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{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "**MIP Era**: CMIP6 \n", "**Institute**: MOHC \n", "**Source ID**: HADGEM3-GC31-MH \n", "**Topic**: Ocean \n", "**Sub-Topics**: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n", "**Properties**: 133 (101 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocean?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:14" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-mh', 'ocean')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Seawater Properties](#2.-Key-Properties--->-Seawater-Properties) \n", "[3. Key Properties --> Bathymetry](#3.-Key-Properties--->-Bathymetry) \n", "[4. Key Properties --> Nonoceanic Waters](#4.-Key-Properties--->-Nonoceanic-Waters) \n", "[5. Key Properties --> Software Properties](#5.-Key-Properties--->-Software-Properties) \n", "[6. Key Properties --> Resolution](#6.-Key-Properties--->-Resolution) \n", "[7. Key Properties --> Tuning Applied](#7.-Key-Properties--->-Tuning-Applied) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid](#9.-Grid) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Discretisation --> Horizontal](#11.-Grid--->-Discretisation--->-Horizontal) \n", "[12. Timestepping Framework](#12.-Timestepping-Framework) \n", "[13. Timestepping Framework --> Tracers](#13.-Timestepping-Framework--->-Tracers) \n", "[14. Timestepping Framework --> Baroclinic Dynamics](#14.-Timestepping-Framework--->-Baroclinic-Dynamics) \n", "[15. Timestepping Framework --> Barotropic](#15.-Timestepping-Framework--->-Barotropic) \n", "[16. Timestepping Framework --> Vertical Physics](#16.-Timestepping-Framework--->-Vertical-Physics) \n", "[17. Advection](#17.-Advection) \n", "[18. Advection --> Momentum](#18.-Advection--->-Momentum) \n", "[19. Advection --> Lateral Tracers](#19.-Advection--->-Lateral-Tracers) \n", "[20. Advection --> Vertical Tracers](#20.-Advection--->-Vertical-Tracers) \n", "[21. Lateral Physics](#21.-Lateral-Physics) \n", "[22. Lateral Physics --> Momentum --> Operator](#22.-Lateral-Physics--->-Momentum--->-Operator) \n", "[23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff](#23.-Lateral-Physics--->-Momentum--->-Eddy-Viscosity-Coeff) \n", "[24. Lateral Physics --> Tracers](#24.-Lateral-Physics--->-Tracers) \n", "[25. Lateral Physics --> Tracers --> Operator](#25.-Lateral-Physics--->-Tracers--->-Operator) \n", "[26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff](#26.-Lateral-Physics--->-Tracers--->-Eddy-Diffusity-Coeff) \n", "[27. Lateral Physics --> Tracers --> Eddy Induced Velocity](#27.-Lateral-Physics--->-Tracers--->-Eddy-Induced-Velocity) \n", "[28. Vertical Physics](#28.-Vertical-Physics) \n", "[29. Vertical Physics --> Boundary Layer Mixing --> Details](#29.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Details) \n", "[30. Vertical Physics --> Boundary Layer Mixing --> Tracers](#30.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Tracers) \n", "[31. Vertical Physics --> Boundary Layer Mixing --> Momentum](#31.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Momentum) \n", "[32. Vertical Physics --> Interior Mixing --> Details](#32.-Vertical-Physics--->-Interior-Mixing--->-Details) \n", "[33. Vertical Physics --> Interior Mixing --> Tracers](#33.-Vertical-Physics--->-Interior-Mixing--->-Tracers) \n", "[34. Vertical Physics --> Interior Mixing --> Momentum](#34.-Vertical-Physics--->-Interior-Mixing--->-Momentum) \n", "[35. Uplow Boundaries --> Free Surface](#35.-Uplow-Boundaries--->-Free-Surface) \n", "[36. Uplow Boundaries --> Bottom Boundary Layer](#36.-Uplow-Boundaries--->-Bottom-Boundary-Layer) \n", "[37. Boundary Forcing](#37.-Boundary-Forcing) \n", "[38. Boundary Forcing --> Momentum --> Bottom Friction](#38.-Boundary-Forcing--->-Momentum--->-Bottom-Friction) \n", "[39. Boundary Forcing --> Momentum --> Lateral Friction](#39.-Boundary-Forcing--->-Momentum--->-Lateral-Friction) \n", "[40. Boundary Forcing --> Tracers --> Sunlight Penetration](#40.-Boundary-Forcing--->-Tracers--->-Sunlight-Penetration) \n", "[41. Boundary Forcing --> Tracers --> Fresh Water Forcing](#41.-Boundary-Forcing--->-Tracers--->-Fresh-Water-Forcing) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Ocean key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of ocean model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Name of ocean model code (NEMO 3.6, MOM 5.0,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of ocean model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OGCM\" \n", "# \"slab ocean\" \n", "# \"mixed layer ocean\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *Basic approximations made in the ocean.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Primitive equations\" \n", "# \"Non-hydrostatic\" \n", "# \"Boussinesq\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *List of prognostic variables in the ocean component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# \"Salinity\" \n", "# \"U-velocity\" \n", "# \"V-velocity\" \n", "# \"W-velocity\" \n", "# \"SSH\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Seawater Properties \n", "*Physical properties of seawater in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Eos Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Wright, 1997\" \n", "# \"Mc Dougall et al.\" \n", "# \"Jackett et al. 2006\" \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Eos Functional Temp\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Temperature used in EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Eos Functional Salt\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Salinity used in EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Practical salinity Sp\" \n", "# \"Absolute salinity Sa\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Eos Functional Depth\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Depth or pressure used in EOS for sea water ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pressure (dbars)\" \n", "# \"Depth (meters)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. Ocean Freezing Point\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.6. Ocean Specific Heat\n", "**Is Required:** TRUE    **Type:** FLOAT    **Cardinality:** 1.1\n", "### *Specific heat in ocean (cpocean) in J/(kg K)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.7. Ocean Reference Density\n", "**Is Required:** TRUE    **Type:** FLOAT    **Cardinality:** 1.1\n", "### *Boussinesq reference density (rhozero) in kg / m3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Bathymetry \n", "*Properties of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Reference Dates\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Reference date of bathymetry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Present day\" \n", "# \"21000 years BP\" \n", "# \"6000 years BP\" \n", "# \"LGM\" \n", "# \"Pliocene\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Type\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is the bathymetry fixed in time in the ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Ocean Smoothing\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe any smoothing or hand editing of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Source\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe source of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Nonoceanic Waters \n", "*Non oceanic waters treatement in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Isolated Seas\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe if/how isolated seas is performed*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. River Mouth\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe if/how river mouth mixing or estuaries specific treatment is performed*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Software Properties \n", "*Software properties of ocean code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Repository\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Code Version\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Code Languages\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Resolution \n", "*Resolution in the ocean grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Range Horizontal Resolution\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Number Of Vertical Levels\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Number of vertical levels resolved on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. Is Adaptive Grid\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Default is False. Set true if grid resolution changes during execution.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. Thickness Level 1\n", "**Is Required:** TRUE    **Type:** FLOAT    **Cardinality:** 1.1\n", "### *Thickness of first surface ocean level (in meters)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Tuning Applied \n", "*Tuning methodology for ocean component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Global Mean Metrics Used\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.N\n", "### *List set of metrics of the global mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Regional Metrics Used\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.N\n", "### *List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Trend Metrics Used\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.N\n", "### *List observed trend metrics used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "*Conservation in the ocean component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Brief description of conservation methodology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *Properties conserved in the ocean by the numerical schemes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Enstrophy\" \n", "# \"Salt\" \n", "# \"Volume of ocean\" \n", "# \"Momentum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Consistency Properties\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Corrected Conserved Prognostic Variables\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Set of variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Was Flux Correction Used\n", "**Is Required:** FALSE    **Type:** BOOLEAN    **Cardinality:** 0.1\n", "### *Does conservation involve flux correction ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid \n", "*Ocean grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of grid in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "*Properties of vertical discretisation in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Coordinates\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of vertical coordinates in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Z-coordinate\" \n", "# \"Z*-coordinate\" \n", "# \"S-coordinate\" \n", "# \"Isopycnic - sigma 0\" \n", "# \"Isopycnic - sigma 2\" \n", "# \"Isopycnic - sigma 4\" \n", "# \"Isopycnic - other\" \n", "# \"Hybrid / Z+S\" \n", "# \"Hybrid / Z+isopycnic\" \n", "# \"Hybrid / other\" \n", "# \"Pressure referenced (P)\" \n", "# \"P*\" \n", "# \"Z**\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Partial Steps\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Using partial steps with Z or Z* vertical coordinate in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Discretisation --> Horizontal \n", "*Type of horizontal discretisation scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Horizontal grid type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Lat-lon\" \n", "# \"Rotated north pole\" \n", "# \"Two north poles (ORCA-style)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Staggering\n", "**Is Required:** FALSE    **Type:** ENUM    **Cardinality:** 0.1\n", "### *Horizontal grid staggering type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"N/a\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Horizontal discretisation scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite difference\" \n", "# \"Finite volumes\" \n", "# \"Finite elements\" \n", "# \"Unstructured grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Timestepping Framework \n", "*Ocean Timestepping Framework*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Diurnal Cycle\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Diurnal cycle type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Via coupling\" \n", "# \"Specific treatment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Timestepping Framework --> Tracers \n", "*Properties of tracers time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Tracers time stepping scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Time Step\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Tracers time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Timestepping Framework --> Baroclinic Dynamics \n", "*Baroclinic dynamics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Baroclinic dynamics type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Preconditioned conjugate gradient\" \n", "# \"Sub cyling\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Baroclinic dynamics scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Time Step\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *Baroclinic time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Timestepping Framework --> Barotropic \n", "*Barotropic time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Splitting\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Time splitting method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"split explicit\" \n", "# \"implicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Time Step\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *Barotropic time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Timestepping Framework --> Vertical Physics \n", "*Vertical physics time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Method\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Details of vertical time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Advection \n", "*Ocean advection*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of advection in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Advection --> Momentum \n", "*Properties of lateral momemtum advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of lateral momemtum advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flux form\" \n", "# \"Vector form\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Scheme Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Name of ocean momemtum advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. ALE\n", "**Is Required:** FALSE    **Type:** BOOLEAN    **Cardinality:** 0.1\n", "### *Using ALE for vertical advection ? (if vertical coordinates are sigma)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.ALE') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Advection --> Lateral Tracers \n", "*Properties of lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Order\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Order of lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Flux Limiter\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Monotonic flux limiter for lateral tracer advection scheme in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Effective Order\n", "**Is Required:** TRUE    **Type:** FLOAT    **Cardinality:** 1.1\n", "### *Effective order of limited lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Passive Tracers\n", "**Is Required:** FALSE    **Type:** ENUM    **Cardinality:** 0.N\n", "### *Passive tracers advected*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ideal age\" \n", "# \"CFC 11\" \n", "# \"CFC 12\" \n", "# \"SF6\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.6. Passive Tracers Advection\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Is advection of passive tracers different than active ? if so, describe.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Advection --> Vertical Tracers \n", "*Properties of vertical tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Flux Limiter\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Monotonic flux limiter for vertical tracer advection scheme in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Lateral Physics \n", "*Ocean lateral physics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of lateral physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of transient eddy representation in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Eddy active\" \n", "# \"Eddy admitting\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Lateral Physics --> Momentum --> Operator \n", "*Properties of lateral physics operator for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Direction\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Direction of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Order\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Order of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Discretisation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Discretisation of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff \n", "*Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Lateral physics momemtum eddy viscosity coeff type in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Constant Coefficient\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Variable Coefficient\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.4. Coeff Background\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.5. Coeff Backscatter\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Lateral Physics --> Tracers \n", "*Properties of lateral physics for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Mesoscale Closure\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there a mesoscale closure in the lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Submesoscale Mixing\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Lateral Physics --> Tracers --> Operator \n", "*Properties of lateral physics operator for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Direction\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Direction of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Order\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Order of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Discretisation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Discretisation of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff \n", "*Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Lateral physics tracers eddy diffusity coeff type in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Constant Coefficient\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Variable Coefficient\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Coeff Background\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.5. Coeff Backscatter\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Lateral Physics --> Tracers --> Eddy Induced Velocity \n", "*Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of EIV in lateral physics tracers in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"GM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Constant Val\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If EIV scheme for tracers is constant, specify coefficient value (M2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Flux Type\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Type of EIV flux (advective or skew)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Added Diffusivity\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Type of EIV added diffusivity (constant, flow dependent or none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Vertical Physics \n", "*Ocean Vertical Physics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of vertical physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Vertical Physics --> Boundary Layer Mixing --> Details \n", "*Properties of vertical physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Langmuir Cells Mixing\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there Langmuir cells mixing in upper ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Vertical Physics --> Boundary Layer Mixing --> Tracers \n", "*Properties of boundary layer (BL) mixing on tracers in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of boundary layer mixing for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Closure Order\n", "**Is Required:** FALSE    **Type:** FLOAT    **Cardinality:** 0.1\n", "### *If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Constant\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If constant BL mixing of tracers, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Background\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Vertical Physics --> Boundary Layer Mixing --> Momentum \n", "*Properties of boundary layer (BL) mixing on momentum in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of boundary layer mixing for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Closure Order\n", "**Is Required:** FALSE    **Type:** FLOAT    **Cardinality:** 0.1\n", "### *If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Constant\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If constant BL mixing of momentum, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Background\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Vertical Physics --> Interior Mixing --> Details \n", "*Properties of interior mixing in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Convection Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of vertical convection in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Non-penetrative convective adjustment\" \n", "# \"Enhanced vertical diffusion\" \n", "# \"Included in turbulence closure\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Tide Induced Mixing\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe how tide induced mixing is modelled (barotropic, baroclinic, none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Double Diffusion\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there double diffusion*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Shear Mixing\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is there interior shear mixing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Vertical Physics --> Interior Mixing --> Tracers \n", "*Properties of interior mixing on tracers in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of interior mixing for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Constant\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If constant interior mixing of tracers, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Profile\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Background\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Vertical Physics --> Interior Mixing --> Momentum \n", "*Properties of interior mixing on momentum in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of interior mixing for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Constant\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If constant interior mixing of momentum, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.3. Profile\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.4. Background\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Uplow Boundaries --> Free Surface \n", "*Properties of free surface in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of free surface in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Free surface scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear implicit\" \n", "# \"Linear filtered\" \n", "# \"Linear semi-explicit\" \n", "# \"Non-linear implicit\" \n", "# \"Non-linear filtered\" \n", "# \"Non-linear semi-explicit\" \n", "# \"Fully explicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.3. Embeded Seaice\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is the sea-ice embeded in the ocean model (instead of levitating) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Uplow Boundaries --> Bottom Boundary Layer \n", "*Properties of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Type Of Bbl\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diffusive\" \n", "# \"Acvective\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Lateral Mixing Coef\n", "**Is Required:** FALSE    **Type:** INTEGER    **Cardinality:** 0.1\n", "### *If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Sill Overflow\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe any specific treatment of sill overflows*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Boundary Forcing \n", "*Ocean boundary forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of boundary forcing in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Surface Pressure\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.3. Momentum Flux Correction\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.4. Tracers Flux Correction\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.5. Wave Effects\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe if/how wave effects are modelled at ocean surface.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.6. River Runoff Budget\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe how river runoff from land surface is routed to ocean and any global adjustment done.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.7. Geothermal Heating\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe if/how geothermal heating is present at ocean bottom.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Boundary Forcing --> Momentum --> Bottom Friction \n", "*Properties of momentum bottom friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of momentum bottom friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Non-linear\" \n", "# \"Non-linear (drag function of speed of tides)\" \n", "# \"Constant drag coefficient\" \n", "# \"None\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Boundary Forcing --> Momentum --> Lateral Friction \n", "*Properties of momentum lateral friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of momentum lateral friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Free-slip\" \n", "# \"No-slip\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Boundary Forcing --> Tracers --> Sunlight Penetration \n", "*Properties of sunlight penetration scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of sunlight penetration scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"1 extinction depth\" \n", "# \"2 extinction depth\" \n", "# \"3 extinction depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.2. Ocean Colour\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is the ocean sunlight penetration scheme ocean colour dependent ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.3. Extinction Depth\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe and list extinctions depths for sunlight penetration scheme (if applicable).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Boundary Forcing --> Tracers --> Fresh Water Forcing \n", "*Properties of surface fresh water forcing in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. From Atmopshere\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of surface fresh water forcing from atmos in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. From Sea Ice\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Type of surface fresh water forcing from sea-ice in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Real salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.3. Forced Mode Restoring\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Type of surface salinity restoring in forced mode (OMIP)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

greenelab/GCB535

22_Prelab_Pharmacology/Prelab_pharmacology.ipynb

1

2760

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pre-lab: Bioinformatics in Pharmacology" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I. Recorded Lecture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Head to Canvas and review the slides and watch the recorded lecture \"Bioinformatics in Pharmacology\" (recorded by John Hogenesch). Then, answer the following questions. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q1. What gene has the most publications associated with it (as of 2007)? Do most genes have a lot of publications assoicated with them? Why do you think that is?" ] }, { "cell_type": "raw", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q2. Describe two genetics-based strategies that could be used to identify new, potential therapeutic drug targets for disease." ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q3. What is the Z' factor? In what context is it utilized, and how?" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## II. Cell-based screening" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our activity for this unit, we have decided to focus on the analysis of data from high-throughput cell screening data. In industry, this is routinely done, for drug discovery, target identification, lead compound optimization, assay development, and more. On Canvas, we have provided you two papers (as some brief overview) to describe the process, features, misconceptions, and examples. See Szymański et al (2012) and Macarron et al (2011). \n", "\n", "However, the analysis of these data requires data management and analytical processing at large input: hundreds, perhaps thousands of plates to analyze, and something that you can not readily do in excel, even though many of the calculations are relatively straight forward." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" }, "name": "Online_database_in_class.ipynb" }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

rjenc29/numerical

notebooks/principal_component_analysis.ipynb

1

348979

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Principal Component Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TL;DR** This notebook provides an overview of Principal Component Analysis and its application." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.decomposition import PCA\n", "from pprint import pprint\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "%matplotlib inline\n", "import ipywidgets as widgets\n", "from scipy.optimize import fmin\n", "\n", "import seaborn as sns\n", "sns.set()\n", "\n", "matplotlib.rcParams['figure.figsize'] = (16, 8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Principal Component Analysis is fuundamentally a mechanism to reduce the dimensionality of large datasets will minimising loss of information. There are a number of applications of PCA by extension - classification / noise filtration / visualisation and more.\n", "\n", "> To build an intuition for how / why PCA works, we're going to use the IRIS dataset, which comprises a collection of measurements of petal and sepal widths and lengths along with which category each measured plant belongs to.\n", "\n", "> There are many excellent tutorials on applying PCA to the IRIS dataset an unsupervised classification model; we're going to instead use the data to try to build some intuition about how and why PCA works.\n", "\n", "Let's take a look at the data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_set = load_iris()\n", "data = data_set.data\n", "target = data_set.target" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal length (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.4</td>\n", " <td>3.9</td>\n", " <td>1.7</td>\n", " <td>0.4</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>4.6</td>\n", " <td>3.4</td>\n", " <td>1.4</td>\n", " <td>0.3</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>5.0</td>\n", " <td>3.4</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>4.4</td>\n", " <td>2.9</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>4.9</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.1</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", "0 5.1 3.5 1.4 0.2 \n", "1 4.9 3.0 1.4 0.2 \n", "2 4.7 3.2 1.3 0.2 \n", "3 4.6 3.1 1.5 0.2 \n", "4 5.0 3.6 1.4 0.2 \n", "5 5.4 3.9 1.7 0.4 \n", "6 4.6 3.4 1.4 0.3 \n", "7 5.0 3.4 1.5 0.2 \n", "8 4.4 2.9 1.4 0.2 \n", "9 4.9 3.1 1.5 0.1 \n", "\n", " species \n", "0 setosa \n", "1 setosa \n", "2 setosa \n", "3 setosa \n", "4 setosa \n", "5 setosa \n", "6 setosa \n", "7 setosa \n", "8 setosa \n", "9 setosa " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.array(data), columns=data_set.feature_names)\n", "df['species'] = data_set.target_names[target]\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data pre-processing: de-meaning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step we're going to take is to pre-process the data by making it mean-centred. We'll come back to why this is necessary (and it is) but for now, let's look at how to achieve it and verify that doesn't affect the variance of our dataset in any way." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal length (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-0.743333</td>\n", " <td>0.446</td>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.943333</td>\n", " <td>-0.054</td>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-1.143333</td>\n", " <td>0.146</td>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-1.243333</td>\n", " <td>0.046</td>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-0.843333</td>\n", " <td>0.546</td>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", "0 -0.743333 0.446 -2.358667 -0.998667\n", "1 -0.943333 -0.054 -2.358667 -0.998667\n", "2 -1.143333 0.146 -2.458667 -0.998667\n", "3 -1.243333 0.046 -2.258667 -0.998667\n", "4 -0.843333 0.546 -2.358667 -0.998667" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def demean(series):\n", " return series - series.mean()\n", "\n", "demeaned_df = df[data_set.feature_names].apply(demean)\n", "demeaned_df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sepal length (cm) 0.685694\n", "sepal width (cm) 0.188004\n", "petal length (cm) 3.113179\n", "petal width (cm) 0.582414\n", "dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.var()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sepal length (cm) 0.685694\n", "sepal width (cm) 0.188004\n", "petal length (cm) 3.113179\n", "petal width (cm) 0.582414\n", "dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demeaned_df.var()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualising the input data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's much easier to build an intuition for PCA when working with 2 dimensions. So we'll extract the petal measurements from the mean-centred data and plot one against the other." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11015c828>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lbd0Z18ciEzKflXm+r0TKU6LFlS9pfFVInucrFk/Kt9ItLLvWQvZ9aVN9TAfM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\nZLXOrK4MdX6O7CkYsGXblrb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"text/plain": [ "<matplotlib.figure.Figure at 0x11012b390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "axes = plt.gca()\n", "axes.set_ylim([-4, 4])\n", "axes.set_xlim([-4, 4])\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "p_x = demeaned_df['petal length (cm)']\n", "p_y = demeaned_df['petal width (cm)']\n", "plt.scatter(p_x, p_y, alpha = 0.4, s=50)\n", "plt.xlabel('petal length (cm)')\n", "plt.ylabel('petal width (cm)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fitting a line (hyperplane) to the input data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There would appear to be an approximately linear relationship between petal length and width, which is intuitively reasonable.\n", "\n", "In the plot below, we additionally draw perpendicular lines (in green) from each data point back to the hyperplane." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_line(angle_in_degrees):\n", "\n", " # original data\n", " plt.scatter(p_x, p_y, alpha = 0.4, s=50)\n", " \n", " # our current fitted line\n", " m = np.tan(np.pi * angle_in_degrees / 360) \n", " x = np.linspace(-4, 4, 3)\n", " y = m * x\n", " plt.plot(x, y, 'r--')\n", "\n", " # perpendicular lines between the original data and the\n", " # current fitted line\n", " p_x_line = (p_x + m * p_y) / (m*m + 1)\n", " p_y_line = m * p_x_line\n", " for idx in range(len(p_x)):\n", " plt.plot([p_x[idx], p_x_line[idx]], [p_y[idx], p_y_line[idx]], color='g', alpha=0.1)\n", " \n", " # average sq distance from origin of perp line intercepts\n", " # i.e. the points where the green line touches the dashed red line\n", " var = np.mean(np.power(p_x_line, 2) + np.power(p_y_line, 2))\n", " \n", " plt.gca().set_aspect('equal', adjustable='box')\n", " axes = plt.gca()\n", " axes.set_ylim([-4, 4])\n", " axes.set_xlim([-4, 4]) \n", " \n", " plt.title('Variance {0:.4f}'.format(var))\n", " \n", " plt.xlabel('petal length (cm)')\n", " plt.ylabel('petal width (cm)')\n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ow3O1utIwNIO4lSDfyDFQ7d/qCudNc6+LTuFl514PVge3+HmSe71zGZPdACGE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\nv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"text/plain": [ "<matplotlib.figure.Figure at 0x11272b240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_line(85) #static plot for arbitrarty slope angle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We introduced a quantity called **variance**:\n", "\n", "> ```python\n", "> var = np.mean(np.power(p_x_line, 2) + np.power(p_y_line, 2))\n", "> ```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we define variance in the general sense for a discrete dataset as:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ Var(x) = \\frac{1}{n} \\sum_{n=1}^{n} (x_i−\\mu)^2 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Noting that $\\mu$ is zero for our de-meaned data set, and that - by Pythogoras - our $x_i$ values are the hypotenuse lengths of triangles with sides p_x_line and p_y_line, we have:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ Var(x) = \\frac{1}{n} \\sum_{n=1}^{n} (x_i-0)^2 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ Var(x) = \\frac{1}{n} \\sum_{n=1}^{n} (x_i)^2 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ Var(x) = \\frac{1}{n} \\sum_{n=1}^{n} ((p.x.line_i)^2 + (p.y.line_i)^2)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interactively changing the orientation of the hyperplane" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could try to fit a stright line through the data as a means of generalising the petal width / length relationship. There are clearly inifinitely many solutions, but certain solutions have interesting properties.\n", "\n", "> Try changing the slope of the line in the interactive plot below. As you change the angle of the line:\n", "\n", "> 1. Make a note of the plot title (variance)\n", "> 2. Take a look at the green lines" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ow3O1utIwNIO4lSDfyDFQ7d/qCudNc6+LTuFl514PVge3+HmSe71zGZPdACGE\n2CX5PtEllxL59jeZv+9cqkcdSX+lF00pNKURtxIjLt+rYy88v8lTQ0+xprwS0zTRNZ3uSA+a0oiZ\nMYLA37CoyyJqtB4la0qn0+6m0QyI2AaGPnIcFTWjBIFPEAQUGgUUiqSdotwsU26WUUrD1sL4TQtb\nC9Pwa+TqORJmiprjEbGNdu71yuJy+qq9GJrenn/e2PBe52w9S9Wtttptxcf8lQ2vAM/UM5SdEhoa\nETOyXV//dCQFWQghxqvZpPmx9+H+9324r9gX68f30p3U6K/201vpQ1MaakOR3djeHa+g4Ts8O/Q0\nKwvL0dBRSqMr3NUu4n7gU3Nr2KaNF3g8sWwZXrWPqJHG0gx60mGO7hz5vq3Rr49PQKGRbxflglPg\nL0tXUS9baMrCUBrhmEPdy1Es9hLS45iaTk86zKsWpJkbn8+q4krWlde1FpptSATb2HBRHaoNtQt+\n1IyO+asbXimeqWcoOgU0pREyQtv3e5hm5JG1EEKMR7WK/Z6zyD1yHytftx+rf3Inwew5zIzOojPU\niaWZ9Fb6KNTzm518pGkar+rcj3069kULdFYUlrIyv4JM7aX4zOG5Wl3prOlzKJWh5OepB0WUBgP5\nGk++OLBZs0bLvV633iVTbFALSijNQ9MVy1Y3eXF1GU9r0lQVNF0xkK/xzMqs5F5PMinIQggxDu7n\nL8f4n4dJHnQk2a/cQJ/d3DBSVMyOz6EjnMZQGr3lPvKN3GYnHxmawQE9r2avjr0IfMWywousKa4e\ncTxiyu5ABQbZgkPECGMog6pXouzmUcD6oQqut/k2pY1zr7PVLKsyQ8SM1iPlmlfG8wPKtSZ+PYwW\nGDh+A9d30JRiIFvD9fwx515vvNd5e3OvN14BLrnXUpCFEGJMgiBgTXEVz7/3dNZcdD7uTbfRlZ7b\nCvSo9FJttuZU58bmkrJTaCqgvzIwah60oRkc2PMaFiYX0fQcluVfYG1pdXv1slIKW8UBDaV04mYH\nMT1OM2hS8Yp4XkDN2Xyx18a5104Tis0Cda9KVE8S0WM0mx6+H+ADIRLE9AS6MgFwA7/9nuPKvQ6l\ntzv3OmSE2rnXuUZut8+9loIshBBb4Xo+tX8+i//Iwzh+k2bYZv17z6UY1IhbCTpDnVTdGn2VXupu\nHV3TmZuYT8JO4gVN+mp95OrZzUaAISPEa2YczLzEAqpulRfyL7I8s5Ln+3qpOy6xkEXK7EBDwwtc\nTC2EoUxdXKZsAAAgAElEQVQcv0E9KBO2Rl8CNLyaeUY8jY6i4pWo+1V0ZWKaOpqm0JXCMnUsPYRS\nCqA1v7zRe0507rUfeGRqmd06YlMWdQkhxCj8IOCZlVnqf36CI5d8kHC1zj/u+jnhV8SpuTVy9SwK\nRcruwA98svUM/ZU+ZsZmYeutVcteYTklp8hgdRClNDpDneia3v6MiBXhkBmvpel5PPjU//Kb0lq6\n9UWk7VnsNbOHeTPiBIWAipun5leIaDH8wCeVhLpXIaaPvsJZUxrdkS7md+dYmemj6pZQQMxIEQub\nKEDT1Ii+9qTDm63g7onMxPV9MvVBVhdXMj+5EEPbvGyEjBCJIEnRKZCtZzfr57ZEzSh+4OH6LoV6\nhc5QZ/sPhd2JjJCFEGIUz6zM4vzPw6S//G76yPHE+R8nG5pFbsgmbIRxvCb5Ro5is0g61EnSTlFy\nigxU+ml6TSzdYn5iAVEzTtWtkKkOjpoHHbfjZFd341aTOKpMjtWUgyGW9w+xqq/IrI44EZWk6fpU\nmmVmd3RwwKIZo57mtDFd0zls7z2Zn56B49YpNIqUnAKv3rOTA/fswvcCHNfD9wJ6Uq1V1puayNzr\nuJUgYka2Ow1sOpARshBCbML1fLyf3c1e3/wEfTH41QUfRv3L0aSDJtWSyZ6zEwzRT73ZaG8z6gp3\n4xOQb+RQmsbMyExCRoh5iXmsKq6k2Chu2A6lNhyR2BoP1R2XbE5nvr0/qxtPUfazDDqr0SyNFQOK\nt7x2HvvO66BQ66buFzF0jWQ4QUaVKTmt9wwb4VH7Yekmh++zJ3vOTtBfHiIdjtAZMUnYydajeMcl\nbG2+t3ljw7nXXuBSclq513Pic9vt39jGe6mHR8rjGemmQikG9NZhGPlGbtS90NOZjJCFEGIT/k/v\nYc+vX0I2bvP8+VdQOfhNVNwyeWeIhu9gqxg94RlYhkW1WaXYKFBpVugJ9xA1Y+TrWQY2JHFFzShz\nY3OJWjFyjRy5WnbEyUeZUh3X84mbaeba+5EyZqJQDLlryTcGGCiUMHSNzliMzkiagIBio0jCTIx6\nmtOmLN2iO9rFrEQXNa9GwSlQdlrvGQ9bWy3GwyT3emLICFkIITbi+R5Dh+yDWvQq/nHueQzOnY/p\nRVCGT8krEAQK25xLWKUoUMclQ6VZQlMK34eIStNULrl6Bg2NdKiLwAsxM7wHvawjUx9C13Q0pegI\npemMh9pFMWF2EzU6GGiupOD2kg/W4qg8NSdC01VEbIuElcQPPErNCgmrNW+br7dGk5Zu4Xo+1YY7\nItVrOPd6OM1LQ8P3Ad8eNf1rNMO51ysKy8nWMujKYGZ05qjXJu0UfuC3i2oq1DHm7394pXimnqHm\n1tqBKaP1a7qRgiyEEAC+T27p32HeAuyZc3jqy99h1aos2YECnlckpMexbZ/k7Cp/XPoibiWGG/hU\nXBctXMEL8jjVELYWI6RZBOEKL/oF/Fo/USOFqenY0TDRDo/B6gCa0tGURtJOMbcnxtrBMpqmoSuD\nLmMevu+ixwv8Yfk/CLsZ4mYrdKQnHeaQAzvJBK1ozLiVaC+m6u9TZPJN3MDHUFo7gUtTqp177QU+\nf1uxmlrZImIkiOiREddtjambzE8sYGVhOYPVfnSl0x3pHvXalN2K2BzOvR4t9WtLhh/rZ2oZSk6Z\n51blKRW1Ufs1nUzPPzOEEGI8mk3iHzmf5uKTWPf0Y0CAoVutWEeioDQcv4KpLIZyLisyvZT9HLZh\n0BHqYv16WLqmRM0v4aoqge7Tu95k2doSZT+LE5RQGjRqYYq5MGEjTH+ll0KjQMkpcsJh85nTHcPz\nfBqOixYYvHrmfrxyxgLWF/P0O6uoBXnQAgbyNZatqrbPKK65NeJmgudWZ1k21Auaj2XoIxK4hkXN\nKH39AdWKTj0o0QhKeKq52XVbM5x7rWsGfdXeEYEmGxvOvTY0c8RBF2M1nAa2dG2RVdkhXBpb7Nd0\nISNkIcTurVIh8f5zsX/zMHNeezBDHZ2sLqwhl7fZozOO2+HhOFEcrYoiYH2fz+yZJoVmFg2NqJ5C\nOXHwG/iBT7lZxPcVlXqA5icIfIecO4TSNGJGkmY5SsesGD6DI3KvTzpiIXXHJVdu0BGzMXSNh/5X\nI23VyTuD9NfWoNBImp2sH6owM9nZzr32/IBKUQMVUGrmiZsdaEp7KYFrno+ha7ieT7moEzeTKBeq\nbhGFImGmR1y3LWEzzLwJyL0OAkWjHMJQdcpekbjSMDVrs35NF9OnJ0IIMV7ZDLVz3krw6MM4bzoW\n564HmDf3IBpNn77GWkxlYmgaYdsmbXXhNF3qbgUjCKGhqPkVco08oBNRaUIqhq4ZlBtFKm6RINCI\naGkIAmpuhapbxiMgaXaPmnsdsgxmpaOELINqw8XQQsyJLCJpdVL1KgzW11Fs5nDd1grp4dzrQq2C\nEzQJaRE8PFz/pcSsjRO4qg0XN/CJGHEiRhxLD+MGLmU3T8N3Rk3/2pKJyL2uNlwCWn/IKFp7r0fr\n13QhBVkIsVvSetcTOu14Msv+zjNnvIWB7/4AolFiVoy90wvRFAw5vYT0KAmrg7jZQTrcDbqHpxxS\nZhdhLQpaEycoY+kWMyOzmWnPIRlK0aRGkwoRM8TM0DwszabuV3H9GhHb3GbudcQ2MJRGxIizILov\nM+w5NPw6g431VP1CO1UrZXeQCEXwgiYQkDBSmJrdfp+NE7iG3xMgZiTptGaQNNP4BNS8IpYxvjnZ\njXOv15RWjyn3elurwjc23F5Ds0iYaaLGS0dabposNh1IQRZC7J4ch3CmQPJt7yJ36eWsqq9vx0N2\nRtPsO2MhTd9joL6WIPBRSpG00uzdPZsmDcpeAVsLY+kmVsjDsB1M3SBkRElYKdKRJJpdpxa0Ro5x\nMwWBIhL3cPz6qLnXQ9UMQ6Uyrtd6FNuTDuMHAbYeZkZ4LkmzNVImlKPQyFOsOnh+QHe0k5kdMape\nlWbQbO/93TSBa+P3BDA0C1sPY2sR0kmLYjM/7kCP4dxr12+OKfcaaOdeu55PseqMelDGpu3VlYGm\n9FH7NV3oV1xxxRUT/aFPPvkkl1xyCaeddtqYrq9Wp9/RXNGoLf3axUzXvk3XfsHoffOaDsVmEa2j\ni+bpZ2Oe9A485VNyilTdGkkr2QrD6ExTqboMlHMUnSJRLcmszhhHHbCAUrXBYDlHzW0Q01PMmREh\nldCo1TyCQMdUFgtnJUjHbYbKeRzfw1Yh5nWmWTA7TMOrY2gmlm4RtxJUmlX+ubqXZ1b1sy5To2/A\noeb47Du/g1rDpVxt4gUQ0WLEoooaBZ5aOkBfpknfgEO14XLAwpnkq2XylQquH2Aqi56O1mrkjcM5\nulJhqvXWezZ9HxUoZncm2Wdukqbv4HhNwkZ4XIEerXnhgKJToNIsk7CSo0Zn6pqOoRlU3SpPrexj\n6eoKawcrrOkvU2k0mT8rSa028vc1WntH69dUF43a27xmwsf7N998M/fffz/h8OjJMkIIsbOoxx4h\nctnFDN3ybRqz59LZ1YmhacyK7oHne+QbOVaXVjE/sQBNabzhlfuycI8oa0t9pEI1XpGeh6ZpvGHf\nfdmzGKe3PEhP1GBWbCalZgnHbWKrGB2ROIauUWwUWFRNkKsVmRG1mRFrjRCz9Uwr4Ut1YOs25Wyc\nRs3E0ytU/Rx2YNCXa43+9l/YiTvvpVStJ5eHWFpcRtkfQHd1QobJwIb8jCP22YuByiBVx2FGrIO4\nvfkCKk2pzd5zeKSZrwfUvTq5RpYOe3wFb9Pc6wXJRaMW5ZARYn2fz0ChiqEaJMyO1iKtDec8z0lH\nxtze6WbCezVv3jy+9rWvTfTHCiF2c/79d+O/9zRKAyuJLl1BEPhk61k830NTGrNjc4ibCUpOkbWl\nte1Ht3OTc5mT6KEZNFhRXI7v+xiawaz4LPZIdFNxi2TqQ8SMGKZh4GlVPFqPvhN2knQ4RWc0ScUr\nkm1kUUq1gzLy9Rw1p0G+4DMzPI+wFsHx65SbBSpugb5Mpf34Oh62ACgUA/aILiCmJyk0s+ScAVy/\nwUC2BkHrUIl4yKbqlrY6VztaUlfSTmHpFo7njDsla9Pc69XFVaM+/nY9n2IBYkYMH5+yWwRahXdL\n5zxvqb3TzYSPkI8//njWrl07rnu6u0c/0WRXJ/3a9UzXvk3XfkGrb+63v8UjX/ww0W6buV/9LqE3\nHENiQzyjpjl0RrrQlEZXd4zl2eWUm2Ucu8icxByUUnR378+y7DIKjQJVK8vCjoUAdLkx1hbXUnJK\n6OEme0Zmk6/ngSbJSAJLt+gKYmRrUfL1PI7noIUdeiJddHpRcvUc5WoFK6SRtJNEo3uTafS3Cplq\n4FIhErVJxkMAFMoNwhELy9DZs3sfMvU+mr5Dwyhga91EYyESMZsuL0ammhnRjjF/X0GcTC2D4zlY\npk8yNPZAD4Curhgr8yspNArUrBzzkvNG5F4P9yFphqm5IQICIkarf07Ta/dhd7RLLFEbHBzfhvJd\nQXd3XPq1i5mufZuu/QLo7opRuWwJ4c9/ls69Eyz/7OUU581i1mABXTcwlYHjl8lq1Q1bcxQxr4vB\nYoEXc6so5GrMiM4CIOZ3MVAqsDy3lkKuxuz4XABMN06jXOTF3Gry4Vo7OSuTbR0jSKBRqQfUAyi7\ndfK5dWTsCkmrg2zFpe5XGChlSDlpCDSMZpp6UKDqFyn7DmszvVTKHdQcD8vQqFUdrGQYv26QCGaS\ncwYYqmapKIdcoZtK2aLacNF0jbL7UjtGOzZxS/zAJF8rkgnKxKwqMTM2ru894qcZKBdZlltDPldj\nj+hsPD+g2nDbfWjoCmg90i7RWl0eiVhUynUatem3pmEsf/TuEgVZCCG2x/ql/4f67lexZ89hzvfu\nYShRJdfIoSuNnsgMlK4wlUnTd9rzpq14yIWsKC5nYEPEZXekB03TWJBcyPLCMjL1DVnOsVmEjTAz\norPoLa9nqDaEUhoJK0m+kecPLyzFKYfxUWiAHfPo6g7432WrqFf6COsJ3KBOuVGnP7sOnCgBrXlb\nZevMnqX4+4o11Ct9hPQEhtKoNprE/NYIUlcGKasLN/CwQg5/XPoCfjVBgIahNBJJ2GNmMO4ziof3\nDmfqGcpOCQ2NiBnZ9o0bbJx7PVQdYumaIl410Y6+rDaahEMm+iZnMu/RFZ3Wj6S3ZfftuRBiWnN9\nl+VWlb9cv4TnfnI7au9X8sr0fsTNOLlGjqHaIH7g4QWtOeGN501tw2Z+vBUP2V/ta8dDGprBouSe\nGJrFQK2fweog0FplPDM6E1M3GaoNUnWrrF3fZKBQpeIXMHSFYeg0qiH+9nSZgWKZelDGoUTEjFKv\nGmTLFapBkSAI0DSDiJ4in9PoLRSoBxWaVNB0RThkUq032+cZa4HJPt3ziBpRVmX6KboZDB00XVEs\nwbped7vOKNY1vR3oUXQKI/ZIj8Vw7vWavjpLh9ZR8jLt6MtwyKC2UR+Gz2R+9d494/qM6WZSCvKc\nOXO46667JuOjhRDTXaVC7BMfw8zkWNSxCG+fV7A0VGKoOkTIDLF/14GEjAiZeoZMdRAfjyAAQ5nt\ngxDgpXhIhcb68joKG15vFeVF6Mqgt7KuXaxjVpwZkRnoSqOv3E9vtkhEj+LhUXbzrUKLTi6rE9ES\nNP0GVa9CsVnAb4aImhHmzggzYwbsPSfF3K4khbxJREtQ96pU3RJVt4yuKSIhkyMOmMnh+83kDQft\nwYELZxLUk4SMCCU3T6GZwQ98NKUoFTVsLYwXuCOOfRyLjQM9Co38mAM9hunKRK93YWgGOWeQUrP1\nB4+uaUTskX3Yf2EnmrbrbGPaGWSELISYNlQuS+r0Uwh//xYiX/8qs+KzWJBchBd4vJh7nnw9R9gM\ns3/X/ti6zWB9iFwtixe4oEBDbx2E0GyFecSsGHPjcwmAtRvFQ4aMEAsTraK8prSaotNaKZy0U3SH\ne6g7LhlnkAAfWwvhBi4+Ho7rE/gaIZUgaiQJAp9qo1VsTWJomOiGh1IBjuuDb2CrGBEjQYBP3a9S\n96p4XkDT89urjqsNF01ZpK0eTN3G8ertPwLcwMcgQtgItx/Nj6com7o5YlX4cHjKWFQbLrpm0WPP\nBqVRcYvt+Es38Ef0QUhBFkJME1rvelKnvBXzr09Q/7czqFx+BUopZsfmMC++AMd3eCH7HKVGkbgV\n51Xp/dCUzkCtn0Ijj+s3NxzIoFN2SlSbVaC1dWl2bDY+PmtKq9rxkBEr0tqvjMbqwsp2se4IpZmb\n3AMF5JxBFBopswtdGViGhqFrhK0QKauLbns2tmXhqjoeNdKhNEmzteJ7+NqoFSFt9dBl74GGRtUr\n49IYERs5HDEZ0iPMCs0nZqbaGdU6irBlkLCS2Lq9XVuabN0mYScJCMg2srj+2DKkh9sVNqLMCS8i\npEeoehUaXm1aRl++XFKQhRC7vMbzT7LunDfQWPYs1fM+ROkbN4FpAq39sfMS85gTm0vVq/J87nkK\n9RJaEGWf5L6Aoq/SR9kpU3cbVOsevk973tT1fHQ/Rpc9A9d3WV1sxUO6no/vWsyKzMHHZ1VxJXW3\njlKKnmgXe3bvQdNvknMGcfwN868K5vbEUKq1cErXdFJWB/GQjRFq4gS1l7YIbXKtpnRiRhICiCWa\nuMFLK5E3jpjUlEbUSGApi4bvEI47GLrW2v9sd2Bq1ohH82MVNsLErcSI/dvjib40NJO4mUJDUXKL\npJKtfm7t/t2NCsbz7GKSTMctGdN1q8l07RdM377t8v1yHIaO25+/00f6lHN55QVfIGy1VgRv3DfP\n93gx9wJ/XPY8bs1mVmgvIkYYPVykGVnP+oEaeiONoduEtBDJhA4ENCohlDLQURjREol0g96BJka9\nG5SOoTSsWI1YRxlLt9kruReWYeH6Hr9//nmWD67HUDY9oVnM6epg3/kdPLcq1zo+cMOq43TSoNDM\nM5ivEtJjRPQIPenwqNd2pHT2e2WMXK5CRyjd3mPsBwHPrMy2r9VRhOINFs6OEDUjJO3UhutaBdX1\nm8TMGDFrfHvQy06JolNi2doSjXIYn9ZBDz3pVpyltkm616btInCxonVA4VRsUEb7/qMPnU8mM/qp\nUbu6sWx7koI8SXb5/wluwXTtF0zfvu3K/Rp+dKo9/Av+uvZx1hx1KPMS8zig69WEjNBmffv7sj7+\nb/0/KbgZkmaaOeFF6JrF6twqcqzD1HR6QnMwNItMpkmTJrM6I8SNDgzNxPN91hXX4uglInqEmeF5\naEpvjQBDZVJddSzNZq+OvTE0A8/3WFdaT29pgM5IijmJ2YQ2hGC43sgoyKbXZKAySL3pMjPeTdR6\nKV5402vjHSYvrl2NUtpme4w3vlbXFJl6ZrPi6/kemXoGP/CIW4lxnVEM8KcXVrA2l8PSLOJGCqVU\n68CHVJj9F3aO/rvaqF1PrehjeaYPQ2nEjBSGZuIHAfvMT28WnTldjKUgyyNrIcQuqfTwT/nbit8x\nUO7HO/at7L94CV2hNGuLq3lm6OnNFh+5nk827zInspCEmaLYzNDfWIvnu1QLcWbYc3EDl1yjn6bX\nJF+rUG8oXD+gsiHeERSlXISYnqTu18g2Btqrmd1qjA6rE8dvsLywDN/30TWdPeKzmJ3soeaV6a/2\n43itR82bRkGauklXtJNYyKLcLIxo/6bXhowQSTs14vHxaNcqpUiH0ujKoNwsU2m2FlRtvKVpvGcU\nu55PtWQS0kO4QZO635pr15RqjYK3EX0JkC/4JIwkPi99t9uKztwdSEEWQuxyQj/4Ls3/eDf1r17F\ns7l/km1ksQyLg2YcSsLqYGVxOS9knx2x+KjacHEDH1O3mRveiy57Fo7fYF15DU3PpcOYxazQAjTN\nbK8mdrw6mm8R0lujtmazNb+c1GeQMjtBKcpu68hCN/BJWTPosDuou7URudc9kRkk7RTFRp6Bav8W\nF0XZut0qtGNYPBU2wsSs+Db3GA+HfGhKH1F8t/eM4uHvMaonCOtRTPVSLKcbtEbBY7nf0kPEjGT7\nuwXwvGCb909nUpCFELuOIEBddzXapR9lkdZB8oz34XhNnh96hmw9S8yMcdCMg4iZCV7Mv8DzQ8+3\nR4/DK34BTN1mVnghUT2Oq2qUgwEMQ6PLnk2cHpQGDUqAT9NvUK54NF0f09QxdA3bNEjbMwjrkQ2r\nmQvoKExdI2nMIqzHqTTLrCmtBsDSLXoiM4hZrVCSgUr/iFHtxkJGiISVHHX0u6mYGSNqRvECl2x9\ny9uZdE2nw+5AKY1CI99erFZrBMTMVlb18BnF2zL8PSqlCOtRDM1s/2wsK6c3/j1Ymo2tv/RoXtfV\nbr3yelLOQx6v6XhW63Q9g3a69gumb992mX75PtFPfwr35uspLtiD0u33MuvVbyLn5Cg4eSrNKhEz\nSjrcSdxKMFQdJNscwm+2TlfSNY1Ko0m53kQphVKKiBGj7tfw9RqDxQr5nKJW1SlU65SdCtlKiYHB\nJmuzBfoGHMq1JvNnxrFMHaUUlmbj+U3qnkO5Xieb81k9WKKY1cnXCxghB893SdhJDM0gZIRouHWK\nzRIKiJiRUY84NPVWkWt4DRpeY7PziTf+ndm6jRd4OF6Dpu8Q0kc/y1jXdCzNotqs8dSKPpauKrN2\nqErvQJ1mE6KxgIbfIKSHRhwGsSlNUyO+x/avJwjo6QgzM731+eit3b9gjySp6NgPwtiVjOU8ZBkh\nCyF2CfGLPkzk5huJLdiX8q13UZjTQ8Nv8Jqeg+mw0+TqGVYUlpGr5+iJ9HBA96sxdZNnsk+zprSa\nIAh41YI0PalwO7IRX2P/GXuxqKebqleg5A0SBAEpvQe3YeO4HnWtTIBHXStSrjkEGxYvDb9HWCXw\nHIVuedSCEpahY5g6utPDuj6HTD1DX7kXoJ17HdJDDNWGGKoNbXFUG7PiRIzImBK2knaqvcc438ht\n8TpLt+jtCxgs1qgGRXQ9QNMV+ZLP+j5/TKNyYLPvcTj68lUL0mP4TW75/t09OnP3fTYghNhlrC+v\nwz98X1614l8o/fAuUskEmXqGolMgZXdwUPfB/O/gX+mv9GFqJkrBHrHZJFI2v83/kacHn8JUFrNi\nszY77B6g/29V5nTWaLgOcc0jaaVYn5mBbwZEYi667xO1LAzVYM1AmeNeO59953VQc1xMXePxp6AS\n5Gn4dTRPJ6xHsXQTGjPQgjIDtX50zaA70k3UjDIjOoPeSi9DtUF0pbdPmtpUwk7iBz51r94+/GK0\n6wBSdge5RpaG16DQyLe3OW3M9XzyBZ+4kaDilSg18yTMNJrSKBYCwnOi1LxK6zCKcOcWR8qaUpt9\nj+NJ29rS/RKdKYQQU5TK56DeCtXIvuWN/N8PvomXTGFoRmtOdEPGsqZrHNT9GpJ2itWlVawtraXQ\nyLMovYh90vviBx5PDf2dgcoAMHIlcrXhgtLpsedi6Sa+XidTywGKuD8TizCWoRPSovh4lNwc2VK9\n/R6O6+MRtLbvKKMVw9nugMaM8LzNcq/jVqKdez1Q+//s3WecXVd97//PWrucfXqZplEbFRdZFjY4\nYINDiYkDxjbFcF1uHPofwg023IQE/2+AG4qDcRxCEkwJgZDgkAsBbBOH4ASMKQbbF4x7kSWrjabP\n6XW3te+DoxlprDIjWXW03q+XHszR3uf81pmyzt77t79rYvbxfcnGctiGPW/ClhCCfKyAKS3aQZuG\nt/etbDMNVTEjTsJIEaFQUfdoOIgUMnJmr0kvJPf62d3fB+u57r/YzHuE3Gw2uf/++9m+fTtCCIaG\nhjj//POJxU7OBaQ1TTs65OgI2SsvIzzlNPjyP+GGLnWvxvb6NlZn12AbNjknT6VTptIpU3B62NB7\nFo9MPsj26lZsYdPfyXJK7hQC5bGpspGHp3/Ni8zzyDu7T63ONBlJI8ayxFoaQQU38ghEm5iRpN8e\nmj1yi5QilC2E1SKKkt3r0DP7C0HGmnvK1hSSfCJFMraGLdXNDNd3YEiTjJ0hG8sRqpCJ1jiT7UkM\naezzqHZmoi11SrMJW33s+57Wmduciu0iDb+BEHLOPcZ7NlQ5RmJOh/NMQ5ZpZAhVuKCjcu3w2u/H\nkna7zU033cRll13Gbbfdxvj4OFNTU9x+++289rWv5aabbqLZbB7NWjVNO0kYmzcRvOm3qW9/imD5\nMhCCofQqklaKuldjuLYd2DtjORPLcGb/WSSsBJsqTzNWG2OyUUa0BhmIraTpN3lo8kEm60VGig06\nXrBX7GTKzGGbFolEgDC6R+d+EKJURIwkQ30FhBHOXqvdc/89qSiivxDHNCQJO8Gq7Jo5udcdL6DV\ntMmYPahIMdEcp9Ku7jNKUghB3sljCLO7+IXX2G9s5cxtTnveYzyzLTBvrTD3mnTVrcwbkakdHvtN\n6rrmmmu44ooreOlLX4qUc+dtpRR333033/nOd/j85z9/xIs8UVOEDuRETkc6kMU6Lli8YzvexmU+\n9Guy//1NVFpFin/0R/D77yO364hWKcXm6iY6QZveeB9LU8sAaPpN6l4NKQx6nB4mWxM8NPkwdz+8\nFXd6CXHy2NImygwTxadxG0mWGOtIWClW9Kd41XkreXpHZTbeURJhxJs8uGmSUkkilI1hCFb0pfi9\ni07vBncoj4SZ2HWdd2485P6iJCudClurW7nv0Un8WgER2RhSkCm4JDNtGg1B1u4jZab2uX+oQqbb\n04xWmoyPhBgytt/X8kOfYqfIUztKuI1u/KcpJH15BxBMlQ9caxRFTLeLPL59kkZNYBvJA0ZkHg7H\n28/i4fScojOjKJr3NMVCtjkcFuM3aLH+4C3WccHiHdvxNK7g7jsJr3kbS0ptWjf+NaOXvw5feSSt\nJGk7091GBWwub8JTLgPJQQYSA0A3Y7nhNzClRcEp8Onb7mJj+SkCAvpYS8IoUKq7lI3NZPMt0mYv\ny+31mNgs70tx6fmr58Q7PrZtki3TY4ShQoQpcvEE0hC7uonz+8yDfnbE5b78n5/+micntmBJmx5z\nBRCX2v0AACAASURBVKa0KVVbBHaVZcvAkQl67AFMGdtnFOXDz0ww3i7RbHVImVlsGdtvbOWDz4yx\nZXocKSBt5jClPbvtTFPagWp9ZMsUW6bHUITEjSRxIzlvROZzcTz9LB5uC5mQ93sNeWaiLZVKfO97\n36NanbsyyDXXXKOvK2iadtioSLHtqXvw0i7T1/8lg294K3mg2C7S9JsIIUlZKUxpsia7ls3VzUw0\nx7ClRd4p7EqtUrSCFjsrEzQqaQr2Sqbd7VTFOCKyCEKbVLCGBCPUw2lK3k567JUMT3ZPITu2STpu\nz8ZsZqwcdVFBmB1MMwXAZKnNupX5fV6r3TMecl86XkClZJE1+6mpKeqqSE4swQ8g8JPEhUlbVakF\nFbJWgckSBCvV7IQZhIpixSeXztNqjdEKath23+7YymdtW66EpK0sjaBKK2yQkYXZbdetzB+w1iBU\nTJfdXTGjZdphk5h0uktWPuu1tMNj3nfzXe96F0888cTRqEXTtJNVFCEQpN9yLc3PfYXh885kZ30n\nwGzs455rFNumzZrsGgxhMlzfQaXT7T7OxLI4hsNwsUorrJGXy+mTp9DLGoJQ4UU1RCTojU5nibUW\nJUPK4Qhu4FFu7I6OnOlGNqVN2syTMHcf3czEQ+7rWu18ivVuQlba7CFvDJKUeYIwQhFBJImFWfrt\nZRhC0ggqdEJ3TpTk7rqsXXVl9qrr2dvaMkbGzBE3Uvvddl9m9pfCIGPlSRpppDAWvL928BZ0H/IN\nN9xwpOvQNO1kFEVM/dX/YltxM2s+/AV6472o01/MtupWxpojEEmyZh8ZO0fVL1PzqkghcUwHx3QY\nyqxiW3ULO+s7MKVJyk6RjeVY2eOC8Qxu1CBl9AFgKJeqmMKjQcLpIWeuoRpO0lRl6tE46cSZ3YUT\n3ADblLPdyHtGQ8LceEhDGhRiBUpuqXv7lZDEjP3fgdKTdmaPKhNGN7IyEhESARKS8Ti2KbFUjEZQ\npRPWsPb4K71nl/SB6tp7W/uA2+7LnvtLYcyJuFzI/trBmzc6s1QqsW3bNjKZDM1mk3q9Tr1eJ50+\nuDU0n4sTItbvIJ0wcYUHabGOCxbv2I7ZuJQi9aEPUvvnL7LFLDO54XR6CyvIOXmkMHhg6zae2DHO\nyHSbUlEQBIJUErzQxTIsTGliGza2EaPilal5NVJmCtu0yThJnhyepNZuEUURhrAxhInnK6QVkMsY\nWCJGTCYJQo9MXtHy2uzYGTA81WRksknL9TEMOad5aV/xkDORlJ2gQyfoYBs2hjT2OWTTkEyUWlSb\n7uwlPyEEfhCSSlos6+0exRrChEiQyQgyaTkbZzkTO+mrCN/fnaa1r7qOZMTlQvY/FIv1dwwWFp05\n70ecer3Ol770JfL5/OxjQgjuuuuu51adpmknL89Dvv/txL9zB0Prz2DkE9ez2ajw0MSvOWfghRSn\nLCy/h5BRasEkjmlDLU8USZYNitn7ji3DIufkiFjJcH0H2+rbOCV7CrZp8/uXnMc/3fUAm0eK+GGE\nYyY4a/Ug2Yxi69Q0Fb9E2sxz5uBa7HSZ4XKRjBXSGxtECkHcMWl3fBIxa69u5GezDZtsLEfFLVN2\ny3utUbyni148xJ33bWd4snvrkmlI1q8uMDSQYrrizr7Wip4CQ8tsmn5jTnLW+lUFRsttnq515q1r\n/aoC7Kf7eyGe6/7awdlvl/WMCy+8kH//93/HcZyjVdNeFmPX3WLtJlys44LFO7ajPq5mk4evvZhf\nFh/k1fZ6Vv799+mkEjw6/TDba9vocfrpTCzHMAxK7gRT7k7iZpr+2HKSMsuLNuRpBjWEkHMmvqnW\nFGPNEWwZ45T8qd3lBXsSPLDpacbLDU7pX0Jfuhu8MdkoMVYt05dO0Zfo5ccPDTPl7aSj2uSsHvJ2\nP0IIVBhx/vOW4IdqQfGQLb+167R69/ar/R0pQ7fBq9xwyadiOLtO/+6rS7vu1Wj6TUxp0eP0IISg\nry/N2Hh1wbGVC+n+PpL7L9Ri/R2DhXVZz/vOrlixYq8Oa03TtEPlfOsbrPrZgwSnruW7H3gTG9UU\nMTPGmT3PYzC5jJHaGJvqjyKAfKyfvthyRCQpeuPU/Cooi7Sd2WshhL5EH73xPjzlsqX6DEopDGmw\nIreENYN5QtmmE3SDPvpTBYZ6CkhDMVqbQkWCfmc5cSOBH3k0g+7fvCBS+KFacLxjwkosaI1iAMc2\nGSwkZydj2HeUZNrOEDfjBMqn7O5eYvFgYid1xOWJYd5T1kIILrnkEk499VQsy5q99/hrX/va0ahP\n07RFZFt1K1z2Sk6JfY6X/dYGfjD6I/5r6/dJGkmWZZdxVt/ZeIHHlpFNbKk/warkmWSNpSSNJtPe\nKGVvHCVWkrbSRJGi4Tcou2UKTgGlICX7cE2PelBla20LfX1nz+ZelzszjVeF2VPMyi0RmB1c1SBj\n5VjiDNEIKniRRzOoERdpLENSa3ndJqcFTEgpK0UUKZr+rkUadh3VPhfZWA4VKdzQpeKW6Scz24C2\n0Lq049+8E/J73vOeo1GHpmmLmLHpaez/uhN+73XUvApbLnk5Z6RX0BYBPxv5Kd/b+l3eeNrl9Cb6\nOGfJOWwZr/D4zi3snGjSb56OZRhgp+gf9Bht7sA21szed9zwm9z79DN06g4hEaaw8GOKgf46Wytb\nydA/J/e63CnNXn/OxwpEUYlMRtBo1EhZGVJmjnpQoRW0aPkBv3iUAyZa7UvazqAiRTtoH7Y86Fys\nG0bSDjr89NFNjI0EB12Xdnyb92PV0NAQP/nJTzj33HMZHBzk29/+NmvWrDkatWmatggYv/4Vude+\nitTHPsyqLZMkrBQVt8xYc5TfGHgh5/afSzNscfvm2yi1i2RiWU7PPo+YTFIMR5gMthBGiqSZoxDr\nJVABO2rbcAOXTCzL9tEOY5U67aiGZUikITDcXsYmQ6pudb+514EKZjOiz1jZSyoV0fIa+KEiITL4\nbgSWj0cL2zSQhmCy0uaJbftfmWlPz86Dfq5mat2ys8H2qSLuIdalHb/mnZD/+I//mBUrVgAwMDDA\nC1/4Qj74wQ8e8cI0TTvxRXf/J49d+xo2U6L2V5+FF5zLUHoIx4hTbE8z0ZrgN1e8jPX59dT8Gv/2\nzO1UmjXcpsN5y36Dlb09GOkpkj1llvelCJopcnYPrnLZXt9O23Np1Sxi0saLPFphtyHINAysdj8W\nMcpumdHGCABxM77X9WcpJL3xHtYP9XLWuhTPPz3DS89aSn+yD0satMImbtgN/ZhNxFrgIgu5WB5L\n2rOrND1XSkGn4WBJk3bYpBO2Dqku7fg074RcrVa56qqrALBtmyuuuIJyuXzEC9M07cRm/9tt5H/v\nKkQQ8tgnPsjG176cKIqwDIuhzCpiMsZUa4Kp1hSvXnMxa7JrKbllvrPpO3RCl6zdyynpDThWjGlv\nJ+OtYUIislYfPfFeOkGLTcUt+FGway1iC1d1Ztf3VQKWJVdhyxjT7SkmWhMAJK0kKSuFikLKbhkV\ndZu/Ck4ByzRRsk2900IhSFs5JIJ2uHtlu4NJqZo5qjWltWuVpufWQdxyAxSQtvNIBJ1DrEs7Ps07\nITuOw09+8pPZr++9917i8fgB9tA07WSn7rmbxLvfihFLsOrGf0Gc/wq2VbcwuisOM2bGWJlZhSFN\nxltjlDslLhm6lFWpFVS8KX5Z+gFhFFCI9bMufQ6OEWfC20HFGycRsxhMLiUTyxGKDmVvnIiItJkj\nbeZm4x1NIUkn4qzJrsWUNhPN7usApOw0CTPR7VzulImiqHubVKyAQODRIIoCDGGSsQqkzOzs2A42\npWomYtMQJg2/QdM/9GVrZ9KzDGHsqmv3+sk6PevEN++E/PGPf5ybbrqJ8847j/POO48bb7yRj33s\nY0ejNk3TTkCdoMNTp/XyzFsvZ/zbt+Od+0rOyG/AkCbPVDcz3hwDIG7FWZkeQiIZaYzQVi1eu+aN\nDKYGaclx7p++i3q7Q7Eo6ZVrQEla5ijT7UmUgqwcIGWniCU9pjqjhCoi9CWhimbX97VMuc/c644X\nUG8YoCx85c2ubSwwkKqbQOWkXLzQI4okgS/mPO/BdjUfSu71vsxdu9mYjc881Lq048u8wSAzyuUy\nlmWRSqXm3/gwW4w3ii/WG+AX67hg8Y7tsI1LKaxf3EPrJS9hZ30nD20bxm/FSVs9xKSNnWzjJ4Yx\npcGZhQ0UEt3l+6pulZ31HYBgKLMKKST//MTX+c7PH8ZqrmRJdE43vzrZ5jWvtpgqdsioVSTMHAKF\nZ02wZWqaZtUhEfVgmQYr+lNc9OIhBgeys2NreA02lzfzi0fH8GoFjMjBkIJCT8j5z+9jdNynVe+m\ncqnIw064jE43qZQsIiUxDTn7vKY8tInPD31KbokoUuSdwgFzr/f7NkdRN6lra2nRdVkv1t8xeI7B\nIO973/v4+c9/Pvt1Pp+fMxn/+Mc/5tprr32OJWqatih4HpPvv5LRd12Kc+f3KU3btFoGLVWlpcog\nFV47jmguIYxCnig9Ts2tAZCNZVmaWkaEYkd9OwLB5vuGiDoOTWsrZesJLEsSdpL8x392KDY6jHQ2\n4UctLNNieiKF15H09Sv6l4ScvjJPIm7x1Pa5vS4pO8XTGwXj5SbVaBxhBpimQblkcNvd29hZLtOJ\nmtimgWPF2brTp9byWDooOGV5Zr/PezC6t1rlEXTjP73w4HObpRC84PQBXv78pbzkzCW8/PlL2bC6\n54SfjLUD3Id8ww03cPPNN3P99dezbt06lixZgmEYjIyM8Nhjj3HhhRfqVaA0TYNGg+w7fo+dT/6I\n0XNPZ/L0XsqVkJ5YPyVvkoZfxZI2KTOHamVYO5hkS/0ZHpt+hLP6nk/KTpF3CgQqYLw5xsOjT1Ft\nwQrjFezgbsriKRLkyBpDuM0UedFPNdrBjtZGVsbX0e5ExMJ+TFGnTZlm6JC2ckyW2vjB7q7jjhdQ\nLEkK1lLK4RjlYIQ+czVCSEpFg+X9ko5oYYYmhojhuyZBaKFQ+LJJXHbz/J/rWsAHk3t9IPOtvayd\nePb7E5VMJrnuuuv41re+xSWXXEJvby89PT1ccsklfPe73+W66647JqevNU07fohSkdzlr8P+8Y84\n5azfQXz002yNqoy0txOTcQp2PwkzTRAFNIIqvgopxAYZSq3CDV2emH6Mtt+9ntqX6Kc/0c+msWma\nagKHJMt5CXH6uo1WqkkUgWomGYwPIYRkpLkDN2gjhU3eWErMiOMpl3bY7HYdd/zZWmfWIk4YWXJy\nCZZ0kFISKoVSAhmmsIWNFBLfD1EqwhQJzCiOKXZPmIejm9kxHTJ2dq/4T+3kNu/HslQqxYUXXng0\natE07ThzoHhGMTFB8S2vorhjKyuuuJLOZz7P80SIN/YrnvZ2EuuYDDgrcJwEjaCKp1yI6sTtFayJ\nr8GPPEYbIzw+/Qhn959DGAr8doozBlfwH3I7DSZIsYTVvBKPGkq6hCKkr7CGhJ2HMEYlmqCiRukV\nK4gZWQJ3gMhs0A6bOEDcsWg2OrTcgGxidxZz0syRpNuhbEiJYQjSTgzT7N5BIqwIKQUigkwsjZS7\nTwcfrm7mhJVAoWh49dnVnJRCx2GexHSPvKZpe1FRxBP7WXZv5lpllMthFvqZ+M0XUb7mg6w2DBxp\ncVb/2WydqDJWG8YUFr3OIEkjQ6gqZDKSZlAja+Q4NXcaSoWMNEb54o/uRDSWEymJlJA0sjSDKk2j\nOynbpGmHFZJJxUS9RDBto5RBUzm0/SqbapuYLHcAA4Qilmxz3pkWv9q4gx3Du8dgmxIvCDGMPVdh\niljRl0IauyddQwpScQsBcybjw93NPJN7Xfca/GLjZtxGfFf85+Jp1NIWTn8E0zRtL09sKzFZaSMN\nsVc8Y3tqlEq7DLEYmX/8d8z3/S+aYZPh+g4A0rE0F69/CT2pOCPNLUw0x4gUrOldwplD/bSDNnWv\nhpSS0wtn8NRmj23FcabDbViWwDJNzlq6hriZwlUN6sEkoVIsy/bz+pedgqda+GF31aaM0UPUTtIO\nPKpiFKIIQ5pEXoJHNhXZPDFOILzZMQwNZrBNgzBUuF5AGCqW96V482vW0Z+Lo8IILwhRYcTZa3s4\na23vnMf6c4d/LeC0nWHHqMt4tUErqs7Gf+o4zJPPgo6QW60W1WqVPe+QWrp06RErStO0YycIFZOl\n7mTsKReJxJQWUgjED+9i8h/ez/D//4es/e03M5gaZMhazZbqM1S9CiP1YZalV1BIFLjkeS/h4clH\n8IMKG/qH6E/3oiJFsV2k6TcRQmJGDtS6qzk1VJEpz6TPXoUTczhnxem84GzYNl3krOUrWNe/hh8/\nNMzSHp9AKWI42DLGeLmP0FI4SRdDFOm3hwAYKQaEq6AdVJFmDlN2T1mvHsxy7vp+6m1/zlrEG1b3\nEKzce93fI70WcBAqWnULR8a6q0yFNVJmdncc5nNoINNOLPNOyDfffDNf+cpXyOfzs48JIbjrrruO\naGGaph0bLbe7ipCNQSuoE6FImzlW/fSHnPsX11GJw4iveLr8FKY06Ev0syrbnZSLnSKGMFmSGqQn\n3sv6nvVsLD/B09UncewYmViGglOg2CnS8Oo0Gi2iSLLEPpURbyOtqEJb1UjILJGSrMgM0ZdL4IYt\ntlVGiJC7VmMqI4RH2zVQKiIZ9WGKMoFsofCJlAHKRAZxIjw85WHKbkdyECkiYLCQ3Gvs++pcPtLd\nzDPvd9LMooIKnnJRkUIKOdtAprupTw7zTsi33norP/rRj+ZMyJqmLV4z8YwACTNNI6givncz/f/0\n9ygrCf/4DU79jTN4ovg4T5aexBQm+XiBNdm1PF1+msn2BIY06Uv0sSS1BF+5PFPt3ub0/L5zSNiJ\n2UnZtF0i6WPKOMvsM3BVjRCftqphyxT9mRRCxtlW3ULDL9L0Bfl4H1mrF+jmVUspEELSZ68gwMWU\nNooIwxDkkmliHROxx9W54y1icub9FkKQNnNEdCdjOP5q1Y6sec+D9Pf3k07PnzCiadrxJwgVtZZ3\nUKsA7RnPaAub3/jm18n9x9/zy7Up7vjsZ6i98CWUShbLnNVEUcjjxceoe3WCAGJBP0EAY80Ryp0S\nHS9A+j0MOMtxQ5dHi4/Q9tsEAbQbFkIICgWFF7pIDKwog1QWnurQ0xNhGhLXEyxNriRm2YhEnapX\nhkjg+d1O6HzKxjYFUgps6ewaRbdRyzQFUhizaxEfjxGTe77fQojZLO7jsVbtyNrvR6+bb74ZgEwm\nw5VXXsnLX/7yOZ2J11xzzZGvTtO0Q7KQLukDWb+qANtKTI+WWH3vPQx6S/jOtR/gwVaD279+Jwn6\nMQxBIhdw9tkBX7r7PzEaKzGIoYSPmZlCRSME9QJmlMA0JDIdsWpNjS/efSdWcxWRkiADcvkIlxoT\nRQuUgZQGhZ4YvQXJnb/aiG0kMYUklUmzajDgF4/sxK3WMEl2rwkvzRBFESNTLYJQzUZcvuq8lUxW\nXZ6udfZ6D443M+/3vr5f2slj3nMhZ5111tGoQ9O0w2hOlzTdD9KTlTZsK7Fhdc+8+0shWLM8Tr4n\nydS/fJ285VD81QjF2q8RYhhTxEjJAu1Klu/dtQMrXcExPAatdcSNBM9sjVGLxljS45E3l2PIBEGt\njx/97GkCu0LSDBi0T8MQDtt2VpF2wNnrMkiVJuM4jJUaPD1cYaDHwRQG0kjSaEimxwSOY+LE2uTN\nHJlYBiGgPxfnVS8aotxw5zRqveD0AZZknSPalHU4SCH221SmnTz2OyHPHAHfdtttXHbZZXP+7+tf\n//qRrUrTtEM20yUdyZCyVyJuJHGM5MK7dhsNMu99N/yP91J/3lqCviTlKMl0aScD8hSmos2Uoq3E\nwxRCWHiNHvLZGM1okmKwg0K0itB3sKI+AlWnGozh2GsRUuLWB0j2CVqqQtEfpmCuIAotOg0LRYQd\nc5EyTqsdEpEgUhEtmpjCQmBRLAmWDi6hFIzTiKZI4mCLGJOlNutW5hfcqHW8OpFq1Q6//U7I//iP\n/0ij0eAb3/gGIyMjs4+HYcgdd9zB1VdffVQK1DTt4Mx07ZpIBJJW2EQgiRnx+bt2p6dpv/P1xH79\nKOlkEvezN1PplHlqfAQv8InbWfrCU2hRwjBsXD9EKkkqXEYsFiNCUfNKhJGBRZKUSmCY3VhI1wsR\nStDLKprGOIawafhVlLKROIjAwY7ZeIEijCJA4ogEQvpIYeJ5IUGocESePlvQDOo0d93SpCJDdyNr\nJ7z9fkweGhra5+O2bfOpT33qiBWkadpzM9O1K4UkbeWQCJphHU+5B+zalTuHib3xVdQ2P8qWyy9m\n9KZPYWAhwwS9GYfAqKOiEEdmyDJEFEVYhgQJScem11pOxujFMAWeaAARKTtHnB5UFBGzDYQUxGyH\nfns1SSMLRogrGkgg7aQhsDGkwBDdJi3HipEw00ghsSwD05BYpiRl5SjEBoiARlCFKMQy5EE3sGna\n8WS/R8gXXHABF1xwAa95zWtYu3btYXkxpRQf/ehH2bhxI7Ztc/311+934tc07dDMdO1OVtoYwpy9\nb7fuV1ndM7DP09Vy41Nkr3wDxugoqT94J1v+4G08vGULtDOYMk6ofOK2pFifxowyCCQCgSmhL+Mw\nszxwTCaIzIiY1cT1a0xXu+v9CiGwTcnSQgIpIkDgyBQRCsOo4okaW8cSRFH3NiYvCMinu+sVzxAC\nVvSnYNdDlrRJGRmqfoUpd5p7HpEohG6I0k5Y+52QX/nKV87eKrAvhxIM8sMf/hDP8/jmN7/JQw89\nxKc+9Sm+8IUvHPTzaJp2YHt27apIkhBZnLTL4BKBH/pYhjW7baQUox95J7XWKD0f+Sjxa/4nxac2\nMl6ZwjE9eowB4naC5T29VBqjuKqGHWUwhCQVt7n4/CFGp1oMTzYIQoVlxFk10EO11aDVqmJFaUwp\nSTgGLz9rKTunmru3lUn6MxHSDHG9OrZIIyIopONkUxYqjOZ0Ha87eylPbS/v0Y0cQ/oOdsylqaqk\nrXz3WvmuBraB/swx/C5o2sHZ74R8yy23EEURn/vc51ixYgVvfOMbMQyDO+64g507dx7Siz3wwAO8\n7GUvA+D5z38+jz322KFVrWnaAe2ra9ePXKpuhZJbmrsGrxB0PvYpyg/+gvJrL2dJGBE0E2TtPHW/\nTNmfJhv1Efox1gz00dez697gzAAx26BS87novCGCUFFuuKTjFv/3iUlaqkrL7xB6Br2pHixTUqp5\ne217/+MTNKMqXuhhRgaZWAYpBSqMOP95S/BDNafreM9xWYbkF4+CS4t22KQRVEib+dkGtj3XQ9a0\n491+J+Rly5YBsHHjRm644YbZx9/xjnfwxje+8ZBerNFozFlD2TAMgiDANA9891Vf3+IMJtHjOvGc\n6GNrekmqbhUpPLLfuxPrjDMRfWfxgpe+imfWr6XpN6lRIpFwyJjLKboWnnLxwybSFKStHL1ZA2EG\n5Ow4Ukg8PySZcsikYqwAqg2XeKJCxhyg7lcIo4B8LAGwz20Tye62Nb+MQJC147PbZrMJMqnYfsfT\nfS2brBWn6Vu4qkPStjGEgeeHtDv+Cf892x89rsVnQZls9913Hy9+8YsB+MlPfvKspcsWLpVK0Ww2\nZ79WSs07GQNMTdUP6fWOZ319aT2uE8xiGZvrgfdPNzPxN9fT37+WgQefolxukw77mKrVmPZ2MlaN\n6IkP4EQFzMin6lWoujWSIosZ9iIUNH0PABVGNBsd3Hb36yBUtFseriEQxDGiiLrfOeC20hAYJImi\niLq37233Zc/XAhszsmj5PuCjwoi4Yy2K79mzLZafxWdbrOOChX3QmHc2vP7667nuuuuYmpoiiiKW\nLVvGX/zFXxxSQeeccw533303F198MQ899BCnnXbaIT2Ppp0IglAdf4vNRxH9f/1Zws9+irHlBaZv\n/Etsv0YQGrTdiGXJlQxH25HxKcquSdYqEAYGKTNNwmkiaIIoIA4Q77hnU5kU4oCxlQez7b7Mt79l\nHifvu6YtwLwT8vr167njjjsol8sIIcjlcof8Yr/zO7/Dz3/+c6666iqiKOKTn/zkIT+Xph2vnmts\n5REThqT+9E+If/XLhCtX0fP125gazPGrjcPs3NkhZqQwhSSdSbFmqc89j4ywtVrHIoVpSAYKeQo9\nUK2XiRtZbGnut5v5YKIgn2tspI6d1BYLEe25yPEePvKRj/CJT3yCN7/5zfvstv7a1752xIubsRhP\nYSzWUzOLdVyw8LE9trXY7fIlQtA9alNRd3H7hcRWHinJP/0TEl/+O4L1G6h+81bUwBIe3TLNRLtI\nrdnCkQkSZgoVRRQbFRpiAhVFFIwlZJwcQkAqFbJ0wCQIBUvT/djzXHI6mLWEn+u6w/vaf7H+POpx\nnXie0ynrK6+8EoBrr7328FWkaYvcTGylNAQ1v4yKFBkrjxTGMV9svvOWd2DsHKb+2S8SZXMEoWKq\n3CGbKdBsunRUC1vFkJhMFyOWLR2k5I3SoEQyimNLh0bNILEsgWd0qPtVCkbhgLdHHkwU5HONjdSx\nk9qJbr8T8oYNGwD48pe/PBsSsmTJkqNWmKadiGZiK20MbBmjFTap+xXSVp4gio56vKMoFqlVx+gs\nGaBw2mmEX/vGXrXOJHp5qoMhTFxfEYQKmwx9sWW0wwaNsEZaSKLIwBJJpIRO2KHilsk7+tSwph0O\n835Uf+9738v09DTXXnstl112GZ/5zGd4+OGHj0ZtmnbCmYmtBHCMJI6MExLSCKoYiINabL7jBYwU\nG3S84JC2lTuHyb3u1RTe/LuoSpGy2z1in1kj2TblbK1SGDhGcjZRqxtRaZC0MuTsXgTQCCoQde/9\nFWECiYUbdu9thkNbe1nTtN3m/etw9tlnc/bZZ3P11Vdz55138sUvfpGvfOUrOtRD0/bh2V2/CTON\nChSdsENvzpsTBbk/gVLced/22TSrmfV9L3rxEKaUC9r20kKH3FVvxBgdwf6D9xEvLKERtLj3qLOz\ngwAAIABJREFU6Wfo1B1CIkwhabk+KfWs+3x3RVTOnIk2pU3SzFL1Kky5RX72iCRCYiCIpdqsWqZ4\nanuFZt08vprYNO0EM+8R8sc+9jFe97rX8c53vpNt27bxZ3/2Z9x7771HozZNOyGtX1WgPxdHhRFe\nEBInzWAuw6pl8dmjyQO5877t7JxqYBiSmG1iGJKdUw3uvG/7grZV999P+pJXYYyO0Pjfn6D50evJ\nODm2j3YYq9RpRzVs00Aagrhj0er4s7WqsNt8dtGLh+aMwYxsZOBgxyRNVcU0wDAlnZbDT389xnC5\nhEdr9nknK22e2FY6Em+vpi1a8x4h12o1oihi9erVrF27ljVr1pBOn7xJKpo2n33FVhpSUOqU6IQd\n6l6NtL3vjOWOFzA82Z1gO6pBW9VJyz5MaTI82T0l7ew67b3ntq2wihd1OHvjVq743J9i+h6lT3+W\n8M1vBXbdE12ziEkbL/JoBXUSZhpDChKOxVnr8guKqPQwZ6+LZ6wCAkm5ZDI4CK2wiRQmtowtfO1l\nTdNmzTshf/rTnwbgmWee4d577+U973kPrVaLn/3sZ0e8OE07kT276zfv5Cm2izT9JkJIUlZqr32K\n9Q5BqDAMia9cWqpKoDx6zBWz+c+DBXOvbb2oQ1OVGYu1CEyLb739f/O8S/4bg7uet+UGhESkzByN\noIJi93XeMIzwQ7XPZrOZMdRaHkGkcMwkEeCGbSIifF+hlMARaXwaRNHu55137WVN0+aYd0LesmUL\n9957L/feey9PPvkkZ599Nq94xSuORm2atqhIISk4BYqdIg2vjkSSsBJztulJO7NHlGmzBz/o0FZ1\nyuEoOWMp+T1ynffcNh9mCKTH8MohPnfjrbTtOC/fY9uZZjMhBGkrP+c1DWP+ZrM9m9XiRpK4kQTA\nsgSmIXEsm4Sce4/1gdZe1jRtb/P+trz//e/nggsu4G1vexvnnHMOUurTT5p2qAxpzE7KNa+KFBLH\ndGb/37FNVvSn2DnVQEpJTg6iIkU7rDOYr86erp7dti/J2q/8Dac98nP++U8+R9tJ0DFhRV9qzrbP\nbjaboaKIpb3Jg46onPHsNYr3fN6FRF9qmrbbvBPyHXfccTTq0LSThilN8rE85U6JqltBiDwxY/fR\n7EUvHprTOZ0Vg/T1FDl7fZLRxghLU92V2AhDrrj9syT//auUepciq2WiWGK2I/vZ9hcxefap/RSL\njXnr3t/+e69RrKMrNe1Q6PNJmnYM2IZNzslT6ZSpdMoUnB4swwLAlJJLz19NxwsoN1zyqRimCZvL\nm5huT2FIkwEjR/qa38f57q0E6zfQvuVfuTCZJ5+KzTky3tO+ms1MQyIXcCvWgfYH9vu4pmkLp39r\nNO0YiRkxMrEsERElt0Sg5gaAOLbJYCGJY5uY0mRNdi2mtJmcegb/rW/A+e6teC8+n8p3/wN7xfLZ\nbecz06h1qJPm/vZ/rs+raSe7/f72/vKXvzzgji960YsOezGadrKJm3FUpKh7NUqdEj1OD4bc93rj\ntmmzJruG7Q99i4mn7iV/0UU0/u6fIB4/ylVrmnYk7HdC/tu//dv97iSEOKqrPWnaYpa0kkSRouE3\nKLtlCk4BKfZ9lOmYDkOvuBz3c/00zvttmGe1JU3TThz7/W2+5ZZbjmYdmnZSS9lpVKRoBS3Kne6k\nvOcqSsbGp0h85ibqf/05HMfB+c1XH8NqNU07Eub9eP2rX/2Kr3zlK7RaLaIoQinF6OgoP/rRj45G\nfZp20sjEsqhIza6ilIvlEUJg/ur/kr36cmS5jPu6y/AuvvRYl6pp2hEwb/fFhz/8YS688ELCMOTq\nq69maGiICy+88GjUpmknnWwsh23YuKFLzati/eiH5P7b6xC1GrW//YKejDVtEZt3QnYchze96U2c\ne+65ZDIZrr/++nkbvjRNOzRCCPKxApa0Cf/tXxHvuByUovbVr+NedfWxLk/TtCNo3gk5FotRqVRY\nvXo1Dz/8MEIIWq3W0ahN005KQgh6R4vkrvsT2ukE1X+9He+ii491WZqmHWHzTshve9vb+MM//EMu\nuOACbr/9di655BI2bNhwNGrTtJNWtPY04h/7G6x/+T7+i88/1uVomnYUzNvUdf7553PRRRchhODW\nW29l27ZtevlFTTsSwhDn61+j87tvBtPEffPbjnVFmqYdRfs9Qh4bG2N0dJSrr76a8fFxRkdHqVQq\npNNp3vWudx3NGjVt8XNdMu9+O+k/fj+Jv/n0sa5G07Rj4IDBIPfffz+Tk5NcffXuZhLTNPmt3/qt\no1Gbpp0URKNO5q1XY//sx3gv+U3a73rPsS5J07RjYL8T8g033ADAl770Jd797ncftYI07WQipqfJ\n/u6bsB56EPeiS6j93T/oKExNO0ktqKnri1/8Itdddx2NRoObb74Zz/OORm3acSYIFbWWRxCqY13K\n4tBokHvdq7EeepD2776Z2j/coidjTTuJzdvU9fGPf5xCocDjjz+OYRjs2LGDD33oQ9x0001Hoz7t\nOKCiiCf2sQ7u+lWFOYvVawcpleomb/k+zQ9/FPR7qWkntXmPkB9//HH+6I/+CNM0icfj3HjjjTz5\n5JNHozbtOPHEthKTlTbSENimgTQEk5U2T2wrHevSTkjGls0QRQC0rvsQzY98TE/GmqbNPyELIfA8\nbzbovlwuzwm91xa3IFRMltpIIaj5JRpBFeguVj9ZauvT1wfJvuu/yL/ypSRu+ET3Af27pGnaLvNO\nyG95y1t4+9vfztTUFH/+53/Om970Jt761rcejdq040DLDQii7qQrkXjKpRnUAAgiRdsLjmV5J5TY\nt79J5s1XgVIEv6HXE9c0ba55ryG/4Q1vYMOGDdx///0opfjCF77AunXrjkZt2nEgETMxd63NmzSz\nqKCMqzqIQOKIJJYhqbW87nbGvJ/vTlrxv/8CqQ9dh8pkqf3zN3X6lqZpe5l3QvZ9n3vuuYf77rsP\n0zSJxWKcfvrp+rT1ScI0ug1ck5XuaeuUmaPul2kGTVp+wC8eZU6j1yt6Use65ONLFJG48XqSf3UT\nYf8A1W/eRnimjp7VNG1v807IH/7wh+l0OlxxxRUopfjud7/Lpk2b+NCHPnQ06tOOA+tXFWC2yzoi\nKbM02lPYsQ4+MWJm91adyUqbhzdNsryQOMYVH1/kdJFw1Woq/3o7atXqY12OpmnHqXkn5Icffpg7\n77xz9utXvvKVXHqpXpP1ZCKFYMPqHoKV3WvGliH52SMRTVWhFpSJqTZpM48UgtHpJkuyjj59HQRg\nmiAEjRs/jahWiAo9x7oqTdOOY/P+1RwcHGT79u2zX09PTzMwMHBEi9KOT6YhScdtvEARIUmZWQLl\nU3QnqPpFAMIwOukbvUSjTvbKy4h//rPdBwxDT8aaps1r3iPkIAh4/etfzwtf+EJM0+SBBx6gr6+P\nt7zlLQB87WtfO+JFaseXmUYvKQ167CWMd7ZT9qeQGCxJDBC35/2xWrTE9DTZ//4mrIcfJEpnaL/n\nvSBP8rMFmqYtyLx/Oa+99to5X7/jHe84YsVoR1YQKlpuMG9HdKPtMVJssqwnSSpu73P/mUYvU9hk\n5CDlYIQpb4xTMj37fe6Fvv6JSg7vIHvFGzCf2Uz76rfQuOmv9WSsadqCzTshn3vuuUejDu0IWmj0\npReG3PL9pxieahCGEYYhWNGX4uqLTmfzcHXO/r05h2bLY+dUkyBUBCSIZaukCy3qXp20nT7o1z+R\nGU89SfaKN2CMj9G69g91FKamaQft5D23eBKZib4UEmxhAN2OaLaV2LB697XNW77/FDunGhhSQhRi\nSMnOqQY3f+sRzlnXP2f/R7cUiYDTVubx/ADb6qEZZnh6R5HBfIehzCqSVnLO60tDYEUSIcQ+X/9E\nFv/7L2CMj9H46J/T/oNr599B0zTtWfT5tEVuJvpSRT5lf4p22AT2jr5stD2GpxpIKWmEU+yMHqAW\nTiCEZLTUwvcVVb9IzS8ThopGy6fR9umETTqijMInY+VQnSyu7zFc204n6MyJ3mwFdar+NIHyF130\nZuOTN1H9P9/Wk7GmaYdMT8iL3Ez0pRQmBgbtsEknbAFzoy9Hik3CsLvggU0KgUk52k4tKBKpiGrL\nxZI2QeRTcssESqFURBR2j5gbQYVAeaSMPGmrB1/57Khto9puz0ZvmtJGEdEIKoRRcMJHb8a+/U1i\n3/yXXV/E8H77Vce2IE3TTmh6Ql7kZjuihSQmMjQaHlW3hhd2MIUkDBUbd5bJJiwMo3vN0zLiFKI1\ngEFFbKMja2QTMRJGGlvYCBng00BKgW3GMMIkgYq6C0+IkKHsMnrjfXTCDlPuTgTdCdmWMRJGCkVE\n3a8giU7Yjuz4lz5P5g/eRerP/hRRrx3rcjRNWwROzL+G2oKZhqQ3F+Puh0ao1D38MMCTZdLxEmaY\n5KePjMw2cLU7PhEQIYiiBA4rqLCFWGoEN1pNTGS7edZUsGJNqs0KW0ZBqYgAhRFrMbQsh5SwJDlI\noALKbonQqSO8Pkxp4hgJIiKaQQMn5Z54TchRROKGT5D867+cjcKM0pljXZWmaYvAifbnUDsE2yca\ndDohEoFlWCTIMDzRZFtpEkSIbRkYUhIBHTdEqQilFLEoy/L4Gp5/aoGK3EzTq+OHioTIkEvGSSUj\nvF3XpGPCIS6TKKUodUpERCxLLycTy7Gk30DZJYIgxAtCYiRYns+zZnmKcqdMtGtt4ONeGJL64//Z\nnYxXrabyvR/oXGpN0w4bfYS8yHW8gJ2TDdIpk1o4STzKYYgU49UmfthgMtiMI9LkjOVECCzL4KVn\nDVBza/SlCyRiqxlvDNPbW8c2JlmXPZtMPMk9j0Qk4xXcoE0UuvQlBjAMSb3u4mY9Sp0SPU4PK1Ir\nUCpADjTIWFCwB0jELExDUumU6YQdKm6ZvFM41m/V/G68kfgtX8XfcBbVb9xK1N9/rCvSNG0R0UfI\ni1yx3u10VgS0oxo1xqh3WhjKwooSNMIyk9HTFP0dRAqiKKLlu6QzEl/UUZGi4CylL76MAI+trSdp\nuB0UgrSVIxIRLVGmEkwSRRExkUIom0D5lN0SUkhWZlYRN+LU/BJNVcSQ3WvV2VgO27BxQ5eqWznG\n79QCXHstrf/v96ne/j09GWuadtjpCXmR60l3F3qwpUNW9hPg0zEnUTLEFHGWGKcjMSiymYYYRwhB\nbyqDI+OEhDSCKgaCM/vWsTS5lKbfYHPtcYSKMIRJb2wQW9jU/DIVbwrDEAyke4gZMbzQo+pWMKXJ\nyswqYobDdHuKqdYkAEII8rECprRoB23q3vHXHCWmp7F+9pPuF+k0zU/eRJTJHtuiNE1blI7JhPyD\nH/yAD3zgA8fipU9YQaiotbwF3bfb8QJGig06XoBjm6zoT6GUwo6yhK0kgfLwrUkMI8CKstjN1QSB\nYpqNmE4d1wvZvtPDbUk85eGkXQwp6DVXMToaMV6dpmZtxQ8CBCYZMYjApORPE0+1MQ1JLpbHkjad\nsEPNrRIzYyxLrqTjRYw2xii2p4HupFxwChjCpOk3afiNI/1WLpjcsZ3ca19F9urLMTZvOtblaJq2\nyB31a8jXX38999xzD2ecccbRfukT0sHETgZKced92xmebBCECtOQrOhP8dJzBrnhqw9QbrhEkU1L\n+JixFmOdERLNQSR5ovYQdfNpnmo/wMg9LjYZICKW6PA/rsrwv/7hLmo1k1BJKnInyZTHC1Y2Mdsr\nu6fERRI7WyLT41LulMg7BfJOnlKnRMNv8tSOCo2aQSNwKLojbMvUecXpG8jH80ghKTgFip0iDa+O\nRJKwju2aysaTT5C98rJuFOb7P0C49pRjWo+maYvfUT9CPuecc/joRz96tF/2hLVn7KVhREijGzv5\nxLbSXtveed92dk41iITCMBWG0Y2+vOGrDxCECjvRIhW3GEysoNNyCOlQYSMdKqRYQiZYQycKqJpP\n0DCHsU2DoONw01cfY7JapSOnESb0yNOoVUx+vmkTiZ4ia5ened6qQVZmVvHMzgqjjRFqbm12ot28\ns8aOUpFO1CRlJxhILKNUd/np049T9+oAGNKg4BQQQlLzqnSCztF+q2eZ999H7nUXdaMwP/5Jmh/6\nM51LrWnaEXfEJuRvfetbXHrppXP+PfLII1x88cUI/cdtQfaMnWyHTap+CS/s7DN2suMFDE92oy+r\n4ThTwTY6qoWvFOWGS0uWGRMPUTSeIAgVcQYwIocd5g940ryFCuOkWEYqXEFZbONp8S2GxS8QQmBG\nKQQGO/gp2/gvFC55uRavbbCltpnhzuP4UYeklSLq5AlCxXB9B02/iVLgNhxMISl5E0x1RrFkjP74\nUorVNtsq22j63VunTGmSj+URCKpuBS/0jvp7bv3iHnJXvB7RqFP77Bdpv+eao16DpmknpyN2yvry\nyy/n8ssvPyzP1deXnn+jE9B846o2XOIJG9syiCuDmq8Aj5gVh9AimXLIpGIADE/UMEwDxzbpsfqY\n9nbSYgLVKUAEDlkc0rSYxvOfIMFpJKJBcupUpo2H2WLcyhnhVWSiIbygRssaY5R7kSpGHy/AVEky\n5nIqbGdH9HOW8gryrEUEJepimmK0g9XxdaAynDIwQCWcpCGncazlpJIJMoZDyZXU/SqeVaU3NoBp\nCSwnomFOsySXw7EcAHqDFKV2CfDIJTJYhnWEvxN7OP+FcPrp8IlPkLn00r3++2T9WTyRLdax6XEt\nPifEfchTU/VjXcJh19eXnndcQahotzzcXZGWKIdGUKFBh6TI0Wx0aDY6tNwAKSAMQjpRBMRIqF7K\n4RihMYEScQzS9LOBER6kZU3g+RY5VrNS/Q6eaFGVm9jEtzk1vIL+6HmEYZtR+XNGzJ+AH2Ol3MBg\n9CJ8OjQZZ4R7WMJLWF94PqP+Y2wpbcbtRJzWu45Y2IvlpplojjFNg1rdIWY7WCoNXovJ1hRt0ydr\n9pHGZnJ6imr5UVZn185Ovn5gUHUrFEtNepweTHlkf1Tl+BhqySDgwJ0/7q5j/Kzvz0K+ZyeixTou\nWLxj0+M68Szkg4a+7ek4ZhrdBi61K8nKkjZJM0sYRfy/9u48vor63v/4a2bOmbMv2SEL+yoFZVNc\nAIvVKipqtUJFKvVXXKpVqxcRF6SouPR6tWrFtRQFRagbrcV70drrLV6wWhERZAkkIWQhCclZc/bz\n+yOYKxVkS3KWfJ6Ph48HxEnm8zmZx3kzM9/zGZM9zJcVDXy4oYZ1m+v4ePNedINKPNF2GduquXCp\nhahKHNW2j3gigkExU8RwTAYLfkMVfqpRMTAgfiG2eG9C6j7K1VW0EqEwPpb82GhiRKg2vE8Tu1DR\n6MUZmMklQD0ey6cUOnIYYB+OxWCjOlhOTN9LkjgFlgLyLQXEiRK3NBFNRDGoOm69EItmxxttwWgN\nUGQrbJ97XeGtIJZoe9iExWDBoTtJJtsmfyWSnfRUqGQS68IF5JxxMtqmL9q+lnHzPIUQ2UCbn4IV\nVqWlpZx77rlHvH0w2PX3EjubzWY6or7y3RaCoRj+YJRoIoEBA0U5NsLxVmpaPJgNZgyahqIqOG1G\n/IEosXiCaCyOSbXQI8/CD07Lp7x2L+GQjpYwYVJyUUyNeBINKAkLJlzkJAfjYRetWi2BZDVOhpBD\nH6LJCIq9hlZDNeZYT/S4EzM9MFhb6Nc/jqfVQ4l1EHbVhWLy4yxoRYlruExunLqTeDKOZgrhDQVQ\nIhaSSQ1dMeF0KBQVqaiKQoG1kFgihi/qpTXWilN3oSoquqYDEI6HCccjmA3mjl1/EI9jn/0rrM89\nTby4hNCVV5F0uw+5+ZH+zjJNtvYF2dub9JV5bDbTYbfJiEvW3ZmqKHyvbx6xXm2PKvz66Uj/9WmA\nJGFqWyvI03tiNlgxaBp9e7o4+YRCfK1RcuwmzLqB3d5KfnKukUTECK35lBY4+HBTEVsC/6DZvwdL\n0MLQgiEomy9hq7qSVr2SrdEXOMU4i+HaJLYlo+T33kO+6RPOLpjB2L4nY9LH89r2V/GE6oi7vuLC\nfj+goTWfXeGv2NS4CU0x0NvVh562YmKJOGpRMzZDjHxTCTaTTigRpM5fS2NrI6qiUWwvIZ6M4414\n2O3bTS9nL1RFxa47SCQTBGNBmkPN+1did0Aoh0I4r/85pndWER1+Ip5XX5fpW0KIlJJrcxnCoKk4\nLDoGTSUYjmFQLZhUM9FEhL3hasLxto8JxZIJkkDPXBvm/eFd5uyNQ3ei6lFcBQF0owG7KY/BztFY\nrAZiuTuoi+zGlsxhUPIydNVGwFzFP7UXSRCjX+IHlJlOwBNv5GP/66haCIfFwcUDfoRdt7HNs5EN\ne/9JT3sxI4pGYFSNfNG4kT3ePQCU2ktxGB0EYj48sb1oqoLdaKfIVoRRNdDY2kBLuIVSexl2ox1v\npIUaf037QyecJhdmra3XlnDzcb+Wis+L64rLML2zisjp42UUphAiLUggZ6Cvn3FsN7pw6wXEkzH2\nhncTjYcxKOpBnzHc29EHm9GOL+KlKbwHg6KSb+7BUOdYUKA2uQG/0oBLyWekcgUmcmihks9Ziqaq\nXDvq/zHYPZTd/iqe/eIp/BE/+dZ8LugzBaNmYu2ev/Flwyb65vRlSO4QVFQ2NW2kPlCPqqiUOXtj\nNVhpCe+jNtAWtg7dSaG1qO1jXK31+KLe9rnX+0KN1AVq20O5I+deK14v2s5ywpMvbDszlscnCiHS\nQEruIR+tbLyncDz3SlRVIRCO4g9F254vrEAw5qc1HqRPfgHFed9ezacoCm7djTfqbds2EiEZM+PU\nczCi0xSvpcZXiTGeg9vQEyelNLAZT2IPisHHNaddytC8E9jevIMK3072eKsZVTCGHFsOLpObnZ6d\n7PRsp9jVgxJLHxLJOE2tDTS2NuLW3dhNDhy6E1/Ehz/iA5LYdQdmgwUVlUDUTzDWilkzkWfJxxdt\n205BwabbURQFs2YhHA8TiYdJksSkHf6ezAGSSVAUkk4n4QsvJnTFDND1I/72bL2/la19Qfb2Jn1l\nniO5hyyBnCLHe+B9vdgrEIyhYYZkHJMtSllPMy5T26Kof6UoCi6TG0/Yg9ESIRpNEo/o2Aw5KElw\n5PppDNdDxIU90QOzUoRP3YY5twlvxMP40okMyR/ClsYv2ekrpz5Qx4i8kyi0F+HQ7Oz0llPlqyDX\nUED/3IGEYmGaQnvZF95HniUPq9GG3WjHF/Xhi3jRVA2r0YbZYAEU/FE/rdEgVqOVHFMu3ogXf9SH\npmhYjda2UDaYCcXChONhUJT2hV+Ho23+EtcVlxGZcCZJdw5Jp/OoV1Nn65tFtvYF2dub9JV5JJDT\n2PEeeIqiUJhjpazITs88G8N6FWO3q/ijXsLxME6T66CLn1RFxam78ES86JYw/Xvm0rcoj5P7DcJi\nAWuOn96lcSYOHs7MCWdwUll/Pqlfz/aWrYRirUzsNYl+rgFsatzILu8uvBEPJxaNJN9agEE1UB2s\nZFvTDvo4+9HL0ZtgLEhDqAFPyEOeJQ+b0YbVYMMb8eKLeNFVHYvRgsVgIZkEX9RLOBbCptvb//Hg\nj/owaeb2VdZmzUwoHiIcD6Eq2mEHhxjWr8N9+cUYqiqIDxtObPiJx/SaZ+ubRbb2Bdnbm/SVeSSQ\n01hHHXiqqmAyamiqisPoIBRrxRf1EolHcerOg4aypmo4dAct4Ra80RacJhtW3UKpoxfesJfGSD3o\nHoYWDqZ/zgAsmo0Nez9lS/NmjIqJ08vOoNTSiy8aN7LTU0441sqwguH0tBVjthr4au92KjwVDHAN\npJezN96Ih4bWvQSifnL2h7LZYMEbbgtls2bBbDBjMViIJxP764/gNDlxGB20RDz4Il6sBiu6pqMq\nKibNROv+UDaoxkMODtHXvItrxlSU1lZ8Tz5DeNr0Y36ts/XNIlv7guztTfrKPBLIaawzDjxVUXHo\nTgJRP76ol0Qygd3oOGgoG1QDVoMVT7gFb8SD1WhD13RKbWXsCzVRE6ylKdhAH1c/huQOJZpIsKnx\nC75s3EiOnse4slPJteSzuXEjO5p3oKAwKG8wQ4sHsrelmd2+Sqp8FQzOG0qJo5SW1hb2ttYTjoXI\nMedi1+2YVB1PxIs34sVmtGEymLAYLMSScbxhD9FkDLfZjcVgxrM/lG1GO0bN2PY5ZVUnFGsLZV3T\n0VTtgB5NK17Fee3VoGl4Fy8lMuWS43p9s/XNIlv7guztTfrKPBLIaayzDryvQ/lfF08djK7p6JqJ\nlkgz3ogXu8GOyWiit6MPtcEaaoO1eMMt9Hb15cTCk/CFvWxp3syGxs8otpYxrmQcNqONL5o2Ud68\nDYtmY3jJUAoNxTS3NlHtr6bav5thOcPpYevBvnAz9cE64vFYeyhrGPBG286UHboTXdOxaJa2QSFh\nD7FEjDxzPkZVxxvxEIj4sesODKoBTW27XN0aayUUD2NSTe2hrHhacE3/MRh1PK++QXTCmcf92mbr\nm0W29gXZ25v0lXkkkNNYZx54mqoddPHUwZgNZnRNpyXcjDfqw6W7MBqM9Hb2YY9vNzXBWlpjIXo5\nezGqxxjqA3VsbfqKzxv/yRD3EMYWj0PHwKamL9nR8hUlrmLy9R70cw+gzl/LHv8e6oN1DM09gUJb\nEU2hBmoDtSiKSo4pB4fJgYKCL+LFH/G1hbJBx2KwEE1E8YRbiCfj5FsLUBQVX8RLIOrHqbvQVG1/\nMBsIxVoJxUNYNEvbgjazmehpZxCaMZP4yFEd8rpm65tFtvYF2dub9JV5JJDTWGcfeAbVgM1gb7/U\na1SNWAyWg25rMVhQFQ1vpAVvxIvbnIOu6ZTZerHbV8WeQDWJRIJSRylje5xCRUs55S3lfLb3n4zK\nG83I4tHEEzG27NvM9uZt9DSXUmTvwcCcQVR5d1ET2MO+0F6GF56ES89hX+s+6gJ70FUdp8mFY/80\nLm/USyAWxGVyYdSMmDQzkUQEb8QLySQFlgKSJPFGvASiwfbV5EbViKKohMN+lCceQh/otQcWAAAg\nAElEQVQwDMXuINGzmGRBxw38yNY3i2ztC7K3N+kr80ggp7GuOPCMmhGLwYon7GlfPGUyHPygsBlt\nxJNxfFEvvqiPHFMuJqOJEnspld4Kdvt3Y1SN9LQXc2rJ6Wxp3MQuz042NPyTUbljGFNyMqFYK9s9\nW9nU8CUDnAPJt+XT19GfXZ6d1ARq8IW9fK9gBE6zk32tTezx78ZkMOE25eDYP/faF/EQjLXi0l3o\nmo5JMxGJh/BGvG0ry61FRBNR/P869zqawHHTL1DeeBVj/V6S503p8NczW98ssrUvyN7epK/MI4Gc\nxrrqwNM1HbNmwhPxtC+eOtRndx26k0g8jD/qIxAL4NbdWIwW8s0F7PZVssdXhd1gp8jeg5F5Y/ii\naQMVvgq+bNrIhF6TGJY3nJDiZ9PeTWzft41h+cPJteZS5uzNTs9O9viriSfiDMkbitVopSHYSG2g\nFqvRimv/mXIoHsYf8RLev8pa13SMWtviLV/Eh6poFFmLCMfDbR+RikdwRcA9YxqO995HGTee6G+f\nA5O5w1/LbH2zyNa+IHt7k74yjwRyGuvKA89kMB+weMpudBzys7suk5tgLIg/6qM13kqOue0+b645\nj12ecqp8lThNOZS4ShidN5ZPGz6hwlfBloZNnFl2FmcMGMf2ul2Ue3ewY99WRuSeRJ4tjzJHL3Y0\nb6fKW4FB0RmcNxhdM9HU2jYi02lyYdftOHVn+0e3ookYTt2JSTNh1IwEowEC0QCaaqDQUkhrLIi/\nvgLrNTPJW/cpkQsuIvDiUrDZO+V1zNY3i2ztC7K3N+kr80ggp7GuPvCsRuu3Fk8d6rO7Lt1FIBbA\nH/URiYfbHqVocuEwOqnyVVLlrSTfnE+xq4QR+Sfyce16Kn27KPfs4vyh59LXOpjKlgp2ecvZ6dnB\nyKLR5Jhz6GnuyVctX1Hl3YnD6GRg7iA0RaWptYm9wXocuhOH7sBh2v/RrYiXZDKJ3Whv+0eFohGM\nBfDH/OiakUJzAerPpxGs2ortkqsIPvnMUY3CPFrZ+maRrX1B9vYmfWUeCeQ0looDz2qwfmvx1KFG\nbH4999of9RFPxnHoTnItuRgVE9X+Kqp8VZQ6yih2FDPQOYh/1P+DXd4d1PlrOb1o4iHnXueYctrn\nXhdYi+ifM+A75177Il6+/uiWSTOjKt+Ye20wk18yBHtuMYkFjxz1KMyjla1vFtnaF2Rvb9JX5pFA\nTmOpOPAUpe2xh7FEDF/E27546nBzr31RL6qiYTPaKLS1rVre46+m2rebEnspvd196G3vzSe169nW\nspXmYMsRzb3e6SmnyNLjO+dee/eHskE1tM+9Nmz8HK8xTtSo4RoyGnX8JOiIZyQfRra+WWRrX5C9\nvUlfmUcCOY2l6sBTFAX7AYunDj/3uiXiwRtpaRvaYbDQ015MOB5ij7+aGn8NvR196JPTlyJbDz5r\n+ISvmja3z70us/Rmc/OXB517Xe4pp8K767vnXhutB8y9dn3wN3rMuBLrV9sxXf4z9EOsGu8M2fpm\nka19Qfb2Jn1lHgnkNJbKA09RlKOae20z2PBEPLSEm9sf8tA299pDbbCG+kAt/dwD6JfTn0JbLuuq\n17GleTNm1cIZvSdQaunFxsbPvzX3Op6IU+mr+M6513ajvX3udehPyym85WbMipH4gt+gDRzapa9b\ntr5ZZGtfkL29SV+ZRwI5jaX6wDuauddGzXjEc6/H9huJx9fKpsYv+KLh8/a513mWgm/NvS5zluGP\nBo9o7rXrD0uIP3Y/MYcD5aW3iI6f2OWvWap/Z50lW/uC7O1N+so8EshpLB0OvOOZe+3cP+LyX+de\nn9BzMANtQ9vnXn9+iLnXdqODvu6+9HH2Oezc65KnnqPHgw9jcxehLH2b5MjRXftC7ZcOv7POkK19\nQfb2Jn1lHgnkNJYuB97Xc6+/Xjx1pHOvPRHvQedeJ5QoBXrP9rnXX31j7vWYnqdg2j/3envzFvLN\nBZQ6yw4791oPRyncXk3rH9+BgYO7+BX6P+nyO+to2doXZG9v0lfmkUBOY+l04LWtYD5w8ZT5O+Ze\noyj49k/++te513tDtbSGIwefe50/5oC519uav6LM3vtbc6+bw818zzEQl2JnX9QL/QeSe91dkJfX\nxa/MgdLpd9aRsrUvyN7epK/MI4GcxtLtwNM1vX3x1OHmXtuN9kPOva4N7aZ83672uden9DyVr5q+\n/Nbca1/Ux/bmrXzVvIUhuSeQa8n9v7nXTeVw31xGr/6YntNvodTdG8Vw8CEmXSndfmcdJVv7guzt\nTfrKPEcSyJ07SUFkFIfuoNReSoIku31VBKKBQ25bbC8hx5RDKNbKLu9OEokEbrObcweei1kz8c+6\nj9mxbxtW3crto+9iYM5A9gSqeeiTBQSiAS4bOJWTi09jX6iRZz/7HXX+OuxmO1Ncp1P4+5f4Yt8W\nPAbQurB/IYRIJQlkcQCX2U2xtYQECaq8FYRioUNuW+bs3b5Su9JXAUBPR0/OLJuEoih8VLuWnS07\nyXfmc8/YBfRy9Ganbxf3f3Qv8UScKwZfyYi8kdS11vLshqdo2f5P+v94GjPereP7Qy5EeX4ZmLru\nc8ZCCJFKEsjiW/KseRRZexBLxKj07CIcCx9y296OPlgNNnwRL7u9lQCUOso4o+RMEsk4/1P9N+q8\ntRS5ezB33D0UWnqwqWkjD378AJqmcdXwqxnsHkp19Ub+cN+5JHbtxHTTbAY8tBQ0OT8WQnQfEsji\noAosBeRbCggnwlT6KoklYgfdTlVV+rj6YjZYaA43U+OtAaB/zgBO7nEaiWScv1a/R1NrE/3c/bl9\nzFzcpjw+3buexz7+DXaDnauH/5z+LRq7jCGq5s8leMc9XTIKUwgh0oks6kqRdF+8cDRzr1VFbZ97\nHVGDREKJA+ZeV/t3H3Tu9XbvNjwhD+NLJzJixGTGlUwg/5KfdnWrRyzdf2fHKlv7guztTfrKPLKo\nSxwXRVEotpfgNLkJRH3s9u0mkUwcdFuDaqCfqz8G1UBtYA/NoX0AjCwazQl5J+CP+ni/cg2BcICx\nJadw4+hfYWpo4b/++ls+qHwft9lNz3FndWV7QgiRViSQxXdSFZUyexkOowNvpIU9/j0kk8mDbqsb\ndAbkDEBTDOz2VeGNeAEYV3w6/V0D8ERaeL/qP4nGo5z7503c+tIOeu9qondADkMhhJB3QnFYmqpR\n5uyN1WClOdREXaD2kKFs1a30dvZBRaXKU4E/4gfgjOIJlNl7sbe1gb8/chXmBXdxcaAni25ez6AR\nZ3dlO0IIkZYkkMURMagGejv7YtLMNLY2sDdYd8ht7bqdUkcvEiSo3P/RKYPBwKSeZ9Jn+SoaPvwz\nXw3vRcs7a4gP6donNgkhRLqSQBZHzKi1za02qDp7g3tpam085LZus5sSeynxZIydnp1EYhFsq//M\nJS/+D6dYBpP30hoSZb26sHohhEhvqZ9HKDKK2WCml7MXld4KagM1aIqG25xz0G3zLPnEknHqA7Xs\n9JQzYMpFKM2P0ufyaSTtB3+qlBBCdFdyhiyOms1oo8zRCwWVPf5qvGHvIbft4YPSN1YTSYTxRwOE\nrp4lYSyEEAchgSyOyZHMvVZ37STngrMZfM+DDNtYi9vsTkGlQgiRGSSQxTH7rrnX2qYvyLngHLTK\nCgK33o426dwUViqEEOlPAlkcl2/Ova7yVhCOhTGu+wj3xZNRG/bif+BhgnfcLaMwhRDiMGRRlzhu\nBZYC4okYDa172bVuNf2nTkOJxvEueoHwpZenujwhhMgIEsjiuCmKQg9bT+LJOMG+FqouO5/8yVcQ\nOeucVJcmhBAZQwJZdAjj559RfOJJqLYIoYefIWIwp7okIYTIKHIPWRyfZBLbgnnknHMmltdXUmAr\nwCxhLIQQR03OkMWxi8Ww33YTlleXEus/gOgpp6a6IiGEyFgSyOLYtLbivPZnmN79C9GTRuJ55XWS\n+fmprkoIITKWBLI4aorPi/PKqej/u5bI+DPxLlkm07eEEOI4yT1kcdSSJjOYTIQvvBjPKysljIUQ\nogN06Rmyz+dj9uzZ+P1+otEod9xxByNHjuzKEsTxaG0FiwV0Hc8fXgGTCTQt1VUJIURW6NIz5MWL\nFzNu3DiWLl3Kgw8+yIIFC7py9+I4aJu+IHfcSPTV77R9wWqVMBZCiA7UpWfIM2fORNd1AOLxOCaT\nqSt3L46Rcd1HOK+ciur1oNbWpLocIYTISkoymUx2xg9euXIlS5YsOeBrCxcuZMSIETQ0NDBr1izu\nvPNOTj755M7Yvegoq1bB1KkQi8FLL8FPfpLqioQQIit1WiAfytatW7n11lu5/fbbmThx4hF9T0OD\nr5Or6noFBY6078u0fBmOX90IJhOe3y8lOukHh/2eTOjrWGVrb9naF2Rvb9JX5ikoOPzi1y69ZL1j\nxw5uvvlmHn/8cYYMGdKVuxZHKxbDsuT3JJ1OPMtWEhsjVzKEEKIzdWkgP/roo0QiER544AEA7HY7\nixYt6soSxJEyGPAsW4Ha2Eh80OBUVyOEEFmvSwNZwjfNxWLY584mfMmlRE87g2RuHvHcvFRXJYQQ\n3YJM6hJtvjEKU9u1E8+pp4OipLoqIYToNiSQBYqnBeeMaejrPiIy4ft4/7BUwlgIIbqYjM7s5pT6\netwXn4++7iNCF/0Iz7IVMgpTCCFSQAK5m3Pc9ksMX35B68z/h++ZF9vGYQohhOhycsm6m/M//B9E\nTxtP6/U3ymVqIYRIITlD7oaM/7sWwxefA5AoKaX1F7+UMBZCiBSTQO5m9NXv4Lr8YpwzpkEolOpy\nhBBC7CeB3I2YXl2K82fTQdPwPfYUmM2pLkkIIcR+EsjdhOXJx3He/AuSLhctr/+J6PfPSnVJQggh\nv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"text/plain": [ "<matplotlib.figure.Figure at 0x11359b898>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<function __main__.plot_line>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "widgets.interact(plot_line,\n", " angle_in_degrees=widgets.FloatSlider(min=0, max=360, step=1, value=85))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you vary the slope of the line, you should find that maximal variance is found at about 45 degrees.\n", "\n", "Minimal variance is around 225 degrees - i.e. a line which is orthogonal to the line of maximum variance. \n", "\n", "The values were about 3.63 and 0.036 respectively.\n", "\n", "Fast-forwarding a little, these are the 'explained variances' which a fitted PCA model returns.\n", "\n", "> ```python\n", "> petal_data = demeaned_df[['petal length (cm)', 'petal width (cm)']].values\n", "> pca = PCA().fit(petal_data)\n", "> pca.explained_variance_\n", ">\n", "> array([ 3.63497866, 0.03597779])\n", "> ``` \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Programattically changing the orientation of the hyperplane" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us programmatically vary the slope of the line and build a plot explained variance as a funtion of angle.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_variance(angle_in_degrees):\n", " \n", " x = p_x\n", " y = p_y\n", "\n", " # our current fitted line\n", " m = np.tan(np.pi * angle_in_degrees / 360) \n", " y = m * x\n", " \n", " # perpendicular lines between the original data and the\n", " # current fitted line\n", " p_x_line = (p_x + m * p_y) / (m*m + 1)\n", " p_y_line = m * p_x_line\n", " \n", " # average sq distance from origin of perp line intercepts\n", " # i.e. the points where the green line touches the dashed red line\n", " var = np.mean(np.power(p_x_line, 2) + np.power(p_y_line, 2))\n", " \n", " return var" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x113549128>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0x7f2aOKwbrruyh7ycHczOx4AAADgVCiureBksU1L1qQrr9CmsEBv3TEpQcmxoWbHgpPz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"text/plain": [ "<matplotlib.figure.Figure at 0x112780550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame({'angle': range(361)})\n", "df['variance'] = df.angle.apply(get_variance)\n", "df = df.set_index('angle')\n", "df.plot()\n", "plt.xlabel('angle (degrees))')\n", "plt.ylabel('variance')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use a solver to find the maxima and minima, which should correspond with our previous findings." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: -3.634979\n", " Iterations: 18\n", " Function evaluations: 36\n", "\n", "Variance: 3.63498 obtained at angle: 45.693 degrees\n" ] } ], "source": [ "angle = fmin(lambda a: -1 * get_variance(a), 50)\n", "var = get_variance(angle)\n", "print('\\nVariance: {0:.5f} obtained at angle: {1:.3f} degrees'.format(var, angle[0]))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.035978\n", " Iterations: 20\n", " Function evaluations: 40\n", "\n", "Variance: 0.03598 obtained at angle: 225.693 degrees\n" ] } ], "source": [ "angle = fmin(get_variance, 200) \n", "var = get_variance(angle)\n", "print('\\nVariance: {0:.5f} obtained at angle: {1:.3f} degrees'.format(var, angle[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solving analytically" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> In some ways, PCA provides us with an analytic mechanism for doing exactly what we did above.\n", "\n", "The above procedure is perfectly valid and tractible for problems with 2 dimensions and small amounts of data. But there are a number of analytic solutions to the problem which scale well and the above is intended just for building intuition.\n", "\n", "What we've discovered so far is that (for our petal dataset) there exists exactly one axis which, when data points are projected onto it, exhibits maximal variance. This is in fact our first Principal Component.\n", "\n", "So we need an analytic approach to decompose the covariance of our data points and recover the principal axes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The elements of a covariance matrix are given by:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\sigma_{jk} = \\frac{1}{n-1} \\sum_{n=1}^{n} (x_{ij}−\\overline{x}_j)(x_{ik}−\\overline{x}_k) $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In matrix notation:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ cov = \\frac{1}{n-1} ((X−\\overline{x})^T(X−\\overline{x})) $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we've already de-meaned our data, our covariance matrix is given by:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 3.11317942, 1.29638747],\n", " [ 1.29638747, 0.58241432]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_data = demeaned_df[['petal length (cm)', 'petal width (cm)']].values\n", "\n", "n = len(petal_data)\n", "cov = 1 / (n - 1) * petal_data.T @ petal_data\n", "cov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can obtain this using numpy directly:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 3.11317942, 1.29638747],\n", " [ 1.29638747, 0.58241432]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cov = np.cov(petal_data.T)\n", "cov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO** : add stuff about maximising variance in matrix form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The eigenvalues and corresponding vectors (organised in ascending eigenvalue order):" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eigenvalues, eigenvectors = np.linalg.eigh(cov)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.03621925, 3.65937449])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eigenvalues" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.38826694, -0.92154695],\n", " [-0.92154695, -0.38826694]])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eigenvectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The eigenvalues look very close to the variance minimum and maximum we found earlier. In fact, they're very closely related - the returned eigenvalues are just scaled differently.\n", "\n", "Recall that we previously wrote down:\n", "\n", "> ```python\n", "> petal_data = demeaned_df[['petal length (cm)', 'petal width (cm)']].values\n", "> pca = PCA().fit(petal_data)\n", "> pca.explained_variance_\n", ">\n", "> array([ 3.63497866, 0.03597779])\n", "> ``` " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.63497866, 0.03597779])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n # number of data points\n", "factor = (n - 1) /n\n", "(factor * eigenvalues)[::-1] # apply factor and flip the order" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpretation of eigenvectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what can we make of the eigenvectors?\n", "\n", "The eigenvector corresponding to the largest eigenvalue is:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.92154695, -0.38826694])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eigenvectors[:, -1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we plot this over out original data, we can visualise this as the first principal component - i.e. the axis which explains maximal variance." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11276e6d8>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GJ6+3aCkAM0wDvy+9Y9PmHc2sXl8HhkH4lp9j//MfxE+ey87fP0zdi68SXXgF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\ngAEIMgAABvhfZmPU2wJpNnYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1135444a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(p_x, p_y, alpha=0.4)\n", "\n", "# slope\n", "m = eigenvectors[:, -1][1]/eigenvectors[:, -1][0]\n", "\n", "e_x = np.linspace(-4, 4, 3)\n", "e_y = m * e_x\n", "\n", "plt.plot(e_x, e_y, 'r--')\n", "\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "axes = plt.gca()\n", "axes.set_ylim([-4, 4])\n", "axes.set_xlim([-4, 4]) \n", "\n", "plt.xlabel('petal length (cm)')\n", "plt.ylabel('petal width (cm)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the angle implied by the first Principal Component against the value we solved for previously." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Angle implied by first eigenvector: 45.693 degrees\n" ] } ], "source": [ "angle = np.arctan(eigenvectors[:, -1][1]/eigenvectors[:, -1][0])*360/np.pi\n", "print('Angle implied by first eigenvector: {0:.3f} degrees'.format(angle))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can trivially add the second eigenvector, which is orthogonal to the first and in fact the only other Principal Component that our two dimensional data has.\n", "\n", "This gives us a new coordinate system whereby the axes are orthogonal to eath other and the variance of the data is maximal on the first axis." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1127a15c0>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CCy9w+OGZz6JeeeUVotFojqNEZKgV3PhbCq7/Fe7YsSRPO2Oow+m13naIsj76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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11276e048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(p_x, p_y, alpha=0.4)\n", "\n", "# slope\n", "m1 = eigenvectors[:, -1][1]/eigenvectors[:, -1][0]\n", "m2 = eigenvectors[:, 0][1]/eigenvectors[:, 0][0]\n", "\n", "e_x1 = np.linspace(-3, 3, 3)\n", "e_y1 = m1 * e_x1\n", "\n", "e_x2 = np.linspace(-0.3, 0.3, 3)\n", "e_y2 = m2 * e_x2\n", "\n", "plt.plot(e_x1, e_y1, 'r--')\n", "plt.plot(e_x2, e_y2, 'r--')\n", "\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "axes = plt.gca()\n", "axes.set_ylim([-4, 4])\n", "axes.set_xlim([-4, 4]) \n", "\n", "plt.xlabel('petal length (cm)')\n", "plt.ylabel('petal width (cm)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the eigenvectors to transform our original data into our new coordinate space:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pc2</th>\n", " <th>pc1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.004526</td>\n", " <td>2.561371</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.004526</td>\n", " <td>2.561371</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-0.034301</td>\n", " <td>2.653526</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.043353</td>\n", " <td>2.469217</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.004526</td>\n", " <td>2.561371</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pc2 pc1\n", "0 0.004526 2.561371\n", "1 0.004526 2.561371\n", "2 -0.034301 2.653526\n", "3 0.043353 2.469217\n", "4 0.004526 2.561371" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transformed_data = petal_data @ eigenvectors\n", "df_trans = pd.DataFrame(transformed_data, columns=['pc2', 'pc1'])\n", "df_trans.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These new features are in fact just linear combinations of our original features. \n", "\n", "We can show this as follows. Recall our original data (demeaned):" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm)\n", "0 -2.358667 -0.998667\n", "1 -2.358667 -0.998667\n", "2 -2.458667 -0.998667\n", "3 -2.258667 -0.998667\n", "4 -2.358667 -0.998667" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df = demeaned_df[['petal length (cm)', 'petal width (cm)']].copy()\n", "petal_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The eigenvector corresponding to the largest eigenvalue was:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.92154695, -0.38826694])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eigenvectors[:, -1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So instead of recording petal width and length, suppose we had recorded a quantity:\n", "(-0.9215469 multiplied by length) + (-0.3882669 multiplied by width)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>new_qty</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>2.561371</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>2.561371</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " <td>2.653526</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " <td>2.469217</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>2.561371</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm) new_qty\n", "0 -2.358667 -0.998667 2.561371\n", "1 -2.358667 -0.998667 2.561371\n", "2 -2.458667 -0.998667 2.653526\n", "3 -2.258667 -0.998667 2.469217\n", "4 -2.358667 -0.998667 2.561371" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df['new_qty'] = -0.92154695 * petal_df['petal length (cm)'] - 0.38826694 * petal_df['petal width (cm)']\n", "petal_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As follows, we can prove that **pc1** data exactly tallies with **new_qty**" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(df_trans.pc1, petal_df.new_qty)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 'new_qty' is often called a 'score' and it would be normal to call the transformed values 'scores' - i.e. the values which each data point corresponds to in the new Principal Component space." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpretation of eigenvalues" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what this means is that if we'd recorded the synthetic quantity (-0.9215469 multiplied by length) + (-0.3882669 multiplied by width), then we'd have **one** collection of data points which almost completely represents the information / variance of the original data which comprised **two** features (length and width). These values would be the PC1 scores.\n", "\n", "So what fraction of total variance would we retain?\n", "\n", "The answer is given by the scaled eigenvalues." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.03597779, 3.63497866])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_eigenvalues = eigenvalues * (n - 1) / n\n", "scaled_eigenvalues" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00980066, 0.99019934])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_eigenvalues / sum(scaled_eigenvalues)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means that using PC1 alone explains 99% of the variance of our original data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Uncorrelated nature of principal components" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One other fact to note is that the transformed data for PC1 and PC2 are uncorrelated (as a consequence of the orthoginal nature of the axes). This should feel intuitively reasonable as moving along one axis does not impact the value on the other." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 0.],\n", " [ 0., 1.]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.around(np.corrcoef(transformed_data.T), 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using sklearn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So let's revisit sklearn PCA and see how we'd use it to recover the above results." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "petal_data = demeaned_df[['petal length (cm)', 'petal width (cm)']].values\n", "pca = PCA().fit(petal_data)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.63497866, 0.03597779])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.99019934, 0.00980066])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_ratio_" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.92154695, 0.38826694],\n", " [-0.38826694, 0.92154695]])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.components_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the transformed values have a flipped sign compared to the results we manually derived above. It doesn't really have any statistical significance and doesn't affect variance. It would be trivial to add a conditioning step to determine a sign which matches sklearn. " ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pc1</th>\n", " <th>pc2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.561371</td>\n", " <td>-0.004526</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.561371</td>\n", " <td>-0.004526</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.653526</td>\n", " <td>0.034301</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.469217</td>\n", " <td>-0.043353</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.561371</td>\n", " <td>-0.004526</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pc1 pc2\n", "0 -2.561371 -0.004526\n", "1 -2.561371 -0.004526\n", "2 -2.653526 0.034301\n", "3 -2.469217 -0.043353\n", "4 -2.561371 -0.004526" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(pca.transform(petal_data), columns=['pc1', 'pc2']).head()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 3.11317942, 1.29638747],\n", " [ 1.29638747, 0.58241432]])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.get_covariance() * n / (n - 1) # rescaled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The power of the sklearn model is that we can very simply reduce down to our desired number of dimesions." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pc1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.561371</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.561371</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.653526</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.469217</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.561371</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pc1\n", "0 -2.561371\n", "1 -2.561371\n", "2 -2.653526\n", "3 -2.469217\n", "4 -2.561371" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_data = demeaned_df[['petal length (cm)', 'petal width (cm)']].values\n", "pca = PCA(n_components=1)\n", "one_dimensional = pd.DataFrame(pca.fit_transform(petal_data), columns=['pc1'])\n", "one_dimensional.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following plot shows the data points transformed into PC1 and then mapped back into the original coordinate system. \n", "\n", "Recalling the interactive chart above, the green dots repesent the projection of each blue data point onto the PC1 best fit line. The difference between the green and blue dots gives an indication of the amount of information / variance which is lost by reducing to one dimension." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1132bbfd0>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ipmrThKbeLMTPC+H6YogGgyEbZBhs6+G67mvY3bnrsnXSyoQhvV0LvwWvpZXk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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1127aac50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trans_data = pca.inverse_transform(one_dimensional.values)\n", "x = trans_data[:, 0]\n", "y = trans_data[:, 1]\n", "\n", "plt.scatter(p_x, p_y, alpha=0.4)\n", "\n", "plt.scatter(x, y, alpha=0.4)\n", "\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "axes = plt.gca()\n", "axes.set_ylim([-4, 4])\n", "axes.set_xlim([-4, 4]) \n", "\n", "plt.xlabel('petal length (cm)')\n", "plt.ylabel('petal width (cm)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Effect of including a feature with zero variance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA is a variance explanation technique. What would happen if we added a feature which had zero variance? Let's say we added a feature called 'animal, vegetable, mineral' which we one-hot encode into three columns: [animal, vegetable, mineral]." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm)\n", "0 -2.358667 -0.998667\n", "1 -2.358667 -0.998667\n", "2 -2.458667 -0.998667\n", "3 -2.258667 -0.998667\n", "4 -2.358667 -0.998667" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df = demeaned_df[['petal length (cm)', 'petal width (cm)']].copy()\n", "petal_df.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>animal</th>\n", " <th>vegetable</th>\n", " <th>mineral</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm) animal vegetable mineral\n", "0 -2.358667 -0.998667 0 1 0\n", "1 -2.358667 -0.998667 0 1 0\n", "2 -2.458667 -0.998667 0 1 0\n", "3 -2.258667 -0.998667 0 1 0\n", "4 -2.358667 -0.998667 0 1 0" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df['animal'] = 0\n", "petal_df['vegetable'] = 1\n", "petal_df['mineral'] = 0\n", "petal_df.head()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pca = PCA().fit(petal_df.values)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.99019934, 0.00980066, 0. , 0. , 0. ])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_ratio_" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.63497866, 0.03597779, 0. , 0. , 0. ])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.92154695, 0.38826694, 0. , 0. , 0. ],\n", " [-0.38826694, 0.92154695, 0. , 0. , 0. ],\n", " [ 0. , 0. , 0. , 0. , 1. ],\n", " [ 0. , 0. , 0. , 1. , 0. ],\n", " [ 0. , 0. , 1. , 0. , 0. ]])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.components_" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pc1</th>\n", " <th>pc2</th>\n", " <th>pc3</th>\n", " <th>pc4</th>\n", " <th>pc5</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.561371</td>\n", " <td>-0.004526</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.561371</td>\n", " <td>-0.004526</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.653526</td>\n", " <td>0.034301</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.469217</td>\n", " <td>-0.043353</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.561371</td>\n", " <td>-0.004526</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pc1 pc2 pc3 pc4 pc5\n", "0 -2.561371 -0.004526 0.0 0.0 0.0\n", "1 -2.561371 -0.004526 0.0 0.0 0.0\n", "2 -2.653526 0.034301 0.0 0.0 0.0\n", "3 -2.469217 -0.043353 0.0 0.0 0.0\n", "4 -2.561371 -0.004526 0.0 0.0 0.0" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(pca.transform(petal_df.values), columns=['pc1', 'pc2', 'pc3', 'pc4', 'pc5']).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you might expect, the features which have no variance are not useful in explaining the variance of the dataset, so PC1 and PC2 are unchanged." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Effect of including a features with different scales" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "What would happen if the dimensions we'd recorded had different scales? So let's say we recorded petal width in meters and petal length in milimeters." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "petal_data = demeaned_df[['petal length (cm)', 'petal width (cm)']].copy()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm)\n", "0 -2.358667 -0.998667\n", "1 -2.358667 -0.998667\n", "2 -2.458667 -0.998667\n", "3 -2.258667 -0.998667\n", "4 -2.358667 -0.998667" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_data.head()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "petal_data['petal length (mm)'] = petal_data['petal length (cm)'] * 10\n", "petal_data['petal width (m)'] = petal_data['petal width (cm)'] /100\n", "del petal_data['petal length (cm)']\n", "del petal_data['petal width (cm)']" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (mm)</th>\n", " <th>petal width (m)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-23.586667</td>\n", " <td>-0.009987</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-23.586667</td>\n", " <td>-0.009987</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-24.586667</td>\n", " <td>-0.009987</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-22.586667</td>\n", " <td>-0.009987</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-23.586667</td>\n", " <td>-0.009987</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (mm) petal width (m)\n", "0 -23.586667 -0.009987\n", "1 -23.586667 -0.009987\n", "2 -24.586667 -0.009987\n", "3 -22.586667 -0.009987\n", "4 -23.586667 -0.009987" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_data.head()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pca = PCA().fit(petal_data)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.09242543e+02, 4.22899390e-06])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 9.99999986e-01, 1.36753301e-08])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_ratio_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, perhaps unsurprisingly as PCA 'works' by explaining the variance in the data, the enormously different scales of the inputs means that one feature dominates the other. This is perhaps something to bear in mind when working with cross-sectional data where features use very different scales." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data pre-conditioning by z-scoring" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what can we do about it? One option is to z-score." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length</th>\n", " <th>petal width</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-1.336794</td>\n", " <td>-1.308593</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-1.336794</td>\n", " <td>-1.308593</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-1.393470</td>\n", " <td>-1.308593</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-1.280118</td>\n", " <td>-1.308593</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-1.336794</td>\n", " <td>-1.308593</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length petal width\n", "0 -1.336794 -1.308593\n", "1 -1.336794 -1.308593\n", "2 -1.393470 -1.308593\n", "3 -1.280118 -1.308593\n", "4 -1.336794 -1.308593" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def zscore(series):\n", " return (series - series.mean()) / series.std()\n", "\n", "petal_data_std = petal_data.apply(zscore)\n", "petal_data_std.columns = ['petal length', 'petal width']\n", "petal_data_std.head()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pca = PCA().fit(petal_data_std)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.94967205, 0.03699462])" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.98137855, 0.01862145])" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_ratio_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many ways of normalising data; z-scoring is just one. So should features *always* be scaled before fitting a PCA model? That's a matter of some debate; a valid counter argument is that it can artificially 'inflate' the contribution of an otherwise relatively unimportant feature. In any event, it makes sense to be explicit about what preconditioning (if any) you've decided to use and why." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A corrollary of z-scoring is that it makes the covariance matrix and correlation matrix equal." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.9627571],\n", " [ 0.9627571, 1. ]])" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.cov(petal_data_std.T)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.9627571],\n", " [ 0.9627571, 1. ]])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(petal_data_std.T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This should come as no great suprise as the act of z-scoring is to rescale by feature standard deviation and by definition:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ cor(X, Y) = \\frac{cov(X, Y)}{\\sigma_X \\sigma_Y}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've hithero chosen to decompose the data's covariance matrix but it may be valid to instead decompose the correlation matrix (e.g. where data scaling is a significant factor). In the event that input features are preconditioned using z-scoring then it makes no difference." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Effect of including a feature which is perfectly correlated with some other feature" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "What happens if we introduce a feature which is perfectly correlated with some other feature?" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm)\n", "0 -2.358667 -0.998667\n", "1 -2.358667 -0.998667\n", "2 -2.458667 -0.998667\n", "3 -2.258667 -0.998667\n", "4 -2.358667 -0.998667" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df = petal_df[['petal length (cm)', 'petal width (cm)']].copy()\n", "petal_df.head()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>length_times_factor</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>-1.886933</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>-1.886933</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-2.458667</td>\n", " <td>-0.998667</td>\n", " <td>-1.966933</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-2.258667</td>\n", " <td>-0.998667</td>\n", " <td>-1.806933</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-2.358667</td>\n", " <td>-0.998667</td>\n", " <td>-1.886933</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm) length_times_factor\n", "0 -2.358667 -0.998667 -1.886933\n", "1 -2.358667 -0.998667 -1.886933\n", "2 -2.458667 -0.998667 -1.966933\n", "3 -2.258667 -0.998667 -1.806933\n", "4 -2.358667 -0.998667 -1.886933" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df['length_times_factor'] = petal_df['petal length (cm)'] * 0.8\n", "petal_df.head()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>length_times_factor</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>petal length (cm)</th>\n", " <td>1.000000</td>\n", " <td>0.962757</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>petal width (cm)</th>\n", " <td>0.962757</td>\n", " <td>1.000000</td>\n", " <td>0.962757</td>\n", " </tr>\n", " <tr>\n", " <th>length_times_factor</th>\n", " <td>1.000000</td>\n", " <td>0.962757</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " petal length (cm) petal width (cm) length_times_factor\n", "petal length (cm) 1.000000 0.962757 1.000000\n", "petal width (cm) 0.962757 1.000000 0.962757\n", "length_times_factor 1.000000 0.962757 1.000000" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "petal_df.corr()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pca = PCA().fit(petal_df.values)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 9.93235835e-01, 6.76416520e-03, 7.80531918e-33])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_ratio_" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 5.61189011e+00, 3.82182664e-02, 4.41008993e-32])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.explained_variance_" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7.42055371e-01, 3.11352335e-01, 5.93644296e-01],\n", " [ -2.43125327e-01, 9.50294546e-01, -1.94500262e-01],\n", " [ 6.24695048e-01, -1.11022302e-16, -7.80868809e-01]])" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.components_" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pc1</th>\n", " <th>pc2</th>\n", " <th>pc3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-3.181366</td>\n", " <td>-0.008567</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-3.181366</td>\n", " <td>-0.008567</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-3.303063</td>\n", " <td>0.031306</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-3.059669</td>\n", " <td>-0.048439</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-3.181366</td>\n", " <td>-0.008567</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pc1 pc2 pc3\n", "0 -3.181366 -0.008567 0.0\n", "1 -3.181366 -0.008567 0.0\n", "2 -3.303063 0.031306 0.0\n", "3 -3.059669 -0.048439 0.0\n", "4 -3.181366 -0.008567 0.0" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(pca.transform(petal_df.values), columns=['pc1', 'pc2', 'pc3']).head()\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what's happening here is that we end up with a third principal component which is not useful at all in explaining variance. \n", "\n", "Indeed, the linear combination of features is zero (i.e. all the scores are zero). Here's ehat happens when we apply the factors to the first data point." ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6.24695048e-01, -1.11022302e-16, -7.80868809e-01])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca.components_[-1]" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-2.2204460492503131e-16" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(pca.components_[-1] * petal_df.values[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And note that if we sum pc3, it's approximately zero." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.pc3.sum()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

hunterherrin/phys202-2015-work

assignments/assignment04/TheoryAndPracticeEx01.ipynb

1

128737

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Theory and Practice of Visualization Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Graphical excellence and integrity" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Find a data-focused visualization on one of the following websites that is a *positive* example of the principles that Tufte describes in *The Visual Display of Quantitative Information*.\n", "\n", "* [Vox](http://www.vox.com/)\n", "* [Upshot](http://www.nytimes.com/upshot/)\n", "* [538](http://fivethirtyeight.com/)\n", "* [BuzzFeed](http://www.buzzfeed.com/)\n", "\n", "Upload the image for the visualization to this directory and display the image inline in this notebook." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "9c86bcce96065a2133bab497403e3291", "grade": true, "grade_id": "theorypracticeex01a", "points": 2 } }, "outputs": [ { "data": { "image/png": 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wNDREUVGREytoK8ZXrlzB5/M5mwTa2towDIP8/Hzy8vKYmJjA7/ejqiqXL18m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kL301PKcjwZKzUHO5ul9POygXf2RVB43VVE2URB6qavDDWv63CUmnng/bLl06ZQpMqWaTWGQEhT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\nMi4xIERlZmF1bHQgUkdCIGNvbG91ciBzcGFjZSAtIHN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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "Image(filename='myfileupload.png')" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Describe in detail the ways in which the visualization exhibits graphical *integrity* and *excellence*:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "80145499593a6a8f756ab550388d18ea", "grade": true, "grade_id": "theorypracticeex01b", "points": 8, "solution": true } }, "source": [ "YOUR ANSWER HERE\n", "\n", "I found this graph to be extremely good. To start off with, there is a key and short synopsis for what the graph is showing. colors are used very well to distinguis between information. Text boxes are placed where necessary, and only necessary labels are shown.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

dsacademybr/PythonFundamentos

Cap04/Notebooks/DSA-Python-Cap04-06-Reduce.ipynb

1

48784

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.8.8\n" ] } ], "source": [ "# Versão da Linguagem Python\n", "from platform import python_version\n", "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reduce" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Importando a função reduce do módulo functools\n", "from functools import reduce" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Criando uma lista\n", "lista = [47,11,42,13]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[47, 11, 42, 13]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lista" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Função \n", "def soma(a,b):\n", " x = a + b\n", " return x" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "113" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Usando reduce com uma função e uma lista. A função vai retornar o valor máximo\n", "reduce(soma, lista)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('arquivos/reduce.png')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Criando uma lista\n", "lst = [47, 11, 42, 13]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "113" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Usando a função reduce() com lambda\n", "reduce(lambda x,y: x+y, lst)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Podemos atribuir a expressão lambda a uma variável\n", "max_find2 = lambda a,b: a if (a > b) else b" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type (max_find2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "47" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reduzindo a lista até o valor máximo, através da função criada com a expressão lambda\n", "reduce(max_find2, lst)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obrigado\n", "\n", "### Visite o Blog da Data Science Academy - <a href=\"http://blog.dsacademy.com.br\">Blog DSA</a>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

dariox2/CADL

session-4/.ipynb_checkpoints/lecture-4-checkpoint.ipynb

1

24522911

apache-2.0

michaelaye/pyciss

tests/test_meta.ipynb

1

7378

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pyciss import meta" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# test_get_meta_df\n", "df = meta.get_meta_df()\n", "assert df.index[0] == \"N1467345444\"\n", "assert df.iloc[-1]['is_lit'] == False" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# test_get_order\n", "# This fishes out the resonance order from the identifier string\n", "# The order is defined as the delta between 1st and 2nd number.\n", "in_ = \"Mimas 4:1\"\n", "expected = 3\n", "assert meta.get_order(in_) == expected" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# test_get_resonances\n", "# test if the reading of resonance file works\n", "df = meta.get_resonances()\n", "assert df.iloc[0]['name'] == 'Titan 2:0'\n", "assert df.iloc[-1]['order'] == 3" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# test_get_prime_resonances\n", "# test if this filters for prime resonances (order = 1)\n", "df = meta.get_prime_resonances()\n", "assert meta.get_order(df.iloc[0]['name']) == 1\n", "assert meta.get_order(df.iloc[-1]['name']) == 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# test_get_janus_epimetheus_resonances\n", "# read higher precision Janus and Epimetheus resonance file\n", "df = meta.get_janus_epimetheus_resonances()\n", "assert df.iloc[0]['name'] == 'Janus1 2:1'\n", "assert df.iloc[-1]['name'] == 'Epimetheus2 15:13'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>moon</th>\n", " <th>reson</th>\n", " <th>radius</th>\n", " <th>order</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Janus1</td>\n", " <td>2:1</td>\n", " <td>96235.150437</td>\n", " <td>1</td>\n", " <td>Janus1 2:1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Janus1</td>\n", " <td>3:1</td>\n", " <td>74051.968798</td>\n", " <td>2</td>\n", " <td>Janus1 3:1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Janus1</td>\n", " <td>3:2</td>\n", " <td>115943.966750</td>\n", " <td>1</td>\n", " <td>Janus1 3:2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Janus1</td>\n", " <td>4:2</td>\n", " <td>95979.967411</td>\n", " <td>2</td>\n", " <td>Janus1 4:2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Janus1</td>\n", " <td>4:3</td>\n", " <td>125250.859597</td>\n", " <td>1</td>\n", " <td>Janus1 4:3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " moon reson radius order name\n", "0 Janus1 2:1 96235.150437 1 Janus1 2:1\n", "1 Janus1 3:1 74051.968798 2 Janus1 3:1\n", "2 Janus1 3:2 115943.966750 1 Janus1 3:2\n", "3 Janus1 4:2 95979.967411 2 Janus1 4:2\n", "4 Janus1 4:3 125250.859597 1 Janus1 4:3" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# test_get_prime_jan_epi\n", "# read and filter for prime resonances of Janus and Epimetheus\n", "df = meta.get_prime_jan_epi()\n", "assert df.iloc[0]['name'] == 'Janus1 2:1'\n", "assert df.iloc[-1]['name'] == 'Epimetheus2 7:6'" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# test_get_all_resonances\n", "# read all moon resonances and merge them into one\n", "df = meta.get_all_resonances()\n", "assert df.iloc[0]['name'] == 'Titan 1:0'\n", "assert df.iloc[-1]['name'] == 'Mimas 3:2'" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pkg_resources as pr" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/Users/klay6683/Dropbox/src/pyciss/pyciss/data/soliton_prediction_parameters.csv'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pr.resource_filename('pyciss', 'data/soliton_prediction_parameters.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py36]", "language": "python", "name": "conda-env-py36-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", 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isc

gditzler/bio-course-materials

notebooks/BioPython-Tutorial.ipynb

1

90117

{ "metadata": { "name": "", "signature": "sha256:ede1c12fc8215f0f83917bb17886f782c3f80dc807173e5ce1fe9ec075ca496a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What is BioPython \n", "\n", "[BioPython](http://biopython.org/wiki/Main_Page) is a set of free Python modules for working with genomic data, as well as other tools that are commonly used for sequence analysis, such as [BLAST](http://blast.ncbi.nlm.nih.gov/Blast.cgi) and [Clustalw](http://www.ebi.ac.uk/Tools/msa/clustalw2/). Or as the BioPython developers put it \n", "\n", ">Basically, the goal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clustering modules, a KD tree data structure etc. and even documentation. \n", "\n", "In terms of getting help, BioPython has a very large [cookbook](http://biopython.org/DIST/docs/tutorial/Tutorial.html) full of examples, and as always, [Google](http://google.com) is your best friend. This *very* brief tutorial covers some of the basic bioinformatics tools that will be useful throughout the course. \n", "\n", "\n", "## BioPython and Support from External Tools\n", "\n", "Biopython includes support for interfacing with or parsing the output from a number of third party command line tools. These tools are not required to install Biopython, but may be of interest. This includes:\n", "\n", "* NCBI Standalone BLAST, which can used with the Bio.Blast module and parsed with the Bio.SearchIO module.\n", "* EMBOSS tools, which can be invoked using the Bio.Emboss module. The Bio.AlignIO module can also parse some alignment formats output by the EMBOSS suite.\n", "* ClustalW, which can be parsed using Bio.AlignIO and invoked using the Bio.Align.Applications module.\n", "* SIMCOAL2 and FDist tools for population genetics can be used via the Bio.PopGen module.\n", "* Bill Pearson\u2019s FASTA tools output can be parsed using the Bio.AlignIO and Bio.SearchIO module.\n", "* Wise2 includes the useful tool dnal.\n", "\n", "See also the listing on http://biopython.org/wiki/Download which should include URLs for these tools, and may also be more up to date.\n", "\n", "If everything has been installed correctly, you should be able to import the module and print out the version that you have installed. Note that I am using version 1.64 and I cannot guarantee that the code below will work with different versions of BioPython. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import Bio as bio\n", "print \"BioPython Version: \"+str(bio.__version__)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "BioPython Version: 1.64\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Started with Python\n", "\n", "If you are new, or uncomfortable with Python, you are encouraged to watch the videos below. \n", "\n", "<a href=\"https://www.youtube.com/watch?v=tKTZoB2Vjuk\n", "\" target=\"_blank\"><img src=\"https://i.ytimg.com/vi_webp/tKTZoB2Vjuk/mqdefault.webp\" \n", "alt=\"IMAGE ALT TEXT HERE\" width=\"240\" height=\"180\" border=\"10\" /></a>\n", "\n", "<a href=\"https://www.youtube.com/watch?v=EPYupizJYQI\n", "\" target=\"_blank\"><img src=\"https://i.ytimg.com/vi_webp/EPYupizJYQI/mqdefault.webp\" \n", "alt=\"IMAGE ALT TEXT HERE\" width=\"240\" height=\"180\" border=\"10\" /></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Sequence Tools\n", "\n", "Sequences in bioinformatics can simply be interpreted as being strings. BioPython represents sequences with the `Seq` object, which contains properties beyond that of a simple string object. For example, the *Alphabet* of the sequence can be specified (and also obtained). More about the `Seq` class can be found on BioPython's [API](http://biopython.org/DIST/docs/api/Bio.Seq.Seq-class.html). It is possible to convert the sequence to a string using Python's built in `str` command (e.g., `str(my_sequence)`). Some useful methods of `Seq` object include:\n", "\n", "* `find`: Find method, like that of a python string\n", "* `startswith`: Does the Seq start with the given prefix? \n", "* `endswith`: Does the Seq end with the given suffix? \n", "* `complement`: Returns the complement sequence \n", "* `reverse_complement`: Returns the reverse complement sequence. \n", "* `transcribe`: Returns the RNA sequence from a DNA sequence \n", "* `back_transcribe`: Returns the DNA sequence from a RNA sequence \n", "* `translate`: Turns a nucleotide sequence into a protein sequence. \n", "\n", "Now let us look at a few simple sequence operations. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio.Seq import Seq \n", "\n", "# working with sequences \n", "my_seq = Seq(\"AGTACACTGGT\") \n", "print \"sequence: AGTACACTGGT\" \n", "print \"complement: \" + my_seq.complement() \n", "print \"reverse complement: \" + my_seq.reverse_complement()\n", "print \"transcribe: \" + my_seq.transcribe() # my_rna = my_seq.transcribe()\n", "print \"my_seq[2:4]: \" + my_seq[2:4]\n", "my_rna = my_seq.transcribe()\n", "my_dna = my_rna.back_transcribe() " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "sequence: AGTACACTGGT\n", "complement: TCATGTGACCA\n", "reverse complement: ACCAGTGTACT\n", "transcribe: AGUACACUGGU\n", "my_seq[2:4]: TA\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example, we converted a simple string in Python to a sequence object, which allowed used to directly call built in functions of the `Seq` class. We could call these methods directly as well (e.g., `transcribe(\"AGTACACTGGT\")`). Also, we did not specify an Alphabet. BioPython provides generic alphabets in `Bio.Alphabet`. Specifying an alphabet can be useful for catching errors in a sequence, or specifying a particular type of sequence. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio.Alphabet import generic_rna,generic_dna \n", "messenger_rna = Seq(\"AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG\", generic_rna) \n", "messenger_rna.translate() " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "Seq('MAIVMGR*KGAR*', HasStopCodon(ExtendedIUPACProtein(), '*'))" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "GC content is a commonly used statistic to quantify a sequence. It is measured as the percentage of nucleotides from either guanine or cytosine. More formally, the GC-content of a sequence is given by: \n", "$$\n", " \\textrm{GC-content} = 100\\times\\frac{N_G + N_C}{N_G + N_C + N_A + N_T}\n", "$$\n", "where $N_G$, $N_C$, $N_A$, and $N_T$ are the counts for the nucleotides *G*, *C*, *A* and *T*, respectively. BioPython allows you to perform this calcuation using either `GC` or `GC123`. Note that `GC` simply calculates the GC-content; however, `CG123` calculates total GC-content plus first, second and third positions' GC-content. Let us also take a moment to examine how to create plots with IPython and Python. If you're using the Python interpreter than you must use `from matplotlib.pylab import *`. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio.Seq import Seq \n", "from Bio.SeqUtils import GC123,GC,GC_skew\n", "seq = \"ataccaggctgaggcccattaatgatgcaatttgctgggcttctctattttctccgtgcttccatcctcttctccgtcggcggggagaagtgaaatgccgtggagatgggcggcggcggcggcgacggcggcgacgagaaagctcaccgggatctctcagtcgcgagtttcagtagcctttaccggccgtcttctctaccgctcgttcggaagcgactccagtgaaagccgcaagaggtcactgccacggggggtcgtatcgatcggggccatcagccttgctggaggtctcgtgctcagcgccgtcaacgacctcgccatcttcaatggatgcacaacgaaggcaattgagcatgctgctgacaaccctgctgttgtggaagcaattggagtgcctatagtcagaggaccgtggtatgatgcttctcttgaggtgggccatcgacggcggtctgtgtcatgcacattccctgtatctgggccacatgggtcaggatttctccagattaaggcaacccgagatggagaggatggtctgctttcgtttctgcggcatcacgactggaagatcctattgctggaggctcatcttgaagcaccatcagatgatgaggaccagagaaagctggttaaggtgaatcttgcaagcagtggccgtggggaagatggggatccagagagtggttaatcttttgtactgaattccatggtgagtggaagatcgtgtcatctgaatggactccaaatattaaatgacatggagatctagggaagcaaaaaaaaaaaaaaaa\"\n", "print GC123(seq)\n", "print GC(seq)\n", "\n", "plot(GC_skew(seq, window=100),c=\"r\")\n", "xlabel(\"Window\")\n", "ylabel(\"(G-C)/(G+C)\")\n", "title(\"GC-skew\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(52.97092288242731, 51.89393939393939, 57.57575757575758, 49.42965779467681)\n", "52.9709228824\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "<matplotlib.text.Text at 0x10c62c7d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10a7c4b50>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also go directly from DNA to amino acids using the `translate` command." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"translate: \" + Seq(seq).translate() # my_rna = my_seq.transcribe()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "translate: IPG*GPLMMQFAGLLYFLRASILFSVGGEK*NAVEMGGGGGDGGDEKAHRDLSVASFSSLYRPSSLPLVRKRLQ*KPQEVTATGGRIDRGHQPCWRSRAQRRQRPRHLQWMHNEGN*ACC*QPCCCGSNWSAYSQRTVV*CFS*GGPSTAVCVMHIPCIWATWVRISPD*GNPRWRGWSAFVSAASRLEDPIAGGSS*STIR**GPEKAG*GESCKQWPWGRWGSREWLIFCTEFHGEWKIVSSEWTPNIK*HGDLGKQKKKK\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Refer to the documentation and [cookbook](http://biopython.org/DIST/docs/tutorial/Tutorial.html#sec17) for BioPython to view more of the sequence tools. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic FileIO\n", "\n", "Different tools in bioinformatics use different standards for containing thier data. For example, GenBank, Fasta, and Biom are all file formats used to storge biological data. BioPython contains several builtin modules for interfacing with theses different file formats using the `SeqIO` class. In this section, we examine converting between file formats as well as manipulating the variables in the `SeqIO` class. \n", "\n", "Let us take a moment to talk about the `SeqRecord` object in BioPython. We learned that sequences in BioPython are stored in a a `Seq` object, which contained some handy functions such as `translation`. We can create a more detailed record that contains a seqence ID, name, the raw sequence, etc. Such a more detailed stucture can be found in the `SeqRecord` class. `SeqRecord` contains a sequence (as a `Seq` object) with identifiers (ID and name), description and optionally annotation and sub-features. The `SeqIO` system will only return `SeqRecord` objects.\n", "\n", "**Quick Python Note**\n", "```python\n", "A = []\n", "for x in range(5):\n", " A.append(x**2)\n", " \n", "B = [x**2 for x in range(5)]\n", "# A and B are are identical (I use this condensed notation) \n", "```\n", "\n", "`SeqIO` contains a `parse` function that can readily handle GenBank and Fasta formats. We have a fasta file in `../data` that looks like:\n", "```\n", ">gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA\n", "CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG\n", "AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG\n", "CCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCCCGGCGCAGTTTGGGCGCCAAGCCATATGAA\n", "...\n", "\n", ">gi|2765657|emb|Z78532.1|CCZ78532 C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA\n", "CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAGAATATATGATCGAGTG\n", "AATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCGTGACCCTGCTTTGTTGTTGG\n", "...\n", "```\n", "which contains 94 sequences, or *records*. We can read in the iterable set of sequence records into a dictionary with the key being the `id` and the value being the sequence. In code this translates to: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio import SeqIO\n", "\n", "# we can parse the fasta file and read the sequences in by looping over \n", "# the iterator returned by SeqIO.parse()\n", "sequences = {} # empty dictionary \n", "for seq_record in SeqIO.parse(\"../data/ls_orchid.fasta\", \"fasta\"):\n", " sequences[seq_record.id] = seq_record.seq\n", "\n", "# an alternative way to do this can be done with one line of code\n", "sequences2 = {s.id:s.seq for s in SeqIO.parse(\"../data/ls_orchid.fasta\", \"fasta\")}\n", "\n", "print sequences2.keys()[0]\n", "print sequences2[sequences2.keys()[0]] # print the first sequence " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gi|2765596|emb|Z78471.1|PDZ78471\n", "CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTTAATCTAGAGGATCGGTTTACTTTGGTCACCCATGGGCATCTGCTCTTACAGTGACCTGGATTTGCCATCGAGCCTCCTTGGGAGCTGTCTTGCTGGCGATCTAAATCGTTGCCCGACGCAGCCTTGCGTCAAGTCACCCCGACACATAATGGAAGGGGGCGGCATGCTGCCTTGACCCTTCCCCAAATTAATTTTTTGACAACTCTCAACATCGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCTTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCAGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTAGTACGGGGGGTCTAAAAGCTGCATGGGCTTTTGCTGGTCCTAAATACGGCAAGAGGTGGACAAAGTATGCTACAACAAAATTGTAGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATTTAAAGACCCTTTTGAACCCCATTAGAGGCCCATCAACCCTGATCAGTTGATGGCCATTTGGTTGCGACCCCAAGTCAGGTGAGGCAACCCGCTGAGTTTAAGC\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also read in Genbank files as well." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# simple conversion from GB to a fasta file\n", "records = SeqIO.parse(\"../data/ls_orchid_full.gbk\", \"genbank\")\n", "count = SeqIO.write(records, \"../data/my_example.fasta\", \"fasta\")\n", "\n", "# read in the sequences from the GB file. note that this is nearly identical to when we \n", "# performed this task with the fasta file in the previous code block\n", "sequences = {s.id:s.seq for s in SeqIO.parse(\"../data/ls_orchid_full.gbk\", \"genbank\")}\n", "\n", "record = SeqIO.read(\"../data/ls_orchid.gbk\", 'genbank')\n", "print \"locus = \" + record.name\n", "print \"definition = \" + record.description\n", "print \"version = \" + record.id\n", "print \"seq = \" + record.seq " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "locus = Z78533\n", "definition = C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.\n", "version = Z78533.1\n", "seq = CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTGAATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGGCCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCCCGGCGCAGTTTGGGCGCCAAGCCATATGAAAGCATCACCGGCGAATGGCATTGTCTTCCCCAAAACCCGGAGCGGCGGCGTGCTGTCGCGTGCCCAATGAATTTTGATGACTCTCGCAAACGGGAATCTTGGCTCTTTGCATCGGATGGAAGGACGCAGCGAAATGCGATAAGTGGTGTGAATTGCAAGATCCCGTGAACCATCGAGTCTTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCTTGGGCGTCGCGCTTCGTCTCTCTCCTGCCAATGCTTGCCCGGCATACAGCCAGGCCGGCGTGGTGCGGATGTGAAAGATTGGCCCCTTGTGCCTAGGTGCGGCGGGTCCAAGAGCTGGTGTTTTGATGGCCCGGAACCCGGCAAGAGGTGGACGGATGCTGGCAGCAGCTGCCGTGCGAATCCCCCATGTTGTCGTGCTTGTCGGACAGGCAGGAGAACCCTTCCGAACCCCAATGGAGGGCGGTTGACCGCCATTCGGATGTGACCCCAGGTCAGGCGGGGGCACCCGCTGAGTTTACGC\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash \n", "cat ../data/my_example.fasta | head -20" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ">Z78533.1 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.\n", "CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAA\n", "CGATCGAGTGAATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGT\n", "GACCCTGATTTGTTGTTGGGCCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCC\n", "CGGCGCAGTTTGGGCGCCAAGCCATATGAAAGCATCACCGGCGAATGGCATTGTCTTCCC\n", "CAAAACCCGGAGCGGCGGCGTGCTGTCGCGTGCCCAATGAATTTTGATGACTCTCGCAAA\n", "CGGGAATCTTGGCTCTTTGCATCGGATGGAAGGACGCAGCGAAATGCGATAAGTGGTGTG\n", "AATTGCAAGATCCCGTGAACCATCGAGTCTTTTGAACGCAAGTTGCGCCCGAGGCCATCA\n", "GGCTAAGGGCACGCCTGCTTGGGCGTCGCGCTTCGTCTCTCTCCTGCCAATGCTTGCCCG\n", "GCATACAGCCAGGCCGGCGTGGTGCGGATGTGAAAGATTGGCCCCTTGTGCCTAGGTGCG\n", "GCGGGTCCAAGAGCTGGTGTTTTGATGGCCCGGAACCCGGCAAGAGGTGGACGGATGCTG\n", "GCAGCAGCTGCCGTGCGAATCCCCCATGTTGTCGTGCTTGTCGGACAGGCAGGAGAACCC\n", "TTCCGAACCCCAATGGAGGGCGGTTGACCGCCATTCGGATGTGACCCCAGGTCAGGCGGG\n", "GGCACCCGCTGAGTTTACGC\n", ">Z78532.1 C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA.\n", "CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAGAATATA\n", "TGATCGAGTGAATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCG\n", "TGACCCTGCTTTGTTGTTGGGCCTCCTCAAGAGCTTTCATGGCAGGTTTGAACTTTAGTA\n", "CGGTGCAGTTTGCGCCAAGTCATATAAAGCATCACTGATGAATGACATTATTGTCAGAAA\n", "AAATCAGAGGGGCAGTATGCTACTGAGCATGCCAGTGAATTTTTATGACTCTCGCAACGG\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Accessing the Entrez Database\n", "\n", "We can also search...." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio import Entrez \n", "from Bio.SeqRecord import SeqRecord\n", "\n", "terms = [\"cytochrome oxidase i AND viridiplantae[Organism] and 450:2000[Sequence Length]\",\n", " \"cytochrome oxidase i AND fungi[Organism] and 450:2000[Sequence Length]\",\n", " \"cytochrome oxidase i AND animalia[Organism] and 450:2000[Sequence Length]\"\n", " ]\n", "Entrez.email = \"[email protected]\" # you should put your email in here!" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "top_records = []\n", "for term in terms: # I could do this in 1 line if I wanted to!\n", " handle = Entrez.esearch(db=\"nucleotide\", term=term)\n", " top_records.append(Entrez.read(handle)[\"IdList\"][0])\n", "\n", "handle = Entrez.efetch(db=\"nucleotide\", id=\",\".join(top_records), rettype=\"gb\", retmode=\"xml\")\n", "genbank_records = Entrez.read(handle)\n", "print \"First Genbank record keys: \" + str(genbank_records[0].keys())\n", "print \"\" \n", "print \"First Genbank sequence (\" + genbank_records[0][\"GBSeq_primary-accession\"] + \"): \" + genbank_records[0][\"GBSeq_sequence\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "First Genbank record keys: [u'GBSeq_moltype', u'GBSeq_source', u'GBSeq_sequence', u'GBSeq_primary-accession', u'GBSeq_definition', u'GBSeq_accession-version', u'GBSeq_topology', u'GBSeq_length', u'GBSeq_feature-table', u'GBSeq_create-date', u'GBSeq_other-seqids', u'GBSeq_division', u'GBSeq_taxonomy', u'GBSeq_comment', u'GBSeq_references', u'GBSeq_update-date', u'GBSeq_organism', u'GBSeq_locus', u'GBSeq_keywords', u'GBSeq_strandedness']\n", "\n", "First Genbank sequence (KC016094): ctttattcccacattagataccagaatagttattgcacgtatgtgtaacgccagtatattaattgcgaaggttgctaagctaatcctaatcaggcaacgagtctttcaggagttctggacacctacgggagttgaggaagtaatcatggctgaacaaagggaggaatgttgccaataaggtctctgtctgctgtatgcatatggctatccttctttctctgggatgcgggatacgtttcactgtccatgaaggaagaaaccactcgaccaagggagaggtggtttgcccggcatggtactgattgttaacgagattgattattcacgctctgcttattgagatcttgggtttcttccccagtatattaattgccggattttccggtgttttacttagctgaacagcactagctccttctctcatgccaaatccaagtcatttagcctagatagcacggggaagaggatcagaccattcattaactccgctaattcaagttctgttcactctcgctacgcacctgagaactataagaaaataattcgctacctcccgctcacaattaatatactggcgccttgttctccacgccattcattcccctggtaaataattctacgagatcgatgtgttaagcatagtgtctctgtctccaaatccgtatcaaccactgcgactctcgctaggatttctattctgaactaatatactgggagctgcgatattgagaatgtgagtcttgctgcaaagcagatgaactacctaactaaactaagggaaggggaagaagggtcaaacacctgacatgactaagcgctagggagactcgctacgcacctgcaccctctgctgcttaccaaattcttagggccgtggtgaactggtcgtcctataccttctgcctatctcatgtgtgtggaaccaggtctttttcggttccagcttaactttcagtaggggtgagaaacgccaacctcccctgttgcctattagtaagcggccccaatagtattactcaaacccagtataaacagaaatataaaaaatagaaaaatatagcaaatataaaaaataaaaaactctgagaactacctaactaaagaagaatagtgctttttctatgattattcagctagtaggcgtggagagctttttgcggggaaacttgcaagtcaagtttggggggaggcgggcgtcgacccaaccttatgagtattcagactataacagtttcgatgaccagtcactcacttttgacagttatatgattccataggatgatccagaattgggtcaatcatgtttattagaagtggacaatagagtggctgtaccagccaaaactcatgtatgtatgattgtaacacccgctgatgtacctcatagttgggctgtaccttctt\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "fasta_records = []\n", "for record in genbank_records:\n", " seq_record = SeqRecord(Seq(record[\"GBSeq_sequence\"]), id=record[\"GBSeq_primary-accession\"], description=record[\"GBSeq_definition\"])\n", " fasta_records.append(seq_record)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "with open(\"../data/my_seqs.fa\",\"w\") as fasta_file:\n", " SeqIO.write(fasta_records, fasta_file, \"fasta\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash \n", "cat ../data/my_seqs.fa" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ">KC016094 UNVERIFIED: Allium sphaerocephalon cytochrome oxidase subunit 2-like (cox2) gene, partial sequence; mitochondrial\n", "ctttattcccacattagataccagaatagttattgcacgtatgtgtaacgccagtatatt\n", "aattgcgaaggttgctaagctaatcctaatcaggcaacgagtctttcaggagttctggac\n", "acctacgggagttgaggaagtaatcatggctgaacaaagggaggaatgttgccaataagg\n", "tctctgtctgctgtatgcatatggctatccttctttctctgggatgcgggatacgtttca\n", "ctgtccatgaaggaagaaaccactcgaccaagggagaggtggtttgcccggcatggtact\n", "gattgttaacgagattgattattcacgctctgcttattgagatcttgggtttcttcccca\n", "gtatattaattgccggattttccggtgttttacttagctgaacagcactagctccttctc\n", "tcatgccaaatccaagtcatttagcctagatagcacggggaagaggatcagaccattcat\n", "taactccgctaattcaagttctgttcactctcgctacgcacctgagaactataagaaaat\n", "aattcgctacctcccgctcacaattaatatactggcgccttgttctccacgccattcatt\n", "cccctggtaaataattctacgagatcgatgtgttaagcatagtgtctctgtctccaaatc\n", "cgtatcaaccactgcgactctcgctaggatttctattctgaactaatatactgggagctg\n", "cgatattgagaatgtgagtcttgctgcaaagcagatgaactacctaactaaactaaggga\n", "aggggaagaagggtcaaacacctgacatgactaagcgctagggagactcgctacgcacct\n", "gcaccctctgctgcttaccaaattcttagggccgtggtgaactggtcgtcctataccttc\n", "tgcctatctcatgtgtgtggaaccaggtctttttcggttccagcttaactttcagtaggg\n", "gtgagaaacgccaacctcccctgttgcctattagtaagcggccccaatagtattactcaa\n", "acccagtataaacagaaatataaaaaatagaaaaatatagcaaatataaaaaataaaaaa\n", "ctctgagaactacctaactaaagaagaatagtgctttttctatgattattcagctagtag\n", "gcgtggagagctttttgcggggaaacttgcaagtcaagtttggggggaggcgggcgtcga\n", "cccaaccttatgagtattcagactataacagtttcgatgaccagtcactcacttttgaca\n", "gttatatgattccataggatgatccagaattgggtcaatcatgtttattagaagtggaca\n", "atagagtggctgtaccagccaaaactcatgtatgtatgattgtaacacccgctgatgtac\n", "ctcatagttgggctgtaccttctt\n", ">KF528054 Uredo rolliniae isolate 189 cytochrome oxidase subunit 3 gene, partial cds; mitochondrial\n", "gtagagggtggtggttatggggtagcactaggttttgtaagcacagtaggtgtaataagt\n", "ctatgatttagggatgtaagtgcagaggggtcgctaggggggtatcatacgtttgatgtt\n", "caacgttctctaaatatgggggtagtactatttattgtaagtgagatctttatttttgta\n", "tcaattttttgagcttattttcattcagctctaagtcctacggtagagctaggttctcag\n", "tggccggcaccaggtgtagagccactgaatgcatttgagattccacttctaaatacgatt\n", "ctgcttctaacctcagcgagctcactaacttatgcacaccatgctctaattaatgggaat\n", "aggaagtcatgtctaattggttttgcggtgactctagcactggcagtaacttttacaggg\n", "tttcaagcactagagtatattgaggcaccttttacaatttcagatggggcatttggtagt\n", "acttttttctttagtacaggttctcatgggatgcacgtaattattgggacgatttttcta\n", "acagtagcactagctcgaattattagttaccaactaacagaccaccaccatctaggtttt\n", "gaggctgcagcactgtattgacattttgttgatgttgtttga\n", ">KF802856 Aspidiotus nerii isolate D0530E cytochrome oxidase subunit 1 (CO1) and cytochrome oxidase subunit 2 (CO2) genes, partial cds; mitochondrial\n", "aataatttatcaactttaggatcaataataacaattatatttatttttatatttttttat\n", "tcaattattgaacttttaatttcaaaacgaaaaattattattattattaaatctaataat\n", "aatgaatgaaaaaataatttaccaactctttctcattcaaatattgaaaataattttata\n", "ttcaataaaaattaaattatatcatgaataaacctaaattttcaaaaccctaattcaatc\n", "aatataattaaaattagaatatttcataattttatattaattattttaattaatattatt\n", "ataattttaattttaataattaatttttcttttattaataaatttaataataaaataatt\n", "ttacaaaatcaaaaattagaaatcttatgaacaattattcccataattatcattattata\n", "attagaataatttcaattaatttattattttcaaataatgaaataaaaaataatatttta\n", "aatattaaaatcattggtaatcaatgattttgaaattatgaatactcattatttaacaaa\n", "aactttaattcatatttattaataaataataattttaatttttttatattagaaacagat\n", "aataattttattattccttttaattatcaattaatacttattttttcatctttagatgtt\n", "attcattcttgaaccattccatcaataaatattaaaatag\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BLAST\n", "\n", "BLAST can be run with BioPython either from using the command line tools -- provided they are installed -- or through the web. In this section, we use the `qblast` from the `Bio.Blast.NCBIWWW` module to call the online version of BLAST. Note that the results would be the same if we were to use the command line tools. As pointed out from the `qblast` documentation, there are three required arguments:\n", "\n", "* The first argument is the blast program to use for the search, as a lower case string. The options and descriptions of the programs are available at <http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml>. Currently `qblast` only works with blastn, blastp, blastx, tblast and tblastx.\n", "* The second argument specifies the database in which to perform the search. Again, the options for this are available on the NCBI web pages at <http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml>. \n", "* The third argument is a string containing your query sequence. This can either be the sequence itself, the sequence in fasta format, or an identifier like a GI number. \n", "\n", "The default output format for `qblast` is XML; however, we shall use other methods in BioPython to convert the results in the XML file to a more interpretable and managable output. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio.Blast import NCBIWWW, NCBIXML\n", "\n", "E_VALUE_THRESH = 0.01\n", "s_len = 100\n", "\n", "# search against the nucleotide database (nt) using BLASTN, and you know the GI number \n", "result_handle = NCBIWWW.qblast(\"blastn\", \"nt\", \"8332116\") # 449020131, 8332116\n", "blast_records = NCBIXML.read(result_handle) \n", "\n", "for alignment in blast_records.alignments[:5]:\n", " for hsp in alignment.hsps: \n", " if hsp.expect < E_VALUE_THRESH: \n", " print \"****Alignment****\"\n", " print \"sequence:\", alignment.title \n", " print \"length:\", alignment.length \n", " print \"e value:\", hsp.expect \n", " print hsp.query[0:s_len] + \"...\" \n", " print hsp.match[0:s_len] + \"...\" \n", " print hsp.sbjct[0:s_len] + \"...\" \n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "****Alignment****\n", "sequence: gi|568824607|ref|XM_006466626.1| PREDICTED: Citrus sinensis cold-regulated 413 plasma membrane protein 2-like (LOC102620025), transcript variant X5, mRNA\n", "length: 878\n", "e value: 4.20976e-99\n", "AAATGGGGAGAGAAATGAAGTACTTGGCCATGAAAACTGATCAATTGGCCGTGGCTAATATGATCGATTCCGATATCAATGAGCTTAAAATGGCAACAAT...\n", "||||||||||| |||| || ||||| |||||||||||| | || | || | ||||| || ||| ||||||||||||| || || ||| ...\n", "AAATGGGGAGAT---TGAATTATTTGGCTATGAAAACTGATGATCAGGTTGCAGCAGAGTTGATCAGCTCTGATTTCAATGAGCTTAAGATTGCTGCAAA...\n", "****Alignment****\n", "sequence: gi|568824605|ref|XM_006466625.1| PREDICTED: Citrus sinensis cold-regulated 413 plasma membrane protein 2-like (LOC102620025), transcript variant X4, mRNA\n", "length: 911\n", "e value: 4.20976e-99\n", "AAATGGGGAGAGAAATGAAGTACTTGGCCATGAAAACTGATCAATTGGCCGTGGCTAATATGATCGATTCCGATATCAATGAGCTTAAAATGGCAACAAT...\n", "||||||||||| |||| || ||||| |||||||||||| | || | || | ||||| || ||| ||||||||||||| || || ||| ...\n", "AAATGGGGAGAT---TGAATTATTTGGCTATGAAAACTGATGATCAGGTTGCAGCAGAGTTGATCAGCTCTGATTTCAATGAGCTTAAGATTGCTGCAAA...\n", "****Alignment****\n", "sequence: gi|568824603|ref|XM_006466624.1| PREDICTED: Citrus sinensis cold-regulated 413 plasma membrane protein 2-like (LOC102620025), transcript variant X3, mRNA\n", "length: 894\n", "e value: 4.20976e-99\n", "AAATGGGGAGAGAAATGAAGTACTTGGCCATGAAAACTGATCAATTGGCCGTGGCTAATATGATCGATTCCGATATCAATGAGCTTAAAATGGCAACAAT...\n", "||||||||||| |||| || ||||| |||||||||||| | || | || | ||||| || ||| ||||||||||||| || || ||| ...\n", "AAATGGGGAGAT---TGAATTATTTGGCTATGAAAACTGATGATCAGGTTGCAGCAGAGTTGATCAGCTCTGATTTCAATGAGCTTAAGATTGCTGCAAA...\n", "****Alignment****\n", "sequence: gi|568824601|ref|XM_006466623.1| PREDICTED: Citrus sinensis cold-regulated 413 plasma membrane protein 2-like (LOC102620025), transcript variant X2, mRNA\n", "length: 922\n", "e value: 4.20976e-99\n", "AAATGGGGAGAGAAATGAAGTACTTGGCCATGAAAACTGATCAATTGGCCGTGGCTAATATGATCGATTCCGATATCAATGAGCTTAAAATGGCAACAAT...\n", "||||||||||| |||| || ||||| |||||||||||| | || | || | ||||| || ||| ||||||||||||| || || ||| ...\n", "AAATGGGGAGAT---TGAATTATTTGGCTATGAAAACTGATGATCAGGTTGCAGCAGAGTTGATCAGCTCTGATTTCAATGAGCTTAAGATTGCTGCAAA...\n", "****Alignment****\n", "sequence: gi|568824599|ref|XM_006466622.1| PREDICTED: Citrus sinensis cold-regulated 413 plasma membrane protein 2-like (LOC102620025), transcript variant X1, mRNA\n", "length: 966\n", "e value: 4.20976e-99\n", "AAATGGGGAGAGAAATGAAGTACTTGGCCATGAAAACTGATCAATTGGCCGTGGCTAATATGATCGATTCCGATATCAATGAGCTTAAAATGGCAACAAT...\n", "||||||||||| |||| || ||||| |||||||||||| | || | || | ||||| || ||| ||||||||||||| || || ||| ...\n", "AAATGGGGAGAT---TGAATTATTTGGCTATGAAAACTGATGATCAGGTTGCAGCAGAGTTGATCAGCTCTGATTTCAATGAGCTTAAGATTGCTGCAAA...\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, we can directly BLAST our sequence if we have it in the Python environment. BioPython is smart enough to pick up on how the third argument should be interpreted for BLASTing. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "seq = \"ataccaggctgaggcccattaatgatgcaatttgctgggcttctctattttctccgtgcttccatcctcttctccgtcggcggggagaagtgaaatgccgtggagatgggcggcggcggcggcgacggcggcgacgagaaagctcaccgggatctctcagtcgcgagtttcagtagcctttaccggccgtcttctctaccgctcgttcggaagcgactccagtgaaagccgcaagaggtcactgccacggggggtcgtatcgatcggggccatcagccttgctggaggtctcgtgctcagcgccgtcaacgacctcgccatcttcaatggatgcacaacgaaggcaattgagcatgctgctgacaaccctgctgttgtggaagcaattggagtgcctatagtcagaggaccgtggtatgatgcttctcttgaggtgggccatcgacggcggtctgtgtcatgcacattccctgtatctgggccacatgggtcaggatttctccagattaaggcaacccgagatggagaggatggtctgctttcgtttctgcggcatcacgactggaagatcctattgctggaggctcatcttgaagcaccatcagatgatgaggaccagagaaagctggttaaggtgaatcttgcaagcagtggccgtggggaagatggggatccagagagtggttaatcttttgtactgaattccatggtgagtggaagatcgtgtcatctgaatggactccaaatattaaatgacatggagatctagggaagcaaaaaaaaaaaaaaaa \"\n", "result_handle = NCBIWWW.qblast(\"blastn\", \"nt\", seq) \n", "blast_records = NCBIXML.read(result_handle) \n", "\n", "for alignment in blast_records.alignments[:5]:\n", " for hsp in alignment.hsps: \n", " if hsp.expect < E_VALUE_THRESH: \n", " print \"****Alignment****\"\n", " print \"sequence:\", alignment.title \n", " print \"length:\", alignment.length \n", " print \"e value:\", hsp.expect \n", " print hsp.query[0:s_len] + \"...\" \n", " print hsp.match[0:s_len] + \"...\" \n", " print hsp.sbjct[0:s_len] + \"...\" \n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "****Alignment****\n", "sequence: gi|226505045|ref|NM_001150016.1| Zea mays uncharacterized LOC100276165 (LOC100276165), mRNA >gi|195621403|gb|EU960414.1| Zea mays clone 224719 hypothetical protein mRNA, complete cds\n", "length: 791\n", "e value: 0.0\n", "ATACCAGGCTGAGGCCCATTAATGATGCAATTTGCTGGGCTTCTCTATTTTCTCCGTGCTTCCATCCTCTTCTCCGTCGGCGGGGAGAAGTGAAATGCCG...\n", "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||...\n", "ATACCAGGCTGAGGCCCATTAATGATGCAATTTGCTGGGCTTCTCTATTTTCTCCGTGCTTCCATCCTCTTCTCCGTCGGCGGGGAGAAGTGAAATGCCG...\n", "****Alignment****\n", "sequence: gi|242044007|ref|XM_002459830.1| Sorghum bicolor hypothetical protein, mRNA\n", "length: 582\n", "e value: 0.0\n", "CGGCGGCGACGAGAAAGCTCACCGGGATCTCTCAGTCGCGAGTTTCAGTAGCCTTTACCGGCCGTCTTCTCTACCGCTCGTTCGGAAGCGACTCCAGTGA...\n", "|||||||| ||||||||||| |||| || | |||| ||| | ||| ||||||||||||| ||||||||||||||||||||||||||||||||| | ...\n", "CGGCGGCGGCGAGAAAGCTCGCCGGAATGCCCCAGTTGCGCACTCCAGGAGCCTTTACCGGCGGTCTTCTCTACCGCTCGTTCGGAAGCGACTCCATCGG...\n", "****Alignment****\n", "sequence: gi|514727640|ref|XM_004956214.1| PREDICTED: Setaria italica serine/threonine-protein kinase PBS1-like (LOC101783701), mRNA\n", "length: 2391\n", "e value: 7.92359e-113\n", "GCTCAGCGCCGTCAACGACCTCGCCATCTTCAATGGATGCACAACGAAGGCAATTGAGCATGCTGCTGACAACCCTGCTGTTGTGGAAGCAATTGGAGTG...\n", "|||||||||||| |||||||||||||||||| ||||||| ||||| |||||||||||| | || ||||||||||| ||||| |||||||||||||||...\n", "GCTCAGCGCCGTTAACGACCTCGCCATCTTCCATGGATGTACAACTAAGGCAATTGAGAAGGCGGCTGACAACCCAAAGGTTGTCGAAGCAATTGGAGTG...\n", "****Alignment****\n", "sequence: gi|357160261|ref|XM_003578660.1| PREDICTED: Brachypodium distachyon uncharacterized LOC100836817 (LOC100836817), mRNA\n", "length: 933\n", "e value: 1.43388e-90\n", "AAGAGGTCACTGCCACGGGGGGTCGTATCGATCGGGGCCATCAGCCTTGCTGGAGGTCTCGTGCTCAGCGCCGTCAACGACCTCGCCATCTTCAATGGAT...\n", "|||||||| | | |||| ||||| | ||||| || | ||||||||| ||||| || ||| ||||| ||| |||||||||||||||||||| ||||||...\n", "AAGAGGTCGCCGGTACGGAGGGTCCTGTCGATTGGTGTCATCAGCCTCGCTGGTGGAGTCGCCCTCAGTGCCCTCAACGACCTCGCCATCTTCCATGGAT...\n", "****Alignment****\n", "sequence: gi|149391396|gb|EF576128.1| Oryza sativa (indica cultivar-group) clone V-E12 unknown mRNA\n", "length: 752\n", "e value: 1.2578e-78\n", "AAGAGGTCACTGCCACGGGGGGTCGTATCGATCGGGGCCATCAGCCTTGCTGGAGGTCTCGTGCTCAGCGCCGTCAACGACCTCGCCATCTTCAATGGAT...\n", "|||||||||| | ||| || |||| ||||| |||| |||||| | ||||| || ||| ||| |||||| |||||||||| || || ||| ||||||...\n", "AAGAGGTCACGGATCCGGAGGATCGTGTCGATTGGGGTTATCAGCATCGCTGGCGGCGTCGCGCTTAGCGCCCTCAACGACCTTGCTATATTCCATGGAT...\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Phylogenetic Trees" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio import Phylo \n", "tree = Phylo.read(\"../data/simple.dnd\", \"newick\")\n", "print tree\n", "Phylo.draw_ascii(tree)\n", "tree.rooted = True \n", "Phylo.draw(tree) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Tree(rooted=False, weight=1.0)\n", " Clade(branch_length=1.0)\n", " Clade(branch_length=1.0)\n", " Clade(branch_length=1.0)\n", " Clade(branch_length=1.0, name='A')\n", " Clade(branch_length=1.0, name='B')\n", " Clade(branch_length=1.0)\n", " Clade(branch_length=1.0, name='C')\n", " Clade(branch_length=1.0, name='D')\n", " Clade(branch_length=1.0)\n", " Clade(branch_length=1.0, name='E')\n", " Clade(branch_length=1.0, name='F')\n", " Clade(branch_length=1.0, name='G')\n", " __________________ A\n", " __________________|\n", " | |__________________ B\n", " __________________|\n", " | | __________________ C\n", " | |__________________|\n", "___________________| |__________________ D\n", " |\n", " | __________________ E\n", " | |\n", " |__________________|__________________ F\n", " |\n", " |__________________ G\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10a743810>" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio.Phylo.PhyloXML import Phylogeny \n", "tree = tree.as_phyloxml() \n", "tree = Phylogeny.from_tree(tree)\n", "tree.root.color = (128, 128, 128) \n", "mrca = tree.common_ancestor({\"name\": \"E\"}, {\"name\": \"F\"}) \n", "mrca.color = \"salmon\" \n", "tree.clade[0, 1].color = \"blue\" \n", "Phylo.draw(tree, branch_labels=lambda c: c.branch_length) \n", "Phylo.draw(tree) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10d8e8790>" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

brianray/puppy_dec_2015

.ipynb_checkpoints/Feature Engineering for Text Data-checkpoint.ipynb

1

118308

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import graphlab as gl" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"votes\": {\"funny\": 0, \"useful\": 5, \"cool\": 2}, \"user_id\": \"rLtl8ZkDX5vH5nAx9C3q5Q\", \"review_id\": \"fWKvX83p0-ka4JS3dc6E5A\", \"stars\": 5, \"date\": \"2011-01-26\", \"text\": \"My wife took me here on my birthday for breakfast and it was excellent. The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure. Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning. It looked like the place fills up pretty quickly so the earlier you get here the better.\\n\\nDo yourself a favor and get their Bloody Mary. It was phenomenal and simply the best I've ever had. I'm pretty sure they only use ingredients from their garden and blend them fresh when you order it. It was amazing.\\n\\nWhile EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious. It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete. It was the best \\\"toast\\\" I've ever had.\\n\\nAnyway, I can't wait to go back!\", \"type\": \"review\", \"business_id\": \"9yKzy9PApeiPPOUJEtnvkg\"}\r\n", "{\"votes\": {\"funny\": 0, \"useful\": 0, \"cool\": 0}, \"user_id\": \"0a2KyEL0d3Yb1V6aivbIuQ\", \"review_id\": \"IjZ33sJrzXqU-0X6U8NwyA\", \"stars\": 5, \"date\": \"2011-07-27\", \"text\": \"I have no idea why some people give bad reviews about this place. It goes to show you, you can please everyone. They are probably griping about something that their own fault...there are many people like that.\\n\\nIn any case, my friend and I arrived at about 5:50 PM this past Sunday. It was pretty crowded, more than I thought for a Sunday evening and thought we would have to wait forever to get a seat but they said we'll be seated when the girl comes back from seating someone else. We were seated at 5:52 and the waiter came and got our drink orders. Everyone was very pleasant from the host that seated us to the waiter to the server. The prices were very good as well. We placed our orders once we decided what we wanted at 6:02. We shared the baked spaghetti calzone and the small \\\"Here's The Beef\\\" pizza so we can both try them. The calzone was huge and we got the smallest one (personal) and got the small 11\\\" pizza. Both were awesome! My friend liked the pizza better and I liked the calzone better. The calzone does have a sweetish sauce but that's how I like my sauce!\\n\\nWe had to box part of the pizza to take it home and we were out the door by 6:42. So, everything was great and not like these bad reviewers. That goes to show you that you have to try these things yourself because all these bad reviewers have some serious issues.\", \"type\": \"review\", \"business_id\": \"ZRJwVLyzEJq1VAihDhYiow\"}\r\n" ] } ], "source": [ "!head -n 2 ../data/yelp/yelp_training_set_review.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SFrame -- Scalable Dataframe\n", "\n", "### Powerful unstructured data processing: read straight up json" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] This commercial license of GraphLab Create is assigned to [email protected].\n", "\n", "[INFO] Start server at: ipc:///tmp/graphlab_server-32308 - Server binary: /Users/alicez/.graphlab/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443536133.log\n", "[INFO] GraphLab Server Version: 1.6.1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Finished parsing file /Users/alicez/Documents/training/Strata NYC 2015/data/yelp/yelp_training_set_review.json\n", "PROGRESS: Parsing completed. Parsed 100 lines in 0.864794 secs.\n", "------------------------------------------------------\n", "Inferred types from first line of file as \n", "column_type_hints=[dict]\n", "If parsing fails due to incorrect types, you can correct\n", "the inferred type list above and pass it to read_csv in\n", "the column_type_hints argument\n", "------------------------------------------------------\n", "PROGRESS: Read 55824 lines. Lines per second: 32872.1\n", "PROGRESS: Finished parsing file /Users/alicez/Documents/training/Strata NYC 2015/data/yelp/yelp_training_set_review.json\n", "PROGRESS: Parsing completed. Parsed 229907 lines in 4.42562 secs.\n" ] }, { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">X1</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 5, 'cool': 2}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 0, 'cool': 0}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 1, 'cool': 0}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 2, 'cool': 1}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 0, 'cool': 0}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 1,<br>'useful': 3, 'cool': 4}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 4,<br>'useful': 7, 'cool': 7}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 1, 'cool': 0}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 0, 'cool': 0}, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'votes': {'funny': 0,<br>'useful': 1, 'cool': 0}, ...</td>\n", " </tr>\n", "</table>\n", "[229907 rows x 1 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tX1\tdict\n", "\n", "Rows: 229907\n", "\n", "Data:\n", "+-------------------------------+\n", "| X1 |\n", "+-------------------------------+\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 1, 'us... |\n", "| {'votes': {'funny': 4, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "| {'votes': {'funny': 0, 'us... |\n", "+-------------------------------+\n", "[229907 rows x 1 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews = gl.SFrame.read_csv('../data/yelp/yelp_training_set_review.json', header=False)\n", "reviews" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'X1': {'business_id': '9yKzy9PApeiPPOUJEtnvkg',\n", " 'date': '2011-01-26',\n", " 'review_id': 'fWKvX83p0-ka4JS3dc6E5A',\n", " 'stars': 5,\n", " 'text': 'My wife took me here on my birthday for breakfast and it was excellent. The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure. Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning. It looked like the place fills up pretty quickly so the earlier you get here the better.\\n\\nDo yourself a favor and get their Bloody Mary. It was phenomenal and simply the best I\\'ve ever had. I\\'m pretty sure they only use ingredients from their garden and blend them fresh when you order it. It was amazing.\\n\\nWhile EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious. It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete. It was the best \"toast\" I\\'ve ever had.\\n\\nAnyway, I can\\'t wait to go back!',\n", " 'type': 'review',\n", " 'user_id': 'rLtl8ZkDX5vH5nAx9C3q5Q',\n", " 'votes': {'cool': 2, 'funny': 0, 'useful': 5}}}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unpack to extract structure" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">business_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stars</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">text</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">type</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-01-26</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My wife took me here on<br>my birthday for break ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-07-27</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I have no idea why some<br>people give bad reviews ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6oRAC4uyJCsJl1X0WZpVSA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-06-14</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IESLBzqUCLdSzSqm0eCSxQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">love the gyro plate. Rice<br>is so good and I also ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">_1QQZuf4zZOyFCvXc0o6Vg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-05-27</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">G-WvGaISbqqaMHlNnByodA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Rosie, Dakota, and I LOVE<br>Chaparral Dog Park!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6ozycU1RpktNG2-1BroVtw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-01-05</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1uJFq2r5QfJG_6ExMRCaGw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">General Manager Scott<br>Petello is a good egg!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-yxfBYGB6SEqszmxJxd97A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2007-12-13</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">m2CKSsepBCoRYWxiRUsxAg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Quiessence is, simply<br>put, beautiful. Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">zp713qNhx8d9KCJJnrw1xA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-02-12</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">riFQ3vxNpP4rWLk_CSri2A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Drop what you're doing<br>and drive here. After I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">hW0Ne_HTHEAgGF1rAdmR-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-07-12</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JL7GXJ9u4YMx7Rzs05NfiQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Luckily, I didn't have to<br>travel far to make my ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wNUea3IXZWD63bbOQaOH-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-08-17</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">XtnfnYmnJYi71yIuGsXIUA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Definitely come for Happy<br>hour! Prices are amaz ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nMHhuYan8e3cONo3PornJA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-08-11</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">jJAIXA46pU1swYyRCdfXtQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nobuo shows his unique<br>talents with everything ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">votes</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 5,<br>'cool': 2} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 0,<br>'cool': 0} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0hT2KtfLiobPvh6cDC8JQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 1,<br>'cool': 0} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">uZetl9T0NcROGOyFfughhg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 2,<br>'cool': 1} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">vYmM4KTsC8ZfQBg-j5MWkw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 0,<br>'cool': 0} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sqYN3lNgvPbPCTRsMFu27g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 1, 'useful': 3,<br>'cool': 4} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wFweIWhv2fREZV_dYkz_1g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 4, 'useful': 7,<br>'cool': 7} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1ieuYcKS7zeAv_U15AB13A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 1,<br>'cool': 0} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vh_DlizgGhSqQh4qfZ2h6A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 0,<br>'cool': 0} ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sUNkXg8-KFtCMQDV6zRzQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'funny': 0, 'useful': 1,<br>'cool': 0} ...</td>\n", " </tr>\n", "</table>\n", "[229907 rows x 8 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tbusiness_id\tstr\n", "\tdate\tstr\n", "\treview_id\tstr\n", "\tstars\tint\n", "\ttext\tstr\n", "\ttype\tstr\n", "\tuser_id\tstr\n", "\tvotes\tdict\n", "\n", "Rows: 229907\n", "\n", "Data:\n", "+------------------------+------------+------------------------+-------+\n", "| business_id | date | review_id | stars |\n", "+------------------------+------------+------------------------+-------+\n", "| 9yKzy9PApeiPPOUJEtnvkg | 2011-01-26 | fWKvX83p0-ka4JS3dc6E5A | 5 |\n", "| ZRJwVLyzEJq1VAihDhYiow | 2011-07-27 | IjZ33sJrzXqU-0X6U8NwyA | 5 |\n", "| 6oRAC4uyJCsJl1X0WZpVSA | 2012-06-14 | IESLBzqUCLdSzSqm0eCSxQ | 4 |\n", "| _1QQZuf4zZOyFCvXc0o6Vg | 2010-05-27 | G-WvGaISbqqaMHlNnByodA | 5 |\n", "| 6ozycU1RpktNG2-1BroVtw | 2012-01-05 | 1uJFq2r5QfJG_6ExMRCaGw | 5 |\n", "| -yxfBYGB6SEqszmxJxd97A | 2007-12-13 | m2CKSsepBCoRYWxiRUsxAg | 4 |\n", "| zp713qNhx8d9KCJJnrw1xA | 2010-02-12 | riFQ3vxNpP4rWLk_CSri2A | 5 |\n", "| hW0Ne_HTHEAgGF1rAdmR-g | 2012-07-12 | JL7GXJ9u4YMx7Rzs05NfiQ | 4 |\n", "| wNUea3IXZWD63bbOQaOH-g | 2012-08-17 | XtnfnYmnJYi71yIuGsXIUA | 4 |\n", "| nMHhuYan8e3cONo3PornJA | 2010-08-11 | jJAIXA46pU1swYyRCdfXtQ | 5 |\n", "+------------------------+------------+------------------------+-------+\n", "+-------------------------------+--------+------------------------+\n", "| text | type | user_id |\n", "+-------------------------------+--------+------------------------+\n", "| My wife took me here on my... | review | rLtl8ZkDX5vH5nAx9C3q5Q |\n", "| I have no idea why some pe... | review | 0a2KyEL0d3Yb1V6aivbIuQ |\n", "| love the gyro plate. Rice ... | review | 0hT2KtfLiobPvh6cDC8JQg |\n", "| Rosie, Dakota, and I LOVE ... | review | uZetl9T0NcROGOyFfughhg |\n", "| General Manager Scott Pete... | review | vYmM4KTsC8ZfQBg-j5MWkw |\n", "| Quiessence is, simply put,... | review | sqYN3lNgvPbPCTRsMFu27g |\n", "| Drop what you're doing and... | review | wFweIWhv2fREZV_dYkz_1g |\n", "| Luckily, I didn't have to ... | review | 1ieuYcKS7zeAv_U15AB13A |\n", "| Definitely come for Happy ... | review | Vh_DlizgGhSqQh4qfZ2h6A |\n", "| Nobuo shows his unique tal... | review | sUNkXg8-KFtCMQDV6zRzQg |\n", "+-------------------------------+--------+------------------------+\n", "+-------------------------------+\n", "| votes |\n", "+-------------------------------+\n", "| {'funny': 0, 'useful': 5, ... |\n", "| {'funny': 0, 'useful': 0, ... |\n", "| {'funny': 0, 'useful': 1, ... |\n", "| {'funny': 0, 'useful': 2, ... |\n", "| {'funny': 0, 'useful': 0, ... |\n", "| {'funny': 1, 'useful': 3, ... |\n", "| {'funny': 4, 'useful': 7, ... |\n", "| {'funny': 0, 'useful': 1, ... |\n", "| {'funny': 0, 'useful': 0, ... |\n", "| {'funny': 0, 'useful': 1, ... |\n", "+-------------------------------+\n", "[229907 rows x 8 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews=reviews.unpack('X1','')\n", "reviews" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Votes are still crammed in a dictionary. Let's unpack it." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">business_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stars</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">text</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">type</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-01-26</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My wife took me here on<br>my birthday for break ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-07-27</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I have no idea why some<br>people give bad reviews ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6oRAC4uyJCsJl1X0WZpVSA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-06-14</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IESLBzqUCLdSzSqm0eCSxQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">love the gyro plate. Rice<br>is so good and I also ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">_1QQZuf4zZOyFCvXc0o6Vg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-05-27</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">G-WvGaISbqqaMHlNnByodA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Rosie, Dakota, and I LOVE<br>Chaparral Dog Park!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6ozycU1RpktNG2-1BroVtw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-01-05</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1uJFq2r5QfJG_6ExMRCaGw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">General Manager Scott<br>Petello is a good egg!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-yxfBYGB6SEqszmxJxd97A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2007-12-13</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">m2CKSsepBCoRYWxiRUsxAg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Quiessence is, simply<br>put, beautiful. Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">zp713qNhx8d9KCJJnrw1xA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-02-12</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">riFQ3vxNpP4rWLk_CSri2A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Drop what you're doing<br>and drive here. After I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">hW0Ne_HTHEAgGF1rAdmR-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-07-12</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JL7GXJ9u4YMx7Rzs05NfiQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Luckily, I didn't have to<br>travel far to make my ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wNUea3IXZWD63bbOQaOH-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-08-17</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">XtnfnYmnJYi71yIuGsXIUA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Definitely come for Happy<br>hour! Prices are amaz ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nMHhuYan8e3cONo3PornJA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-08-11</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">jJAIXA46pU1swYyRCdfXtQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nobuo shows his unique<br>talents with everything ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">cool</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">funny</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">useful</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0hT2KtfLiobPvh6cDC8JQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">uZetl9T0NcROGOyFfughhg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">vYmM4KTsC8ZfQBg-j5MWkw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sqYN3lNgvPbPCTRsMFu27g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wFweIWhv2fREZV_dYkz_1g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1ieuYcKS7zeAv_U15AB13A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vh_DlizgGhSqQh4qfZ2h6A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sUNkXg8-KFtCMQDV6zRzQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", "</table>\n", "[229907 rows x 10 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tbusiness_id\tstr\n", "\tdate\tstr\n", "\treview_id\tstr\n", "\tstars\tint\n", "\ttext\tstr\n", "\ttype\tstr\n", "\tuser_id\tstr\n", "\tcool\tint\n", "\tfunny\tint\n", "\tuseful\tint\n", "\n", "Rows: 229907\n", "\n", "Data:\n", "+------------------------+------------+------------------------+-------+\n", "| business_id | date | review_id | stars |\n", "+------------------------+------------+------------------------+-------+\n", "| 9yKzy9PApeiPPOUJEtnvkg | 2011-01-26 | fWKvX83p0-ka4JS3dc6E5A | 5 |\n", "| ZRJwVLyzEJq1VAihDhYiow | 2011-07-27 | IjZ33sJrzXqU-0X6U8NwyA | 5 |\n", "| 6oRAC4uyJCsJl1X0WZpVSA | 2012-06-14 | IESLBzqUCLdSzSqm0eCSxQ | 4 |\n", "| _1QQZuf4zZOyFCvXc0o6Vg | 2010-05-27 | G-WvGaISbqqaMHlNnByodA | 5 |\n", "| 6ozycU1RpktNG2-1BroVtw | 2012-01-05 | 1uJFq2r5QfJG_6ExMRCaGw | 5 |\n", "| -yxfBYGB6SEqszmxJxd97A | 2007-12-13 | m2CKSsepBCoRYWxiRUsxAg | 4 |\n", "| zp713qNhx8d9KCJJnrw1xA | 2010-02-12 | riFQ3vxNpP4rWLk_CSri2A | 5 |\n", "| hW0Ne_HTHEAgGF1rAdmR-g | 2012-07-12 | JL7GXJ9u4YMx7Rzs05NfiQ | 4 |\n", "| wNUea3IXZWD63bbOQaOH-g | 2012-08-17 | XtnfnYmnJYi71yIuGsXIUA | 4 |\n", "| nMHhuYan8e3cONo3PornJA | 2010-08-11 | jJAIXA46pU1swYyRCdfXtQ | 5 |\n", "+------------------------+------------+------------------------+-------+\n", "+-------------------------------+--------+------------------------+------+-------+\n", "| text | type | user_id | cool | funny |\n", "+-------------------------------+--------+------------------------+------+-------+\n", "| My wife took me here on my... | review | rLtl8ZkDX5vH5nAx9C3q5Q | 2 | 0 |\n", "| I have no idea why some pe... | review | 0a2KyEL0d3Yb1V6aivbIuQ | 0 | 0 |\n", "| love the gyro plate. Rice ... | review | 0hT2KtfLiobPvh6cDC8JQg | 0 | 0 |\n", "| Rosie, Dakota, and I LOVE ... | review | uZetl9T0NcROGOyFfughhg | 1 | 0 |\n", "| General Manager Scott Pete... | review | vYmM4KTsC8ZfQBg-j5MWkw | 0 | 0 |\n", "| Quiessence is, simply put,... | review | sqYN3lNgvPbPCTRsMFu27g | 4 | 1 |\n", "| Drop what you're doing and... | review | wFweIWhv2fREZV_dYkz_1g | 7 | 4 |\n", "| Luckily, I didn't have to ... | review | 1ieuYcKS7zeAv_U15AB13A | 0 | 0 |\n", "| Definitely come for Happy ... | review | Vh_DlizgGhSqQh4qfZ2h6A | 0 | 0 |\n", "| Nobuo shows his unique tal... | review | sUNkXg8-KFtCMQDV6zRzQg | 0 | 0 |\n", "+-------------------------------+--------+------------------------+------+-------+\n", "+--------+\n", "| useful |\n", "+--------+\n", "| 5 |\n", "| 0 |\n", "| 1 |\n", "| 2 |\n", "| 0 |\n", "| 3 |\n", "| 7 |\n", "| 1 |\n", "| 0 |\n", "| 1 |\n", "+--------+\n", "[229907 rows x 10 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews = reviews.unpack('votes', '')\n", "reviews" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick data visualization" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Canvas is accessible via web browser at the URL: http://localhost:59655/index.html\n", "Opening Canvas in default web browser.\n" ] } ], "source": [ "reviews.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Represent datetime" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reviews['date'] = reviews['date'].str_to_datetime(str_format='%Y-%m-%d')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Munge votes and add a new column" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">business_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stars</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">text</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">type</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-01-26 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My wife took me here on<br>my birthday for break ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-07-27 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I have no idea why some<br>people give bad reviews ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6oRAC4uyJCsJl1X0WZpVSA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-06-14 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IESLBzqUCLdSzSqm0eCSxQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">love the gyro plate. Rice<br>is so good and I also ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">_1QQZuf4zZOyFCvXc0o6Vg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-05-27 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">G-WvGaISbqqaMHlNnByodA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Rosie, Dakota, and I LOVE<br>Chaparral Dog Park!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6ozycU1RpktNG2-1BroVtw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-01-05 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1uJFq2r5QfJG_6ExMRCaGw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">General Manager Scott<br>Petello is a good egg!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-yxfBYGB6SEqszmxJxd97A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2007-12-13 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">m2CKSsepBCoRYWxiRUsxAg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Quiessence is, simply<br>put, beautiful. Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">zp713qNhx8d9KCJJnrw1xA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-02-12 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">riFQ3vxNpP4rWLk_CSri2A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Drop what you're doing<br>and drive here. After I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">hW0Ne_HTHEAgGF1rAdmR-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-07-12 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JL7GXJ9u4YMx7Rzs05NfiQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Luckily, I didn't have to<br>travel far to make my ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wNUea3IXZWD63bbOQaOH-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-08-17 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">XtnfnYmnJYi71yIuGsXIUA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Definitely come for Happy<br>hour! Prices are amaz ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nMHhuYan8e3cONo3PornJA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-08-11 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">jJAIXA46pU1swYyRCdfXtQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nobuo shows his unique<br>talents with everything ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">cool</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">funny</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">useful</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">total_votes</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0hT2KtfLiobPvh6cDC8JQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">uZetl9T0NcROGOyFfughhg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">vYmM4KTsC8ZfQBg-j5MWkw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sqYN3lNgvPbPCTRsMFu27g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wFweIWhv2fREZV_dYkz_1g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1ieuYcKS7zeAv_U15AB13A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vh_DlizgGhSqQh4qfZ2h6A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sUNkXg8-KFtCMQDV6zRzQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", "</table>\n", "[229907 rows x 11 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tbusiness_id\tstr\n", "\tdate\tdatetime\n", "\treview_id\tstr\n", "\tstars\tint\n", "\ttext\tstr\n", "\ttype\tstr\n", "\tuser_id\tstr\n", "\tcool\tint\n", "\tfunny\tint\n", "\tuseful\tint\n", "\ttotal_votes\tint\n", "\n", "Rows: 229907\n", "\n", "Data:\n", "+------------------------+---------------------+------------------------+-------+\n", "| business_id | date | review_id | stars |\n", "+------------------------+---------------------+------------------------+-------+\n", "| 9yKzy9PApeiPPOUJEtnvkg | 2011-01-26 00:00:00 | fWKvX83p0-ka4JS3dc6E5A | 5 |\n", "| ZRJwVLyzEJq1VAihDhYiow | 2011-07-27 00:00:00 | IjZ33sJrzXqU-0X6U8NwyA | 5 |\n", "| 6oRAC4uyJCsJl1X0WZpVSA | 2012-06-14 00:00:00 | IESLBzqUCLdSzSqm0eCSxQ | 4 |\n", "| _1QQZuf4zZOyFCvXc0o6Vg | 2010-05-27 00:00:00 | G-WvGaISbqqaMHlNnByodA | 5 |\n", "| 6ozycU1RpktNG2-1BroVtw | 2012-01-05 00:00:00 | 1uJFq2r5QfJG_6ExMRCaGw | 5 |\n", "| -yxfBYGB6SEqszmxJxd97A | 2007-12-13 00:00:00 | m2CKSsepBCoRYWxiRUsxAg | 4 |\n", "| zp713qNhx8d9KCJJnrw1xA | 2010-02-12 00:00:00 | riFQ3vxNpP4rWLk_CSri2A | 5 |\n", "| hW0Ne_HTHEAgGF1rAdmR-g | 2012-07-12 00:00:00 | JL7GXJ9u4YMx7Rzs05NfiQ | 4 |\n", "| wNUea3IXZWD63bbOQaOH-g | 2012-08-17 00:00:00 | XtnfnYmnJYi71yIuGsXIUA | 4 |\n", "| nMHhuYan8e3cONo3PornJA | 2010-08-11 00:00:00 | jJAIXA46pU1swYyRCdfXtQ | 5 |\n", "+------------------------+---------------------+------------------------+-------+\n", "+-------------------------------+--------+------------------------+------+-------+\n", "| text | type | user_id | cool | funny |\n", "+-------------------------------+--------+------------------------+------+-------+\n", "| My wife took me here on my... | review | rLtl8ZkDX5vH5nAx9C3q5Q | 2 | 0 |\n", "| I have no idea why some pe... | review | 0a2KyEL0d3Yb1V6aivbIuQ | 0 | 0 |\n", "| love the gyro plate. Rice ... | review | 0hT2KtfLiobPvh6cDC8JQg | 0 | 0 |\n", "| Rosie, Dakota, and I LOVE ... | review | uZetl9T0NcROGOyFfughhg | 1 | 0 |\n", "| General Manager Scott Pete... | review | vYmM4KTsC8ZfQBg-j5MWkw | 0 | 0 |\n", "| Quiessence is, simply put,... | review | sqYN3lNgvPbPCTRsMFu27g | 4 | 1 |\n", "| Drop what you're doing and... | review | wFweIWhv2fREZV_dYkz_1g | 7 | 4 |\n", "| Luckily, I didn't have to ... | review | 1ieuYcKS7zeAv_U15AB13A | 0 | 0 |\n", "| Definitely come for Happy ... | review | Vh_DlizgGhSqQh4qfZ2h6A | 0 | 0 |\n", "| Nobuo shows his unique tal... | review | sUNkXg8-KFtCMQDV6zRzQg | 0 | 0 |\n", "+-------------------------------+--------+------------------------+------+-------+\n", "+--------+-------------+\n", "| useful | total_votes |\n", "+--------+-------------+\n", "| 5 | 7 |\n", "| 0 | 0 |\n", "| 1 | 1 |\n", "| 2 | 3 |\n", "| 0 | 0 |\n", "| 3 | 8 |\n", "| 7 | 18 |\n", "| 1 | 1 |\n", "| 0 | 0 |\n", "| 1 | 1 |\n", "+--------+-------------+\n", "[229907 rows x 11 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews['total_votes'] = reviews['funny'] + reviews['cool'] + reviews['useful']\n", "reviews" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filter rows to remove reviews with no votes" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype: int\n", "Rows: 229907\n", "[1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, ... ]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews['total_votes'] > 0" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">business_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stars</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">text</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">type</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-01-26 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My wife took me here on<br>my birthday for break ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6oRAC4uyJCsJl1X0WZpVSA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-06-14 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">IESLBzqUCLdSzSqm0eCSxQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">love the gyro plate. Rice<br>is so good and I also ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">_1QQZuf4zZOyFCvXc0o6Vg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-05-27 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">G-WvGaISbqqaMHlNnByodA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Rosie, Dakota, and I LOVE<br>Chaparral Dog Park!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-yxfBYGB6SEqszmxJxd97A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2007-12-13 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">m2CKSsepBCoRYWxiRUsxAg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Quiessence is, simply<br>put, beautiful. Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">zp713qNhx8d9KCJJnrw1xA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-02-12 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">riFQ3vxNpP4rWLk_CSri2A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Drop what you're doing<br>and drive here. After I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">hW0Ne_HTHEAgGF1rAdmR-g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2012-07-12 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JL7GXJ9u4YMx7Rzs05NfiQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Luckily, I didn't have to<br>travel far to make my ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nMHhuYan8e3cONo3PornJA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-08-11 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">jJAIXA46pU1swYyRCdfXtQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nobuo shows his unique<br>talents with everything ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">AsSCv0q_BWqIe3mX2JqsOQ</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-06-16 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E11jzpKz9Kw5K7fuARWfRw</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">The oldish man who owns<br>the store is as sweet as ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">e9nN4XxjdHj4qtKCOPq_vg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2011-10-21 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3rPt0LxF7rgmEUrznoH22w</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wonderful Vietnamese<br>sandwich shoppe. Their ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">h53YuCiIDfEFSJCQpk8v1g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2010-01-11 00:00:00</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cGnKNX3I9rthE0-TH24-qA</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">They have a limited time<br>thing going on right now ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">review</td>\n", " </tr>\n", "</table>\n", "<table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">cool</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">funny</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">useful</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">total_votes</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; 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padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sqYN3lNgvPbPCTRsMFu27g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">wFweIWhv2fREZV_dYkz_1g</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1ieuYcKS7zeAv_U15AB13A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sUNkXg8-KFtCMQDV6zRzQg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-OMlS6yWkYjVldNhC31wYg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C1rHp3dmepNea7XiouwB6Q</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">UPtysDF6cUDUxq2KY-6Dcg</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " </tr>\n", "</table>\n", "[? rows x 11 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use len(sf) to force materialization.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tbusiness_id\tstr\n", "\tdate\tdatetime\n", "\treview_id\tstr\n", "\tstars\tint\n", "\ttext\tstr\n", "\ttype\tstr\n", "\tuser_id\tstr\n", "\tcool\tint\n", "\tfunny\tint\n", "\tuseful\tint\n", "\ttotal_votes\tint\n", "\n", "Rows: Unknown\n", "\n", "Data:\n", "+------------------------+---------------------+------------------------+-------+\n", "| business_id | date | review_id | stars |\n", "+------------------------+---------------------+------------------------+-------+\n", "| 9yKzy9PApeiPPOUJEtnvkg | 2011-01-26 00:00:00 | fWKvX83p0-ka4JS3dc6E5A | 5 |\n", "| 6oRAC4uyJCsJl1X0WZpVSA | 2012-06-14 00:00:00 | IESLBzqUCLdSzSqm0eCSxQ | 4 |\n", "| _1QQZuf4zZOyFCvXc0o6Vg | 2010-05-27 00:00:00 | G-WvGaISbqqaMHlNnByodA | 5 |\n", "| -yxfBYGB6SEqszmxJxd97A | 2007-12-13 00:00:00 | m2CKSsepBCoRYWxiRUsxAg | 4 |\n", "| zp713qNhx8d9KCJJnrw1xA | 2010-02-12 00:00:00 | riFQ3vxNpP4rWLk_CSri2A | 5 |\n", "| hW0Ne_HTHEAgGF1rAdmR-g | 2012-07-12 00:00:00 | JL7GXJ9u4YMx7Rzs05NfiQ | 4 |\n", "| nMHhuYan8e3cONo3PornJA | 2010-08-11 00:00:00 | jJAIXA46pU1swYyRCdfXtQ | 5 |\n", "| AsSCv0q_BWqIe3mX2JqsOQ | 2010-06-16 00:00:00 | E11jzpKz9Kw5K7fuARWfRw | 5 |\n", "| e9nN4XxjdHj4qtKCOPq_vg | 2011-10-21 00:00:00 | 3rPt0LxF7rgmEUrznoH22w | 5 |\n", "| h53YuCiIDfEFSJCQpk8v1g | 2010-01-11 00:00:00 | cGnKNX3I9rthE0-TH24-qA | 5 |\n", "+------------------------+---------------------+------------------------+-------+\n", "+-------------------------------+--------+------------------------+------+-------+\n", "| text | type | user_id | cool | funny |\n", "+-------------------------------+--------+------------------------+------+-------+\n", "| My wife took me here on my... | review | rLtl8ZkDX5vH5nAx9C3q5Q | 2 | 0 |\n", "| love the gyro plate. Rice ... | review | 0hT2KtfLiobPvh6cDC8JQg | 0 | 0 |\n", "| Rosie, Dakota, and I LOVE ... | review | uZetl9T0NcROGOyFfughhg | 1 | 0 |\n", "| Quiessence is, simply put,... | review | sqYN3lNgvPbPCTRsMFu27g | 4 | 1 |\n", "| Drop what you're doing and... | review | wFweIWhv2fREZV_dYkz_1g | 7 | 4 |\n", "| Luckily, I didn't have to ... | review | 1ieuYcKS7zeAv_U15AB13A | 0 | 0 |\n", "| Nobuo shows his unique tal... | review | sUNkXg8-KFtCMQDV6zRzQg | 0 | 0 |\n", "| The oldish man who owns th... | review | -OMlS6yWkYjVldNhC31wYg | 1 | 1 |\n", "| Wonderful Vietnamese sandw... | review | C1rHp3dmepNea7XiouwB6Q | 1 | 0 |\n", "| They have a limited time t... | review | UPtysDF6cUDUxq2KY-6Dcg | 1 | 0 |\n", "+-------------------------------+--------+------------------------+------+-------+\n", "+--------+-------------+\n", "| useful | total_votes |\n", "+--------+-------------+\n", "| 5 | 7 |\n", "| 1 | 1 |\n", "| 2 | 3 |\n", "| 3 | 8 |\n", "| 7 | 18 |\n", "| 1 | 1 |\n", "| 1 | 1 |\n", "| 3 | 5 |\n", "| 1 | 2 |\n", "| 2 | 3 |\n", "+--------+-------------+\n", "[? rows x 11 columns]\n", "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n", "You can use len(sf) to force materialization." ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews = reviews[reviews['total_votes'] > 0]\n", "reviews" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "147084" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.num_rows()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification task\n", "\n", "### Predict which reviews will be voted \"funny,\" based on review text.\n", "\n", "#### First, the labels. Reviews with at least one vote for \"funny\" is funny." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reviews['funny'] = reviews['funny'] > 0" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">text</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">funny</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My wife took me here on<br>my birthday for break ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">love the gyro plate. Rice<br>is so good and I also ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Rosie, Dakota, and I LOVE<br>Chaparral Dog Park!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Quiessence is, simply<br>put, beautiful. Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Drop what you're doing<br>and drive here. After I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Luckily, I didn't have to<br>travel far to make my ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nobuo shows his unique<br>talents with everything ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">The oldish man who owns<br>the store is as sweet as ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wonderful Vietnamese<br>sandwich shoppe. Their ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">They have a limited time<br>thing going on right now ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " </tr>\n", "</table>\n", "[147084 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\ttext\tstr\n", "\tfunny\tint\n", "\n", "Rows: 147084\n", "\n", "Data:\n", "+-------------------------------+-------+\n", "| text | funny |\n", "+-------------------------------+-------+\n", "| My wife took me here on my... | 0 |\n", "| love the gyro plate. Rice ... | 0 |\n", "| Rosie, Dakota, and I LOVE ... | 0 |\n", "| Quiessence is, simply put,... | 1 |\n", "| Drop what you're doing and... | 1 |\n", "| Luckily, I didn't have to ... | 0 |\n", "| Nobuo shows his unique tal... | 0 |\n", "| The oldish man who owns th... | 1 |\n", "| Wonderful Vietnamese sandw... | 0 |\n", "| They have a limited time t... | 0 |\n", "+-------------------------------+-------+\n", "[147084 rows x 2 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews = reviews[['text','funny']]\n", "reviews" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### To save time, take just a small subset" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reviews = reviews[:10000]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create bag-of-words representation of text" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "word_delims = [\"\\r\", \"\\v\", \"\\n\", \"\\f\", \"\\t\", \" \", \n", " '~', '`', '!', '@', '#', '$', '%', '^', '&', '*', '-', '_', '+', '=', \n", " ',', '.', ';', ':', '\\\"', '?', '|', '\\\\', '/', \n", " '<', '>', '(', ')', '[', ']', '{', '}']\n", "\n", "reviews['bow'] = gl.text_analytics.count_words(reviews['text'], delimiters=word_delims)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create tf-idf representation of the bag of words" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reviews['tf_idf'] = gl.text_analytics.tf_idf(reviews['bow'])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reviews['tf_idf'] = reviews['tf_idf'].apply(lambda x: x['docs'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">text</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">funny</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bow</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tf_idf</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My wife took me here on<br>my birthday for break ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'anyway': 1, 'looks': 1,<br>'go': 1, 'toast': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'anyway':<br>3.564893474332945, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">love the gyro plate. Rice<br>is so good and I also ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'plate': 1,<br>'selection': 1, 'love': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and':<br>0.08621169681906551, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Rosie, Dakota, and I LOVE<br>Chaparral Dog Park!!! ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 8, 'does': 1,<br>'all': 1, 'surrounded': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and':<br>0.6896935745525241, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Quiessence is, simply<br>put, beautiful. Full ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'45': 1, 'seated': 1,<br>'just': 1, 'bring': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just':<br>1.0552654841647438, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Drop what you're doing<br>and drive here. After I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'cute': 1, 'condesa': 1,<br>'desolate': 1, 'mexic ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'cute':<br>3.575550768806933, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Luckily, I didn't have to<br>travel far to make my ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'presence': 1,<br>\"didn't\": 1, 'as': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and':<br>0.17242339363813103, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nobuo shows his unique<br>talents with everything ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'pork': 1,<br>'features': 1, 'go': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and':<br>0.08621169681906551, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">The oldish man who owns<br>the store is as sweet as ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'cookies': 2,<br>'sweet': 1, 'is': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and':<br>0.08621169681906551, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wonderful Vietnamese<br>sandwich shoppe. Their ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'selection': 1, 'have':<br>2, 'baguettes': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'selection':<br>2.6882475738060303, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">They have a limited time<br>thing going on right now ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'limited': 1,<br>'all': 1, 'on': 1, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and':<br>0.08621169681906551, ...</td>\n", " </tr>\n", "</table>\n", "[10000 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\ttext\tstr\n", "\tfunny\tint\n", "\tbow\tdict\n", "\ttf_idf\tdict\n", "\n", "Rows: 10000\n", "\n", "Data:\n", "+-------------------------------+-------+-------------------------------+\n", "| text | funny | bow |\n", "+-------------------------------+-------+-------------------------------+\n", "| My wife took me here on my... | 0 | {'anyway': 1, 'looks': 1, ... |\n", "| love the gyro plate. Rice ... | 0 | {'and': 1, 'plate': 1, 'se... |\n", "| Rosie, Dakota, and I LOVE ... | 0 | {'and': 8, 'does': 1, 'all... |\n", "| Quiessence is, simply put,... | 1 | {'45': 1, 'seated': 1, 'ju... |\n", "| Drop what you're doing and... | 1 | {'cute': 1, 'condesa': 1, ... |\n", "| Luckily, I didn't have to ... | 0 | {'and': 2, 'presence': 1, ... |\n", "| Nobuo shows his unique tal... | 0 | {'and': 1, 'pork': 1, 'fea... |\n", "| The oldish man who owns th... | 1 | {'and': 1, 'cookies': 2, '... |\n", "| Wonderful Vietnamese sandw... | 0 | {'selection': 1, 'have': 2... |\n", "| They have a limited time t... | 0 | {'and': 1, 'limited': 1, '... |\n", "+-------------------------------+-------+-------------------------------+\n", "+-------------------------------+\n", "| tf_idf |\n", "+-------------------------------+\n", "| {'anyway': 3.5648934743329... |\n", "| {'and': 0.0862116968190655... |\n", "| {'and': 0.6896935745525241... |\n", "| {'just': 1.055265484164743... |\n", "| {'cute': 3.575550768806933... |\n", "| {'and': 0.1724233936381310... |\n", "| {'and': 0.0862116968190655... |\n", "| {'and': 0.0862116968190655... |\n", "| {'selection': 2.6882475738... |\n", "| {'and': 0.0862116968190655... |\n", "+-------------------------------+\n", "[10000 rows x 4 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a train-test split\n", "\n", "#### Returns immediately because SFrame operations are lazily evaluated." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_sf, test_sf = reviews.random_split(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train classifiers on bow and tf-idf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dictionaries are automatically interpreted as sparse features. \n", "\n", "Not demonstrated here, but any string/categorical columns are automatically interpreted as sparse features as well." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Logistic regression:\n", "PROGRESS: --------------------------------------------------------\n", "PROGRESS: Number of examples : 8039\n", "PROGRESS: Number of classes : 2\n", "PROGRESS: Number of feature columns : 1\n", "PROGRESS: Number of unpacked features : 30218\n", "PROGRESS: Number of coefficients : 30219\n", "PROGRESS: Starting L-BFGS\n", "PROGRESS: --------------------------------------------------------\n", "PROGRESS: +-----------+----------+-----------+--------------+-------------------+\n", "PROGRESS: | Iteration | Passes | Step size | Elapsed Time | Training-accuracy |\n", "PROGRESS: +-----------+----------+-----------+--------------+-------------------+\n", "PROGRESS: | 1 | 6 | 0.000002 | 1.235960 | 0.480781 |\n", "PROGRESS: | 2 | 9 | 5.000000 | 1.387001 | 0.574450 |\n", "PROGRESS: | 3 | 10 | 5.000000 | 1.453472 | 0.506779 |\n", "PROGRESS: | 4 | 11 | 5.000000 | 1.527113 | 0.593233 |\n", "PROGRESS: | 5 | 12 | 5.000000 | 1.606156 | 0.484389 |\n", "PROGRESS: | 6 | 14 | 1.000000 | 1.753020 | 0.567110 |\n", "PROGRESS: +-----------+----------+-----------+--------------+-------------------+\n" ] } ], "source": [ "m1 = gl.logistic_classifier.create(train_sf, \n", " 'funny', \n", " features=['bow'], \n", " validation_set=None, \n", " feature_rescaling=False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Logistic regression:\n", "PROGRESS: --------------------------------------------------------\n", "PROGRESS: Number of examples : 8039\n", "PROGRESS: Number of classes : 2\n", "PROGRESS: Number of feature columns : 1\n", "PROGRESS: Number of unpacked features : 30218\n", "PROGRESS: Number of coefficients : 30219\n", "PROGRESS: Starting L-BFGS\n", "PROGRESS: --------------------------------------------------------\n", "PROGRESS: +-----------+----------+-----------+--------------+-------------------+\n", "PROGRESS: | Iteration | Passes | Step size | Elapsed Time | Training-accuracy |\n", "PROGRESS: +-----------+----------+-----------+--------------+-------------------+\n", "PROGRESS: | 1 | 6 | 0.000009 | 0.375903 | 0.483891 |\n", "PROGRESS: | 2 | 10 | 21.000000 | 0.716405 | 0.709790 |\n", "PROGRESS: | 3 | 11 | 21.000000 | 0.942466 | 0.624083 |\n", "PROGRESS: | 4 | 17 | 1.294279 | 1.391701 | 0.750591 |\n", "PROGRESS: | 5 | 18 | 1.294279 | 1.503106 | 0.500684 |\n", "PROGRESS: | 6 | 20 | 1.000000 | 1.689104 | 0.770121 |\n", "PROGRESS: | 10 | 24 | 1.000000 | 2.161948 | 0.872621 |\n", "PROGRESS: +-----------+----------+-----------+--------------+-------------------+\n" ] } ], "source": [ "m2 = gl.logistic_classifier.create(train_sf, \n", " 'funny', \n", " features=['tf_idf'], \n", " validation_set=None, \n", " feature_rescaling=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate on validation set and compare performance" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'accuracy': 0.6017338092809791, 'confusion_matrix': Columns:\n", " \ttarget_label\tint\n", " \tpredicted_label\tint\n", " \tcount\tint\n", " \n", " Rows: 4\n", " \n", " Data:\n", " +--------------+-----------------+-------+\n", " | target_label | predicted_label | count |\n", " +--------------+-----------------+-------+\n", " | 0 | 1 | 380 |\n", " | 0 | 0 | 685 |\n", " | 1 | 0 | 401 |\n", " | 1 | 1 | 495 |\n", " +--------------+-----------------+-------+\n", " [4 rows x 3 columns]}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m1_res = m1.evaluate(test_sf)\n", "m1_res" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'accuracy': 0.6078531361550229, 'confusion_matrix': Columns:\n", " \ttarget_label\tint\n", " \tpredicted_label\tint\n", " \tcount\tint\n", " \n", " Rows: 4\n", " \n", " Data:\n", " +--------------+-----------------+-------+\n", " | target_label | predicted_label | count |\n", " +--------------+-----------------+-------+\n", " | 0 | 0 | 722 |\n", " | 0 | 1 | 343 |\n", " | 1 | 0 | 426 |\n", " | 1 | 1 | 470 |\n", " +--------------+-----------------+-------+\n", " [4 rows x 3 columns]}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m2_res = m2.evaluate(test_sf)\n", "m2_res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baseline accuracy (what if we classify everything as the majority class)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Percentage of 'funny' reviews:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.45690973992860784" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "float(test_sf['funny'].sum())/test_sf.num_rows()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Percentage of not funny reviews:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.5430902600713922" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1.0 - float(test_sf['funny'].sum())/test_sf.num_rows()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

Cyb3rWard0g/HELK

docker/helk-jupyter/notebooks/sigma/win_alert_ad_user_backdoors.ipynb

1

4512

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Active Directory User Backdoors\n", "Detects scenarios where one can control another users or computers account without having to use their credentials." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Active Directory User Backdoors\n", " id: 300bac00-e041-4ee2-9c36-e262656a6ecc\n", " description: Detects scenarios where one can control another users or computers\n", " account without having to use their credentials.\n", " references:\n", " - https://msdn.microsoft.com/en-us/library/cc220234.aspx\n", " - https://adsecurity.org/?p=3466\n", " - https://www.harmj0y.net/blog/redteaming/another-word-on-delegation/\n", " author: '@neu5ron'\n", " tags:\n", " - attack.t1098\n", " - attack.credential_access\n", " - attack.persistence\n", " logsource:\n", " product: windows\n", " service: security\n", " definition1: 'Requirements: Audit Policy : Account Management > Audit User Account\n", " Management, Group Policy : Computer Configuration\\Windows Settings\\Security\n", " Settings\\Advanced Audit Policy Configuration\\Audit Policies\\Account Management\\Audit\n", " User Account Management'\n", " definition2: 'Requirements: Audit Policy : DS Access > Audit Directory Service\n", " Changes, Group Policy : Computer Configuration\\Windows Settings\\Security Settings\\Advanced\n", " Audit Policy Configuration\\Audit Policies\\DS Access\\Audit Directory Service\n", " Changes'\n", " category: null\n", " detection:\n", " selection1:\n", " EventID: 4738\n", " filter1:\n", " AllowedToDelegateTo: null\n", " filter2:\n", " AllowedToDelegateTo: '-'\n", " selection2:\n", " EventID: 5136\n", " AttributeLDAPDisplayName: msDS-AllowedToDelegateTo\n", " selection3:\n", " EventID: 5136\n", " ObjectClass: user\n", " AttributeLDAPDisplayName: servicePrincipalName\n", " selection4:\n", " EventID: 5136\n", " AttributeLDAPDisplayName: msDS-AllowedToActOnBehalfOfOtherIdentity\n", " condition: (selection1 and not 1 of filter*) or selection2 or selection3 or selection4\n", " falsepositives:\n", " - Unknown\n", " level: high\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying Elasticsearch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from elasticsearch import Elasticsearch\n", "from elasticsearch_dsl import Search\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Elasticsearch client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n", "searchContext = Search(using=es, index='logs-endpoint-winevent-security-*', doc_type='doc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Elasticsearch Query" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = searchContext.query('query_string', query='((((event_id:\"4738\" AND (NOT ((NOT _exists_:user_attribute_allowed_todelegate) OR (user_attribute_allowed_todelegate:\"\\-\")))) OR (event_id:\"5136\" AND dsobject_attribute_name:\"msDS\\-AllowedToDelegateTo\")) OR (event_id:\"5136\" AND dsobject_class:\"user\" AND dsobject_attribute_name:\"servicePrincipalName\")) OR (event_id:\"5136\" AND dsobject_attribute_name:\"msDS\\-AllowedToActOnBehalfOfOtherIdentity\"))')\n", "response = s.execute()\n", "if response.success():\n", " df = pd.DataFrame((d.to_dict() for d in s.scan()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

jallen2/Research-Trend

.ipynb_checkpoints/ClemsonDTM_Data_Mining-checkpoint.ipynb

1

7572

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Number of Topics: 55\n", "done\n" ] } ], "source": [ "from bs4 import BeautifulSoup\n", "from gensim import corpora\n", "from pprint import pprint \n", "from collections import defaultdict\n", "import glob \n", "\n", "\n", "#Array storage for Award IDs and Abstracts \n", "awardabs_ar=[]\n", "aID_ar=[]\n", "doct_div2 = []\n", "doc_div = []\n", " \n", "for items in glob.glob(\"201*/*.xml\"): #Iterate through all xml files in the directory/file name given\n", " \n", " #Open files\n", " item = open(items)\n", " \n", " #Store Data\n", " xml = item.read()\n", " \n", " #Convert data to text \n", " soup = BeautifulSoup(xml,'xml')\n", " \n", " #store the current name of the institution \n", " clemson = soup.Name.string\n", " \n", " #Check if the name matches Clemson University \n", " #if clemson==\"Clemson University\":\n", " #Add award ID \n", " ID = soup.AwardID.string\n", " aID_ar.append(ID)\n", " \n", " #filter data if not None Type, Error checking \n", " if soup.AbstractNarration.string is not None: \n", " abst = soup.AbstractNarration.string\n", " #Adds abstracts to its list \n", " awardabs_ar.append(abst)\n", " \n", " #Determine the number of topics if not None Type, error checking \n", " if soup.Directorate.LongName.string is not None:\n", " doct = soup.Directorate.LongName.string\n", " if soup.Division.LongName.string is not None:\n", " div = soup.Division.LongName.string\n", " #concatenate Directorate and Divison names for testing \n", " test = doct + \"\\t\" + div\n", " \n", " #All Directorates and Divisions stored \n", " doc_div.append(test)\n", " \n", " #No duplicate Directorate and Division combinations \n", " if test not in doct_div2:\n", " doct_div2.append(test)\n", "\n", "topic = len(doct_div2)\n", "print(\"\\nNumber of Topics:\", topic)\n", "\n", "#All Abstracts \n", "file=open(\"cu_tigers.txt\",\"w\") \n", "for lines in awardabs_ar:\n", " file.write(lines)\n", " file.write('\\n')\n", "file.close()\n", "\n", "#All Xml Award IDs \n", "file2=open(\"cu_IDs.txt\",\"w\") \n", "for lines in aID_ar:\n", " file2.write(lines)\n", " file2.write('\\n')\n", "file2.close()\n", "\n", "#No Dublicate Directorates and Division Combos \n", "file3=open(\"cu_doc_div2.txt\",\"w\") \n", "for lines in doct_div2:\n", " file3.write(lines)\n", " file3.write('\\n')\n", "file3.close()\n", "\n", "#All Directorates and Divisions \n", "file4=open(\"cu_doc_div.txt\",\"w\") \n", "for lines in doc_div:\n", " file4.write(lines)\n", " file4.write('\\n')\n", "file4.close()\n", "\n", "print(\"done\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Number of Topics: 25\n", "done\n" ] } ], "source": [ "from bs4 import BeautifulSoup\n", "from gensim import corpora\n", "from pprint import pprint \n", "from collections import defaultdict\n", "import glob \n", "\n", "\n", "#Array storage for Award IDs and Abstracts \n", "awardabs_ar=[]\n", "aID_ar=[]\n", "doct_div2 = []\n", "doc_div = []\n", " \n", "for items in glob.glob(\"201*/*.xml\"): #Iterate through all xml files in the directory/file name given\n", " \n", " #Open files\n", " item = open(items)\n", " \n", " #Store Data\n", " xml = item.read()\n", " \n", " #Convert data to text \n", " soup = BeautifulSoup(xml,'xml')\n", " \n", " #store the current name of the institution \n", " clemson = soup.Name.string\n", " \n", " #Check if the name matches Clemson University \n", " if clemson==\"Clemson University\":\n", " #Add award ID \n", " ID = soup.AwardID.string\n", " aID_ar.append(ID)\n", " \n", " #filter data if not None Type, Error checking \n", " if soup.AbstractNarration.string is not None: \n", " abst = soup.AbstractNarration.string\n", " #Adds abstracts to its list \n", " awardabs_ar.append(abst)\n", " \n", " #Determine the number of topics if not None Type, error checking \n", " if soup.Directorate.LongName.string is not None:\n", " doct = soup.Directorate.LongName.string\n", " if soup.Division.LongName.string is not None:\n", " div = soup.Division.LongName.string\n", " #concatenate Directorate and Divison names for testing \n", " test = doct + \" \" + div\n", " \n", " #All Directorates and Divisions stored \n", " doc_div.append(test)\n", " \n", " #No duplicate Directorate and Division combinations \n", " if test not in doct_div2:\n", " doct_div2.append(test)\n", "\n", "topic = len(doct_div2)\n", "print(\"\\nNumber of Topics:\", topic)\n", "\n", "#All Abstracts \n", "file=open(\"cu_tigers.txt\",\"w\") \n", "for lines in awardabs_ar:\n", " file.write(lines)\n", " file.write('\\n')\n", "file.close()\n", "\n", "#All Xml Award IDs \n", "file2=open(\"cu_IDs.txt\",\"w\") \n", "for lines in aID_ar:\n", " file2.write(lines)\n", " file2.write('\\n')\n", "file2.close()\n", "\n", "#No Dublicate Directorates and Division Combos \n", "file3=open(\"cu_doc_div2.txt\",\"w\") \n", "for lines in doct_div2:\n", " file3.write(lines)\n", " file3.write('\\n')\n", "file3.close()\n", "\n", "#All Directorates and Divisions \n", "file4=open(\"cu_doc_div.txt\",\"w\") \n", "for lines in doc_div:\n", " file4.write(lines)\n", " file4.write('\\n')\n", "file4.close()\n", "\n", "#Total Topic count\n", "file5=open(\"TotalTopicCount.txt\",\"w\")\n", "file5.write(str(topic))\n", "file5.close()\n", "\n", "print(\"done\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Anaconda 2.5.0 (Python 3)", "language": "python", "name": "anaconda_2.5.0_python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

sserkez/ocelot

test/workshop/3_space_charge.ipynb

1

84570

{ "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "name": "", "signature": "sha256:430512a24c8fb14242d6c68a3b3fa21e026963f41f0acde27971315914942cc1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "*This notebook was created by [Sergey Tomin](http://www.xfel.eu/organization/staff/tomin_sergey/) for Workshop: [Designing future X-ray FELs](http://www.xrayfels.co.uk/). Source and license info is on [GitHub](https://github.com/iagapov/ocelot/tree/dev/docs). August 2016.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial N3. Space Charge.\n", "*Second order tracking with space charge effect of the 200k particles.*\n", "\n", "As an example, we will use lattice file (converted to Ocelot format) of the European XFEL Injector. \n", "\n", "The space charge forces are calculated by solving the Poisson equation in the bunch frame. \n", "Then the Lorentz transformed electromagnetic field is applied as a kick in the laboratory frame.\n", "For the solution of the Poisson equation we use an integral representation of the electrostatic potential by convolution of the free-space Green's function with the charge distribution. The convolution equation is solved with the help of the Fast Fourier Transform (FFT). The same algorithm for solution of the 3D Poisson equation is used, for example, in [ASTRA](http://www.desy.de/~mpyflo/).\n", "\n", "#### This example will cover the following topics:\n", "* Initialization of the Space Charge objects and the places of their applying\n", "* tracking of second order with space charge effect.\n", "\n", "#### Requirements \n", "* injector_lattice.py - input file, the The European XFEL Injector lattice.\n", "* beam_6MeV.ast - input file, initial beam distribution in ASTRA format (was obtained from s2e simulation performed with ASTRA)." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Import of modules" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# the output of plotting commands is displayed inline within frontends, \n", "# directly below the code cell that produced it\n", "%matplotlib inline\n", "\n", "from time import time \n", "\n", "# this python library provides generic shallow (copy) and deep copy (deepcopy) operations \n", "from copy import deepcopy\n", "\n", "# import from Ocelot main modules and functions\n", "from ocelot import *\n", "\n", "# import from Ocelot graphical modules\n", "from ocelot.gui.accelerator import *\n", "\n", "# import injector lattice\n", "from ocelot.test.workshop.injector_lattice import *\n", "\n", "# load beam distribution\n", "# this function convert Astra beam distribution to Ocelot format - ParticleArray. ParticleArray is designed for tracking.\n", "# in order to work with converters we have to import specific module from ocelot.adaptors\n", "from ocelot.adaptors.astra2ocelot import *" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "initializing ocelot...\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Change RF parameters for the comparison with ASTRA" ] }, { "cell_type": "code", "collapsed": false, "input": [ "phi1=18.7268\n", "V1=18.50662e-3/np.cos(phi1*pi/180)\n", "\n", "C_A1_1_1_I1.v = V1; C_A1_1_1_I1.phi = phi1\n", "C_A1_1_2_I1.v = V1; C_A1_1_2_I1.phi = phi1\n", "C_A1_1_3_I1.v = V1; C_A1_1_3_I1.phi = phi1\n", "C_A1_1_4_I1.v = V1; C_A1_1_4_I1.phi = phi1\n", "C_A1_1_5_I1.v = V1; C_A1_1_5_I1.phi = phi1\n", "C_A1_1_6_I1.v = V1; C_A1_1_6_I1.phi = phi1\n", "C_A1_1_7_I1.v = V1; C_A1_1_7_I1.phi = phi1\n", "C_A1_1_8_I1.v = V1; C_A1_1_8_I1.phi = phi1\n", "\n", "phi13=180\n", "V13=-20.2E-3/8/np.cos(phi13*pi/180)\n", "\n", "C3_AH1_1_1_I1.v=V13; C3_AH1_1_1_I1.phi=phi13\n", "C3_AH1_1_2_I1.v=V13; C3_AH1_1_2_I1.phi=phi13\n", "C3_AH1_1_3_I1.v=V13; C3_AH1_1_3_I1.phi=phi13\n", "C3_AH1_1_4_I1.v=V13; C3_AH1_1_4_I1.phi=phi13\n", "C3_AH1_1_5_I1.v=V13; C3_AH1_1_5_I1.phi=phi13\n", "C3_AH1_1_6_I1.v=V13; C3_AH1_1_6_I1.phi=phi13\n", "C3_AH1_1_7_I1.v=V13; C3_AH1_1_7_I1.phi=phi13\n", "C3_AH1_1_8_I1.v=V13; C3_AH1_1_8_I1.phi=phi13" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "p_array_init = astraBeam2particleArray(filename='beam_6MeV.ast')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('Astra to Ocelot: charge = ', 2.5000000000000012e-10)\n", "('Astra to Ocelot: particles number = ', 200000)\n", "('Astra to Ocelot: energy = ', 0.0065579389982232342)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "('Astra to Ocelot: s pos = ', 3.2000000000000002)\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": true, "input": [ "# initialization of tracking method\n", "method = MethodTM()\n", "\n", "# for second order tracking we have to choose SecondTM \n", "method.global_method = SecondTM\n", "\n", "# for first order tracking uncomment next line\n", "# method.global_method = TransferMap\n", "\n", "# we will start simulation from point 3.2 from the gun. For this purpose marker was created (start_sim=Marker()) \n", "# and placed in 3.2 m after gun \n", "# Q_38_I1 is quadrupole between RF cavities 1.3 GHz and 3.9 GHz\n", "# C3_AH1_1_8_I1 is the last section of the 3.9 GHz cavity\n", "lat = MagneticLattice(cell, start=start_sim, stop=Q_38_I1, method=method)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initializing SpaceCharge" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sc1 = SpaceCharge()\n", "sc1.nmesh_xyz = [63, 63, 63]\n", "sc1.low_order_kick = False\n", "sc1.step = 1\n", "\n", "sc5 = SpaceCharge()\n", "sc5.nmesh_xyz = [63, 63, 63]\n", "sc5.step = 5\n", "sc5.low_order_kick = False" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "navi = Navigator(lat)\n", "\n", "# add physics processes from the first element to the last of the lattice\n", "navi.add_physics_proc(sc1, lat.sequence[0], C_A1_1_2_I1)\n", "navi.add_physics_proc(sc5, C_A1_1_2_I1, lat.sequence[-1])\n", "\n", "# definiing of unit step in [m]\n", "navi.unit_step = 0.02\n", "\n", "# deep copy of the initial beam distribution \n", "p_array = deepcopy(p_array_init)\n", "start = time()\n", "tws_track, p_array = track(lat, p_array, navi)\n", "\n", "print(\"time exec: \", time() - start, \"sec\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.04 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.06 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.08 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.1 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.14 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.16 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.18 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.2 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.24 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.26 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.28 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.3 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.34 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.36 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.38 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.4 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.44 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.46 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.48 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.5 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.54 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.56 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.58 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.6 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.64 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.66 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.68 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.7 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.74 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.76 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.78 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.8 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.84 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.86 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.88 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.9 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.94 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.96 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 0.98 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.0 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.04 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.06 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.08 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.1 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.14 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.16 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.18 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.2 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.24 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.26 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.28 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.3 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.34 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.36 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.38 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.4 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.44 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.46 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.48 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.5 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.54 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.56 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.58 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.6 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.64 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.66 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.68 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.7 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 1.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 2.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 3.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 4.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 5.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 6.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 7.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 8.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 9.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 10.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.52 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.62 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.72 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.82 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 11.92 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 12.02 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 12.12 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 12.22 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 12.32 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 12.42 / 12.6974 : applied: SpaceCharge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "z = 12.6974 / 12.6974 : applied: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "('time exec: ', 222.24300003051758, 'sec')\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# you can change top_plot argument, for example top_plot=[\"alpha_x\", \"alpha_y\"]\n", "plot_opt_func(lat, tws_track, top_plot=[\"E\"], fig_name=0, legend=False)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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yv/LycjZv3kxUVBTx8fE899xzhIeHs2rVMGpqYMoU8MUgTetBGWNMP/XjH//Y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\nQRljjAlKFlDGGGOCkgWUMcaYoGQBZYw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"text": [ "<matplotlib.figure.Figure at 0xf40ad50>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison with ASTRA\n", "Beam tracking with ASTRA was performed by Igor Zagorodnov (DESY). " ] }, { "cell_type": "code", "collapsed": false, "input": [ "sa, bx_sc, by_sc, bx_wo_sc, by_wo_sc = np.loadtxt(\"astra_sim.txt\", usecols=(0, 1, 2, 3, 4), unpack=True)\n", "\n", "s = [tw.s for tw in tws_track]\n", "bx = [tw.beta_x for tw in tws_track]\n", "by = [tw.beta_y for tw in tws_track]\n", "ax = plot_API(lat, legend=False)\n", "ax.plot(s, bx, \"r\", label=\"Ocelot, bx\")\n", "ax.plot(sa-3.2, bx_sc, \"b-\",label=\"ASTRA, bx\")\n", "ax.plot(s, by, \"r\", label=\"Ocelot, by\")\n", "ax.plot(sa-3.2, by_sc, \"b-\",label=\"ASTRA, by\")\n", "ax.legend()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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gpl-3.0

python-visualization/folium_contrib

notebooks/GoogleEarthEngine_layer.ipynb

1

13592

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Google Earth Engine Tile Layers in Folium" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2.1\n" ] } ], "source": [ "import ee\n", "import folium\n", "\n", "ee.Initialize()\n", "print(folium.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def folium_gee_map(image,vis_params=None,folium_kwargs={}):\n", " \"\"\"\n", " Function to view Google Earth Engine tile layer as a Folium map.\n", " \n", " Parameters\n", " ----------\n", " image : Google Earth Engine Image.\n", " vis_params : Dict with visualization parameters.\n", " folium_kwargs : Keyword args for Folium Map.\n", " \"\"\"\n", " \n", " # Get the MapID and Token after applying parameters\n", " image_info = image.getMapId(vis_params)\n", " mapid = image_info['mapid']\n", " token = image_info['token']\n", " folium_kwargs['attr'] = ('Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a> ')\n", " folium_kwargs['tiles'] = \"https://earthengine.googleapis.com/map/%s/{z}/{x}/{y}?token=%s\"%(mapid,token)\n", " \n", " return folium.Map(**folium_kwargs)\n", "\n", "def folium_gee_layer(folium_map,image,vis_params=None,folium_kwargs={}):\n", " \"\"\"\n", " Function to add Google Earch Engine tile layer as a Folium layer.\n", " \n", " Parameters\n", " ----------\n", " folium_map : Folium map to add tile to.\n", " image : Google Earth Engine Image.\n", " vis_params : Dict with visualization parameters.\n", " folium_kwargs : Keyword args for Folium Map.\n", " \"\"\"\n", " \n", " # Get the MapID and Token after applying parameters\n", " image_info = image.getMapId(vis_params)\n", " mapid = image_info['mapid']\n", " token = image_info['token']\n", " folium_kwargs['attr'] = ('Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a> ')\n", " folium_kwargs['tiles'] = \"https://earthengine.googleapis.com/map/%s/{z}/{x}/{y}?token=%s\"%(mapid,token)\n", " \n", " layer = folium.TileLayer(**folium_kwargs)\n", " layer.add_to(folium_map)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;base64,
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        \" style=\"position:absolute;width:100%;height:100%;left:0;top:0;\"></iframe></div></div>" ], "text/plain": [ "<folium.folium.Map at 0x7f7efab16160>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get an area to look at\n", "lat = 39.495159\n", "lon = -107.3689237\n", "zoom_start=10\n", "\n", "# Open Street Map Base\n", "m = folium.Map(location=[lat, lon], tiles=\"OpenStreetMap\", zoom_start=zoom_start)\n", "\n", "# Add GEE Terrain Layer\n", "image = ee.Image('srtm90_v4')\n", "vis_params = {'min':0.0, 'max':3000, 'palette':'00FFFF,0000FF'}\n", "folium_gee_layer(m,image,vis_params=vis_params,folium_kwargs={'overlay':True,'name':'SRTM'})\n", "\n", "# Create a reference to the image collection\n", "l8 = ee.ImageCollection('LANDSAT/LC8_L1T_TOA')\n", "# Filter the collection down to a two week period\n", "filtered = l8.filterDate('2013-05-01', '2013-05-15');\n", "# Use the mosaic reducer, to select the most recent pixel in areas of overlap\n", "l8_image = filtered.median()\n", "l8_vis_params = {'min': 0, 'max':0.3, 'bands':'B4,B3,B2'}\n", "folium_gee_layer(m,l8_image,l8_vis_params,folium_kwargs={'overlay':True,'name':'Visual'})\n", "m.add_child(folium.LayerControl())\n", "m" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

mitkovetta/nfbia15-deep-learning

examples/mnist_cifar/Using Theano and Lasagne to classify MNIST and CIFAR.ipynb

1

1434758

mit

jieshen-sjtu/caffe-for-DDNet

examples/selective_search_demo.ipynb

3

412090

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This approach follows ideas described in Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. *Rich feature hierarchies for accurate object detection and semantic segmentation*. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n", "\n", "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to run the Matlab Selective Search code.\n", "\n", "Let's run detection on an image of a couple of cats frolicking (one of the ImageNet detection challenge pictures), which we will download from the web. You'll need a prototxt specifying the network, and a trained model.\n", "\n", "We will use `models/imagenet.prototxt` and the caffe_reference_imagenet_model which you can download by `models/get_caffe_reference_imagenet_model.sh`. The learned model should be at `models/caffe_reference_imagenet_model`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!mkdir _temp\n", "!curl http://farm1.static.flickr.com/220/512450093_7717fb8ce8.jpg > _temp/cat.jpg\n", "!echo `pwd`/_temp/cat.jpg > _temp/cat.txt\n", "!python ../python/caffe/detection/detector.py --crop_mode=selective_search --pretrained_model=../models/caffe_reference_imagenet_model --model_def=../models/imagenet.prototxt _temp/cat.txt _temp/cat.h5" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running this outputs a DataFrame with the filenames, selected windows, and their ImageNet scores to an HDF5 file.\n", "(We only ran on one image, so the filenames will all be the same.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "\n", "df = pd.read_hdf('_temp/cat.h5', 'df')\n", "print(df.shape)\n", "print(df.iloc[0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(223, 5)\n", "feat [6.90396e-06, 1.27811e-06, 1.82159e-06, 1.1020...\n", "ymin 0\n", "xmin 0\n", "ymax 500\n", "xmax 496\n", "Name: /Users/karayev/work/caffe-bvlc/examples/_temp/cat.jpg, dtype: object\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, `detector.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n", "Simply list an image per line in the `images_file`, and it will process all of them.\n", "\n", "Although this guide gives an example of ImageNet detection, `detector.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories.\n", "Refer to `python detector.py --help` and the `images_dim` and `images_mean_file` parameters to describe your data set.\n", "No need for hardcoding.\n", "\n", "Anyway, let's now load ImageNet class names and make a DataFrame of the features. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('../data/ilsvrc12/synset_words.txt') as f:\n", " labels_df = pd.DataFrame([\n", " {\n", " 'synset_id': l.strip().split(' ')[0],\n", " 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n", " }\n", " for l in f.readlines()\n", " ])\n", "labels_df.sort('synset_id')\n", "feats_df = pd.DataFrame(np.vstack(df.feat.values), columns=labels_df['name'])\n", "print(feats_df.iloc[0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "name\n", "tench 0.000007\n", "goldfish 0.000001\n", "great white shark 0.000002\n", "tiger shark 0.000001\n", "hammerhead 0.000007\n", "electric ray 0.000004\n", "stingray 0.000007\n", "cock 0.000060\n", "hen 0.003055\n", "ostrich 0.000010\n", "brambling 0.000004\n", "goldfinch 0.000001\n", "house finch 0.000004\n", "junco 0.000002\n", "indigo bunting 0.000001\n", "...\n", "daisy 0.000002\n", "yellow lady's slipper 0.000002\n", "corn 0.000020\n", "acorn 0.000011\n", "hip 0.000003\n", "buckeye 0.000010\n", "coral fungus 0.000005\n", "agaric 0.000019\n", "gyromitra 0.000039\n", "stinkhorn 0.000002\n", "earthstar 0.000025\n", "hen-of-the-woods 0.000035\n", "bolete 0.000037\n", "ear 0.000008\n", "toilet tissue 0.000019\n", "Name: 0, Length: 1000, dtype: float32\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the activations." ] }, { "cell_type": "code", "collapsed": false, "input": [ "gray()\n", "matshow(feats_df.values)\n", "xlabel('Classes')\n", "ylabel('Windows')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "<matplotlib.text.Text at 0x107290150>" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x106877510>" ] }, { "metadata": {}, 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nTu0GpAULFuD48eNRi0cQBEEQBEGESOq9NBPJptUchrktlLAzo0mAnV8Q/21F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\nqr7GS78edZ2UyS+uPjdMp3JJbvsy50/dcIruEuZGVlCHW1E6RfQyv1e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"text": [ "<matplotlib.figure.Figure at 0x106877590>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take max across all windows and plot the top classes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "max_s = feats_df.max(0)\n", "max_s.sort(ascending=False)\n", "print(max_s[:10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "name\n", "proboscis monkey 0.923392\n", "tiger cat 0.918685\n", "milk can 0.783663\n", "American black bear 0.637560\n", "broccoli 0.612832\n", "tiger 0.515798\n", "platypus 0.514660\n", "dhole 0.509583\n", "lion 0.496187\n", "dingo 0.482885\n", "dtype: float32\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, there are indeed cats in there (and some nonsense).\n", "Picking good localizations is work in progress; manually, we see that the third and thirteenth top detections correspond to the two cats." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Find, print, and display max detection.\n", "window_order = pd.Series(feats_df.values.max(1)).order(ascending=False)\n", "\n", "i = window_order.index[3]\n", "j = window_order.index[13]\n", "\n", "# Show top predictions for top detection.\n", "f = pd.Series(df['feat'].iloc[i], index=labels_df['name'])\n", "print('Top detection:')\n", "print(f.order(ascending=False)[:5])\n", "print('')\n", "\n", "# Show top predictions for 10th top detection.\n", "f = pd.Series(df['feat'].iloc[j], index=labels_df['name'])\n", "print('10th detection:')\n", "print(f.order(ascending=False)[:5])\n", "\n", "# Show top detection in red, 10th top detection in blue.\n", "im = imread('_temp/cat.jpg')\n", "imshow(im)\n", "currentAxis = plt.gca()\n", "\n", "det = df.iloc[i]\n", "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n", "\n", "det = df.iloc[j]\n", "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Top detection:\n", "name\n", "tiger cat 0.882021\n", "tiger 0.075015\n", "tabby 0.024404\n", "lynx 0.012947\n", "Egyptian cat 0.004409\n", "dtype: float32\n", "\n", "10th detection:\n", "name\n", "tiger cat 0.681169\n", "Pembroke 0.063924\n", "dingo 0.050501\n", "golden retriever 0.027614\n", "tabby 0.021413\n", "dtype: float32\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "<matplotlib.patches.Rectangle at 0x108516c90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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sYubFjL2Ita0Ko+mE7UsN3ERHyBxsp0+jusro3RE5ASdnCXlVYa6kYWg5Eztl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dup0uk+mM3f3LLFdTPvqR55CFOq+8eoPV0kfRIxodm7qt4M5nSKnKeLRkq/WAq3td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X6wwQ/X1AikYcnLN+8J2wi5D29Xv0LQJd5efk1aBRRiznK3YLHb0h8NEI0GSRVA0yjqmqKt\nSJuKq9kMTZbYm4wJQw+xlTiYnOMnBUnd8rMXL5G6Nr9+/hJFA0mpEOWSk+NDUi/k00cfM3S6zOcz\nusMRtVhxM39Jf+oSJit2oceLV19x/ugecRyh6BLr7Y7FxmfurXl7fYXbHVCWMvv7j2hVA703QrO6\ndNweTVHzu7/1I3qyiX+bIMQ6I93EKSzE9ANJnZ0f8fjZE3TT5NHTTwiSHUHiI5kybreHretI1ARB\nQBgnmN0+oqKx3WyQZA1Ls9kb7RH6O3beGsPQMFWV5WpBEpcosk1ViAi1TIuArEns4pCTkzNG3R66\nINKzBtzOrhkPhsThBsvRuLm7QVE1NNXgbn6LYTgYjstiG3G5WZC39W/E4rdGAvsHB2iGhqQINHVF\nIzTQtPjBhqaNqJuAhgLL6GLqBmVRIggSZZUxHNzDdQYEUUSFyN7JCZpm4nkxlmlTtRL3zp+y2gZ0\nemNawSBIS0yniywCbcbeXpfu0IW2ZjodfcPqDYZTI5IiSvDk4VMG9hhd6rDfvc/3Pvk3JEFMW+Sk\nWUrTtriOwdX1HcvlhpYGb5VSli1ZmuJaOqORQ8d0qcMCuRXodFwuLi5wXZskSVHVD9VcfhDRGQzI\nmpaD0/vMNyumR1PMrommG4iixueffx9F+RCK0u32sZ0Ox8dnvPjV17RNw8HBEU1Tszfa46h7gpyp\nHE1O2MU7HMcGUSbMMnqjA9a7Hf5mhyDCsD9AERWeX75iHd2xmF/w7vaCXRVydXfF+9sF508+xxR7\neNsIPw65ur0hzErSIiMrK7bRmscPf4hX1GySW8SOzMnZMXlVfnNKnHBwOuXd1TUSMrok8PblP/Lq\n/R2LdUiY56iayCePfpsffPwFbZTy1Ze/4GZzg6JryIpFON9RJDlV3RA2JderO0RNZRsFFK1AmCUE\naYhuOIxGY1RZZLtdcHhwwGDQJ9ltOex1+fThAxxF5qOnz9BUg08++ZjA8zg9OiHe+bRpwR/84Hf4\n5OEjiixBlgpGAwfLVNj6K7bbFU+ePUESW6qi5P31JZudjzPoUIgFQRExnPbwIo+Vv8AdOKxmc5RG\noIxTTqcHDPpDciqqCmRJ5+52SVuCWIr0nR51KWJZNnHkU5GhGjYX8/c8efoQoSq4mL2mEgP+/ud/\nSxpl+NsQXXc4OryPrQ9wbAdFVulYU6z+kM9+8L3fiMVvjQTyJMAxXKIwI4lzLMPBdDSWwYxWD0iS\nkH7PJct8SrFgF26pqwxBzJG1hhcvf8noYIwfb9ks5jydPuZ3nv0+3//oD+i4EyrVpTZ04rZGVHW6\nbgdTV/DCNdsiwDAkduGGbbZgG8zwvLc05Yy2XOKHlzRViq1rxLstz558yu3C58H5Q95dXCEJErmf\n8+ThM7KyRdMbvvPR95h0jzg9fIrQGuwd7rHdLZElkSSKEJuG7cbDdV2yMmfn5xRZSt1EJMkFZbaE\nqqQtJdbLLTerBRd3b5nNb+m4A2bzDYbZRTVaXnz9C+LApz8Y82Z9zce/9Tm7dM0yn1PLMr//O/+B\nT598woODY07HB/QkCyHK6Zodvvj0J1CbPLh/jqqoCDRoqkyQbRB0sDs9irri/NFTRNni+esFumHw\nWw8+58ga8f0nPyTxFabTfTqDMZnwQd2mlVh6S1qxAVViuDehaGvivGS1XREVHr/86h9Iq4S1t6Au\nYz7/zud03YqHzw5YXvtIRclf/umf8fUvXrOeb+k5+1i1y2y+xHVEVrM7tt6G/mjM69cvkVSdyfQB\n797d8ejxY/ZPzqhbhbv5Gj/0WXpbBEnicG/K5bu3aIrM189fcHv7K77++d+xuHiF3voE3hx/HfLk\n+BFtkfHko3v89Bd/zpdvf8Y23OF2+5huB8PsUGUVSbylqRtevH1H08rsje6RpTlZXqIoOnkTM/dj\nUCEpYkCkqQWiXczh3j5ff/UVCBK22+PNuyvevnmPLilcvLlgs14iCgpFIaDKKqtwSVVXHxKKvAWS\nVHN0NCVNQuIo4KOPPiaqUlzbJdzEbDcrBsMeq82coi04nBzw3fMvuHzx/Ddi8dvbDvynMVEUcXJ0\nn7Kq2WYpeVuSU5LkKR3dAQQkQWRdlZilyWgwJM43NKrH5GCfK29Of9BBwyT0Z1xu3rAtL+nv2Uyd\nLqQzhkaO1tZEfsDe5JjtLkQxFMosJy4ETu49Al2mkRt2ye5DIksGtmHT0VzkVqfIZYxBn6+e/4Kj\nyQcbriwJ5G1K0RYoRY+T0QFtmtC0KbphE+ceSAKO67KeexiOQ5wllHnOZrPm2ZNPuLt9g2tbFFX2\nYa0m6aR5SUODadqkmQ+VQBJVgEYjyeQxSLRM+g5NniFqIp6/RMhzBpMOVRohFSJhvGa9vKNj9RiO\nxmTFjiwLGDgDEBpmdxes1lcMxzaCCKJrcPX+iqKoqUWRJA/Jy4jHjx9xMDjly5//E7vS52b5EtkQ\nSHcJv/eHf8TOC6nLnNOjh9zNnmPoBqJgEccpaVqg6wpvXl9y7/hTXLvPzo/J8hzbNXj7/jmqoiPK\nJopaM+5OOD09J8w9ju+d8OUvvsLRdBIuGU4lrI7I1XrJwd4eN7dX7B2O6fX7aErLzfUbDF2jEVuS\nKmS43+Pi3YY/+N1/RxL57HYber0+x6enbFYhh51zHF0iSj2WK48/+sP/yOt/+lsmxzbv3r8jr1oE\n2WATzRkNp5RlSafbpUpyvNUat9cjTBP2+/soRYNpCrRCxnq1omlLzk6e0JYFVR6QZw1Of8Th/VMu\nry84f/wxm3CLJBmIisLAdpErgU8++5j3769w+n2G0yltlXI5v0CTTUzRJk5iwiQAoaJNYhy3SxjW\nnOwfEXgbHt5/xHK5hLZEEGoWG5+PPnrEVz/7K6a9Hn/9p6t/XZpAmuaUZYogFKw3t3j+HEmWWC1X\n5IXMOvKY+QviKkRrCzRVZhutQS3wwh3r3Yx+12Q0OGR6eI/+wUcMDk+581akGby6/jlL7yvevPln\nbm/ekFcFm/UG1zRo0pyuOWDUPSL0SupQY2AcotYOqtDBcSbczec0dYmpmJRJSs+QeTAecH56D1vr\n4FoWI7OPUVb83vd/gmONaSUVx+2RZBF1IWCpFsEqYG84wfe2KIKMaeh03S6Xby9I4xhdHeBafapS\nZDFb0ukMEESNJC0QWhFNNzE0DVvTqJOUSb+Hv94hSxZZBB17TDDPMQ2TtAxQZIm8ijEdE7NvoDop\nt/NXFEVC0Ra8X17y+t1zSjGjEBMKIWOxXRAufcadIYoioNsqjdCS56Bofb76+jllmSKWDboiMRkN\nUQ2F+c3XyFVNmSVsVld09T5yJeBoGo5p45oqVdyyP7zH2zdveP3mgp2X8tkX/4YwbqEa8Jf/8znv\nL+cMO3u8v7igs98ntQW21Yb73zlHmMpcLZds0oiw2TAa9SjqjCfPHpPlAVkeEIYxnc6IKIpw+zpZ\nXZHEKf/L7/+Q+e1bFK3m8PAQ29DJspCT41PqakPblBxNT/nBpz/m+S//CcVs+NWLX9G2oBsaQRQw\nm0XcLG6R9Ja3795xevqInrXPsDPAVGWyXcjdYo6gtqSlz2DoItUSmbdgdvEa0zHZhB55E/HV+1+R\n6yJRlaEpKh3b4nc/+/f4ixS5Z2K4Qw5OjpHlitXmhigMGI8PsKwu/dEUtzNEaAV8z6OoWwxR5ur5\nCzQkpLYhT0L2h/vEQYWQw+98+h3W7y+5f/8estX7jVj81kjgdrFmcrzHl6//ET9c0rdEvJsLHL2P\noXYQZRNN0diGO4SmRTEKWqVi62dIWBhGD1UwUEQdQZaJ4jlb7yWKDKZkEsYlqeDiVR1uty2j4TGK\non1ojBE1Qi+gI7vcG5/iihZiLHFgHaM0FtPxIYPhPlfzG0xLQStX7FsaZVyT+TvG1pCe0sFuDL54\n8D1ev/4ZReLh6D38TcDhcA+lkijjEllQ0RUbzVAoywxFUjgcHjO0e/T1PnUasVyuGHUmUEoMRJeO\n4DByJ3SMCY7SI/AiaAUkWeFu9YbptMf9g3PO9p/g0GfoThBaFanVKRoQTR0/ith4t7x+/SsEsSCI\nPQzLQZR0vCihEQSGeyOW6xl+uCbPYgzNwNUHVEHD0Bzy6PRjiiDDUGQsEY6Hh1QbBSUymAynDFyX\nIlgzsvrsdQ8oi5rl+paqScniHYvbOd7K4zuffQdJaFBUgdP7p3z95tekYc6Xf3fLcM/m+N4D0rJB\nLBSulxfUeUKY1Hz99i1RnSBZh/z6aw/J6FLWKWVZsl2vkai4vb3m8OiA8cGUhgpLlri/PyZY+qxm\nM3S5pq0zVusZdsdC12p24TUPPjrA0jRIRZJ4h901CasaRR/y4ME9vM0a3TbZPzj6cLa+3tC0DZud\nR2c6JC1SdNnAtDS65oDLV+9RxJbLu9foto7veYRBBE2JoFS0Us3W36EbJkmeYVomfdMhiLb8+Cc/\nwbJ6zC5vGHdHJH5CEaUE24CHRw9xVZeqqDANk6Zs6Xf6JGnAjb/i4fe+YLHdocoG0W5Lmkb0XZeu\n6bCdL3l4do/1ckeeFb8Ri98aCTz7+If8+sVb+r0Buqoi1iJHgwOkBMSqxnH6iJKO0Er46xRFUVku\nl2RJQxGXpHGEIhS0Zcv8esXVm0u2Gx9T7XI4nKLVkGyTD4WgaU5NRS3WjPpD+p0h49EEXdbRMNAl\ng5EzxhZ7jHoTsjBBakVkw+XNzUsOjo9oa5Np/wipVUnjFNuxaagJ05SsjJEVAVPXsBQNCtjr7zOw\nuvQdF9dxUVqTcXfCyeSMOikQWuhbE/yNj9PpUUUFmmZiShJdw6XNKq7eXUNZsDfep9MdMp/N2Owy\nRFnk57/4a2Z3LxDqENv+0MEopC11kxKUC8J8gaRK3+y1ZUI/5OXzV7RIuB2Hre+zC1KKgm++B7ju\nkNOjcxRRxVBUhFLA0BX29gZcvnmD73s8PntGsatQMckygduLO8I7n25RQbkky2SyBKJdTscZ0ev1\nif01Z3tDuqpCXUSU5Y5ar/mv/8d/Yu/JMZoBVr/D5fWG1tXJIgVb6PFkcg8xFbH1Hq4z5erKQ3Ni\nGjnCtg2apiRYB3z96xdc3tyR1ODFBaIsc3x2iJeuiRufLPOwHYnZ7IY0TTA6MlGbImkCe9MxRVUg\nCApxWqNqBu/fvCXPa3ZZSrfv8Pr5K4osYzAZs/CX9Ho2hmrQkXXKJObti1ccTw/YrFY0Tc2wP+Cr\nV2/IJAPF3MN0hoiahqJomIrJz//2H5gMp/g7n//+V/+NmpDri59Bu6Ulwffm+FHAm9sbXr+7RNIN\nLm/e8ebNG8zugNc3d8y2AR999gmVUCIocHs3o21FFt6c2fyW5WbDcrPiL/7ur3AHffwg+o1Y/NY0\ngX/3n59wcfcGoRXI85rN2kMsRfYPj1iub+l0XdpvGncHHY3tYo2uaqiaSyNU2K5Cr+fSBCLRKsJQ\nBRpJ5rB/zOzyko47QhZ06rJgPBphGDaO2aUtauRGomPYGKqNJsqIjYghqQytLpImU8QhJ0enmEqX\nLMmZdu5TCy3vL18QJxt6/S5+4n0TlppTVxJlWdAWObog4TgOS3+NqNSEoYfjWIitgC5LXLx6gaZ+\nSABWHY1GtOiM+jS1SFlX6KJMWaZkbUW/O0CRJDTJQJUUsiyjakrGkz02/pY0CynzBLfTxdBkOq5D\nkoZc3r7FchWq8kNxqakb1FVBpzekKUT2BiN0VSTLY0zNoOfsYRgG85lPv9Ml2NxgSiY9d5+Fd0te\nxRwfHZFnHyzJD84f8ebyPfPlEqlpqKIcIQow9m1Uy6WqGga9AR3LxTZ0VAWCZMXtasHZ/TN0S8A2\nDJarK/Yn+4ThhsHIwO0pTA7H2GKHy3/+ElFVKSsRmuzDebmhEWYhiAazzYzJUZdwsUWqJXrTfTpO\nn93GZ9gdcH15hSKp6KpOXTXkdUt3OsYLI7I2oaxhOOhRCgXz7Yb+eIre7XK7XUGZMz3eZxWFpHHC\nLk6wbAXT7OAYJnmc4+12JOGOumq4P94jzncohka0q1AaEaGGOBEZDoYsbuekUczE7lHGGf/h9/4j\nv37+K8I6oT/o0dUL/NUCWbZpgSiIOH36jOMHJzRiyi4O0C2D03vfbFtUk3sHD/jZ//wbsqyhaVS6\nrs3zV79mm2zJK5Fnn3zEi/df0hn0KeuGMIn58n9s/3VpAt1hj5ODe7jWhAeHH/Hs0Q85OXmCgMRw\nMqHMc9q8QhJFTEMgTn1USef86Wes/R3IAorc4f3bC6psR1lGGJJOsPU5OzrjdP8JD04+43D/KZbd\nh0ZGqEQM2aKtQNNskiggTWKKLCbeeuR5SLScUecZZZDSUVVO+8fsdhui4I5K8FH1lqV3TZTvCFMf\nw7ZRVY2KnLjaIaotaZXQCC07b4tt2dzd3LBc3+D5txiOCkKO45gkTcWzp89YLDdkWYilacRJTFU1\nyK1MmefQNAhVScfUsAyNR+f32XpLBtMxdSUyHp3Rdw8QMXn/5pKe2eHjJ98h3LWoUg/bPOD9u0s2\nmw1yK6OKNXWVoWkyQtlg6SaHe3tsNwEH+/vf1GlVXM8vUeQWRVEwbQfDHVCUUDYlM+8apUmZra6R\nOwZtT+GXzxdEhYuiKWgqaFpNml7hbRe4toti9nn66ad4TU2wixFVlelknzhNsCwDbzXH6OYIgYc3\nf83Z/X2saYfj83POH57g2AatUiPh4nQG1IpELZS0tJR5y9QeoBUyZqXz9qt3nE+fYikDwmVOkQk8\nOPuEzWXGg/0H2MKIYXePWpO4mi1oNYGXF2+YrW959uQxq2SGYrd0bIOTo3v8+Ec/oKpq4niHICjM\ngw1O32ZwtI8z6hEHAdtlSJHIGIpFndekVcX9vSG7uztOxgO6poMiKyhI/PT//h+8eX/N/vQIUa4o\nKolkLlAVMprWJalbkiDlL/78z2kSn2G3w27n84+/+AcsVWevN4Y4Z2gfcLj/CEqB1WqFoBjYdo/J\nwZRt5CFpKpIhMfc9WvH/RdDo/1fvy3c/Jwg8KEoOexO0WieLc5omw9RkijwjiSOSTcRyc0dv7OIY\nQ5rG4eTeIY7V50+WepYAACAASURBVObymsPjAzRTwbRNRsMhSZ7y/voKbzfH33lIgoyuatR5glCB\nLhn0uuMP6nWe4Kceu8InqnZE+Ya8DNEUiTje4G9n3Hlv2CTXLP05ttNHUlySrKYsa+pKxDQMun2T\ntPRZ5wsukxm7JiSvM0xLx1utOdg7BLHh9dU7Sqnl1eWMVhbJkpwouME1VFQRbF2n1+0x7I3Y6zj0\nZAGLHE2MKaodbZuiyjWWbhBstvzbH/82Sb6hlkKMrsPA3SP0QrpGj45uIlQCRwdP6Y2OcHs9FFEh\nTXc0bUIQ+LRiQluJ/OrLv0ekRRJL0iykUmX8JuDOv6TMd6iigR/u2DubEJLy8v0lbS3z9MF9jk8m\nPPjiMQ9/+Bm25JDvClTJpC4bBEnBtESub99SBAVVlnJ78SVFmiHWBVQFbb1FFhQEZMKoYLGeM5y6\nCKZAUZfIcsDV9Zd0OgYHg2PCYIWlSAh5SRbWfPr9H3/YgTcJRZajGiZt05BVGYKsofY6JG3Kyxcv\nePjglJvrS7oDC0FLuL56x/G9M4SixrVNTEklXO0w1D7X15ecTk7pmyZ//1e/QBdV4uUWS1JBzvDW\nN9RVidEbIA5dzMGQqhI4GN/j7T9HHE3OMJwpeVggGzbvv77E7Y7w0oi9+8ec3TuiCAuKHGYLH8Xs\n0NAwW83YPzumrUu+ePZd9ruH9GSdgWLy6aNn1EVBFsc4vSmdgwPODg8YWQ6WpjPojsgLkU8/f8bb\ni7eYVp9WkGlbkcne0W/E4rfnE8gLpt0xoiCQFAGVFHATvmO2vWY1n6OKAsUuY70IiDYFlmzx8YNP\nmL39ir4yoQlEDFmm13PQLJm2aVgtF3S7XVoNosIjybbomkQWpYw7BwycA4RSps4/iF2CJBFVJX4R\nE1YRbxeXJGVGlCQk8ZaqDJBEmTAMkUWZJheQJRVqBaV2kBuFuqzY+jtUzcGx99GtHmEW44Ur5t6M\nom346vU7yqSl43ZxrAEHD5+x2sVkWUZTlczmdxiWhttxcLsORRmx3cyRJYkyA1qByA84Pjomimq8\nbYCit6z9S8omZhUFvLl6idMVODs7Z3Z3idIaCLXIV//8d3Q1m5PRA8a9fcSmJU93zOZXFLXGsDfE\nkBz2RnvkUczx4TGOZrM/PqCuJGx7wtmDU6o2+XBS2zFBqunsTYi3KW9/9jXZassmCVDLlGB2SxGF\nhFGKHydEaUCY+/Rcm+tXd3x8NGXYt1Eai7IIyIqARsgxNAXXHrLzc2RZQWp1JuMxfjInzTxUqUYt\nKrRa/fDXzQrWm4ib5S276IZKWdIbyzx+8pjpwQl2v8/ecJ94V5KmEs+enJMXd/RGInfzN9zN36M4\nEGYFsiFg9wWW6xv8wOfe/ac0ucxs8ZqsCvnJH/42Rd2SJyWr2YphZ0SraDSqwuzuFnGvh74/YHh8\nwNHJE/7LH/1vaLWEWlXomsYuDvjRFz9g2ulzMD2kaVtUTaWpK3RFp2wEhoeHaJaOLgnIeYaRp9yb\nDqnyipW/xehoLNa3VE2OrEBZlXQsE1kEf+szGu/R0Tt4d1v+/C9+iun2kDWdIAhItwlV9C9H/8O3\nSAJjtw95gbe9YetdUpZrpvt7GNYYQXRI2pbhvUNGR6f0O094dPrb+IHPsN9l0h1yONmnLDI8b4FI\nTb/fY9DvUaYFcRmx9pfIWsPWm+PaNpZhkQc5tu6gyypVUdBUBUVTYrouZrfLq9trFMum35uy3mxY\nrJakSUaWVaRZQSM0GJaDoiocHIzZejdsN3doyoeKr0F3jFiI+CsPgZq6KTm+f4ZuOByffIJtHNI0\nFtbwkLpRmU5GLFYJmmzRtcZQyyRhxWh0QqtZSFaHg3uPqVFIooTBYMBoNGEyGWHqOn/z13/DaLCP\nZtvkRYGhamRJwLB3xKR7zPHkEY8ePsXz1oTBjml/RFvktFWG3bVwel1CL8HRdSbDEcvbOW1Zs0tL\nDKND6Cfkacbf/vSvCSOfxeqWq+tLOr0+haTQqBbbTCeKVIa9DrtoTcdQERqBqlaIU41WcEFRqdqQ\n8/sPWb9LqPKI1WyFXFn0HBuBmuvrOW9eXdI2ImWWkeY7FBGiVUYpdNlGIUvvHbKiYutdPnr4Eb3O\ngLZtWe9W7MIVVxcv2HorJnuHtMgojcSxPeG//uh/5/LFnLcX/8xme8Pjx48wtD7+tmC7uyBrQnxv\nxoOH99B0yJIt9+89ZW/8hPX6hp5j84Mf/ojx8QkYFo1u0bYC1CUHZ0fs/A+RYF3X4ubqBY7Uo2MY\ntFWNLKtYtk2l64RBws3zCzq6TRKnnD44h1ZCVnT2zk4pxJo4Cxg4JkXt8X7xFa3dssl3bLIQsWOw\no+Am3pDqO95dviTe5nz/O99j2N9jb3LGdlfy5NlnTA8O6Y+mrOZbTMPEi7a/EYvfmjD4O//lmMXd\nFUf3PmK9ndNWEoPuGMfeR9Itlt4VR84+XatHp+ewWq+Is4gojmnlmloqKOqGbbAlT1LassFUDKoW\nDN2mLnNc00WRVZRGo05TVrPnaLrIZrdhF+0I4jWy0KDJCnkWMRhOmRw/Zpe1JFFBHK7QZIWTgxNA\nopUgTSqKsiKpYpqmQmxq0tj7cPorqIRpgKyYDDoTDMsiiWuSKEVoBbK2QtSgqRNkWUCUTTq9LnWR\n0tYSXatD3bREcYpruKRJgqRoqJrCxvdRFZUwS4iTFV1nwJPzRyRbH0cWOeh06douy9ma8WDCerum\nalMsS+f25jWCEjOfvaM/6LGIQ7K6wVJV0iSAukFRbJ6ef4ev3r/g9NEZNwuPcXePNANRbdnu1nz0\n5CNKoWY0PWG22JCHN1j9LrouUVc5iqYi6jJmzyETGhpNIC4TEAVqUfigScgiMiNG0x5pnZO3EXd3\nazRDxtZdqjxj6+8Y9Ya8/OkL0l2BY30ofDWUKZrc40eff06wWbL1tgxdgySu6fcHuHYXTVJwUMmz\nO5pmxLCnMlu/5G7+munklKYRaaKKSa+HojSECdRNl1IukaSWe2fn3NxdoRg1a2/BdLzHYrmkzmqe\nnj1mvVzQNbuM+xOoW9JdSF4WHO/t8/zVa86m95hfX6O6Dev1Fb2JS1akOJaLXMPx4SnbKGQ8mfDi\n1UtUVaCuc3bBHVdX7+l3HYJgTSPJXN0uGA736A5cPH+L7VrUTU5RRsS7As2Y0jYFV6uvuNy8Zp2X\nfO9Hn/FPX/6cvekRF69fIikVo0kfRVL5xz+7/dclDKZRxF7vhDxoECqRtshospam2HK4N8Yy+5hW\nB0vvkKUNgqJSUoKYIwqw28VEUYoi69hOl1YQyeuaIkroql2EpGZ7c4EhScTZjCRdYloySbpAEItv\nLq1UBv0hQbJB0HIatSbMMibTY/7N938PUbTZP9gjKwoMyyAMCoRWYbp/SBiG5HmBaepEu5AyDQn9\nGVQFrmFgaRZllSJKMt//wRe4nSH9/gRBFLm4eosgCTRAkSTYhoIuSHg7H1XqoWsGZVPT704wDYtW\nUOlP97lZbTAVBaVxkVuN+fU1yS5ElGxqyWCXV4imRlSEVG1BKeS8ffc1tq1QCBJSp8Pl+g5RFOgY\nFsv5grwq0Lo2dqdLnKT0hha/+vWX2K5NWaccDg9RGpHD8R7rrUeWpgy7HSTB53j/kGG/IA12tG2G\nn4cY+wOWSUArQuL7pPGOOPCJ/QBRU4jqloV3AZJEK1vErUguFERVhGg1mL0OaS2zDjPOnp1zdNpH\nVTsorYhr2LiqwMWL5+TbFFN22cwSnMaimGXE84i2iilrj0Zc0XUlXrz6GlkqODm5hyTUjNUhvc6U\neOsT+1ukOqWuY8pKwgt9Xl79LZ9+/oyNv2S93ZDmLavtglpKeX35KyRDYhuGqJpG09Tc3d3yycNP\n8DcR5ycPqcUGc9AiGCrudIjZsTBVBT9Z09YFy/Udmi6TxxEn4xG6WNOQkEoxD56dkuQRcVYzGpxz\ncvAMsRUp8oqOa7PZBsxXdxRZRF7l/PiL/5UvPvu3bLOcSh8gagX/1//5pzzbP8dtbCIvRJNVHE2D\nKPuNWPzWSEDSFNqmpKl90l3FF5/9Lk2T0zQpF6+/YjoYkVcNkmqQVRXuoEuYh0iKSJJWlDn03Qmi\noNEgEucpQbSDtqbKIqRWxLJcfH/F7d0VUbZjF6UEUY5tD0iDhLbKycuM7c7jdnHBeOpiGfCPf/fn\nlOWW/ckBuqkRxj4de8rRwR6iJKNpBlVVMuwP8PyMhx//mNLokAkybnfAzt+SFGuKwsPQZO4uN5we\nHdEUFU0hMRk/RJcdyiRBbiXKsGW12iHXLY7Z+yBwyTZ5EZOm+YegC9mh4w7w/B22apNFIU3VcP7s\nU5JaohBtFkHOKsqZbz381EO1NARNRTNNwjwnrGuGhyfEYYIiCOiyQlM3yIpCvNugKzJC1XA46UOZ\nINUFebOk3zeQZYE8z9AkiTdvXmKoDrQKTaPQ7boYtoFmq8RJQpGlSHXJQa+LmtdkXogsiSBUOEOb\n3pHFrsw4GT0gv8pRKxOpkj+EnEoSx2dnnNx7jNsboEo6tmIx6vWI6xu8ZM0m3lG0EvvjE0bOiK5i\n/D/MvcmvJFl65fezeTbz2f2952+IOSIj56xKFtnF7uZQRLcEQd1LrmrPf4S10H9AQDstCGhBElpQ\nbKE5VpNMVuUcc8SbB5/d3G2eexEEIaGZggBCyP52BphdwBbn4N77fecc0qihOx4hOzXbZEkuFSy2\nf8fto12qyiTNE6qy4eRyQhAUaI7HalZhKBIqK96/t0cdRFiNQTyfcdQf8vjWHe4M7tA32nSsNpbX\nZulvefjoIWcnpwiNyHj/kIvzc2RVRhUl8rKm0+1xdn6G1x4SxQK2u0vL9NBdC7fbxnZ0smxDnYYk\nkc9g6JGmW64mlxSqgqCZFEWIIMPVYo7r9lmuU3rWDkos0pF63GrfZqSc8Td/+b9QKz79UQ+javho\nvINXVOTBkh//2m/QH4xZLjbs/GNY0D9X3xsJzLcb/HyLgIbrDfjmxXPyWkAVTRzDwZYVdEdkm8/w\nXIPlYkpVNQRhiG3apGnGdDLDX2yIswwBkaaChpL1xidMCmarBVfX1xiWiyS5HNx+TIrIydU5lRAi\nVhKG7rGzM6IoKhbXl/zVX/4pdkshqbaImkFZ5Yz2drHdHq7bppEF/HDLoD/C8zq8c/c9NmFMmqcY\nrkRJitfr0OsdstN+F7XWEMqK89NXuJbBu+98SL/t4pgqHcvAM0363R263V0cs0eRp4zH96lKlbwA\nBB1NsdEakXC1wnMHxFmAJKa0PJfVZonSlKiKSJbGb70AzR6226OoBBZBgp/JFIWOabTY+AG3Dm6x\nWfh4jkNdVESbJcvFGevNa8oypM4j1DSkqVM20ZIw2yLpGooMbcNi0GkRBQmqZ6IbHebLBdswpS4F\n8jTDdmyuJ5csl0vCNMF2LQRZICtiiiIhSVJcz+Hs5IL1zYwoDtC0FrrQYtQ6RK5EgtWUOL4ijlcU\nmw1JniDqBrUksN0mxEnMm29POD55w8VkglRXXExPSDKfbTQjihwyKWYd+wRxgOX0KLH4tU9/g7Mn\nz7l8dsKDB/dRJI1+x2R6+QpDEzFEl6ubG+K8pJFqkmzF0p9R1iVVLTDou1xePMXpmLw5fUlZZWR1\nwMH+kG3kEyT+23Qgq0KsRfb2dhn2RogyrKM11+tLgmROWm9QOhrT7RXfvHnO/dsfkG0TJEPAtTTm\nywW7ewfczKZ8/sVnyFpNVcbseUPGowe0OjL/+1//r/xyec3u/Q8YaRpikrF/cIggNQiGyPniFavV\nJXfffYxifDfUvzcS0GSNWpSxXI/bj95nfHAbU5KxFItuq0tdVZyfnZCla7bbFZZhYusmrt0hzzbY\nqky31WZnNKIsS+IkR9Ms+t0jZMmkad7ehnotF1EUiOOUy+spiApHt/f56snnDHZ7NEVNk1k49i5J\n0jDa2cN2HXw/YBsHVKKBqHrUNPirBYf7eyiiQBymDIcHTCczXEvE81TW8ymObqPhUMYKZSYjKyai\nLGAZJrpiUhclaRCwnK2QGoHV4pqXT59w//ZtijxHkoHaoEhKOtaQvfZ9OlYLuQTPlsmDFYfjHdbB\nmkkwYZMsWGcr/vN//j9xXZu6TklzkNQWlVihezaVpDI+vEPLbHNzPGWziVBVg6ZS6HRdEEC3LBbr\nOQgKa3+FIBegZsy314iqhqLJpHGCqjrMpzMMpUITRcRS4vDwDlEQ0dQ1WVFQliWO5xEnEb1Oi07L\nZtjvMRjsszc4JJdkovkax9EZ3t7B6pjIgoXbGWCZNi3V4skvvubrr56QNzlJU6FbBmJV4ZoW09k1\n63WIoTbsHz1CsRx03aQJa/KJRhmYWImJLpp43QF3bj/AFQ3aGPz8z/+csdvD07qYfZnp+oqikanq\nCkO3ufPgQ5JUYb4KyCn49sVXdPt90iRFRWZycUW83hCHPt2OA4KIbXks5gtcx8DUJCyvIUsrur0O\ncRSx8K+5WZyyTWdsoymXN2cEUUBebcmlDNttozcOo+EOi5MTYlZUWszF5ITxeI9bBzvs2wNIa+6+\n8z7hesH1esqirjkc3yeZbJFrmU6/w7beMo0XXAWnNHJCy+nw5osvubq4/k4sfm8kcDg4oO206OgW\n5WZJEyUMW10MTcJUVaRa5Gh0izqtUUSZNAwxJRGhLEhiH6HO0Q0LWVHY+gGHu7fp2COqUuBq8prb\ntw9QJBsqDc9oY5smrZ5HmAdczU84enCLIAuJgpQ7+3fZG9zC0HRMVYWqRJJUDNMizTK8Vo+Lqwtc\nr810MsFQddq2x+p6ymi4g7+aMTk/oWVbqHKNrgm0XIPx/j40DbIIcbLFMDWicEO0SXE0G6mRMDWH\n4XDA6etvUMuA1flTdl2XtiuwWs8pq4KNv2KbrBjvjkizOV99+w90u7uoWosXL69QVJcPP/yEly9e\nIwgigpgRxFOKssRwbRRbpcxD8mDDfn8HQ7bx3D4XFzdsg4A4jbm4mRAWGU0j4Zm7ZKFAU6kMhwck\nVcibq1cIkshmuyHPc5I4RDcMZFkmilL6ox0qQaChJo5jbMvGcC0s06UUanb2d5nOJvjLAI0+3VaX\noJqieRajwT5tr00YhZwcn6Agsjvs47keAhp22yNKYmzXJYgjPvz4Yw4O9pgvrnnvnY8wVY1212XU\n6rM3vM2HD/8VqzcznMJgennKfHZGmvqsgxtaA4O777+LZPe4uHzJcLfHePQAyxjQ6XZ48uUXfHLn\nA9qCjlw02K5KXIZcT2e0XI9hr8fOsI+ltnl09xN0WePug/vESUJZlgTRhqyq6LQPSaOEF6+eEqQB\naVxgWC283oBlUCLrHZLUYLku6XgdVqsbTNHkoLNPlmQEaQRiztHODsFiyWd//Ut6/R0+e/aEtRAw\nnV7RrCOMOKdl2sRxiiorNE1BLGWojk1VGbTUIwbGmEfvvP+dWPzeSMBCpuO2UAyNlALXNqmyBJqa\nLN2SRktsrSIJA1quzWo2o85L0nhNltaIoklSJHz+5S9xbY10s6JtOyTllt7AJa8Sur1DbH1Ex9mh\nrgSSLMTp2OR1SZpJdDr7tDt7SI1BW28hFQm6kFFufUZuD6WSMQQNf7Uizpdczc9R5JJgdUW0nFCE\nK3zfR2gk7hzeZX/8Doulj7+dsA4vmExPKbKQdLOlajLWmxnQMOgeoAg6ZVEzm235zV//n4i2MaII\npq0xmV3g9loopskqOSYs1+RawnoTE1Yioq5yc3mFXFa8d/8jZpdTIGXvYERdylR5RJ1XtNwhaZKx\n3S4IVhPWy2v2RkPKCGyly9HBHQShh6i5dAZ94qIkCmNGnX0e3v6IOlHZLmKWqxLJ6NId7GGZDqrm\n4fWH/P3nf0WQrciJaZQGxVSIs5iHjx69NUtFpmW0kSWJ169f4S9vEJuMf/fhj9kuNiSbkDt7D5ES\nmSoPCJZnRPEpp/PPsXoN7Z5Bx3NR5Iq8rvHTFMnSePb0lNXqitawz+TVMVKVskrnGLpMsprgT1+j\ndGryQkdXFZo6pBEbgjCga9lMwiVRMUPMdIpA4PTVKcPuEKoaz3M4Pv2arX9DkTboOJiSjSCVvDj7\nBaJUocoahiJSJiEH3X00QYe8ps4yvJZFJVjsjo64ujzGslzCOOHO0T0kUcRrd9nZGbPyN0yWF3i2\nzfX5CWZXIsqWaB0PVTIYjW8zW06ZLK5oFJWjdx5TVTVtVcCPJ7gtkceP72C6Jtlmjm0Z1LVII8j0\nR3sMO7vYuo07dhm9c5uri+fficXvrUX4P/6HR8ShTyXCfLNCFQWqumS+nRKXWySpoapqFFklTEIE\nBLI4R9ZMZNVmvHPEcjnHNFSqNMYwZAShQNFkup0daAxkSSfOAuIsJk5KnLZHkvsIlYxp2ORxzU7v\nENdskSQxohCThBtauk1dVOwOetSNwGh0QJZFRImPLul0Wj3KIsbQVG7deZeiyFkv12QZNAis4xWi\npVCmIrVQ0JCCoWN3e8R5hWba5PVbIdBwd49gm7LezJFlGVGxEOSG56+eY9niW8VcuEIUIC1qsrIm\nTzLERiIKt5iGQ7vTR5MVKGuCYI5lKEiSyGK5IksjsvhtbJdluYTbhCiLMQwdR7XZHR+ymJ6hmhKu\n28HQXFaLJYqiY1oaG3/Fg9v3KTcFOiJVnlOrFcvlnE5nh7KssAyDKAzIy4SGlDJOoajp2Db39x+y\nXKzIipqD0QHbxYZwvUKsGzTFYjK/QZQkwiAijwsUyaBobARBRkBk5Qf0R/sMhwNczePu/mNGzgB/\ntWawO+Tq7Ix7Dw7IhJImrHn59TFZXrBZZbR6Dtt0S1HXjNo9qrIhzEtqsUAUJSzdwDJMNMXk7PwE\nQag4vznFMEWMZsje3gOWmyllIxElKbppY5k6i8WMefAMBZXdwQ6Xr15xtLuPbbjMJxG3Du6zml7j\nmSbPXjzn9u07HJ8/QRTeCuLSdYgiS1zNbrBchfl8gS7a5HFJLTSYps1iscZ2u0R5hm31GXRM0nBC\nlvpMF9dogkBTCKiaQ1nXGKbBy8s3KC2DRqiZzSe0Wh1m02vC2Rm6XPOX/8f0n20R/r8Gkv7/Wf5i\njqDVxFlEUcSoRp8syimFiqpIqBqItwndbpdwE9Dv77BdxVimx2Yd4q8jWs5bB1/bPYBaYr6Y4LYF\nvOE+cVwiIBJGNYZj0h/tM/enFEWJKlp0Wz08a0jV/KOlWR5TZTJNZeHHIUIacrN4w7Az5vL4BaJS\ncHVzjjW2CYmpahnFMDh99YIwjtEli0FnnyCKKQSJtEyxTZ28KlitlhzsjojrEtmxiLKQQg8wRYXT\n629wtSG74ztoYk6ayZzPL2h3ZF6/eo6sS9x/8C5ZmVGkFXWYsLd7xNOvv2K8u0OVVyhqQYXAsD9k\nuZ7ir33COKIRVA5v3UFo9cnyhq7bxpIUyjIhjbYURcr12QRRroiTEFmsMTWPnfEulA3rjY8jGriV\nSaa1EKqMtIzJqpTRaAexMimEhGC9QFUl4m2K09botloEs4DI3/B08S2abhImIbUNVZHghxV+8PYs\n7LW6rFZziqJAbemYqsew3WIym+F0bAQxZ+B6HL/+nAf3H7G9mXBx8oa98RBBNhntlsyW19idATcn\nM26P7xMEMZm/Zj1fU1sGjnOLPFUwzR6rySV21yXKI5oyISsLilqglgV0vYWhlFSNRiOoPH/1hN3x\nmGWwpNcfMJtPqauMzXaObOhswiWvn31DuQl5PV9Sywa3bt0nD2puje/x+vUv+fUf/ZBpFGF3WuiG\nxux4ykcPPuB8MaE7OiAtfTx9yMM7H/Dmmy8YtvvEaY1iq5xdX7NzdAs1U0iqNX42I68F7PYI1XBo\naoEwit6OkScrUqEgibdoVFS1yMXlG7q2gyS+teb7rvrejgOdlodrdUnjCF2EPEvQTAWKCl2xERuN\njttFrSUUJOIoYTjoU6YJQ9ej5dhsEp/uYEAjGgz39zBcgyTO2PhLlotzJpMTmioj9kPSPERRZLru\nkHFnh+xmzeVXT2jKjKvpa1bXZxSblH5/n+7wPkmpsw595vMZhmEQhgm9zgC35dHp9Gl3d7mZLJGV\nHFUTEEsBA4F+e0CwWiKVGZ2Ox+V2RvfwDoblkaQFsqqyCUPiqqIRFFpe/639WAFlLNDv7nKw94ga\nj48//U1kq8Xn33yGqeySJypSJWPILq7T5d33fohhOIx6I4QGrm8m6LbHbB2h6W12RnskccTVzSWz\n6QUvXn/NVy9/wdnNKyarS1bBhLV/TY2A44xQJJntZsX51TnbOCZKa/b37tOUIpfXrxENhUazURUP\nU7FwNB1TNNgf3UHMDEb2GLWxSOIaU+3Q6u+QEhMnGwadAdE2AEliuvbRLR1RKnj96gVNWSIBiqDS\nclq03R0URSfNYx6//z6v3xxjtTVmmw1B6WMNHbxWF8fQENQG2bC5ub6kqWvqRuLe+Ii7t47YJAqa\n5iDmIl5rwLNnzzAkg6dfHRMkJX4V8OLcR7HadAe3eXm2Qs5hMDhgI+TETc7p+TFNVZGFMYNul7Ks\n8LwuSVgRBTBbbQmLhm0aM19f8+Kbz/Aci0ayMe0OSbwlKzfQiFQZtGyXv/3Lv0EtG4xSwBEdPMPj\n+vIJu0MPqZFwzTZllvP+o8fIpyHb1RJRKkFu0A0RIYbjZzN0zXl772PrTG4mfPLoV7jbv0/HGqEK\nyttMAgGslk0qld+Jxe+NBBYbnyhLKcqcrCiwTJPtcoGl6dSlyHITEGQxN4spURKxmM+5uZmyXi0x\nTYu6rqmLGKFKMdWCq+kzymbJcLhLWUKaZ8iKjGFamLaBKIjIkkSTF1iChiYrOK0WmvLWoVWUFDRZ\no6or0jIHCfJaIGreagu2aUzba1NVFXmeUdcilu2gmC5uq0e33+bs5DnT+QtG3Q4OFpKkMRo+oigb\nkrik2+mSJlvG+4co6og3ry4x5DY9b5e9nUMUyWC9mXJ2dspgeEDa5HQGtzncf0wSL8nLmFF3QLJN\n+PDxr/DkuBbngAAAIABJREFUyTOaJidKtohijWMbGHLDqNPG1DTqvKTjtOnZQ0adHdrtAYbXxrLb\n5EVDmjcMBnuIskEcpeiqQxbnDLw2nmUz6vVotVyOnzzn9nCfMi6wFBXPcKjKhjjx2WyuCNZr7h28\nQxVDHJZcXk5AqJGFkuGgj26qbII5o909bKvDo7t3UQWR6fQGRzM42Duk3+my1x6yWd4wW6zoD/tU\nTcZqe0ahg+KOyEU4v7rCchxmmwtW2yuKqmK1iUlL8IZjWoMjrsMNrttn6PQwEo3NxQWiUDIY3KLJ\nDW4N7hGvC/Japje4zYuXJ0hpyL1dh8yQKBuJtmez1x0zPZ/iqR5qo5BtcjpWnzyu0cQKXXfIG4vh\nzj2EWsfVbNqWS5pGvDh/Qlw1DLs7yLWMVjdspgvCJMLtD6iakixYYioaQi1Q1CVxA4cP7+IHU2rA\nEEW0aMvBcI+mlAmjCLlqcWvnMR89/pgiydjrD7k5v0RuZLJ1RrSI6LUGSHnGQbuFqIqsZkvqLPtO\nLH5vJLBzsM/VbI4kKXiOx2q7pm4KHNsi3QRUecF6s6GWVGynheNYeF2L4XAPxApVk7F1iTTcEG3X\n6KKJYw45PnmDaSookgpNTRCGvHz1GsOQoS5ZL5dkWYrnuaiqTLD1CbKYytGQ2iZxkxEXGRUCoq6g\n2Sa1ILJNU2RdQpUM4iDH1ofc2f8EqbLxZ2sWK5+gjigJWCwucY0WQpqgBymuXCEKNTeLN/jbS0Qa\nxr0x9+/eRSglbL0NhUqeVoSbDbcP7lHHNcl8y+ODd9gb3iEvSwxTphFi9vZcBBKEOuRmccxydU4W\nLVlPrynzCl23aNKCtusRrHxMVcOQVTq6hYmMo7q0Wl12Dw/JANfz6A09knxLp+1gqBp5mjK9viJJ\nQnodjyzMEBsZRdCJthlpmoJQYZoqdTVHU0RUU2N3eIQkiGzDNZPZDUm+wXIMNFPk7PQlmmqAUGIa\nYIoNYgNJEGDrDqen3yIwxzRqtn5BkYn4i4CO6/Ds9ILr2Zq0rnl9fkycR6yCFWGZ0BkN2R1/yMP3\nf4U4D3CGLoalMtrWeMuKlmPz8ttvuXN0yM7+gJan8+OPP0KroIyWvHv/gLJ6zc31c7ZhzCafEEeX\nvHrxDfE2xtYcXNVFrAU0WWC7XtG1b7PZRvjxkqtlwM3NGrmSCDcxl9PXtPo6Sb7k5fFrPr7/qxi1\nRNfr0O8POTy8w2w2py5rfP8SRW1od26xjkuevHyBaojYtsnVZMa8CbhcXIJkcbTzKRa7tO0dpLzB\n0W2ERkSoRPS6QqkChGrDzenn3Lujsri6wBV6KGaXyeS7TUW+NxKYnS94eOces+spjuORVTmKo3Ny\n9gbPMum2PFotj1qQ0HSTqsxYzM7Ii5DL6Ze8ePNXVMWK+fSE3b0Omb+myRq81og3r18jCzVN0aDI\nCrfv7PHll78kDEPavT6z1Zz5Zk3elKzjKT3PYrla8OLl07cSV1HBtDwESUfRG6o6ZOjaBFcn9B2L\nvu297SLUIWVV0Nrtspav8aslpSxjDFq89l8zmT9j7FkkfowghCiqjqN2Sf0t12+eIgsgyCJey0SS\nBDRVwdY92paBoTa0Whovn3+GWGWUeUG/02UyP+XZy5+z3rzCMUoUDbIsoSpi2rZGp9PDdDwOxvtQ\n1f8Ygd6BJocmxZYVqjIniVKGuyNEXcVSLaJFzGa6RBFrlotrjl8+wWuVXM2+pO5XiC2d/YM9tuEW\ny9aRZfntFrduyPIC1Rb46NPfJA4qLLNNXeoY1oCi1tmmUKkioi6z2MyZzCes/C2D9h57B4fUUoWm\niwiqSxLBcnVNI2eoukJTy3hyD6kqsR2Rg90+tmJQRDW6ouOZHVTBYDaZcnz+GapbUoQr3MsbPrBd\nNHJm1yuW2xV1E3Izf04jZVBBlon869/4LZKtSiF3qYwuO+MhVSKySGIOH99j//ERpQ7dfp+O3uNq\n7nP/7ockWYJuDRmNH1AR8eN/+1sUkkPrcJfPT/8a2RbJq4zhTodffP6fMN0RUVbhOkNkRWRnZ8Te\n/pC8LEjTjOurC/YGB4jIVKKEKsoM3DF3PvqQVldBq3Oe/fnfYik5z7/5K27techlwKjX4t0HdxHL\nnO1sweHeARkJmyLBGtjM8yU9z2Kvf/c7sfi9kcDRzi6TszNu7R2QbgLKMEVV3k7HrVYhrtlBFQ00\nTSSMAmbzObKiMoknfDV5hU9IVITIAqRp9FagUcvoksr+3kMU2SaKZ+T5HCp4fO8RSiMTbnM000Kz\nPHJJYB5OeTN/Q2fPxei6rDdrhHpLHNwQb5cojYoiFJRpioXD+bPPWF79DcHiGeur5xhyhSQLlE2D\n3bbR3A4pAoWoIOo2yBqoCkEQQiHRV0d0jDY3F6fklU9eFyy3azRX4mbxhCyb8c2TfyCM1mRJSZzE\nfPvNF0h1RR4V7I8fIUo2UZgRBjFiI5JUb1Nu/MUSVzbo2g5BFBDHMYoqMZ1OAAjDCFlT8bwWqiTz\n8ttnOKbF1ZsTgssJpiCzWfsIgoDndQmjEM2zkA2B2WrCV1/+kr3xLi9ePSPNQsoqRVUkbNvm+OUL\nvvq7/4sPH/xr9ro77A4tDkd3kco+rtFnMd0gCDLL+QzHsBn1RqzikCBYsAwmTNMVjWbR7R7QqA7X\n8wWW1wEF5tsZD3bv4C83TK5eEwRTZF3GtFziKCTYhOwd9kEVmW6XvFlv+UUa8b89+4LjqsLs9khj\nOJ9f4w1NympBHk/IlITPX/wCP1lw9u2E2zt3+bu/eIJp9Xnv/r+iqUSGgx7Rdsvx8StifLIk4/G9\nT9jf2Wc8tBiPXH791/4HvvnqJXsHI1bZArPjUKYldVlyuZrS3T1iNtuiZzLzmwmbcMp7jz/A9+ek\ncUASJLT0Hv5kSzgLKbcZhmngqBAnN9RRiJgXfPreeyxmN3zy3vtsFhOksmE7W5AWazKhQXFbTG8W\n9M0W+bymq4wpVm/FaZZuficWvz8BUZWj6gaKrLLxNyiihlQrSMrbvD7XaVOUOWWZIEsie7sjREEg\n2Ca4VgtZUFEkE9vroikO++MjyqxGEVVubs4IwxBZ1tANmyqNycIKQ7YxFQ0klVWw5fXlawoxJ6tD\nrq4vGAw6uF6PYWdEv7cDhcBmteDy7IxGComslLUWcJ2uOZseEyQx44N9bjav2L01xm3tUqYFmqDi\nmi5RMmG6PsftKTSCir+8Ity+4fz5Zxzs7rBaRGi6R9GUnE/OQCwwDIteZ4cyF9GVEZ41wNR1LN1A\nqCTyuEYUFIoqhbKh7ThESUyUJgiiAFVFtAlQNIWiyun3Rwx7HSRFQbcMyqIkz3JarkORF6gCyKqK\n23WRHZOMCllTaYQSVdRYX21Yni057I3ZBj7ffP1LTF1lsZhgOwarzYI4S0ljH52SaHtBmr4lsDCa\nM+yOkMUa191BlDQERUQVNbK8xmsNsKw2VWmw2BbERcDJzQmCUJM3Jb/46gv29o/Ii4yzV2+gKNDN\nHdLaYu/RHTIlZVktmBUz3lw+42J+htpx0Edtqv0O7R89RLnl0R62UA3w0yVFvWG9nvHVt08ZHY2I\nmwTTc9A1k2dPvuWDD26TJj7h1mcTzLm8mLD2fYq6AqXCcQVOT74gXG/IswDP0VC1mscfHCHICd22\nzq7bQUgK7g4foNdQlgsODvZ4+eUvaLdFhnt9VqslcRoi1Q26pPH4zgcUmxillum7PSJ/Q5GnyCRU\nUoGgNwiayGi0h1Lr6KpHExtUiUxVyhzeOUA0RMoqJFwXNKGNIRh0WyKOYkP636GfQCpcMT4ckeUN\nd48eMWztEoeAoLL1l+RRhGYI1GUMdUSeJ6RZTb/TxjUHKGYHQdU5vThltplxOj1hEc6Y+eeYpoSk\nlGyjCFECTWgxah1w6+GntLpdluff0EQblvMLprMN7zz4Ia7bIs9Sqqbm/Po1y80VtuewDiM01yQp\nM0oRjB0XsdtHbtl8/KP/yIuTY0IzZhWuEAsJWbKoUjAaA9s5YiuELGcZrurRNjps6oiz62/o6W0e\n99/jndH7GFqLTRYg2Dts6xKjbWJanbc7kDjm4Z2PaDt3qasKsc4okoykzPALnyxac9BxGXl9BNng\nenbBJoyIy4jWgceT118hqhGLzSVZ1XB2cc7J8y9Zzy7R5ZLF9AJ/MSVIt2RlimzovLp4iaTLXF4t\nsNwBq0VAx2txa3cfMW9oygan0+Yfvv0av0rJpJKzyRWrICDNZkg1JHlKGEVE9SlpXbAz6GF7Nrv7\nByhOF800OD9/ynK7YufWbeK8QjZUilxhcn7Bg6NDXFXHlD0suUOh1DQISJqGJhksX04pRIHdW7vs\njvv0un2iICZLU3RBY2+vjelZ1GJFLVWMRjt4msVkk3Mei7iDB9SxjitLvHnxglosSKuSTz7+mJ7d\nIoimZAncu32XR/cfc/fWbR7cv0chZJRSxv7dIYpgspmv+PLr/8Raes5N+Io6yXFEG0WqyIQlvf4Q\nBYGFf839Hz5mGYeYXpdci2l39sm2bW4d7jGdfU2nJSBKOXkZY0gyUXDOclFht3bJiNnmCXvtu1y/\neoVS1/R6Q/b3xm8NWJx7eFqPKo6YHy/pd8bUAsSbCuIMV7a/E4vf27DQv/sP+2RRjihWuJaKWKZI\njcT9+z8i3qZ4rsy2iNC1hsmLN3z00ce8vjql0z3CNlukcUkp2OyPH7CNCsIoRnd0ttGaIm9ospKH\nDx+wWq8YDw/o9PYI/Q1Pv/ovyIqIbFio9pC7d95FaSDcbKjIaUSJxWJGUkZklYRk2Vgth3WwwesO\nqHINRdOpCxOrr3F2fUxtQ7bOyAqZVnuMaXWZzwOkWmGnvUNT68i6RRgsmc7PGLgWQVpzfX1MVRW4\nxgDVMHCtDopacnr8Gs9uIakgiAJ1E6Ko4Ptr3JZOHId0+x5QIsgVyXaLbdqoisnx2SlVI2BYGrru\n8eLFCxxHx9A8bM1gMLSp6xLbaiFIkOc5rfaAUf9tpv3RnQfY7gDL67N/eJezi1OuLm9oqJENgZKa\n3m6PTRKyv3+EY7TeGopKBoNWh+vLM+qyoawqGkHienJKVizY7bS5evOcg51bpHlFWWSMBj1ubs5x\nPQnHMCmikCyIUGyTk9PntC2Xz//LZxyf3mB3NSRLII5WlEXGItyQFxD4IdH2ml6ny/HrU3YGu6yW\nZ+yOwNDfxqTrak0tpczjJW31kI7VRWgtaWpwDQtbbaFrFvv7Y5brNWWVoegSu+MDou2cwUBlMT8l\nLn2kqmFo9vGMAVnydoLV6XZ5evaMvcEBmtTCtE1Wq0tkWUdUFabTFYrVJ1ymjAePWc9fU+cxiuHy\n+M5ttosAy4Qk8ZH0iCCZkVUVrmuwSTMcq8d0MqMQBG7tdohmFxR5SV1VnJx8i24aXN9M6A1c6kbE\nMduslgk7u0ckccVsOWE+veHvf77678tPQFdtZK2g5RmE2yVF4dNkW0xFQZBKysKg09pn49c4vSOW\ns4wPDz/FEqHIIxxLY8froAoy4XqFUufUQUyy2rLb6aApCpG/wNZ1zq6/5Wb2gqqu+Ojej7i9+zHb\nyEdQK+qqxF+ekKRTbt1+RCO2UW0Ly3GRJAFXNkj8mI0foSoe0GYTJbz3o3/P3z/9O/r3dnj27bfE\n24hWt0dNQZyGvPPOe+RZyDZ661loKRYNNXVd4g12sV0Hp9WhaQI8x8WULJoqYb664Ee/+tuIkkwY\nBEDKYn3F2p/T63XYrNY0ZUkYxsRVTZkrWEqH9TIiyiLu3XuIaesIosr51YRf/zf/M2s/RtVyZpNX\nSGUNAizXK5oahqMd8jwjq0MEKWd1dYq49WmSDeeXL4nTOXcfDtjZb+OHW+yWSRxtqfyY1dmMlt5F\nKd96CuZJRMvRqIoCz+rz8P6Pca0hZQNP3vySsI4RDYlVcEl/0EFTeuRoIEhcn07ZLiMevnML1ZTo\n7exitce0x7foj3uEUcr5RY6ETFZmSFZBmq64vrnG0VzEUuVXPvmE1WJOu33EqgzZ4hPXPkG5IKmW\nuJZFJd7Q3a8pRY0iicgCeOedh8xma86vbgjDBFlskGUJUaqpcpnLyylpk+PXa9bFmm28Zb1OUWUV\nGZkdp8u97iFSVoIwIQyu2dvfBwmKokGQLHpuh/1+jyo4pt4KyLLIZP6UKNogCjKL6YI8K5GlgizN\nEEWT2SwgjkyyMuPw9gH2qOT56dec+Zd4ux2+ffMV3XGXF2evWQdb0jygzEKEMKEnC2TX19hpiSGq\nDI4G34nF/08kcHR0xPvvv89HH33Ep59+CsBqteInP/kJ9+/f53d+53fwff+f3v/93/997t27x8OH\nD/mzP/uzf3ZNAYkqSUk3PnG0oahqOt0+oX9Gx2vheS4ta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"text": [ "<matplotlib.figure.Figure at 0x10732eb10>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's cool. Both of these detections are tiger cats. Let's take all 'tiger cat' detections and NMS them to get rid of overlapping windows." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def nms_detections(dets, overlap=0.5):\n", " \"\"\"\n", " Non-maximum suppression: Greedily select high-scoring detections and\n", " skip detections that are significantly covered by a previously\n", " selected detection.\n", "\n", " This version is translated from Matlab code by Tomasz Malisiewicz,\n", " who sped up Pedro Felzenszwalb's code.\n", "\n", " Parameters\n", " ----------\n", " dets: ndarray\n", " each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n", " overlap: float\n", " minimum overlap ratio (0.5 default)\n", "\n", " Output\n", " ------\n", " dets: ndarray\n", " remaining after suppression.\n", " \"\"\"\n", " if np.shape(dets)[0] < 1:\n", " return dets\n", "\n", " x1 = dets[:, 0]\n", " y1 = dets[:, 1]\n", " x2 = dets[:, 2]\n", " y2 = dets[:, 3]\n", "\n", " w = x2 - x1\n", " h = y2 - y1\n", " area = w * h\n", "\n", " s = dets[:, 4]\n", " ind = np.argsort(s)\n", "\n", " pick = []\n", " counter = 0\n", " while len(ind) > 0:\n", " last = len(ind) - 1\n", " i = ind[last]\n", " pick.append(i)\n", " counter += 1\n", "\n", " xx1 = np.maximum(x1[i], x1[ind[:last]])\n", " yy1 = np.maximum(y1[i], y1[ind[:last]])\n", " xx2 = np.minimum(x2[i], x2[ind[:last]])\n", " yy2 = np.minimum(y2[i], y2[ind[:last]])\n", "\n", " w = np.maximum(0., xx2 - xx1 + 1)\n", " h = np.maximum(0., yy2 - yy1 + 1)\n", "\n", " o = w * h / area[ind[:last]]\n", "\n", " to_delete = np.concatenate(\n", " (np.nonzero(o > overlap)[0], np.array([last])))\n", " ind = np.delete(ind, to_delete)\n", "\n", " return dets[pick, :]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "scores = feats_df['tiger cat']\n", "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n", "dets = np.hstack((windows, scores[:, np.newaxis]))\n", "nms_dets = nms_detections(dets)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show top 3 NMS'd detections for 'tiger cat' in the image." ] }, { "cell_type": "code", "collapsed": false, "input": [ "imshow(im)\n", "currentAxis = plt.gca()\n", "colors = ['r', 'b', 'y']\n", "for c, det in zip(colors, nms_dets[:3]):\n", " currentAxis.add_patch(\n", " Rectangle((det[0], det[1]), det[2], det[3],\n", " fill=False, edgecolor=c, linewidth=5)\n", " )" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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YMakNXlvhtNmhaGjMrTYYTyw6zTahH7I0v0r/dMzGhRV2jh6xtrGEkNjYwx5L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SAAAg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8/f8WD0EZbSJZhn5KnA/dNPWF2m3N+/jy306bX2qDSJq8mMRhN4dXHOZHnL0yePWcQTFLvB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\nWyJZLFP68DraaoFSvYhuwtDvEaUjB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"text": [ "<matplotlib.figure.Figure at 0x1072b00d0>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove the temp directory to clean up." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import shutil\n", "shutil.rmtree('_temp')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 } ], "metadata": {} } ] }

bsd-2-clause

yidongxiainl/lammps

python/examples/pylammps/interface_usage.ipynb

17

9845

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using LAMMPS with iPython and Jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n", "2. Compile LAMMPS as a shared library and enable exceptions and PNG support\n", " ```bash\n", " cd $LAMMPS_DIR/src\n", " make mpi mode=shlib LMP_INC=\"-DLAMMPS_PNG -DLAMMPS_EXCEPTIONS\" JPG_LIB=\"-lpng\"\n", " ```\n", "\n", "3. Create a python virtualenv\n", " ```bash\n", " virtualenv testing\n", " source testing/bin/activate\n", " ```\n", "\n", "4. Inside the virtualenv install the lammps package\n", " ```\n", " (testing) cd $LAMMPS_DIR/python\n", " (testing) python install.py\n", " (testing) cd # move to your working directory\n", " ```\n", "\n", "5. Install jupyter and ipython in the virtualenv\n", " ```bash\n", " (testing) pip install ipython jupyter\n", " ```\n", "\n", "6. Run jupyter notebook\n", " ```bash\n", " (testing) jupyter notebook\n", " ```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from lammps import IPyLammps" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L = IPyLammps()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 3d Lennard-Jones melt\n", "\n", "L.units(\"lj\")\n", "L.atom_style(\"atomic\")\n", "L.atom_modify(\"map array\")\n", "\n", "L.lattice(\"fcc\", 0.8442)\n", "L.region(\"box block\", 0, 4, 0, 4, 0, 4)\n", "L.create_box(1, \"box\")\n", "L.create_atoms(1, \"box\")\n", "L.mass(1, 1.0)\n", "\n", "L.velocity(\"all create\", 1.44, 87287, \"loop geom\")\n", "\n", "L.pair_style(\"lj/cut\", 2.5)\n", "L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n", "\n", "L.neighbor(0.3, \"bin\")\n", "L.neigh_modify(\"delay 0 every 20 check no\")\n", "\n", "L.fix(\"1 all nve\")\n", "\n", "L.variable(\"fx atom fx\")\n", "\n", "L.run(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.image(zoom=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Queries about LAMMPS simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.system" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.system.natoms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.communication" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.fixes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.computes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.dumps" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.groups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with LAMMPS Variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L.variable(\"a index 2\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L.variable(\"t equal temp\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "if sys.version_info < (3, 0):\n", " # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n", " x = float(L.lmp_print('\"${a}\"'))\n", "else:\n", " # In Python 3 the print function can be redefined.\n", " # x = float(L.print('\"${a}\"')\")\n", " \n", " # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n", " x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['t'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.eval(\"v_t/2.0\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L.variable(\"b index a b c\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['b'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.eval(\"v_b\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['b'].definition" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L.variable(\"i loop 10\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['i'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.next(\"i\")\n", "L.variables['i'].value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.eval(\"ke\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing Atom data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[x for x in dir(L.atoms[0]) if not x.startswith('__')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].position" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].velocity" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].force" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.atoms[0].type" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.variables['fx'].value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing thermo data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.runs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.runs[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.runs[0].thermo" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "L.runs[0].thermo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving session to as LAMMPS input file" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L.write_script(\"in.output\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dir(L.runs[0].thermo)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

dogwood008/DeepFX

histdata_converter.ipynb

1

4445

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# histdata.comでDLした1分足のデータを任意の足に変換する\n", "\n", "# http://www.histdata.com/download-free-forex-historical-data/?/ascii/1-minute-bar-quotes/usdjpy/2017/10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from hist_data import HistData, BitcoinHistData" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "def get_new_index(old_dataframe, freq='5min'):\n", " start = hd.data()[0:1].index[0]\n", " end = hd.data()[-1:].index[0]\n", " new_index = pd.date_range(start, end, None, freq)\n", " return new_index\n", "\n", "def create_dataframe(dataarray):\n", " new_df = pd.DataFrame.from_records(dataarray,\\\n", " index=['Date'], columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])\n", " return new_df\n", "\n", "def create_new_dataarray(hist_data, new_index, i):\n", " old_dataframe = hist_data.data()\n", " start = new_index[i].to_pydatetime()\n", " end = new_index[i+1].to_pydatetime()\n", " slice = old_dataframe.loc[start:end][:-1]\n", " if len(slice) is 0:\n", " return\n", " open = slice.ix[0:1]['Open'][0]\n", " high = max(slice['High'])\n", " low = min(slice['Low'])\n", " close = slice.ix[-1:]['Close'][0]\n", " if type(hist_data) == HistData:\n", " volume = slice.sum()['Volume']\n", " elif type(hist_data) == BitcoinHistData:\n", " volume = slice.sum()['Volume_(BTC)']\n", " return np.array([start, open, high, low, close, volume])\n", "\n", "def create_new_dataframe(hist_data, freq='5min'):\n", " old_dataframe = hist_data.data()\n", " new_index = get_new_index(old_dataframe, freq)\n", " datalist = [create_new_dataarray(hist_data, new_index, i) for i in range(len(new_index) - 1)]\n", " none_removed_array = np.array([x for x in datalist if x is not None])\n", " new_df = create_dataframe(none_removed_array)\n", " return new_df\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "if False:\n", " read_filepath = 'historical_data/DAT_ASCII_USDJPY_M1_201710.csv'\n", " write_filepath = 'historical_data/DAT_ASCII_USDJPY_M1_201710_h1.csv'\n", " hd = HistData(read_filepath)\n", " new_df = create_new_dataframe(hd, freq='1h')\n", " new_df.to_csv(write_filepath, sep=';', header=['Open', 'High', 'Low', 'Close', 'Volume'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "header is included\n" ] } ], "source": [ "if True:\n", " read_filepath = 'historical_data/coincheckJPY_1-min_data_2014-10-31_to_2017-10-20.csv'\n", " write_filepath = 'historical_data/coincheckJPY_1-min_data_2014-10-31_to_2017-10-20_h1.csv'\n", " hd = BitcoinHistData(read_filepath)\n", " new_df = create_new_dataframe(hd, freq='1h')\n", " new_df.to_csv(write_filepath, sep=';', header=['Open', 'High', 'Low', 'Close', 'Volume'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

regardscitoyens/sunshine-data

exploitations/rpps/rpps.ipynb

1

17986

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('~/Downloads/exports-etalab-2017.04.22/declaration_avantage_2017_04_22_04_00.csv', delimiter=';', dtype=str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>entreprise_identifiant</th>\n", " <th>denomination_sociale</th>\n", " <th>ligne_identifiant</th>\n", " <th>ligne_rectification</th>\n", " <th>benef_categorie_code</th>\n", " <th>categorie</th>\n", " <th>benef_nom</th>\n", " <th>benef_prenom</th>\n", " <th>benef_qualite_code</th>\n", " <th>qualite</th>\n", " <th>...</th>\n", " <th>benef_etablissement_codepostal</th>\n", " <th>benef_etablissement_ville</th>\n", " <th>benef_denomination_sociale</th>\n", " <th>benef_objet_social</th>\n", " <th>ligne_type</th>\n", " <th>avant_date_signature</th>\n", " <th>avant_montant_ttc</th>\n", " <th>avant_nature</th>\n", " <th>avant_convention_lie</th>\n", " <th>semestre</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>SQZVJHFY</td>\n", " <td>PFIZER SAS</td>\n", " <td>2014103500</td>\n", " <td>N</td>\n", " <td>[PRS]</td>\n", " <td>Professionnel de santé</td>\n", " <td>LAURENT</td>\n", " <td>PHILIPPE</td>\n", " <td>[10]</td>\n", " <td>Médecin</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>[A]</td>\n", " <td>04/04/2014</td>\n", " <td>76</td>\n", " <td>REPAS</td>\n", " <td>NaN</td>\n", " <td>S1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>MBZJSQUV</td>\n", " <td>GEDEON RICHTER France - Division Santé de la F...</td>\n", " <td>20150760</td>\n", " <td>N</td>\n", " <td>[PRS]</td>\n", " <td>Professionnel de santé</td>\n", " <td>ANANI</td>\n", " <td>VALENTIN</td>\n", " <td>[10]</td>\n", " <td>Médecin</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>[A]</td>\n", " <td>06/05/2015</td>\n", " <td>29</td>\n", " <td>REPAS</td>\n", " <td>NaN</td>\n", " <td>S1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>LQQNVAGG</td>\n", " <td>DENTALINOV</td>\n", " <td>MANUEL_139614</td>\n", " <td>N</td>\n", " <td>[PRS]</td>\n", " <td>Professionnel de santé</td>\n", " <td>CHOURAQUI</td>\n", " <td>JEAN FRANCOIS</td>\n", " <td>[40]</td>\n", " <td>Chirurgien-dentiste</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>[A]</td>\n", " <td>24/02/2015</td>\n", " <td>56</td>\n", " <td>REPAS</td>\n", " <td>FORMATION JOURNEE NUMERIQUE 4 FEV 15</td>\n", " <td>S1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>QUDWAYXU</td>\n", " <td>Laboratoires Lohmann &amp; Rauscher</td>\n", " <td>S12014_CPLT102</td>\n", " <td>N</td>\n", " <td>[PRS]</td>\n", " <td>Professionnel de santé</td>\n", " <td>MATHIS</td>\n", " <td>FREDERIC</td>\n", " <td>[01]</td>\n", " <td>Préparateur en pharmacie et préparateur en pha...</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>[A]</td>\n", " <td>13/03/2014</td>\n", " <td>15</td>\n", " <td>REPAS</td>\n", " <td>NaN</td>\n", " <td>S1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>CLQGPUAY</td>\n", " <td>LABORATOIRES URGO</td>\n", " <td>16526</td>\n", " <td>N</td>\n", " <td>[PRS]</td>\n", " <td>Professionnel de santé</td>\n", " <td>PIMONT</td>\n", " <td>Marie Noelle</td>\n", " <td>[60]</td>\n", " <td>Infirmier</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>[A]</td>\n", " <td>02/04/2015</td>\n", " <td>28</td>\n", " <td>REPAS</td>\n", " <td>NaN</td>\n", " <td>S1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 36 columns</p>\n", "</div>" ], "text/plain": [ " entreprise_identifiant denomination_sociale \\\n", "0 SQZVJHFY PFIZER SAS \n", "1 MBZJSQUV GEDEON RICHTER France - Division Santé de la F... \n", "2 LQQNVAGG DENTALINOV \n", "3 QUDWAYXU Laboratoires Lohmann & Rauscher \n", "4 CLQGPUAY LABORATOIRES URGO \n", "\n", " ligne_identifiant ligne_rectification benef_categorie_code \\\n", "0 2014103500 N [PRS] \n", "1 20150760 N [PRS] \n", "2 MANUEL_139614 N [PRS] \n", "3 S12014_CPLT102 N [PRS] \n", "4 16526 N [PRS] \n", "\n", " categorie benef_nom benef_prenom benef_qualite_code \\\n", "0 Professionnel de santé LAURENT PHILIPPE [10] \n", "1 Professionnel de santé ANANI VALENTIN [10] \n", "2 Professionnel de santé CHOURAQUI JEAN FRANCOIS [40] \n", "3 Professionnel de santé MATHIS FREDERIC [01] \n", "4 Professionnel de santé PIMONT Marie Noelle [60] \n", "\n", " qualite ... \\\n", "0 Médecin ... \n", "1 Médecin ... \n", "2 Chirurgien-dentiste ... \n", "3 Préparateur en pharmacie et préparateur en pha... ... \n", "4 Infirmier ... \n", "\n", " benef_etablissement_codepostal benef_etablissement_ville \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " benef_denomination_sociale benef_objet_social ligne_type \\\n", "0 NaN NaN [A] \n", "1 NaN NaN [A] \n", "2 NaN NaN [A] \n", "3 NaN NaN [A] \n", "4 NaN NaN [A] \n", "\n", " avant_date_signature avant_montant_ttc avant_nature \\\n", "0 04/04/2014 76 REPAS \n", "1 06/05/2015 29 REPAS \n", "2 24/02/2015 56 REPAS \n", "3 13/03/2014 15 REPAS \n", "4 02/04/2015 28 REPAS \n", "\n", " avant_convention_lie semestre \n", "0 NaN S1 \n", "1 NaN S1 \n", "2 FORMATION JOURNEE NUMERIQUE 4 FEV 15 S1 \n", "3 NaN S1 \n", "4 NaN S1 \n", "\n", "[5 rows x 36 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>identifiant_type</th>\n", " <th>benef_identifiant_valeur</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>RPPS</td>\n", " <td>10002808250</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>RPPS</td>\n", " <td>10002259124</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>AUTRE</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>AUTRE</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>AUTRE</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " identifiant_type benef_identifiant_valeur\n", "0 RPPS 10002808250\n", "1 RPPS 10002259124\n", "2 AUTRE NaN\n", "3 AUTRE NaN\n", "4 AUTRE NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['identifiant_type', 'benef_identifiant_valeur']].head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.71668428788702609" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# % de RPPS dans le type d'identifiant\n", "total_count = df.shape[0]\n", "rpps_id_count = (df['identifiant_type'] == 'RPPS').sum()\n", "rpps_id_count / total_count" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.99989534944681957" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# % d'avantage sans RPPS\n", "df[df['identifiant_type'] == 'RPPS']['benef_identifiant_valeur'].notnull().sum() / rpps_id_count" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 6893204\n", "identifiant_type\n", "RPPS 0.716684\n", "AUTRE 0.271417\n", "ORDRE 0.010217\n", "SIREN 0.001605\n", "FINESS 0.000076\n", "Name: identifiant_type, dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/fmassot/Envs/RC3/lib/python3.4/site-packages/ipykernel/__main__.py:2: FutureWarning: sort is deprecated, use sort_values(inplace=True) for INPLACE sorting\n", " from ipykernel import kernelapp as app\n" ] } ], "source": [ "identifiant_type_count = df.groupby('identifiant_type').identifiant_type.count()\n", "identifiant_type_count.sort(ascending=False)\n", "print('total', total_count)\n", "print(identifiant_type_count / total_count)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.49759852052187947" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# % d'identifiant AUTRE non nul\n", "df[df['identifiant_type'] == 'AUTRE'].benef_identifiant_valeur.notnull().sum() / identifiant_type_count.AUTRE" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/fmassot/Envs/RC3/lib/python3.4/site-packages/ipykernel/__main__.py:3: FutureWarning: sort is deprecated, use sort_values(inplace=True) for INPLACE sorting\n", " app.launch_new_instance()\n" ] }, { "data": { "text/plain": [ "qualite\n", "Infirmier 0.039359\n", "Médecin 0.037760\n", "Préparateur en pharmacie et préparateur en pharmacie hospitalière 0.013506\n", "Pharmacien 0.004212\n", "Manipulateur d’électroradiologie médicale 0.003823\n", "Audioprothésiste 0.002916\n", "Opticien-lunetier 0.002871\n", "Chirurgien-dentiste 0.002149\n", "Technicien de laboratoire médical 0.001552\n", "Aide soignant 0.001439\n", "Prothésiste et orthésiste pour l’appareillage des personnes handicapées 0.000991\n", "Diététicien 0.000909\n", "Orthoptiste 0.000575\n", "Masseur-kinésithérapeute 0.000568\n", "Sage-femme 0.000415\n", "Auxiliaire de puériculture 0.000252\n", "Orthophoniste 0.000215\n", "Pédicure-podologue 0.000068\n", "Ergothérapeute 0.000054\n", "Psychomotricien 0.000023\n", "Editeur de presse 0.000006\n", "Ambulancier 0.000006\n", "Name: qualite, dtype: float64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# On regarde parmi les nuls quels sont les types de professionnel \n", "autre_with_null_id_by_qualite_count = df[(df.identifiant_type == 'AUTRE') & (df.benef_identifiant_valeur.isnull())].groupby('qualite').qualite.count()\n", "autre_with_null_id_by_qualite_count.sort(ascending=False)\n", "autre_with_null_id_by_qualite_count / total_count" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 1 }

agpl-3.0

VictorQuintana91/Thesis

notebooks/sample_scripts/test_apriori_association_rules.ipynb

1

3537

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting apyori\n", " Downloading apyori-1.1.1.tar.gz\n", "Building wheels for collected packages: apyori\n", " Running setup.py bdist_wheel for apyori ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /Users/falehalrashidi/Library/Caches/pip/wheels/fc/a6/7b/9a3b9a1eb6034c23f1f6b8ff65593ded70afa3ff1a6e0e32e2\n", "Successfully built apyori\n", "Installing collected packages: apyori\n", "Successfully installed apyori-1.1.1\n", "\u001b[33mYou are using pip version 9.0.1, however version 9.0.2 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install apyori" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting pip\n", " Downloading pip-9.0.2-py2.py3-none-any.whl (1.4MB)\n", "\u001b[K 100% |████████████████████████████████| 1.4MB 1.2MB/s ta 0:00:01\n", "\u001b[?25hInstalling collected packages: pip\n", " Found existing installation: pip 9.0.1\n", " Uninstalling pip-9.0.1:\n", " Successfully uninstalled pip-9.0.1\n", "Successfully installed pip-9.0.2\n" ] } ], "source": [ "!pip install --upgrade pip" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from apyori import apriori\n", "\n", "transactions = [\n", " ['beer', 'nuts', 'sugar'],\n", " ['beer', 'cheese', 'sugar'],\n", " ['beer', 'cheese'],\n", " ['beer', 'cheese', 'sugar'],\n", "]\n", "results = apriori(transactions)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'generator' object has no attribute 'printResults'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-16-630f701587d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprintResults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'printResults'" ] } ], "source": [ "results.printResults()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

openai/openai-python

examples/finetuning/olympics-1-collect-data.ipynb

1

33676

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Collect Wikipedia data about Olympic Games 2020\n", "\n", "The idea of this project is to create a question answering model, based on a few paragraphs of provided text. Base GPT-3 models do a good job at answering questions when the answer is contained within the paragraph, however if the answer isn't contained, the base models tend to try their best to answer anyway, often leading to confabulated answers. \n", "\n", "To create a model which answers questions only if there is sufficient context for doing so, we first create a dataset of questions and answers based on paragraphs of text. In order to train the model to answer only when the answer is present, we also add adversarial examples, where the question doesn't match the context. In those cases, we ask the model to output \"No sufficient context for answering the question\". \n", "\n", "We will perform this task in three notebooks:\n", "1. The first (this) notebook focuses on collecting recent data, which GPT-3 didn't see during it's pre-training. We picked the topic of Olympic Games 2020 (which actually took place in the summer of 2021), and downloaded 713 unique pages. We organized the dataset by individual sections, which will serve as context for asking and answering the questions.\n", "2. The [second notebook](olympics-2-create-qa.ipynb) will utilize Davinci-instruct to ask a few questions based on a Wikipedia section, as well as answer those questions, based on that section.\n", "3. The [third notebook](olympics-3-train-qa.ipynb) will utilize the dataset of context, question and answer pairs to additionally create adversarial questions and context pairs, where the question was not generated on that context. In those cases the model will be prompted to answer \"No sufficient context for answering the question\". We will also train a discriminator model, which predicts whether the question can be answered based on the context or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.1 Data extraction using the wikipedia API\n", "Extracting the data will take about half an hour, and processing will likely take about as much." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "909" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import wikipedia\n", "\n", "\n", "def filter_olympic_2020_titles(titles):\n", " \"\"\"\n", " Get the titles which are related to Olympic games hosted in 2020, given a list of titles\n", " \"\"\"\n", " titles = [title for title in titles if '2020' in title and 'olympi' in title.lower()]\n", " \n", " return titles\n", "\n", "def get_wiki_page(title):\n", " \"\"\"\n", " Get the wikipedia page given a title\n", " \"\"\"\n", " try:\n", " return wikipedia.page(title)\n", " except wikipedia.exceptions.DisambiguationError as e:\n", " return wikipedia.page(e.options[0])\n", " except wikipedia.exceptions.PageError as e:\n", " return None\n", "\n", "def recursively_find_all_pages(titles, titles_so_far=set()):\n", " \"\"\"\n", " Recursively find all the pages that are linked to the Wikipedia titles in the list\n", " \"\"\"\n", " all_pages = []\n", " \n", " titles = list(set(titles) - titles_so_far)\n", " titles = filter_olympic_2020_titles(titles)\n", " titles_so_far.update(titles)\n", " for title in titles:\n", " page = get_wiki_page(title)\n", " if page is None:\n", " continue\n", " all_pages.append(page)\n", "\n", " new_pages = recursively_find_all_pages(page.links, titles_so_far)\n", " for pg in new_pages:\n", " if pg.title not in [p.title for p in all_pages]:\n", " all_pages.append(pg)\n", " titles_so_far.update(page.links)\n", " return all_pages\n", "\n", "\n", "pages = recursively_find_all_pages([\"2020 Summer Olympics\"])\n", "len(pages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2 Filtering the Wikipedia pages and splitting them into sections by headings\n", "We remove sections unlikely to contain textual information, and ensure that each section is not longer than the token limit" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('Bermuda at the 2020 Summer Olympics',\n", " 'Equestrian',\n", " \"Bermuda entered one dressage rider into the Olympic competition by finishing in the top four, outside the group selection, of the individual FEI Olympic Rankings for Groups D and E (North, Central, and South America), marking the country's recurrence to the sport after an eight-year absence. The quota was later withdrawn, following an injury of Annabelle Collins' main horse Joyero and a failure to obtain minimum eligibility requirements (MER) aboard a new horse Chuppy Checker.\",\n", " 104)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "import re\n", "from typing import Set\n", "from transformers import GPT2TokenizerFast\n", "\n", "import numpy as np\n", "from nltk.tokenize import sent_tokenize\n", "\n", "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n", "\n", "def count_tokens(text: str) -> int:\n", " \"\"\"count the number of tokens in a string\"\"\"\n", " return len(tokenizer.encode(text))\n", "\n", "def reduce_long(\n", " long_text: str, long_text_tokens: bool = False, max_len: int = 590\n", ") -> str:\n", " \"\"\"\n", " Reduce a long text to a maximum of `max_len` tokens by potentially cutting at a sentence end\n", " \"\"\"\n", " if not long_text_tokens:\n", " long_text_tokens = count_tokens(long_text)\n", " if long_text_tokens > max_len:\n", " sentences = sent_tokenize(long_text.replace(\"\\n\", \" \"))\n", " ntokens = 0\n", " for i, sentence in enumerate(sentences):\n", " ntokens += 1 + count_tokens(sentence)\n", " if ntokens > max_len:\n", " return \". \".join(sentences[:i][:-1]) + \".\"\n", "\n", " return long_text\n", "\n", "discard_categories = ['See also', 'References', 'External links', 'Further reading', \"Footnotes\",\n", " \"Bibliography\", \"Sources\", \"Citations\", \"Literature\", \"Footnotes\", \"Notes and references\",\n", " \"Photo gallery\", \"Works cited\", \"Photos\", \"Gallery\", \"Notes\", \"References and sources\",\n", " \"References and notes\",]\n", "\n", "\n", "def extract_sections(\n", " wiki_text: str,\n", " title: str,\n", " max_len: int = 1500,\n", " discard_categories: Set[str] = discard_categories,\n", ") -> str:\n", " \"\"\"\n", " Extract the sections of a Wikipedia page, discarding the the references and other low information sections\n", " \"\"\"\n", " if len(wiki_text) == 0:\n", " return []\n", "\n", " # find all headings and the coresponding contents\n", " headings = re.findall(\"==+ .* ==+\", wiki_text)\n", " for heading in headings:\n", " wiki_text = wiki_text.replace(heading, \"==+ !! ==+\")\n", " contents = wiki_text.split(\"==+ !! ==+\")\n", " contents = [c.strip() for c in contents]\n", " assert len(headings) == len(contents) - 1\n", "\n", " cont = contents.pop(0).strip()\n", " outputs = [(title, \"Summary\", cont, count_tokens(cont)+4)]\n", " \n", " # discard the discard categories, accounting for a tree structure\n", " max_level = 100\n", " keep_group_level = max_level\n", " remove_group_level = max_level\n", " nheadings, ncontents = [], []\n", " for heading, content in zip(headings, contents):\n", " plain_heading = \" \".join(heading.split(\" \")[1:-1])\n", " num_equals = len(heading.split(\" \")[0])\n", " if num_equals <= keep_group_level:\n", " keep_group_level = max_level\n", "\n", " if num_equals > remove_group_level:\n", " if (\n", " num_equals <= keep_group_level\n", " ):\n", " continue\n", " keep_group_level = max_level\n", " if plain_heading in discard_categories:\n", " remove_group_level = num_equals\n", " keep_group_level = max_level\n", " continue\n", " nheadings.append(heading.replace(\"=\", \"\").strip())\n", " ncontents.append(content)\n", " remove_group_level = max_level\n", "\n", " # count the tokens of each section\n", " ncontent_ntokens = [\n", " count_tokens(c)\n", " + 3\n", " + count_tokens(\" \".join(h.split(\" \")[1:-1]))\n", " - (1 if len(c) == 0 else 0)\n", " for h, c in zip(nheadings, ncontents)\n", " ]\n", "\n", " # Create a tuple of (title, section_name, content, number of tokens)\n", " outputs += [(title, h, c, t) if t<max_len \n", " else (title, h, reduce_long(c, max_len), count_tokens(reduce_long(c,max_len))) \n", " for h, c, t in zip(nheadings, ncontents, ncontent_ntokens)]\n", " \n", " return outputs\n", "\n", "# Example page being processed into sections\n", "bermuda_page = get_wiki_page('Bermuda at the 2020 Summer Olympics')\n", "ber = extract_sections(bermuda_page.content, bermuda_page.title)\n", "\n", "# Example section\n", "ber[-1]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2.1 We create a dataset and filter out any sections with fewer than 40 tokens, as those are unlikely to contain enough context to ask a good question." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Token indices sequence length is longer than the specified maximum sequence length for this model (1060 > 1024). Running this sequence through the model will result in indexing errors\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>heading</th>\n", " <th>content</th>\n", " <th>tokens</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020 Summer Olympics</td>\n", " <td>Summary</td>\n", " <td>The 2020 Summer Olympics (Japanese: 2020年夏季オリン...</td>\n", " <td>713</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020 Summer Olympics</td>\n", " <td>Host city selection</td>\n", " <td>The International Olympic Committee (IOC) vote...</td>\n", " <td>126</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020 Summer Olympics</td>\n", " <td>Impact of the COVID-19 pandemic</td>\n", " <td>In January 2020, concerns were raised about th...</td>\n", " <td>369</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020 Summer Olympics</td>\n", " <td>Qualifying event cancellation and postponement</td>\n", " <td>Concerns about the pandemic began to affect qu...</td>\n", " <td>298</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020 Summer Olympics</td>\n", " <td>Effect on doping tests</td>\n", " <td>Mandatory doping tests were being severely res...</td>\n", " <td>163</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title heading \\\n", "0 2020 Summer Olympics Summary \n", "1 2020 Summer Olympics Host city selection \n", "2 2020 Summer Olympics Impact of the COVID-19 pandemic \n", "3 2020 Summer Olympics Qualifying event cancellation and postponement \n", "4 2020 Summer Olympics Effect on doping tests \n", "\n", " content tokens \n", "0 The 2020 Summer Olympics (Japanese: 2020年夏季オリン... 713 \n", "1 The International Olympic Committee (IOC) vote... 126 \n", "2 In January 2020, concerns were raised about th... 369 \n", "3 Concerns about the pandemic began to affect qu... 298 \n", "4 Mandatory doping tests were being severely res... 163 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = []\n", "for page in pages:\n", " res += extract_sections(page.content, page.title)\n", "df = pd.DataFrame(res, columns=[\"title\", \"heading\", \"content\", \"tokens\"])\n", "df = df[df.tokens>40]\n", "df = df.drop_duplicates(['title','heading'])\n", "df = df.reset_index().drop('index',axis=1) # reset index\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save the section dataset\n", "We will save the section dataset, for the [next notebook](olympics-2-create-qa.ipynb)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df.to_csv('olympics-data/olympics_sections.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.3 (Optional) Exploring the data " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Concerns and controversies at the 2020 Summer Olympics 51\n", "United States at the 2020 Summer Olympics 46\n", "Great Britain at the 2020 Summer Olympics 42\n", "Canada at the 2020 Summer Olympics 39\n", "Olympic Games 39\n", "Name: title, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.title.value_counts().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There appear to be winter and summer Olympics 2020. We chose to leave a little ambiguity and noise in the dataset, even though we were interested in only Summer Olympics 2020." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True 3567\n", "False 305\n", "Name: title, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.title.str.contains('Summer').value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 3774\n", "True 98\n", "Name: title, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.title.str.contains('Winter').value_counts()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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vV+WZxWjg7NxzaTHg/Ii4RNJdwGRJ3wL+Dpyepz8d+Lmk6cCzpB5QRMSdks4H7gLmAQfky1tmZjZAKksWEXE78M4W5Q/QojdTRLwE7NrDso4Gju7vGM3MrBz/gtvMzDpysjAzs46cLMzMrCMnCzMz68jJwszMOnKyMDOzjpwszMysIycLMzPrqGOykLS5pOF5eE9Jx0tas/rQzMysLsqcWZwCvChpQ+AQ0m3Gz6k0KjMzq5UyyWJevqHfzsBJEXEysFy1YZmZWZ2UuTfUHEmHAXsCW0hajPTUOzMzGyLKnFl8nPQ87YkR8QTpeRLfqzQqMzOrlY5nFjlBHF8Yfxi3WZiZDSllekN9VNJ9kmZLel7SHEnPD0RwZmZWD2XaLL4LfCgi7q46GDMzq6cybRZPOlGYmQ1tZc4sbpZ0HvB7UkM3ABHx26qCMjOzeimTLN4EvAhsXSgLwMnCzGyIKNMbap+BCMTMzOqrTG+o1SX9TtJT+fUbSasPRHBmZlYPZRq4zwQuAlbNr4tzmZmZDRFlksUqEXFmRMzLr7OAVSqOy8zMaqRMsngm35p8WH7tCTxTdWBmZlYfZZLFvsDHgCeAx4FdgI6N3pLGSLpG0l2S7pR0UC4/UtJMSbfm1/aFeQ6TNF3SvZK2KZRvm8umS5rU2zdpZmYLp0xvqIeAnfqw7HnAIRFxi6TlgKmSrsx1J0TE94sTS1of2A14O6lt5CpJb8nVJwMfBB4FbpJ0UUTc1YeYzMysD3pMFpK+HBHflfQj0u8quomIz7dbcEQ8TjoTISLmSLobWK3NLDsDkyPiZeBBSdOBTXPd9Ih4IMc1OU/rZGFmNkCUnmvUokL6UERcLGlCq/qIOLv0SqSxwLXAO4AvAnsDzwM3k84+Zkk6Cbg+In6R5zkduDwvYtuI+GQu3wt4d0Qc2LSO/YD9AEaNGrXJ5MmTW8Yyd+5cRowY0TbeaTNnl31r/WqD1ZbvNl4m1rpwrNVwrNVwrK2NHz9+akSMa1XX45lFRFycB1+MiF8X6yTtWnblkkYAvwEOjojnJZ0CHEU6WzkKOI7ULrJQIuI04DSAcePGRVdXV8vppkyZQk91DXtPunRhw+mTGXt0dRsvE2tdONZqONZqONbeK9PAfVjJsteRtAQpUfyycS+piHgyIl6NiPnAT1lwqWkmMKYw++q5rKdyMzMbIO3aLLYDtgdWk3RioepNpMbrtiQJOB24OyKOL5SPzu0ZAB8B7sjDFwG/knQ8qYF7PeBGQMB6ktYiJYndgP8p9/bMzKw/tOsN9RipTWEnYGqhfA7whRLL3hzYC5gm6dZcdjiwu6SNSJehZgCfBoiIOyWdT2q4ngccEBGvAkg6ELgCGAacERF3lli/mZn1k3ZtFrcBt0n6HfBC4cA9DFiq04Ij4i+ks4Jml7WZ52jg6Bbll7Wbz8zMqlWmzeKPwDKF8WWAq6oJx8zM6qhMslg6IuY2RvLwstWFZGZmdVMmWbwgaePGiKRNgH9VF5KZmdVNmSflHQz8WtJjpDaI/wA+XmVQZmZWL2XuDXWTpLcBb81F90bEK9WGZWZmdVLmSXnLAocCB0XEHcBYSTtWHpmZmdVG2Sfl/Rt4Tx6fCXyrsojMzKx2yiSLdSLiu8ArABHxIq1/P2FmZouoMsni35KWId+mXNI6wMuVRmVmZrVSpjfUEcAfgDGSfkm6jcfeVQZlZmb1UqY31JWSbgE2I11+Oiginq48MjMzq40yvaE2B16KiEuBkcDhktasOjAzM6uPMm0WpwAvStqQ9JS7+4FzKo3KzMxqpUyymBfp2as7AydHxMnActWGZWZmdVKmgXuOpMOAPYEtJC0GLFFtWGZmVidlziw+TuoqOzEiniA91vR7lUZlZma1UqY31BPA8YXxh3GbhZnZkFLmzMLMzIY4JwszM+vIycLMzDrq2GYhaT3gGGB9YOlGeUSsXWFcZmZWI2VvUX4KMA8YT2rc/kWVQZmZWb2USRbLRMTVgCLioYg4Etih2rDMzKxOyiSLl/MP8e6TdKCkjwAjOs0kaYykayTdJelOSQfl8hUlXSnpvvx3hVwuSSdKmi7pdkkbF5Y1IU9/n6QJfXyvZmbWR2WSxUHAssDngU2AvYAyB+x5wCERsT7pjrUHSFofmARcHRHrAVfncYDtgPXyaz/SpS8krUi6Tfq7gU2BIxoJxszMBkaZH+XdlAfnAvuUXXBEPA48nofnSLobWI10j6muPNnZwBTSM753Bs7J96G6XtJISaPztFdGxLMAkq4EtgXOLRuLmZktnB6ThaQfRMTBki4mPyWvKCJ2KrsSSWOBdwI3AKNyIgF4AhiVh1cDHinM9mgu66nczMwGSLszi5/nv99fmBVIGgH8Bjg4Ip6XFjy+OyJC0usSUR/Xsx/p8hWjRo1iypQpLaebO3duj3UNh2wwrz9C6rXmuMrEWheOtRqOtRqOtfd6TBYRMTX//VNfFy5pCVKi+GVE/DYXPylpdEQ8ni8zPZXLZwJjCrOvnstmsuCyVaN8Sot4TwNOAxg3blx0dXU1TwKkA3JPdQ17T7q0bX1VZuzR1W28TKx14Vir4Vir4Vh7r8cGbknTcq+klq9OC1Y6hTgduDsiji9UXcSCBvIJwIWF8k/kXlGbAbPz5aorgK0lrZAbtrfOZWZmNkDaXYbaMf89IP9tXJbakxZtGC1sTuo5NU3SrbnscOBY4HxJE4GHgI/lusuA7YHpwIvkxvSIeFbSUUCjof2bjcZuMzMbGO0uQz0EIOmDEfHOQtWhkm5hQZfXnub/C6AeqrdqMX2wIDE1150BnNFufWZmVp0yv7OQpM0LI/9dcj4zM1tElHms6kTgDEnL5/HngH0ri8jMzGqnzI/ypgIbNpJFRMyuPCozM6uVjpeTJI2SdDowOSJmS1o/N06bmdkQUabt4SxSV9VV8/g/gIMrisfMzGqoTLJYOSLOB+YDRMQ84NVKozIzs1opkyxekLQS+bcVjR/MVRqVmZnVSpneUF8k/bp6HUl/BVYBdqk0KjMzq5UyvaFukfR+4K2kH9ndGxGvVB6ZmZnVRsdkIWlp4LPAe0mXov4s6dSIeKnq4MzMrB7KXIY6B5gD/CiP/w/pPlG7VhWUmZnVS5lk8Y78aNSGayTdVVVAZmZWP2V6Q92Se0ABIOndwM3VhWRmZnVT5sxiE+A6SQ/n8TWAeyVNI90s9r8qi87MzGqhTLLYtvIozMys1npMFpLeFBHPkxq3X8cPIDIzGzranVn8ivS0vKmkLrPFBxkFsHaFcZmZWY20e1LejvnvWs11+fnaZmY2RJS5Rfk3m8YXA35RWURmZlY7ZbrOjpF0GICkpYDfAfdVGpWZmdVKmWSxL7BBThgXA9dExJGVRmVmZrXSrjfUxoXRHwI/Af4KXCtp44i4pergzMysHtr1hjquaXwWsH4uD2DLqoIyM7N6adcbavxABmJmZvXVY5uFpD3z3y+2enVasKQzJD0l6Y5C2ZGSZkq6Nb+2L9QdJmm6pHslbVMo3zaXTZc0qe9v1czM+qrdZajh+e9yfVz2WcBJpFucF50QEd8vFkhaH9gNeDuwKnCVpLfk6pOBDwKPAjdJuigifNdbM7MB1C5ZPCnpzRHxjb4sOCKulTS25OQ7A5Mj4mXgQUnTgU1z3fSIeABA0uQ8rZOFmdkAUkS0rpAuAN4DvAhcR+oJdV1E3NFyhtbLGAtcEhHvyONHAnsDz5Nuc35IRMySdBJwfUT8Ik93OnB5Xsy2EfHJXL4X8O6IOLDFuvYD9gMYNWrUJpMnT24Z09y5cxkxYkTbuKfNnF32LfarDVZbvtt4mVjrwrFWw7FWw7G2Nn78+KkRMa5VXbsG7l0AJK1FShr/DXxa0hrATRGxfU/ztnEKcBSpN9VRpJ5V+/ZhOa3iPQ04DWDcuHHR1dXVcropU6bQU13D3pMu7Y+Qem3GHl3dxsvEWheOtRqOtRqOtfc63qI8Ih7Mv9xeJr+Wzn97LSKebAxL+ilwSR6dCYwpTLp6LqNNuZmZDZB2vaEOl3SxpOuBw4AlSQ3W/9XXbrWSRhdGPwI0LmldBOwmaal8JrMecCNwE7CepLUkLUlqBL+oL+s2M7O+a3dm8QngBdItPq4DboiI0hfzJZ0LdAErS3oUOALokrQR6TLUDODTABFxp6TzSQ3X84ADIuLVvJwDgSuAYcAZEXFnL96fmZn1g3ZtFm+TtCKpraILmCRpBHAbqaH7zHYLjojdWxSf3mb6o4GjW5RfBlzWbl1mZlattm0W+Wl4l0j6A+lZ3FuQzgb2BdomCzMzW3S0u5HgTqSzis1JP5a7k9R99hDSZSkzMxsi2p1Z7E1KDl8GpkbEvwckIjMzq512bRYfHchAzMysvso8/MjMzIY4JwszM+uo3Y/yrs5/vzNw4ZiZWR21a+AeLem/gZ3y3V5VrPRjVc3Mho52yeLrwNdI92M6vqnOj1U1MxtC2vWGugC4QNLXIuKoAYzJzMxqpsxdZ4/KP9DbIhdNiYhL2s1jZmaLlo69oSQdAxxEusnfXcBBkr5ddWBmZlYfHc8sgB2AjSJiPoCks4G/A4dXGZiZmdVH2d9ZjCwML9/TRGZmtmgqc2ZxDPB3SdeQus9uAUyqNCozM6uVMg3c50qaArwrFx0aEU9UGpWZmdVKmTMLIuJx/DhTM7Mhy/eGMjOzjpwszMyso7bJQtIwSfcMVDBmZlZPbZNFRLwK3CtpjQGKx8zMaqhMA/cKwJ2SbgReaBRGxE6VRWVmZrVSJll8rfIozMys1sr8zuJPktYE1ouIqyQtCwyrPjQzM6uLMjcS/BRwAfCTXLQa8PsS850h6SlJdxTKVpR0paT78t8VcrkknShpuqTbJW1cmGdCnv4+SRN6+f7MzKwflOk6ewCwOfA8QETcB7y5xHxnAds2lU0Cro6I9YCrWXDbkO2A9fJrP+AUSMkFOAJ4N7ApcEQjwZiZ2cApkyxejoh/N0YkLU56Ul5bEXEt8GxT8c7A2Xn4bODDhfJzIrkeGClpNLANcGVEPBsRs4AreX0CMjOziimi/XFf0neB54BPAJ8DPgvcFRFf6bhwaSxwSUS8I48/FxEj87CAWRExUtIlwLER8ZdcdzVwKNAFLB0R38rlXwP+FRHfb7Gu/UhnJYwaNWqTyZMnt4xp7ty5jBgxom3c02bO7vTWKrHBat1v6Fsm1rpwrNVwrNVwrK2NHz9+akSMa1VXpjfUJGAiMA34NHAZ8LOFDSoiQlLHM5ReLO804DSAcePGRVdXV8vppkyZQk91DXtPurS/wuqVGXt0dRsvE2tdONZqONZqONbeK9Mban5+4NENpMtP90an05GePSlpdEQ8ni8zPZXLZwJjCtOtnstmks4uiuVT+rhuMzProzK9oXYA7gdOBE4Cpkvaro/ruwho9GiaAFxYKP9E7hW1GTA73+n2CmBrSSvkhu2tc5mZmQ2gMpehjgPGR8R0AEnrAJcCl7ebSdK5pLOClSU9SurVdCxwvqSJwEPAx/LklwHbA9OBF4F9ACLiWUlHATfl6b4ZEc2N5mZmVrEyyWJOI1FkDwBzOs0UEbv3ULVVi2mD1EW31XLOAM4oEaeZmVWkx2Qh6aN58GZJlwHnk9osdmXBN30zMxsC2p1ZfKgw/CTw/jz8T2CZyiIyM7Pa6TFZRMQ+AxmImZnVV8c2C0lrkX6MN7Y4vW9RbmY2dJRp4P49cDpwMTC/0mjMzKyWyiSLlyLixMojMTOz2iqTLH4o6Qjgj8DLjcKIuKWyqMzMrFbKJIsNgL2ALVlwGSryuJmZDQFlksWuwNrF25SbmdnQUuZ5FncAIyuOw8zMaqzMmcVI4B5JN9G9zcJdZ83MhogyyeKIyqMwM7NaK/M8iz8NRCBmZlZfZX7BPYcFz9xeElgCeCEi3lRlYGZmVh9lziyWawzn52bvDGxWZVBmZlYvZXpDvSaS3wPbVBOOmZnVUZnLUB8tjC4GjANeqiwiMzOrnTK9oYrPtZgHzCBdijIzsyGiTJuFn2thZjbEtXus6tfbzBcRcVQF8ZiZWQ21O7N4oUXZcGAisBLgZGFmNkS0e6zqcY1hScsBBwH7AJOB43qaz8zMFj1t2ywkrQh8EdgDOBvYOCJmDURgZmZWHz3+zkLS94CbgDnABhFxZH8lCkkzJE2TdKukm3PZipKulHRf/rtCLpekEyVNl3S7pI37IwYzMyuv3Y/yDgFWBb4KPCbp+fyaI+n5flj3+IjYKCLG5fFJwNURsR5wdR4H2A5YL7/2A07ph3WbmVkvtGuz6NWvu/vBzkBXHj4bmAIcmsvPiYgArpc0UtLoiHh8gOMzMxuylI7BA7xS6UFgFukGhT+JiNMkPRcRI3O9gFkRMVLSJcCxEfGXXHc1cGhE3Ny0zP1IZx6MGjVqk8mTJ7dc99y5cxkxYkTb+KbNnL0wb6/PNlht+W7jZWKtC8daDcdaDcfa2vjx46cWrvZ0U+YX3FV4b0TMlPRm4EpJ9xQrIyIk9SqLRcRpwGkA48aNi66urpbTTZkyhZ7qGvaedGlvVt1vZuzR1W28TKx14Vir4Vir4Vh7b6AvNQEQETPz36eA3wGbAk9KGg2Q/z6VJ58JjCnMvnouMzOzATLgyULS8Py7DSQNB7YmPef7ImBCnmwCcGEevgj4RO4VtRkw2+0VZmYDazAuQ40CfpeaJVgc+FVE/CE/4/t8SROBh4CP5ekvA7YHpgMvkn4YaGZmA2jAk0VEPABs2KL8GWCrFuUBHDAAoQ26sU1tJYdsMG/A2k9mHLvDgKzHzN6YBqXNwszM3licLMzMrCMnCzMz68jJwszMOnKyMDOzjpwszMysIycLMzPryMnCzMw6crIwM7OOnCzMzKwjJwszM+vIycLMzDpysjAzs46cLMzMrCMnCzMz68jJwszMOnKyMDOzjgbjsapWQ81P6eutvj7Vz0/oM3tj8JmFmZl15GRhZmYdOVmYmVlHThZmZtaRk4WZmXXkZGFmZh29YbrOStoW+CEwDPhZRBw7yCFZP1jYLrt90dduvv3F3YXtjegNcWYhaRhwMrAdsD6wu6T1BzcqM7Oh441yZrEpMD0iHgCQNBnYGbhrUKMy64PenE0N9llQb7SL1WdTb3yKiMGOoSNJuwDbRsQn8/hewLsj4sDCNPsB++XRtwL39rC4lYGnKwy3PznWajjWajjWagxkrGtGxCqtKt4oZxYdRcRpwGmdppN0c0SMG4CQFppjrYZjrYZjrUZdYn1DtFkAM4ExhfHVc5mZmQ2AN0qyuAlYT9JakpYEdgMuGuSYzMyGjDfEZaiImCfpQOAKUtfZMyLizj4uruOlqhpxrNVwrNVwrNWoRaxviAZuMzMbXG+Uy1BmZjaInCzMzKyjIZMsJG0r6V5J0yVNqkE8YyRdI+kuSXdKOiiXryjpSkn35b8r5HJJOjHHf7ukjQch5mGS/i7pkjy+lqQbckzn5c4HSFoqj0/P9WMHOM6Rki6QdI+kuyW9p67bVdIX8v//DknnSlq6LttV0hmSnpJ0R6Gs19tR0oQ8/X2SJgxgrN/L+8Dtkn4naWSh7rAc672StimUV36caBVroe4QSSFp5Tw+qNu1m4hY5F+kRvH7gbWBJYHbgPUHOabRwMZ5eDngH6RbmXwXmJTLJwHfycPbA5cDAjYDbhiEmL8I/Aq4JI+fD+yWh08FPpOHPwucmod3A84b4DjPBj6Zh5cERtZxuwKrAQ8CyxS259512a7AFsDGwB2Fsl5tR2BF4IH8d4U8vMIAxbo1sHge/k4h1vXzMWApYK18bBg2UMeJVrHm8jGkTjwPASvXYbt2i6/qD0QdXsB7gCsK44cBhw12XE0xXgh8kPTL89G5bDRwbx7+CbB7YfrXphug+FYHrga2BC7JO+/ThQ/ja9s47/DvycOL5+k0QHEunw/Aaiqv3XYlJYtH8gd+8bxdt6nTdgXGNh2Ae7Udgd2BnxTKu01XZaxNdR8BfpmHu33+G9t1II8TrWIFLgA2BGawIFkM+nZtvIbKZajGh7Lh0VxWC/lywjuBG4BREfF4rnoCGJWHB/s9/AD4MjA/j68EPBcR81rE81qsuX52nn4grAX8EzgzXzL7maTh1HC7RsRM4PvAw8DjpO00lXpu14bebsfB3m8b9iV9Q4caxippZ2BmRNzWVFWbWIdKsqgtSSOA3wAHR8TzxbpIXxkGvW+zpB2BpyJi6mDHUsLipFP8UyLincALpMslr6nRdl2BdEPMtYBVgeHAtoMaVC/UZTt2IukrwDzgl4MdSyuSlgUOB74+2LG0M1SSRS1vFyJpCVKi+GVE/DYXPylpdK4fDTyVywfzPWwO7CRpBjCZdCnqh8BISY0fdhbjeS3WXL888MwAxfoo8GhE3JDHLyAljzpu1w8AD0bEPyPiFeC3pG1dx+3a0NvtOKifPUl7AzsCe+TkRpuYBivWdUhfGG7Ln7HVgVsk/UedYh0qyaJ2twuRJOB04O6IOL5QdRHQ6NkwgdSW0Sj/RO4dsRkwu3A5oFIRcVhErB4RY0nb7v8iYg/gGmCXHmJtvIdd8vQD8g00Ip4AHpH01ly0FelW9rXbrqTLT5tJWjbvD41Ya7ddC3q7Ha8Atpa0Qj6T2jqXVU7pgWlfBnaKiBeb3sNuuXfZWsB6wI0M0nEiIqZFxJsjYmz+jD1K6vzyBHXarlU2iNTpRepV8A9Sb4ev1CCe95JO4W8Hbs2v7UnXoK8G7gOuAlbM04v0AKj7gWnAuEGKu4sFvaHWJn3IpgO/BpbK5Uvn8em5fu0BjnEj4Oa8bX9P6i1Sy+0KfAO4B7gD+Dmph04ttitwLqkt5RXSAWxiX7Yjqb1gen7tM4CxTidd1298vk4tTP+VHOu9wHaF8sqPE61ibaqfwYIG7kHdrsWXb/dhZmYdDZXLUGZmthCcLMzMrCMnCzMz68jJwszMOnKyMDOzjpws7A0v36XzuML4lyQd2U/LPkvSLp2nXOj17Kp0h9xrmsrHSvqfEvPvLemk6iK0oc7JwhYFLwMfbdzWuS4Kv8IuYyLwqYgY31Q+FuiYLMyq5mRhi4J5pOcUf6G5ovnMQNLc/LdL0p8kXSjpAUnHStpD0o2Spklap7CYD0i6WdI/8n2yGs/2+J6km/JzBj5dWO6fJV1E+jV2czy75+XfIek7uezrpB9pni7pe02zHAu8T9KtSs++WFrSmXkZf5fUnFyQtIOkv0laWdLWefgWSb/O9yJD0gxJ38jl0yS9LZe/P6/r1rz85cr/G2xR5mRhi4qTgT0kLd+LeTYE9gf+E9gLeEtEbAr8DPhcYbqxwKbADsCpkpYmnQnMjoh3Ae8CPpVvHQHpXlQHRcRbiiuTtCrpuQpbkn5l/i5JH46Ib5J+cb5HRPxvU4yTgD9HxEYRcQJwAOkefhuQblN9do6nsY6P5Hm2z0VfBT4QERvndXyxsOync/kpwJdy2ZeAAyJiI+B9wL/ab0IbKpwsbJEQ6Y695wCf78VsN0XE4xHxMul2Cn/M5dNICaLh/IiYHxH3kR4y8zbSvXg+IelW0q3lVyLdYwjgxoh4sMX63gVMiXTjwMZdULfoRbyQzkB+ARAR95AelNNISlsChwI7RMQs0sNy1gf+muOcAKxZWFbj5pVTC+/3r8Dxkj4PjIwFt0q3Ic7JwhYlPyB94x9eKJtH3s8lLUZ6AlrDy4Xh+YXx+aRbnTc03xMnSPfs+Vz+xr9RRKwVEY1k88LCvImFcD/pqYuN5CHgykKM60fExML0jff7Kvn9RsSxwCeBZUhJ5m0DE7rVnZOFLTIi4lnSI0mLB8QZwCZ5eCdgiT4seldJi+V2jLVJN5+7AviM0m3mkfQWpYcstXMj8P7cljCMdBnpTx3mmUNKAA1/BvZorBNYI8cD6Szj/wHnSHo7cD2wuaR18/TD8zw9krROpLugfod0F1YnCwOcLGzRcxxQ7BX1U9IB+jbSYzP78q3/YdKB/nJg/4h4idSucRfpuQN3kB5r2bb3U6RbS08i3YL8NmBqRFzYbh7SnXNflXSbpC8APwYWkzQNOA/YO19Ga6zjHlIy+TXwJtIzvc+VdDvwNzof/A/Oje+3k+6KenmH6W2I8F1nzcysI59ZmJlZR04WZmbWkZOFmZl15GRhZmYdOVmYmVlHThZmZtaRk4WZmXX0/wFZfduL32Si2AAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "\n", "df = pd.read_csv('olympics-data/olympics_sections.csv')\n", "df[['tokens']].hist()\n", "# add axis descriptions and title\n", "plt.xlabel('Number of tokens')\n", "plt.ylabel('Number of Wikipedia sections')\n", "plt.title('Distribution of number of tokens in Wikipedia sections')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the majority of section are fairly short (less than 500 tokens)." ] } ], "metadata": { "interpreter": { "hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8" }, "kernelspec": { "display_name": "Python 3.7.3 64-bit ('base': conda)", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }

mit

tensorflow/docs-l10n

site/ja/tfx/tutorials/serving/rest_simple.ipynb

1

22579

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "MhoQ0WE77laV" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "_ckMIh7O7s6D" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "jYysdyb-CaWM" }, "source": [ "# TensorFlow Serving を使用して TensorFlow モデルをトレーニングしてサービングする" ] }, { "cell_type": "markdown", "metadata": { "id": "E6FwTNtl3S4v" }, "source": [ "**注意: このノートブックは、Google Colab でのみ実行するように設計されています**。これは root アクセスのあるシステムにパッケージをインストールします。ローカルの Jupyter ノートブックで実行する場合は、注意して続行してください。\n", "\n", "注：この例は、Jupyter スタイルのノートブックで今すぐ実行できます。セットアップは必要ありません。「Google Colabで実行」をクリックするだけです\n", "\n", "<div class=\"devsite-table-wrapper\"><table class=\"tfo-notebook-buttons\" align=\"left\">\n", "<tr>\n", "<td><a target=\"_blank\" href=\"https://www.tensorflow.org/tfx/tutorials/serving/rest_simple\"> <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org で表示</a></td>\n", "<td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/tfx/tutorials/serving/rest_simple.ipynb\"> <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab で実行</a></td>\n", "<td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/tfx/tutorials/serving/rest_simple.ipynb\"> <img width=\"32px\" src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub でソースを表示</a></td>\n", "<td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/tfx/tutorials/serving/rest_simple.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n", "</tr>\n", "</table></div>" ] }, { "cell_type": "markdown", "metadata": { "id": "FbVhjPpzn6BM" }, "source": [ "このガイドでは、ニューラルネットワークモデルをトレーニングし、スニーカーやシャツなどの衣類の[画像](https://github.com/zalandoresearch/fashion-mnist)を分類し、トレーニングしたモデルを保存してから、[TensorFlow Serving](https://www.tensorflow.org/serving/) でサービングします。ここでは TensorFlow でのモデリングとトレーニングではなく、TensorFlow Serving に焦点が当てられているため、モデリングとトレーニングに焦点を当てた完全な例については、[基本的な分類の例](https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/keras/basic_classification.ipynb)を参照してください。\n", "\n", "このガイドでは、高レベル API である [tf.keras](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/keras.ipynb) を使用して、TensorFlow でモデルを構築およびトレーニングします。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FWkuJabJSKGB" }, "outputs": [], "source": [ "import sys\n", "\n", "# Confirm that we're using Python 3\n", "assert sys.version_info.major == 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dzLKpmZICaWN" }, "outputs": [], "source": [ "# TensorFlow and tf.keras\n", "print(\"Installing dependencies for Colab environment\")\n", "!pip install -Uq grpcio==1.26.0\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "# Helper libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import subprocess\n", "\n", "print('TensorFlow version: {}'.format(tf.__version__))" ] }, { "cell_type": "markdown", "metadata": { "id": "5jAk1ZXqTJqN" }, "source": [ "## モデルの作成" ] }, { "cell_type": "markdown", "metadata": { "id": "yR0EdgrLCaWR" }, "source": [ "### Fashion-MNIST データセットをインポートする\n", "\n", "このガイドでは、[Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) データセットを使用します。このデータセットには、10 カテゴリの 70,000 のグレースケール画像が含まれています。次のように、画像は低解像度（28 x 28ピクセル）で個々の衣料品を示しています。\n", "\n", "<table>\n", " <tr><td> <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\" alt=\"Fashion MNIST sprite\"> </td></tr>\n", " <tr><td align=\"center\"> <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST サンプル</a> (作成者： Zalando、MIT ライセンス).<br> </td></tr>\n", "</table>\n", "\n", "Fashion MNIST は、画像処理のための機械学習での \"Hello, World\" としてしばしば登場する MNIST データセットの代替として開発されたデータセットです。データをインポートして読み込むだけで、TensorFlow から直接 Fashion MNIST にアクセスできます。\n", "\n", "注意：これらは実際には画像ですが、バイナリ画像オブジェクトではなく、NumPy 配列として読み込まれます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7MqDQO0KCaWS" }, "outputs": [], "source": [ "fashion_mnist = keras.datasets.fashion_mnist\n", "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n", "\n", "# scale the values to 0.0 to 1.0\n", "train_images = train_images / 255.0\n", "test_images = test_images / 255.0\n", "\n", "# reshape for feeding into the model\n", "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)\n", "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)\n", "\n", "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n", " 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n", "\n", "print('\\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))\n", "print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))" ] }, { "cell_type": "markdown", "metadata": { "id": "PDu7OX8Nf5PY" }, "source": [ "### モデルをトレーニングして評価する\n", "\n", "ここではモデリングには焦点を当てていないので、可能な限り単純な CNN を使用します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LTNN0ANGgA36" }, "outputs": [], "source": [ "model = keras.Sequential([\n", " keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3, \n", " strides=2, activation='relu', name='Conv1'),\n", " keras.layers.Flatten(),\n", " keras.layers.Dense(10, name='Dense')\n", "])\n", "model.summary()\n", "\n", "testing = False\n", "epochs = 5\n", "\n", "model.compile(optimizer='adam', \n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=[keras.metrics.SparseCategoricalAccuracy()])\n", "model.fit(train_images, train_labels, epochs=epochs)\n", "\n", "test_loss, test_acc = model.evaluate(test_images, test_labels)\n", "print('\\nTest accuracy: {}'.format(test_acc))" ] }, { "cell_type": "markdown", "metadata": { "id": "AwGPItyphqXT" }, "source": [ "## モデルを保存する\n", "\n", "トレーニング済みモデルを TensorFlow Serving に読み込むには、最初にモデルを [SavedModel](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/saved_model) 形式で保存する必要があります。これにより、明確に定義されたディレクトリ階層に protobuf ファイルが作成され、バージョン番号が含まれます。[TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving) を使用すると、推論リクエストを行うときに使用するモデルのバージョン、つまり「サービング可能」を選択できます。各バージョンは、指定されたパスの下の異なるサブディレクトリにエクスポートされます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0w5Rq8SsgWE6" }, "outputs": [], "source": [ "# Fetch the Keras session and save the model\n", "# The signature definition is defined by the input and output tensors,\n", "# and stored with the default serving key\n", "import tempfile\n", "\n", "MODEL_DIR = tempfile.gettempdir()\n", "version = 1\n", "export_path = os.path.join(MODEL_DIR, str(version))\n", "print('export_path = {}\\n'.format(export_path))\n", "\n", "tf.keras.models.save_model(\n", " model,\n", " export_path,\n", " overwrite=True,\n", " include_optimizer=True,\n", " save_format=None,\n", " signatures=None,\n", " options=None\n", ")\n", "\n", "print('\\nSaved model:')\n", "!ls -l {export_path}" ] }, { "cell_type": "markdown", "metadata": { "id": "FM7B_RuDYoIj" }, "source": [ "## 保存したモデルを調べる\n", "\n", "コマンドラインユーティリティ`saved_model_cli`を使用して、SavedModel の [MetaGraphDefs](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/MetaGraphDef)（モデル）と [SignatureDefs](../signature_defs)（呼び出すことができるメソッド）を確認します。TensorFlow ガイドの [SavedModel CLI](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/saved_model.md#cli-to-inspect-and-execute-savedmodel) に関する説明を参照してください。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LU4GDF_aYtfQ" }, "outputs": [], "source": [ "!saved_model_cli show --dir {export_path} --all" ] }, { "cell_type": "markdown", "metadata": { "id": "lSPWuegUb7Eo" }, "source": [ "これはモデルについて多く情報を与えてくれます。ここでは、モデルをトレーニングしたばかりなので、入力と出力はすでにわかっていますが、知らない場合は、これは重要な情報になります。たとえば、これがグレースケール画像データであることなど、完全な情報は提供されませんが、有用な情報を提供してくれます。" ] }, { "cell_type": "markdown", "metadata": { "id": "DBgsyhytS6KD" }, "source": [ "## TensorFlow Serving を使用してモデルをサービングする\n", "\n", "**注意: これを Google Colab で実行していない場合は、**以下のセルは root アクセスのあるシステムにパッケージをインストールします。ローカルの Jupyter ノートブックで実行する場合は、注意して続行してください。\n", "\n", "### TensorFlow Serving ディストリビューション URI をパッケージソースとして追加する\n", "\n", "この Colab は Debian 環境で実行されるため、[Aptitude](https://wiki.debian.org/Aptitude) を使用して TensorFlow Serving をインストールする準備をします。`tensorflow-model-server`パッケージを Aptitude が認識しているパッケージのリストに追加します。root として実行していることに注意してください。\n", "\n", "注意：この例では TensorFlow Serving をネイティブで実行していますが、[Docker コンテナで実行することもできます](https://www.tensorflow.org/tfx/serving/docker)。これは TensorFlow Serving を使い始めるのに最も簡単な方法の 1 つです。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "v2hF_ChoOrEd" }, "outputs": [], "source": [ "import sys\n", "# We need sudo prefix if not on a Google Colab.\n", "if 'google.colab' not in sys.modules:\n", " SUDO_IF_NEEDED = 'sudo'\n", "else:\n", " SUDO_IF_NEEDED = ''" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EWg9X2QHlbGS" }, "outputs": [], "source": [ "# This is the same as you would do from your command line, but without the [arch=amd64], and no sudo\n", "# You would instead do:\n", "# echo \"deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \\\n", "# curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -\n", "\n", "!echo \"deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\" | {SUDO_IF_NEEDED} tee /etc/apt/sources.list.d/tensorflow-serving.list && \\\n", "curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | {SUDO_IF_NEEDED} apt-key add -\n", "!{SUDO_IF_NEEDED} apt update" ] }, { "cell_type": "markdown", "metadata": { "id": "W1ZVp_VOU7Wu" }, "source": [ "### TensorFlow Serving のインストール\n", "\n", "必要なのは以下のコマンドライン 1 行だけです。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ygwa9AgRloYy" }, "outputs": [], "source": [ "!{SUDO_IF_NEEDED} apt-get install tensorflow-model-server" ] }, { "cell_type": "markdown", "metadata": { "id": "k5NrYdQeVm52" }, "source": [ "### TensorFlow Serving の実行をはじめる\n", "\n", "これから TensorFlow Serving の実行を開始し、モデルを読み込み始めます。読み込み後、REST を使用して推論リクエストの作成を開始できます。以下の重要なパラメータがあります。\n", "\n", "- `rest_api_port`: REST リクエストで使用するポート。\n", "- `model_name`: これを REST リクエストの URL で使用します。任意の名前を使用します。\n", "- `model_base_path`: これは、モデルを保存したディレクトリへのパスです。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aUgp3vUdU5GS" }, "outputs": [], "source": [ "os.environ[\"MODEL_DIR\"] = MODEL_DIR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kJDhHNJVnaLN" }, "outputs": [], "source": [ "%%bash --bg \n", "nohup tensorflow_model_server \\\n", " --rest_api_port=8501 \\\n", " --model_name=fashion_model \\\n", " --model_base_path=\"${MODEL_DIR}\" >server.log 2>&1\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IxbeiOCUUs2z" }, "outputs": [], "source": [ "!tail server.log" ] }, { "cell_type": "markdown", "metadata": { "id": "vwg1JKaGXWAg" }, "source": [ "## TensorFlow Serving でモデルにリクエストを出す\n", "\n", "まず、テストデータからランダムな例を見てみましょう。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Luqm_Jyff9iR" }, "outputs": [], "source": [ "def show(idx, title):\n", " plt.figure()\n", " plt.imshow(test_images[idx].reshape(28,28))\n", " plt.axis('off')\n", " plt.title('\\n\\n{}'.format(title), fontdict={'size': 16})\n", "\n", "import random\n", "rando = random.randint(0,len(test_images)-1)\n", "show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "TKnEHeTrbh3L" }, "source": [ "興味深いことに気が付きましたか？次に、3 つの推論リクエストのバッチ用の JSON オブジェクトを作成し、モデルがどの程度認識しているかを確認しましょう。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2dsD7KQG1m-R" }, "outputs": [], "source": [ "import json\n", "data = json.dumps({\"signature_name\": \"serving_default\", \"instances\": test_images[0:3].tolist()})\n", "print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))" ] }, { "cell_type": "markdown", "metadata": { "id": "ReQd4QESIwXN" }, "source": [ "### REST リクエストを行う" ] }, { "cell_type": "markdown", "metadata": { "id": "iT3J-lHrhOYQ" }, "source": [ "#### サーバブルの最新バージョン\n", "\n", "予測リクエストを POST としてサーバーの REST エンドポイントに送信し、3 つの例を渡します。特定のバージョンを指定せずに、サーバーにサーバブルの最新バージョンを提供するように依頼します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vGvFyuIzW6n6" }, "outputs": [], "source": [ "# docs_infra: no_execute\n", "!pip install -q requests\n", "\n", "import requests\n", "headers = {\"content-type\": \"application/json\"}\n", "json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)\n", "predictions = json.loads(json_response.text)['predictions']\n", "\n", "show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(\n", " class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0]))" ] }, { "cell_type": "markdown", "metadata": { "id": "YJH8LtM4XELp" }, "source": [ "#### 特定バージョンのサーバブル\n", "\n", "次に、特定のバージョンのサーバブルのを指定します。1 つしかないので、バージョン 1 を選択しましょう。また、3 つの結果すべてを確認します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zRftRxeR1tZx" }, "outputs": [], "source": [ "# docs_infra: no_execute\n", "headers = {\"content-type\": \"application/json\"}\n", "json_response = requests.post('http://localhost:8501/v1/models/fashion_model/versions/1:predict', data=data, headers=headers)\n", "predictions = json.loads(json_response.text)['predictions']\n", "\n", "for i in range(0,3):\n", " show(i, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(\n", " class_names[np.argmax(predictions[i])], np.argmax(predictions[i]), class_names[test_labels[i]], test_labels[i]))" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "rest_simple.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

GoogleCloudPlatform/tensorflow-without-a-phd

tensorflow-rnn-tutorial/old-school-tensorflow/run-on-cloud-ml-engine.ipynb

5

5328

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Covert notebook to raw python\n", "To run on ML-Engine, we strip all cells tagged \"display\" to remove the dependency on matplotlib.\n", "The remote server/cluster does not have a display anyway, just logs.\n", "(To view/edit tags on notebook cells: View>Cell Toolbar>Tags)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Convert notebook to raw python format and remove all cells tagged \"display\"\n", "NOTEBOOK='02_RNN_generator_temperatures_solution.ipynb'\n", "jupyter nbconvert tutorial/${NOTEBOOK} \\\n", " --to python --TagRemovePreprocessor.remove_cell_tags={\\\"display\\\"} \\\n", " --output task.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*** Windows users, please copy-paste this command into your command prompt: ***\n", "```bash\n", "jupyter nbconvert \"tutorial/02_RNN_generator_temperatures_solution.ipynb\" --to python --output task.py --TagRemovePreprocessor.remove_cell_tags={\\\"display\\\"} \n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## To run on ML-Engine\n", "If you are using your own GCP account you have to first:\n", "1. Create a Google Cloud Platform project\n", "1. Enable billing\n", "1. Create a Google Cloud Storage bucket (put in in region us-central1)\n", "1. Enable the necessary APIs and request the necessary quotas\n", "\n", "If you are using a lab account through Qwiklabs (Available soon):\n", "1. Please [register your email address here](https://docs.google.com/forms/d/e/1FAIpQLScDruivAynhrL9XMyEozLZRRCuMLg-X0BFC3ct0VqHs_sW1cg/viewform?usp=sf_link) so that we can white-list you on Qwiklabs.\n", "1. Go to Qwiklabs for the last part of the workshop. Qwiklabs will provision a free lab account on Google Cloud Platform with a GPU quota for you:\n", "[https://events.qwiklabs.com/classrooms/&lt;available soon&gt;](https://events.qwiklabs.com/classrooms/XXXX)\n", "1. Create a Google Cloud Storage bucket (put in in region us-central1)\n", "\n", "And fill in the info in the variables below.\n", "You can try running on a GPU by using --scale-tier=BASIC_GPU or a CPU using --scale-tier=BASIC" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "BUCKET='ml1-demo-martin'\n", "PROJECT='cloudml-demo-martin'\n", "\n", "REGION='us-central1'\n", "JOBNAME=sines_$(date -u +%y%m%d_%H%M%S)\n", "OUTDIR=\"gs://${BUCKET}/sinejobs/${JOBNAME}\"\n", "gcloud ml-engine jobs submit training $JOBNAME \\\n", " --region=$REGION \\\n", " --module-name=tutorial.task \\\n", " --package-path=tutorial \\\n", " --job-dir=$OUTDIR \\\n", " --scale-tier=BASIC_GPU \\\n", " --runtime-version=1.6 \\\n", " -- \\\n", " --data-dir=\"gs://good-temperatures-public\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*** Windows users, please copy-paste this command into your command prompt (replace &lt;PROJECT&gt; and &lt;BUCKET&gt; with your own values and adjust the job name job001 if needed): ***\n", "```bash\n", "gcloud ml-engine jobs submit training job001 --region=\"us-central1\" --project=<PROJECT> --module-name=\"tutorial.task\" --package-path=\"tutorial\" --job-dir=\"gs://<BUCKET>/sinejobs/job001\" --scale-tier=BASIC_GPU --runtime-version=1.6 -- --data-dir=\"gs://good-temperatures-public\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## To test-run locally as if the code was running on ML-Engine\n", "Warning: this will use the \"python\" command to run (usually mapped to python 2 on mac, same as ML-Engine)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gcloud ml-engine local train \\\n", " --module-name=tutorial.task \\\n", " --package-path=tutorial \\\n", " --job-dir=\"checkpoints\" \\\n", " -- \\\n", " --data-dir=\"gs://good-temperatures-public\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2018 Google LLC\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] } ], "metadata": { "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

seanwood/gcc-nmf

notebooks/lowLatencySpeechEnhancement.ipynb

1

4935083

mit

tensorflow/docs

site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb

1

42796

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2019 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "# Distributed Training with DTensors\n" ] }, { "cell_type": "markdown", "metadata": { "id": "r6P32iYYV27b" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/distribute/dtensor_ml_tutorial\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/distribute/dtensor_ml_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "kiF4jjX4O1mF" }, "source": [ "## Overview\n", "\n", "DTensor provides a way for you to distribute the training of your model across devices to improve efficiency, reliability and scalability. For more details on DTensor concepts, see [The DTensor Programming Guide](https://www.tensorflow.org/guide/dtensor_overview).\n", "\n", "In this tutorial, you will train a Sentiment Analysis model with DTensor. Three distributed training schemes are demonstrated with this example:\n", "\n", " - Data Parallel training, where the training samples are sharded (partitioned) to devices.\n", " - Model Parallel training, where the model variables are sharded to devices.\n", " - Spatial Parallel training, where the features of input data are sharded to devices. (Also known as [Spatial Partitioning](https://cloud.google.com/blog/products/ai-machine-learning/train-ml-models-on-large-images-and-3d-volumes-with-spatial-partitioning-on-cloud-tpus))\n", "\n", "The training portion of this tutorial is inspired [A Kaggle guide on Sentiment Analysis](https://www.kaggle.com/code/anasofiauzsoy/yelp-review-sentiment-analysis-tensorflow-tfds/notebook) notebook. To learn about the complete training and evaluation workflow (without DTensor), refer to that notebook.\n", "\n", "This tutorial will walk through the following steps:\n", "\n", "- First start with some data cleaning to obtain a `tf.data.Dataset` of tokenized sentences and their polarity.\n", "\n", "- Next build an MLP model with custom Dense and BatchNorm layers. Use a `tf.Module` to track the inference variables. The model constructor takes additional `Layout` arguments to control the sharding of variables.\n", "\n", "- For training, you will first use data parallel training together with `tf.experimental.dtensor`'s checkpoint feature. Then continue with Model Parallel Training and Spatial Parallel Training.\n", "\n", "- The final section briefly describes the interaction between `tf.saved_model` and `tf.experimental.dtensor` as of TensorFlow 2.9.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "YD80veeg7QtW" }, "source": [ "## Setup\n", "\n", "DTensor is part of TensorFlow 2.9.0 release." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-RKXLJN-7Yyb" }, "outputs": [], "source": [ "!pip install --quiet --upgrade --pre tensorflow tensorflow-datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "tcxP4_Zu7ciQ" }, "source": [ "Next, import `tensorflow` and `tensorflow.experimental.dtensor`. Then configure TensorFlow to use 8 virtual CPUs.\n", "\n", "Even though this example uses CPUs, DTensor works the same way on CPU, GPU or TPU devices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dXcB26oP7dUd" }, "outputs": [], "source": [ "import tempfile\n", "import numpy as np\n", "import tensorflow_datasets as tfds\n", "\n", "import tensorflow as tf\n", "\n", "from tensorflow.experimental import dtensor\n", "print('TensorFlow version:', tf.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oHtO6MJLUXlz" }, "outputs": [], "source": [ "def configure_virtual_cpus(ncpu):\n", " phy_devices = tf.config.list_physical_devices('CPU')\n", " tf.config.set_logical_device_configuration(phy_devices[0], [\n", " tf.config.LogicalDeviceConfiguration(),\n", " ] * ncpu)\n", "\n", "configure_virtual_cpus(8)\n", "DEVICES = [f'CPU:{i}' for i in range(8)]\n", "\n", "tf.config.list_logical_devices('CPU')" ] }, { "cell_type": "markdown", "metadata": { "id": "omYd4jbF7j_I" }, "source": [ "## Download the dataset\n", "\n", "Download the IMDB reviews data set to train the sentiment analysis model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fW4w4QlFVHhx" }, "outputs": [], "source": [ "train_data = tfds.load('imdb_reviews', split='train', shuffle_files=True, batch_size=64)\n", "train_data" ] }, { "cell_type": "markdown", "metadata": { "id": "ki3mpfi4aZH8" }, "source": [ "## Prepare the data\n", "\n", "First tokenize the text. Here use an extension of one-hot encoding, the `'tf_idf'` mode of `tf.keras.layers.TextVectorization`.\n", "\n", "- For the sake of speed, limit the number of tokens to 1200.\n", "- To keep the `tf.Module` simple, run `TextVectorization` as a preprocessing step before the training.\n", "\n", "The final result of the data cleaning section is a `Dataset` with the tokenized text as `x` and label as `y`.\n", "\n", "**Note**: Running `TextVectorization` as a preprocessing step is **neither a usual practice nor a recommended one** as doing so assumes the training data fits into the client memory, which is not always the case.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zNpxjku_57Lg" }, "outputs": [], "source": [ "text_vectorization = tf.keras.layers.TextVectorization(output_mode='tf_idf', max_tokens=1200, output_sequence_length=None)\n", "text_vectorization.adapt(data=train_data.map(lambda x: x['text']))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "q16bjngoVwQp" }, "outputs": [], "source": [ "def vectorize(features):\n", " return text_vectorization(features['text']), features['label']\n", "\n", "train_data_vec = train_data.map(vectorize)\n", "train_data_vec" ] }, { "cell_type": "markdown", "metadata": { "id": "atTqL9kE5wz4" }, "source": [ "## Build a neural network with DTensor\n", "\n", "Now build a Multi-Layer Perceptron (MLP) network with `DTensor`. The network will use fully connected Dense and BatchNorm layers.\n", "\n", "`DTensor` expands TensorFlow through single-program multi-data (SPMD) expansion of regular TensorFlow Ops according to the `dtensor.Layout` attributes of their input `Tensor` and variables.\n", "\n", "Variables of `DTensor` aware layers are `dtensor.DVariable`, and the constructors of `DTensor` aware layer objects take additional `Layout` inputs in addition to the usual layer parameters.\n", "\n", "Note: As of TensorFlow 2.9, Keras layers such as `tf.keras.layer.Dense`, and `tf.keras.layer.BatchNormalization` accepts `dtensor.Layout` arguments. Refer to the [DTensor Keras Integration Tutorial](/tutorials/distribute/dtensor_keras_tutorial) for more information using Keras with DTensor." ] }, { "cell_type": "markdown", "metadata": { "id": "PMCt-Gj3b3Jy" }, "source": [ "### Dense Layer\n", "\n", "The following custom Dense layer defines 2 layer variables: $W_{ij}$ is the variable for weights, and $b_i$ is the variable for the biases.\n", "\n", "$$\n", "y_j = \\sigma(\\sum_i x_i W_{ij} + b_j)\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": { "id": "nYlFUJWNjl4N" }, "source": [ "### Layout deduction\n", "\n", "This result comes from the following observations:\n", "\n", "- The preferred DTensor sharding for operands to a matrix dot product $t_j = \\sum_i x_i W_{ij}$ is to shard $\\mathbf{W}$ and $\\mathbf{x}$ the same way along the $i$-axis.\n", "\n", "- The preferred DTensor sharding for operands to a matrix sum $t_j + b_j$, is to shard $\\mathbf{t}$ and $\\mathbf{b}$ the same way along the $j$-axis.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VpKblz7Yb16G" }, "outputs": [], "source": [ "class Dense(tf.Module):\n", "\n", " def __init__(self, input_size, output_size,\n", " init_seed, weight_layout, activation=None):\n", " super().__init__()\n", "\n", " random_normal_initializer = tf.function(tf.random.stateless_normal)\n", "\n", " self.weight = dtensor.DVariable(\n", " dtensor.call_with_layout(\n", " random_normal_initializer, weight_layout,\n", " shape=[input_size, output_size],\n", " seed=init_seed\n", " ))\n", " if activation is None:\n", " activation = lambda x:x\n", " self.activation = activation\n", " \n", " # bias is sharded the same way as the last axis of weight.\n", " bias_layout = weight_layout.delete([0])\n", "\n", " self.bias = dtensor.DVariable(\n", " dtensor.call_with_layout(tf.zeros, bias_layout, [output_size]))\n", "\n", " def __call__(self, x):\n", " y = tf.matmul(x, self.weight) + self.bias\n", " y = self.activation(y)\n", "\n", " return y" ] }, { "cell_type": "markdown", "metadata": { "id": "tfVY_vAKbxM0" }, "source": [ "### BatchNorm\n", "\n", "A batch normalization layer helps avoid collapsing modes while training. In this case, adding batch normalization layers helps model training avoid producing a model that only produces zeros.\n", "\n", "The constructor of the custom `BatchNorm` layer below does not take a `Layout` argument. This is because `BatchNorm` has no layer variables. This still works with DTensor because 'x', the only input to the layer, is already a DTensor that represents the global batch.\n", "\n", "Note: With DTensor, the input Tensor 'x' always represents the global batch. Therefore `tf.nn.batch_normalization` is applied to the global batch. This differs from training with `tf.distribute.MirroredStrategy`, where Tensor 'x' only represents the per-replica shard of the batch (the local batch)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "riBA9pfhlPFq" }, "outputs": [], "source": [ "class BatchNorm(tf.Module):\n", "\n", " def __init__(self):\n", " super().__init__()\n", "\n", " def __call__(self, x, training=True):\n", " if not training:\n", " # This branch is not used in the Tutorial.\n", " pass\n", " mean, variance = tf.nn.moments(x, axes=[0])\n", " return tf.nn.batch_normalization(x, mean, variance, 0.0, 1.0, 1e-5)" ] }, { "cell_type": "markdown", "metadata": { "id": "q4R4MPz5prh4" }, "source": [ "A full featured batch normalization layer (such as `tf.keras.layers.BatchNormalization`) will need Layout arguments for its variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "unFcP99zprJj" }, "outputs": [], "source": [ "def make_keras_bn(bn_layout):\n", " return tf.keras.layers.BatchNormalization(gamma_layout=bn_layout,\n", " beta_layout=bn_layout,\n", " moving_mean_layout=bn_layout,\n", " moving_variance_layout=bn_layout,\n", " fused=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "v8Dj7AJ_lPs0" }, "source": [ "### Putting Layers Together\n", "\n", "Next, build a Multi-layer perceptron (MLP) network with the building blocks above. The diagram below shows the axis relationships between the input `x` and the weight matrices for the two `Dense` layers without any DTensor sharding or replication applied." ] }, { "cell_type": "markdown", "metadata": { "id": "udFGAO-NrZw6" }, "source": [ "<img src=\"https://www.tensorflow.org/images/dtensor/no_dtensor.png\" alt=\"The input and weight matrices for a non distributed model.\" class=\"no-filter\">\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8DCQ0aQ5rQtB" }, "source": [ "The output of the first `Dense` layer is passed into the input of the second `Dense` layer (after the `BatchNorm`). Therefore, the preferred DTensor sharding for the output of first `Dense` layer ($\\mathbf{W_1}$) and the input of second `Dense` layer ($\\mathbf{W_2}$) is to shard $\\mathbf{W_1}$ and $\\mathbf{W_2}$ the same way along the common axis $\\hat{j}$,\n", "\n", "$$\n", "\\mathsf{Layout}[{W_{1,ij}}; i, j] = \\left[\\hat{i}, \\hat{j}\\right] \\\\\n", "\\mathsf{Layout}[{W_{2,jk}}; j, k] = \\left[\\hat{j}, \\hat{k} \\right]\n", "$$\n", "\n", "Even though the layout deduction shows that the 2 layouts are not independent, for the sake of simplicity of the model interface, `MLP` will take 2 `Layout` arguments, one per Dense layer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "junyS-965opl" }, "outputs": [], "source": [ "from typing import Tuple\n", "\n", "class MLP(tf.Module):\n", "\n", " def __init__(self, dense_layouts: Tuple[dtensor.Layout, dtensor.Layout]):\n", " super().__init__()\n", "\n", " self.dense1 = Dense(\n", " 1200, 48, (1, 2), dense_layouts[0], activation=tf.nn.relu)\n", " self.bn = BatchNorm()\n", " self.dense2 = Dense(48, 2, (3, 4), dense_layouts[1])\n", "\n", " def __call__(self, x):\n", " y = x\n", " y = self.dense1(y)\n", " y = self.bn(y)\n", " y = self.dense2(y)\n", " return y\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9dgLmebHhr7h" }, "source": [ "The trade-off between correctness in layout deduction constraints and simplicity of API is a common design point of APIs that uses DTensor.\n", "It is also possible to capture the dependency between `Layout`'s with a different API. For example, the `MLPStricter` class creates the `Layout` objects in the constructor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wEZR7UlihsYX" }, "outputs": [], "source": [ "class MLPStricter(tf.Module):\n", "\n", " def __init__(self, mesh, input_mesh_dim, inner_mesh_dim1, output_mesh_dim):\n", " super().__init__()\n", "\n", " self.dense1 = Dense(\n", " 1200, 48, (1, 2), dtensor.Layout([input_mesh_dim, inner_mesh_dim1], mesh),\n", " activation=tf.nn.relu)\n", " self.bn = BatchNorm()\n", " self.dense2 = Dense(48, 2, (3, 4), dtensor.Layout([inner_mesh_dim1, output_mesh_dim], mesh))\n", "\n", "\n", " def __call__(self, x):\n", " y = x\n", " y = self.dense1(y)\n", " y = self.bn(y)\n", " y = self.dense2(y)\n", " return y" ] }, { "cell_type": "markdown", "metadata": { "id": "GcQi7D5mal2L" }, "source": [ "To make sure the model runs, probe your model with fully replicated layouts and a fully replicated batch of `'x'` input." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zOPuYeQwallh" }, "outputs": [], "source": [ "WORLD = dtensor.create_mesh([(\"world\", 8)], devices=DEVICES)\n", "\n", "model = MLP([dtensor.Layout.replicated(WORLD, rank=2),\n", " dtensor.Layout.replicated(WORLD, rank=2)])\n", "\n", "sample_x, sample_y = train_data_vec.take(1).get_single_element()\n", "sample_x = dtensor.copy_to_mesh(sample_x, dtensor.Layout.replicated(WORLD, rank=2))\n", "print(model(sample_x))" ] }, { "cell_type": "markdown", "metadata": { "id": "akrjDstEpDv9" }, "source": [ "## Moving data to the device\n", "\n", "Usually, `tf.data` iterators (and other data fetching methods) yield tensor objects backed by the local host device memory. This data must be transferred to the accelerator device memory that backs DTensor's component tensors.\n", "\n", "`dtensor.copy_to_mesh` is unsuitable for this situation because it replicates input tensors to all devices due to DTensor's global perspective. So in this tutorial, you will use a helper function `repack_local_tensor`, to facilitate the transfer of data. This helper function uses `dtensor.pack` to send (and only send) the shard of the global batch that is intended for a replica to the device backing the replica.\n", "\n", "This simplified function assumes single-client. Determining the correct way to split the local tensor and the mapping between the pieces of the split and the local devices can be laboring in a multi-client application.\n", "\n", "Additional DTensor API to simplify `tf.data` integration is planned, supporting both single-client and multi-client applications. Please stay tuned." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3t5WvQR4Hvo4" }, "outputs": [], "source": [ "def repack_local_tensor(x, layout):\n", " \"\"\"Repacks a local Tensor-like to a DTensor with layout.\n", "\n", " This function assumes a single-client application.\n", " \"\"\"\n", " x = tf.convert_to_tensor(x)\n", " sharded_dims = []\n", "\n", " # For every sharded dimension, use tf.split to split the along the dimension.\n", " # The result is a nested list of split-tensors in queue[0].\n", " queue = [x]\n", " for axis, dim in enumerate(layout.sharding_specs):\n", " if dim == dtensor.UNSHARDED:\n", " continue\n", " num_splits = layout.shape[axis]\n", " queue = tf.nest.map_structure(lambda x: tf.split(x, num_splits, axis=axis), queue)\n", " sharded_dims.append(dim)\n", "\n", " # Now we can build the list of component tensors by looking up the location in\n", " # the nested list of split-tensors created in queue[0].\n", " components = []\n", " for locations in layout.mesh.local_device_locations():\n", " t = queue[0]\n", " for dim in sharded_dims:\n", " split_index = locations[dim] # Only valid on single-client mesh.\n", " t = t[split_index]\n", " components.append(t)\n", "\n", " return dtensor.pack(components, layout)" ] }, { "cell_type": "markdown", "metadata": { "id": "2KKCDcjG7zj2" }, "source": [ "## Data parallel training\n", "\n", "In this section, you will train your MLP model with data parallel training. The following sections will demonstrate model parallel training and spatial parallel training.\n", "\n", "Data parallel training is a commonly used scheme for distributed machine learning:\n", "\n", " - Model variables are replicated on N devices each.\n", " - A global batch is split into N per-replica batches.\n", " - Each per-replica batch is trained on the replica device.\n", " - The gradient is reduced before weight up data is collectively performed on all replicas.\n", "\n", "Data parallel training provides nearly linear speedup regarding the number of devices." ] }, { "cell_type": "markdown", "metadata": { "id": "UMsLUyTGq3oL" }, "source": [ "### Creating a data parallel mesh\n", "\n", "A typical data parallelism training loop uses a DTensor `Mesh` that consists of a single `batch` dimension, where each device becomes a replica that receives a shard from the global batch.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_data_para.png\" alt=\"Data parallel mesh\" class=\"no-filter\">\n", "\n", "\n", "The replicated model runs on the replica, therefore the model variables are fully replicated (unsharded)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C0IyOlxmeu4I" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"batch\", 8)], devices=DEVICES)\n", "\n", "model = MLP([dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh),\n", " dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh),])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OREKwBybo1gZ" }, "source": [ "### Packing training data to DTensors\n", "\n", "The training data batch should be packed into DTensors sharded along the `'batch'`(first) axis, such that DTensor will evenly distribute the training data to the `'batch'` mesh dimension.\n", "\n", "**Note**: In DTensor, the `batch size` always refers to the global batch size. The batch size should be chosen such that it can be divided evenly by the size of the `batch` mesh dimension." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8xMYkTpGocY8" }, "outputs": [], "source": [ "def repack_batch(x, y, mesh):\n", " x = repack_local_tensor(x, layout=dtensor.Layout(['batch', dtensor.UNSHARDED], mesh))\n", " y = repack_local_tensor(y, layout=dtensor.Layout(['batch'], mesh))\n", " return x, y\n", "\n", "sample_x, sample_y = train_data_vec.take(1).get_single_element()\n", "sample_x, sample_y = repack_batch(sample_x, sample_y, mesh)\n", "\n", "print('x', sample_x[:, 0])\n", "print('y', sample_y)" ] }, { "cell_type": "markdown", "metadata": { "id": "uONSiqOIkFL1" }, "source": [ "### Training step\n", "\n", "This example uses a Stochastic Gradient Descent optimizer with the Custom Training Loop (CTL). Consult the [Custom Training Loop guide](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch) and [Walk through](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) for more information on those topics.\n", "\n", "The `train_step` is encapsulated as a `tf.function` to indicate this body is to be traced as a TensorFlow Graph. The body of `train_step` consists of a forward inference pass, a backward gradient pass, and the variable update.\n", "\n", "Note that the body of `train_step` does not contain any special DTensor annotations. Instead, `train_step` only contains high-level TensorFlow operations that process the input `x` and `y` from the global view of the input batch and the model. All of the DTensor annotations (`Mesh`, `Layout`) are factored out of the train step." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BwUFzLGDtQT6" }, "outputs": [], "source": [ "# Refer to the CTL (custom training loop guide)\n", "@tf.function\n", "def train_step(model, x, y, learning_rate=tf.constant(1e-4)):\n", " with tf.GradientTape() as tape:\n", " logits = model(x)\n", " # tf.reduce_sum sums the batch sharded per-example loss to a replicated\n", " # global loss (scalar).\n", " loss = tf.reduce_sum(\n", " tf.nn.sparse_softmax_cross_entropy_with_logits(\n", " logits=logits, labels=y))\n", " parameters = model.trainable_variables\n", " gradients = tape.gradient(loss, parameters)\n", " for parameter, parameter_gradient in zip(parameters, gradients):\n", " parameter.assign_sub(learning_rate * parameter_gradient)\n", "\n", " # Define some metrics\n", " accuracy = 1.0 - tf.reduce_sum(tf.cast(tf.argmax(logits, axis=-1, output_type=tf.int64) != y, tf.float32)) / x.shape[0]\n", " loss_per_sample = loss / len(x)\n", " return {'loss': loss_per_sample, 'accuracy': accuracy}" ] }, { "cell_type": "markdown", "metadata": { "id": "0OYTu4j0evWT" }, "source": [ "### Checkpointing\n", "\n", "You can checkpoint a DTensor model using `dtensor.DTensorCheckpoint`. The format of a DTensor checkpoint is fully compatible with a Standard TensorFlow Checkpoint. There is ongoing work to consolidate `dtensor.DTensorCheckpoint` into `tf.train.Checkpoint`.\n", "\n", "When a DTensor checkpoint is restored, `Layout`s of variables can be different from when the checkpoint is saved. This tutorial makes use of this feature to continue the training in the Model Parallel training and Spatial Parallel training sections.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rsInFFJg7x9t" }, "outputs": [], "source": [ "CHECKPOINT_DIR = tempfile.mkdtemp()\n", "\n", "def start_checkpoint_manager(mesh, model):\n", " ckpt = dtensor.DTensorCheckpoint(mesh, root=model)\n", " manager = tf.train.CheckpointManager(ckpt, CHECKPOINT_DIR, max_to_keep=3)\n", "\n", " if manager.latest_checkpoint:\n", " print(\"Restoring a checkpoint\")\n", " ckpt.restore(manager.latest_checkpoint).assert_consumed()\n", " else:\n", " print(\"new training\")\n", " return manager\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9r77ky5Jgp1j" }, "source": [ "### Training loop\n", "\n", "For the data parallel training scheme, train for epochs and report the progress. 3 epochs is insufficient for training the model -- an accuracy of 50% is as good as randomly guessing.\n", "\n", "Enable checkpointing so that you can pick up the training later. In the following section, you will load the checkpoint and train with a different parallel scheme." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UaLn-vGZgqbS" }, "outputs": [], "source": [ "num_epochs = 2\n", "manager = start_checkpoint_manager(mesh, model)\n", "\n", "for epoch in range(num_epochs):\n", " step = 0\n", " pbar = tf.keras.utils.Progbar(target=int(train_data_vec.cardinality()), stateful_metrics=[])\n", " metrics = {'epoch': epoch}\n", " for x,y in train_data_vec:\n", "\n", " x, y = repack_batch(x, y, mesh)\n", "\n", " metrics.update(train_step(model, x, y, 1e-2))\n", "\n", " pbar.update(step, values=metrics.items(), finalize=False)\n", " step += 1\n", " manager.save()\n", " pbar.update(step, values=metrics.items(), finalize=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "YRFJEhum7EGD" }, "source": [ "## Model Parallel Training\n", "\n", "If you switch to a 2 dimensional `Mesh`, and shard the model variables along the second mesh dimension, then the training becomes Model Parallel.\n", "\n", "In Model Parallel training, each model replica spans multiple devices (2 in this case):\n", "\n", "- There are 4 model replicas, and the training data batch is distributed to the 4 replicas.\n", "- The 2 devices within a single model replica receive replicated training data.\n", "\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_model_para.png\" alt=\"Model parallel mesh\" class=\"no-filter\">\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5gZE9IT5Dzwl" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"batch\", 4), (\"model\", 2)], devices=DEVICES)\n", "model = MLP([dtensor.Layout([dtensor.UNSHARDED, \"model\"], mesh), \n", " dtensor.Layout([\"model\", dtensor.UNSHARDED], mesh)])" ] }, { "cell_type": "markdown", "metadata": { "id": "Ihof3DkMFKnf" }, "source": [ "As the training data is still sharded along the batch dimension, you can reuse the same `repack_batch` function as the Data Parallel training case. DTensor will automatically replicate the per-replica batch to all devices inside the replica along the `\"model\"` mesh dimension." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dZf56ynbE_p1" }, "outputs": [], "source": [ "def repack_batch(x, y, mesh):\n", " x = repack_local_tensor(x, layout=dtensor.Layout(['batch', dtensor.UNSHARDED], mesh))\n", " y = repack_local_tensor(y, layout=dtensor.Layout(['batch'], mesh))\n", " return x, y" ] }, { "cell_type": "markdown", "metadata": { "id": "UW3OXdhNFfpv" }, "source": [ "Next run the training loop. The training loop reuses the same checkpoint manager as the Data Parallel training example, and the code looks identical.\n", "\n", "You can continue training the data parallel trained model under model parallel training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LLC0wgii7EgA" }, "outputs": [], "source": [ "num_epochs = 2\n", "manager = start_checkpoint_manager(mesh, model)\n", "\n", "for epoch in range(num_epochs):\n", " step = 0\n", " pbar = tf.keras.utils.Progbar(target=int(train_data_vec.cardinality()))\n", " metrics = {'epoch': epoch}\n", " for x,y in train_data_vec:\n", " x, y = repack_batch(x, y, mesh)\n", " metrics.update(train_step(model, x, y, 1e-2))\n", " pbar.update(step, values=metrics.items(), finalize=False)\n", " step += 1\n", " manager.save()\n", " pbar.update(step, values=metrics.items(), finalize=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "BZH-aMrVzi2L" }, "source": [ "## Spatial Parallel Training" ] }, { "cell_type": "markdown", "metadata": { "id": "u-bK6IZ9GCS9" }, "source": [ "When training data of very high dimensionality (e.g. a very large image or a video), it may be desirable to shard along the feature dimension. This is called [Spatial Partitioning](https://cloud.google.com/blog/products/ai-machine-learning/train-ml-models-on-large-images-and-3d-volumes-with-spatial-partitioning-on-cloud-tpus), which was first introduced into TensorFlow for training models with large 3-d input samples.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_spatial_para.png\" alt=\"Spatial parallel mesh\" class=\"no-filter\">\n", "\n", "DTensor also supports this case. The only change you need to do is to create a Mesh that includes a `feature` dimension, and apply the corresponding `Layout`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jpc9mqURGpmK" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"batch\", 2), (\"feature\", 2), (\"model\", 2)], devices=DEVICES)\n", "model = MLP([dtensor.Layout([\"feature\", \"model\"], mesh), \n", " dtensor.Layout([\"model\", dtensor.UNSHARDED], mesh)])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "i07Wrv-jHBc1" }, "source": [ "Shard the input data along the `feature` dimension when packing the input tensors to DTensors. You do this with a slightly different repack function, `repack_batch_for_spt`, where `spt` stands for Spatial Parallel Training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DWR8qF6BGtFL" }, "outputs": [], "source": [ "def repack_batch_for_spt(x, y, mesh):\n", " # Shard data on feature dimension, too\n", " x = repack_local_tensor(x, layout=dtensor.Layout([\"batch\", 'feature'], mesh))\n", " y = repack_local_tensor(y, layout=dtensor.Layout([\"batch\"], mesh))\n", " return x, y" ] }, { "cell_type": "markdown", "metadata": { "id": "Ygl9dqMUHTVN" }, "source": [ "The Spatial parallel training can also continue from a checkpoint created with other parallell training schemes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "p3NnpHSKo-hx" }, "outputs": [], "source": [ "num_epochs = 2\n", "\n", "manager = start_checkpoint_manager(mesh, model)\n", "for epoch in range(num_epochs):\n", " step = 0\n", " metrics = {'epoch': epoch}\n", " pbar = tf.keras.utils.Progbar(target=int(train_data_vec.cardinality()))\n", "\n", " for x, y in train_data_vec:\n", " x, y = repack_batch_for_spt(x, y, mesh)\n", " metrics.update(train_step(model, x, y, 1e-2))\n", "\n", " pbar.update(step, values=metrics.items(), finalize=False)\n", " step += 1\n", " manager.save()\n", " pbar.update(step, values=metrics.items(), finalize=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "vp4L59CpJjYr" }, "source": [ "## SavedModel and DTensor\n", "\n", "The integration of DTensor and SavedModel is still under development. This section only describes the current status quo for TensorFlow 2.9.0.\n", "\n", "As of TensorFlow 2.9.0, `tf.saved_model` only accepts DTensor models with fully replicated variables.\n", "\n", "As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors, not DTensors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "49HfIq_SJZoj" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"world\", 1)], devices=DEVICES[:1])\n", "mlp = MLP([dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh), \n", " dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)])\n", "\n", "manager = start_checkpoint_manager(mesh, mlp)\n", "\n", "model_for_saving = tf.keras.Sequential([\n", " text_vectorization,\n", " mlp\n", "])\n", "\n", "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n", "def run(inputs):\n", " return {'result': model_for_saving(inputs)}\n", "\n", "tf.saved_model.save(\n", " model_for_saving, \"/tmp/saved_model\",\n", " signatures=run)" ] }, { "cell_type": "markdown", "metadata": { "id": "h6Csim_VMGxQ" }, "source": [ "As of TensorFlow 2.9.0, you can only call a loaded signature with a regular Tensor, or a fully replicated DTensor (which will be converted to a regular Tensor)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HG_ASSzR4IWW" }, "outputs": [], "source": [ "sample_batch = train_data.take(1).get_single_element()\n", "sample_batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qW8yKPrhKQ5b" }, "outputs": [], "source": [ "loaded = tf.saved_model.load(\"/tmp/saved_model\")\n", "\n", "run_sig = loaded.signatures[\"serving_default\"]\n", "result = run_sig(sample_batch['text'])['result']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GahGbv0ZmkJb" }, "outputs": [], "source": [ "np.mean(tf.argmax(result, axis=-1) == sample_batch['label'])" ] }, { "cell_type": "markdown", "metadata": { "id": "Ks-Vs9qsH6jO" }, "source": [ "## What's next?\n", "\n", "This tutorial demonstrated building and training an MLP sentiment analysis model with DTensor.\n", "\n", "Through `Mesh` and `Layout` primitives, DTensor can transform a TensorFlow `tf.function` to a distributed program suitable for a variety of training schemes.\n", "\n", "In a real-world machine learning application, evaluation and cross-validation should be applied to avoid producing an over-fitted model. The techniques introduced in this tutorial can also be applied to introduce parallelism to evaluation.\n", "\n", "Composing a model with `tf.Module` from scratch is a lot of work, and reusing existing building blocks such as layers and helper functions can drastically speed up model development.\n", "As of TensorFlow 2.9, all Keras Layers under `tf.keras.layers` accepts DTensor layouts as their arguments, and can be used to build DTensor models. You can even directly reuse a Keras model with DTensor without modifying the model implementation. Refer to the [DTensor Keras Integration Tutorial](https://www.tensorflow.org/tutorials/distribute/dtensor_keras_tutorial) for information on using DTensor Keras. " ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "dtensor_ml_tutorial.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

douglas-larocca/ipyfuturize

demo.ipynb

1

5982

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ipyfuturize\n", "\n", "A cell magic for [futurize](http://python-future.org/futurize.html) (based on [2to3](https://docs.python.org/2/library/2to3.html))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%install_ext https://raw.github.com/douglas-larocca/ipyfuturize/master/ipyfuturize.py" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load_ext ipyfuturize" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Usage: futurize [options] file|dir ...\r\n", "\r\n", "Options:\r\n", " -h, --help show this help message and exit\r\n", " -V, --version Report the version number of futurize\r\n", " -a, --all-imports Add all __future__ and future imports to each module\r\n", " -1, --stage1 Modernize Python 2 code only; no compatibility with Python 3 (or dependency on\r\n", " ``future``)\r\n", " -2, --stage2 Take modernized (stage1) code and add a dependency on ``future`` to provide Py3\r\n", " compatibility.\r\n", " -0, --both-stages Apply both stages 1 and 2\r\n", " -u, --unicode-literals\r\n", " Add ``from __future__ import unicode_literals`` to implicitly convert all\r\n", " unadorned string literals '' into unicode strings\r\n", " -f FIX, --fix=FIX Each FIX specifies a transformation; default: all. Either use '-f division -f\r\n", " metaclass' etc. or use the fully-qualified module name: '-f\r\n", " lib2to3.fixes.fix_types -f libfuturize.fixes.fix_unicode_keep_u'\r\n", " -j PROCESSES, --processes=PROCESSES\r\n", " Run 2to3 concurrently\r\n", " -x NOFIX, --nofix=NOFIX\r\n", " Prevent a fixer from being run.\r\n", " -l, --list-fixes List available transformations\r\n", " -p, --print-function Modify the grammar so that print() is a function\r\n", " -v, --verbose More verbose logging\r\n", " --no-diffs Don't show diffs of the refactoring\r\n", " -w, --write Write back modified files\r\n", " -n, --nobackups Don't write backups for modified files.\r\n", " -o OUTPUT_DIR, --output-dir=OUTPUT_DIR\r\n", " Put output files in this directory instead of overwriting the input files.\r\n", " Requires -n. For Python >= 2.7 only.\r\n", " -W, --write-unchanged-files\r\n", " Also write files even if no changes were required (useful with --output-dir);\r\n", " implies -w.\r\n", " --add-suffix=ADD_SUFFIX\r\n", " Append this string to all output filenames. Requires -n if non-empty. For Python\r\n", " >= 2.7 only.ex: --add-suffix='3' will generate .py3 files.\r\n" ] } ], "source": [ "%%futurize?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example from [http://python-future.org/quickstart.html#to-convert-existing-python-2-code](http://python-future.org/quickstart.html#to-convert-existing-python-2-code)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "\n", " var cell = IPython.notebook.get_selected_cell();\n", " var content = \"from __future__ import print_function\\nfrom future import standard_library\\nstandard_library.install_aliases()\\nfrom builtins import next\\nfrom builtins import object\\nimport configparser # Py2 module name\\n\\nclass Upper(object):\\n def __init__(self, iterable):\\n self._iter = iter(iterable)\\n def __next__(self): # Py2-style iterator interface\\n return next(self._iter).upper()\\n def __iter__(self):\\n return self\\n\\nitr = Upper('hello')\\nprint(next(itr), end=' ')\\nfor letter in itr:\\n print(letter, end=' ') # Py2-style print statement\";\n", " \n", " cell.code_mirror.setValue(content);\n", " " ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%futurize\n", "import ConfigParser # Py2 module name\n", "\n", "class Upper(object):\n", " def __init__(self, iterable):\n", " self._iter = iter(iterable)\n", " def next(self): # Py2-style iterator interface\n", " return next(self._iter).upper()\n", " def __iter__(self):\n", " return self\n", "\n", "itr = Upper('hello')\n", "print next(itr),\n", "for letter in itr:\n", " print letter, # Py2-style print statement" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0a0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

seregaxvm/rtestbook

TestBook.ipynb

1

2453002

agpl-3.0

encima/Comp_Thinking_In_Python

3_Homework_Answers.ipynb

2

11591

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q1", "locked": true, "solution": false } }, "source": [ "When would 2 variables be equal but not have the same `identity`?" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": true, "grade_id": "q1_ans", "locked": false, "points": 2, "solution": true } }, "source": [ "When they have different addresses in memory" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q2", "locked": true, "solution": false } }, "source": [ "What is the symbol for identity comparison?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q2_ans", "locked": false, "points": 1, "solution": true } }, "outputs": [], "source": [ "is" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q3", "locked": true, "solution": false } }, "source": [ "Which of the values below return True when used in the following statement?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "q3_code", "locked": true, "solution": false } }, "outputs": [], "source": [ "num_wheels >= 1024 and num_wheels < 2456" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q3_b", "locked": true, "solution": false } }, "source": [ "a.) num_wheels = 2456\n", "b.) num_wheels = 1765\n", "c.) num_wheels = 1024\n", "d.) num_wheels = 8000\n", "e.) num_wheels = 1028\n", "f.) num_wheels = 1020" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": true, "grade_id": "q3_ans", "locked": false, "points": 2, "solution": true } }, "source": [ "b, c, e" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q4", "locked": true, "solution": false } }, "source": [ "Fix the if statements below so that the checks are correct and the right statements are printed for the age and drivers info.\n", "\n", "You may also want to look at the precedence section in the lecture to ensure it is right in all situations.\n", "\n", "You DO NOT need to edit variables, just if statements." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "nbgrader": { "grade": false, "grade_id": "q4_ans", "locked": false, "points": 2, "solution": true } }, "outputs": [], "source": [ "age = 23\n", "has_license = True\n", "min_drive_age = 17\n", "cheap_ins_age = 25\n", "\n", "def age_check(age):\n", " if age <= 18:\n", " print(\"Yay, you can drink legally!\")\n", " return True\n", " else:\n", " return False\n", " \n", "def drive_check(age, has_license):\n", " if age < min_drive_age and has_license:\n", " print(\"Not legally able to drive but have a license? Do you think I am a fool?\")\n", " return False\n", " if age > min_drive_age and not has_license:\n", " print(\"You can drive but you need to get your license!\")\n", " return False\n", " elif age < cheap_ins_age and has_license and age > min_drive_age: #precedence can be used here to make things cleaner\n", " print(\"You can drive, but insurance is not gonna be cheap\")\n", " return True\n", " elif age <= cheap_ins_age and has_license:\n", " print(\"Go you, cheap insurance all round!\")\n", " return True\n", " else:\n", " return False" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "nbgrader": { "grade": true, "grade_id": "q4_tests", "locked": true, "points": 6, "solution": false } }, "outputs": [ { "ename": "AssertionError", "evalue": "True != False", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-10-5f98c1bd9d8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnose\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0massert_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mage_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrive_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_license\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrive_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/python/2.7.12_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.pyc\u001b[0m in \u001b[0;36massertEqual\u001b[0;34m(self, first, second, msg)\u001b[0m\n\u001b[1;32m 511\u001b[0m \"\"\"\n\u001b[1;32m 512\u001b[0m \u001b[0massertion_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getAssertEqualityFunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0massertion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0massertNotEqual\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/python/2.7.12_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.pyc\u001b[0m in \u001b[0;36m_baseAssertEqual\u001b[0;34m(self, first, second, msg)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0mstandardMsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%s != %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msafe_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msafe_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msecond\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_formatMessage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstandardMsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfailureException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0massertEqual\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAssertionError\u001b[0m: True != False" ] } ], "source": [ "from nose.tools import assert_equal\n", "\n", "assert_equal(True, age_check(age))\n", "assert_equal(True, drive_check(age, has_license))\n", "assert_equal(False, drive_check(18, False))\n", "assert_equal(False, drive_check(14, True))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q5", "locked": true, "solution": false } }, "source": [ "Write the output of the variable `str_two` once the following code block has run:\n", "\n", "```python\n", "str_one = \"Hello\"\n", "str_two = str_one\n", "str_one = \"Hi\"\n", "print(str_two)\n", "```" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "nbgrader": { "grade": true, "grade_id": "q5_ans", "locked": false, "points": 1, "solution": true } }, "outputs": [ { "data": { "text/plain": [ "'Hello'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"Hello\"" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q6", "locked": true, "solution": false } }, "source": [ "Explain the difference between mutable and immutable" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": true, "grade_id": "q6_ans", "locked": false, "points": 4, "solution": true } }, "source": [ "Mutable types can be changed once they have been created but immutable types maintain their original state on creation and cannot be changed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Create Assignment", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

zyv/ctci-solutions

notebooks/task_v6_04_02_haskell.ipynb

1

5394

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Minimal Tree" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data Tree a = Empty | Node a (Tree a) (Tree a) deriving (Show)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "split3 :: (Ord a) => [a] -> ([a], a, [a])\n", "split3 [x] = ([], x, [])\n", "split3 xs =\n", " let\n", " (left, middle : right) = splitAt (length xs `div` 2) xs\n", " in\n", " (left, middle, right)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "minimalBST :: (Ord a) => [a] -> (Tree a)\n", "minimalBST [] = Empty\n", "minimalBST xs =\n", " let\n", " (left, middle, right) = split3 xs\n", " in\n", " Node middle (minimalBST left) (minimalBST right)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fromTree :: (Ord a) => (Tree a) -> [a]\n", "fromTree Empty = []\n", "fromTree (Node middle left right) =\n", " (fromTree left) ++ middle : (fromTree right) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1,2,3,4,5]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fromTree $ minimalBST [1, 2, 3, 4, 5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tests" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import Test.QuickCheck" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "testRoundTrip :: [Int] -> Bool\n", "testRoundTrip xs =\n", " fromTree (minimalBST xs) == xs" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "+++ OK, passed 100 tests." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quickCheck testRoundTrip" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "height :: (Ord a) => (Tree a) -> Int\n", "height Empty = 0\n", "height (Node middle left right) =\n", " 1 + max (height left) (height right)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "testMinimalHeight :: [Int] -> Bool\n", "testMinimalHeight xs =\n", " let \n", " expected :: [Int] -> Int\n", " expected [] = 0\n", " expected xs = 1 + truncate (logBase 2 $ fromIntegral $ length xs)\n", " in\n", " height (minimalBST xs) == expected xs" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "+++ OK, passed 100 tests." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quickCheck testMinimalHeight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check whether the binary search tree invariant holds for a given binary tree (see Task v6 04.05):" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "validBST :: (Ord a) => Tree a -> Bool\n", "validBST tree =\n", " let\n", " checkNode :: (Ord a) => Tree a -> Maybe a -> Maybe a -> Bool\n", " checkNode Empty _ _ = True\n", " checkNode (Node middle left right) lower upper =\n", " maybe True (< middle) lower &&\n", " maybe True (> middle) upper &&\n", " checkNode left lower (Just middle) &&\n", " checkNode right (Just middle) upper\n", " in\n", " checkNode tree Nothing Nothing" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "False" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "validBST $ minimalBST [1, 2, 3, 4, 5]\n", "validBST $ minimalBST [1, 2, 7, 4, 5]" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell", "language": "haskell", "name": "haskell" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "name": "haskell", "version": "7.10.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ericmjl/protein-systematic-characterization

experiments/20161006-cpec/cpec-translate.ipynb

1

27508

{ "cells": [ { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from Bio import AlignIO\n", "#Alignment conducted with multiple alignment tool @ http://www.ebi.ac.uk/Tools/msa/clustalo/\n", "aln = AlignIO.read('cpec-align.clustal', 'clustal')\n", "\n", "from Bio.Seq import Seq\n", "from Bio.SeqRecord import SeqRecord\n", "from Bio.Alphabet import generic_dna\n", "from Bio import SeqIO\n", "from Bio.Alphabet import IUPAC\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [], "source": [ "aln[0].id = \"L16\"\n", "aln[1].id = \"L11\"\n", "aln[2].id = \"L1\"\n", "aln[3].id = \"L6\"\n", "aln[4].id = \"L3\"\n", "aln[5].id = \"L4\"\n", "\n", "L10 = aln[7] #sequence read with forward primer not used because of poor quality\n", "aln[6].id = \"vic\"" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "Find good start and end indices to trim mutant sequences at\n", "\"\"\"\n", "\n", "def is_good(index, sequence):\n", " good = 0\n", " for next_base in sequence[index+1:index+100]: #100 chosen as large enough to ensure good \n", " if next_base != \"-\":\n", " good += 1 \n", " if good > 98:\n", " return True\n", "def find_end(index, sequence):\n", " for more_index, more_base in enumerate(sequence[index:]):\n", " if more_base == \"-\" and is_good(more_index, sequence[index:]): #Find first non-isolated occurrence of \"-\" (a single occurence indicates a frameshift mutation)\n", " return more_index\n", " return False\n", " \n", "start_list = []\n", "end_list = []\n", "\n", "for sequence in aln[0:6]:\n", " for index, base in enumerate(sequence):\n", " if base != \"-\":\n", " if is_good(index, sequence):\n", " \n", " start_list.append(index)\n", " if find_end(index, sequence) != False:\n", " end_list.append(index + find_end(index, sequence))\n", "\n", " break\n", "\n", "from collections import Counter\n", "data = Counter(end_list)\n", "\n", "\n", "end = data.most_common(1)[0][0] \n", "start = max(start_list) \n" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'L16': 'AGCAAAAGCAGGTCAATTATATTCAGTATGGAAAGAATAAAAGAACTACGGAATCTGATGTCGCAGTCTCGCACTCGCGAGATACTGACAAAAACCACAGTGGACCATATGGCCATAATTAAGAAGTACACATCGGGGAGACAGGAAAAGAACCCGTCACTTAGGATGAAATGGATGATGGCAATGAAATATCCAATCACTGCTGACAAAAGGGTAACAGAAATGGTTCCGGAGAGAAATGAACAAGGACAAACTCTATGGAGTAAAATGAGTGATGCTGGATCAGATAGAGTGATGGTATCACCTTTGGCTGTAACATGGTGGAATAGGAATGGACCCGTGACAAGTACGGTCCATTACCCAAAAGTGTACAAAACTTATTTCGACAAAGTCGAAAGGTTAAAACATGGAACCTTTGGCCCTGTCCATTTCAGAAATCAAGTCAAGATACGCAGAAGAGTAGACATAAACCCTGGTCATGCAGACCTCAGTGCCAAAGAGGCACAAGATGTAATTATGGAAGTTGTTTTTCCCAATGAAGTGGGAGCCAGAATACTAACATCAGAATCACAACTAACAATAACTAAGGAGAAAAAAGAAGAGCTCCGAGATTGCAAAATTTCTCCCTTGATGGTCGCATACATGTTAGAGAGAGAACTTGTACGAAAAACAAGATTTCTCCCAGTTGCTGGCGGAACAAGCAGTATATACATTGAAGTTTTACATTTGACTCAGGGAACGTGTTGGGAACAAATGTACACTCCAGGTGGAGGAGTGAGGAATGACGATGTTGACCAAAGCCTAATTATTGCGGCCAGGAACATAGTAAGAAGAGCCGCAGTATCAGCAGATCCACTAGCATCTTTATTGGAGATGTGCCACAGCACGCAAATTGGCGGAACAAGGATGGTGGACATTCTTAGACAGAACCCGACTGAAGAACAAGCTGTGGATATATGCAAGGCTGCAATGGGATTGAGAATCAGCTCATCCTTCAGCTTTGGTGGCTTTACATTTAAAAGAACAAGCGGGTCATCAGTCAAAAAAGAAGAAGAGGTGCTTACAGGCAATCTCCAAACATTGAGAATAAGAGTACATGAGGGGTATGAGGAGTTCACAATGGTAGGGAAAAGAGCAACAGCTATACTAAGAAAAGCAACCAGAAGATTGGTTCAACTCATAGTGAGTGGAAGAGACGAACAGTCAATAGCCGAAGCAATAATCGTGGCCATGGTGTTTTCACAAGAAGATTGCATGATAAAAGCAGTTAGAGGTGACCTGAATTTTGTCAACAGAGCAAATCAGCGGTTGAACCCCATGCATCAGCTTTTAAGGCATTTTCAGAAAGATGCGAAAGTGCTCTTTCAAAATTGGGGAGTTGAACACATCGACAGTGTGATGGGAATGGTTGGAGTATTACCAGACATGACTCCAAGCACAGAGATGTCAATGAGAGGAATAAGAGTCAGCAAAATGGGTGTGGATGAATACTCCAGCACAGAGAGGGTGGTGGTTAGCATTGATCGGTTTTTGAGAGTTCGAGACCAACGTGGGAATGTATTATTATCTCCTGAGGAGGTCAGTGAAACACAGGGAACTGAGAGACTGACAATAACTTATTCATCGTCGATGATGTGGGAGATTAACGGTCCTGAGTCGGTTTTGGTCAATACCTATCAATGGATCATCAGAAATTGGGAAGCTGTCAAAATTCAATGGTCTCAGAATCCTGCAATGTTGTACAACAAAATGGAATTTGAACCATTTCAATCTTTAGTCCCCAAGGCCACTAGAAGCCAATACAGTGGGTTTGTCAGAACTCTATTCCAACAAATGAGAGACGTACTTGGGACATTTGACACTGCCCAGATAATAAAGCTTCTCCCGTTGCAGCTGCTCCACCGAAGCGAAGCAGAATGCAGTTCTCTTAACTGACTGTGAATGTGAGGGGATCAGGGATGAGAATACTTGTAAGGGGCAATTCTCCTGTATTCAACTACAACAAAACCAGTAAAAGGCTAACAATTCTCGGAAAAGATGCCGGCACTTTAATTGAAGACCCAGATGAAAGCACATCAGGAGTGGAGTCCGCCGTCTTGAGAGGGTTCCTCATTATAGGTAAGGAAGACAGGAGATATGGACCAGCATTGAGCATCAATGAACTGAGTAACCTTGCAAAAGGGGAAAAGGCTAATGTGCTAATCGGGCAAGGAGACGTGGTGTTGGTAATGAAACGAAAACGGGAACTCTAGCATACTTACTGACAGCCAGACAGCGACCAAAAGAATTCGGATGGCCATCAATTAATGCTGAATAGTTTAAAAACGACCTTGTTTCTAC', 'L3': 'AGCAAAAGCAGGTCAATTATATTCAGTATGGAAAGAATAAAAGAACTACGGAATCTGATGTCGCAGTCTCGCACTCGCGAGATACTGACAAAAACCACAGTGGACCATATGGCCATAATTAAGAAGTACACATCGGGGAGACAGGAAAAGAACCCGTCACTTAGGATGAAATGGATGATGGCAATGAAATATCCAATCACTGCTGACAAAAGGGTAACAGAAATGGTTCCGGAGAGAAATGAACAAGGACAAACTCTATGGAGTAAAATGAGTGATGCTGGATCAGATAGAGTGATGGTATCACCTTTGGCTGTAACATGGTGGAATAGGAATGGACCCGTGACAAGTACGGTCCATTACCCAAAAGTGTACAAAACTTATTTCGACAAAGTCGAAAGGTTAAAACATGGAACCTTTGGCCCTGTCCATTTCAGAAATCAAGTCAAGATACGCAGAAGAGTAGACATAAACCCTGGTCATGCAGACCTCAGTGCCAAAGAGGCACAAGATGTAATTATGGAAGTTGTTTTTCCCAATGAAGTGGGAGCCAGAATACTAACATCAGAATCACAACTAACAATAACTAAGGAGAAAAAAGAAGAGCTCCGAGATTGCAAAATTTCTCCCTTGATGGTCGCATACATGTTAGAGAGAGAACTTGTACGAAAAACAAGATTTCTCCCAGTTGCTGGCGGAACAAGCAGTATATACATTGAAGTTTTACATTTGACTCAGGGAACGTGTTGGGAACAAATGTACACTCCAGGTGGAGGAGTGAGGAATGACGATGTTGACCAAAGCCTAATTATTGCGGCCAGGAACATAGTAAGAAGAGCCGCAGTATCAGCAGATCCACTAGCATCTTTATTGGAGATGTGCCACAGCACGCAAATTGGCGGAACAAGGATGGTGGACATTCTTAGACAGAACCCGACTGAAGAACAAGCTGTGGATATATGCAAGGCTGCAATGGGATTGAGAATCAGCTCATCCTTCAGCTTTGGTGGCTTTACATTTAAAAGAACAAGCGGGTCATCAGTCAAAAAAGAAGAAGAGGTGCTTACAGGCAATCTCCAAACATTGAGAATAAGAGTACATGAGGGGTATGAGGAGTTCACAATGGTAGGGAAAAGAGCAACAGCTATACTAAGAAAAGCAACCAGAAGATTGGTTCAACTCATAGTGAGTGGAAGAGACGAACAGTCAATAGCCGAAGCAATAATCGTGGCCATGGTGTTTTCACAAGAAGATTGCATGATAAAAGCAGTTAGAGGTGACCTGAATTTTGTCAACAGAGCAAATCAGCGGTTGAACCCCATGCATCAGCTTTTAAGGCATTTTCAGAAAGATGCGAAAGTGCTCTTTCAAAATTGGGGAGTTGAACACATCGACAGTGTGATGGGAATGGTTGGAGTATTACCAGACATGACTCCAAGCACAGAGATGTCAATGAGAGGAATAAGAGTCAGCAAAATGGGTGTGGATGAATACTCCAGCACAGAGAGGGTGGTGGTTAGCATTGATCGGTTTTTGAGAGTTCGAGACCAACGTGGGAATGTATTATTATCTCCTGAGGAGGTCAGTGAAACACAGGGAACTGAGAGACTGACAATAACTTATTCATCGTCGATGATGTGGGAGATTAACGGTCCTGAGTCGGTTTTGGTCAATACCCATCAATGGATCATCAGAAATTGGGAAGCTGTCAAAATTCAATGGTCTCAGAATCCTGCAATGTTGTACAACAAAATGGAATTTGAACCATTTCAATCTTTAGTCCCCAAGGCCATTGGAAGCCAATACAGTGGGTTTGTCAGAACTCTATTCCAACAAATGAGAGACGTACTTGGGACATTTGACACTGCCCAGATAATAAAGCTTCTCCCTTTTGCAGCTGCTCCACCGAAGCAAAGCAGAATGCAGTTCTCTTCACTGACTGTGAATGTGAGGGGATCAGGGATGAGAATACTTGTAAGGGGCAATTCTCCTGTATTCAACTACAACAAAACCACTAAAAGGCTAACAATTCTCGGAAAAGATGCCGGCACTTTAATTGAAGACCCAGATGAAAGCACATCAGGAGTGGAGTCCGCCGTCTTGAGAGGGTTCCTCATTATAGGTAAGGAAGACAGAAGATACGGACCAGCATTAAGCATCAATGAACTGAGTAACCTTGCAAAAGGGGAAAAGGCTAATGTGCTAATCGGGCAAGGAGACGTGGTGTTGGTAATGAAACGAAAACGGGACTCTAGCATACTTACTGACAGCCAGACAGCGACCAAAAGAATTCGGATGGCCATCAATTAATGCTGAATAGTTTAAAAACGACCTTGTTTCTAC', 'L4': 'AGCAAAAGCAGGTCAATTATATTCAGTATGGAAAGAATAAAAGAACTACGGAATCTGATGTCGCAGTCTCGCACTCGCGAGATACTGACAAAAACCACAGTGGACCATATGGCCATAATTAAGAAGTACACATCGGGGAGACAGGAAAAGAACCCGTCACTTAGGATGAAATGGATGATGGCAATGAAATATCCAATCACTGCTGACAAAAGGGTAACAGAAATGGTTCCGGAGAGAAATGAACAAGGACAAACTCTATGGAGTAAAATGAGTGATGCTGGATCAGATAGAGTGATGGTATCACCTTTGGCTGTAACATGGTGGAATAGGAATGGACCCGTGACAAGTACGGTCCATTACCCAAAAGTGTACAAAACTTATTTCGACAAAGTCGAAAGGTTAAAACATGGAACCTTTGGCCCTGTCCATTTCAGAAATCAAGTCAAGATACGCAGAAGAGTAGACATAAACCCTGGTCATGCAGACCTCAGTGCCAAAGAGGCACAAGATGTAATTATGGAAGTTGTTTTTCCCAATGAAGTGGGAGCCAGAATACTAACATCAGAATCACAACTAACAATAACTAAGGAGAAAAAAGAAGAGCTCCGAGATTGCAAAATTTCTCCCTTGATGGTCGCATACATGTTAGAGAGAGAACTTGTACGAAAAACAAGATTTCTCCCAGTTGCTGGCGGAACAAGCAGTATATACATTGAAGTTTTACATTTGACTCAGGGAACGTGTTGGGAACAAATGTACACTCCAGGTGGAGGAGTGAGGAATGACGATGTTGACCAAAGCCTAATTATTGCGGCCAGGAACATAGTAAGAAGAGCCGCAGTATCAGCAGATCCACTAGCATCTTTATTGGAGATGTGCCACAGCACGCAAATTGGCGGAACAAGGATGGTGGACATTCTTAGACAGAACCCGACTGAAGAACAAGCTGTGGATATATGCAAGGCTGCAATGGGATTGAGAATCAGCTCATCCTTCAGCTTTGGTGGCTTTACATTTAAAAGAACAAGCGGGTCATCAGTCAAAAAAGAAGAAGAGGTGCTTACAGGCAATCTCCAAACATTGAGAATAAGAGTACATGAGGGGTATGAGGAGTTCACAATGGTAGGGAAAAGAGCAACAGCTATACTAAGAAAAGCAACCAGAAGATTGGTTCAACTCATAGTGAGTGGAAGAGACGAACAGTCAATAGCCGAAGCAATAATCGTGGCCATGGTGTTTTCACAAGAAGATTGCATGATAAAAGCAGTTAGAGGTGACCTGAATTTTGTCAACAGAGCAAATCAGCGGTTGAACCCCATGCATCAGCTTTTAAGGCATTTTCAGAAAGATGCGAAAGTGCTCTTTCAAAATTGGGGAGTTGAACACATCGACAGTGTGATGGGAATGGTTGGAGTATTACCAGACATGACTCCAAGCACAGAGATGTCAATGAGAGGAATAAGAGTCAGCAAAATGGGTGTGGATGAATACTCCAGCACAGAGAGGGTGGTGGTTAGCATTGATCGGTTTTTGAGAGTTCGAGACCAACGTGGGAATGTATTATTATCTCCTGAGGAGGTCAGTGAAACACAGGGAACTGAGAGACTGACAATAACTTATTCATCGTCGATGATGTGGGAGATTAACGGTCCTGAGTCGGTTTTGGTCAATACCTATCAATGGATCATCAGAAATTGGGAAGCTGTCAAAATTCAATGGTCTCAGAATCCTGCAATGTTGTACAACAAAATGGAATTTGAACCATTTCAATCTTTAGTCCCCAAGGCCATTAGAAGCCAATACAGTGGGTTTGTCAGAACTCTATTCCAACAAATGAGAGACGTACTTGGGACATTTGACACTGCCCAGATAATAAAGCTTCACCCTTTTGCAGCTGCTCCACCGAAGCAAAGCAGAATGCAGTTCTCTTCACTGACTGTGAATGTGAGGGGATCAGGGATGAGAATACTTGTAAGGGGCAATTCTCCTGTATTCAACTACAACAAAACCACTAAAAGGCTAACAATTCTCGGAAAAGATGCCGGCACTTTAATTGAAGACCTAGATGAAAGCACATCAGGAGTGGAGTCCGCCGTCTTGAGAGGGTTCCTCATTATAGGTAAGGAAGACAGAAGATACGGACCAGCATTAAGCATCAATGAACTGAGTAACCTTGCAAAAGGGGAAGAGGCTAATGTGCTAATCGGGCAAGGAGACGTGGTGTTGGTAATGAAACGAAAACGGGACTCTAGCATACTTACTGACAGCCAGACNGCGACCGAAAGAATTCGGATGGCCATCAATTAATGCTGAATAGTTTAAAAACGACCTTGTTTCTAC', 'L1': 'AGCAAAAGCAGGTCAATTATATTCAGTATGGAAAGAATAAAAGAACTACGGAATCTGATGTCGCAGTCTCGCACTCGCGAGATACTGACAAAAACCACAGTGGACCATATGGCCATAATTAAGAAGTACACATCGGGGAGACAGGAAAAGAACCCGTCACTTAGGATGAAATGGATGATGGCAATGAAATATCCAATCACTGCTGACAAAAGGGTAACAGAAATGGTTCCGGAGAGAAATGAACAAGGACAAACTCTATGGAGTAAAATGAGTGATGCTGGATCAGATAGAGTGATGGTATCACCTTTGGCTGTAACATGGTGGAATAGGAATGGACCCGTGACAAGTACGGTCCATTACCCAAAAGTGTACAAAACTTATTTCGACAAAGTCGAAAGGTTAAAACATGGAACCTTTGGCCCTGTCCATTTCAGAAATCAAGTCAAGATACGCAGAAGAGTAGACATAAACCCTGGTCATGCAGACCTCAGTGCCAAAGAGGCACAAGATGTAATTATGGAAGTTGTTTTTCCCAATGAAGTGGGAGCCAGAATACTAACATCAGAATCACAACTAACAATAACTAAGGAGAAAAAAGAAGAGCTCCGAGATTGCAAAATTTCTCCCTTGATGGTCGCATACATGTTAGAGAGAGAACTTGTACGAAAAACAAGATTTCTCCCAGTTGCTGGCGGAACAAGCAGTATATACATTGAAGTTTTACATTTGACTCAGGGAACGTGTTGGGAACAAATGTACACTCCAGGTGGAGGAGTGAGGAATGACGATGTTGACCAAAGCCTAATTATTGCGGCCAGGAACATAGTAAGAAGAGCCGCAGTATCAGCAGATCCACTAGCATCTTTATTGGAGATGTGCCACAGCACGCAAATTGGCGGAACAAGGATGGTGGACATTCTTAGACAGAACCCGACTGAAGAACAAGCTGTGGATATATGCAAGGCTGCAATGGGATTGAGAATCAGCTCATCCTTCAGCTTTGGTGGCTTTACATTTAAAAGAACAAGCGGGTCATCAGTCAAAAAAGAAGAAGAGGTGCTTACAGGCAATCTCCAAACATTGAGAATAAGAGTACATGAGGGGTATGAGGAGTTCACAATGGTAGGGAAAAGAGCAACAGCTATACTAAGAAAAGCAACCAGAAGATTGGTTCAACTCATAGTGAGTGGAAGAGACGAACAGTCAATAGCCGAAGCAATAATCGTGGCCATGGTGTTTTCACAAGAAGATTGCATGATAAAAGCAGTTAGAGGTGACCTGAATTTTGTCAACAGAGCAAATCAGCGGTTGAACCCCATGCATCAGCTTTTAAGGCATTTTCAGAAAGATGCGAAAGTGCTCTTTCAAAATTGGGGAGTTGAACACATCGACAGTGTGATGGGAATGGTTGGAGTATTACCAGACATGACTCCAAGCACAGAGATGTCAATGAGAGGAATAAGAGTCAGCAAAATGGGTGTGGATGAATACTCCAGCACAGAGAGGGTGGTGGTTAGCATTGATCGGTTTTTGAGAGTTCGAGACCAACGTGGGAATGTATTATTATCTCCTGAGGAGGTCAGTGAAACACAGGGAACTGAGAGACTGACAATAACTTATTCATCGTCGATGATGTGGGAGATTAACGGTCCTGAGTCGGTTTTGGTCAATACCTATCAATGGATCATCAGAAATTGGGAAGCTGTCAAAATTCAATGGTCTCAGAATCCTGCAATGTTGTACAACAAAATGGAATTTGAACCATTTCAATCTTTAGTCCCCAAGGCCATTAGAAGCCAATACAGTGGGTTTGTCAGAACTCTATTCCAACAAATGAGAGACGTACTTGGGACATTTGACACTGCCCAGATAATAAAGCTTCTCCCTTTTGCAGCTGCTCCACCGAAGCAAAGCAGTATGCAGTTCTCTTCACTGACTGTGAATGTGAGGGGATCAGGGATGAGAATACTTGTAAGGGGCAATTCTCCTGTATTCAACTACAACAAAACCACTAAAAGGCTAACAATTCTCGGAAAAGATGCCGGCACTTTAATTGAAGACCCAGATGAAAGCACATCAGGAGTGGAGTCCGCCGTCTTGAGAGGGTTCCTCATTATAGGTAAGGAAGACAGAAGATACGGACCAGCATTAAGCATCAATGAACTGAGTAACCCTGCAAAAGGGGAAAAGGCTAATGTGCTAATCGGGCAAGGAGACGTGGTGTTGGTAATGAAACGAAAACGGGACTCTAGCATACTTACTGACAGCCAGACAGCGACCAAAAGAATTCGGATGGCCATCAATTAATGCTGAATAGTTTAAAAACGACCTTGTTTCTAC', 'L6': 'AGCAAAAGCAGGTCAATTATATTCAGTATGGAAAGAATAAAAGAACTACGGAATCTGATGTCGCAGTCTCGCACTCGCGAGATACTGACAAAAACCACAGTGGACCATATGGCCATAATTAAGAAGTACACATCGGGGAGACAGGAAAAGAACCCGTCACTTAGGATGAAATGGATGATGGCAATGAAATATCCAATCACTGCTGACAAAAGGGTAACAGAAATGGTTCCGGAGAGAAATGAACAAGGACAAACTCTATGGAGTAAAATGAGTGATGCTGGATCAGATAGAGTGATGGTATCACCTTTGGCTGTAACATGGTGGAATAGGAATGGACCCGTGACAAGTACGGTCCATTACCCAAAAGTGTACAAAACTTATTTCGACAAAGTCGAAAGGTTAAAACATGGAACCTTTGGCCCTGTCCATTTCAGAAATCAAGTCAAGATACGCAGAAGAGTAGACATAAACCCTGGTCATGCAGACCTCAGTGCCAAAGAGGCACAAGATGTAATTATGGAAGTTGTTTTTCCCAATGAAGTGGGAGCCAGAATACTAACATCAGAATCACAACTAACAATAACTAAGGAGAAAAAAGAAGAGCTCCGAGATTGCAAAATTTCTCCCTTGATGGTCGCATACATGTTAGAGAGAGAACTTGTACGAAAAACAAGATTTCTCCCAGTTGCTGGCGGAACAAGCAGTATATACATTGAAGTTTTACATTTGACTCAGGGAACGTGTTGGGAACAAATGTACACTCCAGGTGGAGGAGTGAGGAATGACGATGTTGACCAAAGCCTAATTATTGCGGCCAGGAACATAGTAAGAAGAGCCGCAGTATCAGCAGATCCACTAGCATCTTTATTGGAGATGTGCCACAGCACGCAAATTGGCGGAACAAGGATGGTGGACATTCTTAGACAGAACCCGACTGAAGAACAAGCTGTGGATATATGCAAGGCTGCAATGGGATTGAGAATCAGCTCATCCTTCAGCTTTGGTGGCTTTACATTTAAAAGAACAAGCGGGTCATCAGTCAAAAAAGAAGAAGAGGTGCTTACAGGCAATCTCCAAACATTGAGAATAAGAGTACATGAGGGGTATGAGGAGTTCACAATGGTAGGGAAAAGAGCAACAGCTATACTAAGAAAAGCAACCAGAAGATTGGTTCAACTCATAGTGAGTGGAAGAGACGAACAGTCAATAGCCGAAGCAATAATCGTGGCCATGGTGTTTTCACAAGAAGATTGCATGATAAAAGCAGTTAGAGGTGACCTGAATTTTGTCAACAGAGCAAATCAGCGGTTGAACCCCATGCATCAGCTTTTAAGGCATTTTCAGAAAGATGCGAAAGTGCTCTTTCAAAATTGGGGAGTTGAACACATCGACAGTGTGATGGGAATGGTTGGAGTATTACCAGACATGACTCCAAGCACAGAGATGTCAATGAGAGGAATAAGAGTCAGCAAAATGGGTGTGGATGAATACTCCAGCACAGAGAGGGTGGTGGTTAGCATTGATCGGTTTTTGAGAGTTCGAGACCAACGTGGGAATGTATTATTATCTCCTGAGGAGGTCAGTGAAACACAGGGAACTGAGAGACTGACAATAACTTATTCATCGTCGATGATGTGGGAGATTAACGGTCCTGAGTCGGTTTTGGTCAAAACCTATCAATGGATCATCAGAAATTGGGAAGTTGTCAAAATTCAATGGTCTCAGAATCCTATAATGTTGTACAACAAAATGGAATTTGAACCATTTCAATCTTTAGTCCCCAAGGCCATTAGAAGCCAATACAGTGGGTTTGTCAGAACTCTATTCCAACCAATGAGAGACGTACTTGGGACATTTGACACTGCCCAGATAATAAAGCTTCTCCCTTTTGCAGCTGCTCCACCGAAGCAAAGCAGAATGCAGTTCTCTTCACTGACTGTGAATGTGAGGGGATCAGGGATGAGAATACTTGTAAGGGGCAATTCTCCTGTATTCAACTACAACAAAACCACTAAAAGGCTAACAATTCTCGGAAAAGATGCCGGCACTTTAATTGAAGACCCAGATGATAGCACATCAGGAGTGGAGTCCGCCGTCTTGAGAGGGTTCCTCATTATAGGTAAGGAAGACAGAAGATACGGACCAGCATTAAGCATCAATGAACTGAGTAACCTTGCAAAAGGGGAAAAGGCTAATGTGCTAATCGGGCAAGGAGACGTGGTGTTGGTAGTGAAACGAAAACGGGACTCTAGCATACTTACTGACAGCCAGACAGCGACCAAAAGAATTCGGATGGCCATCAATTAATGCTGAATAGTTTAAAAACGACCTTGTTTCTAC', 'L11': 'AGCAAAAGCAGGTCAATTATATTCAGTATGGAAAGAATAAAAGAACTACGGAATCTGATGTCGCAGTCTCGCACTCGCGAGATACTGACAAAAACCACAGTGGACCATATGGCCATAATTAAGAAGTACACATCGGGGAGACAGGAAAAGAACCCGTCACTTAGGATGAAATGGATGATGGCAATGAAATATCCAATCACTGCTGACAAAAGGGTAACAGAAATGGTTCCGGAGAGAAATGAACAAGGACAAACTCTATGGAGTAAAATGAGTGATGCTGGATCAGATAGAGTGATGGTATCACCTTTGGCTGTAACATGGTGGAATAGGAATGGACCCGTGACAAGTACGGTCCATTACCCAAAAGTGTACAAAACTTATTTCGACAAAGTCGAAAGGTTAAAACATGGAACCTTTGGCCCTGTCCATTTCAGAAATCAAGTCAAGATACGCAGAAGAGTAGACATAAACCCTGGTCATGCAGACCTCAGTGCCAAAGAGGCACAAGATGTAATTATGGAAGTTGTTTTTCCCAATGAAGTGGGAGCCAGAATACTAACATCAGAATCACAACTAACAATAACTAAGGAGAAAAAAGAAGAGCTCCGAGATTGCAAAATTTCTCCCTTGATGGTCGCATACATGTTAGAGAGAGAACTTGTACGAAAAACAAGATTTCTCCCAGTTGCTGGCGGAACAAGCAGTATATACATTGAAGTTTTACATTTGACTCAGGGAACGTGTTGGGAACAAATGTACACTCCAGGTGGAGGAGTGAGGAATGACGATGTTGACCAAAGCCTAATTATTGCGGCCAGGAACATAGTAAGAAGAGCCGCAGTATCAGCAGATCCACTAGCATCTTTATTGGAGATGTGCCACAGCACGCAAATTGGCGGAACAAGGATGGTGGACATTCTTAGACAGAACCCGACTGAAGAACAAGCTGTGGATATATGCAAGGCTGCAATGGGATTGAGAATCAGCTCATCCTTCAGCTTTGGTGGCTTTACATTTAAAAGAACAAGCGGGTCATCAGTCAAAAAAGAAGAAGAGGTGCTTACAGGCAATCTCCAAACATTGAGAATAAGAGTACATGAGGGGTATGAGGAGTTCACAATGGTAGGGAAAAGAGCAACAGCTATACTAAGAAAAGCAACCAGAAGATTGGTTCAACTCATAGTGAGTGGAAGAGACGAACAGTCAATAGCCGAAGCAATAATCGTGGCCATGGTGTTTTCACAAGAAGATTGCATGATAAAAGCAGTTAGAGGTGACCTGAATTTTGTCAACAGAGCAAATCAGCGGTTGAACCCCATGCATCAGCTTTTAAGGCATTTTCAGAAAGATGCGAAAGTGCTCTTTCAAAATTGGGGAGTTGAACACATCGACAGTGTGATGGGAATGGTTGGAGTATTACCAGACATGACTCCAAGCACAGAGATGTCAATGAGAGGAATAAGAGTCAGCAAAATGGGTGTGGATGAATACTCCAGCACAGAGAGGGTGGTGGTTAGCATTGATCGGTTTTTGAGAGTTCGAGACCAACGTGGGAATGTATTATTATCTCCTGAGGAGGTCAGTGAAACACAGGGAACTGAGAGACTGACAATAACTTATTCATCGTCGATGATGTGGGAGATTAACGGTCCTGAGTCGGTTTTGGTCAATACCTATCAATGGATCATCAGAAATTGGGAAGCTGTCAAAATTCAATGGTCTCAGAATACTGCAATGTTGTACAACAAAATGGAATTTGAACCATTTCAATCTTTAGTCCCCAAGGCCATTAGAAGCCAATACAGTGGGTTTGTCAGAACTCTATTCCAACAAATGAGAGACGTACTCGGGACATTTGACACTGCCCAGATAATAAAGCTTCTCCCTTTTGCAGCTGCTCCACCGAAGCAAAGCAGAATGCAGTTCTCTTCACTGACTGTGAATGTGAGGGGATCAGGGATGAGAATACTTGTAAGGGGCAATTCTCCTGTATTCAACTACAACAAAACCACTAAAAGGCTAACAATTCTCGGAAAAGATGCCGGCACTTTAATTGAAGACCCAGATGAAAGCACATCAGGAGTGGAGTCCGCCGTCTTGAGAGGGTTCCTCATTATAGGTAAGGAAGACAGAAGATACGGACCAGCATTAAGCATCAATGAACTGAGTAACCTTGCAAAAGGGGAAAAGGCTAATGTGCTAATCGGGCAAGGAGACGTGGTGTTGGTAATGAAACGAAAACGGGACTCTAGCATACTTACTGACAGCCAGACAGCGACCAAAAGAATTCGGATGGCCATCAATTAATGCTGAATAGTTTAAAAACGACCTTGTTTCTAC'}\n" ] } ], "source": [ "\"\"\"\n", "Complete full PB2 sequences for each mutant by combining with template sequence\n", "\"\"\"\n", "vic_end = find_end(end, aln[6]) #Stripping the - at the end of the template sequence - NOT USED\n", "\n", "full_seq_dict = {}\n", "for sequence in aln[0:6]:\n", " full_seq = str(aln[6].seq)[0:start] + str(sequence.seq)[start:end] + str(aln[6].seq)[end:]\n", " full_seq_dict[sequence.id] = full_seq.replace(\"-\", \"\") #remove gaps resulting from deletions or insertions or frameshift mutations\n", "print(full_seq_dict)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'L16': 'SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVNTYQWIIRNWEAVKIQWSQNPAMLYNKMEFEPFQSLVPKATRSQYSGFVRTLFQQMRDVLGTFDTAQIIKLLPLQLLHRSEAECSSLN*L*M*GDQG*EYL*GAILLYSTTTKPVKG*QFSEKMPAL*LKTQMKAHQEWSPPS*EGSSL*VRKTGDMDQH*ASMN*VTLQKGKRLMC*SGKETWCW**NENGNSSILTDSQTATKRIRMAIN*C*IV*KRPCFY', 'L3': 'SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVNTHQWIIRNWEAVKIQWSQNPAMLYNKMEFEPFQSLVPKAIGSQYSGFVRTLFQQMRDVLGTFDTAQIIKLLPFAAAPPKQSRMQFSSLTVNVRGSGMRILVRGNSPVFNYNKTTKRLTILGKDAGTLIEDPDESTSGVESAVLRGFLIIGKEDRRYGPALSINELSNLAKGEKANVLIGQGDVVLVMKRKRDSSILTDSQTATKRIRMAIN*C*IV*KRPCFY', 'L4': 'SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVNTYQWIIRNWEAVKIQWSQNPAMLYNKMEFEPFQSLVPKAIRSQYSGFVRTLFQQMRDVLGTFDTAQIIKLHPFAAAPPKQSRMQFSSLTVNVRGSGMRILVRGNSPVFNYNKTTKRLTILGKDAGTLIEDLDESTSGVESAVLRGFLIIGKEDRRYGPALSINELSNLAKGEEANVLIGQGDVVLVMKRKRDSSILTDSQTATERIRMAIN*C*IV*KRPCFY', 'L1': 'SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVNTYQWIIRNWEAVKIQWSQNPAMLYNKMEFEPFQSLVPKAIRSQYSGFVRTLFQQMRDVLGTFDTAQIIKLLPFAAAPPKQSSMQFSSLTVNVRGSGMRILVRGNSPVFNYNKTTKRLTILGKDAGTLIEDPDESTSGVESAVLRGFLIIGKEDRRYGPALSINELSNPAKGEKANVLIGQGDVVLVMKRKRDSSILTDSQTATKRIRMAIN*C*IV*KRPCFY', 'L6': 'SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVKTYQWIIRNWEVVKIQWSQNPIMLYNKMEFEPFQSLVPKAIRSQYSGFVRTLFQPMRDVLGTFDTAQIIKLLPFAAAPPKQSRMQFSSLTVNVRGSGMRILVRGNSPVFNYNKTTKRLTILGKDAGTLIEDPDDSTSGVESAVLRGFLIIGKEDRRYGPALSINELSNLAKGEKANVLIGQGDVVLVVKRKRDSSILTDSQTATKRIRMAIN*C*IV*KRPCFY', 'L11': 'SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVNTYQWIIRNWEAVKIQWSQNTAMLYNKMEFEPFQSLVPKAIRSQYSGFVRTLFQQMRDVLGTFDTAQIIKLLPFAAAPPKQSRMQFSSLTVNVRGSGMRILVRGNSPVFNYNKTTKRLTILGKDAGTLIEDPDESTSGVESAVLRGFLIIGKEDRRYGPALSINELSNLAKGEKANVLIGQGDVVLVMKRKRDSSILTDSQTATKRIRMAIN*C*IV*KRPCFY'}\n", "SKSRSIIFSMERIKELRNLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPSLRMKWMMAMKYPITADKRVTEMVPERNEQGQTLWSKMSDAGSDRVMVSPLAVTWWNRNGPVTSTVHYPKVYKTYFDKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITKEKKEELRDCKISPLMVAYMLERELVRKTRFLPVAGGTSSIYIEVLHLTQGTCWEQMYTPGGGVRNDDVDQSLIIAARNIVRRAAVSADPLASLLEMCHSTQIGGTRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKKEEEVLTGNLQTLRIRVHEGYEEFTMVGKRATAILRKATRRLVQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGVEHIDSVMGMVGVLPDMTPSTEMSMRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTERLTITYSSSMMWEINGPESVLVNTYQWIIRNWEAVKIQWSQNPAMLYNKMEFEPFQSLVPKAIRSQYSGFVRTLFQQMRDVLGTFDTAQIIKLLPFAAAPPKQSRMQFSSLTVNVRGSGMRILVRGNSPVFNYNKTTKRLTILGKDAGTLIEDPDESTSGVESAVLRGFLIIGKEDRRYGPALSINELSNLAKGEKANVLIGQGDVVLVMKRKRDSSILTDSQTATKRIRMAIN*C*IV*KRPCFY\n" ] } ], "source": [ "\"\"\"\n", "Translate sequences\n", "\"\"\"\n", "\n", "full_protein_dict = {}\n", "for name, sequence in full_seq_dict.items():\n", " coding_dna = Seq(sequence, generic_dna)\n", " full_protein_dict[name] = str(coding_dna.translate())\n", " \n", "print(full_protein_dict)\n", "\n", "strip_vic = str(aln[6].seq).replace(\"-\", \"\")\n", "seq_strip_vic = Seq(strip_vic, generic_dna)\n", "vic_trans = seq_strip_vic.translate()\n", "print(vic_trans)\n" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\nAlign translated sequences\\n'" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "Align translated sequences\n", "\"\"\"\n", "\n", "#Used Clustal Omega Multiple Alignment because I still haven't downloaded ClustalW yet" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'L16': 112, 'L3': 1, 'L4': 4, 'L1': 2, 'L6': 5, 'L11': 1}\n", "\n", "Length of sequence of interest: 206\n" ] } ], "source": [ "\"\"\"\n", "Identify number of mutations in each sequence\n", "\"\"\"\n", "\n", "aln_aa = (AlignIO.read('cpec-protein-align-auto.clustal', 'clustal')) #I can't access the .seq and .id attributes with parse\n", "\n", "import math\n", "\n", "#arbitrarily decided to opt for max proportion of mutant sequence\n", "protein_start = int(math.floor(start/3)) \n", "protein_end = int(math.ceil(end/3))\n", "\n", "def is_diff(mutant, vic):\n", " num_mut = 0\n", " where_mut = []\n", " for aa_index, aa in enumerate(mutant[protein_start:protein_end]):\n", " if aa != vic[aa_index+protein_start]: \n", " num_mut += 1\n", " where_mut.append(aa_index+protein_start)\n", " return (num_mut, where_mut)\n", "\n", "output_handle = open(\"cpec-proteins-forward.fasta\", \"w\")\n", "\n", "mut_dict = {}\n", "\n", "\"\"\"\n", "Save sequences in a SeqRecord and write to a file\n", "\"\"\"\n", "\n", "for sequence in aln_aa[0:6]:\n", " #[0]:number of mutations, [1]: location of mutations\n", " mut_dict[sequence.id] = is_diff(sequence.seq, aln_aa[6].seq)[0]\n", " mut_str = str(mut_dict[sequence.id])\n", "\n", " record = SeqRecord(Seq(str(sequence.seq).replace(\"-\", \"\"), IUPAC.protein), id=sequence.id, name=\"cpec-\"+sequence.id+\"-forward\",description = \"Number of mutations: \" + mut_str)\n", " SeqIO.write(record, output_handle, \"fasta\")\n", "output_handle.close()\n", "\n", "print(mut_dict)\n", "print(\"\")\n", "print(\"Length of sequence of interest:\", len(vic_trans[protein_start:protein_end])) #number of amino acids in sequence of interest\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Delete gaps in amino acids\n", "#Don't need locations in saved file\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

Gelvazio/CODIGOS_ABERTOS

PROJETOS DE USUARIOS MEU GITHUB/SITE PYTHON/ApendiceE/ApendiceE.ipynb

2

10302

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python para Desenvolvedores](http://ricardoduarte.github.io/python-para-desenvolvedores/#conteudo)\n", "===================================\n", "2&ordf; edi\u00e7\u00e3o, revisada e ampliada\n", "-----------------------------------\n", "\n", "Ap\u00eandice E: BrOffice.org\n", "=============================\n", "_____________________________\n", "[BrOffice.org](http://www.broffice.org/) \u00e9 um conhecido pacote de automa\u00e7\u00e3o de escrit\u00f3rios de c\u00f3digo aberto, que inclui editor de textos, planilha e outros aplicativos. Al\u00e9m disso, o BrOffice.org tamb\u00e9m suporta Python (entre outras linguagens):\n", "\n", "+ Como linguagem de macro, permitindo a automatiza\u00e7\u00e3o de tarefas.\n", "+ Para a constru\u00e7\u00e3o de extens\u00f5es (*add ons*).\n", "+ Em um servi\u00e7o para atender conex\u00f5es, atrav\u00e9s de uma API chamada UNO (*Universal Network Objects*).\n", "\n", "Exemplo de macro:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# A macro deve ser executada a partir do\n", "# BrOffice.org Calc\n", "\n", "def plan():\n", " \"\"\"\n", " Preenche uma planilha\n", " \"\"\"\n", "\n", " # Obt\u00eam o documento para o contexto de script\n", " doc = XSCRIPTCONTEXT.getDocument()\n", "\n", " # A primeira planilha do documento\n", " sheet = doc.getSheets().getByIndex(0)\n", "\n", " col = lin = 0\n", " a = ord('A')\n", "\n", " # Cria uma linha com os t\u00edtulos para as colunas\n", " for titulo in ('Jan', 'Fev', 'Mar', 'Total'):\n", "\n", " col += 1\n", " sheet.getCellByPosition(col, lin).setString(titulo)\n", "\n", " # E coloca uma f\u00f3rmula com somat\u00f3rio na \u00faltima linha\n", " coluna = chr(a + col)\n", " formula = '=SUM(%s2:%s6)' % (coluna, coluna)\n", " sheet.getCellByPosition(col, lin + 6).setFormula(formula)\n", "\n", " for lin in xrange(1, 6):\n", "\n", " # Numera as linhas\n", " sheet.getCellByPosition(0, lin).setValue(lin)\n", "\n", " # Coloca somat\u00f3rios no fim de cada linha\n", " formula = '=SUM(B%d:D%d)' % (lin + 1, lin + 1)\n", " sheet.getCellByPosition(4, lin).setFormula(formula)\n", " \n", " # Preenche os dados\n", " for col in (1, 2, 3):\n", " sheet.getCellByPosition(col, lin).setFormula('=10*RAND()')\n", "\n", " # Substitui a f\u00f3rmula pelo valor\n", " val = sheet.getCellByPosition(col, lin).getValue()\n", " sheet.getCellByPosition(col, lin).setValue(val)\n", "\n", " return None" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sa\u00edda:\n", "\n", "\n", "\n", "Para que o BrOffice.org possa identificar o *script* escrito em Python como um arquivo de macro, ele precisa estar na pasta para *scripts* em Python, que no Windows fica em `Basis\\share\\Scripts\\python`, dentro da pasta de instala\u00e7\u00e3o do BrOffice.org.\n", "\n", "Exemplo de gera\u00e7\u00e3o de relat\u00f3rio em PDF atrav\u00e9s do editor de texto (Writer), atrav\u00e9s da Python UNO Bridge:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Para iniciar o BrOffice.org como servidor:\n", "# swriter.exe -headless\n", "# \"-accept=pipe,name=py;urp;StarOffice.ServiceManager\"\n", "\n", "import os\n", "import uno\n", "from com.sun.star.beans import PropertyValue\n", "\n", "# Dados...\n", "mus = [('Artista', 'Faixa'),\n", " ('King Crimson', 'Starless'), ('Yes', 'Siberian Khatru'),\n", " ('Led Zeppellin', 'No Quarter'), ('Genesis', 'Supper\\'s Ready')]\n", "\n", "# Obt\u00eam o n\u00famero e o tamanho dos registros\n", "rows = len(mus)\n", "cols = len(mus[0])\n", "\n", "# Inicio do \"Boiler Plate\"...\n", "\n", "# Contexto de componente local\n", "loc = uno.getComponentContext()\n", "\n", "# Para resolver URLs\n", "res = loc.ServiceManager.createInstanceWithContext(\n", " 'com.sun.star.bridge.UnoUrlResolver', loc)\n", "\n", "# Contexto para a URL\n", "con = res.resolve('uno:pipe,name=py;urp;StarOffice.ComponentContext')\n", "\n", "# Documento corrente\n", "desktop = con.ServiceManager.createInstanceWithContext(\n", " 'com.sun.star.frame.Desktop', con)\n", "\n", "# Fim do \"Boiler Plate\"...\n", "\n", "# Cria um documento novo no Writer\n", "doc = desktop.loadComponentFromURL('private:factory/swriter',\n", " '_blank', 0, ())\n", "\n", "# Cursor de texto\n", "cursor = doc.Text.createTextCursor()\n", "\n", "# Muda as propriedades do texto\n", "cursor.setPropertyValue('CharFontName', 'Verdana')\n", "cursor.setPropertyValue('CharHeight', 20)\n", "cursor.setPropertyValue('CharWeight', 180)\n", "\n", "# Insere o texto no documento\n", "doc.Text.insertString(cursor, 'M\u00fasicas favoritas\\n', 0)\n", "\n", "# Cria tabela\n", "tab = doc.createInstance('com.sun.star.text.TextTable')\n", "tab.initialize(rows, cols)\n", "doc.Text.insertTextContent(cursor, tab, 0)\n", "\n", "# Preenche a tabela\n", "for row in xrange(rows):\n", " for col in xrange(cols):\n", " cel = chr(ord('A') + col) + str(row + 1)\n", " tab.getCellByName(cel).setString(mus[row][col])\n", "\n", "# Propriedades para exportar o documento\n", "props = []\n", "p = PropertyValue()\n", "p.Name = 'Overwrite'\n", "p.Value = True # Sobrescreve o documento anterior\n", "props.append(p)\n", "\n", "p = PropertyValue()\n", "p.Name = 'FilterName'\n", "p.Value = 'writer_pdf_Export' # Writer para PDF\n", "props.append(p)\n", "\n", "# URL de destino, no qual o arquivo PDF ser\u00e1 salvo\n", "url = uno.systemPathToFileUrl(os.path.abspath('musicas.pdf'))\n", "\n", "# Salva o documento como PDF\n", "doc.storeToURL(url, tuple(props))\n", "\n", "# Fecha o documento\n", "doc.close(True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sa\u00edda (arquivo PDF):\n", "\n", "\n", "\n", "A API do BrOffice.org \u00e9 bastante completa e simplifica v\u00e1rias atividades que s\u00e3o lugar comum em programas para ambiente desktop." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Helvetica, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .note{\n", " border-bottom: 1px black dotted;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x50f8f98>" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }

gpl-2.0

Leguark/pynoddy

docs/notebooks/4-Create-model.ipynb

1

63709

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a model from scratch\n", "==============\n", "\n", "We describe here how to generate a simple history file for computation with Noddy using the functionality of pynoddy. If possible, it is advisable to generate the history files with the Windows GUI for Noddy as this method provides, to date, a simpler and more complete interface to the entire functionality. \n", "\n", "For completeness, pynoddy contains the functionality to generate simple models, for example to automate the model construction process, or to enable the model construction for users who are not running Windows. Some simple examlpes are shown in the following." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib import rc_params" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Arvo:400,700,400italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=PT+Mono' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Shadows+Into+Light' rel='stylesheet' type='text/css'>\n", "<link rel=\"stylesheet\" type=\"text/css\" href=\"http://fonts.googleapis.com/css?family=Tangerine\">\n", "<link href='http://fonts.googleapis.com/css?family=Philosopher:400,700,400italic,700italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Libre+Baskerville:400,400italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Lora:400,400italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Karla:400,400italic' rel='stylesheet' type='text/css'>\n", "\n", "<style>\n", "\n", "@font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", "}\n", "\n", "#notebook_panel { /* main background */\n", " background: #888;\n", " color: #f6f6f6;\n", "}\n", "\n", "div.cell { /* set cell width to about 80 chars */\n", " width: 800px;\n", "}\n", "\n", "div #notebook { /* centre the content */\n", " background: #fff; /* white background for content */\n", " width: 1000px;\n", " margin: auto;\n", " padding-left: 1em;\n", "}\n", "\n", "#notebook li { /* More space between bullet points */\n", "margin-top:0.8em;\n", "}\n", "\n", "/* draw border around running cells */\n", "div.cell.border-box-sizing.code_cell.running { \n", " border: 3px solid #111;\n", "}\n", "\n", "/* Put a solid color box around each cell and its output, visually linking them together */\n", "div.cell.code_cell {\n", " background: #ddd; /* rgba(230,230,230,1.0); */\n", " border-radius: 10px; /* rounded borders */\n", " width: 900px;\n", " padding: 1em;\n", " margin-top: 1em;\n", "}\n", "\n", "div.text_cell_render{\n", " font-family: 'Arvo' sans-serif;\n", " line-height: 130%;\n", " font-size: 115%;\n", " width:700px;\n", " margin-left:auto;\n", " margin-right:auto;\n", "}\n", "\n", "\n", "/* Formatting for header cells */\n", ".text_cell_render h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " /* font-family: 'Tangerine', serif; */\n", " /* font-family: 'Libre Baskerville', serif; */\n", " /* font-family: 'Karla', sans-serif;\n", " /* font-family: 'Lora', serif; */\n", " font-size: 50px;\n", " text-align: center;\n", " /* font-style: italic; */\n", " font-weight: 400;\n", " /* font-size: 40pt; */\n", " /* text-shadow: 4px 4px 4px #aaa; */\n", " line-height: 120%;\n", " color: rgb(12,85,97);\n", " margin-bottom: .5em;\n", " margin-top: 0.1em;\n", " display: block;\n", "}\t\n", ".text_cell_render h2 {\n", " /* font-family: 'Arial', serif; */\n", " /* font-family: 'Lora', serif; */\n", " font-family: 'Alegreya Sans', sans-serif;\n", " font-weight: 700;\n", " font-size: 24pt;\n", " line-height: 100%;\n", " /* color: rgb(171,165,131); */\n", " color: rgb(12,85,97);\n", " margin-bottom: 0.1em;\n", " margin-top: 0.1em;\n", " display: block;\n", "}\t\n", "\n", ".text_cell_render h3 {\n", " font-family: 'Arial', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " font-style: italic;\n", " color: rgb(95,92,72);\n", "}\n", "\n", ".text_cell_render h4 {\n", " font-family: 'Arial', serif;\n", "}\n", "\n", ".text_cell_render h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: grey;\n", " font-style: italic;\n", " margin-bottom: .1em;\n", " margin-top: 0.1em;\n", " display: block;\n", "}\n", "\n", ".text_cell_render h6 {\n", " font-family: 'PT Mono', sans-serif;\n", " font-weight: 300;\n", " font-size: 10pt;\n", " color: grey;\n", " margin-bottom: 1px;\n", " margin-top: 1px;\n", "}\n", "\n", ".CodeMirror{\n", " font-family: \"PT Mono\";\n", " font-size: 100%;\n", "}\n", "\n", "</style>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "css_file = 'pynoddy.css'\n", "HTML(open(css_file, \"r\").read())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys, os\n", "import matplotlib.pyplot as plt\n", "# adjust some settings for matplotlib\n", "from matplotlib import rcParams\n", "# print rcParams\n", "rcParams['font.size'] = 15\n", "# determine path of repository to set paths corretly below\n", "repo_path = os.path.realpath('../..')\n", "import pynoddy.history" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rcParams.update({'font.size': 20})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defining a stratigraphy\n", "--------------\n", "\n", "We start with the definition of a (base) stratigraphy for the model. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Combined: model generation and output vis to test:\n", "history = \"simple_model.his\"\n", "output_name = \"simple_out\"\n", "reload(pynoddy.history)\n", "reload(pynoddy.events)\n", "\n", "# create pynoddy object\n", "nm = pynoddy.history.NoddyHistory()\n", "# add stratigraphy\n", "strati_options = {'num_layers' : 8,\n", " 'layer_names' : ['layer 1', 'layer 2', 'layer 3', \n", " 'layer 4', 'layer 5', 'layer 6', \n", " 'layer 7', 'layer 8'],\n", " 'layer_thickness' : [1500, 500, 500, 500, 500, 500, 500, 500]}\n", "nm.add_event('stratigraphy', strati_options )\n", "\n", "nm.write_history(history)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute the model\n", "reload(pynoddy)\n", "pynoddy.compute_model(history, output_name) " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1073fcb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot output\n", "import pynoddy.output\n", "reload(pynoddy.output)\n", "nout = pynoddy.output.NoddyOutput(output_name)\n", "nout.plot_section('y', layer_labels = strati_options['layer_names'][::-1], \n", " colorbar = True, title=\"\",\n", " savefig = False, fig_filename = \"ex01_strati.eps\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a fault event\n", "----------\n", "\n", "As a next step, let's now add the faults to the model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reload(pynoddy.history)\n", "reload(pynoddy.events)\n", "nm = pynoddy.history.NoddyHistory()\n", "# add stratigraphy\n", "strati_options = {'num_layers' : 8,\n", " 'layer_names' : ['layer 1', 'layer 2', 'layer 3', 'layer 4', 'layer 5', 'layer 6', 'layer 7', 'layer 8'],\n", " 'layer_thickness' : [1500, 500, 500, 500, 500, 500, 500, 500]}\n", "nm.add_event('stratigraphy', strati_options )\n", "\n", "\n", "\n", "\n", "# The following options define the fault geometry:\n", "fault_options = {'name' : 'Fault_E',\n", " 'pos' : (6000, 0, 5000),\n", " 'dip_dir' : 270,\n", " 'dip' : 60,\n", " 'slip' : 1000}\n", "\n", "nm.add_event('fault', fault_options)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1: <pynoddy.events.Stratigraphy at 0x1073fc590>,\n", " 2: <pynoddy.events.Fault at 0x107565fd0>}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nm.events" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nm.write_history(history)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute the model\n", "pynoddy.compute_model(history, output_name) " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1074dac90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot output\n", "reload(pynoddy.output)\n", "nout = pynoddy.output.NoddyOutput(output_name)\n", "nout.plot_section('y', layer_labels = strati_options['layer_names'][::-1], \n", " colorbar = True, title = \"\",\n", " savefig = False, fig_filename = \"ex01_fault_E.eps\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The following options define the fault geometry:\n", "fault_options = {'name' : 'Fault_1',\n", " 'pos' : (5500, 3500, 0),\n", " 'dip_dir' : 270,\n", " 'dip' : 60,\n", " 'slip' : 1000}\n", "\n", "nm.add_event('fault', fault_options)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nm.write_history(history)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute the model\n", "pynoddy.compute_model(history, output_name) " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1074ea610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot output\n", "reload(pynoddy.output)\n", "nout = pynoddy.output.NoddyOutput(output_name)\n", "nout.plot_section('y', layer_labels = strati_options['layer_names'][::-1], colorbar = True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nm1 = pynoddy.history.NoddyHistory(history)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(10000.0, 7000.0, 5000.0)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nm1.get_extent()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Complete Model Set-up\n", "--------------------\n", "\n", "And here now, combining all the previous steps, the entire model set-up with base stratigraphy and two faults:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reload(pynoddy.history)\n", "reload(pynoddy.events)\n", "nm = pynoddy.history.NoddyHistory()\n", "# add stratigraphy\n", "strati_options = {'num_layers' : 8,\n", " 'layer_names' : ['layer 1', 'layer 2', 'layer 3',\n", " 'layer 4', 'layer 5', 'layer 6', \n", " 'layer 7', 'layer 8'],\n", " 'layer_thickness' : [1500, 500, 500, 500, 500, \n", " 500, 500, 500]}\n", "nm.add_event('stratigraphy', strati_options )\n", "\n", "# The following options define the fault geometry:\n", "fault_options = {'name' : 'Fault_W',\n", " 'pos' : (4000, 3500, 5000),\n", " 'dip_dir' : 90,\n", " 'dip' : 60,\n", " 'slip' : 1000}\n", "\n", "nm.add_event('fault', fault_options)\n", "# The following options define the fault geometry:\n", "fault_options = {'name' : 'Fault_E',\n", " 'pos' : (6000, 3500, 5000),\n", " 'dip_dir' : 270,\n", " 'dip' : 60,\n", " 'slip' : 1000}\n", "\n", "nm.add_event('fault', fault_options)\n", "nm.write_history(history)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Change cube size\n", "nm1 = pynoddy.history.NoddyHistory(history)\n", "nm1.change_cube_size(50)\n", "nm1.write_history(history)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute the model\n", "pynoddy.compute_model(history, output_name) " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107784e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot output\n", "reload(pynoddy.output)\n", "nout = pynoddy.output.NoddyOutput(output_name)\n", "nout.plot_section('y', layer_labels = strati_options['layer_names'][::-1], \n", " colorbar = True, title=\"\",\n", " savefig = True, fig_filename = \"ex01_faults_combined.eps\",\n", " cmap = 'YlOrRd') # note: YlOrRd colourmap should be suitable for colorblindness!\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

natasha/natasha

docs.ipynb

1

47515

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Natasha" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Natasha solves basic NLP tasks for Russian language: tokenization, sentence segmentatoin, word embedding, morphology tagging, lemmatization, phrase normalization, syntax parsing, NER tagging, fact extraction.\n", "\n", "Library is just a wrapper for lower level tools from <a href=\"https://github.com/natasha\">Natasha project</a>:\n", "\n", "* <a href=\"https://github.com/natasha/razdel\">Razdel</a> — token, sentence segmentation for Russian\n", "* <a href=\"https://github.com/natasha/navec\">Navec</a> — compact Russian embeddings\n", "* <a href=\"https://github.com/natasha/slovnet\">Slovnet</a> — modern deep-learning techniques for Russian NLP, compact models for Russian morphology, syntax, NER.\n", "* <a href=\"https://github.com/natasha/yargy\">Yargy</a> — rule-based fact extraction similar to Tomita parser.\n", "* <a href=\"https://github.com/natasha/ipymarkup\">Ipymarkup</a> — NLP visualizations for NER and syntax markups.\n", "\n", "Consider using these lower level tools for realword tasks. Natasha models are optimized for news articles, on other domains quality may be worse." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from natasha import (\n", " Segmenter,\n", " MorphVocab,\n", " \n", " NewsEmbedding,\n", " NewsMorphTagger,\n", " NewsSyntaxParser,\n", " NewsNERTagger,\n", " \n", " PER,\n", " NamesExtractor,\n", " DatesExtractor,\n", " MoneyExtractor,\n", " AddrExtractor,\n", "\n", " Doc\n", ")\n", "\n", "segmenter = Segmenter()\n", "morph_vocab = MorphVocab()\n", "\n", "emb = NewsEmbedding()\n", "morph_tagger = NewsMorphTagger(emb)\n", "syntax_parser = NewsSyntaxParser(emb)\n", "ner_tagger = NewsNERTagger(emb)\n", "\n", "names_extractor = NamesExtractor(morph_vocab)\n", "dates_extractor = DatesExtractor(morph_vocab)\n", "money_extractor = MoneyExtractor(morph_vocab)\n", "addr_extractor = AddrExtractor(morph_vocab)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Doc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Doc` aggregates annotators, initially it has just `text` field defined:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Doc(text='Посол Израиля на Украине Йоэль Лион признался, чт...)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text = 'Посол Израиля на Украине Йоэль Лион признался, что пришел в шок, узнав о решении властей Львовской области объявить 2019 год годом лидера запрещенной в России Организации украинских националистов (ОУН) Степана Бандеры. Свое заявление он разместил в Twitter. «Я не могу понять, как прославление тех, кто непосредственно принимал участие в ужасных антисемитских преступлениях, помогает бороться с антисемитизмом и ксенофобией. Украина не должна забывать о преступлениях, совершенных против украинских евреев, и никоим образом не отмечать их через почитание их исполнителей», — написал дипломат. 11 декабря Львовский областной совет принял решение провозгласить 2019 год в регионе годом Степана Бандеры в связи с празднованием 110-летия со дня рождения лидера ОУН (Бандера родился 1 января 1909 года). В июле аналогичное решение принял Житомирский областной совет. В начале месяца с предложением к президенту страны Петру Порошенко вернуть Бандере звание Героя Украины обратились депутаты Верховной Рады. Парламентарии уверены, что признание Бандеры национальным героем поможет в борьбе с подрывной деятельностью против Украины в информационном поле, а также остановит «распространение мифов, созданных российской пропагандой». Степан Бандера (1909-1959) был одним из лидеров Организации украинских националистов, выступающей за создание независимого государства на территориях с украиноязычным населением. В 2010 году в период президентства Виктора Ющенко Бандера был посмертно признан Героем Украины, однако впоследствии это решение было отменено судом. '\n", "doc = Doc(text)\n", "doc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After applying `segmenter` two new fields appear `sents` and `tokens`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Doc(text='Посол Израиля на Украине Йоэль Лион признался, чт..., tokens=[...], sents=[...])" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[DocSent(stop=218, text='Посол Израиля на Украине Йоэль Лион признался, чт..., tokens=[...]),\n", " DocSent(start=219, stop=257, text='Свое заявление он разместил в Twitter.', tokens=[...])]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[DocToken(stop=5, text='Посол'),\n", " DocToken(start=6, stop=13, text='Израиля'),\n", " DocToken(start=14, stop=16, text='на'),\n", " DocToken(start=17, stop=24, text='Украине'),\n", " DocToken(start=25, stop=30, text='Йоэль')]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "doc.segment(segmenter)\n", "display(doc)\n", "display(doc.sents[:2])\n", "display(doc.tokens[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After applying `morph_tagger` and `syntax_parser`, tokens get 5 new fields `id`, `pos`, `feats`, `head_id`, `rel` — annotation in <a href=\"https://universaldependencies.org/\">Universal Dependencies format</a>:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[DocToken(stop=5, text='Посол', id='1_1', head_id='1_7', rel='nsubj', pos='NOUN', feats=<Anim,Nom,Masc,Sing>),\n", " DocToken(start=6, stop=13, text='Израиля', id='1_2', head_id='1_1', rel='nmod', pos='PROPN', feats=<Inan,Gen,Masc,Sing>),\n", " DocToken(start=14, stop=16, text='на', id='1_3', head_id='1_4', rel='case', pos='ADP'),\n", " DocToken(start=17, stop=24, text='Украине', id='1_4', head_id='1_1', rel='nmod', pos='PROPN', feats=<Inan,Loc,Fem,Sing>),\n", " DocToken(start=25, stop=30, text='Йоэль', id='1_5', head_id='1_1', rel='appos', pos='PROPN', feats=<Anim,Nom,Masc,Sing>)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "doc.tag_morph(morph_tagger)\n", "doc.parse_syntax(syntax_parser)\n", "display(doc.tokens[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After applying `ner_tagger` doc gets `spans` field with PER, LOC, ORG annotation:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[DocSpan(start=6, stop=13, type='LOC', text='Израиля', tokens=[...]),\n", " DocSpan(start=17, stop=24, type='LOC', text='Украине', tokens=[...]),\n", " DocSpan(start=25, stop=35, type='PER', text='Йоэль Лион', tokens=[...]),\n", " DocSpan(start=89, stop=106, type='LOC', text='Львовской области', tokens=[...]),\n", " DocSpan(start=152, stop=158, type='LOC', text='России', tokens=[...])]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "doc.tag_ner(ner_tagger)\n", "display(doc.spans[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Natasha wraps <a href=\"https://github.com/natasha/ipymarkup\">Ipymarkup</a> to provide ASCII visualizations for morphology, syntax and NER annotations. `doc` and `sents` have 3 methods: `morph.print()`, `syntax.print()` and `ner.print()`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Посол Израиля на Украине Йоэль Лион признался, что пришел в шок, узнав\n", " LOC──── LOC──── PER─────── \n", " о решении властей Львовской области объявить 2019 год годом лидера \n", " LOC────────────── \n", "запрещенной в России Организации украинских националистов (ОУН) \n", " LOC─── ORG─────────────────────────────────────── \n", "Степана Бандеры. Свое заявление он разместил в Twitter. «Я не могу \n", "PER──────────── ORG──── \n", "понять, как прославление тех, кто непосредственно принимал участие в \n", "ужасных антисемитских преступлениях, помогает бороться с \n", "антисемитизмом и ксенофобией. Украина не должна забывать о \n", " LOC──── \n", "преступлениях, совершенных против украинских евреев, и никоим образом \n", "не отмечать их через почитание их исполнителей», — написал дипломат. \n", "11 декабря Львовский областной совет принял решение провозгласить 2019\n", " ORG────────────────────── \n", " год в регионе годом Степана Бандеры в связи с празднованием 110-летия\n", " PER──────────── \n", " со дня рождения лидера ОУН (Бандера родился 1 января 1909 года). В \n", " ORG \n", "июле аналогичное решение принял Житомирский областной совет. В начале \n", " ORG──────────────────────── \n", "месяца с предложением к президенту страны Петру Порошенко вернуть \n", " PER──────────── \n", "Бандере звание Героя Украины обратились депутаты Верховной Рады. \n", "PER──── LOC──── ORG─────────── \n", "Парламентарии уверены, что признание Бандеры национальным героем \n", " PER──── \n", "поможет в борьбе с подрывной деятельностью против Украины в \n", " LOC──── \n", "информационном поле, а также остановит «распространение мифов, \n", "созданных российской пропагандой». Степан Бандера (1909-1959) был \n", " PER─────────── \n", "одним из лидеров Организации украинских националистов, выступающей за \n", " ORG───────────────────────────────── \n", "создание независимого государства на территориях с украиноязычным \n", "населением. В 2010 году в период президентства Виктора Ющенко Бандера \n", " PER─────────── PER──── \n", "был посмертно признан Героем Украины, однако впоследствии это решение \n", " LOC──── \n", "было отменено судом. \n" ] } ], "source": [ "doc.ner.print()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Посол NOUN|Animacy=Anim|Case=Nom|Gender=Masc|Number=Sing\n", " Израиля PROPN|Animacy=Inan|Case=Gen|Gender=Masc|Number=Sing\n", " на ADP\n", " Украине PROPN|Animacy=Inan|Case=Loc|Gender=Fem|Number=Sing\n", " Йоэль PROPN|Animacy=Anim|Case=Nom|Gender=Masc|Number=Sing\n", " Лион PROPN|Animacy=Anim|Case=Nom|Gender=Masc|Number=Sing\n", " признался VERB|Aspect=Perf|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Mid\n", " , PUNCT\n", " что SCONJ\n", " пришел VERB|Aspect=Perf|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act\n", " в ADP\n", " шок NOUN|Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing\n", " , PUNCT\n", " узнав VERB|Aspect=Perf|Tense=Past|VerbForm=Conv|Voice=Act\n", " о ADP\n", " решении NOUN|Animacy=Inan|Case=Loc|Gender=Neut|Number=Sing\n", " властей NOUN|Animacy=Inan|Case=Gen|Gender=Fem|Number=Plur\n", " Львовской ADJ|Case=Gen|Degree=Pos|Gender=Fem|Number=Sing\n", " области NOUN|Animacy=Inan|Case=Gen|Gender=Fem|Number=Sing\n", " объявить VERB|Aspect=Perf|VerbForm=Inf|Voice=Act\n", " 2019 ADJ\n", " год NOUN|Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing\n", " годом NOUN|Animacy=Inan|Case=Ins|Gender=Masc|Number=Sing\n", " лидера NOUN|Animacy=Anim|Case=Gen|Gender=Masc|Number=Sing\n", " запрещенной VERB|Aspect=Perf|Case=Gen|Gender=Fem|Number=Sing|Tense=Past|VerbForm=Part|Voice=Pass\n", " в ADP\n", " России PROPN|Animacy=Inan|Case=Loc|Gender=Fem|Number=Sing\n", " Организации PROPN|Animacy=Inan|Case=Gen|Gender=Fem|Number=Sing\n", " украинских ADJ|Case=Gen|Degree=Pos|Number=Plur\n", " националистов NOUN|Animacy=Anim|Case=Gen|Gender=Masc|Number=Plur\n", " ( PUNCT\n", " ОУН PROPN|Animacy=Inan|Case=Nom|Gender=Fem|Number=Sing\n", " ) PUNCT\n", " Степана PROPN|Animacy=Anim|Case=Gen|Gender=Masc|Number=Sing\n", " Бандеры PROPN|Animacy=Anim|Case=Gen|Gender=Masc|Number=Sing\n", " . PUNCT\n" ] } ], "source": [ "sent = doc.sents[0]\n", "sent.morph.print()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ┌──► Посол nsubj\n", " │ Израиля \n", " │ ┌► на case\n", " │ └─ Украине \n", " │ ┌─ Йоэль \n", " │ └► Лион flat:name\n", "┌─────┌─└─── признался \n", "│ │ ┌──► , punct\n", "│ │ │ ┌► что mark\n", "│ └►└─└─ пришел ccomp\n", "│ │ ┌► в case\n", "│ └──►└─ шок obl\n", "│ ┌► , punct\n", "│ ┌────►┌─└─ узнав advcl\n", "│ │ │ ┌► о case\n", "│ │ ┌───└►└─ решении obl\n", "│ │ │ ┌─└──► властей nmod\n", "│ │ │ │ ┌► Львовской amod\n", "│ │ │ └──►└─ области nmod\n", "│ └─└►┌─┌─── объявить nmod\n", "│ │ │ ┌► 2019 amod\n", "│ │ └►└─ год obj\n", "│ └──►┌─ годом obl\n", "│ ┌─────└► лидера nmod\n", "│ │ ┌►┌─── запрещенной acl\n", "│ │ │ │ ┌► в case\n", "│ │ │ └►└─ России obl\n", "│ ┌─└►└─┌─── Организации nmod\n", "│ │ │ ┌► украинских amod\n", "│ │ ┌─└►└─ националистов nmod\n", "│ │ │ ┌► ( punct\n", "│ │ └►┌─└─ ОУН parataxis\n", "│ │ └──► ) punct\n", "│ └──────►┌─ Степана appos\n", "│ └► Бандеры flat:name\n", "└──────────► . punct\n" ] } ], "source": [ "sent.syntax.print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lemmatization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tokens have `lemmatize` method, it uses `pos` and `feats` assigned by `morph_tagger` to get word normal form. `morph_vocab` is just a wrapper for <a href=\"https://pymorphy2.readthedocs.io/en/latest/\">Pymorphy2</a>:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Посол': 'посол',\n", " 'Израиля': 'израиль',\n", " 'на': 'на',\n", " 'Украине': 'украина',\n", " 'Йоэль': 'йоэль',\n", " 'Лион': 'лион',\n", " 'признался': 'признаться',\n", " ',': ',',\n", " 'что': 'что',\n", " 'пришел': 'прийти'}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for token in doc.tokens:\n", " token.lemmatize(morph_vocab)\n", " \n", "{_.text: _.lemma for _ in doc.tokens[:10]}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Phrase normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider phrase \"Организации украинских националистов\", one can not just inflect every word independently to get normal form: \"Организация украинский националист\". Spans have method `normalize` that uses syntax annotation by `syntax_parser` to inflect phrases:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'Израиля': 'Израиль',\n", " 'Украине': 'Украина',\n", " 'Йоэль Лион': 'Йоэль Лион',\n", " 'Львовской области': 'Львовская область',\n", " 'России': 'Россия',\n", " 'Организации украинских националистов (ОУН)': 'Организация украинских националистов (ОУН)',\n", " 'Степана Бандеры': 'Степан Бандера',\n", " 'Twitter': 'Twitter',\n", " 'Украина': 'Украина',\n", " 'Львовский областной совет': 'Львовский областной совет',\n", " 'ОУН': 'ОУН',\n", " 'Житомирский областной совет': 'Житомирский областной совет',\n", " 'Петру Порошенко': 'Петр Порошенко',\n", " 'Бандере': 'Бандера',\n", " 'Украины': 'Украина',\n", " 'Верховной Рады': 'Верховная Рада',\n", " 'Бандеры': 'Бандера',\n", " 'Степан Бандера': 'Степан Бандера',\n", " 'Организации украинских националистов': 'Организация украинских националистов',\n", " 'Виктора Ющенко': 'Виктор Ющенко',\n", " 'Бандера': 'Бандера'}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for span in doc.spans:\n", " span.normalize(morph_vocab)\n", " \n", "{_.text: _.normal for _ in doc.spans}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fact extraction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To split names like \"Виктор Ющенко\", \"Бандера\" and \"Йоэль Лион\" into parts use `names_extractor` and spans method `extract_fact`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Йоэль Лион': {'first': 'Йоэль', 'last': 'Лион'},\n", " 'Степан Бандера': {'first': 'Степан', 'last': 'Бандера'},\n", " 'Петр Порошенко': {'first': 'Петр', 'last': 'Порошенко'},\n", " 'Бандера': {'last': 'Бандера'},\n", " 'Виктор Ющенко': {'first': 'Виктор', 'last': 'Ющенко'}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for span in doc.spans:\n", " if span.type == PER:\n", " span.extract_fact(names_extractor)\n", " \n", "{_.normal: _.fact.as_dict for _ in doc.spans if _.fact}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One may use Natasha components independently. It is not mandatory to construct `Doc` object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Segmenter`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Segmenter` is just a wrapper for <a href=\"https://github.com/natasha/razdel\">Razdel</a>, it has two methods `tokenize` and `sentenize`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Token(start=0, stop=13, text='Кружка-термос')\n", "Token(start=14, stop=16, text='на')\n", "Token(start=17, stop=20, text='0.5')\n", "Token(start=20, stop=21, text='л')\n", "Token(start=22, stop=23, text='(')\n" ] } ], "source": [ "tokens = list(segmenter.tokenize('Кружка-термос на 0.5л (50/64 см³, 516;...)'))\n", "for token in tokens[:5]:\n", " print(token)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sent(start=1, stop=23, text='- \"Так в чем же дело?\"')\n", "Sent(start=24, stop=40, text='- \"Не ра-ду-ют\".')\n", "Sent(start=42, stop=57, text='И т. д. и т. п.')\n", "Sent(start=58, stop=77, text='В общем, вся газета')\n" ] } ], "source": [ "text = '''\n", "- \"Так в чем же дело?\" - \"Не ра-ду-ют\".\n", " И т. д. и т. п. В общем, вся газета\n", "'''\n", "sents = list(segmenter.sentenize(text))\n", "for sent in sents:\n", " print(sent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `MorphVocab`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`MorphVocab` is a wrapper for <a href=\"pymorphy2.readthedocs.io/en/latest/\">Pymorphy2</a>. `MorphVocab` adds cache and adapts grammems to Universal Dependencies format:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[MorphForm(normal='стать', pos='VERB', feats={'VerbForm': 'Fin', 'Aspect': 'Perf', 'Number': 'Plur', 'Tense': 'Past', 'Mood': 'Ind'}),\n", " MorphForm(normal='сталь', pos='NOUN', feats={'Animacy': 'Inan', 'Gender': 'Fem', 'Number': 'Sing', 'Case': 'Gen'}),\n", " MorphForm(normal='сталь', pos='NOUN', feats={'Animacy': 'Inan', 'Gender': 'Fem', 'Number': 'Sing', 'Case': 'Dat'}),\n", " MorphForm(normal='сталь', pos='NOUN', feats={'Animacy': 'Inan', 'Gender': 'Fem', 'Number': 'Sing', 'Case': 'Loc'}),\n", " MorphForm(normal='сталь', pos='NOUN', feats={'Animacy': 'Inan', 'Gender': 'Fem', 'Number': 'Plur', 'Case': 'Nom'}),\n", " MorphForm(normal='сталь', pos='NOUN', feats={'Animacy': 'Inan', 'Gender': 'Fem', 'Number': 'Plur', 'Case': 'Acc'})]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forms = morph_vocab('стали')\n", "forms" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CacheInfo(hits=214, misses=634, maxsize=10000, currsize=634)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "morph_vocab.__call__.cache_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also `MorphVocab` adds method `lemmatize`. Given `pos` and `feats` it selects the most suitable morph form and returns its `normal` field:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'стать'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "morph_vocab.lemmatize('стали', 'VERB', {})" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'сталь'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "morph_vocab.lemmatize('стали', 'X', {'Case': 'Gen'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Embedding`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Embedding` is a wrapper for <a href=\"https://github.com/natasha/navec/\">Navec</a> — compact pretrained word embeddings for Russian language:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Words in vocab + 2 for pad and unk: 250002\n" ] } ], "source": [ "print('Words in vocab + 2 for pad and unk: %d' % len(emb.vocab.words) )" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.3309305 , 0.18249014, 0.23347412, 0.14935994, -0.17402406,\n", " -0.47864223, -0.24524143, 0.15673256, -0.08669729, -0.11727095],\n", " dtype=float32)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "emb['навек'][:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `MorphTagger`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`MorphTagger` wraps <a href=\"https://github.com/natasha/slovnet\">Slovnet morphology tagger</a>. Tagger has list of words as input and returns markup object. Markup has `print` method that outputs morph tags ASCII visualization:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Европейский ADJ|Case=Nom|Degree=Pos|Gender=Masc|Number=Sing\n", " союз NOUN|Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing\n", " добавил VERB|Aspect=Perf|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act\n", " в ADP\n", " санкционный ADJ|Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|Number=Sing\n", " список NOUN|Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing\n", " девять NUM|Case=Nom\n", " политических ADJ|Case=Gen|Degree=Pos|Number=Plur\n", " деятелей NOUN|Animacy=Anim|Case=Gen|Gender=Masc|Number=Plur\n" ] } ], "source": [ "words = ['Европейский', 'союз', 'добавил', 'в', 'санкционный', 'список', 'девять', 'политических', 'деятелей']\n", "markup = morph_tagger(words)\n", "markup.print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `SyntaxParser`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`SyntaxParser` wraps <a href=\"https://github.com/natasha/slovnet\">Slovnet syntax parser</a>. Interface is similar to `MorphTagger`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ┌► Европейский amod\n", " ┌►└─ союз nsubj\n", " ┌─└─── добавил \n", " │ ┌──► в case\n", " │ │ ┌► санкционный amod\n", "┌─└►└─└─ список obl\n", "│ ┌──► девять nummod:gov\n", "│ │ ┌► политических amod\n", "└──►└─└─ деятелей nmod\n" ] } ], "source": [ "words = ['Европейский', 'союз', 'добавил', 'в', 'санкционный', 'список', 'девять', 'политических', 'деятелей']\n", "markup = syntax_parser(words)\n", "markup.print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `NERTagger`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`NERTagger` wraps <a href=\"https://github.com/natasha/slovnet\">Slovnet NER tagger</a>. Interface is similar to `MorphTagger` but has untokenized text as input:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Посол Израиля на Украине Йоэль Лион признался, что пришел в шок, узнав\n", " LOC──── LOC──── PER─────── \n", " о решении властей Львовской области объявить 2019 год годом лидера \n", " LOC────────────── \n", "запрещенной в России Организации украинских националистов (ОУН) \n", " LOC─── ORG─────────────────────────────────────── \n", "Степана Бандеры. Свое заявление он разместил в Twitter. 11 декабря \n", "PER──────────── ORG──── \n", "Львовский областной совет принял решение провозгласить 2019 год в \n", "ORG────────────────────── \n", "регионе годом Степана Бандеры в связи с празднованием 110-летия со дня\n", " PER──────────── \n", " рождения лидера ОУН (Бандера родился 1 января 1909 года).\n", " ORG \n" ] } ], "source": [ "text = 'Посол Израиля на Украине Йоэль Лион признался, что пришел в шок, узнав о решении властей Львовской области объявить 2019 год годом лидера запрещенной в России Организации украинских националистов (ОУН) Степана Бандеры. Свое заявление он разместил в Twitter. 11 декабря Львовский областной совет принял решение провозгласить 2019 год в регионе годом Степана Бандеры в связи с празднованием 110-летия со дня рождения лидера ОУН (Бандера родился 1 января 1909 года).'\n", "markup = ner_tagger(text)\n", "markup.print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Extractor`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to `names_extractor` Natasha bundles several other extractors: `dates_extractor`, `money_extractor` and `addr_extractor`. All extractors are based on <a href=\"https://github.com/natasha/yargy\">Yargy-parser</a>, meaning that they work correctly only on small predefined set of texts. For realword tasks consider writing your own grammar, see <a href=\"https://github.com/natasha/yargy#documentation\">Yargy docs</a> for more." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `DatesExtractor`" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Match(\n", " start=0,\n", " stop=10,\n", " fact=Date(\n", " year=2017,\n", " month=1,\n", " day=24\n", " )\n", " ),\n", " Match(\n", " start=12,\n", " stop=20,\n", " fact=Date(\n", " year=2015,\n", " month=None,\n", " day=None\n", " )\n", " ),\n", " Match(\n", " start=22,\n", " stop=28,\n", " fact=Date(\n", " year=2014,\n", " month=None,\n", " day=None\n", " )\n", " ),\n", " Match(\n", " start=30,\n", " stop=38,\n", " fact=Date(\n", " year=None,\n", " month=4,\n", " day=1\n", " )\n", " ),\n", " Match(\n", " start=40,\n", " stop=51,\n", " fact=Date(\n", " year=2017,\n", " month=5,\n", " day=None\n", " )\n", " ),\n", " Match(\n", " start=53,\n", " stop=68,\n", " fact=Date(\n", " year=2017,\n", " month=5,\n", " day=9\n", " )\n", " )]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text = '24.01.2017, 2015 год, 2014 г, 1 апреля, май 2017 г., 9 мая 2017 года'\n", "list(dates_extractor(text))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `MoneyExtractor`" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Match(\n", " start=0,\n", " stop=18,\n", " fact=Money(\n", " amount=1599059.38,\n", " currency='EUR'\n", " )\n", " ),\n", " Match(\n", " start=20,\n", " stop=32,\n", " fact=Money(\n", " amount=420,\n", " currency='USD'\n", " )\n", " ),\n", " Match(\n", " start=34,\n", " stop=44,\n", " fact=Money(\n", " amount=20000000,\n", " currency='RUB'\n", " )\n", " ),\n", " Match(\n", " start=46,\n", " stop=54,\n", " fact=Money(\n", " amount=20000,\n", " currency='RUB'\n", " )\n", " ),\n", " Match(\n", " start=56,\n", " stop=133,\n", " fact=Money(\n", " amount=881913.98,\n", " currency='RUB'\n", " )\n", " )]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text = '1 599 059, 38 Евро, 420 долларов, 20 млн руб, 20 т. р., 881 913 (Восемьсот восемьдесят одна тысяча девятьсот тринадцать) руб. 98 коп.'\n", "list(money_extractor(text))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `NamesExtractor`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`names_extractor` should be applied only to spans of text. To extract single fact use method `find`:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=16,\n", " fact=Name(\n", " first='Мустафа',\n", " last='Джемилев',\n", " middle=None\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=12,\n", " fact=Name(\n", " first='О',\n", " last='Дерипаска',\n", " middle=None\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=22,\n", " fact=Name(\n", " first='Фёдор',\n", " last='Шаляпин',\n", " middle='Иванович'\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=8,\n", " fact=Name(\n", " first=None,\n", " last='Янукович',\n", " middle=None\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lines = [\n", " 'Мустафа Джемилев',\n", " 'О. Дерипаска',\n", " 'Фёдор Иванович Шаляпин',\n", " 'Янукович'\n", "]\n", "for line in lines:\n", " display(names_extractor.find(line))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `AddrExtractor`" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=48,\n", " fact=Addr(\n", " parts=[AddrPart(\n", " value='Россия',\n", " type='страна'\n", " ),\n", " AddrPart(\n", " value='Вологодская',\n", " type='область'\n", " ),\n", " AddrPart(\n", " value='Череповец',\n", " type='город'\n", " ),\n", " AddrPart(\n", " value='Победы',\n", " type='проспект'\n", " )]\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=56,\n", " fact=Addr(\n", " parts=[AddrPart(\n", " value='692909',\n", " type='индекс'\n", " ),\n", " AddrPart(\n", " value='РФ',\n", " type='страна'\n", " ),\n", " AddrPart(\n", " value='Приморский',\n", " type='край'\n", " ),\n", " AddrPart(\n", " value='Находка',\n", " type='город'\n", " ),\n", " AddrPart(\n", " value='Добролюбова',\n", " type='улица'\n", " )]\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Match(\n", " start=0,\n", " stop=29,\n", " fact=Addr(\n", " parts=[AddrPart(\n", " value='Народного Ополчения',\n", " type='улица'\n", " ),\n", " AddrPart(\n", " value='9к',\n", " type='дом'\n", " )]\n", " )\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lines = [\n", " 'Россия, Вологодская обл. г. Череповец, пр.Победы 93 б',\n", " '692909, РФ, Приморский край, г. Находка, ул. Добролюбова, 18',\n", " 'ул. Народного Ополчения д. 9к.3'\n", "]\n", "for line in lines:\n", " display(addr_extractor.find(line))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

sirex/ckan-ivpk-import

notebook.ipynb

1

95265

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.read_json('data/ivpk-export.jsonl', lines=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(757, 29)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2 lygis</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3 lygis</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Alternatyvus pavadinimas</th>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>Apibūdinimas</th>\n", " <td>Pateikiama informacija: duomenų data, kelio nu...</td>\n", " </tr>\n", " <tr>\n", " <th>Atnaujinimo dažnumas</th>\n", " <td>Kartą per metus</td>\n", " </tr>\n", " <tr>\n", " <th>Duomenų formatas</th>\n", " <td>CSV formatas</td>\n", " </tr>\n", " <tr>\n", " <th>Duomenų išsamumas</th>\n", " <td>Visi duomenys sukaupti</td>\n", " </tr>\n", " <tr>\n", " <th>Duomenų patikimumas</th>\n", " <td>Įstaiga prisiima atsakomybę</td>\n", " </tr>\n", " <tr>\n", " <th>Ieškoti tik jau pateiktuose rezultatuose</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Internetinis adresas</th>\n", " <td>http://www.lakd.lt/files/atviri_duomenys/danga...</td>\n", " </tr>\n", " <tr>\n", " <th>Kategorija (informacijos sritis)</th>\n", " <td>Transportas ir ryšiai</td>\n", " </tr>\n", " <tr>\n", " <th>Kategorija (informacijos sritis) 1 lygis</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Kodas</th>\n", " <td>6055</td>\n", " </tr>\n", " <tr>\n", " <th>Kontaktiniai duomenys</th>\n", " <td>(8 5) 232 9640 [email protected]</td>\n", " </tr>\n", " <tr>\n", " <th>Pavadinimas</th>\n", " <td>2013 m. viršutinės kelio dangos duomenys</td>\n", " </tr>\n", " <tr>\n", " <th>Publikavimo data iki:</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Publikavimo data nuo:</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Reikšminiai žodžiai</th>\n", " <td>keliai, dangos</td>\n", " </tr>\n", " <tr>\n", " <th>Rinkmenos aprašymo publikavimo duomenys</th>\n", " <td>2016-12-05 17:07:50</td>\n", " </tr>\n", " <tr>\n", " <th>Rinkmenos duomenų teikimo sąlygos</th>\n", " <td>Skelbiama internete</td>\n", " </tr>\n", " <tr>\n", " <th>Rinkmenos pabaigos data</th>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>Rinkmenos pradžios data</th>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>Rinkmenos rūšis</th>\n", " <td>Kita</td>\n", " </tr>\n", " <tr>\n", " <th>Rinkmenos tvarkytojas</th>\n", " <td>Lietuvos automobilių kelių direkcija</td>\n", " </tr>\n", " <tr>\n", " <th>Sukaupta duomenu (%) iki:</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Sukaupta duomenu (%) nuo:</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Tvarkytojas</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>key</th>\n", " <td>http://opendata.gov.lt/index.php?vars=/public/...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", " NaN\n", "2 lygis NaN\n", "3 lygis NaN\n", "Alternatyvus pavadinimas \n", "Apibūdinimas Pateikiama informacija: duomenų data, kelio nu...\n", "Atnaujinimo dažnumas Kartą per metus\n", "Duomenų formatas CSV formatas\n", "Duomenų išsamumas Visi duomenys sukaupti\n", "Duomenų patikimumas Įstaiga prisiima atsakomybę\n", "Ieškoti tik jau pateiktuose rezultatuose NaN\n", "Internetinis adresas http://www.lakd.lt/files/atviri_duomenys/danga...\n", "Kategorija (informacijos sritis) Transportas ir ryšiai\n", "Kategorija (informacijos sritis) 1 lygis NaN\n", "Kodas 6055\n", "Kontaktiniai duomenys (8 5) 232 9640 [email protected]\n", "Pavadinimas 2013 m. viršutinės kelio dangos duomenys\n", "Publikavimo data iki: NaN\n", "Publikavimo data nuo: NaN\n", "Reikšminiai žodžiai keliai, dangos\n", "Rinkmenos aprašymo publikavimo duomenys 2016-12-05 17:07:50\n", "Rinkmenos duomenų teikimo sąlygos Skelbiama internete\n", "Rinkmenos pabaigos data 2013\n", "Rinkmenos pradžios data 2013\n", "Rinkmenos rūšis Kita\n", "Rinkmenos tvarkytojas Lietuvos automobilių kelių direkcija\n", "Sukaupta duomenu (%) iki: NaN\n", "Sukaupta duomenu (%) nuo: NaN\n", "Tvarkytojas NaN\n", "key http://opendata.gov.lt/index.php?vars=/public/..." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(1).T" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PDF formatas 311\n", "HTML formatas 297\n", "XLS formatas 189\n", "DOC/DOCX formatas 120\n", "XML formatas 110\n", "Kita 87\n", "CSV formatas 37\n", "ESRI Shapefile formatas 20\n", "JPG/PNG/GIF formatas 19\n", "TIFF formatas 14\n", "TXT formatas 8\n", "GML formatas 3\n", "R2004) 2\n", "R2000 2\n", "MICROSTATION DESIGN IR KT. 1\n", "XLSX 1\n", "Informacija teikiama internetiniame puslapyje http 1\n", "LIZARDTECH MRSID 1\n", "MAPINFO TAB 1\n", "ESRI SHAPE 1\n", "TIFF 1\n", "Duomenys kaupiami ir saugomi Vaistų registro centr 1\n", "JPEG 1\n", "ESRI PERSONAL GEODATABASE 1\n", "ESRI PERSONAL GEODATABASE (FILE) 1\n", "AUTOCAD DWG (R14 1\n", "Microsoft SQL 1\n", "MAPINFO MIF 1\n", "HTML 1\n", "AUTOCAD DXF (R14 1\n", "dtype: int64" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Duomenų formatas'].str.split(r',|\\t+').apply(pd.Series).stack().str.strip().value_counts()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Finansinių ataskaitų rinkiniai 5\n", " 3\n", "Biudžeto vykdymo ataskaitų rinkiniai 2\n", "Nuasmeninti Lietuvos Respublikos teismų sprendimai, nuosprendžiai, nutarimai ir nutartys 1\n", "Miškų sanitarinės būklės apžvalga 1\n", "Name: Pavadinimas, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.Pavadinimas.value_counts().head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Finansinių ataskaitų rinkiniai 5.0\n", " 3.0\n", "Biudžeto vykdymo ataskaitų rinkiniai 2.0\n", "Name: Pavadinimas, dtype: float64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Pavadinimas'].value_counts().map(lambda x: x if x > 1 else None).dropna()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.set_option('display.max_colwidth', 70)\n", "pd.set_option('display.width', 1000)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Kodas Pavadinimas Rinkmenos tvarkytojas key\n", "0 NaN http://opendata.gov.lt/index.php?vars=/public/public/search/15/\n", "1 5781 Finansinių ataskaitų rinkiniai Audito, apskaitos, turto vertinimo ir nemokumo valdymo tarnyba pri... http://opendata.gov.lt/index.php?vars=/public/public/print/292/\n", "2 NaN http://opendata.gov.lt/index.php?vars=/public/public/search/300/\n", "3 NaN http://opendata.gov.lt/index.php?vars=/public/public/search/150/\n", "4 6203 Biudžeto vykdymo ataskaitų rinkiniai Finansinių nusikaltimų tyrimo tarnyba http://opendata.gov.lt/index.php?vars=/public/public/print/714/\n", "5 6204 Finansinių ataskaitų rinkiniai Finansinių nusikaltimų tyrimo tarnyba http://opendata.gov.lt/index.php?vars=/public/public/print/715/\n", "6 6579 Finansinių ataskaitų rinkiniai Lietuvos Respublikos valstybės kontrolė http://opendata.gov.lt/index.php?vars=/public/public/print/1090/\n", "7 6242 Finansinių ataskaitų rinkiniai Kupškio rajono savivaldybės administracija http://opendata.gov.lt/index.php?vars=/public/public/print/753/\n", "8 6566 Finansinių ataskaitų rinkiniai Visagino savivaldybės administracija http://opendata.gov.lt/index.php?vars=/public/public/print/1077/\n", "9 6577 Biudžeto vykdymo ataskaitų rinkiniai Lietuvos Respublikos valstybės kontrolė http://opendata.gov.lt/index.php?vars=/public/public/print/1088/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/sirex/.venvs/databot/lib/python3.5/site-packages/ipykernel/__main__.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " if __name__ == '__main__':\n" ] } ], "source": [ "print(data.set_index('Pavadinimas')[data['Pavadinimas'].value_counts() > 1].reset_index()[['Kodas', 'Pavadinimas', 'Rinkmenos tvarkytojas', 'key']])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Kodas</th>\n", " <th>Pavadinimas</th>\n", " <th>Alternatyvus pavadinimas</th>\n", " <th>key</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>15</th>\n", " <td></td>\n", " <td></td>\n", " <td>NaN</td>\n", " <td>http://opendata.gov.lt/index.php?vars=/public/public/search/15/</td>\n", " </tr>\n", " <tr>\n", " <th>193</th>\n", " <td></td>\n", " <td></td>\n", " <td>NaN</td>\n", " <td>http://opendata.gov.lt/index.php?vars=/public/public/search/300/</td>\n", " </tr>\n", " <tr>\n", " <th>238</th>\n", " <td></td>\n", " <td></td>\n", " <td>NaN</td>\n", " <td>http://opendata.gov.lt/index.php?vars=/public/public/search/150/</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Kodas Pavadinimas Alternatyvus pavadinimas \\\n", "15 NaN \n", "193 NaN \n", "238 NaN \n", "\n", " key \n", "15 http://opendata.gov.lt/index.php?vars=/public/public/search/15/ \n", "193 http://opendata.gov.lt/index.php?vars=/public/public/search/300/ \n", "238 http://opendata.gov.lt/index.php?vars=/public/public/search/150/ " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data['Pavadinimas'] == ''][['Kodas', 'Pavadinimas', 'Alternatyvus pavadinimas', 'key']]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Rinkmenos tvarkytojas</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Lietuvos automobilių kelių direkcija</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Lietuvos statistikos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Lietuvos automobilių kelių direkcija</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Lietuvos automobilių kelių direkcija</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Lietuvos automobilių kelių direkcija</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Lietuvos automobilių kelių direkcija</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Lietuvos bankas</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Lietuvos Respublikos Vyriausybės kanceliarija</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Lietuvos saugios laivybos administracija</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Lietuvos bankas</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Lietuvos saugios laivybos administracija</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Lietuvos Respublikos finansų ministerija</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>Lietuvos Respublikos valstybinis patentų biuras</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>Lietuvos Respublikos valstybinis patentų biuras</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>Lietuvos Respublikos valstybinis patentų biuras</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>Lietuvos Respublikos valstybinis patentų biuras</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>Lietuvos saugios laivybos administracija</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>Lietuvos nacionalinė Martyno Mažvydo biblioteka</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>Lietuvos hidrometeorologijos tarnyba prie Apli...</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>Audito, apskaitos, turto vertinimo ir nemokumo...</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>Lietuvos nacionalinė Martyno Mažvydo biblioteka</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>Audito, apskaitos, turto vertinimo ir nemokumo...</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>Audito, apskaitos, turto vertinimo ir nemokumo...</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>Audito, apskaitos, turto vertinimo ir nemokumo...</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>Lietuvos Respublikos ginklų fondas prie Vidaus...</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>Lietuvos Respublikos ginklų fondas prie Vidaus...</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>Lietuvos Respublikos ginklų fondas prie Vidaus...</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>Lietuvos Respublikos ginklų fondas prie Vidaus...</td>\n", " </tr>\n", " <tr>\n", " <th>64</th>\n", " <td>Lietuvos Respublikos finansų ministerija</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>Lietuvos Respublikos finansų ministerija</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>482</th>\n", " <td>Lietuvos bioetikos komitetas</td>\n", " </tr>\n", " <tr>\n", " <th>487</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " <tr>\n", " <th>488</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " <tr>\n", " <th>489</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " <tr>\n", " <th>490</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " <tr>\n", " <th>523</th>\n", " <td>Lietuvos Respublikos teisingumo ministerija</td>\n", " </tr>\n", " <tr>\n", " <th>524</th>\n", " <td>Lietuvos standartizacijos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>525</th>\n", " <td>Lietuvos standartizacijos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>573</th>\n", " <td>VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba</td>\n", " </tr>\n", " <tr>\n", " <th>574</th>\n", " <td>VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba</td>\n", " </tr>\n", " <tr>\n", " <th>575</th>\n", " <td>VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba</td>\n", " </tr>\n", " <tr>\n", " <th>576</th>\n", " <td>VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba</td>\n", " </tr>\n", " <tr>\n", " <th>582</th>\n", " <td>Lietuvos statistikos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>583</th>\n", " <td>Lietuvos statistikos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>584</th>\n", " <td>Lietuvos statistikos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>585</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>586</th>\n", " <td>Lietuvos statistikos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>587</th>\n", " <td>Lietuvos statistikos departamentas</td>\n", " </tr>\n", " <tr>\n", " <th>608</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>609</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>610</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>611</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>627</th>\n", " <td>Lietuvos Respublikos užsienio reikalų ministerija</td>\n", " </tr>\n", " <tr>\n", " <th>703</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>705</th>\n", " <td>Lietuvos metrologijos inspekcija</td>\n", " </tr>\n", " <tr>\n", " <th>709</th>\n", " <td>VĮ \"Lietuvos naftos produktų agentūra\"</td>\n", " </tr>\n", " <tr>\n", " <th>754</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " <tr>\n", " <th>755</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " <tr>\n", " <th>756</th>\n", " <td>Lietuvos Respublikos valstybės kontrolė</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>180 rows × 1 columns</p>\n", "</div>" ], "text/plain": [ " Rinkmenos tvarkytojas\n", "0 Lietuvos automobilių kelių direkcija\n", "1 Lietuvos statistikos departamentas\n", "2 Lietuvos automobilių kelių direkcija\n", "3 Lietuvos automobilių kelių direkcija\n", "4 Lietuvos automobilių kelių direkcija\n", "5 Lietuvos automobilių kelių direkcija\n", "17 Lietuvos bankas\n", "24 Lietuvos Respublikos Vyriausybės kanceliarija\n", "25 Lietuvos saugios laivybos administracija\n", "26 Lietuvos bankas\n", "28 Lietuvos saugios laivybos administracija\n", "29 Lietuvos Respublikos finansų ministerija\n", "31 Lietuvos Respublikos valstybinis patentų biuras\n", "32 Lietuvos Respublikos valstybinis patentų biuras\n", "33 Lietuvos Respublikos valstybinis patentų biuras\n", "34 Lietuvos Respublikos valstybinis patentų biuras\n", "35 Lietuvos saugios laivybos administracija\n", "36 Lietuvos nacionalinė Martyno Mažvydo biblioteka\n", "37 Lietuvos hidrometeorologijos tarnyba prie Apli...\n", "40 Audito, apskaitos, turto vertinimo ir nemokumo...\n", "42 Lietuvos nacionalinė Martyno Mažvydo biblioteka\n", "56 Audito, apskaitos, turto vertinimo ir nemokumo...\n", "57 Audito, apskaitos, turto vertinimo ir nemokumo...\n", "58 Audito, apskaitos, turto vertinimo ir nemokumo...\n", "60 Lietuvos Respublikos ginklų fondas prie Vidaus...\n", "61 Lietuvos Respublikos ginklų fondas prie Vidaus...\n", "62 Lietuvos Respublikos ginklų fondas prie Vidaus...\n", "63 Lietuvos Respublikos ginklų fondas prie Vidaus...\n", "64 Lietuvos Respublikos finansų ministerija\n", "65 Lietuvos Respublikos finansų ministerija\n", ".. ...\n", "482 Lietuvos bioetikos komitetas\n", "487 Lietuvos Respublikos valstybės kontrolė\n", "488 Lietuvos Respublikos valstybės kontrolė\n", "489 Lietuvos Respublikos valstybės kontrolė\n", "490 Lietuvos Respublikos valstybės kontrolė\n", "523 Lietuvos Respublikos teisingumo ministerija\n", "524 Lietuvos standartizacijos departamentas\n", "525 Lietuvos standartizacijos departamentas\n", "573 VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba\n", "574 VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba\n", "575 VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba\n", "576 VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba\n", "577 VŠĮ Lietuvos žemės ūkio konsultavimo tarnyba\n", "582 Lietuvos statistikos departamentas\n", "583 Lietuvos statistikos departamentas\n", "584 Lietuvos statistikos departamentas\n", "585 Lietuvos metrologijos inspekcija\n", "586 Lietuvos statistikos departamentas\n", "587 Lietuvos statistikos departamentas\n", "608 Lietuvos metrologijos inspekcija\n", "609 Lietuvos metrologijos inspekcija\n", "610 Lietuvos metrologijos inspekcija\n", "611 Lietuvos metrologijos inspekcija\n", "627 Lietuvos Respublikos užsienio reikalų ministerija\n", "703 Lietuvos metrologijos inspekcija\n", "705 Lietuvos metrologijos inspekcija\n", "709 VĮ \"Lietuvos naftos produktų agentūra\"\n", "754 Lietuvos Respublikos valstybės kontrolė\n", "755 Lietuvos Respublikos valstybės kontrolė\n", "756 Lietuvos Respublikos valstybės kontrolė\n", "\n", "[180 rows x 1 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data['Rinkmenos tvarkytojas'].fillna('').str.contains('lietuvos', case=False)][['Rinkmenos tvarkytojas']]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(107,)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Rinkmenos tvarkytojas'].value_counts().shape" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "spl = data['Reikšminiai žodžiai'].str.split(';')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sergamumas 20\n", "licencijos 20\n", "Gyventojai 16\n", "pardavimai 14\n", "statistika 13\n", "Žemė 13\n", "prekyba 13\n", "ligotumas 12\n", "Žemės danga 12\n", "prisirašymo įstaiga 12\n", "stacionaro ligonis 12\n", "ekonominės veiklos rūšis 12\n", "juridinis asmuo 11\n", "žemės ūkis 11\n", "ataskaitos 10\n", "dirbantieji 10\n", "mirtis 10\n", "įmonė 10\n", "pagrindinė mirties priežastis 9\n", "ataskaita 9\n", "Licencijos 9\n", "Klaipėdos miesto savivaldybės administracija 8\n", "Telekomunikacijos 8\n", "elektros energija 8\n", "IRT prekyba 8\n", "IRT paslaugos 8\n", "IRT pramonė 8\n", "ištekliai 7\n", "leidimas 7\n", "keliai 7\n", " ..\n", "juridinis pagrindas 1\n", "sutarties nutraukimas 1\n", "bankroto ir restruktūrizavimo administratorių pažymėjimai 1\n", "Ministras Pirmininkas darbotvarkė 1\n", "veiklos programa 1\n", "viešieji pirkimai 1\n", "Saugomos teritorijos 1\n", "draudimo 1\n", "2014-2020 1\n", "gamtiniai veiksniai 1\n", "sanitarinė būklė apžvalga 1\n", "melioracijos projektai 1\n", "ekspedicija 1\n", "pranešimas apie sprendimą byloje 1\n", "Tuskulėnų aukos 1\n", "vidaus vandenų transporto priemonės 1\n", "maisto saugos kontrolė 1\n", "Sąvartynai 1\n", "darbuotojų atstovai 1\n", "vykdymas ataskaitos 1\n", "Spaudo numeris 1\n", "įsipareigojimai užsieniui 1\n", "augalų apsauga 1\n", "NA būklė 1\n", "Veikliųjų medžiagų gamintojai 1\n", "vanduo monitoringas ežeras hidrocheminiai hidrobiologiniai 1\n", "Garantinio fondo tarybos posėdžiai 1\n", "pelno (nuostolio) ataskaita 1\n", "Aplinka atliekos išleidžiama 1\n", "GDR10LT 1\n", "dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spl.apply(pd.Series).stack().str.split(',').apply(pd.Series).stack().str.strip().value_counts()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20 sergamumas\n", "20 licencijos\n", "16 Gyventojai\n", "14 pardavimai\n", "13 statistika\n", "13 Žemė\n", "13 prekyba\n", "12 ligotumas\n", "12 Žemės danga\n", "12 prisirašymo įstaiga\n", "12 stacionaro ligonis\n", "12 ekonominės veiklos rūšis\n", "11 juridinis asmuo\n", "11 žemės ūkis\n", "10 ataskaitos\n", "10 dirbantieji\n", "10 mirtis\n", "10 įmonė\n", "9 pagrindinė mirties priežastis\n", "9 ataskaita\n", "9 Licencijos\n", "8 Klaipėdos miesto savivaldybės administracija\n", "8 Telekomunikacijos\n", "8 elektros energija\n", "8 IRT prekyba\n", "8 IRT paslaugos\n", "8 IRT pramonė\n", "7 ištekliai\n", "7 leidimas\n", "7 keliai\n", "7 dokumentai\n", "7 \n", "7 Gamtinės dujos\n", "7 Bankai\n", "7 stacionariniai ligoniai\n", "7 ambulatoriniai ligoniai\n", "7 sveikatos priežiūros veikla\n", "6 asmens duomenys\n", "6 žemėlapiai\n", "6 Rokiškio rajono savivaldybė\n", "6 šiluma\n", "6 užsienio banko skyriai (filialai)\n", "6 duomenų apsauga\n", "5 sveikata\n", "5 biodujos\n", "5 IRT sektorius\n", "5 apygardos teismas\n", "5 plotas\n", "5 išmokos\n", "5 projektai\n", "5 administratorius\n", "5 internetas\n", "5 Archyvai\n", "5 demografija\n", "5 ORT10LT\n", "5 parama kaimui\n", "5 žemės sklypai\n", "5 biokuras\n", "5 aeronuotrauka\n", "5 mažmeninė prekyba\n", "5 finansai\n", "5 rastras\n", "4 statistiniai duomenys\n", "4 lytis\n", "4 interneto svetainė\n", "4 laikinas nedarbingumas\n", "4 ekonominė veiklos rūšis\n", "4 darbingumo lygis\n", "4 deklaravimas\n", "4 duomenys\n", "4 Akcizai\n", "4 Elektros energija\n", "4 bankrotas\n", "4 apsilankymas\n", "4 nedarbingumo atvejis\n", "4 elektroninės paslaugos\n", "4 juridiniai asmenys\n", "4 tabakas\n", "4 stebėsena\n", "4 pirkimai\n", "4 amžius\n", "4 Hidrografija\n", "4 Šilutės rajono savivaldybė\n", "4 neįgalumo lygis\n", "4 kainos\n", "4 ortofotografinis vaizdas\n", "4 nedarbingumo pažymėjimas\n", "4 rekomendacijos\n", "4 gyvensena\n", "4 ortografinis vaizdas\n", "4 saugumas\n", "4 Neįgaliųjų socialinė integracija\n", "4 Garantinis fondas\n", "3 dangos\n", "3 balansinė ataskaita\n", "3 kompiuteris\n", "3 ženklinimas\n", "3 sklypai\n", "3 rezultatai\n", "3 reljefas\n", "3 pretendentai\n", "3 Lietuvoje\n", "3 rinkinys\n", "3 Biudžetas\n", "3 statistinė informacija\n", "3 Erdviniai duomenys\n", "3 atkūrimas\n", "3 planas\n", "3 centralizuotas šilumos tiekimas\n", "3 tarifai\n", "3 kuras\n", "3 registras\n", "3 žemės plotas\n", "3 žemėtvarka\n", "3 Administraciniai vienetai\n", "3 techniniai atidėjiniai\n", "3 informacinis portalas\n", "3 sąrašas\n", "3 nuosavybės teisė\n", "3 įgyvendinimas\n", "3 sprendimai\n", "3 ortofoto\n", "3 paslauga\n", "3 perdavimas\n", "3 serialiniai leidiniai\n", "3 darbo užmokestis\n", "3 Investuok\n", "3 atvejo trukmė\n", "3 Žemės ūkis\n", "3 Kokybė\n", "3 hidrografija\n", "3 dyzelinas\n", "3 plombavimo schemos\n", "3 Biržų rajono savivaldybė\n", "3 gyventojai\n", "3 bankroto byla\n", "3 deklaruoti mokesčiai\n", "3 fiziniai asmenys\n", "3 chirurginės operacijos\n", "3 šilumos tiekimas\n", "3 žemės apskaita\n", "3 išieškojimas\n", "3 Žemės danga (Land cover)\n", "3 įkainiai\n", "3 atsinaujinantys ištekliai\n", "3 georeferenciniai duomenys\n", "3 finansinės ataskaitos\n", "3 chirurginės procedūros\n", "3 knygos\n", "3 juridiniai faktai\n", "3 Ginklai\n", "3 vidaus vandenys\n", "2 suskystintos dujos\n", "2 paslaugos teikėjas\n", "2 Žemėtvarka\n", "2 apmokėjimo suma\n", "2 medicininė reabilitacija\n", "2 testamentas\n", "2 atranka\n", "2 restruktūrizavimas\n", "2 planavimas\n", "2 planavimo dokumentai\n", "2 skaičius\n", "2 patento paraiška\n", "2 slauga\n", "2 moksliniai tyrimai\n", "2 žemės naudojimo paskirtis\n", "2 paskolos\n", "2 šeškai\n", "2 nuotekos\n", "2 ribotai veiksnus\n", "2 tyrimas\n", "2 bylos numeris\n", "2 radiotechniniai objektai\n", "2 skalūnų alyva\n", "2 mazutas\n", "2 fondas\n", "2 valstybinė\n", "2 keleivis\n", "2 nuomojama\n", "2 mirties priežastis\n", "2 duomenų rinkiniai\n", "2 elektromagnetinė spinduliuotė\n", "2 genocidas\n", "2 stacionarinė reabilitacija\n", "2 pavadinimas\n", "2 nuotekų tvarkymas\n", "2 jonizuojančiosios spinduliuotės šaltiniai\n", "2 TOP50LKS\n", "2 parama\n", "2 teritorijų planavimo dokumentas\n", "2 veiklos\n", "2 licencijuojama veikla\n", "2 vaistai\n", "2 atvejai\n", "2 kontrolė\n", "2 gyvybės draudimo įmonės\n", "2 Išvados\n", "2 veiklos ataskaitos\n", "2 priežiūra\n", "2 restruktūrizavimo planas\n", "2 elektroniniai ištekliai\n", "2 gamtinės dujos\n", "2 atmintinės\n", "2 žemės naudotojų grupės\n", "2 ribos\n", "2 centralizuotas nuotekų tvarkymas\n", "2 Veiklos\n", "2 kartografija\n", "2 aukščio taškai\n", "2 naftos produktai\n", "2 kraujo donorystė\n", "2 vaikų išlaikymo fondas\n", "2 Matavimo priemonė\n", "2 atsinaujinantys energijos ištekliai\n", "2 mokesčiai\n", "2 karo nusikaltimai\n", "2 aprašymo turinys\n", "2 ambulatorinė reabilitacija\n", "2 žemės reforma\n", "2 Įvaikinimo tarnyba\n", "2 žuvininkystė\n", "2 bioetika\n", "2 žemės danga\n", "2 Negaliojantys leidimai\n", "2 palikimas\n", "2 analizė\n", "2 Leidimai\n", "2 ortografinis vaizdas (Orthoimagery)\n", "2 kontrolės\n", "2 valstybiniai miškai\n", "2 georeferencinio pagrindo žemėlapiai\n", "2 perįgaliojimas\n", "2 matavimo indai\n", "2 biudžetas\n", "2 Teismas\n", "2 Savivaldybės biudžetas\n", "2 Elektroniniai valdžios vartai\n", "2 kraujas\n", "2 Valstybės vaiko teisių apsaugos ir įvaikinimo tarnyba\n", "2 kultūros vertybės\n", "2 apskaita\n", "2 fondai\n", "2 Namų ūkiai\n", "2 įmonės vadovas\n", "2 turto areštas\n", "2 parama žemės ūkiui\n", "2 katės\n", "2 MM kodas\n", "2 auditas\n", "2 nacionalinė parama\n", "2 balansas\n", "2 skola\n", "2 medicina\n", "2 garso įrašai\n", "2 KGB\n", "2 Administracinis suskirstymas\n", "2 terminalai\n", "2 prijungimas\n", "2 natos\n", "2 Transporto tinklai\n", "2 nuoma\n", "2 kvalifikacinis pažymėjimas\n", "2 konsultacijos\n", "2 savivaldybė\n", "2 valstybinė apskaita\n", "2 restruktūrizavimo byla\n", "2 nafta\n", "2 alkoholis\n", "2 paslaugos\n", "2 darbuotojai\n", "2 donacija\n", "2 medicininiai duomenys\n", "2 valstybės tarnautojai\n", "2 Degalinės\n", "2 veiklos ataskaita\n", "2 Žemės naudojimas\n", "2 valstybės tarnyba\n", "2 kelių tinklas\n", "2 Statistika\n", "2 Geografiniai pavadinimai\n", "2 Lietuvos žuvininkystės sektoriaus 2014-2020 m. veiksmų programos statistika\n", "2 kraujo donorai\n", "2 nelegalus darbas\n", "2 Leidimas\n", "2 represijos\n", "2 politinis persekiojimas\n", "2 dizainas\n", "2 kelionių organizatoriai\n", "2 privati\n", "2 medicinos prietaisas\n", "2 Biržų rajono savivaldybės administracija\n", "2 išvados\n", "2 eismo intensyvumas\n", "2 turto padalijimas\n", "2 topografinis žemėlapis\n", "2 geriamasis vanduo\n", "2 Fiziniai asmenys\n", "2 profesinės ligos\n", "2 nefasuoti naftos produktai\n", "2 kita papildoma informacija\n", "2 alimentai\n", "2 deklaracija\n", "2 investicijos.\n", "2 PVM mokėtojas\n", "2 leidimai\n", "2 darbo sauga\n", "2 išorinė reklama\n", "2 pelno (nuostolių) ataskaita.\n", "2 suskystintos naftos dujos\n", "2 gimimas\n", "2 susirgimai\n", "2 ne gyvybės draudimo įmonės\n", "2 centralizuotas vandens tiekimas\n", "2 TLK-10-AM\n", "2 darbo sutartis\n", "2 Profesinis mokymas\n", "2 Paslaugos kodas\n", "2 VIAP\n", "2 Dyzeliniai degalai\n", "2 apmokėjimo atvejis\n", "2 Valstybės\n", "2 geriamojo vandens tiekimas\n", "2 skirstymas\n", "2 alkoholiniai gėrimai\n", "2 tpd\n", "2 sutartys\n", "2 Rastrinis žemėlapis\n", "2 įregistravimas\n", "2 transportas\n", "2 daiktinės teisės\n", "2 paciento teisės\n", "2 restruktūrizuojamos įmonės\n", "2 Profesinė reabilitacija\n", "2 panauda\n", "2 GDR50LT\n", "2 Muitinės įstaiga\n", "2 viešinimas\n", "2 rinkiniai\n", "2 akmens anglis\n", "2 galvijas\n", "2 šaudmenys\n", "2 teroras\n", "2 administracinės ribos\n", "2 ''3''\n", "2 Topografija\n", "2 SSSR\n", "2 medicinos mokslo tiriamieji darbai\n", "2 apskritis\n", "2 Skolininkas\n", "2 vedybų sutartis\n", "2 Nekilnojamieji daiktai\n", "2 stacionaro dienos atvejis\n", "2 vykdymo ataskaitų rinkiniai\n", "2 akvakultūra\n", "2 žuvys\n", "2 rinkos kaina\n", "2 Kelių transportas\n", "2 Finansinių ataskaitų rinkiniai\n", "2 meteorologija\n", "2 ''e''\n", "2 pardavimas\n", "2 Biudžeto lėšos\n", "2 LSSR\n", "2 veikla\n", "2 pajamos\n", "1 įkeitimas\n", "1 Prekės kodas\n", "1 žinybinis registras\n", "1 Banderolės\n", "1 tabako gaminiai\n", "1 veterinarinė kontrolė\n", "1 ekonominiai rodikliai\n", "1 Lygiagrečiai importuojamas vaistinis preparatas\n", "1 Užsienio bankų kontroliuojamos finansų įmonės\n", "1 Paskolų ir indėlių palūkanų normos.\n", "1 ekonominių rodiklių projekcijos\n", "1 Įstaigos\n", "1 rengėjai sąrašas miškotvarka\n", "1 skirstomieji tinklai\n", "1 Nekilnojami daiktai\n", "1 registrų funkcionavimas\n", "1 žemėtvarkos projektai\n", "1 nustatymas\n", "1 apleistos žemės\n", "1 apskričių centrai\n", "1 Registras\n", "1 vienadienių indėlių sąskaitos\n", "1 tarpininkai\n", "1 privatūs projektai kadastras miškotvarka\n", "1 sveikatos priežiūros ištekliai\n", "1 anglų kalbos kursai\n", "1 kaimo plėtrai ir žuvininkystei taisyklės\n", "1 bylos rūšis\n", "1 pakartotinis\n", "1 laboratorija\n", "1 Tremtis\n", "1 rodiklių projekcijos\n", "1 bankų sektoriaus rodikliai\n", "1 muziejai\n", "1 paslaugos neįgaliesiems\n", "1 viešasis\n", "1 mokestis\n", "1 Natura 2000 BAST\n", "1 Tarpbankinio skolinimo sandoriai ir jų palūkanų normos\n", "1 Verslo liudijimai\n", "1 melioracija\n", "1 licencija\n", "1 Lietuvos banko valdybos nutarimai\n", "1 SEŽP_05LT\n", "1 Vyriausybės skolinimasis\n", "1 Šilutės r. savivaldybė\n", "1 susisiekimo komunikacijos\n", "1 sveikatos priežiūros paslaugos\n", "1 prekybos automatų modeliai\n", "1 dirbantys pagal darbo sutartis\n", "1 atmintinė darbuotojui\n", "1 kilnojamieji daiktai\n", "1 Višieji pirkimai\n", "1 vartotojo vadovas\n", "1 Socialinis dialogas įmonėje\n", "1 Molėtų rajono savivaldybės administracija\n", "1 susirašinėjimo su NMA istorija\n", "1 galiojimo laikas\n", "1 įsakymai\n", "1 valstybės ir savivaldybių institucijos ir įstaigos\n", "1 Sveikata\n", "1 KPP statistika\n", "1 duomenų valdytojas\n", "1 efektyvumo rodikliai\n", "1 teikiamų paslaugų aprašymai\n", "1 fasuotos prekės\n", "1 sportas\n", "1 Hidrologija\n", "1 jūrinio laipsnio diplomai\n", "1 korupcijos prevencija\n", "1 Kredito unijų pelno (nuostolio) ataskaita\n", "1 įgaliojimas\n", "1 ketv.)\n", "1 bankroto procesas\n", "1 atominės\n", "1 laisva žemė\n", "1 finansinio stabilumo rodikliai\n", "1 moduliai\n", "1 geros praktikos vadovai\n", "1 naftos produktų kaina\n", "1 administraciniai vienetai\n", "1 Sprendimas\n", "1 Aukcionas\n", "1 skolos gražinimo grafikas\n", "1 finansinės sąskaitos\n", "1 visi darbai\n", "1 atestatas liudijimas energetikos įrenginys eksploatavimo veikla\n", "1 neveiksnus\n", "1 kategorija\n", "1 pažyma apie statybas\n", "1 įstaigos\n", "1 suvestinė\n", "1 galiojančios sutartys\n", "1 rezervo karininkai\n", "1 taršos sklaida\n", "1 tarpusavio\n", "1 srautas\n", "1 generatoriai\n", "1 Orų prognozė\n", "1 finansinė sąskaitos\n", "1 dujos\n", "1 laisvos žemės plotas\n", "1 Pasienio kontrolės punktai\n", "1 Švietimo ir mokslo registrų sąrašas\n", "1 degalai\n", "1 Geležinkelia\n", "1 atmintinė darbdaviui\n", "1 apie darbo ginčų komisijas\n", "1 informacinės technologijos\n", "1 pastatai\n", "1 Paminklai\n", "1 gyvybės draudimo sudarytos sutartys ir išmokos\n", "1 teritorijų planavimo sąlygos\n", "1 nekilnojamasis turtas\n", "1 Sveikatos priežiūros specialistų (vaistininkų ir vaistininkų padėjėjų (farmakotechnikų)) spaudų numeriai\n", "1 bibliotekos\n", "1 darbotvarkė protokolas posėdis pasitarimas Vyriausybė\n", "1 debesuotumas\n", "1 indėliai\n", "1 vandens telkinys\n", "1 apyvartiniai taršos leidimai\n", "1 Arkliai\n", "1 EGM_1000LT\n", "1 Krantai\n", "1 medicinos norma\n", "1 Administracijos veiklos planas\n", "1 kenkėjai\n", "1 kreditorių reikalavimai\n", "1 autobusų stotis.\n", "1 Rokiškio rajono savivaldybės\n", "1 elektroninės DSS deklaracijos\n", "1 vaikai\n", "1 automobilių statymo\n", "1 aprašas\n", "1 kūno kultūra\n", "1 hipoteka\n", "1 matininkas\n", "1 biosferos poligonai\n", "1 šaltas vanduo\n", "1 vaizdo įrašai\n", "1 ūkis\n", "1 darbo laikas\n", "1 dokumento rengėjas\n", "1 oro tarša\n", "1 Mokymo įstaigos\n", "1 2007-2013\n", "1 makroekonominiai rodikliai\n", "1 prekyba tabako gaminiais\n", "1 inventorizacija statistika\n", "1 asmeninis ūkis\n", "1 kvietimas teikti paraiškas\n", "1 atkuriamieji sklypai\n", "1 Keliai\n", "1 komitetas\n", "1 žaibų išlydžiai\n", "1 išleidžiami teršalai išmetimai\n", "1 elektroninė erdvė\n", "1 nesudėtingi statiniai\n", "1 Lietuvos bankas\n", "1 kvalifikacijos liudijimai\n", "1 augalinės kilmės produkcija\n", "1 makroekonominių rodiklių projekcijos\n", "1 disponuojamosios pajamos\n", "1 laikinosios sveikatos priežiūros praktikos\n", "1 išleistuvai\n", "1 Kredito unijų balansinė ataskaita\n", "1 kvalifikacijos tobulinimas.\n", "1 Visuomenės sveikata\n", "1 Pienas\n", "1 turtas\n", "1 privalomieji draudimai\n", "1 Lietuvos kaimo plėtros 2014-2020 m. programa\n", "1 kokybės valdymas\n", "1 institucinių sektorių turtas ir įsipareigojimai\n", "1 klimato kaita\n", "1 Europos Sąjungos dokumentai\n", "1 norminiai aktai\n", "1 DSS komisija\n", "1 protokolai\n", "1 meteorologinės apžvalgos\n", "1 Draudikai\n", "1 atitikties deklaracija\n", "1 supirkimo tarifai\n", "1 EKA įmonės\n", "1 išvados nr.\n", "1 žvejyba\n", "1 nuotoliniai mokymai\n", "1 etiketės\n", "1 Kaimo verslas\n", "1 geoportal.lt\n", "1 auditorius\n", "1 slaugos praktika\n", "1 Prašymai dėl informacijos\n", "1 Vaistinio preparato indentifikatorius\n", "1 Topografinis žemėlapis\n", "1 sklypas\n", "1 Bokštai\n", "1 draudžiama eiti pareigas\n", "1 sprendinių brėžiniai\n", "1 pažymėjimas\n", "1 vietovardžiai\n", "1 įdarbinimas\n", "1 monitoringas\n", "1 valdytojas\n", "1 garso\n", "1 kokybės vadyba\n", "1 draustiniai\n", "1 Žemės kadastras\n", "1 didmeninė prekyba\n", "1 mūsų tautiečiams ir moksleiviams švenčiant dainų šventes\n", "1 schemos\n", "1 Sutikimas\n", "1 darbdavių atstovai\n", "1 50000\n", "1 bandomasis laikotarpis\n", "1 vanduo\n", "1 išduoti leidimai\n", "1 Dozės galia\n", "1 specifikacijos\n", "1 Uosto statistika\n", "1 pramoginiai laivai\n", "1 Viešojo saugumo tarnyba\n", "1 žalia nafta\n", "1 vaizdo kameros\n", "1 garantinio\n", "1 bitės\n", "1 Bendrosios praktikos slaugytojo licencija\n", "1 bičių šeimos\n", "1 Viešai skelbiami pareiškėjai ir paramos gavėjai\n", "1 byla\n", "1 Mokymo įstaiga\n", "1 EuroGlobalMap\n", "1 vėjo energija\n", "1 verslui\n", "1 buitiniai vartotojai\n", "1 Paraiškų\n", "1 valstybiniai\n", "1 stabilizavimo\n", "1 vidaus vandens telkiniai\n", "1 krizių valdymas\n", "1 GDB250LT\n", "1 inspekciniai\n", "1 nuobaudos\n", "1 masinė kapavietė\n", "1 išmoka\n", "1 apžvalga\n", "1 Energetikos teisės aktai\n", "1 asmuo\n", "1 administracinė teritorija\n", "1 skolos rodikliai\n", "1 Rekomendacijos\n", "1 Asmens kortelė\n", "1 Geriamojo vandens tiekimas\n", "1 Dziudo\n", "1 MN\n", "1 Mokymasis visą gyvenimą\n", "1 kandidatai\n", "1 Valstybės vaiko teisių apsaugos ir įvaikinimo tarnybos antikorupcinė programa\n", "1 kvalifikacija\n", "1 muzikos leidiniai\n", "1 leidimas-higienos pasas\n", "1 sprogmenų naudojimas\n", "1 atitikties įvertinimo įstaigos\n", "1 oro užterštumas\n", "1 patentas\n", "1 Pavardė\n", "1 juose atliktos operacijos\n", "1 viršutinės kainų ribos\n", "1 pinigai P1\n", "1 Bendroji skola (BS)\n", "1 būstas\n", "1 pirmasis darbas\n", "1 gimdyvė\n", "1 seimo rinkimų rezultatai 2016\n", "1 vandens valymas\n", "1 EDV\n", "1 vandens toksinio rodiklio ribinė vertė\n", "1 Europos socialinis fondas\n", "1 eilė\n", "1 galvijo produktyvumo rodikliai\n", "1 įvaikinimo statistika\n", "1 VILIBID\n", "1 skolos grąžinimas\n", "1 suvaržymai\n", "1 plitimas\n", "1 miškai žemėlapiai geoinformacija\n", "1 stebint futbolo rungtynes\n", "1 ūkio subjektas\n", "1 žemės sklypas\n", "1 planavimo organizatorius\n", "1 B kategorijos ginklai\n", "1 multimedija\n", "1 Valstybės tarnautojų mokymo paslaugų teikėjai\n", "1 Europos Sąjungos parama Programų ir projektų administravimas Vieša ir privati partnerystė\n", "1 Alkoholis\n", "1 Valstybės sienos apsaugos tarnyba prie Lietuvos Respiblikos vidaus reikalų ministerijos\n", "1 planuojama ūkinė veikla\n", "1 PVM mokėtojai\n", "1 Socialinės įmonės\n", "1 informacija apie įdarbinimą\n", "1 pažymėjimai\n", "1 nustatyta\n", "1 PVM deklaracija\n", "1 bendradarbiavimas\n", "1 krovinių vežimas\n", "1 mokymų programos\n", "1 apskaitos prietaisai\n", "1 Pramoninė nuosavybė\n", "1 bankrutuojanti\n", "1 Urbanizuotos teritorijos\n", "1 GDB50LT\n", "1 „stribų“ būstinės\n", "1 korupcija\n", "1 antikorupcinės programos vykdymas\n", "1 finansinės būklės ataskaitos\n", "1 atliekų tvarkymas\n", "1 antikorupcinės priemonės\n", "1 kovinės sporto šakos\n", "1 LR ginklų fondas prie LRVRM\n", "1 saugusis dokumentas\n", "1 Veiklos ataskaitos\n", "1 kelių transporto priemonė\n", "1 Pirminiai\n", "1 dauginės mirties priežastys\n", "1 Atliekos aplinka tarša\n", "1 Paraiška išmokoms iš Garantinio fondo gauti\n", "1 Chemija medžiagos preparatai\n", "1 skaičiai\n", "1 draudikų sąrašai\n", "1 centrinė koordinavimo grupė\n", "1 saugomos teritorijos\n", "1 grupė\n", "1 kartografinis pagrindas\n", "1 pareiškėjai\n", "1 kai investavimo rizika tenka draudėjui\n", "1 kovotojų kelias\n", "1 darbuotojams skirtos išmokos iš Garantinio fondo\n", "1 apvalioji mediena\n", "1 Lietuvos žuvininkystės sektoriaus veiksmų programos statistika\n", "1 testai\n", "1 sumokėta suma\n", "1 aktas\n", "1 Georeferencinių duomenų rinkinys\n", "1 Žemės informacinė sistema\n", "1 projektų vykdytojai\n", "1 kompensacinis PVM tarifas\n", "1 ligos kodas\n", "1 privalomas\n", "1 skiriamos išmokos bankrutuojančių ir bankrutavusių įmonių darbuotojams\n", "1 ŽIS\n", "1 privatizavimo\n", "1 planuojamų tikrinti administratorių sąrašas.\n", "1 teisės aktai\n", "1 Įgaliojimas\n", "1 įsakymas\n", "1 sąmata\n", "1 Oficialiosios tarptautinės atsargos (OA)\n", "1 Kaimo plėtros programos statistika\n", "1 perdavimas nuosavybėn\n", "1 bylos.\n", "1 kūno kultūra ir sportas\n", "1 apyrašai\n", "1 augalų veislės\n", "1 nekilnojamojo turto sąrašas\n", "1 asmens sveikatos priežiūros įstaiga\n", "1 arešto aktas\n", "1 dokumento rūšis\n", "1 išeivija\n", "1 užsienio bankų kontroliuojamų finansų įmonių sąrašai\n", "1 atliekami tyrimai\n", "1 apdraustieji asmenys (objektai)\n", "1 plotai\n", "1 portfelinės investicijos\n", "1 laisvos vietos\n", "1 erdvinai duomenų rinkiniai\n", "1 Biudžeto vykdymo ataskaitos\n", "1 gyvybės draudimo įmonių techniniai atidėjiniai\n", "1 lošimo įrenginių tipai\n", "1 administracinės paslaugos\n", "1 Laisvos darbo vietos\n", "1 FR0512\n", "1 finansiniai metai\n", "1 nepriklausomi šilumos gamintojai\n", "1 Viešosios ir (arba) administracinės paslaugos\n", "1 mokymo programos\n", "1 mirusiųjų skaičius\n", "1 žemės plotų žiniaraštis\n", "1 kokybės rodikliai\n", "1 valiutos pirkimas ir pardavimas\n", "1 data\n", "1 Garantinio fondo lėšos\n", "1 darbo pasiūlymai\n", "1 testų aprašai\n", "1 keleiviai\n", "1 kaimo plėtrai ir žuvininkystei\n", "1 konkursai\n", "1 potvarkiai\n", "1 Lietuvos centrinės kredito unijos pelno (nuostolio) ataskaita\n", "1 varžytinės\n", "1 ortofotgrafinis vaizdas\n", "1 bylos tipas\n", "1 Patikimas sąrašas\n", "1 pranešimo forma\n", "1 Patikimasis sąrašas\n", "1 geležinkeliai\n", "1 poveikio visuomenės sveikatai vertinimo ataskaita\n", "1 Lietuvos centrinė kredito unija\n", "1 Rezervinis (stabilizavimo) fondas\n", "1 Elektroninė demokratija\n", "1 ūkininkas\n", "1 brošiūrų\n", "1 Paslaugos\n", "1 Lietuvos Respublikos užsienio reikalų ministerija\n", "1 pakabinamieji varikliai\n", "1 išsilavinimas\n", "1 vidaus tarnybos pulkai\n", "1 restruktūrizavimo administratorius\n", "1 Bankrotas\n", "1 transporto piemonės\n", "1 egzekucija\n", "1 Parama bitininkystės sektoriui\n", "1 nepriklausomi gamintojai\n", "1 miškas ligos kenkėjai\n", "1 viešoji įstaiga\n", "1 rinkimų rezultatai\n", "1 Nacionalinis biudžetas\n", "1 Žūvančiųjų gelbėjimo kryžius\n", "1 asignavimai\n", "1 mokėjimo prašymų ir kt. dokumentų formos\n", "1 įvaikinti vaikai\n", "1 standartas\n", "1 Kupiškio rajono savivaldybės kontroliuojamų viešojo sektoriaus subjektų konsoliduotųjų finansinių ataskaitų rinkiniai\n", "1 gyvuliai\n", "1 individualių įgūdžių atnaujinimo kursai\n", "1 azartiniai lošimai\n", "1 Egzaminai\n", "1 globėjų ir įtėvių mokymo ir konsultavimo projektai\n", "1 Molėtų rajono\n", "1 Jūrų uostai\n", "1 darbuotojų skaičius\n", "1 Paveldo objektai\n", "1 KGB veikla\n", "1 veiklos priežiūra\n", "1 Administracinės ribos\n", "1 pavojingi meteorologiniai reiškiniai\n", "1 GIS duomenys\n", "1 VVP pardavimo ir priešlaikinio išpirkimo aukcionai.\n", "1 pasirašytos įmokos\n", "1 inicijavimas\n", "1 IMŪEPIS\n", "1 rinkimai\n", "1 laisvos valstybinės žemės plotai\n", "1 vertinimo rezultatai\n", "1 Seimas sesija posėdžiai\n", "1 Jūrų laivas\n", "1 pašarai\n", "1 Mel_DR10LT\n", "1 LEIP\n", "1 Mokėjimo įstaigos\n", "1 kaimo plėtros programos (KPP) statistika\n", "1 VĮ Turto bankui perduoti kreditoriniai reikalavimai\n", "1 oro monitoringas\n", "1 TSL\n", "1 Teritorijų planavimo dokumentų rengimas\n", "1 ginklų gamyba\n", "1 Darbingo amžiaus asmenys\n", "1 biudžeto\n", "1 infrastruktūra\n", "1 rekreacines zonos\n", "1 pieno išmoka\n", "1 žemės paėmimas\n", "1 numatomų išmokėjimų techninis atidėjinys\n", "1 kadastriniai matavimai\n", "1 Muitinės deklaracija\n", "1 Mėnesio\n", "1 detalusis planas\n", "1 sambo\n", "1 bankrutuojanti ar bankrutavusi įmonė\n", "1 ne gyvybės draudimo sutartys ir išmokos\n", "1 Universalios paslaugos siuntos paštas laiškas\n", "1 žemė\n", "1 saulės šviesa\n", "1 ligos\n", "1 žemės naudojimas\n", "1 augalų apsaugos produktų naudotojai\n", "1 planuojami pirkimai\n", "1 užsienio\n", "1 darbo santykiai\n", "1 valda\n", "1 atmosferos slėgis\n", "1 arkliai\n", "1 matavimo priemonė\n", "1 Ataskaitos\n", "1 lygiagretaus importo leidimo numeris\n", "1 bankrotas pripažintas tyčiniu\n", "1 ožka\n", "1 sertifikatai\n", "1 upė ežeras tvenkinys vanduo\n", "1 Laisvos vietos vaikų socialinės globos įstaigose\n", "1 kvalifikacijos tobulinimas\n", "1 SSRS karo tribunolas\n", "1 prekių ženklas\n", "1 pesticidai\n", "1 P2\n", "1 Šunys\n", "1 proceso dalyvis\n", "1 bankomatų ir kortelių skaitytuvų skaičius\n", "1 reforma\n", "1 matavimas\n", "1 mokėjimai negrynaisiais pinigais ir grynųjų pinigų operacijos\n", "1 aplinka oras tarša koncentracija\n", "1 pirmas pulkas\n", "1 vėjo jėgainė\n", "1 Jūra\n", "1 Tiesioginės išmokos\n", "1 kaimas\n", "1 palyginamieji duomenys\n", "1 kontaktai telefonas\n", "1 Lietuvos periodinės spaudos straipsniai\n", "1 Lietuvos georeferencinių erdvinių duomenų bazė\n", "1 grūdai\n", "1 specialus parengimas\n", "1 saugus kvalifikuotas elektroninis parašas sertifikavimo paslaugų teikėjai\n", "1 vėjo kryptis greitis\n", "1 mokesčių mokėtojo pavadinimas\n", "1 savavališka statyba\n", "1 SŽNS\n", "1 ORT2LT\n", "1 Medicinos praktikos licencija\n", "1 vaistininko praktikos licencijos\n", "1 konsoliduotosios finansinės ataskaitos\n", "1 dvaro sodyba\n", "1 klasifikatoriai\n", "1 Bendrasis planas\n", "1 Ūkininko ūkis\n", "1 ligos pavadinimas\n", "1 miškų žemėlapis\n", "1 bankrutuojančios ar bankrutavusios įmonės\n", "1 statyba\n", "1 registravimas\n", "1 Patikrinimai\n", "1 išregistruota\n", "1 lizingo sutartis\n", "1 kovotojai\n", "1 nepriklausomi tiekėjai\n", "1 Yad Vašemo\n", "1 techninis atidėjinys\n", "1 rodik\n", "1 pirminis\n", "1 statybos užbaigimo dokumentai\n", "1 procesas ne teismo tvarka\n", "1 jūrininkas\n", "1 pakaitinis valstybės tarnautojas\n", "1 suvaržymai.\n", "1 fizinių asmenų bankrotas\n", "1 šablonas\n", "1 atliekų apdorojimas\n", "1 draudimo įmokų grąžinimo techninis atidėjinys\n", "1 darbo vietų atitiktis DSS normų reikalavimams\n", "1 pelno (nuotolio) ataskaita\n", "1 nesąžiningos sąlygos\n", "1 Vyriausybės vertybiniai popieriai\n", "1 išdavimo data\n", "1 Degalai\n", "1 nacionalinis\n", "1 vandens tiekimas\n", "1 turizmo paslaugos\n", "1 globos kainos\n", "1 bankų sąrašai\n", "1 Šilutės rajono savivaldybės administracija\n", "1 drenažas\n", "1 vėjo gūsiai\n", "1 Seimas posėdžiai transliacijos spaudos konferencijos\n", "1 Privatizavimo fondas\n", "1 Leidėjai\n", "1 įstatymas\n", "1 ES investicijos\n", "1 KGB agentas\n", "1 perkeltų įmokų techninis atidėjinys\n", "1 Jūrlapiai\n", "1 auto ralį\n", "1 organai audiniai donorinė ligoninė transplantacija inkstai širdis kepenys ragenos plaučiai kasos-inksto širdies-plaučių\n", "1 mokėtojas\n", "1 Radiacija monitoringas RADIS aplinka\n", "1 reguliuojantys teisės aktai\n", "1 ligos diagnozės\n", "1 Įmonių restruktūrizavimas\n", "1 agreguoti ketvirtiniai duomenys\n", "1 tvarka\n", "1 skaidymas lapais\n", "1 nedarbas\n", "1 Ataskaita\n", "1 mirtinas NA\n", "1 bankroto administravimas\n", "1 apyskaitos\n", "1 gyvybės draudimo išmokos\n", "1 nušalinimo metodinės rekomendacijos\n", "1 draudimo tarpininkų sąrašai\n", "1 mokėjimo įstaigų (sąrašai)\n", "1 laikinasis įdarbinimas\n", "1 ožkos\n", "1 seimo rinkimai\n", "1 pokyčių tendencijos\n", "1 veterinariniai preparatai\n", "1 Bankroto ir restruktūrizavimo administratoriai\n", "1 minint religines ir tautines dienas\n", "1 ūkinis vertingumas\n", "1 viešasis aukcionas\n", "1 Medicinos priemonė\n", "1 restruktūrizuojamos\n", "1 Galvijai\n", "1 sėklinė bazė sąvadas miškas\n", "1 ortofoto žemėlapis\n", "1 laukiančiųjų eilėje skaičius vaikų socialinės globos įstaigose\n", "1 naujagimis\n", "1 valstybės institucijų atstovai\n", "1 sveikatos apsauga\n", "1 šaudmenų gamyba\n", "1 parama kaimo plėtrai\n", "1 žemių nusausinimas\n", "1 vertinimo išvados\n", "1 Leidimai archeologiniams tyrimams\n", "1 pavojingi gaminiai\n", "1 bankrutavusi\n", "1 matomumas.\n", "1 forma\n", "1 kūno kultūra ir sportas leidimai\n", "1 Visuomenės sveikatos priežiūros įstaigos licencija\n", "1 Aplinka oras tarša\n", "1 informacija kontaktams\n", "1 atnaujintų (mosednizuotų) daugiabučių namų ataskaitos\n", "1 GDB10LT\n", "1 prašymas dėl darbo santykių pasibaigimo\n", "1 Pelkės\n", "1 Vidaus vandens telkiniai\n", "1 vertės\n", "1 sunkus NA\n", "1 pirkimo-pardavimo su atpirkimo teise sutartis\n", "1 socialinės paslaugos\n", "1 TOP 500\n", "1 Metinės\n", "1 nuotekų varkymas\n", "1 Pasaulio tautų teisuolių vardai\n", "1 žemės sklypų planai\n", "1 Darbingumas\n", "1 veterinariniai diagnostikumai\n", "1 miškų ūkis\n", "1 jūrininko knygelės\n", "1 aptarnavimas\n", "1 dauginamoji medžiaga tiekėjų sąrašas\n", "1 Viešosios\n", "1 negyvagimis\n", "1 kadastro žemėlapis\n", "1 tipas\n", "1 Ortofotografinis vaizdas (Orthoimagery)\n", "1 tvarkytojai\n", "1 Oficialiai patvirtintų tiekėjų sąrašas\n", "1 Žydų gelbėtojai\n", "1 LKS-94\n", "1 paveldotvarka\n", "1 specialiosios praktikos\n", "1 paviršinių vandenų būklė\n", "1 Dokumentai\n", "1 bendrieji reikalavimai\n", "1 augalų apsaugos produktai\n", "1 ežerai\n", "1 gauti skundai\n", "1 BS užsieniui tvarkymas\n", "1 nacionalinis įvaikinimas\n", "1 pretendentų sąrašas\n", "1 rinkos operatorius\n", "1 vežimo leidimai\n", "1 Teritorijų valdymas/apribojimas/reguliavimo zonos\n", "1 turto vertė\n", "1 ORT10LT 2013\n", "1 PFI turtas ir įsipareigojimai\n", "1 viešasis maitinimas\n", "1 Socialinis dialogas (rekomendacijos)\n", "1 žemės įvertinimas\n", "1 Naujienos iš aplinkosaugos srities\n", "1 politinės partijos\n", "1 licencijavimas\n", "1 teisių apribojimas\n", "1 pranešimo turinys\n", "1 laikinai registruotas gavėjas\n", "1 formos\n", "1 standartizuotas mirtingumas\n", "1 Reljefas\n", "1 Mokėjimų balansas\n", "1 augalų ligos\n", "1 Įmonių bankroto valdymo departamentas prie ŪM\n", "1 Geodeziniai punktai\n", "1 šaudmenų perdirbimas\n", "1 transportas elektroniniai ryšiai eismo sauga logistika paštas\n", "1 Finansinių ataskaitų rinkiniai\n", "1 Policijos nuovados\n", "1 šunys\n", "1 VDI leidiniai\n", "1 tinklai\n", "1 Personalas\n", "1 grynasis išorės turtas\n", "1 technologinės apsaugos priemonės\n", "1 darbo teisės konsultacijos\n", "1 komisiniai\n", "1 švietimo įstaigos\n", "1 Sprendimai\n", "1 A kategorijos ginklai\n", "1 mokėjimo korteles\n", "1 prekyba alkoholiniais gėrimais\n", "1 upė būklė monitoringas vanduo hidrocheminiai hidrobiologiniai\n", "1 kiti techniniai atidėjiniai\n", "1 sveikatos\n", "1 kapai\n", "1 Švenčionių rajono savivaldybės kontroliuojamų viešojo sektoriaus subjektų ir konsoliduotų finansinių ataskaitų rinkiniai\n", "1 žalos padengimo techninis atidėjinys\n", "1 Analizės\n", "1 Atitiktis\n", "1 parama žuvininkystei\n", "1 aplinka\n", "1 EBM_100LT\n", "1 trumpalaikė skola pagal likutinį terminą\n", "1 geodezininkas\n", "1 juridiniai\n", "1 dokumento pavadinimas\n", "1 Apžvalgos\n", "1 finansinių ataskaitų rinkiniai\n", "1 upės\n", "1 P3\n", "1 Civiliniai orlaiviai\n", "1 Išradimas.\n", "1 Muitinės tarpininkas\n", "1 Lietuvos makroekonominių rodiklių projekcijos\n", "1 santuoka\n", "1 oficialiosios tarptautinės atsargos\n", "1 Garantinio fondo paraiškos\n", "1 Farmacinės veiklos licencijos\n", "1 apskričių DSS komisijos\n", "1 gyvenamoji vietovė\n", "1 Valstybės ir savivaldybių įstaigų darbuotojų tarnybinės komandiruotės\n", "1 visuomenės sveikatos priežiūros įstaiga\n", "1 finansiniai\n", "1 techninės ekspertizės įmonė.\n", "1 Sutartys\n", "1 augalų kenkėjai\n", "1 duomenų byla\n", "1 tvarkaraštis\n", "1 LSSRS KGB vadovai\n", "1 įvykdymas\n", "1 atleidimo iš darbo pažyma\n", "1 arklio vertinimo duomenys\n", "1 gyvybės draudimo įmokos ir išmokos\n", "1 KGB kadriniai darbuotojai\n", "1 radioaktyviosios atliekos\n", "1 žirgų Sartuose lenktynes\n", "1 sutarties keitimas\n", "1 sektorius\n", "1 valstybės vaikų globos namai\n", "1 gyventojų apšvita\n", "1 ne gyvybės draudimo įmokos ir išmokos\n", "1 dauginamoji medžiaga\n", "1 SŽNS_DR10LT\n", "1 schema\n", "1 gyvulių užkrečiamų ligų kontrolė\n", "1 bylos baigtis\n", "1 atliktas darbas\n", "1 visuomenės sveikatos priežiūra\n", "1 vykdomos studijų programos\n", "1 įvertinimas\n", "1 Alytaus miesto savivaldybės specialieji planai\n", "1 kovinis sambo\n", "1 tarptautinė paraiška\n", "1 grynoji skola\n", "1 posėdžio data\n", "1 projekcijų perspektyvos\n", "1 kadastras\n", "1 pareigūnai\n", "1 pareigybės\n", "1 Veikliųjų medžiagų importuotojai\n", "1 10000\n", "1 Skaitmeninis reljefo modelis\n", "1 žalinimas\n", "1 hidroenergija\n", "1 gynyba\n", "1 Lietuvos Respublikos vaistinių preparatų registras\n", "1 vertinimo pobūdis\n", "1 GDR250LT-SR\n", "1 Ignalinos\n", "1 gamtos paveldo objektai\n", "1 Gyvenamoji vieta\n", "1 Valstybės ir savivaldybių institucijos ir įstaigos\n", "1 gamintojai\n", "1 Sertifikavimo paslaugų teikėjai\n", "1 perdavimo tinklas\n", "1 sveikatinimo paslauga\n", "1 kelionės\n", "1 mokestiniai metai\n", "1 aukštis\n", "1 sudarytos sutartys\n", "1 pajamų surinkimas\n", "1 tpdr\n", "1 radiacinė sauga\n", "1 statinio nugriovimas\n", "1 Žemės mokestis\n", "1 kontrolės grupės\n", "1 Ekspertų veikla\n", "1 studijų kryptys\n", "1 Hipoteka\n", "1 Navigacija\n", "1 skundų nagrinėjimas\n", "1 demografinė būklė\n", "1 parama būstui gauti ar išsinuomoti\n", "1 persiuntimas\n", "1 Viešųjų pirkimų įstatymo 91 straipsnis\n", "1 Švietimo ir mokslo informacinės sistemos\n", "1 bendrinis klausimynas\n", "1 Elektrinės\n", "1 Turimas turtas užsienyje\n", "1 Kasos aparatai\n", "1 miestai\n", "1 gyvybės draudimo įmokos\n", "1 apvaliosios medienos elektroninė pardavimų sistema\n", "1 oras\n", "1 teisė vykdyti veiklą\n", "1 valstybės\n", "1 likutis (pozicija) laikotarpio pabaigoje\n", "1 vėjas\n", "1 laukiančiųjų eilėje skaičius socialinės globos įstaigose\n", "1 ADOC\n", "1 Kaimo plėtra\n", "1 MP registras\n", "1 Muitinė\n", "1 pirkimas-pardavimas su atpirkimo teise\n", "1 namų ūkiai\n", "1 Seimas\n", "1 1944-1947 m.\n", "1 socialinio\n", "1 skundai\n", "1 Valstybinis Vilniaus žydų muziejus.\n", "1 išsilavinimas.\n", "1 panakinta licencija\n", "1 triukšmas žemėlapis geležinkelis traukiniai\n", "1 gyvulių bandų sveikatingumo statusas\n", "1 Mokesčių mokėtojai\n", "1 radioaktyviosios medžiagos\n", "1 Jūros regionai\n", "1 vrk atviri duomenys\n", "1 kontroliuojamieji gyvuliai\n", "1 prancūzų kalbos kursai\n", "1 kompensacija\n", "1 transporto išlaidų kompensacija\n", "1 Europos žandarmerijos pajėgos\n", "1 išankstiniai\n", "1 negalumas\n", "1 reguliavimas\n", "1 elektroninė asmenų bei organizacijų informacijos paieškos sistema\n", "1 tiesioginių išmokų statistika\n", "1 PARAMA\n", "1 Tabakas\n", "1 Fasuoto prekės\n", "1 prekių ženklai\n", "1 GDR50LT-SR\n", "1 D kategorijos ginklai\n", "1 Laukiančiu kirsti Lietuvos Respublikos valstybės sieną transporto priemonių skaičius pasienio kontrolės punktuose\n", "1 apribojimas\n", "1 statybą leidžiantis dokumentas\n", "1 komunikacijos\n", "1 tyčinis bankrotas\n", "1 inžineriniai tinklai\n", "1 mažos\n", "1 melioracijos planai\n", "1 specialieji poreikiai\n", "1 valstybinė žemė\n", "1 ataskaitinis laikotarpis\n", "1 pirkimas-pardavimas\n", "1 brėžinys\n", "1 Ūkininkai kompensacininkai\n", "1 registrų steigimas\n", "1 Finansinis\n", "1 sukarinta tarnyba.\n", "1 Buvę NKVD-MVD-MGB pastatai\n", "1 testatorius\n", "1 atlikti veiksmai\n", "1 archyvai\n", "1 keleivių vežimas\n", "1 atnaujinimo data\n", "1 avys\n", "1 suvestiniai duomenys\n", "1 žemės ūkio ir maisto produktai\n", "1 hospitolizuotųjų skaičius\n", "1 Teršalai mėnesio koncentracija oras kokybė\n", "1 Natura 2000\n", "1 sveikatos statistika\n", "1 paveldėjimas\n", "1 objektas\n", "1 serija ir Nr.\n", "1 skolinimasis. mokėtinos sumos\n", "1 derinimo eiga\n", "1 hospitalinė infekcija\n", "1 savaeigė\n", "1 išradimas\n", "1 Antstolis\n", "1 transporto tinklai\n", "1 Veterinarinių vaistų registras\n", "1 Jūrų regionai\n", "1 Laikinasis leidimas\n", "1 regioniniai parkai\n", "1 Bankrutuojančios ir bankrutavusios įmonės\n", "1 licencijos pavojingi slėginiai įrenginiai priežiūra\n", "1 nacionaliniai parkai\n", "1 Informacija apie pateiktas paraiškas ir jų būklę\n", "1 emisijos atsiskaitymas apyvartiniai taršos leidimai Kioto vienetai ATL TMV PTMV Bendro įgyvendinimo projektai\n", "1 rizikos veiksniai\n", "1 Švietimo ir mosklo klasifikatoriai\n", "1 užimtumas\n", "1 šilumos suvartojimas\n", "1 prevenciniai patikrinimai\n", "1 tiekimas\n", "1 straipsniai\n", "1 laikinojo įdarbinimo įmonės\n", "1 nekilnojamieji\n", "1 Pieninis galvijas\n", "1 kokybės tyrimai\n", "1 URM\n", "1 Vidaus vandenų laivų registras\n", "1 laukiančių kirsti Lietuvos Respublikos valstybės sieną\n", "1 OA pokyčio veiksniai\n", "1 apsaugos zonos\n", "1 dviračių-pėsčiųjų takai\n", "1 materialinė atsakomybė\n", "1 vertinimas\n", "1 Vaizdinė informacija apie transporto priemonių\n", "1 gelbėjimo darbai\n", "1 naftos dujos\n", "1 fiziniai\n", "1 metodinė informacija\n", "1 Draudimo tarpininkai\n", "1 Aplinka apsauga žemėlapis oras tarša vanduo monitoringas\n", "1 aiškinimai\n", "1 sužeisti\n", "1 Viešosios paslaugos\n", "1 ekstremali situacija\n", "1 tarptautinis įvaikinimas\n", "1 valstybės siena\n", "1 Išradimai\n", "1 geodeziniai punktai\n", "1 ūkinė komercinė veikla\n", "1 Priežiūra (pagalba)\n", "1 namų valdos\n", "1 Geležinkeliai\n", "1 žemėlapis\n", "1 akcinė bendrovė\n", "1 palikėjas\n", "1 informacija\n", "1 žvejybos plotai\n", "1 Pažedimų prevencija\n", "1 karštas vanduo\n", "1 pinigų plovimo prevencija\n", "1 Asmens sveikatos priežiūros įstaigos licencija\n", "1 Laisva\n", "1 bokštai\n", "1 ūkiniai gyvūnai\n", "1 sumokėti mokesčiai\n", "1 ES parama\n", "1 apsaugos zonų ribos\n", "1 elektrinės\n", "1 vartojimo išlaidos\n", "1 Interneto svetainė\n", "1 vykdymo būklė\n", "1 atliekos\n", "1 registravimo data\n", "1 drenavimo sistema\n", "1 tiesioginės investicijos\n", "1 viešai skelbiami įspėjimai\n", "1 Perdraudimo tarpininkai\n", "1 metinės\n", "1 oro temperatūra\n", "1 KGB pavaduotojai\n", "1 parama už pieną\n", "1 Mokesčių mokėtojo identifikacinis numeris (juridinio asmens kodas)\n", "1 įgaliotinis\n", "1 valdymo apribojimas\n", "1 nusikalstamos veikos\n", "1 skundas\n", "1 terminas\n", "1 Atmintinos vietos\n", "1 aptarnauti EKA\n", "1 vertė\n", "1 Išradimas\n", "1 antras pulkas\n", "1 kultūros vertybių apsaugos finansavimas\n", "1 reikalavimai\n", "1 jūrininkų registras\n", "1 C kategorijos ginklai\n", "1 radiacinis fonas\n", "1 aukcionai\n", "1 rinkos\n", "1 bankroto ir restruktūrizavimo administratoriai\n", "1 jėgainė\n", "1 tiekėjai\n", "1 TOP 50\n", "1 Auditas\n", "1 arklio pasas\n", "1 Lietuvos žemėlapis\n", "1 ligos diagnozė\n", "1 būklė\n", "1 visuomeninis tiekimas\n", "1 ES lėšos\n", "1 apsauginė juosta\n", "1 parduodamas iš varžytynių turtas\n", "1 keliai.\n", "1 Dokumento blanko pavadinimas\n", "1 mirtingumas\n", "1 DGK posėdžių paieška\n", "1 sąrašai\n", "1 specialiosios sąlygos\n", "1 VILIBOR palūkanų normos\n", "1 temperatūra\n", "1 Klimatas\n", "1 jaunojo specialisto mokymo programa\n", "1 studijų krypčių apžvalgos\n", "1 grūdų kokybės tyrimai\n", "1 pažymėjimo Nr.\n", "1 trumpalaikė reklama\n", "1 mokėjimų balanso einamoji\n", "1 Varžytynės\n", "1 įgaliotojas\n", "1 Metinių pirkimų planas\n", "1 valstybės tarnautojų pareigos\n", "1 biudžeto vykdymo ataskaitos\n", "1 veikla Lietuvoje neįsisteigus\n", "1 infostatyba\n", "1 administratoriai\n", "1 registruotas gavėjas\n", "1 Horizontalės\n", "1 dvaras\n", "1 pakartotiniai\n", "1 energijos efektyvumas\n", "1 atstovų sąrašas\n", "1 įsipareigojimai\n", "1 Sprogmenys\n", "1 tikrinimas\n", "1 disponavimo apribojimas\n", "1 techninių\n", "1 hidrelektrinė\n", "1 Stebėsena\n", "1 priekaba\n", "1 konsoliduotos ataskaitos\n", "1 apskaitos politika\n", "1 varžytynių vykdytojas\n", "1 dantys\n", "1 detalieji planai\n", "1 biografija\n", "1 donorai recipientai inkstai parinkimas transplantacija operacija parinkimas LSMUL VULSK\n", "1 elektroniniai pinigai.\n", "1 Incidentas internetas virusas kenkejiškas kodas trojanas spam ddos ataka botnet duomenų saugumas\n", "1 potvynių rizika\n", "1 veterinariniai vaistai\n", "1 Europos patentas\n", "1 PVM. Grąžinimas.\n", "1 Analitinės\n", "1 socialinių įmonių sąrašas\n", "1 lizingo gavėjas\n", "1 Klientų aptarnavimo vietos\n", "1 sugyventinių sutartis\n", "1 taikymo vadovai\n", "1 oro kokybė\n", "1 mokėtinos sumos\n", "1 apsikeitimo ir atpirkimo sandoriai\n", "1 neįgaliųjų socialinės įmonės\n", "1 žemės fondas\n", "1 rašytinis pritarimas\n", "1 Viešai skelbiama informacija apie išmokėtą paramą\n", "1 prekyba mediena\n", "1 biocidiniai produktai\n", "1 bankrutuojančios ir bankrutavusios įmonės\n", "1 reklamos įrengimas\n", "1 Plėtros planas\n", "1 AŽ_DRLT\n", "1 reikalavimai gaminiai\n", "1 areštas\n", "1 bendrosios pajamos\n", "1 Matavimo vienetas\n", "1 pasėliai\n", "1 biomasė\n", "1 Neveiksnus\n", "1 prašymai dėl neteisėtai išvežto ar laikomo vaiko\n", "1 ekstradicija\n", "1 pavyzdys\n", "1 šilumos tiekimo įmonės\n", "1 įkeitimai\n", "1 kombainas\n", "1 piktžolės\n", "1 viešas aukcionas\n", "1 ORT10LT 2012\n", "1 žemės konsolidacija\n", "1 statiniai\n", "1 neformalus mokymas ir savišvieta\n", "1 augalų apsaugos produktų platintojai\n", "1 žemės naudojimo sąlygos\n", "1 kvietimai teikti paraiškas\n", "1 Įmonių skelbimas apie negalėjimą arba neketinimą vykdyti įsipareigojimus\n", "1 Specialus apmokestinimo momentas\n", "1 Vaikų socialinės globos įstaigos\n", "1 Veikla\n", "1 radiotechninės dalies projektas\n", "1 privatizavimas\n", "1 Trusted list\n", "1 planavimo ataskaita\n", "1 užterštumo sklaidos žemėlapiai\n", "1 atliekų surinkimas\n", "1 rastrinis topografinis žemėlapis\n", "1 Techninė būklė\n", "1 Viešasis sektorius\n", "1 Makroekonomika\n", "1 Eismo įvykiai\n", "1 asmens būklė\n", "1 nebaigtas statyti jūrų laivas\n", "1 institucijos\n", "1 rankraščiai\n", "1 ES fondai\n", "1 Valstybinis registras\n", "1 arklio sertifikatas\n", "1 klaidos ir praleidimai\n", "1 nepilnametis\n", "1 NMA administruojamų paramos priemonių žemės ūkiui\n", "1 Trusted Services List\n", "1 ne teismo tvarka\n", "1 Periodinės įmokos\n", "1 įstatymai\n", "1 sandėlis\n", "1 Farmacijos įmonių ir farmacijos specialistų (vaistininkų) licencijos\n", "1 laivai uostas atvykimas išvykimas švartavimas krantinės\n", "1 temos\n", "1 Gamintojai\n", "1 kvalifikacijos pažymėjimai\n", "1 Vyriausybė kanceliarija naujienos\n", "1 ataskaitų formos\n", "1 ginklų perdirbimas\n", "1 apylinkės teismas\n", "1 kokybės įvertinimas\n", "1 prevenciniai tikrinimai\n", "1 Natura 2000 PAST\n", "1 VL numeris\n", "1 turizmo centrai\n", "1 sertifikatas\n", "1 Prekyba\n", "1 perdraudimo tarpininkų sąrašai\n", "1 žuvo\n", "1 blankas\n", "1 projektas\n", "1 šilumos kainų pokyčiai\n", "1 rezervatai\n", "1 regioninė plėtra\n", "1 patikrinimai\n", "1 Muitinės sandėlis\n", "1 viešo paskelbimo data\n", "1 nelaimingas atsitikimas darbe\n", "1 lageriai\n", "1 kiekiai\n", "1 vidaus kreditas.\n", "1 objektų teritorijų ribos\n", "1 Lietuvos banko valdyba\n", "1 Teritorinių statistinių vienetų klasifikatorius (NUTS)\n", "1 statinio projektas\n", "1 akreditavimas\n", "1 kitos investicijos\n", "1 Gyvenvietės\n", "1 turizmo paslaugų teikėjai\n", "1 arklys\n", "1 Transplantacija recipientai pacientai eilė inkstai širdis kepenys ragenos plaučiai kasos-inksto širdies-plaučių\n", "1 Valstybės vaiko teisių apsaugos ir įvaikinimo tarnybos prie Socialinės apsaugos ir darbo ministerijos\n", "1 sutikimai\n", "1 Atestuota\n", "1 lituanika\n", "1 Laisvos vietos socialinės globos įstaigose\n", "1 asmenys\n", "1 Sandėliai\n", "1 eilės pasienio kontrolės punktuose ir palaukimo aikštelėse\n", "1 tpdris\n", "1 dinamika\n", "1 teisės suteikimas\n", "1 traktorius\n", "1 ERM_250LT\n", "1 augalų dauginamoji medžiaga\n", "1 projekcijos\n", "1 Valiutų kursai\n", "1 kovotojas\n", "1 sprogmenys\n", "1 filialai\n", "1 srautas per laikotarpį (mėn.\n", "1 EuroBoundaryMap\n", "1 Elektrininiai dokumentai\n", "1 Georeferencinių duomenų bazė\n", "1 sprogmenų gamyba\n", "1 adresai\n", "1 gaisrai\n", "1 sprogdinimas\n", "1 sytartis\n", "1 dešimtaidienio\n", "1 statybų pradžia\n", "1 įmokos\n", "1 Maisto sauga\n", "1 suaugusiųjų švietimas\n", "1 Įmonių bankrotas\n", "1 sveikatos priežiūros specialistas\n", "1 nutarimas\n", "1 EuroRegionalMap\n", "1 benzinas\n", "1 šilumos kainos\n", "1 išlygos taikymo sąlygos\n", "1 informacija apie pareiškėjus ir paramos gavėjus\n", "1 saulės\n", "1 Oro uostai\n", "1 KGB dokumentai\n", "1 biosferos rezervatai\n", "1 pirkimo-pardavimo išsimokėtinai sutartis\n", "1 patentai\n", "1 neįgalumas\n", "1 energijos vartojimo auditas\n", "1 Interesantai\n", "1 GF lėšų panaudojimas\n", "1 Matavimo priemonių registras\n", "1 ginklų taisymas\n", "1 metinė pelno mokesčio deklaracija\n", "1 Garantinio fondo taryba\n", "1 Ūkio subjektai\n", "1 elektroniniai valdžios vartai\n", "1 IVP programa\n", "1 ligoninė\n", "1 detalūs ketvirtiniai duomenys\n", "1 Lietuvos kaimo plėtros 2007-2013 m. programa\n", "1 Mirties bausmė\n", "1 Tema\n", "1 jūrų laivų registras\n", "1 klimato indeksas\n", "1 žirgai\n", "1 lizingo davėjas\n", "1 Ligoninės\n", "1 likutis (sukauptosios investicijos) laikotarpio (ketvirčio) pabaigoje\n", "1 rezervinio\n", "1 Buhalterinė apskaita\n", "1 lizingas\n", "1 BS pagal valiutas\n", "1 vandens šaltiniai\n", "1 Sanglaudos politika\n", "1 patento paraiška.\n", "1 švenčiant Valstybines šventes\n", "1 adresas\n", "1 reklamos registras\n", "1 Audito\n", "1 tarptautinės atsargos ir užsienio valiutų likvidumas\n", "1 meteorologinis rekordas\n", "1 nesaugus\n", "1 ikiteisminis tyrimas\n", "1 avis\n", "1 VED\n", "1 aplinkos oras\n", "1 palikimą priėmęs asmuo\n", "1 Veikliųjų medžiagų platintojai.\n", "1 Atestatai\n", "1 skirta\n", "1 supaprastinti\n", "1 Strateginis veiklos planas\n", "1 ne gyvybės draudimo techniniai atidėjiniai\n", "1 procesiniai dokumentai\n", "1 stebėsenos planai\n", "1 sandėliai\n", "1 bendrosios dotacijos kompensacija\n", "1 paramos administravimas\n", "1 politikos\n", "1 Valiuta\n", "1 gimdymas\n", "1 naudojimo aprobojimas\n", "1 GIMK\n", "1 kalendoriniai metai\n", "1 Administracinių ribų duomenų bazė\n", "1 Vyriausybė naujienos veikla\n", "1 gidai\n", "1 radaras\n", "1 pirkimas-pardavimas išsimokėtinai\n", "1 juridinis pagrindas\n", "1 sutarties nutraukimas\n", "1 bankroto ir restruktūrizavimo administratorių pažymėjimai\n", "1 Ministras Pirmininkas darbotvarkė\n", "1 veiklos programa\n", "1 viešieji pirkimai\n", "1 Saugomos teritorijos\n", "1 draudimo\n", "1 2014-2020\n", "1 gamtiniai veiksniai\n", "1 sanitarinė būklė apžvalga\n", "1 melioracijos projektai\n", "1 ekspedicija\n", "1 pranešimas apie sprendimą byloje\n", "1 Tuskulėnų aukos\n", "1 vidaus vandenų transporto priemonės\n", "1 maisto saugos kontrolė\n", "1 Sąvartynai\n", "1 darbuotojų atstovai\n", "1 vykdymas ataskaitos\n", "1 Spaudo numeris\n", "1 įsipareigojimai užsieniui\n", "1 augalų apsauga\n", "1 NA būklė\n", "1 Veikliųjų medžiagų gamintojai\n", "1 vanduo monitoringas ežeras hidrocheminiai hidrobiologiniai\n", "1 Garantinio fondo tarybos posėdžiai\n", "1 pelno (nuostolio) ataskaita\n", "1 Aplinka atliekos išleidžiama\n", "1 GDR10LT\n" ] } ], "source": [ "for k, v in data['Reikšminiai žodžiai'].str.split(';').apply(pd.Series).stack().str.split(',').apply(pd.Series).stack().str.strip().value_counts().items():\n", " print(v, k)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'aa'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'aaa'[:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

ragavvenkatesan/Convolutional-Neural-Networks

pantry/tutorials/notebooks/Generative Adversarial networks.ipynb

1

55542

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Networks(GAN)\n", "GAN is one of the areas in the Neural Networks with a very fast pace of reasearch. Every week there is new GAN. To explain the concept of GAN, let's use a small anecdote to stage this concept. In old movies to sketch a criminal there will be an artist and a witness. Witness tells artist some details and witness validates his art and says if it is correct or not. If the imageis not similar to the criminal, artist will redraw it again with further changes. This process will be repeated until artist produces an image which is accepted by the witness. In other words witness unable to differentiate the artists imaginary art from the crimial. At this point they stop.\n", "\n", "GAN works similar to this idea. We have a generator network that generates random images and a Descriminator network that clssifies whether that image is fake or real. If the image is fake the descriminator discards the image and if image is real, it accepts it. This process continues until generator generates all real images. The generator is a decoder network from the autoencoder we discussed in the tutorial before. We take a random codeword and we pass it to the generator network to generate image. We take that generated image and feed it to descriminator to tell if it is a real or fake image. To achieve that we always keep our descriminator a step ahead.\n", "\n", "The following code shows the implementation of GAN using YANN:\n", "For GAN in YANN we need to use the yann.special.gan package which has similar functionalities like a network." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n", " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n", "\n", "Using gpu device 0: GeForce GTX 750 Ti (CNMeM is enabled with initial size: 80.0% of memory, cuDNN 5110)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " creating a new dataset to run through\n", ". Setting up dataset \n", ".. setting up skdata\n", "... Importing mnist from skdata\n", ".. setting up dataset\n", ".. training data\n", ".. validation data \n", ".. testing data \n", ". Dataset 60713 is created.\n", ". Time taken is 0.890919 seconds\n", ". Initializing the network\n", ".. Setting up the datastream\n", ".. Setting up the visualizer\n", ".. Adding random layer z\n", ".. Adding input layer x\n", ".. Adding dot_product layer G(z)\n", ".. Adding dot_product layer D(x)\n", ".. Adding flatten layer 4\n", ".. Adding dot_product layer D(G(z))\n", ".. Adding dot_product layer real\n", ".. Adding dot_product layer fake\n", ".. Adding classifier layer softmax\n", ".. Adding tensor layer discriminator_task\n", ".. Adding objective layer discriminator_obj\n", ".. Adding tensor layer objective_task\n", ".. Adding objective layer generator_obj\n", ".. Adding objective layer classifier_obj\n", ".. Saving the network down as an image\n", ".. This method will be deprecated with the implementation of a visualizer,also this works only for tree-like networks. This will cause errors in printing DAG-style networks.\n", " |-\n", " |-\n", " |-\n", " |- id: objective_task\n", " |-=================------------------\n", " |- type: tensor\n", " |- output shape: (1,)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: generator_obj\n", " |-=================------------------\n", " |- type: objective\n", " |- output shape: (1,)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: x\n", " |-=================------------------\n", " |- type: input\n", " |- output shape: (500, 1, 28, 28)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: 4\n", " |-=================------------------\n", " |- type: flatten\n", " |- output shape: (500, 784)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: D(x)\n", " |-=================------------------\n", " |- type: dot_product\n", " |- output shape: (500, 800)\n", " |- batch norm is OFF\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: real\n", " |-=================------------------\n", " |- type: dot_product\n", " |- output shape: (500, 1)\n", " |- batch norm is OFF\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: softmax\n", " |-=================------------------\n", " |- type: classifier\n", " |- output shape: (500, 10)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: classifier_obj\n", " |-=================------------------\n", " |- type: objective\n", " |- output shape: (1,)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: z\n", " |-=================------------------\n", " |- type: random\n", " |- output shape: (100, 32)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: G(z)\n", " |-=================------------------\n", " |- type: dot_product\n", " |- output shape: (100, 784)\n", " |- batch norm is OFF\n", " |------------------------------------\n", " | |-\n", " | |-\n", " | |-\n", " | |- id: D(G(z))\n", " | |-=================------------------\n", " | |- type: dot_product\n", " | |- output shape: (100, 800)\n", " | |- batch norm is OFF\n", " | |------------------------------------\n", " | |-\n", " | |-\n", " | |-\n", " | |- id: fake\n", " | |-=================------------------\n", " | |- type: dot_product\n", " | |- output shape: (100, 1)\n", " | |- batch norm is OFF\n", " | |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: G(z)\n", " |-=================------------------\n", " |- type: dot_product\n", " |- output shape: (100, 784)\n", " |- batch norm is OFF\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: D(G(z))\n", " |-=================------------------\n", " |- type: dot_product\n", " |- output shape: (100, 800)\n", " |- batch norm is OFF\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: fake\n", " |-=================------------------\n", " |- type: dot_product\n", " |- output shape: (100, 1)\n", " |- batch norm is OFF\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: discriminator_task\n", " |-=================------------------\n", " |- type: tensor\n", " |- output shape: (1,)\n", " |------------------------------------\n", " |-\n", " |-\n", " |-\n", " |- id: discriminator_obj\n", " |-=================------------------\n", " |- type: objective\n", " |- output shape: (1,)\n", " |------------------------------------\n", ".. Cooking the network\n", ".. Setting up the resultor\n", ".. Setting up the optimizer\n", ".. Setting up the optimizer\n", ".. Setting up the optimizer\n", ". Training\n", ".\n", "\n", "\n", ".. Pre-Training Epoch: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "| validation 0% ETA: --:--:-- \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Softmax Cost : 19.0017\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| validation 100% Time: 0:00:00 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Validation accuracy : 88.69\n", ".. Training accuracy : 87.728\n", ".. Best training accuracy\n", ".. Best validation accuracy\n", ".\n", "\n", "\n", ".. Pre-Training Epoch: 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "| validation 0% ETA: --:--:-- \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Softmax Cost : 0.634408\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| validation 100% Time: 0:00:00 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Validation accuracy : 89.11\n", ".. Training accuracy : 87.636\n", ".. Best validation accuracy\n", ".\n", "\n", "\n", ".. Pre-Training Epoch: 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "| validation 0% ETA: --:--:-- \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Softmax Cost : 0.690765\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| validation 100% Time: 0:00:00 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Validation accuracy : 90.52\n", ".. Training accuracy : 89.722\n", ".. Best training accuracy\n", ".. Best validation accuracy\n", ".. Pre- Training complete.Took 0.181362733333 minutes\n", ".\n", "\n", "\n", ".. Epoch: 0 Era: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "| validation 6% ETA: 0:00:01 \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Sigmoid D(x) : 0.622721\n", ".. Generator Sigmoid D(G(z)) : 0.422515\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| validation 100% Time: 0:00:00 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Validation accuracy : 82.69\n", ".. Training accuracy : 80.798\n", ".\n", "\n", "\n", ".. Epoch: 1 Era: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "| validation 0% ETA: --:--:-- \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Sigmoid D(x) : 0.584707\n", ".. 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Validation accuracy : 88.23\n", ".. Training accuracy : 87.546\n", ".\n", "\n", "\n", ".. Epoch: 18 Era: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "| validation 0% ETA: --:--:-- \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Sigmoid D(x) : 0.647033\n", ".. Generator Sigmoid D(G(z)) : 0.546981\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| validation 100% Time: 0:00:00 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Validation accuracy : 90.56\n", ".. Training accuracy : 89.936\n", ".. Best training accuracy\n", ".. Best validation accuracy\n", ".\n", "\n", "\n", ".. Epoch: 19 Era: 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "| training 100% Time: 0:00:00 \n", "/ validation 35% ETA: 0:00:00 \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".. Discriminator Sigmoid D(x) : 0.523178\n", ".. 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Validation accuracy : 89.79\n", ".. Training accuracy : 88.632\n", ".. Training complete.Took 1.48964608333 minutes\n" ] } ], "source": [ "from yann.special.gan import gan \n", "from theano import tensor as T \n", "\n", "def shallow_gan_mnist ( dataset= None, verbose = 1 ):\n", " \"\"\"\n", " This function is a demo example of a generative adversarial network. \n", " This is an example code. You should study this code rather than merely run it. \n", "\n", " Args: \n", " dataset: Supply a dataset. \n", " verbose: Similar to the rest of the dataset.\n", "\n", " Notes:\n", " This method is setup for MNIST.\n", " \"\"\"\n", " optimizer_params = { \n", " \"momentum_type\" : 'polyak', \n", " \"momentum_params\" : (0.65, 0.9, 50), \n", " \"regularization\" : (0.000, 0.000), \n", " \"optimizer_type\" : 'rmsprop', \n", " \"id\" : \"main\"\n", " }\n", "\n", "\n", " dataset_params = {\n", " \"dataset\" : dataset,\n", " \"type\" : 'xy',\n", " \"id\" : 'data'\n", " }\n", "\n", " visualizer_params = {\n", " \"root\" : '.',\n", " \"frequency\" : 1,\n", " \"sample_size\": 225,\n", " \"rgb_filters\": False,\n", " \"debug_functions\" : False,\n", " \"debug_layers\": True, \n", " \"id\" : 'main'\n", " } \n", " \n", " # intitialize the network\n", " net = gan ( borrow = True,\n", " verbose = verbose ) \n", " \n", " net.add_module ( type = 'datastream', \n", " params = dataset_params,\n", " verbose = verbose ) \n", " \n", " net.add_module ( type = 'visualizer',\n", " params = visualizer_params,\n", " verbose = verbose \n", " ) \n", "\n", " #z - latent space created by random layer\n", " net.add_layer(type = 'random',\n", " id = 'z',\n", " num_neurons = (100,32), \n", " distribution = 'normal',\n", " mu = 0,\n", " sigma = 1,\n", " verbose = verbose)\n", " \n", " #x - inputs come from dataset 1 X 784\n", " net.add_layer ( type = \"input\",\n", " id = \"x\",\n", " verbose = verbose, \n", " datastream_origin = 'data', # if you didnt add a dataset module, now is \n", " # the time. \n", " mean_subtract = False )\n", "\n", " net.add_layer ( type = \"dot_product\",\n", " origin = \"z\",\n", " id = \"G(z)\",\n", " num_neurons = 784,\n", " activation = 'tanh',\n", " verbose = verbose\n", " ) # This layer is the one that creates the images.\n", " \n", " #D(x) - Contains params theta_d creates features 1 X 800. \n", " net.add_layer ( type = \"dot_product\",\n", " id = \"D(x)\",\n", " origin = \"x\",\n", " num_neurons = 800,\n", " activation = 'relu',\n", " regularize = True, \n", " verbose = verbose\n", " )\n", "\n", " net.add_layer ( type = \"dot_product\",\n", " id = \"D(G(z))\",\n", " origin = \"G(z)\",\n", " input_params = net.dropout_layers[\"D(x)\"].params, \n", " num_neurons = 800,\n", " activation = 'relu',\n", " regularize = True,\n", " verbose = verbose\n", " )\n", "\n", "\n", " #C(D(x)) - This is the opposite of C(D(G(z))), real\n", " net.add_layer ( type = \"dot_product\",\n", " id = \"real\",\n", " origin = \"D(x)\",\n", " num_neurons = 1,\n", " activation = 'sigmoid',\n", " verbose = verbose\n", " )\n", "\n", " #C(D(G(z))) fake - the classifier for fake/real that always predicts fake \n", " net.add_layer ( type = \"dot_product\",\n", " id = \"fake\",\n", " origin = \"D(G(z))\",\n", " num_neurons = 1,\n", " activation = 'sigmoid',\n", " input_params = net.dropout_layers[\"real\"].params, # Again share their parameters \n", " verbose = verbose\n", " )\n", "\n", " \n", " #C(D(x)) - This is the opposite of C(D(G(z))), real\n", " net.add_layer ( type = \"classifier\",\n", " id = \"softmax\",\n", " origin = \"D(x)\",\n", " num_classes = 10,\n", " activation = 'softmax',\n", " verbose = verbose\n", " )\n", " \n", " # objective layers \n", " # discriminator objective \n", " net.add_layer (type = \"tensor\",\n", " input = - 0.5 * T.mean(T.log(net.layers['real'].output)) - \\\n", " 0.5 * T.mean(T.log(1-net.layers['fake'].output)),\n", " input_shape = (1,),\n", " id = \"discriminator_task\"\n", " )\n", "\n", " net.add_layer ( type = \"objective\",\n", " id = \"discriminator_obj\",\n", " origin = \"discriminator_task\",\n", " layer_type = 'value',\n", " objective = net.dropout_layers['discriminator_task'].output,\n", " datastream_origin = 'data', \n", " verbose = verbose\n", " )\n", " #generator objective \n", " net.add_layer (type = \"tensor\",\n", " input = - 0.5 * T.mean(T.log(net.layers['fake'].output)),\n", " input_shape = (1,),\n", " id = \"objective_task\"\n", " )\n", " net.add_layer ( type = \"objective\",\n", " id = \"generator_obj\",\n", " layer_type = 'value',\n", " origin = \"objective_task\",\n", " objective = net.dropout_layers['objective_task'].output,\n", " datastream_origin = 'data', \n", " verbose = verbose\n", " ) \n", "\n", " #softmax objective. \n", " net.add_layer ( type = \"objective\",\n", " id = \"classifier_obj\",\n", " origin = \"softmax\",\n", " objective = \"nll\",\n", " layer_type = 'discriminator',\n", " datastream_origin = 'data', \n", " verbose = verbose\n", " )\n", " \n", " from yann.utils.graph import draw_network\n", " draw_network(net.graph, filename = 'gan.png') \n", " net.pretty_print()\n", " \n", " net.cook ( objective_layers = [\"classifier_obj\", \"discriminator_obj\", \"generator_obj\"],\n", " optimizer_params = optimizer_params,\n", " discriminator_layers = [\"D(x)\"],\n", " generator_layers = [\"G(z)\"], \n", " classifier_layers = [\"D(x)\", \"softmax\"], \n", " softmax_layer = \"softmax\",\n", " game_layers = (\"fake\", \"real\"),\n", " verbose = verbose )\n", " \n", " learning_rates = (0.05, 0.01 ) \n", "\n", " net.train( epochs = (20), \n", " k = 2, \n", " pre_train_discriminator = 3,\n", " validate_after_epochs = 1,\n", " visualize_after_epochs = 1,\n", " training_accuracy = True,\n", " show_progress = True,\n", " early_terminate = True,\n", " verbose = verbose)\n", " \n", " return net\n", "\n", "if __name__ == '__main__':\n", " \n", " from yann.special.datasets import cook_mnist_normalized_zero_mean as c \n", " # from yann.special.datasets import cook_cifar10_normalized_zero_mean as c\n", " print \" creating a new dataset to run through\"\n", " data = c (verbose = 2)\n", " dataset = data.dataset_location() \n", "\n", " net = shallow_gan_mnist ( dataset, verbose = 2 )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

jmhsi/justin_tinker

data_science/courses/temp/tutorials/meanshift.ipynb

1

25548

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering with pytorch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clustering techniques are unsupervised learning algorithms that try to group unlabelled data into \"clusters\", using the (typically spatial) structure of the data itself.\n", "\n", "The easiest way to demonstrate how clustering works is to simply generate some data and show them in action. We'll start off by importing the libraries we'll be using today." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import math, numpy as np, matplotlib.pyplot as plt, operator, torch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_clusters=6\n", "n_samples =250" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate our data, we're going to pick 6 random points, which we'll call centroids, and for each point we're going to generate 250 random points about it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "centroids = np.random.uniform(-35, 35, (n_clusters, 2))\n", "slices = [np.random.multivariate_normal(centroids[i], np.diag([5., 5.]), n_samples)\n", " for i in range(n_clusters)]\n", "data = np.concatenate(slices).astype(np.float32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we can see each centroid marked w/ X, and the coloring associated to each respective cluster." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_data(centroids, data, n_samples):\n", " colour = plt.cm.rainbow(np.linspace(0,1,len(centroids)))\n", " for i, centroid in enumerate(centroids):\n", " samples = data[i*n_samples:(i+1)*n_samples]\n", " plt.scatter(samples[:,0], samples[:,1], c=colour[i], s=1)\n", " plt.plot(centroid[0], centroid[1], markersize=10, marker=\"x\", color='k', mew=5)\n", " plt.plot(centroid[0], centroid[1], markersize=5, marker=\"x\", color='m', mew=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_data(centroids, data, n_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean shift" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most people that have come across clustering algorithms have learnt about **k-means**. Mean shift clustering is a newer and less well-known approach, but it has some important advantages:\n", "* It doesn't require selecting the number of clusters in advance, but instead just requires a **bandwidth** to be specified, which can be easily chosen automatically\n", "* It can handle clusters of any shape, whereas k-means (without using special extensions) requires that clusters be roughly ball shaped.\n", "\n", "The algorithm is as follows:\n", "* For each data point x in the sample X, find the distance between that point x and every other point in X\n", "* Create weights for each point in X by using the **Gaussian kernel** of that point's distance to x\n", " * This weighting approach penalizes points further away from x\n", " * The rate at which the weights fall to zero is determined by the **bandwidth**, which is the standard deviation of the Gaussian\n", "\n", "* Update x as the weighted average of all other points in X, weighted based on the previous step\n", "\n", "This will iteratively push points that are close together even closer until they are next to each other." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So here's the definition of the gaussian kernel, which you may remember from high school..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from numpy import exp, sqrt, array" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def gaussian(d, bw): return exp(-0.5*((d/bw))**2) / (bw * math.sqrt(2*math.pi))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " This person at the science march certainly remembered!\n", "\n", "<img src=\"images/normal.jpg\" width=400>\n", "\n", "Since all of our distances are positive, we'll only be using the right-hand side of the gaussian. Here's what that looks like for a couple of different choices of bandwidth (bw)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x=np.linspace(0,5)\n", "fig, ax = plt.subplots()\n", "ax.plot(x, gaussian(x, 1), label='bw=1');\n", "ax.plot(x, gaussian(x, 2.5), label='bw=2.5')\n", "ax.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our implementation, we choose the bandwidth to be 2.5. (One easy way to choose bandwidth is to find which bandwidth covers one third of the data, which you can try implementing as an exercise.)\n", "\n", "We'll also need to be able to calculate the distance between points - here's the function we'll use:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def distance(x, X): return sqrt(((x-X)**2).sum(1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try it out. (More on how this function works shortly)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = distance(array([2,3]), array([[1,2],[2,3],[-1,1]])); d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can feed the distances into our gaussian function to see what weights we would get in this case." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gaussian(d, 2.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can put these steps together to define a single iteration of the algorithm." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def meanshift_iter(X):\n", " # Loop through every point\n", " for i, x in enumerate(X):\n", " # Find distance from point x to every other point in X\n", " dist = distance(x, X)\n", " # Use gaussian to turn into array of weights\n", " weight = gaussian(dist, 2.5)\n", " # Weighted sum (see next section for details)\n", " X[i] = (weight[:,None]*X).sum(0) / weight.sum()\n", " return X" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X=meanshift_iter(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results show that, as we hoped, all the points have moved closer to their \"true\" cluster centers (even although the algorithm doesn't know where the centers actually are)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_data(centroids, X, n_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By repeating this a few times, we can make the clusters more accurate." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def meanshift(data):\n", " X = np.copy(data)\n", " # Loop through a few epochs\n", " # A full implementation would automatically stop when clusters are stable\n", " for it in range(5): X = meanshift_iter(X)\n", " return X" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%time X=meanshift(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that mean shift clustering has almost reproduced our original clustering. The one exception are the very close clusters, but if we really wanted to differentiate them we could lower the bandwidth.\n", "\n", "What is impressive is that this algorithm nearly reproduced the original clusters without telling it how many clusters there should be. (In the chart below we are offsetting the centroids a bit to the right, otherwise we couldn't be able to see the points since they're now on top of each other)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_data(centroids+2, X, n_samples)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Broadcasting" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "How did our distance function `sqrt(((x-X)**2).sum(1))` work over a matrix without us writing any loops? The trick is that we used *broadcasting*. The term broadcasting was first used by Numpy, although is now used in other libraries such as [Tensorflow](https://www.tensorflow.org/performance/xla/broadcasting) and Matlab; the rules can vary by library.\n", "\n", "From the [Numpy Documentation](https://docs.scipy.org/doc/numpy-1.10.0/user/basics.broadcasting.html):\n", "\n", " The term broadcasting describes how numpy treats arrays with \n", " different shapes during arithmetic operations. Subject to certain \n", " constraints, the smaller array is “broadcast” across the larger \n", " array so that they have compatible shapes.Broadcasting provides a \n", " means of vectorizing array operations so that looping occurs in C\n", " instead of Python. It does this without making needless copies of \n", " data and usually leads to efficient algorithm implementations.\n", " \n", "In addition to the efficiency of broadcasting, it allows developers to write less code, which typically leads to fewer errors.\n", "\n", "Operators (+,-,\\*,/,>,<,==) are usually element-wise. Here's some examples of element-wise operations:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "a = np.array([10, 6, -4])\n", "b = np.array([2, 8, 7])\n", "\n", "a + b, a < b" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Now this next example clearly can't be element-wise, since the second parameter is a scalar, not a 1d array." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "a > 0" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "So how did this work? The trick was that numpy automatically *broadcast* the scalar `0` so that had the same `shape` as a. We can manually see how numpy broadcasts by using `broadcast_to()`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "a.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "np.broadcast_to(0, a.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "np.broadcast_to(0, a.shape).shape" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Here's another example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "a + 1" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "It works with higher-dimensional arrays too, for instance 2d (matrices):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "m = np.array([[1, 2, 3], [4,5,6], [7,8,9]]); m" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "m * 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "m.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "np.broadcast_to(2, m.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can use the same trick to broadcast a vector to a matrix:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "c = np.array([10,20,30]); c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "m + c" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Let's see what numpy has done with `c` in this case:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "np.broadcast_to(c, m.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Interesting - we see that it has duplicated `c` across rows. What if `c` was a column vector, i.e. a 3x1 array?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "# Indexing an axis with None adds a unit axis in that location\n", "cc = c[:,None]; cc" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "m + cc" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Let's see what numpy has done with `c` in this case:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "np.broadcast_to(cc, m.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that numpy isn't actually replicating the memory of the axes being broadcast - it's simply looping over the same locations multiple times. This is very efficient both for compute and memory.\n", "\n", "The behaviour of numpy's broadcasting seems quite intuitive, but you'll want to remember the explicit broadcasting rules to use this technique effectively:\n", "\n", "When operating on two arrays, Numpy/PyTorch compares their shapes element-wise. It starts with the **trailing dimensions**, and works its way forward. Two dimensions are **compatible** when\n", "\n", "- They are equal, or\n", "- One of them is 1.\n", "\n", "When axes have the same dimension, no broadcasting is required. Any axes of dimension 1 are replicated to match the other array.\n", "\n", "Arrays do not need to have the same number of dimensions. For example, if you have a 256 x 256 x 3 array of RGB values, and you want to scale each color in the image by a different value, you can multiply the image by a one-dimensional array with 3 values. Lining up the sizes of the trailing axes of these arrays according to the broadcast rules, shows that they are compatible:\n", "\n", " Image (3d array): 256 x 256 x 3\n", " Scale (1d array): 3\n", " Result (3d array): 256 x 256 x 3\n", " \n", "Numpy will insert additional unit axes as required to make the array few fewer dimensions math. So in this case the Scale array would be first reshaped automatically to 1x1x3, and then broadcast to 256 x 256 x 3. The [numpy documentation](https://docs.scipy.org/doc/numpy-1.13.0/user/basics.broadcasting.html#general-broadcasting-rules) includes several examples of what dimensions can and can not be broadcast together." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can now see how our `distance()` function works:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "a=array([2,3])\n", "b=array([[1,2],[2,3],[-1,1]])\n", "c=(a-b)**2; c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "w=gaussian(sqrt(c.sum(1)), 2.5); w" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "...and we can also now pull apart our weighted average:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "w.shape, b.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "w[:,None]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "w[:,None]*b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "(w[:,None]*b).sum(0) / w.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPU-accelerated mean shift in pytorch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now look at using [PyTorch](http://pytorch.org/), a Python framework for dynamic neural networks with GPU acceleration, which was released by Facebook's AI team.\n", "\n", "PyTorch has two overlapping, yet distinct, purposes. As described in the [PyTorch documentation](http://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html):\n", "\n", "<img src=\"images/what_is_pytorch.png\" alt=\"pytorch\" style=\"width: 80%\"/>\n", "\n", "The neural network functionality of PyTorch is built on top of the Numpy-like functionality for fast matrix computations on a GPU. Although the neural network purpose receives much more attention, both are very useful. Today we'll use PyTorch to accelerate our meanshift algorithm by running it on the GPU.\n", "\n", "If you want to learn more PyTorch, you can try this [introductory tutorial](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) or this [tutorial to learn by examples](http://pytorch.org/tutorials/beginner/pytorch_with_examples.html).\n", "\n", "One advantage of pytorch is that it's very similar to numpy. For instance, in fact, our definitions of `gaussian` and `distance` and `meanshift_iter` are identical. So we'll simply import PyTorch's alternate implementations of the two numpy functions we use:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from torch import exp, sqrt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we'll use the exact same code as before, but first convert our numpy array to a GPU PyTorch tensor." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def meanshift(data):\n", " X = torch.from_numpy(np.copy(data)).cuda()\n", " for it in range(5): X = meanshift_iter(X)\n", " return X" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try it out..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%time X = meanshift(data).cpu().numpy()\n", "plot_data(centroids+2, X, n_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It works, but this implementation actually takes longer. Oh dear! What do you think is causing this?\n", "\n", "Each iteration launches a new cuda kernel, which takes time and slows the algorithm down as a whole. Furthermore, each iteration doesn't have enough processing to do to fill up all of the threads of the GPU. To use the GPU effectively, we need to process a *batch* of data at a time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPU batched algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To process a batch of data, we need batched versions of our functions. Here's a version of `distance()` that works on batches:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def distance_b(a,b): return sqrt(((a[None,:] - b[:,None]) ** 2).sum(2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=torch.rand(2,2)\n", "b=torch.rand(3,2)\n", "distance_b(b, a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how the two parameters to `distance_b()` have a unit axis added in two different places (`a[None,:]` and `b[:,None]`). This is a handy trick which effectively generalizes the concept of an 'outer product' to any function. In this case, we use it to get the distance from every point in `a` (our batch) to every point in `b` (the whole dataset).\n", "\n", "Now that we have a suitable distance function, we can make some minor updates to our meanshift function to handle batches of data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def meanshift(data, bs=500):\n", " n = len(data)\n", " X = torch.from_numpy(np.copy(data)).cuda()\n", " for it in range(5):\n", " for i in range(0,n,bs):\n", " s = slice(i,min(n,i+bs))\n", " weight = gaussian(distance_b(X, X[s]), 2.5)\n", " num = (weight[:,:,None] * X).sum(1)\n", " X[s] = num / weight.sum(1)[:,None]\n", " return X" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "torch.from_numpy(np.copy(data)).cuda()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although each iteration still has to launch a new cuda kernel, there are now fewer iterations, and the acceleration from updating a batch of points more than makes up for it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%time X = meanshift(data).cpu().numpy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's more like it! We've gone from 2000ms to 26ms, which is a speedup of over 7000%. Oh, and it even gives the right answer!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_data(centroids+2, X, n_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## course.fast.ai" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you found this interesting, you might enjoy the 30+ hours of deep learning lessons at [course.fast.ai](http://course.fast.ai). There's also a very active forum of deep learning practitioners and learners at [forums.fast.ai](http://forums.fast.ai). Hope to see you there! :)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

sbitzer/pyEPABC

examples/narrow_posteriors.ipynb

1

85670

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Why EP-ABC can produce too narrow posteriors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you use EP-ABC for inference you may notice that your posterior distributions appear suspiciously narrow, i.e., you may not belief the certainty indicated by EP-ABC inference. Your suspicions can be correct: The posteriors inferred by EP-ABC sometimes tend to be too narrow. The fault lies within the recursive sampling process used in EP-ABC: The main mechanism is to maintain an estimate of the posterior distribution from which you sample and then re-estimate the posterior distribution based on a subset of the samples compatible with a data point. If the distribution from which you sample has some [sampling error](https://en.wikipedia.org/wiki/Sampling_error), your next estimate of that distribution will deviate even more from the underlying distribution, especially, if the sampling error consistently deviates in one direction. This is exactly what can happen in individual runs of EP-ABC.\n", "\n", "In the following, I will demonstrate drifting distribution estimates by recursively sampling from a Gaussian. Ideally, the estimated mean and standard deviation would remain stable, but sampling error lets them drift. The question, therefore, is how strong the drift is and whether it has a trend." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling theory\n", "It is known, but rarely appreciated, that the square root of an unbiased estimate of [variance](https://en.wikipedia.org/wiki/Variance) consistently underestimates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation). Because we use standard deviations when sampling from a Gaussian (also in EP-ABC), recursively sampling from a Gaussian will shrink its standard deviation, even though we may have no bias in estimating the variance of the distribution. Actually, it's not so hard to compute an [unbiased estimate of the standard deviation](https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation), at least approximately: Instead of dividing by $N-1$, as in the unbiased estimate of variance, we only have to divide by $N-1.5$. I have implemented this in EP-ABC to reduce the shrinking of posteriors. Below you can see for yourself what effect this has." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some plotting functions\n", "I here define some plotting functions used below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_mean_with_std(mean, std, std_mult=2, xvals=None, ax=None):\n", " if xvals is None:\n", " xvals = np.arange(mean.shape[0])\n", " if ax is None:\n", " ax = plt.axes()\n", " \n", " ax.plot(mean, 'k', lw=3)\n", " ax.fill_between(xvals, mean + std_mult*std, mean - std_mult*std, \n", " edgecolor='k', facecolor='0.7')\n", "\n", "def plot_single_trajectory(means, stds, rep=None):\n", " if rep is None:\n", " rep = np.random.randint(means.shape[0])\n", " \n", " xvals = np.arange(means.shape[1])\n", " \n", " fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)\n", " ax1.set_title('repetition %d' % rep)\n", " plot_mean_with_std(means[rep, :], stds[rep, :], xvals=xvals, ax=ax1)\n", " \n", " ax2.set_ylabel('mean')\n", " ax2.plot(xvals, means[rep, :], 'k', lw=3, label='mean')\n", " ax3.set_ylabel('std')\n", " ax3.plot(xvals, stds[rep, :], 'k', lw=1, label='std')\n", " \n", " diff = means[rep, :] - means[rep, 0]\n", " print('largest deviation from initial mean: %6.1f%% (of initial std)' \n", " % (diff[np.abs(diff).argmax()] / stds[rep, 0] * 100, ) )\n", " diff = stds[rep, :] - stds[rep, 0]\n", " print('largest deviation from initial std: %6.1f%%' \n", " % (diff[np.abs(diff).argmax()] / stds[rep, 0] * 100, ) )\n", " \n", "def plot_distribution_drift(means, stds):\n", " fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n", " \n", " ax1.set_title('average drift of mean (+- 2*std)')\n", " plot_mean_with_std(means.mean(axis=0), means.std(axis=0), ax=ax1)\n", " ax2.set_title('average drift of std (+- 2*std)')\n", " plot_mean_with_std(stds.mean(axis=0), stds.std(axis=0), ax=ax2)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recursive Sampling of a Gaussian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell repeatedly runs a recursive sampling process and plots the average drifts of mean and standard deviation across the repetitions." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "after 200 steps with 500 samples:\n", "difference in mean mean: 15.9% (of initial std)\n", "difference in mean std: 5.3%\n" ] }, { "data": { "image/png": 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R0UFvby9ut5vs7GySkpJi9p6UUnR2dgJgs9kiNsz+/n5qa2tpaGiItEysVmuk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\nmUzS3d1NR0cHHR0dhMNho0xDQ0NGOVPHO0BfXx9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8277299be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# number of recursive steps\n", "nsteps = 200\n", "# number of samples drawn from distribution\n", "nsamples = 500\n", "# number of repetitions of recursive sampling\n", "nrep = 100\n", "\n", "# initial mean\n", "mu = 23\n", "# initial standard deviation\n", "sigma = 100\n", "\n", "# degrees of freedom determining the divisor for\n", "# the estimation of standard deviation (1.5~unbiased)\n", "ddof = 1.5\n", "\n", "means = np.full((nrep, nsteps), np.nan)\n", "means[:, 0] = mu\n", "stds = np.full((nrep, nsteps), np.nan)\n", "stds[:, 0] = sigma\n", "\n", "for r in range(nrep):\n", " for s in range(1, nsteps):\n", " S = np.random.normal(means[r, s-1], stds[r, s-1], nsamples)\n", " means[r, s] = S.mean()\n", " stds[r, s] = S.std(ddof=ddof)\n", " \n", "plot_distribution_drift(means, stds)\n", "print('after %d steps with %d samples:' % (nsteps, nsamples))\n", "print('difference in mean mean: %6.1f%% (of initial std)' % (\n", " (means[:, -1].mean() - mu) / sigma * 100, ))\n", "print('difference in mean std: %6.1f%%' % ((stds[:, -1].mean() - sigma) / \n", " sigma * 100, ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The generated plots should have a relatively flat, thick, black line in the middle which suggests that mean and standard deviation did not change much when averaged across repetitions. The shading, representing the area of double standard deviation across repetitions, however, should become wider as more recursive sampling steps are taken, indicating that in some repetitions there was a considerable drift of the distribution (see below for plots of individual trajectories).\n", "\n", "You may now want to see how these curves change, when you manipulate the number of samples, or degrees of freedom (ddof) used to estimate standard deviations from samples. Increasing the number of samples is generally good and leads to a reduction of mean drift and a narrowing of the shading, meaning that also the drift in individual repetitions of recursive sampling is small. Setting `ddof=0`, which is the standard setting of numpy (!), leads to severe shrinkage of the sampled distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Individual recursive sampling trajectories\n", "To get a feeling for the variability of recursive sampling trajectories across repetitions the following cell will plot a single (random) trajectory computed above." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "largest deviation from initial mean: 54.6% (of initial std)\n", "largest deviation from initial std: 26.2%\n" ] }, { "data": { "image/png": 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NYuzYsfTq1euS3qderyctLY38/Hy0Wi3h4eHodDpWr15Nfn4+Go2G/Px85s6dy6JFi3Bw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\nszWH2RhjVQAcNCbNQNncPYwL0B2SukD9Q1KRyGdISh9vvvkmAwMDOWX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"text/plain": [ "<matplotlib.figure.Figure at 0x7f827756f3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_single_trajectory(means, stds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "It is remarkable how strongly mean and standard deviation of the sampled Gaussian can drift in a single run of the recursive sampling process. Their largest deviations from their initial values often exceed 50% of the initial standard deviation and the mean moves more than the standard deviation. This may look like a severe problem for EP-ABC at first, but note that the posterior in EP-ABC cannot freely drift like here. It is strongly constrained by the data. These constraints particularly prevent the mean from drifting and prevent the standard deviation from increasing. However, the constraints are less efficient in preventing shrinkage of the posterior, if the data is explained well enough by a subregion covered by the true posterior. For example, if your data is explained equally well by either of two sets of parameter values, shrinkage of the posterior induced by random drifts due to sampling error may force the EP-ABC posterior to cover only one set of parameter values, instead of both of them. It is possible to identify these situations by repeating the inference with EP-ABC and checking the consistency of the inferred posteriors. Sampling errors and the resulting drift of posteriors can also be prevented by setting the minimum number of accepted samples in EP-ABC (`minacc`) to a high value, e.g., 500 or 1000." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

LargePanda/ccg_tweet_wikification

.ipynb_checkpoints/random_example-checkpoint.ipynb

1

130349

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from textblob import TextBlob\n", "import pickle" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "WordList(['linda', 'bond', 'widow', u'tyrone power', u'tv version', 'royale'])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text = \"Linda Christian, the first Bond girl, dies at 87 . Widow of Tyrone Power was in TV version of Casino Royale\"\n", "blob = TextBlob(text)\n", "blob.noun_phrases" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "\n", "with open(\"NEEL_tweets(with_grams).pickle\", \"rb\") as f:\n", " tweet_corpus = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['93314579924393984',\n", " '94884467910512640',\n", " '93141776474456064',\n", " '91707256726499328',\n", " '92955019615272961',\n", " '102380900473507840',\n", " '93404134618841088',\n", " '102312601190539265',\n", " '99288973943390209',\n", " '102125361248215040',\n", " '101574290444521472',\n", " '100658108661112832',\n", " '72058654651711488',\n", " '100977986563604480',\n", " '95953148530790400',\n", " '91927932758540291',\n", " '96222040377393152',\n", " '102000318501490688',\n", " '92681654858035201',\n", " 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u'the',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'DT'}],\n", " [u'perfect',\n", " {u'CharacterOffsetBegin': u'68',\n", " u'CharacterOffsetEnd': u'75',\n", " u'Lemma': u'perfect',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'JJ'}],\n", " [u'position',\n", " {u'CharacterOffsetBegin': u'76',\n", " u'CharacterOffsetEnd': u'84',\n", " u'Lemma': u'position',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NN'}],\n", " [u'to',\n", " {u'CharacterOffsetBegin': u'85',\n", " u'CharacterOffsetEnd': u'87',\n", " u'Lemma': u'to',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'TO'}],\n", " [u'be',\n", " {u'CharacterOffsetBegin': u'88',\n", " u'CharacterOffsetEnd': u'90',\n", " u'Lemma': u'be',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'VB'}],\n", " [u'in',\n", " {u'CharacterOffsetBegin': u'91',\n", " u'CharacterOffsetEnd': u'93',\n", " u'Lemma': u'in',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'IN'}],\n", " [u'.',\n", " {u'CharacterOffsetBegin': u'93',\n", " u'CharacterOffsetEnd': u'94',\n", " u'Lemma': u'.',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'.'}]]},\n", " {u'dependencies': [[u'root', u'ROOT', u'Praise'],\n", " [u'nn', u'Praise', u'P.R.A.Y.'],\n", " [u'amod', u'Praise', u'='],\n", " [u'nn', u'Yield', u'Repent'],\n", " [u'appos', u'Yield', u'Ask'],\n", " [u'appos', u'Praise', u'Yield']],\n", " u'parsetree': u'(ROOT (NP (NP (NN P.R.A.Y.) (JJ =) (NN Praise)) (, ,) (NP (NNP Repent) (, ,) (NNP Ask) (, ,) (NNP Yield)) (. .)))',\n", " u'text': u'P.R.A.Y. = Praise, Repent, Ask, Yield.',\n", " u'words': [[u'P.R.A.Y.',\n", " {u'CharacterOffsetBegin': u'95',\n", " u'CharacterOffsetEnd': u'103',\n", " u'Lemma': u'p.r.a.y.',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NN'}],\n", " [u'',\n", " {u'': u'',\n", " u'CharacterOffsetBegin': u'104',\n", " u'CharacterOffsetEnd': u'105',\n", " u'Lemma': u'',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'JJ'}],\n", " [u'Praise',\n", " {u'CharacterOffsetBegin': u'106',\n", " u'CharacterOffsetEnd': u'112',\n", " u'Lemma': u'praise',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NN'}],\n", " [u',',\n", " {u'CharacterOffsetBegin': u'112',\n", " u'CharacterOffsetEnd': u'113',\n", " u'Lemma': u',',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u','}],\n", " [u'Repent',\n", " {u'CharacterOffsetBegin': u'114',\n", " u'CharacterOffsetEnd': u'120',\n", " u'Lemma': u'Repent',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NNP'}],\n", " [u',',\n", " {u'CharacterOffsetBegin': u'120',\n", " u'CharacterOffsetEnd': u'121',\n", " u'Lemma': u',',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u','}],\n", " [u'Ask',\n", " {u'CharacterOffsetBegin': u'122',\n", " u'CharacterOffsetEnd': u'125',\n", " u'Lemma': u'Ask',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NNP'}],\n", " [u',',\n", " {u'CharacterOffsetBegin': u'125',\n", " u'CharacterOffsetEnd': u'126',\n", " u'Lemma': u',',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u','}],\n", " [u'Yield',\n", " {u'CharacterOffsetBegin': u'127',\n", " u'CharacterOffsetEnd': u'132',\n", " u'Lemma': u'Yield',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NNP'}],\n", " [u'.',\n", " {u'CharacterOffsetBegin': u'132',\n", " u'CharacterOffsetEnd': u'133',\n", " u'Lemma': u'.',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'.'}]]},\n", " {u'dependencies': [[u'root', u'ROOT', u'#TLS']],\n", " u'parsetree': u'(ROOT (NP (NN #TLS)))',\n", " u'text': u'#TLS',\n", " u'words': [[u'#TLS',\n", " {u'CharacterOffsetBegin': u'134',\n", " u'CharacterOffsetEnd': u'138',\n", " u'Lemma': u'#tls',\n", " u'NamedEntityTag': u'O',\n", " u'PartOfSpeech': u'NN'}]]}]},\n", " 'tweet_info': {u'contributors': None,\n", " u'coordinates': None,\n", " u'created_at': u'Wed Jul 20 18:14:24 +0000 2011',\n", " u'entities': {u'hashtags': [{u'indices': [134, 138], u'text': u'TLS'}],\n", " u'symbols': [],\n", " u'urls': [],\n", " u'user_mentions': []},\n", " u'favorite_count': 194,\n", " u'favorited': False,\n", " u'geo': None,\n", " u'id': 93745589786902528,\n", " u'id_str': u'93745589786902528',\n", " u'in_reply_to_screen_name': None,\n", " u'in_reply_to_status_id': None,\n", " u'in_reply_to_status_id_str': None,\n", " u'in_reply_to_user_id': None,\n", " u'in_reply_to_user_id_str': None,\n", " u'is_quote_status': False,\n", " u'lang': u'en',\n", " u'place': None,\n", " u'retweet_count': 449,\n", " u'retweeted': False,\n", " u'source': u'<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>',\n", " u'text': u'God will never drop you any farther than your knees and that is the perfect position to be in. P.R.A.Y. = Praise, Repent, Ask, Yield. #TLS',\n", " u'truncated': False,\n", " u'user': {u'contributors_enabled': False,\n", " u'created_at': u'Thu Oct 15 15:52:52 +0000 2009',\n", " u'default_profile': False,\n", " u'default_profile_image': False,\n", " u'description': u'True Love Forever!',\n", " u'entities': {u'description': {u'urls': []}},\n", " u'favourites_count': 24,\n", " u'follow_request_sent': False,\n", " u'followers_count': 1250455,\n", " u'following': False,\n", " u'friends_count': 27,\n", " u'geo_enabled': False,\n", " u'has_extended_profile': False,\n", " u'id': 82647069,\n", " u'id_str': u'82647069',\n", " u'is_translation_enabled': False,\n", " u'is_translator': False,\n", " u'lang': u'en',\n", " u'listed_count': 7879,\n", " u'location': u'',\n", " u'name': u'The Love Stories',\n", " u'notifications': False,\n", " u'profile_background_color': u'EBEBEB',\n", " u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme7/bg.gif',\n", " u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme7/bg.gif',\n", " u'profile_background_tile': False,\n", " u'profile_banner_url': u'https://pbs.twimg.com/profile_banners/82647069/1412285293',\n", " u'profile_image_url': u'http://pbs.twimg.com/profile_images/517788093605097472/Zj4O8z4B_normal.jpeg',\n", " u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/517788093605097472/Zj4O8z4B_normal.jpeg',\n", " u'profile_link_color': u'990000',\n", " u'profile_sidebar_border_color': u'DFDFDF',\n", " u'profile_sidebar_fill_color': u'F3F3F3',\n", " u'profile_text_color': u'333333',\n", " u'profile_use_background_image': True,\n", " u'protected': False,\n", " u'screen_name': u'thelovestories',\n", " u'statuses_count': 31257,\n", " u'time_zone': u'Central Time (US & Canada)',\n", " u'url': None,\n", " u'utc_offset': -18000,\n", " u'verified': False}},\n", " 'url_mapping': {},\n", " 'usermention_mapping': {}}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_corpus['93745589786902528']" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n", "11\n" ] } ], "source": [ "with open(\"tweet_text_ids.txt\", \"wb\") as g:\n", " with open(\"tweet_text.txt\", \"wb\") as f:\n", " for tw in tweet_corpus.values():\n", " g.write(str(tw['tweet_info']['id'])+\"\\n\")\n", " text = tw['tweet_info']['text']\n", " if \"\\n\" in text or \"\\t\" in text:\n", " print \"11\"\n", " text = text.replace(\"\\n\", \" \")\n", " text = text.replace(\"\\t\", \" \")\n", " f.write(text.encode(\"utf8\")+\"\\n\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = \"plpl \\nko\"" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "plpl \n", "ko\n" ] } ], "source": [ "print p" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ko'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p[6:]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'ascii' codec can't decode byte 0xe2 in position 37: ordinal not in range(128)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-59-67f6011cc69b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tweet_text.txt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0xe2 in position 37: ordinal not in range(128)" ] } ], "source": [ "with open(\"tweet_text.txt\", \"rb\") as f:\n", " for line in f:\n", " line.encode(\"utf8\")" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"output.txt\", \"rb\") as f:\n", " col = []\n", " for line in f:\n", " col.append(line.strip())" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(\"tweet_text_ids.txt\", \"rb\") as f:\n", " ids = []\n", " for line in f:\n", " ids.append(line.strip())" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output = {}\n", "for i in range(len(col)):\n", " output[ids[i]] = col[i]" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'93314579924393984': [],\n", " '94884467910512640': ['Christian', 'Tyrone Power'],\n", " '94259157615648770': [],\n", " '93141776474456064': ['Diego'],\n", " '91707256726499328': [],\n", " '97794908630425600': [],\n", " '92955019615272961': [],\n", " 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" '92643760508387328': [],\n", " '94063258499153920': [],\n", " '93500846666551296': [],\n", " '100571949746495488': [],\n", " '96985217134366720': [],\n", " '93899167105167360': ['Blames Crisis'],\n", " '100712904470429696': [],\n", " '94404324922363904': [],\n", " '96004045017321473': [],\n", " '95103154651275264': [],\n", " '91853498135752704': [],\n", " '100689319639007232': [],\n", " '93484337873420288': [],\n", " '95072342140653568': [],\n", " '100992810420936704': ['America'],\n", " '100644788671488000': [],\n", " '99611423281393664': [],\n", " '97665015968047104': ['Heat', 'Erik Spoelstra'],\n", " '100901337633521664': ['Guardian'],\n", " '93708758311632896': [],\n", " '102580796497461248': ['State Fair'],\n", " '95533101521977344': [],\n", " '92110996134576128': ['Geng'],\n", " '91840950862741504': [],\n", " '92744177418379265': [],\n", " '100671608204701696': [],\n", " '98181224346034176': [],\n", " '91872307479449600': ['Dog', 'Chelsea'],\n", " '93923166950395904': [],\n", " 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'Videographers'],\n", " '93348468701151232': [],\n", " '92817093799587841': [],\n", " '94575922270838784': ['Field'],\n", " '91655593139511296': ['Johnson'],\n", " '92275090007404544': [],\n", " '94709694458900480': [],\n", " '102401405851140096': [],\n", " '99029732041031680': ['Furo', 'Rivers United U19', 'Helsinki', 'Nigeria'],\n", " '99287434281484288': [],\n", " '92689105703550976': [],\n", " '97376969657823233': [],\n", " '92006871375945728': [],\n", " '92884204265811970': [],\n", " '101371988911726592': [],\n", " '102865437901070336': [],\n", " '93273993544597504': [],\n", " '92968056053186562': ['Brothers'],\n", " '93443989793030144': [],\n", " '92446791315890176': [],\n", " '94518623552552961': [],\n", " '102411827916455936': [],\n", " '92713454162554880': [],\n", " '92284144960286721': [],\n", " '92201997121499136': [],\n", " '96956789559537665': [],\n", " '99556024939331584': [],\n", " '93324903985577984': [],\n", " '92294980080644096': [],\n", " '100529934270873600': [],\n", " '99653392141533184': ['States', 'AAA'],\n", " '96211977642315776': [],\n", " '95951788217352193': [],\n", " '93080394370519040': [],\n", " '96427426464284673': [],\n", " '96836366918352896': [],\n", " '97819281215074304': [],\n", " '95396113888194560': ['Jazeera Misr', 'Adli'],\n", " '96651234517323776': [],\n", " '94029148615483393': [],\n", " '92297618285273088': ['Award', 'Human Rights'],\n", " '91708290513383425': [],\n", " '92435485066145792': [],\n", " '99641382926434304': [],\n", " '95438229242773504': [],\n", " '92624675829596160': [],\n", " '93538364686483456': [],\n", " '102620482892869633': [],\n", " '94813064473481216': ['Winehouse'],\n", " '93731915043049473': [],\n", " '93031292421144576': ['Villa'],\n", " '93211671123861504': [],\n", " '93330419516313600': [],\n", " '102087976670076928': ['Herrera', 'Joel Campbell'],\n", " '92928036990746625': [],\n", " '100925943694110720': [],\n", " '96228351936692224': [],\n", " '92990179672985600': [],\n", " 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'101841857687977984': ['London Square'],\n", " '101671774474870785': [],\n", " '92247182312349696': [],\n", " '92508293959917568': [],\n", " '94815768675487745': [],\n", " '94759766982799360': ['Citizen'],\n", " '96116890295992320': [],\n", " '91786366156935168': [],\n", " '96354439341936640': ['Longwell'],\n", " '98068716322951169': [],\n", " '95128487727349760': ['Grand Prix'],\n", " '92926655382818819': [],\n", " '93059420296183808': [],\n", " '92370537531183104': [],\n", " '94201131856707584': [],\n", " '102923598414622720': [],\n", " '93291567711916032': [],\n", " '96726597469618176': [],\n", " '94510033202724865': ['Risk', 'Heat Stroke'],\n", " '97027268685213696': [],\n", " '97002463147720705': [],\n", " '93900949902462977': [],\n", " '93942339093004288': [],\n", " '96145938346811393': ['Madrid', 'Sergio Aguero', 'Manchester', 'City'],\n", " '97373690815197184': ['Knox'],\n", " '93750898479730688': ['Croft', 'Tom Smith'],\n", " '92848660353794049': [],\n", " '93378613063327744': [],\n", " '91904202460241920': [],\n", " '99954453586780161': ['Wenger', 'Barack Obama'],\n", " '92804182280634371': [],\n", " '93068195799371776': ['and Chandler'],\n", " '92648687725051905': [],\n", " '96979327224266752': ['Klinsmann'],\n", " '102441571353509888': [],\n", " '93709909803274240': [],\n", " '93105665089867776': [],\n", " '91871796252512256': [],\n", " '100720839560933376': [],\n", " '93671907987177473': [],\n", " '94807286882635776': [],\n", " '101197985895034880': [],\n", " '93761496332517376': [],\n", " '95999339016626176': [],\n", " ...}" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for o in output.keys():\n", " output[o] = get_entities(output[o])" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_entities(line_text):\n", " segments = line_text.split(\" \")\n", " col = []\n", " for seg in segments:\n", " res = normalize(seg)\n", " if res is not None:\n", " col.append(res)\n", " fin = []\n", " temp = []\n", " for c in col:\n", " if c[1] == \"B\":\n", " if len(temp) == 0:\n", " pass\n", " else:\n", " fin.append(\" \".join(temp))\n", " temp = [c[0]]\n", " elif c[1] == \"I\":\n", " temp.append(c[0])\n", " return fin\n", " " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Century Fox Animation', 'Blue Sky Studios Announce Ice-Age Casting']" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_entities(\"CONFIRMED/O :/O Twentieth/B-ENTITY Century/I-ENTITY Fox/I-ENTITY Animation/I-ENTITY &/O Blue/B-ENTITY Sky/I-ENTITY Studios/I-ENTITY Announce/I-ENTITY Ice-Age/I-ENTITY Casting/I-ENTITY &/O Nicki/B-ENTITY Minaj/I-ENTITY is/O on/O the/O LINEUP/O http://t.co/IlQgO34/O\")" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def normalize(word):\n", " index = word.rfind('/')\n", " pure = word[:index]\n", " if \"I-\" in word[index+1:]:\n", " return (pure, \"I\")\n", " if \"B-\" in word[index+1:]:\n", " return (pure, \"B\")\n", " else:\n", " return None" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp1 = [1,2,3,4]\n", "temp2 = [3,4,5,6]\n", "len(set(temp1) - set(temp2))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for tweet in tweet_corpus.values():\n", " tweet[\"mention_set\"] = set([item['mention'] for item in tweet['goldens']])\n", " \n", "stats = {\"tp\":0., \"fp\":0., \"tn\":0., \"fn\":0.}\n", "\n", "for tweet in tweet_corpus.values():\n", " tid = str(tweet['tweet_info']['id'])\n", " ners = set(output[tid])\n", " golds = tweet[\"mention_set\"]\n", " \n", " stats[\"fp\"] += len(set(ners) - set(golds)))\n", " stats[\"fn\"] += len(set(golds) - set(ners)))\n", " stats[\"tp\"] += len(set(golds) - set(ners)))\n", " \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

AlbertoAlfredo/exercicios-cursos

ExerciciosGerais/slides/34.Prática em Python/scripts/3.deep_learning.ipynb

1

6286

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Formação Cientista de Dados - Fernando Amaral e Jones Granatyr\n", "# Deep Learning" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Importação das bibliotecas\n", "import matplotlib.pyplot as plt\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout\n", "from keras.utils import np_utils\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "from keras.datasets import mnist\n", "#pip install tensorflow (executar no Anaconda Prompt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Obtenção dos dados e divisão automática entre treinamento e teste\n", "(X_treinamento, y_treinamento), (X_teste, y_teste) = mnist.load_data()\n", "# Visualização de imagens específicas\n", "plt.imshow(X_treinamento[21], cmap = 'gray')\n", "plt.title(y_treinamento[21])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Mudança de dimensão, originalmente está em 28x28 e precisamos 784\n", "X_treinamento = X_treinamento.reshape((len(X_treinamento), np.prod(X_treinamento.shape[1:])))\n", "X_teste = X_teste.reshape((len(X_teste), np.prod(X_teste.shape[1:])))\n", "X_teste[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Transformação dos dados para float para podermos normalizar os dados\n", "X_treinamento = X_treinamento.astype('float32')\n", "X_teste = X_teste.astype('float32')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Normalização (255 é o valor máximo de um pixel)\n", "X_treinamento /= 255\n", "X_teste /= 255" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Transformação para o formato dummy (temos 10 classes)\n", "y_treinamento = np_utils.to_categorical(y_treinamento, 10)\n", "y_teste = np_utils.to_categorical(y_teste, 10)\n", "y_teste[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Estrutura da rede neural: 784 - 64 - 64 - 64 - 10\n", "# Dropout é utilizado para zerar uma porcentagem dos neurônios, para evitar o overfitting\n", "modelo = Sequential()\n", "modelo.add(Dense(units = 64, activation = 'relu', input_dim = 784))\n", "modelo.add(Dropout(0.2))\n", "modelo.add(Dense(units = 64, activation = 'relu'))\n", "modelo.add(Dropout(0.2))\n", "modelo.add(Dense(units = 64, activation = 'relu'))\n", "modelo.add(Dropout(0.2))\n", "#camada de saida, softmax probabilidade\n", "modelo.add(Dense(units = 10, activation = 'softmax'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização da estrutura da rede neural\n", "modelo.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Configuração dos parâmetros da rede neural e treinamento (utilizando base de dados de validação)\n", "# Na variável historico temos os histórico das execuções (erro e accuracy)\n", "modelo.compile(optimizer = 'adam', loss = 'categorical_crossentropy',\n", " metrics = ['accuracy'])\n", "historico = modelo.fit(X_treinamento, y_treinamento, epochs = 20,\n", " validation_data = (X_teste, y_teste))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Gráfico para visualizar os erros e accuracy\n", "historico.history.keys()\n", "#evolução do erro, azul\n", "plt.plot(historico.history['val_loss'])\n", "#performance da rede\n", "plt.plot(historico.history['val_accuracy'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Obtenção das previsões \n", "previsoes = modelo.predict(X_teste)\n", "previsoes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# valor máximo (com a probabilidade maior por serem 10 saídas) e geração da matriz de confusão\n", "y_teste_matriz = [np.argmax(t) for t in y_teste]\n", "y_previsoes_matriz = [np.argmax(t) for t in previsoes]\n", "confusao = confusion_matrix(y_teste_matriz, y_previsoes_matriz)\n", "confusao" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Previsão com um novo registro, convertendo o array para o formato de matriz\n", "#número 4\n", "y_treinamento[20]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#passo a mesma posição para o modelo prever\n", "novo = X_treinamento[20]\n", "#de matriz para vetor\n", "novo = np.expand_dims(novo, axis = 0)\n", "#previsao\n", "pred = modelo.predict(novo)\n", "#maior valor\n", "pred = [np.argmax(pred) for t in pred]\n", "pred" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

statsmodels/statsmodels.github.io

v0.13.1/examples/notebooks/generated/generic_mle.ipynb

2

32412

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Likelihood Estimation (Generic models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: \n", "\n", "1. Probit model for binary dependent variables\n", "2. Negative binomial model for count data\n", "\n", "The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Probit model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:58.433804Z", "iopub.status.busy": "2021-11-12T23:37:58.427242Z", "iopub.status.idle": "2021-11-12T23:37:59.499600Z", "shell.execute_reply": "2021-11-12T23:37:59.498626Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``Spector`` dataset is distributed with ``statsmodels``. You can access a vector of values for the dependent variable (``endog``) and a matrix of regressors (``exog``) like this:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.503538Z", "iopub.status.busy": "2021-11-12T23:37:59.502570Z", "iopub.status.idle": "2021-11-12T23:37:59.519319Z", "shell.execute_reply": "2021-11-12T23:37:59.520013Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 32\n", "\n", " Number of Variables - 4\n", "\n", " Variable name definitions::\n", "\n", " Grade - binary variable indicating whether or not a student's grade\n", " improved. 1 indicates an improvement.\n", " TUCE - Test score on economics test\n", " PSI - participation in program\n", " GPA - Student's grade point average\n", "\n", " GPA TUCE PSI\n", "0 2.66 20.0 0.0\n", "1 2.89 22.0 0.0\n", "2 3.28 24.0 0.0\n", "3 2.92 12.0 0.0\n", "4 4.00 21.0 0.0\n" ] } ], "source": [ "data = sm.datasets.spector.load_pandas()\n", "exog = data.exog\n", "endog = data.endog\n", "print(sm.datasets.spector.NOTE)\n", "print(data.exog.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Them, we add a constant to the matrix of regressors:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.523253Z", "iopub.status.busy": "2021-11-12T23:37:59.522330Z", "iopub.status.idle": "2021-11-12T23:37:59.529565Z", "shell.execute_reply": "2021-11-12T23:37:59.530233Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "exog = sm.add_constant(exog, prepend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create your own Likelihood Model, you simply need to overwrite the loglike method." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.533445Z", "iopub.status.busy": "2021-11-12T23:37:59.532500Z", "iopub.status.idle": "2021-11-12T23:37:59.539828Z", "shell.execute_reply": "2021-11-12T23:37:59.540498Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "class MyProbit(GenericLikelihoodModel):\n", " def loglike(self, params):\n", " exog = self.exog\n", " endog = self.endog\n", " q = 2 * endog - 1\n", " return stats.norm.logcdf(q*np.dot(exog, params)).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate the model and print a summary:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.543674Z", "iopub.status.busy": "2021-11-12T23:37:59.542761Z", "iopub.status.idle": "2021-11-12T23:37:59.660562Z", "shell.execute_reply": "2021-11-12T23:37:59.661343Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.400588\n", " Iterations: 292\n", " Function evaluations: 494\n", " MyProbit Results \n", "==============================================================================\n", "Dep. Variable: GRADE Log-Likelihood: -12.819\n", "Model: MyProbit AIC: 33.64\n", "Method: Maximum Likelihood BIC: 39.50\n", "Date: Fri, 12 Nov 2021 \n", "Time: 23:37:59 \n", "No. Observations: 32 \n", "Df Residuals: 28 \n", "Df Model: 3 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -7.4523 2.542 -2.931 0.003 -12.435 -2.469\n", "GPA 1.6258 0.694 2.343 0.019 0.266 2.986\n", "TUCE 0.0517 0.084 0.617 0.537 -0.113 0.216\n", "PSI 1.4263 0.595 2.397 0.017 0.260 2.593\n", "==============================================================================\n" ] } ], "source": [ "sm_probit_manual = MyProbit(endog, exog).fit()\n", "print(sm_probit_manual.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare your Probit implementation to ``statsmodels``' \"canned\" implementation:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.664691Z", "iopub.status.busy": "2021-11-12T23:37:59.663749Z", "iopub.status.idle": "2021-11-12T23:37:59.672581Z", "shell.execute_reply": "2021-11-12T23:37:59.673251Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.400588\n", " Iterations 6\n" ] } ], "source": [ "sm_probit_canned = sm.Probit(endog, exog).fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.677060Z", "iopub.status.busy": "2021-11-12T23:37:59.675659Z", "iopub.status.idle": "2021-11-12T23:37:59.685340Z", "shell.execute_reply": "2021-11-12T23:37:59.686015Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "const -7.452320\n", "GPA 1.625810\n", "TUCE 0.051729\n", "PSI 1.426332\n", "dtype: float64\n", "[-7.45233176 1.62580888 0.05172971 1.42631954]\n" ] } ], "source": [ "print(sm_probit_canned.params)\n", "print(sm_probit_manual.params)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.689761Z", "iopub.status.busy": "2021-11-12T23:37:59.688359Z", "iopub.status.idle": "2021-11-12T23:37:59.701242Z", "shell.execute_reply": "2021-11-12T23:37:59.701927Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " const GPA TUCE PSI\n", "const 6.464166 -1.169668 -0.101173 -0.594792\n", "GPA -1.169668 0.481473 -0.018914 0.105439\n", "TUCE -0.101173 -0.018914 0.007038 0.002472\n", "PSI -0.594792 0.105439 0.002472 0.354070\n", "[[ 6.46416769e+00 -1.16966616e+00 -1.01173181e-01 -5.94788997e-01]\n", " [-1.16966616e+00 4.81472112e-01 -1.89134585e-02 1.05438225e-01]\n", " [-1.01173181e-01 -1.89134585e-02 7.03758394e-03 2.47189243e-03]\n", " [-5.94788997e-01 1.05438225e-01 2.47189243e-03 3.54069512e-01]]\n" ] } ], "source": [ "print(sm_probit_canned.cov_params())\n", "print(sm_probit_manual.cov_params())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the ``GenericMaximumLikelihood`` class provides automatic differentiation, so we did not have to provide Hessian or Score functions in order to calculate the covariance estimates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Example 2: Negative Binomial Regression for Count Data\n", "\n", "Consider a negative binomial regression model for count data with\n", "log-likelihood (type NB-2) function expressed as:\n", "\n", "$$\n", " \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n", " \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n", " \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n", "$$\n", "\n", "with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n", "and the negative binomial heterogeneity parameter $\\alpha$. \n", "\n", "Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n", "simply as:\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.705938Z", "iopub.status.busy": "2021-11-12T23:37:59.704486Z", "iopub.status.idle": "2021-11-12T23:37:59.709876Z", "shell.execute_reply": "2021-11-12T23:37:59.710528Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import numpy as np\n", "from scipy.stats import nbinom" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.714342Z", "iopub.status.busy": "2021-11-12T23:37:59.712940Z", "iopub.status.idle": "2021-11-12T23:37:59.719635Z", "shell.execute_reply": "2021-11-12T23:37:59.720287Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "def _ll_nb2(y, X, beta, alph):\n", " mu = np.exp(np.dot(X, beta))\n", " size = 1/alph\n", " prob = size/(size+mu)\n", " ll = nbinom.logpmf(y, size, prob)\n", " return ll" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### New Model Class\n", "\n", "We create a new model class which inherits from ``GenericLikelihoodModel``:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.724111Z", "iopub.status.busy": "2021-11-12T23:37:59.722736Z", "iopub.status.idle": "2021-11-12T23:37:59.728273Z", "shell.execute_reply": "2021-11-12T23:37:59.728928Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.732622Z", "iopub.status.busy": "2021-11-12T23:37:59.731215Z", "iopub.status.idle": "2021-11-12T23:37:59.742414Z", "shell.execute_reply": "2021-11-12T23:37:59.743081Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "class NBin(GenericLikelihoodModel):\n", " def __init__(self, endog, exog, **kwds):\n", " super(NBin, self).__init__(endog, exog, **kwds)\n", " \n", " def nloglikeobs(self, params):\n", " alph = params[-1]\n", " beta = params[:-1]\n", " ll = _ll_nb2(self.endog, self.exog, beta, alph)\n", " return -ll \n", " \n", " def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):\n", " # we have one additional parameter and we need to add it for summary\n", " self.exog_names.append('alpha')\n", " if start_params == None:\n", " # Reasonable starting values\n", " start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n", " # intercept\n", " start_params[-2] = np.log(self.endog.mean())\n", " return super(NBin, self).fit(start_params=start_params, \n", " maxiter=maxiter, maxfun=maxfun, \n", " **kwds) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two important things to notice: \n", "\n", "+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n", "+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n", " \n", "That's it! You're done!\n", "\n", "### Usage Example\n", "\n", "The [Medpar](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/doc/COUNT/medpar.html)\n", "dataset is hosted in CSV format at the [Rdatasets repository](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets). We use the ``read_csv``\n", "function from the [Pandas library](https://pandas.pydata.org) to load the data\n", "in memory. We then print the first few columns: \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.746833Z", "iopub.status.busy": "2021-11-12T23:37:59.745444Z", "iopub.status.idle": "2021-11-12T23:37:59.750577Z", "shell.execute_reply": "2021-11-12T23:37:59.751229Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:37:59.754877Z", "iopub.status.busy": "2021-11-12T23:37:59.753488Z", "iopub.status.idle": "2021-11-12T23:38:00.327753Z", "shell.execute_reply": "2021-11-12T23:38:00.328443Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>los</th>\n", " <th>hmo</th>\n", " <th>white</th>\n", " <th>died</th>\n", " <th>age80</th>\n", " <th>type</th>\n", " <th>type1</th>\n", " <th>type2</th>\n", " <th>type3</th>\n", " <th>provnum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " los hmo white died age80 type type1 type2 type3 provnum\n", "0 4 0 1 0 0 1 1 0 0 30001\n", "1 9 1 1 0 0 1 1 0 0 30001\n", "2 3 1 1 1 1 1 1 0 0 30001\n", "3 9 0 1 0 0 1 1 0 0 30001\n", "4 1 0 1 1 1 1 1 0 0 30001" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n", "\n", "medpar.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model we are interested in has a vector of non-negative integers as\n", "dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n", "``type3``, ``hmo``, ``white``.\n", "\n", "For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.332216Z", "iopub.status.busy": "2021-11-12T23:38:00.331267Z", "iopub.status.idle": "2021-11-12T23:38:00.336872Z", "shell.execute_reply": "2021-11-12T23:38:00.337548Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "y = medpar.los\n", "X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]].copy()\n", "X[\"constant\"] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we fit the model and extract some information: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.340714Z", "iopub.status.busy": "2021-11-12T23:38:00.339795Z", "iopub.status.idle": "2021-11-12T23:38:00.736175Z", "shell.execute_reply": "2021-11-12T23:38:00.736885Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 3.209014\n", " Iterations: 805\n", " Function evaluations: 1238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/statsmodels/statsmodels/statsmodels/base/model.py:2694: UserWarning: df_model + k_constant differs from nparams\n", " warnings.warn(\"df_model + k_constant differs from nparams\")\n", "/home/runner/work/statsmodels/statsmodels/statsmodels/base/model.py:2696: UserWarning: df_resid differs from nobs - nparams\n", " warnings.warn(\"df_resid differs from nobs - nparams\")\n" ] } ], "source": [ "mod = NBin(y, X)\n", "res = mod.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Extract parameter estimates, standard errors, p-values, AIC, etc.:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.741014Z", "iopub.status.busy": "2021-11-12T23:38:00.739556Z", "iopub.status.idle": "2021-11-12T23:38:00.754064Z", "shell.execute_reply": "2021-11-12T23:38:00.753582Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [ 0.2212642 0.70613942 -0.06798155 -0.12903932 2.31026565 0.44575147]\n", "Standard errors: [0.05059259 0.07613047 0.05326095 0.06854137 0.06794697 0.01981542]\n", "P-values: [1.22298069e-005 1.76979353e-020 2.01818957e-001 5.97480106e-002\n", " 2.15240570e-253 4.62688812e-112]\n", "AIC: 9604.95320583016\n" ] } ], "source": [ "print('Parameters: ', res.params)\n", "print('Standard errors: ', res.bse)\n", "print('P-values: ', res.pvalues)\n", "print('AIC: ', res.aic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, you can obtain a full list of available information by typing\n", "``dir(res)``.\n", "We can also look at the summary of the estimation results." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.761450Z", "iopub.status.busy": "2021-11-12T23:38:00.760983Z", "iopub.status.idle": "2021-11-12T23:38:00.767382Z", "shell.execute_reply": "2021-11-12T23:38:00.767005Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " NBin Results \n", "==============================================================================\n", "Dep. Variable: los Log-Likelihood: -4797.5\n", "Model: NBin AIC: 9605.\n", "Method: Maximum Likelihood BIC: 9632.\n", "Date: Fri, 12 Nov 2021 \n", "Time: 23:38:00 \n", "No. Observations: 1495 \n", "Df Residuals: 1490 \n", "Df Model: 4 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "type2 0.2213 0.051 4.373 0.000 0.122 0.320\n", "type3 0.7061 0.076 9.275 0.000 0.557 0.855\n", "hmo -0.0680 0.053 -1.276 0.202 -0.172 0.036\n", "white -0.1290 0.069 -1.883 0.060 -0.263 0.005\n", "constant 2.3103 0.068 34.001 0.000 2.177 2.443\n", "alpha 0.4458 0.020 22.495 0.000 0.407 0.485\n", "==============================================================================\n" ] } ], "source": [ "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.771747Z", "iopub.status.busy": "2021-11-12T23:38:00.771306Z", "iopub.status.idle": "2021-11-12T23:38:00.848087Z", "shell.execute_reply": "2021-11-12T23:38:00.847693Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " NegativeBinomial Regression Results \n", "==============================================================================\n", "Dep. Variable: los No. Observations: 1495\n", "Model: NegativeBinomial Df Residuals: 1490\n", "Method: MLE Df Model: 4\n", "Date: Fri, 12 Nov 2021 Pseudo R-squ.: 0.01215\n", "Time: 23:38:00 Log-Likelihood: -4797.5\n", "converged: True LL-Null: -4856.5\n", "Covariance Type: nonrobust LLR p-value: 1.404e-24\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "type2 0.2212 0.051 4.373 0.000 0.122 0.320\n", "type3 0.7062 0.076 9.276 0.000 0.557 0.855\n", "hmo -0.0680 0.053 -1.276 0.202 -0.172 0.036\n", "white -0.1291 0.069 -1.883 0.060 -0.263 0.005\n", "constant 2.3103 0.068 34.001 0.000 2.177 2.443\n", "alpha 0.4457 0.020 22.495 0.000 0.407 0.485\n", "==============================================================================\n" ] } ], "source": [ "res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n", "print(res_nbin.summary())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.853886Z", "iopub.status.busy": "2021-11-12T23:38:00.853269Z", "iopub.status.idle": "2021-11-12T23:38:00.857669Z", "shell.execute_reply": "2021-11-12T23:38:00.856792Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type2 0.221218\n", "type3 0.706173\n", "hmo -0.067987\n", "white -0.129053\n", "constant 2.310279\n", "alpha 0.445748\n", "dtype: float64\n" ] } ], "source": [ "print(res_nbin.params)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-11-12T23:38:00.862017Z", "iopub.status.busy": "2021-11-12T23:38:00.861573Z", "iopub.status.idle": "2021-11-12T23:38:00.867512Z", "shell.execute_reply": "2021-11-12T23:38:00.867855Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type2 0.050592\n", "type3 0.076131\n", "hmo 0.053261\n", "white 0.068541\n", "constant 0.067947\n", "alpha 0.019815\n", "dtype: float64\n" ] } ], "source": [ "print(res_nbin.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we could compare them to results obtained using the MASS implementation for R:\n", "\n", " url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/csv/COUNT/medpar.csv'\n", " medpar = read.csv(url)\n", " f = los~factor(type)+hmo+white\n", " \n", " library(MASS)\n", " mod = glm.nb(f, medpar)\n", " coef(summary(mod))\n", " Estimate Std. Error z value Pr(>|z|)\n", " (Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257\n", " factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05\n", " factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20\n", " hmo -0.06795522 0.05321375 -1.277024 2.015939e-01\n", " white -0.12906544 0.06836272 -1.887951 5.903257e-02\n", "\n", "### Numerical precision \n", "\n", "The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }

bsd-3-clause

bosmanoglu/Digital_Image_Processing_Book

en/Figure_3_12.ipynb

1

3484

{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#DTMF tonlarini hazirla ve Fourier Transformunu goster\n", "import numpy as np\n", "import bqplot.pyplot as pl\n", "import bqplot\n", "## DTMF Tonlari: 697,770,852,941 Hz ve 1209,1336,1477,1633 Hz\n", "t=np.linspace(0,1,8000) # ornekleme araligi ornekleyecegimiz frekansin en az iki kati olmali.\n", "ton1=np.sin(2*np.pi*697*t) + np.sin(2*np.pi*1209*t)\n", "ton2=np.sin(2*np.pi*697*t) + np.sin(2*np.pi*1336*t)\n", "#ton4=np.sin(2*np.pi*770*t) + np.sin(2*np.pi*1209*t)\n", "pl.figure(1, title='Zamanda Isaret');pl.clear();pl.plot(t[0:100],ton1[0:100], 'r');\n", "pl.plot(t[0:100],ton2[0:100], 'g');\n", "#pl.plot(t[0:100],ton4[0:100], 'b');\n", "pl.show() #pl.title('1 tonu');pl.xlabel('Zaman (sn)');pl.ylabel('Genlik (volt)')\n", "## Frekans uzayi gosterimi\n", "Fourier_ton1=np.fft.fft(ton1);\n", "Fourier_ton2=np.fft.fft(ton2);\n", "#Fourier_ton4=np.fft.fft(ton4);\n", "freq = np.fft.fftfreq(t.shape[-1], d=np.diff(t).mean())\n", "pl.figure(2, title='Frekans Uzayi Gosterimi');pl.clear();pl.plot(freq[0:4000], 20*np.log10(abs(Fourier_ton1[0:4000])), 'r');\n", "pl.plot(freq[0:4000], 20*np.log10(abs(Fourier_ton2[0:4000])), 'g');\n", "#pl.plot(freq[0:4000], 20*np.log10(abs(Fourier_ton4[0:4000])), 'b');\n", "pl.show();\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "widgets": { "state": { "1d3062a5c64448dba8ab690fb1a94e64": { "views": [ { "cell_index": 0 } ] }, "422083937bdd46dfbd40c60b209e03b0": { "views": [ { "cell_index": 0 } ] }, "9896fb5c04754ef393d30eb70d261c3a": { "views": [ { "cell_index": 0 } ] }, "9988e00dd02045c6ae4e6efba519e9ed": { "views": [ { "cell_index": 0 } ] }, "a3046a449b9f425991072b6775b4d7f6": { "views": [ { "cell_index": 0 } ] }, "ada30fcadc8244cf972ad144c0c3b7fe": { "views": [ { "cell_index": 0 } ] }, "c9f2bbfb4a794a06b8640cfa844a6b1b": { "views": [ { "cell_index": 0 } ] }, "d06e14845c874379921d6dffb31cf5b7": { "views": [ { "cell_index": 0 } ] }, "da3dd06d44a549cd94a0f71c7ffb7676": { "views": [ { "cell_index": 0 } ] }, "e83ac6eec117467d822b83a6a413ff70": { "views": [ { "cell_index": 0 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

computational-class/cjc2016

code/09.06-Linear-Regression.ipynb

2

265776

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# In Depth: Linear Regression" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "<!--BOOK_INFORMATION-->\n", "\n", "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "<!--NAVIGATION-->\n", "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Naive Bayes (discussed earlier in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks\n", "- linear regression models are a good starting point for regression tasks.\n", " - can be fit very quickly, and \n", " - are very interpretable.\n", " \n", "The simplest form of a linear regression model (i.e., fitting a straight line to data) \n", "- Extended to model more complicated data behavior.\n", "- We will see how linear models can be generalized to account for more complicated patterns in data.\n", "\n", "We begin with the standard imports:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T01:48:28.419326Z", "start_time": "2018-12-26T01:48:26.363658Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns; sns.set()\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Simple Linear Regression\n", "\n", "We will start with the most familiar linear regression, a straight-line fit to data.\n", "A straight-line fit is a model of the form\n", "$$\n", "y = ax + b\n", "$$\n", "where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*.\n", "\n", "Consider the following data, which is scattered about a line with a slope of 2 and an intercept of -5:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T01:59:07.712292Z", "start_time": "2018-12-26T01:59:07.534304Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.RandomState(1)\n", "x = 10 * rng.rand(50)\n", "y = 2 * x - 5 + rng.randn(50)\n", "plt.scatter(x, y);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can use Scikit-Learn's ``LinearRegression`` estimator to fit this data and construct the best-fit line:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T01:59:08.889365Z", "start_time": "2018-12-26T01:59:08.703013Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "model = LinearRegression(fit_intercept=True)\n", "\n", "model.fit(x[:, np.newaxis], y)\n", "\n", "xfit = np.linspace(0, 10, 1000)\n", "ytest = 2*xfit -5\n", "yfit = model.predict(xfit[:, np.newaxis])\n", "\n", "plt.scatter(x, y)\n", "plt.plot(xfit, yfit);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore.\n", "Here the relevant parameters are ``coef_`` and ``intercept_``:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T01:59:13.031371Z", "start_time": "2018-12-26T01:59:13.027600Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model slope: 2.02720881036\n", "Model intercept: -4.99857708555\n" ] } ], "source": [ "print(\"Model slope: \", model.coef_[0])\n", "print(\"Model intercept:\", model.intercept_)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We see that the results are very close to the inputs, as we might hope." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Model evaluation for regression**\n", "\n", "- RMSE\n", "- R Square\n", "\n", "https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T02:32:43.445443Z", "start_time": "2018-12-26T02:32:43.441417Z" }, "code_folding": [], "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Root mean square error 均方根误差,亦称标准误差\n", "# https://en.wikipedia.org/wiki/Root-mean-square_deviation\n", "def rmse(y_test, y_pred): \n", " mse = np.mean((y_test - y_pred) ** 2)\n", " return mse ** 0.5" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T02:32:47.989491Z", "start_time": "2018-12-26T02:32:47.983715Z" }, "code_folding": [], "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# R square\n", "def R2(y_test, y_pred): \n", " residuals_sum_of_squares = np.sum((y_pred - y_test)**2)\n", " total_sum_of_squares = np.sum((y_test - np.mean(y_test))**2)\n", " return 1 - residuals_sum_of_squares/total_sum_of_squares\n", "# https://en.wikipedia.org/wiki/Coefficient_of_determination" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T02:35:37.441154Z", "start_time": "2018-12-26T02:35:37.436570Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 0.1584\n", "R2 score: 0.9992\n" ] } ], "source": [ "print('RMSE: %.4f' % rmse(ytest, yfit))\n", "print('R2 score: %.4f' % R2(ytest, yfit))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T06:10:19.758587Z", "start_time": "2018-12-26T06:10:19.755636Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T02:35:47.008382Z", "start_time": "2018-12-26T02:35:47.002317Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 0.1584\n", "R2 score: 0.9992\n", "Variance score: 0.9998\n" ] } ], "source": [ "print('RMSE: %.4f' % mean_squared_error(ytest, yfit) ** 0.5)\n", "print('R2 score: %.4f' % r2_score(ytest, yfit))\n", "print('Variance score: %.4f' % explained_variance_score(ytest, yfit))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The ``LinearRegression`` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form\n", "$$\n", "y = a_0 + a_1 x_1 + a_2 x_2 + \\cdots\n", "$$\n", "where there are multiple $x$ values.\n", "Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyper-plane to points in higher dimensions." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Building some example data using NumPy**\n", "\n", "The <font color = 'red'>multidimensional nature of such regressions</font> makes them more difficult to visualize" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T01:53:32.046192Z", "start_time": "2018-12-26T01:53:32.040784Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "rng = np.random.RandomState(1)\n", "X = 10 * rng.rand(100, 3)\n", "y = 0.5 + np.dot(X, [1.5, -2., 1.])\n", "# $y$ is constructed from three random $x$ values" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "we can use the single ``LinearRegression`` estimator to fit lines, planes, or hyperplanes to our data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-12-26T01:52:01.908289Z", "start_time": "2018-12-26T01:52:01.891454Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5\n", "[ 1.5 -2. 1. ]\n" ] } ], "source": [ "model.fit(X, y)\n", "print(model.intercept_)\n", "print(model.coef_)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Basis Function Regression 基函数回归\n", "\n", "One trick you can use to adapt linear regression to nonlinear relationships between variables\n", "- to transform the data according to *basis functions*.\n", "\n", "We have seen one version of this before, in the ``PolynomialRegression`` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb).\n", "\n", "The idea is to take our multidimensional linear model:\n", "$$\n", "y = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3 + \\cdots\n", "$$\n", "and build the $x_1, x_2, x_3,$ and so on, from our single-dimensional input $x$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "That is, we let $x_n = f_n(x)$, where $f_n()$ is some function that transforms our data.\n", "\n", "For example, if $f_n(x) = x^n$, our model becomes a polynomial regression:\n", "$$\n", "y = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \\cdots\n", "$$\n", "\n", "Notice that this is *still a linear model*\n", "- the linearity refers to the fact that the coefficients $a_n$ never multiply or divide each other.\n", "- What we have effectively done is taken our one-dimensional $x$ values and projected them into a higher dimension, so that a linear fit can fit more complicated relationships between $x$ and $y$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Polynomial basis functions 多项式基函数\n", "\n", "> polynomial, Synonym: multinomial, 多项式\n", "\n", "This polynomial projection is useful enough that it is built into Scikit-Learn, using the ``PolynomialFeatures`` transformer:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T09:39:14.565054Z", "start_time": "2018-05-20T09:39:14.558498Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 4., 8.],\n", " [ 3., 9., 27.],\n", " [ 4., 16., 64.]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "x = np.array([2, 3, 4])\n", "poly = PolynomialFeatures(3, include_bias=False)\n", "poly.fit_transform(x[:, None])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We see here that the transformer has converted our one-dimensional array into a three-dimensional array by taking the exponent of each value.\n", "- This new, higher-dimensional data representation can then be plugged into a linear regression.\n", "- As we saw in [Feature Engineering](05.04-Feature-Engineering.ipynb), the cleanest way to accomplish this is to use a pipeline.\n", "\n", "Let's make a 7th-degree polynomial model in this way:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T09:40:06.921714Z", "start_time": "2018-05-20T09:40:06.917263Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from sklearn.pipeline import make_pipeline\n", "poly_model = make_pipeline(PolynomialFeatures(7),\n", " LinearRegression())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "With this transform in place, we can use the linear model to fit much more complicated relationships between $x$ and $y$. \n", "\n", "For example, here is a sine wave with noise:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Our linear model, through the use of 7th-order polynomial basis functions, can provide an excellent fit to this non-linear data!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T09:56:35.280127Z", "start_time": "2018-05-20T09:56:35.146469Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { 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swmQ08ObpZvyBwLxn5iQLmblPIfQkXF+Wzz++eJGtqwr5vfet1HpY07KnsoTfvHuLQ2dbeGj9grvliIUA+Nie5ahNvbS6Bu8pI33xmguT0YA9w8LomI8ndizh52/HZuY7kw1P0y0wlpNlZdvqIk5cvsP5a65p31vQG5m5T9O7l4Oz3z1JtL3ZYbdSWVFAU8cgja39Wg9HJJhfvH2d5o7B+/oDdA+McfBMC785cYt0q5nO/tGEmPnOpMDYI1XBrJ43TjXPuhRxspPgPg3Dox7Oqh0UOtJRFudqPZwZ2VsZfDM6clFurIr3TKecxpjXz851xdQ0hq+4OJ0mHPNpJgXGlhTbURblcq2lD4s5fJhLs5h03f9Agvs0VF9tZ8zrT8qljZVLHBTk2Dhd1yHVIsVd0+kPAFBRmpNQM9+ZVJ58ZEtw9p6q1SJlzX0ajtXcwWCAB9Yu0HooM2Y0GHho/QJePnqD03Ud7NowP+lsIrlFy5YJsVpMKIsds27CEQszqTy5sbyAvGxrxDcn95gvbCMQvZCZ+xQ6eoa50dbP6qV5OOzJ+RFu57oFGICjl8I31hapJ9oSR8imigLsGWmzbsIRS9PZX2I0Gti3qTTi8bxsbetAxZoE9ymcqu0AYNuqIo1HMnt52TbWLMujsbWf251DWg9HJIjQEofVEj4MpNvM9zxuNk1mtLZrw0KMEVZSE32vylzJsswUqmvbMZsMbKoo0Hooc/Lg+gVcvtHNsUttfCIJXpQiNiZv239qdxnn6124PfcvXVy81sXH9/iwWkxzajKjpax0C1tXF3HySjvZGRYGRzzT3jCV7CS4R9HiGqTVNUTlioI5V8LTWuUKJ5k2Mycut/HR3culkUeKibRLc29lybSbU8+2yYzW9laWcPJKOytKc/n43rKkenOaC3mFR3F3SWZ18i7JhFjMRnasKaZ/2MOlCKltQr8i7dI8eKZ5Vs2pk0l5SQ4lBZlcaOjElmZOicAOEtwjCgQCnKptJ81iZENZci/JhDy4Ppjt8+7lOxqPRMRTtJz2S43drC8P//zWy5q0wWBg98aF+PwBjtW0aT2cuJHgHsHNOwN09IywsbxAN0W3FhVmUeLM5GJjJ0OjHq2HI+Jkqm37+zeXsnZZ3t2vJdMN0+l6YG0xaWYjRy604k+RDmUS3CMI9SDVw5JMiMFgYPvqIry+AGfqOrQejoiTqbbt52XbGPMGN/r82Sc3TquIV7LJsFnYsrIQV+8o15p7tR5OXOjnrzeP/IEAp2o7SLeaddembvvqYgBOXpFKkaliqm373f2j1Df3smqJg9VL83SxFBPOznXBZcnjNe8tS+q5xrtky4TR0NJHz4CbB9ctiFiXIlnl59hQFuWiNvfS1TdKfo5N6yGJOIhWP/3nhxuBYClrPatYnHu3FMfTD5fxq2M3dV3jXYJ7GNU6XJKZaPuaItTmXqpr23n/9iVaD0fEQaRt+x6vjxOX72DPsLBpih2ryc7j9bOhLJ+3zrXyL7+8wtWbPXePTW76oQf6eIuaRz6/nzN1HdgzLKxcklwVIKeramUhZpOBdy/fIZAiN5dE0ORt+2frXQyOeHhw3QLd7n2Y2JrvrfG2k3W3esI+Nt6VLmNJn3/NOai91cPAsIeqlYW6+Xg2WabNwoayAlo7h2juGNR6OEJD71wI1hvSc0G5iTn+IZNr2Ifoqca7PqPXHJy6mvy1ZKZj+5rg9Z28KjdWU9Wd7mHqmoI3Uovykm/n6XRMp279RHrZuAUS3O/h8fo5W+/CYbdSXpqj9XBian1ZARlWM9VX21Mm71fc68iF4BKFnm+kTrdufYheNm7BHG6oKopiBL4NbADcwOdUVW2YcPxbwE5gYPxLT6qq2jeHscbc5etdjLi97NqwAGOSNeWYKYvZyKYKJ8dq2mhs7WNFqT7vL4jwPF4/x2vukJVuoXKFfm+kTlW3Xs/FxOaSLfNhwKaq6g5FUbYD3wCenHB8E/CYqqqdcxngfJtcFW8ivWfJTLZlVSHHato4XdshwT3FXGrsZHDEw6NbFuku3XeiUI5/KBNmsk0VTt63bbEui4nNJbg/CLwGoKrqSUVRqkIHxmf1K4D/oyhKEfA9VVW/P6eRzlGkqnihvFb3mI8LDZ0UOtJZUmTXcqhxs2qJg0ybmdNqB5/cv0L3n1bEe0IbeR5cl3zdxWYqXI7/hvJ8Ttd1cK7exe88qq/duCFzCe7ZwMRlFp+iKGZVVb1AJvC/gX8ETMBhRVHOqKp6KdIPczgyMJtn/87pdEYPyM//suaed+9QXmtGehrPfngd75xvYczjZ+/mRRQWZs96HPE01TVPx84NJbxRfQvXwBhrk6BA2nxcc7KZ72vuGRjl0vUuykpzqFyTmMF9vq/5Tz61mdExLz39bhzZVmxpZr7zn5f4zfEb3O51s0kpnNfzzcZ8X/Ncgns/MHE0xvHADjAMfFNV1WEARVEOEVybjxjce3qGZz0Qp9OOyzUQ8bjb4+P4xdawx45fvM3jWxdxsPoWAGuXOqL+rEQx1TVP17qlDt6ovsWbJ29SFKH+SKKYr2tOJrG45jdONeH3B9i6sjAhf5+x/DubgYG+EQaA9csc/Ob4Dd44cYNFeekxOd90zeWaI70pzOWzyHHg/QDja+41E45VAMcURTEpimIhuIRzbg7nmpOpquLd6Rqi5noXpc5MSgoy4zw6ba1ckktWuoUzqgt/pORfoSvHau5gMgaLyKWyspIc8rNtnK13MaaTjUsTzSW4vwyMKopyAvgn4E8VRfmyoigfUlW1Fvh34CRwBPixqqpX5j7c2ZmqKt6NtgG8vgBbdZ7bHo7JaKRKcdI/NEZ9ilTLS2VN7QO0uAZZX5aPPSNN6+FoymgwsG11EaNjPl02sJn1soyqqn7gC5O+XDfh+NeBr8/258+naHfMKysKODu+yWFris5ktqws5O0LtzlV18HKJQ6thyNiKNSsIhVupE7H9tVF/PbkLU5ebadqpfbr7vNJf7eII4jUwf3xbYupvdnDsgXZFOZqu+6mlYrFuWRnWDirduDz+7UejogRr8/PySvtZKVbWFemr1LWs1U63sDmUmMnwzprYJMyVSEjVcU7fK4FfyDAtlX6eteeCZPRyOaVhRw+14ra1MvqpXlTf5NIOjWNXQyOeNhfVarbImGzsX11ES8duc5Z1cVDOqqxk3J/4clV8aqvtmMAtqTgevtEW8c/koaaggv9OT7eO3fnWlmSmSh0ry20iVEvUi64T9TdP0p9Sx8Vi3Jx2BM7DTDWVpTmkpOZxrl6Fz6/X9cdalJR//AYFxs6KXVmsbgoS+vhJBRnbjrLFmRTd6uXgeExrYczb1JmWSac0+N9RFP1RupERqOBTRVODp9v5V9+eZlbdwZ026EmFZ2p68DnD/DA2mIMshP5PltWFnKjrZ9z9S62rymOWKIkmaR0cK++2o7RYKBK0W/hpJnYpASD+7n698oB6bFDTSoKLT+mSt2kmapSnPzscAO/PnGTV0/c1MXEJvlGPE/ae4a5eWeA1cscKZ/vG7Ks2E6kOZ2eOtSkmu7+Ua7J8mNUBbnpZGdY6O5309XvJsB7E5sXDzVM+f2JKGWDe+jGod6bcszE4IiHSHtU9dShJtXcfa7LrD0it8eH1xc+DThZJzYpGdwDgQCnrrZjNhl1Xct6pnKyrNjTw6/U6alDTaqprg0uP26W5ceI+gbdDLvDB/BkndikZHBv7hiktXOIDeX5ZNhS+rbDPawWE1URPsnoqUNNKmnvHuaWLD9OKSfLSn6UEiXJOLFJieA+Oa2verxvaKoXTgrnmf0rKHIEd+oaeG8nr5461KSSU6EGNLL8GFWoREk4yTqx0fW0NVyDjo0rCjhf30m61cR62YJ9H5PRyEd2Lec7v7rCvk0lfGxvOVaLCbfHR1ffcNKnh6WaU7Udsvw4TU/vK2fE7b3byCQ/O7lb7+k6uL94qOG+Bh1vnQ3WdX9w3QIsc2gOomfrludjNhmpa+7FbDLwwsH6iB2sROJqGV9+3FThlOXHaTAZjXz2A6tp6RiiuWOAr/zuJvKzbVoPa9Z0++p0e3ycH6/2GI7cXIos3Wpm7bI8Wl1DfP+3tRw806Kb9LBUkmo9gefL1tWF+ANw5Ua31kOZE90G92gNOoC768oivNCb38Vr4fubJ2t6WKoIBAKcqm3HapHlx5mqGm+5d6Yuuess6Ta4R2vQYUsz4Ujij1vxsKG8AKMB3aWHpYobbQO4ekepXJGcNwO15MxNZ2mxnas3exgcSd4ywLoN7tHufq8ry5cn/BTSraao6V/Jmh6WKkJZMlI3aXa2rCzEHwhEXdpNdLoN7nBvg47Qtvp0q5lnn1il6biSwYuHGugZiDwzT9b0sFTgH1+SyRi/dyJmbvN4CewzqgT3hBRq0PHcs9t4avdyAB7bsgizSYJSNNFuRhsNsLdyYdKmh6WCa8299A6OsVlxSlOOWSrMTWdJkZ2rN7sZStIOTSnxl7daTFxs7MIAPLCuWOvhJLxoN6MDwGNbF0saZAKrlloy86JqpROfP8CFCEkFiS4lXqF3uoe51tLH6qUOCnIkS2Yq0W5G58lae0Lz+vycqesgOzONlYul2flcJHvWTEoE9+PjHd93rpf2YtOhx63YqaL2VjDDY4tSiNEoTTnmoigvg0WFWVy52c3wqFfr4cyY7oO7z+/neE0bGVYzm2QL9rRNvBkdsmejrLUnulNXZePSfKpSnHh9AS42JN/SjO6D+5Ub3fQOjrFtTRFpMuOctok3o/dXlQJQsThX1toTmMfr49w1F/nZVpaXZGs9HF2oups1k3xLM7p/pb59/jYAD8mSzKxYLSZ2bVgIwLkkTgtLBZcauxlx+9iyqgij9EmdFwvyMylxZlJzvZsRd3Itzeg6uHf0jnCxoZPlC7NZWiwzmdkqKcik0JFOzfVuxiaVHJhcTlloR8r7xkaVUojX5+diY3Itzei6VNzb51oJAA9vLtV6KEnNYDCwucLJgeomrtzspnKFM2w5ZakWqZ3RMS8XGzopystgcVGW1sPRlSrFya+O3eBsnYvtq5MnlVq3r0K3x8fRS7fJzrDcTWkSs7dpPHvmTF0HHT3DvHDwmlSLTCAXGjoZ8/rZtqoQgyzJzKsSZxYL8jO4dL2L0bHkWZrRbXCvvtrO0KiXXRtLsJh1e5lxs7g4C6vFyMmr7Xzluyc5cr417OOkWqQ2Tl0N3vDbKksyMVGlFOLx+rnU2KX1UKZNl1HP7w/wWnUTJqOBvZUlWg9HF35+uBG3x08gEPy/PxD+cVItMv6GRj3UXO9iUWEWCwsytR6OLlUlYa0ZXQb3M2oHd7qHeWBtMQ677Kacq6kan0wk1SLj75zqwucPsHWVLD/GSqkzkyJHOpcak+eTqe6Cuz8Q4NUTNzEY4P07lmg9HF2YqvHJRLKDNf6qJUsm5gwGA1UrCxnz+KlJkqUZ3QX3C9c6aXENsW11EUWODK2HowvRas0YDWAwBJsJ768qlR2scdY3NEbtrR7KFmZTkCt1k2Lpbq2ZJNnQpKtUSK/Pz8/fbsRggCd2LNV6OLoRqjUzsdl4yO6NC3ls62JysqwyY9fAmboOAgG5kRoPi4uycObauNjYxZjHl/A73nU1cz98rpX27mH2VJaQn2OTzTXzKFRrJm/8HobRaODhzSU880gFhY4MCewaqa5txwBskfX2mAstzbjHfFxOgubZugnuPQNufnXsBulWEz6fn68+f5K/+O5Jvvr8SV44WI/P79d6iEktVGvm7z+/nbXL8vD7A+zZWCIbljTU1TdKQ0sfyuJccuUmdlwk09KMLl6ZgUCAH71Wx7Dby5IiO+9cbJPNNTFitZh4YG1wl97ZJO4vqQenx+uMS5/U+FlabKcgx8aFa514vIk9YdRFcP/5W9e41NjFqiUOOnqGwz5GNtfMn/VlBZiMBs5JcNdU9dV2TEaD7MCOI4Mh+PseHfNxJcGXZpI+uDe09PFvB2rJz7by0V3L6BkYC/s42VwzfzJsZlYtddDUPoird0Tr4aSkO93D3GofYM2yPLLSLVoPJ6VsXjleiiPBl2ZmnS2jKIoR+DawAXADn1NVtWHC8WeBPwK8wHOqqr46x7GGlW41sXllIU8+sBSnI528bCtBJ+idAAAXwklEQVRdYXKyZXPN/NpU4eTy9W5O13VQpTglWybOQhUgZeNS/C1fkE1etpXz1zrx+vwJ24R8LqP6MGBTVXUH8BXgG6EDiqIUA38M7AQeA/5BUZSYRNYSZxZ/8+wOSguzpD1cHG0oywfgl0evy43rOAsEAlRfbcdiNlIp3cXiLlgltZARt5erNxN3aWYuwf1B4DUAVVVPAlUTjm0Fjquq6lZVtQ9oANbP4VzTNrE9nFE218TMgeomALy+gNy4jrMW1xBtXcOsL8sn3aqrrSpJY0uo1kxd4t53msszIxvom/B/n6IoZlVVvWGODQA50X6Yw5GB2Tz7mbXTab/77z/51GZGx7z09LtxZFuxpenzBTDxmuNpdMwbsTrepcYu/uip9Jj9zrW6Zi1NvuYDp5sBeGTbUt3+PhL9uvLzs8h75QoXGjpx5GXOy9LMfF/zXF6B/cDE0RjHA3u4Y3agN9oP64mQ5TIdTqcdl2vgvq+bgYG+Ee4/kvwiXXM8dPQM4+oJfyO1s3eExptdFMag9IOW16yVydccCAQ4fKYZa5qJpc4MXf4+kuXvXLmigLfOtnD0TBNrl+fP6WfN5ZojvSnM5e3mOPB+AEVRtgM1E46dAh5SFMWmKEoOsAq4PIdzReT2+GjrHJI0xziKVmtGblzH1vW2fjr7Rtm0oiDht7/r3ZYEb549l5n7y8AjiqKcAAzAHyiK8mWgQVXVVxRF+RZwlOAbyF+pqjo69+G+5542bwNu8uzS5i1eotWakRvXsSVNORJHeUkOOZlpnKvv5NOP+RMu7sw6uKuq6ge+MOnLdROOPw88P9ufP5UXDzXcE1xCN/QAntlfEavTinGhG9SnazvoGxojzWxk18aFcuM6hvz+AKfq2sm0mVmzLE/r4aQ8o9HAJsXJ4XOt1DX1smZpYv1NEuutZpqiNY+QnajxEao18w9/tJ2CHBsAH9tdlnCzFz2pb+6lb3CMzUphwuZWp5ot47uDz46XgnB7fAlTsDAp00iiNY8I7USNxQ09cT9bmpktqwo5cLKJKze6I+4zEHN36m5TDtm4lCgqFuViz7Bwtt6FyWTgwrVOuvvd5GVrv0yclG//ckMvsWyuCAYbqTUTO16fnzOqi5zMNJTFDq2HI8YZjQY2VzgZGPbw1tnWhCpYmJTBXXaiJpalC+w47FYuNAS3Y4v5d/VmD4MjHrasLMRoNGg9HDHB+vKCiMe0XCZOyuAOshM1kRgNBjatcDI06qW+Oep2BjFL1VfvALBNyvsmnCJH5PaGWhYsTMo1d3jvht5Tu8swpVnwjXlkxq6hTRUFvHWuhbP1LlYnWNZAsnN7fJy71klBjo3lC7O1Ho6YJC/bhtVixO25/1OrlsvESTtzD7FaTCwoyJTArrGKxblk2sycr3fhDwS0Ho6uXGrswj3mY9vqIgwGWZJJNFaLKeIOVS2XiZM+uIvEYDIa2biigN7BMW7c7td6OLpy8oosySS6z39oNbY0EwaCOzoTYZlYgruYN5I1M/8GRzzUXO+i1JlJqTNL6+GICCwmEw9vLiUAfPLhcp57dhvP7K/QdN+HBHcxb9Ysc2C1mDhb7yIgSzPz4t1Lt/H6AjJrTwI71gR7C1+52ZMQy8QS3MW8sZhNrCvLp6NnhNbOIa2HowvvnG8FpJZMMlhYkMmSYjuXr3fTPxS+3Wc8SXAX82pTRTDn95wqSzNz1Tfo5lKDi7KSbJy5kdPtROLYsaYYfyBwdzexliS4i3m1oawAs8kg6+7z4FRdB/4AbJNZe9LYtroIo8HAu+M3wbUkwV3Mq3SrmVVL8mjqGMTV+15Dj0QqqJQsTl1tx2iALRLck0ZOZhqrlzm40TZAW5e2S5NJu4lJJK7NipOa612cq3exv6r0vbr7CVJQKRl09I7QeLufjRVOcjLTtB6OmIEH1hRz+Xo3Jy7f4andZZqNQ4K7mHcbywswGOBsvYuu/lGpuz8Lp64G12x3V5ZoPBIxU5sqnGRYzRy71MaTDy7TrDyzTJ3EvMvOTGNFaS6NLX1361xPJnX3o6uubcdsMrJj3UKthyJmKM1iYsfaYvqGxrjYEL6RfDxIcBcxsXVVIQGgZzB8SpiWBZUSXUvHIK2uIdaX5ZOZbtF6OGIWdm8Ivim/c/G2ZmOQ4C5iYrNSiMEAZlP4WihSdz+y6vE0uu2ycSlplRZmsWyBncvXuzS7sSrBXcRETmYaq5Y48PrC71SVuvvh+QMB3r1yh3SrifVl4YtRicTm8/t54WA9HT0jBIDnfnSGFw7W4/PHt9eBBHcRM6FdleUlOVJ3f5rUpl66+91UKYWkyZtfUnrxUAMHz7QwNOoFYGTMp0lXJsmWETGzqcLJv72uMub18dyz2+gbdJOTZZUZexTvXg5ufnlgbbHGIxGz4fb4OB9hA9/5+k6e2l0Wt+e/zNxFzGSlW1izLI+m9kF6BoJNyyWwR+b2+DitdpCfbWPFolythyNmoW/QTXd/+ESBeCcRSHAXMbV1VbAMcCLU2kh05+tduMd87FhbjFGaciSlnCwrednhEwXsGWlxTSKQ4C5iqnKFE7PJSPXVdikDPIUTsiST9KwWE5UVzrDH0m3muH5yleAuYirdamZ9WT5tXcO0uqQMcCS9g26u3Oxm+cJsivMytB6OmIOn95Wzv6r0bhJBnt1Kps2Mq2eE3jguy8gNVRFzW1cVcq7exam6dkoLpZtQOCevtBMIyKxdD0xGI8/sr+Cp3WV3kwjevXKHH7+m8tbZlrjVm5GZu4i5DWUFpFmMnLraIUszEZy4fAeT0SBNOXTEajHdTSLYsaaY7AwLh861MDjiicv5JbiLmLOmmdhYXkBH7wg37wxoPZyE09Q+QItrkPVl+WRJuQFdslpMPL59CSNuH2+cbo7LOSW4i7jYvjq43BDK4xbvCTV2eGDtAo1HImJpT2UJ2RkWDp5pjsvsXYK7iIu1y/PISrdQXduO1xffbdiJzOvz8+6VdjJtZik3oHOh2fvomI83TjfF/HwS3EVcmE1Gtq0uYmDYw+Ub3VoPJ2HUNHbRPzTG9jXFWMzyctS7PZUlZGem8fqp5pgXFJNnk4ibUCbICVmauevopTYAHlovSzJ65/P7eelIIz6fH4/Xz9/+4HRMC4pJcBdxs7TYzoL8DC5c62R4ND4ZA4msZ8DNxcZOlhbbWVxk13o4IsYmFxQb8/pjWlBMgruIG4PBwANri/H6/JyO0KEplZy43EYgAA9tkG5LehetoNg51cXomHfezynBXcSVZM0EBQIBjl5sI81sZJvktutetIJi3QNueiIcmwsJ7iKu8nNsrFycS31LH67eEa2Hoxm1qZeO3hGqVhaSYZON4noXraAYQFff/L8WJLiLuNsxfmM1lN+dio5eCvbWlBupqSFaQTGH3Yo9M23ezynBXcRdlVJImtnIiZo7KVmOYHjUwxnVRZEjnQqp254yJhcUC3Ul+/oXd7CkOHvezzerz4OKoqQDPwEKgQHg91RVdU16zCtAPuABRlRVfXyOYxU6kW41s1lx8u6VdtSmXlYucWg9pLg6ebUdj9fPQxsWYpC67SkjXEGxWJYAnu3M/YtAjaqqDwE/Br4a5jHlwIOqqu6RwC4m2zWeIfLOxdsajyS+AoEAh8+3YjIa2CkVIFPSxIJisTTb4P4g8Nr4vw8A+yceVBSlCMgFfq0oyjFFUZ6Y/RCFHlUsyqU4L4MzqituVfISQX1zL62uITYrzrh25RGpZ8plGUVRPgv86aQvtwN94/8eAHImHU8DvgF8E8gDjiuKckpV1YjJzQ5HBmbz7N/JnM7U2wSS7Nf8+APL+MGrV6i52cOHdk2vxnWyX/MPXlMB+Oi+imlfS7Jf82zINc/dlMFdVdXvAd+b+DVFUf4TCI3EDvRO+rY7wHdUVfUCHYqinAcUIGJw7+kZnsGw7+V02nG5UquUrB6uecNyByajgd8ev8H2lc4p15+T/Zp7B92cuHSbEmcmzizLtK4l2a95NuSaZ/694cx2WeY48P7xfz8OHJ10fD/wMwBFUbKAtUDtLM8ldCo7I43KCietnUNcv92v9XBi7p2Lt/H5A+zbVCo3UkXMzTa4/wuwRlGUY8Dngb8FUBTl64qibFVV9QBwTVGUk8AbwF+qqto5LyMWurJ7/MbqEZ3fWB12ezl0rhVbmontq2VHqoi9WaVCqqo6DHw8zNf/fMK/vzSHcYkUsWqpg4IcG6dq2/nUwytIt+prt6bP7+fFQw2cvHKHwREvNouRl49e5+l95ZiMss1ExI48u4SmjAYDuzYsZMzj12Up4FAlwMGRYGGoUU9sKwEKESLBXWhu14aFmE0GDp5twa+jHavRKgGer+/E7fHFeUQilUhwF5rLzkxj26oi2ruHuaqjLk3RKgH2DIzSNzj/lQCFCJHgLhLCw1WlABw826LxSOZPTpaVnKzwBaEcdptsYhIxJcFdJISlxdmUl+ZwqbGL9u7Z73lIJFaLKWIAr6woiPn2c5HaJLiLhLF/c3D2/pZOZu8jbi8dPcOkmY3k2a33VAJ8el+51sMTOqevvDOR1DZVOHHYrRyraeMju5YnfVrksUttjLh9fPjBZTy2bXFcKgEKESIzd5EwzCYjeytLGB3zceRCcm9q8vr8vHmmGYvZyN5NJXGrBChEiAR3kVD2birBmmbijdNNeLx+rYcza+9euUNn3ygPrV+APWP+u+wIMRUJ7iKhZNos7Nm4kN7BsaRtw+fz+/nNu7cwGQ28f/sSrYcjUpQEd5FwHt2yGLPJwIGTt/D7k29T06naDjp6Rnho/QLysm1aD0ekKAnuIuE47FYeWFtMe88IZyPs8ExUfn+AV0/clFm70JwEd5GQHt+2BIMBfvPuzaRqon1G7aCta5gda4spyE3XejgihUlwFwmpKC+DLSsLaWof5MK15KgW7fP7+dWxGxgNBj6wQ2btQlsS3EXCevLBZRgM8PLR60lRUOx4zR3auobZtWEBRY4MrYcjUpwEd5GwFuRn8sCaYlpcQxw808zomFfrIUXk9vj45dHrpJmNfHDnMq2HI4TsUBWJy+f3w3g3up++1cBbZ1vYUF6QkI0uDp5ppndwjA/sWILDLgXBhPYS6xUixAQvHmrgeM17ue6u3tGEbHQxMDzGb082kWkz8/g2WWsXiUGCu0hIydTo4qUjjYy4vXzowWVk2OTDsEgMEtxFQkqWRhfXb/dz9GIbJc5M9m0q0Xo4QtwlwV0kpJwsK3nZ4deuszPSEqLRhT8Q4N/fVAkAv/tIRcLdBxCpTZ6NIiFZLSYqK5xhj5lMBtLM2j9137l4mxttA2xfXYSy2IHb46OjZzihloxE6pIFQpGwQg0tztd30jMwSn6ODZ8/QFe/m5NX2tmxtlizsXX1jfKzQw2kW008tXs5Lxys53y9i+5+N3nZViornAmZ1SNShwR3kbBMRiPP7K/gqd1l9A26KVuaz/Vb3Xzte9W8cLCe8tIcAoFA3BtgBAIBfnigltExH3/w/pW8frqZg2fe6x7V1e+++/9n9lfEbVxCTCTTCpHwQo0ubGlmnLnpfHTXcoZGvXztX6v5yndP8tXnT/LCwfpgXnwcHLl4mys3e1hfls+WlYVJk9UjUosEd5F0XL0jAIyNN/MIzZTjkf/e4hrkpwevkWE183vvW0n/0FhSZPWI1CPBXSQVt8cXsZBYrGfKo2Nevv3yZca8fj77gVU47NaoWT1pFhNZGZaYjUeIaCS4i6Qym/z3+chiCa6z13Gne5hHtyy6m8kTLatndMzHL4/emPU5hZgLuaEqkkpoptwVJsDnZlnvyX/3+f28eKhhXrJYXj56g1O1HZSX5PCxPWX3HPvwQ8s5duk2o2P3r/mfr+/kqd1l0hhbxJ3M3EVSiTZTTrea7sl/f/FQAwfPtNDV7ybA7Nfm37l4m1dP3MSZa+O/PrUOs+nel83g8BjuMIEdZN1daEeCu0g6T+8rZ39VKXnj1RfHC0fS2jnM3/7wNF6fb95q0xy9eJsfHagjK93Clz6+geyMtPseE23d3WG3JcRuWpF6JLiLpBPKf19Xlg/AxDYeTe2D/N8/OktP/+icslgCgQBvnm7mhwfqyEy38Gef3MiC/Mywj432aaKyokCWZIQmZM1dJB2f388Lb9Zz9OLtsMebOwb5yZv15NrT6BkYu+/4VLNpj9fPfxys5+0Lt8nOTOPLn9jA4iJ71DFN3k3rsNuorCi4+3Uh4k2Cu0g6Lx5q4PD58IE95OrNHmxp4WfM0WbTN+/0871Xa2ntHGJxYRZ//LH15GXbphzT5N208d41K8RkEtxFUom2lh6SZ7eyfU0Rr59qBsBiNuL1+nHYrWxSnGFn080dg7xWfYuTV9oJAHsrS/jE3nKsEd4gIgntphVCaxLcRVLpG3SHTYOcaMOKAj62p5ytq4r4yZv1NLT0AWAeD/KHzrWSYTUzOubjTvcwalMPLa4hABYVZvH0vnJWL82L+bUIEUsS3EVSycmykpuVRu/g/WvpIfs3lwKwuMjOX/zOJupu9XDk4m0uXOvk7Qv3L+eYTQY2lhfw0PoFbFhRgNFguO8xQiQbCe4iqVgtJipXFERcc8/Ptt2zRm4wGFi1NI9VS/Pw+vy0uobo6B1hxO3FlmYiP9vG4iI7lgSoDy/EfJLgLpLOM49U0NDaT3PH4H3Hot0sNZuMLCm2s6Q4euaLEHowp+mKoigfURTlhQjHnlUU5YyiKCcVRXliLucRYiKT0chf/34VezeV4MiyYjAEZ+z7q0ol9VCIcbOeuSuK8k3gMeBCmGPFwB8DVYANOKYoypuqqso+bDEvTEYjn35U4RN7yyX1UIgw5jJzPwF8McKxrcBxVVXdqqr2AQ3A+jmcS4iwQqmHEtiFuNeUM3dFUT4L/OmkL/+BqqovKoqyJ8K3ZQN9E/4/AOREO4/DkYHZPPsXqNOZeuuocs2pQa45Ncz3NU8Z3FVV/R7wvRn+3H5g4kjtQG+0b+jpGZ7hKd7jdNpxuQZm/f3JSK45Ncg1p4a5XHOkN4VYZcucAv5eURQbYAVWAZdjdC4hhBCTzGtwVxTly0CDqqqvKIryLeAowXX9v1JVdXQ+zyWEECIyQyAQmPpRceByDcx6IPIxLjXINacGueYZf2/YLdWyLU8IIXQoYWbuQggh5o/M3IUQQockuAshhA5JcBdCCB2S4C6EEDokwV0IIXRIgrsQQuiQBHchhNChpO7EpCiKEfg2sAFwA59TVbVB21HFlqIoFuD7wFKCdXueU1X1FU0HFQeKohQCZ4FHVFWt03o8saYoyl8AHwLSgG+PF/DTrfHn9Y8IPq99wLN6/jsrirIN+J+qqu5RFKUc+CEQIFiD67+oquqf6zmSfeb+YcCmquoO4CvANzQeTzz8LtClqupDwOPA/6fxeGJu/IX/XWBE67HEw3gp7QeAncBuYJGmA4qP9wNmVVUfAP4O+HuNxxMziqL8OfCvBBsZAfwj8NXx17QBeHI+zpPswf1B4DUAVVVPEuz8pHc/B7424f9erQYSR/8L+A4Qviu2/jwG1AAvA78GXtV2OHFRD5jHP41nAx6NxxNLjcBHJ/x/M3Bk/N8HgP3zcZJkD+6Tm4L4FEVJ6qWmqaiqOqiq6oCiKHbgF8BXtR5TLCmK8vuAS1XV17UeSxwVEJyofBz4AvDviqKELQ6lI4MEl2TqgOeBb2k6mhhSVfUl7n3zMqiqGqoDM2Vjo+lK9uA+uSmIUVVV3c9kFUVZBBwG/k1V1bANynXkD4FHFEV5G9gI/Hi8R6+edQGvq6o6pqqqCowCTo3HFGt/SvCaKwjeQ/vReD+IVDBxfX3KxkbTlezB/TjBtToURdlO8KOsrimKUgS8AfwPVVW/r/V4Yk1V1V2qqu5WVXUPwWbsn1FV9Y7Gw4q1Y8D7FEUxKIqyEMgkGPD1rIf3PoV3AxYgVRrjnp/QsvRxgn0w5izZlzBeJjirO0HwRsQfaDyeePhLwAF8TVGU0Nr746qqpsTNxlSgquqriqLsItjRzEgwe8Kn8bBi7Z+A7yuKcpRghtBfqqo6pPGY4uW/A88ripIG1BJcbp0zKfkrhBA6lOzLMkIIIcKQ4C6EEDokwV0IIXRIgrsQQuiQBHchhNAhCe5CCKFDEtyFEEKH/n890uUqTeBVggAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.RandomState(1)\n", "x = 10 * rng.rand(50)\n", "y = np.sin(x) + 0.1 * rng.randn(50)\n", "xfit = np.linspace(0, 10, 1000)\n", "\n", "poly_model.fit(x[:, np.newaxis], y)\n", "yfit = poly_model.predict(xfit[:, np.newaxis])\n", "\n", "plt.scatter(x, y)\n", "plt.plot(xfit, yfit);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Gaussian basis functions 高斯基函数\n", "\n", "Of course, other basis functions are possible.\n", "For example, one useful pattern is to fit a model that is not a sum of polynomial bases, but a sum of Gaussian bases.\n", "The result might look something like the following figure:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n", "<img src = \"img/figures/05.06-gaussian-basis.png\" width = \"400px\">\n", "\n", "<center>[figure source in Appendix](#Gaussian-Basis)</center>\n", "\n", "<font size = '4pt'>The shaded regions in the plot are the scaled basis functions, and when added together they reproduce the smooth curve through the data.</font>\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n", "These Gaussian basis functions are not built into Scikit-Learn, \n", "- but we can write a custom transformer that will create them\n", "- Scikit-Learn transformers are implemented as Python classes; \n", " - reading Scikit-Learn's source is a good way to see how they can be created:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The simplest case of a normal distribution is known as the ''standard normal distribution''.\n", "\n", "$$\n", "f(x \\mid \\mu, \\sigma^2) = \\frac{1}{\\sqrt{2\\pi\\sigma^2} } e^{ -\\frac{(x-\\mu)^2}{2\\sigma^2} } \\sim e^{ -0.5 (\\frac{x-\\mu}{\\sigma})^2}\n", "$$" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:22:00.734658Z", "start_time": "2018-05-20T15:22:00.710792Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "from sklearn.base import BaseEstimator, TransformerMixin\n", "\n", "class GaussianFeatures(BaseEstimator, TransformerMixin):\n", " \"\"\"Uniformly spaced Gaussian features for one-dimensional input\"\"\"\n", " def __init__(self, N, sigma_factor=2.0):\n", " self.N = N\n", " self.sigma_factor = sigma_factor\n", " \n", " @staticmethod\n", " def _gauss_basis(x, mu, sigma, axis=None):\n", " arg = (x - mu) / sigma\n", " return np.exp(-0.5 * np.sum(arg ** 2, axis))\n", " \n", " def fit(self, X, y=None):\n", " # create N centers spread along the data range\n", " self.mu_ = np.linspace(X.min(), X.max(), self.N)\n", " self.sigma_ = self.sigma_factor * (self.mu_[1] - self.mu_[0])\n", " return self\n", " \n", " def transform(self, X):\n", " return self._gauss_basis(X[:, :, np.newaxis], self.mu_,\n", " self.sigma_, axis=1)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:22:01.648815Z", "start_time": "2018-05-20T15:22:01.503183Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.RandomState(1)\n", "x = 10 * rng.rand(50)\n", "y = np.sin(x) + 0.1 * rng.randn(50)\n", "xfit = np.linspace(0, 10, 1000)\n", "\n", "gauss_model = make_pipeline(GaussianFeatures(20),\n", " LinearRegression())\n", "gauss_model.fit(x[:, np.newaxis], y)\n", "yfit = gauss_model.predict(xfit[:, np.newaxis])\n", "\n", "plt.scatter(x, y)\n", "plt.plot(xfit, yfit)\n", "plt.xlim(0, 10);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "There is nothing magic about polynomial basis functions: \n", "- You should have some sort of intuition about **the generating process of your data**; \n", "- If you think one basis or another might be appropriate, you can use them as well." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Regularization 正则化\n", "\n", "The introduction of basis functions into our linear regression makes the model much more flexible, \n", "- but it also can very quickly lead to over-fitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\n", "\n", "For example, if we choose too many Gaussian basis functions, we end up with results that don't look so good:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:33:09.422378Z", "start_time": "2018-05-20T15:33:09.258227Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = make_pipeline(GaussianFeatures(30),\n", " LinearRegression())\n", "model.fit(x[:, np.newaxis], y)\n", "\n", "plt.scatter(x, y)\n", "plt.plot(xfit, model.predict(xfit[:, np.newaxis]))\n", "\n", "plt.xlim(0, 10)\n", "plt.ylim(-5, 1.5);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "With the data projected to the 30-dimensional basis, the model has far too much flexibility and goes to extreme values between locations where it is constrained by data.\n", "\n", "We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:35:27.115635Z", "start_time": "2018-05-20T15:35:26.843888Z" }, "code_folding": [ 0 ], "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def basis_plot(model, title=None): \n", " fig, ax = plt.subplots(2, sharex=True)\n", " model.fit(x[:, np.newaxis], y)\n", " ax[0].scatter(x, y)\n", " ax[0].plot(xfit, model.predict(xfit[:, np.newaxis]))\n", " ax[0].set(xlabel='x', ylabel='y', ylim=(-5, 1.5))\n", " \n", " if title:\n", " ax[0].set_title(title)\n", "\n", " ax[1].plot(model.steps[0][1].mu_,\n", " model.steps[1][1].coef_)\n", " ax[1].set(xlabel='basis location',\n", " ylabel='coefficient',\n", " xlim=(0, 10))\n", " \n", "model = make_pipeline(GaussianFeatures(30), LinearRegression())\n", "basis_plot(model)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This is typical over-fitting behavior when basis functions overlap: \n", "- the coefficients of adjacent basis functions blow up and cancel each other out.\n", "\n", "We know that such behavior is problematic" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "It would be nice if we could limit such spikes expliticly in the model \n", "- by **penalizing large values of the model parameters**.\n", "\n", "Such a penalty is known as *regularization*, and comes in several forms." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Ridge regression ($L_2$ Regularization) 岭回归\n", "\n", "\n", "*ridge regression* or $L_2$ *regularization*, sometimes also called *Tikhonov regularization*.\n", "- Perhaps the most common form of regularization\n", "\n", "This proceeds by penalizing the **sum of squares** (2-norms) of the model coefficients; \n", "- The penalty on the model fit would be \n", "$$\n", "P = \\alpha\\sum_{n=1}^N \\theta_n^2\n", "$$\n", "\n", "where $\\alpha$ is a free parameter that controls the strength of the penalty.\n", "\n", "This type of penalized model is built into Scikit-Learn with the ``Ridge`` estimator:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:44:24.582352Z", "start_time": "2018-05-20T15:44:24.362999Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import Ridge\n", "model = make_pipeline(GaussianFeatures(30), Ridge(alpha=0.1))\n", "basis_plot(model, title='Ridge Regression')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The $\\alpha$ parameter is essentially a knob controlling the complexity of the resulting model.\n", "- In the limit $\\alpha \\to 0$, we recover the standard linear regression result; \n", "- in the limit $\\alpha \\to \\infty$, all model responses will be suppressed.\n", "\n", "One advantage of ridge regression in particular is that it can be computed very efficiently\n", "- at hardly more computational cost than the original linear regression model." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Lasso regression ($L_1$ regularization) 套索回归\n", "\n", "Lasso regression involves penalizing the **sum of absolute values** (1-norms) of regression coefficients:\n", "$$\n", "P = \\alpha\\sum_{n=1}^N |\\theta_n|\n", "$$\n", "Though this is conceptually very similar to ridge regression, the results can differ surprisingly: \n", "- for example, due to geometric reasons lasso regression tends to favor *sparse models* where possible: \n", " - it preferentially sets model coefficients to exactly zero.\n", "\n", "We can see this behavior in duplicating the ridge regression figure, but using L1-normalized coefficients:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:47:04.365611Z", "start_time": "2018-05-20T15:47:04.159181Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/sklearn/linear_model/coordinate_descent.py:466: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations\n", " ConvergenceWarning)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import Lasso\n", "model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001))\n", "basis_plot(model, title='Lasso Regression')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "With the lasso regression penalty, **the majority of the coefficients are exactly zero**, \n", "- with the functional behavior being modeled by a small subset of the available basis functions.\n", "\n", "As with ridge regularization, the $\\alpha$ parameter tunes the strength of the penalty, and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Example: Predicting Bicycle Traffic" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "To predict the number of bicycle trips across Seattle's Fremont Bridge based on weather, season, and other factors.\n", "\n", "We have seen this data already in [Working With Time Series](03.11-Working-with-Time-Series.ipynb).\n", "\n", "- we will join the bike data with another dataset, and \n", "- try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor.\n", "\n", "- the NOAA makes available their daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) (I used station ID USW00024233) \n", "- we can easily use Pandas to join the two data sources.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "We will perform a simple linear regression to relate weather and other information to bicycle counts, in order to estimate how a change in any one of these parameters affects the number of riders on a given day.\n", "\n", "In particular, this is an example of how the tools of Scikit-Learn can be used in a statistical modeling framework, in which the parameters of the model are assumed to have interpretable meaning.\n", "\n", "Let's start by loading the two datasets, indexing by date:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:53:35.910674Z", "start_time": "2018-05-20T15:53:20.663864Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import pandas as pd\n", "counts = pd.read_csv('data/Fremont_Bridge.csv', index_col='Date', parse_dates=True)\n", "weather = pd.read_csv('data/BicycleWeather.csv', index_col='DATE', parse_dates=True)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Next we will compute the total daily bicycle traffic, and put this in its own dataframe:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:53:53.996710Z", "start_time": "2018-05-20T15:53:53.981379Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "daily = counts.resample('d').sum()\n", "daily['Total'] = daily.sum(axis=1)\n", "daily = daily[['Total']] # remove other columns" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We saw previously that the patterns of use generally vary from day to day; let's account for this in our data by adding binary columns that indicate the day of the week:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:54:09.994337Z", "start_time": "2018-05-20T15:54:09.942189Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n", "for i in range(7):\n", " daily[days[i]] = (daily.index.dayofweek == i).astype(float)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Similarly, we might expect riders to behave differently on holidays; let's add an indicator of this as well:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:54:34.478168Z", "start_time": "2018-05-20T15:54:34.445100Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from pandas.tseries.holiday import USFederalHolidayCalendar\n", "cal = USFederalHolidayCalendar()\n", "holidays = cal.holidays('2012', '2016')\n", "daily = daily.join(pd.Series(1, index=holidays, name='holiday'))\n", "daily['holiday'].fillna(0, inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We also might suspect that the hours of daylight would affect how many people ride; let's use the standard astronomical calculation to add this information:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:55:20.848224Z", "start_time": "2018-05-20T15:55:20.530107Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "(8, 17)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def hours_of_daylight(date, axis=23.44, latitude=47.61):\n", " \"\"\"Compute the hours of daylight for the given date\"\"\"\n", " days = (date - pd.datetime(2000, 12, 21)).days\n", " m = (1. - np.tan(np.radians(latitude))\n", " * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\n", " return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\n", "\n", "daily['daylight_hrs'] = list(map(hours_of_daylight, daily.index))\n", "daily[['daylight_hrs']].plot()\n", "plt.ylim(8, 17)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can also add the average temperature and total precipitation to the data.\n", "In addition to the inches of precipitation, let's add a flag that indicates whether a day is dry (has zero precipitation):" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:55:35.967003Z", "start_time": "2018-05-20T15:55:35.952760Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# temperatures are in 1/10 deg C; convert to C\n", "weather['TMIN'] /= 10\n", "weather['TMAX'] /= 10\n", "weather['Temp (C)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\n", "\n", "# precip is in 1/10 mm; convert to inches\n", "weather['PRCP'] /= 254\n", "weather['dry day'] = (weather['PRCP'] == 0).astype(int)\n", "\n", "daily = daily.join(weather[['PRCP', 'Temp (C)', 'dry day']])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Finally, let's add a counter that increases from day 1, and measures how many years have passed.\n", "This will let us measure any observed annual increase or decrease in daily crossings:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:55:51.546978Z", "start_time": "2018-05-20T15:55:51.528230Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "daily['annual'] = (daily.index - daily.index[0]).days / 365." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now our data is in order, and we can take a look at it:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:56:04.238306Z", "start_time": "2018-05-20T15:56:04.217949Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Total</th>\n", " <th>Mon</th>\n", " <th>Tue</th>\n", " <th>Wed</th>\n", " <th>Thu</th>\n", " <th>Fri</th>\n", " <th>Sat</th>\n", " <th>Sun</th>\n", " <th>holiday</th>\n", " <th>daylight_hrs</th>\n", " <th>PRCP</th>\n", " <th>Temp (C)</th>\n", " <th>dry day</th>\n", " <th>annual</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-10-03</th>\n", " <td>3521.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>11.277359</td>\n", " <td>0.0</td>\n", " <td>13.35</td>\n", " <td>1.0</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04</th>\n", " <td>3475.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>11.219142</td>\n", " <td>0.0</td>\n", " <td>13.60</td>\n", " <td>1.0</td>\n", " <td>0.002740</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-05</th>\n", " <td>3148.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>11.161038</td>\n", " <td>0.0</td>\n", " <td>15.30</td>\n", " <td>1.0</td>\n", " <td>0.005479</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-06</th>\n", " <td>2006.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>11.103056</td>\n", " <td>0.0</td>\n", " <td>15.85</td>\n", " <td>1.0</td>\n", " <td>0.008219</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-07</th>\n", " <td>2142.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>11.045208</td>\n", " <td>0.0</td>\n", " <td>15.85</td>\n", " <td>1.0</td>\n", " <td>0.010959</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Total Mon Tue Wed Thu Fri Sat Sun holiday daylight_hrs \\\n", "Date \n", "2012-10-03 3521.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 11.277359 \n", "2012-10-04 3475.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 11.219142 \n", "2012-10-05 3148.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 11.161038 \n", "2012-10-06 2006.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 11.103056 \n", "2012-10-07 2142.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 11.045208 \n", "\n", " PRCP Temp (C) dry day annual \n", "Date \n", "2012-10-03 0.0 13.35 1.0 0.000000 \n", "2012-10-04 0.0 13.60 1.0 0.002740 \n", "2012-10-05 0.0 15.30 1.0 0.005479 \n", "2012-10-06 0.0 15.85 1.0 0.008219 \n", "2012-10-07 0.0 15.85 1.0 0.010959 " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "daily.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "With this in place, we can choose the columns to use, and fit a linear regression model to our data.\n", "We will set ``fit_intercept = False``, because the daily flags essentially operate as their own day-specific intercepts:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:56:39.750887Z", "start_time": "2018-05-20T15:56:39.734285Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Drop any rows with null values\n", "daily.dropna(axis=0, how='any', inplace=True)\n", "\n", "column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday',\n", " 'daylight_hrs', 'PRCP', 'dry day', 'Temp (C)', 'annual']\n", "X = daily[column_names]\n", "y = daily['Total']\n", "\n", "model = LinearRegression(fit_intercept=False)\n", "model.fit(X, y)\n", "daily['predicted'] = model.predict(X)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Finally, we can compare the total and predicted bicycle traffic visually:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:56:48.137178Z", "start_time": "2018-05-20T15:56:47.862115Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "daily[['Total', 'predicted']].plot(alpha=0.5);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "It is evident that we have missed some key features, especially during the summer time.\n", "\n", "- Either our features are not complete\n", " - i.e., people decide whether to ride to work based on more than just these\n", "- or there are some nonlinear relationships that we have failed to take into account \n", " - e.g., perhaps people ride less at both high and low temperatures\n", "\n", "Nevertheless, our rough approximation is enough to give us some insights, and we can take a look at the coefficients of the linear model to estimate how much each feature contributes to the daily bicycle count:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:58:02.441342Z", "start_time": "2018-05-20T15:58:02.435225Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Mon 504.882756\n", "Tue 610.233936\n", "Wed 592.673642\n", "Thu 482.358115\n", "Fri 177.980345\n", "Sat -1103.301710\n", "Sun -1133.567246\n", "holiday -1187.401381\n", "daylight_hrs 128.851511\n", "PRCP -664.834882\n", "dry day 547.698592\n", "Temp (C) 65.162791\n", "annual 26.942713\n", "dtype: float64" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params = pd.Series(model.coef_, index=X.columns)\n", "params" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "These numbers are difficult to interpret without some measure of their uncertainty.\n", "We can compute these uncertainties quickly using bootstrap resamplings of the data:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:58:23.047893Z", "start_time": "2018-05-20T15:58:20.770355Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from sklearn.utils import resample\n", "np.random.seed(1)\n", "err = np.std([model.fit(*resample(X, y)).coef_\n", " for i in range(1000)], 0)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "With these errors estimated, let's again look at the results:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "ExecuteTime": { "end_time": "2018-05-20T15:58:37.008473Z", "start_time": "2018-05-20T15:58:37.001643Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " effect error\n", "Mon 505.0 86.0\n", "Tue 610.0 83.0\n", "Wed 593.0 83.0\n", "Thu 482.0 85.0\n", "Fri 178.0 81.0\n", "Sat -1103.0 80.0\n", "Sun -1134.0 83.0\n", "holiday -1187.0 163.0\n", "daylight_hrs 129.0 9.0\n", "PRCP -665.0 62.0\n", "dry day 548.0 33.0\n", "Temp (C) 65.0 4.0\n", "annual 27.0 18.0\n" ] } ], "source": [ "print(pd.DataFrame({'effect': params.round(0),\n", " 'error': err.round(0)}))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- We first see that there is a relatively stable trend in the weekly baseline: \n", " - there are many more riders on weekdays than on weekends and holidays.\n", "- We see that for each additional hour of daylight, 129 ± 9 more people choose to ride; \n", "- a temperature increase of one degree Celsius encourages 65 ± 4 people to grab their bicycle; \n", "- a dry day means an average of 548 ± 33 more riders, and each inch of precipitation means 665 ± 62 more people leave their bike at home.\n", "\n", "Once all these effects are accounted for, we see a modest increase of 27 ± 18 new daily riders each year.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Our model is almost certainly missing some relevant information. \n", " - For example, nonlinear effects \n", " - such as effects of precipitation *and* cold temperature \n", " - nonlinear trends within each variable \n", " - such as disinclination to ride at very cold and very hot temperatures\n", "- Additionally, we have thrown away some of the finer-grained information\n", " - such as the difference between a rainy morning and a rainy afternoon, \n", "- and we have ignored correlations between days\n", " - such as the possible effect of a rainy Tuesday on Wednesday's numbers, \n", " - or the effect of an unexpected sunny day after a streak of rainy days.\n", " \n", "These are all potentially interesting effects, and you now have the tools to begin exploring them if you wish!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<!--NAVIGATION-->\n", "< [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) | [Contents](Index.ipynb) | [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) >" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "268px", "left": "1058px", "top": "113px", "width": "180px" }, "toc_section_display": false, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }

mit

ACM-USAL/seminario-python

codigo/Notebooks/Python_Biotec - 2_Bio.ipynb

1

2700567

mit

eblur/newdust

examples/giant_grains.ipynb

1

6978

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from newdust.graindist import *\n", "from newdust import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "A0 = 10.0\n", "MD = 1.e22 * constants.m_p * 0.009 # g cm^-2\n", "\n", "SIL = composition.CmSilicate()\n", "GRA_para = composition.CmGraphite(orient='para')\n", "GRA_perp = composition.CmGraphite(orient='perp')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "EVALS = np.logspace(-1,np.log10(20.),100)\n", "THVALS = np.logspace(0.0,2.0,5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(EVALS, SIL.ip(EVALS, unit='kev'), 'g', label='Silicate')\n", "plt.plot(EVALS, GRA_para.ip(EVALS, unit='kev'), 'b--', label='Graphite (parallel)')\n", "plt.plot(EVALS, GRA_perp.ip(EVALS, unit='kev'), 'b:', label='Graphite (perpendicular)')\n", "plt.loglog()\n", "plt.xlabel(\"Energy (keV)\", size=14)\n", "plt.ylabel(\"Imaginary part\", size=14)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(EVALS, 1.0 - SIL.rp(EVALS, unit='kev'), 'g', label='Silicate')\n", "plt.plot(EVALS, 1.0 - GRA_para.rp(EVALS, unit='kev'), 'b--', label='Graphite (parallel)')\n", "plt.plot(EVALS, 1.0 - GRA_perp.rp(EVALS, unit='kev'), 'b:', label='Graphite (perpendicular)')\n", "plt.loglog()\n", "plt.xlabel(\"Energy (keV)\", size=14)\n", "plt.ylabel(\"1 - Real part\", size=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No real difference between grain sizes for the parallel vs perpendicular graphite." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ABIG = 1.0\n", "big_sil = SingleGrainPop('Grain', 'Silicate', 'Mie', amax=ABIG, md=MD)\n", "big_gra = SingleGrainPop('Grain', 'Graphite', 'Mie', amax=ABIG, md=MD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "big_sil.calculate_ext(EVALS, unit='kev', theta=THVALS)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "big_gra.calculate_ext(EVALS, unit='kev', theta=THVALS)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = plt.subplot(111)\n", "big_sil.plot_ext(ax, 'all')\n", "plt.loglog()\n", "ax.set_ylim(0.01, 2)\n", "plt.title('Silicate')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = plt.subplot(111)\n", "big_gra.plot_ext(ax, 'all')\n", "plt.loglog()\n", "ax.set_ylim(0.01, 2)\n", "plt.title('Graphite')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "inds = [0, 50, -10]\n", "ms = dict(zip(inds,['d','o','s']))\n", "for i in inds:\n", " plt.plot(THVALS, big_sil.int_diff[i], 'g', ls='',\n", " marker=ms[i], markersize=10, label='%.2f keV' % EVALS[i])\n", " plt.plot(THVALS, big_gra.int_diff[i], 'b', ls='', marker=ms[i], markersize=10)\n", "plt.loglog()\n", "plt.legend(loc='lower left', frameon=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "giant_sil = SingleGrainPop('Grain', 'Silicate', 'Mie', amax=A0, md=MD)\n", "giant_gra = SingleGrainPop('Grain', 'Graphite', 'Mie', amax=A0, md=MD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "giant_sil.calculate_ext(EVALS, unit='kev', theta=THVALS)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "giant_gra.calculate_ext(EVALS, unit='kev', theta=THVALS)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = plt.subplot(111)\n", "giant_sil.plot_ext(ax, 'all')\n", "plt.loglog()\n", "ax.set_ylim(0.01, 2)\n", "plt.title('Silicate')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = plt.subplot(111)\n", "giant_gra.plot_ext(ax, 'all')\n", "plt.loglog()\n", "ax.set_ylim(0.01, 2)\n", "plt.title('Graphite')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "inds = [0, 50, -10]\n", "ms = dict(zip(inds,['d','o','s']))\n", "for i in inds:\n", " plt.plot(THVALS, giant_sil.int_diff[i], 'g', ls='',\n", " marker=ms[i], markersize=10, label='%.2f keV' % EVALS[i])\n", " plt.plot(THVALS, giant_gra.int_diff[i], 'b', ls='', marker=ms[i], markersize=10)\n", "plt.loglog()\n", "plt.legend(loc='lower left', frameon=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a giant comparison plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = plt.subplot(111)\n", "big_gra.plot_ext(ax, 'abs', color='b', lw=1, label='1 um gra')\n", "big_sil.plot_ext(ax, 'abs', color='g', lw=1, label='1 um sil')\n", "giant_gra.plot_ext(ax, 'abs', color='b', lw=2, label='10 um gra')\n", "giant_sil.plot_ext(ax, 'abs', color='g', lw=2, label='10 um sil')\n", "plt.loglog()\n", "plt.xlim(0.1, 20)\n", "plt.ylim(0.001, 2)\n", "plt.title(\"Absorption\")\n", "plt.legend(loc='lower left', frameon=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Starts to flatten out around $E_{kev} < \\sqrt{a_{um}}$" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-2-clause

thsant/sklearn-intro

02._Regressao.ipynb

1

294645

{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:df43c9ace0a541daf492aa31e0d23f7f3d875bfbbe45feaf34938359459c55ac" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Regress\u00e3o" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Exemplo - O problema dos pre\u00e7os de im\u00f3veis em Boston" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_boston\n", "boston = load_boston()\n", "print boston.DESCR" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Boston House Prices dataset\n", "\n", "Notes\n", "------\n", "Data Set Characteristics: \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive\n", " \n", " :Median Value (attribute 14) is usually the target\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in $1000's\n", "\n", " :Missing Attribute Values: None\n", "\n", " :Creator: Harrison, D. and Rubinfeld, D.L.\n", "\n", "This is a copy of UCI ML housing dataset.\n", "http://archive.ics.uci.edu/ml/datasets/Housing\n", "\n", "\n", "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", "\n", "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", "prices and the demand for clean air', J. Environ. Economics & Management,\n", "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", "pages 244-261 of the latter.\n", "\n", "The Boston house-price data has been used in many machine learning papers that address regression\n", "problems. \n", " \n", "**References**\n", "\n", " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", "\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "boston.data.shape" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "(506, 13)" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "num_samples = boston.data.shape[0]\n", "num_samples" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "506" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "boston.data[0]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([ 6.32000000e-03, 1.80000000e+01, 2.31000000e+00,\n", " 0.00000000e+00, 5.38000000e-01, 6.57500000e+00,\n", " 6.52000000e+01, 4.09000000e+00, 1.00000000e+00,\n", " 2.96000000e+02, 1.53000000e+01, 3.96900000e+02,\n", " 4.98000000e+00])" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "boston.target.shape" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "(506,)" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Dist\u00e2ncia aos centros de emprego vs. pre\u00e7o" ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12,8)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(boston.data[:,5], boston.target)\n", "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n", "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "<matplotlib.text.Text at 0x7f8a59970a90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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P2Lp1LXa7PdjxRC56msZORETkAhMfH8/Rox4yM4cDNcjMfIbExHDWr18f7GgikgUV0CIi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\nQDcEDtvLh+11Ece0atWKL7+cy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"text": [ "<matplotlib.figure.Figure at 0x7f8a68b43ad0>" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Produzindo conjuntos de treinamento e teste" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import ShuffleSplit" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "ssplit = ShuffleSplit(num_samples, n_iter=1, test_size=0.25)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "for train_idx, test_idx in ssplit:\n", " pass" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "train_idx" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "array([ 15, 362, 303, 100, 371, 389, 327, 44, 471, 274, 287, 52, 305,\n", " 410, 318, 230, 504, 169, 302, 189, 282, 211, 124, 39, 475, 481,\n", " 236, 30, 441, 454, 496, 155, 498, 505, 372, 49, 38, 117, 323,\n", " 386, 51, 262, 192, 113, 103, 123, 110, 417, 59, 349, 2, 220,\n", " 324, 258, 499, 25, 494, 23, 253, 404, 308, 19, 21, 359, 167,\n", " 298, 387, 411, 191, 63, 62, 447, 421, 442, 229, 135, 108, 235,\n", " 46, 56, 202, 128, 415, 289, 458, 140, 444, 428, 422, 381, 182,\n", " 76, 373, 172, 457, 166, 150, 132, 459, 337, 463, 204, 119, 3,\n", " 319, 143, 67, 352, 116, 495, 88, 228, 351, 254, 48, 249, 147,\n", " 503, 10, 353, 478, 345, 174, 398, 357, 339, 28, 433, 227, 111,\n", " 92, 440, 238, 85, 12, 199, 179, 41, 165, 134, 129, 177, 58,\n", " 270, 267, 322, 115, 0, 354, 244, 142, 106, 154, 466, 16, 137,\n", " 181, 401, 163, 45, 431, 291, 383, 489, 316, 33, 178, 286, 183,\n", " 131, 206, 271, 184, 394, 449, 355, 365, 311, 414, 36, 120, 473,\n", " 393, 399, 257, 40, 451, 9, 295, 151, 233, 278, 133, 336, 213,\n", " 460, 424, 239, 375, 112, 173, 201, 209, 66, 138, 14, 73, 197,\n", " 332, 397, 1, 141, 130, 148, 70, 377, 118, 122, 176, 250, 476,\n", " 380, 500, 180, 306, 296, 265, 11, 162, 310, 114, 75, 472, 315,\n", " 403, 20, 405, 136, 156, 486, 335, 292, 255, 80, 369, 96, 146,\n", " 231, 175, 437, 284, 341, 50, 224, 426, 453, 370, 275, 5, 264,\n", " 126, 186, 443, 81, 317, 84, 125, 309, 205, 90, 497, 203, 465,\n", " 89, 320, 22, 217, 107, 139, 358, 97, 241, 427, 487, 400, 492,\n", " 218, 348, 490, 269, 346, 7, 280, 430, 501, 225, 8, 196, 429,\n", " 456, 188, 366, 251, 402, 468, 61, 462, 419, 300, 304, 242, 74,\n", " 455, 388, 87, 450, 69, 94, 313, 77, 99, 364, 98, 79, 240,\n", " 95, 185, 145, 261, 331, 330, 368, 78, 461, 237, 432, 164, 266,\n", " 158, 207, 17, 27, 226, 293, 435, 436, 72, 288, 325, 273, 215,\n", " 413, 260, 57, 425, 491, 104, 438, 342, 452, 340, 200, 65, 159,\n", " 214, 420, 219, 276, 268, 326, 198, 395, 26, 152, 149, 232, 439,\n", " 170, 54])" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "n_train = train_idx.shape[0]\n", "n_train" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "379" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "test_idx" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "array([361, 263, 53, 407, 409, 171, 445, 391, 37, 378, 297, 418, 356,\n", " 367, 343, 484, 408, 406, 127, 334, 467, 256, 68, 392, 483, 31,\n", " 6, 344, 502, 379, 281, 24, 474, 222, 82, 246, 329, 301, 285,\n", " 194, 234, 109, 412, 385, 277, 221, 376, 363, 64, 396, 83, 469,\n", " 374, 212, 482, 312, 223, 279, 479, 382, 4, 338, 252, 290, 195,\n", " 477, 485, 390, 384, 283, 248, 144, 187, 157, 216, 208, 18, 210,\n", " 161, 347, 93, 328, 307, 423, 91, 247, 160, 153, 259, 121, 416,\n", " 480, 101, 493, 29, 464, 86, 321, 245, 299, 190, 102, 488, 13,\n", " 71, 34, 434, 35, 42, 294, 243, 470, 350, 47, 193, 314, 446,\n", " 32, 43, 105, 333, 448, 360, 60, 168, 55, 272])" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "n_test = test_idx.shape[0]\n", "n_test" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "127" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "n_train + n_test" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "506" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Exemplo - regress\u00e3o utilizando apenas RM\n", "\n", "Vamos prever o pre\u00e7o dos im\u00f3veis utilizando apenas o **n\u00famero de c\u00f4modos** (RM)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = boston.data[train_idx,5].reshape(n_train,1)\n", "X.shape" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "(379, 1)" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "y = boston.target[train_idx]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Treinamento\n", "\n", "Aqui, vamos utilizar a **regress\u00e3o linear**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import LinearRegression" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "regr = LinearRegression()\n", "\n", "regr.fit(X, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(X, y)\n", "plot(X, regr.predict(X))\n", "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n", "xlim((3,9))\n", "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')\n", "ylim((0,55))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "(0, 55)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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T5oLSTLOIiIhIKfLkk8/w3nursNtnABlYrd157bUhPPTQMG+HVmjffQc332xu//kntGvn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\nDUWSJKnnvJPXng58L2nXAxuIaYbbfItIs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"text": [ "<matplotlib.figure.Figure at 0x7f8a597b2310>" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Erro quadr\u00e1tico m\u00e9dio" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mean((regr.predict(X) - y)**2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "46.796244399594485" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Score\n", "\n", "O coeficiente $R^2$ \u00e9 definido como $(1 - \\frac{u}{v})$ em que\n", "\n", "$u = \\sum_{i} (y_i - \\hat{y_i})^2$\n", "\n", "$v = \\sum_{i} (y_i - \\mu_y)^2$\n", "\n", "O melhor *score* poss\u00edvel \u00e9 1.0 e o pior poss\u00edvel 0.0" ] }, { "cell_type": "code", "collapsed": false, "input": [ "regr.score(X, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "0.48450411944260396" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Teste" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X_t = boston.data[test_idx,5].reshape(n_test, 1)\n", "y_t = boston.target[test_idx]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Erro quadr\u00e1tico m\u00e9dio" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mean((regr.predict(X_t) - y_t)**2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "34.312008286451317" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "*Score*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "regr.score(X_t, y_t)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "0.47519490527319708" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(X_t, y_t)\n", "plot(X_t, regr.predict(X_t))\n", "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n", "xlim((3,9))\n", "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')\n", "ylim((0,55))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "(0, 55)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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bFJgldzj7V7IHaMzlrcolgD+Bap4oykEtzSIikmcYhkGpUhU4e3Ym0AqIJji4EYsWfUzr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\nd5g/31xOToa4OGtjEhERETkfqjSLX23dalaX58+H9983E2glzCIiIhL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"text": [ "<matplotlib.figure.Figure at 0x7f8a597238d0>" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(X, y, c='b', marker='o')\n", "scatter(X_t, y_t, c='r', marker='s')\n", "plot(X, regr.predict(X))\n", "plot(X_t, regr.predict(X_t), 'r-')\n", "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n", "xlim((3,9))\n", "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')\n", "ylim((0,55))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "(0, 55)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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geA1oi6dqOns7l8vM999/z3vvvcexY8cCFW5ArOj1eHbC/GP728EwlDBLiWik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\n/cCtwD5SUv5h+3Yf9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ipVxM5+/C0hTz1wcYO+RpUXiQgtW3Zk8+amJCf/H4axkJCQN/j7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\nIC3+AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x7f8a51710290>" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Exemplo - regress\u00e3o utilizando todas as dimens\u00f5es" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Treinamento" ] }, { "cell_type": "code", "collapsed": false, "input": [ "regr = LinearRegression()\n", "X = boston.data[train_idx]\n", "y = boston.target[train_idx]\n", "regr.fit(X, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Erro quadr\u00e1tico m\u00e9dio" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mean((regr.predict(X) - y)**2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "22.91041859376821" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "*Score*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "regr.score(X, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "0.74762448229637368" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Teste" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Erro quadr\u00e1tico m\u00e9dio" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X_t = boston.data[test_idx]\n", "y_t = boston.target[test_idx]\n", "mean((regr.predict(X_t) - y_t)**2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "19.870238886388343" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "*Score*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "regr.score(X_t, y_t)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "0.69608300062305262" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Exerc\u00edcio\n", "\n", "### A base de dados sobre diabetes\n", "\n", "Dez vari\u00e1veis idade, sexo, \u00edndice de massa corporal, press\u00e3o arterial m\u00e9dia e seis medi\u00e7\u00f5es sangu\u00edneas de $n = 442$ pacientes com **diabetes**. \n", "\n", "Medida quantitativa de interesse: **\u00edndice de progress\u00e3o da doen\u00e7a ap\u00f3s 1 ano**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_diabetes\n", "diabetes = load_diabetes()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Exerc\u00edcio\n", "\n", "Realizar regress\u00e3o para prever o \u00edndice de progress\u00e3o da doen\u00e7a a partir das 10 vari\u00e1vies" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Solu\u00e7\u00e3o" ] }, { "cell_type": "code", "collapsed": false, "input": [ "num_samples = diabetes.data.shape[0]\n", "num_samples" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "442" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "ssplit = ShuffleSplit(num_samples, n_iter=1, test_size=0.25)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "for train_idx, test_idx in ssplit:\n", " pass" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "train_idx" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "array([ 88, 325, 273, 279, 104, 146, 403, 48, 331, 401, 329, 381, 191,\n", " 4, 80, 101, 202, 47, 93, 422, 278, 321, 298, 38, 197, 174,\n", " 286, 225, 149, 404, 129, 272, 91, 39, 190, 166, 36, 239, 398,\n", " 17, 209, 82, 54, 409, 97, 318, 378, 419, 154, 208, 96, 275,\n", " 441, 355, 372, 284, 171, 22, 19, 315, 55, 6, 192, 258, 42,\n", " 52, 289, 30, 291, 268, 1, 428, 185, 218, 200, 241, 326, 130,\n", " 345, 319, 238, 250, 227, 211, 353, 263, 232, 430, 314, 281, 138,\n", " 77, 344, 67, 396, 357, 100, 170, 63, 2, 413, 342, 49, 0,\n", " 231, 254, 386, 150, 245, 244, 237, 135, 358, 307, 145, 434, 407,\n", " 248, 352, 420, 79, 399, 94, 242, 178, 277, 303, 123, 85, 113,\n", " 297, 87, 16, 346, 433, 229, 310, 293, 195, 205, 379, 371, 167,\n", " 259, 194, 44, 46, 189, 156, 251, 230, 287, 429, 269, 31, 78,\n", " 21, 9, 81, 98, 26, 305, 240, 290, 59, 157, 369, 196, 158,\n", " 410, 349, 347, 72, 252, 122, 366, 356, 341, 221, 306, 423, 116,\n", " 115, 343, 440, 23, 338, 246, 391, 380, 118, 300, 3, 15, 400,\n", " 132, 181, 322, 99, 389, 106, 323, 282, 226, 177, 110, 27, 262,\n", " 186, 164, 64, 188, 56, 224, 90, 324, 382, 361, 212, 376, 128,\n", " 50, 213, 183, 274, 161, 20, 83, 141, 421, 437, 333, 288, 395,\n", " 152, 412, 162, 5, 313, 351, 133, 105, 438, 74, 121, 108, 163,\n", " 142, 387, 45, 139, 173, 111, 117, 62, 109, 75, 266, 34, 214,\n", " 223, 260, 439, 264, 426, 408, 112, 144, 165, 172, 29, 406, 204,\n", " 71, 370, 126, 417, 255, 362, 270, 257, 402, 393, 125, 120, 10,\n", " 160, 431, 89, 360, 193, 187, 219, 153, 309, 61, 40, 43, 102,\n", " 35, 168, 228, 182, 367, 92, 243, 363, 179, 103, 432, 340, 312,\n", " 137, 124, 397, 236, 151, 184, 140, 424, 203, 261, 60, 12, 114,\n", " 247, 65, 8, 416, 234, 392])" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "test_idx" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "array([267, 155, 332, 143, 334, 37, 276, 285, 180, 339, 25, 253, 235,\n", " 41, 256, 350, 11, 427, 368, 336, 365, 216, 308, 377, 220, 296,\n", " 107, 316, 418, 384, 411, 292, 385, 280, 68, 394, 294, 374, 233,\n", " 302, 176, 359, 364, 13, 58, 169, 175, 425, 28, 53, 301, 199,\n", " 119, 57, 84, 14, 70, 383, 136, 330, 7, 24, 76, 95, 436,\n", " 348, 271, 354, 222, 311, 388, 415, 304, 249, 335, 18, 206, 295,\n", " 217, 435, 127, 373, 131, 147, 265, 283, 210, 320, 73, 327, 215,\n", " 207, 299, 414, 66, 134, 148, 32, 198, 69, 51, 337, 375, 328,\n", " 33, 390, 405, 317, 86, 159, 201])" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "regr = LinearRegression()\n", "X = diabetes.data[train_idx]\n", "y = diabetes.target[train_idx]\n", "regr.fit(X, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Erro quadr\u00e1tico m\u00e9dio" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mean((regr.predict(X) - y)**2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "2854.5161687412092" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "X_t = diabetes.data[test_idx]\n", "y_t = diabetes.target[test_idx]\n", "mean((regr.predict(X_t) - y_t)**2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "2957.3328332580109" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "regr.score(X_t, y_t)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "0.49383612489744777" ] } ], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Lasso \n", "\n", "- Lasso \u00e9 capaz de produzir **modelos esparsos**\n", " - Modelos que usam um **subconjunto** das vari\u00e1veis\n", "- O ideia \u00e9 reduzir os coeficientes de algumas vari\u00e1veis **zero**\n", "- Modelos Lasso s\u00e3o assim mais f\u00e1ceis de interpretar\n", "- Pode ser entendido como uma aplica\u00e7\u00e3o da **faca de Occam**\n", " - *Prefira modelos mais **simples** *" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ssplit = ShuffleSplit(num_samples, n_iter=1, test_size=0.25)\n", "\n", "for train_idx, test_idx in ssplit:\n", " pass\n", "\n", "X = boston.data[train_idx]\n", "y = boston.target[train_idx]\n", "\n", "X_t = boston.data[test_idx]\n", "y_t = boston.target[test_idx]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import Lasso" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "prompt_number": 133 }, { "cell_type": "code", "collapsed": false, "input": [ "lasso_regr = Lasso()\n", "lasso_regr.set_params(alpha=0.001)\n", "lasso_regr.fit(X, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 134, "text": [ "Lasso(alpha=0.001, copy_X=True, fit_intercept=True, max_iter=1000,\n", " normalize=False, positive=False, precompute='auto', tol=0.0001,\n", " warm_start=False)" ] } ], "prompt_number": 134 }, { "cell_type": "code", "collapsed": false, "input": [ "lasso_regr.score(X_t, y_t)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 135, "text": [ "0.7053705432249564" ] } ], "prompt_number": 135 }, { "cell_type": "code", "collapsed": false, "input": [ "boston_vars = ['CRIM (per capita crime rate by town)',\n", " 'ZN (proportion of residential land zoned for lots over 25,000 sq.ft.)',\n", " 'INDUS (proportion of non-retail business acres per town)',\n", " 'CHAS (Charles River dummy variable, = 1 if tract bounds river; 0 otherwise)',\n", " 'NOX nitric oxides concentration (parts per 10 million)',\n", " 'RM (average number of rooms per dwelling)',\n", " 'AGE (proportion of owner-occupied units built prior to 1940)',\n", " 'DIS (weighted distances to five Boston employment centres)',\n", " 'RAD (index of accessibility to radial highways)',\n", " 'TAX (full-value property-tax rate per $10,000)',\n", " 'PTRATIO (pupil-teacher ratio by town)',\n", " 'B (1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town)',\n", " 'LSTAT (lower status of the population)']" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 136 }, { "cell_type": "code", "collapsed": false, "input": [ "for descr, coefficient in zip(boston_vars, lasso_regr.coef_):\n", " print \"%.3f\\t%s\" % (coefficient, descr)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-0.107\tCRIM (per capita crime rate by town)\n", "0.061\tZN (proportion of residential land zoned for lots over 25,000 sq.ft.)\n", "0.051\tINDUS (proportion of non-retail business acres per town)\n", "2.754\tCHAS (Charles River dummy variable, = 1 if tract bounds river; 0 otherwise)\n", "-17.696\tNOX nitric oxides concentration (parts per 10 million)\n", "3.505\tRM (average number of rooms per dwelling)\n", "0.032\tAGE (proportion of owner-occupied units built prior to 1940)\n", "-1.401\tDIS (weighted distances to five Boston employment centres)\n", "0.432\tRAD (index of accessibility to radial highways)\n", "-0.016\tTAX (full-value property-tax rate per $10,000)\n", "-0.883\tPTRATIO (pupil-teacher ratio by town)\n", "0.010\tB (1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town)\n", "-0.668\tLSTAT (lower status of the population)\n" ] } ], "prompt_number": 137 } ], "metadata": {} } ] }

cc0-1.0

imaginebog/kmc_proc

notebooks/process_descs.ipynb

1

33863

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subject,structure,area,d1,d2,d3 from descriptors\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "from pandas.io import sql" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pandas.read_sql(q,conn,index_col=[\"subject\",\"structure\"])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>area</th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d3</th>\n", " </tr>\n", " <tr>\n", " <th>subject</th>\n", " <th>structure</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8 </th>\n", " <th>ctx-lh-parahippocampal</th>\n", " <td> 2278.972681</td>\n", " <td> 37.429935</td>\n", " <td> 27.662001</td>\n", " <td> 18.309098</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <th>ctx-lh-entorhinal</th>\n", " <td> 1301.127051</td>\n", " <td> 31.906112</td>\n", " <td> 22.805641</td>\n", " <td> 12.805795</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <th>ctx-lh-unknown</th>\n", " <td> 624.890113</td>\n", " <td> 71.958321</td>\n", " <td> 65.579963</td>\n", " <td> 24.555925</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <th>ctx-lh-postcentral</th>\n", " <td> 6899.567492</td>\n", " <td> 91.104336</td>\n", " <td> 38.530314</td>\n", " <td> 20.813732</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <th>ctx-rh-parahippocampal</th>\n", " <td> 2580.549424</td>\n", " <td> 39.370039</td>\n", " <td> 22.284075</td>\n", " <td> 15.277122</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ " area d1 d2 d3\n", "subject structure \n", "8 ctx-lh-parahippocampal 2278.972681 37.429935 27.662001 18.309098\n", "144 ctx-lh-entorhinal 1301.127051 31.906112 22.805641 12.805795\n", "8 ctx-lh-unknown 624.890113 71.958321 65.579963 24.555925\n", "144 ctx-lh-postcentral 6899.567492 91.104336 38.530314 20.813732\n", "8 ctx-rh-parahippocampal 2580.549424 39.370039 22.284075 15.277122" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "descs=[\"area\",\"d1\",\"d2\",\"d3\"]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "df2=df.unstack()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "df2.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"10\" halign=\"left\">area</th>\n", " <th>...</th>\n", " <th colspan=\"10\" halign=\"left\">d3</th>\n", " </tr>\n", " <tr>\n", " <th>structure</th>\n", " <th>3rd-Ventricle</th>\n", " <th>4th-Ventricle</th>\n", " <th>5th-Ventricle</th>\n", " <th>Brain-Stem</th>\n", " <th>CC-Full</th>\n", " <th>CC_Anterior</th>\n", " <th>CC_Central</th>\n", " <th>CC_Mid_Anterior</th>\n", " <th>CC_Mid_Posterior</th>\n", " <th>CC_Posterior</th>\n", " <th>...</th>\n", " <th>ctx-rh-rostralanteriorcingulate</th>\n", " <th>ctx-rh-rostralmiddlefrontal</th>\n", " <th>ctx-rh-superiorfrontal</th>\n", " <th>ctx-rh-superiorparietal</th>\n", " <th>ctx-rh-superiortemporal</th>\n", " <th>ctx-rh-supramarginal</th>\n", " <th>ctx-rh-temporalpole</th>\n", " <th>ctx-rh-transversetemporal</th>\n", " <th>ctx-rh-unknown</th>\n", " <th>non-WM-hypointensities</th>\n", " </tr>\n", " <tr>\n", " <th>subject</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8 </th>\n", " <td> 1042.746860</td>\n", " <td> 2324.878508</td>\n", " <td> NaN</td>\n", " <td> 19901.106811</td>\n", " <td>NaN</td>\n", " <td> 931.075837</td>\n", " <td> 349.613667</td>\n", " <td> 399.028359</td>\n", " <td> 326.393362</td>\n", " <td> 938.169249</td>\n", " <td>...</td>\n", " <td> 13.867242</td>\n", " <td> 34.474535</td>\n", " <td> 36.268758</td>\n", " <td> 29.907202</td>\n", " <td> 21.840511</td>\n", " <td> 32.846882</td>\n", " <td> 14.357744</td>\n", " <td> 9.730251</td>\n", " <td> 29.612856</td>\n", " <td> 31.402270</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 641.619491</td>\n", " <td> 1403.124541</td>\n", " <td> 0.035986</td>\n", " <td> 19105.559066</td>\n", " <td>NaN</td>\n", " <td> 827.894232</td>\n", " <td> 552.128907</td>\n", " <td> 449.101797</td>\n", " <td> 415.486599</td>\n", " <td> 760.369338</td>\n", " <td>...</td>\n", " <td> 16.315684</td>\n", " <td> 28.157098</td>\n", " <td> 28.987894</td>\n", " <td> 31.327106</td>\n", " <td> 24.768581</td>\n", " <td> 39.578074</td>\n", " <td> 16.031983</td>\n", " <td> 9.157265</td>\n", " <td> 29.442350</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 394.184189</td>\n", " <td> 997.982595</td>\n", " <td> NaN</td>\n", " <td> 14207.624250</td>\n", " <td>NaN</td>\n", " <td> 762.970895</td>\n", " <td> 376.312508</td>\n", " <td> 449.414371</td>\n", " <td> 332.295052</td>\n", " <td> 872.130594</td>\n", " <td>...</td>\n", " <td> 10.291902</td>\n", " <td> 27.141029</td>\n", " <td> 31.444940</td>\n", " <td> 33.197328</td>\n", " <td> 20.185973</td>\n", " <td> 35.341697</td>\n", " <td> 17.011382</td>\n", " <td> 7.816032</td>\n", " <td> 25.034827</td>\n", " <td> 24.233473</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 353.600531</td>\n", " <td> 993.396872</td>\n", " <td> NaN</td>\n", " <td> 19291.102927</td>\n", " <td>NaN</td>\n", " <td> 852.189519</td>\n", " <td> 372.058946</td>\n", " <td> 341.943886</td>\n", " <td> 377.845119</td>\n", " <td> 980.551577</td>\n", " <td>...</td>\n", " <td> 12.437163</td>\n", " <td> 28.664058</td>\n", " <td> 36.267630</td>\n", " <td> 33.355277</td>\n", " <td> 24.461873</td>\n", " <td> 31.105284</td>\n", " <td> 16.346414</td>\n", " <td> 11.192667</td>\n", " <td> 27.950776</td>\n", " <td> 34.210083</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 1109.025713</td>\n", " <td> 1909.614231</td>\n", " <td> 0.190569</td>\n", " <td> 20935.992450</td>\n", " <td>NaN</td>\n", " <td> 860.965305</td>\n", " <td> 456.710971</td>\n", " <td> 453.471441</td>\n", " <td> 405.456617</td>\n", " <td> 1001.375148</td>\n", " <td>...</td>\n", " <td> 18.343729</td>\n", " <td> 32.607878</td>\n", " <td> 37.655168</td>\n", " <td> 29.434078</td>\n", " <td> 34.904352</td>\n", " <td> 38.723101</td>\n", " <td> 12.684097</td>\n", " <td> 13.565896</td>\n", " <td> 29.122445</td>\n", " <td> 8.558598</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 464 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ " area \\\n", "structure 3rd-Ventricle 4th-Ventricle 5th-Ventricle Brain-Stem CC-Full \n", "subject \n", "8 1042.746860 2324.878508 NaN 19901.106811 NaN \n", "9 641.619491 1403.124541 0.035986 19105.559066 NaN \n", "15 394.184189 997.982595 NaN 14207.624250 NaN \n", "19 353.600531 993.396872 NaN 19291.102927 NaN \n", "25 1109.025713 1909.614231 0.190569 20935.992450 NaN \n", "\n", " \\\n", "structure CC_Anterior CC_Central CC_Mid_Anterior CC_Mid_Posterior \n", "subject \n", "8 931.075837 349.613667 399.028359 326.393362 \n", "9 827.894232 552.128907 449.101797 415.486599 \n", "15 762.970895 376.312508 449.414371 332.295052 \n", "19 852.189519 372.058946 341.943886 377.845119 \n", "25 860.965305 456.710971 453.471441 405.456617 \n", "\n", " ... d3 \\\n", "structure CC_Posterior ... ctx-rh-rostralanteriorcingulate \n", "subject ... \n", "8 938.169249 ... 13.867242 \n", "9 760.369338 ... 16.315684 \n", "15 872.130594 ... 10.291902 \n", "19 980.551577 ... 12.437163 \n", "25 1001.375148 ... 18.343729 \n", "\n", " \\\n", "structure ctx-rh-rostralmiddlefrontal ctx-rh-superiorfrontal \n", "subject \n", "8 34.474535 36.268758 \n", "9 28.157098 28.987894 \n", "15 27.141029 31.444940 \n", "19 28.664058 36.267630 \n", "25 32.607878 37.655168 \n", "\n", " \\\n", "structure ctx-rh-superiorparietal ctx-rh-superiortemporal \n", "subject \n", "8 29.907202 21.840511 \n", "9 31.327106 24.768581 \n", "15 33.197328 20.185973 \n", "19 33.355277 24.461873 \n", "25 29.434078 34.904352 \n", "\n", " \\\n", "structure ctx-rh-supramarginal ctx-rh-temporalpole \n", "subject \n", "8 32.846882 14.357744 \n", "9 39.578074 16.031983 \n", "15 35.341697 17.011382 \n", "19 31.105284 16.346414 \n", "25 38.723101 12.684097 \n", "\n", " \n", "structure ctx-rh-transversetemporal ctx-rh-unknown non-WM-hypointensities \n", "subject \n", "8 9.730251 29.612856 31.402270 \n", "9 9.157265 29.442350 NaN \n", "15 7.816032 25.034827 24.233473 \n", "19 11.192667 27.950776 34.210083 \n", "25 13.565896 29.122445 8.558598 \n", "\n", "[5 rows x 464 columns]" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "df2.index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "Int64Index([8, 9, 15, 19, 25, 29, 31, 35, 44, 51, 53, 54, 56, 64, 65, 69, 71, 72, 75, 83, 90, 93, 95, 107, 108, 113, 119, 121, 123, 124, 125, 128, 129, 134, 138, 141, 143, 144, 145, 149, 151, 153, 154, 156, 157, 161, 165, 172, 173, 175, 176, 177, 182, 185, 186, 195, 197, 198, 201, 202, 205, 208, 210, 212, 216, 219, 221, 225, 227, 230, 231, 232, 235, 237, 253, 256, 261, 263, 264, 266, 277, 288, 292, 293, 300, 301, 304, 307, 310, 313, 314, 319, 320, 322, 327, 331, 332, 333, 344, 346, ...], dtype='int64')" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "table_subjects = [ 8, 9, 15, 19, 25, 29, 31, 35, 44, 51, 53, 54, 56, 64, 65, 69, 71, 72, 73, 75, 83, 90, 93, 95, 107, 108, 113, 119, 121, 123, 124, 125, 128, 129, 134, 138, 141, 143, 144, 145, 149, 151, 153, 154, 156, 157, 161, 165, 172, 173, 175, 176, 177, 182, 185, 186, 195, 197, 198, 201, 202, 205, 208, 210, 212, 216, 219, 221, 225, 227, 230, 231, 232, 235, 237, 253, 256, 259, 261, 263, 264, 266, 277, 288, 292, 293, 300, 301, 304, 307, 310, 312, 313, 314, 319, 320, 322, 327, 331, 332, 333, 344, 346, 348, 353, 355, 356, 357, 358, 364, 369, 371, 374, 381, 390, 396, 399, 402, 409, 413, 416, 417, 423, 424, 426, 427, 429, 431, 432, 440, 452, 456, 458, 464, 469, 472, 478, 480, 483, 484, 485, 491, 496, 499, 504, 517, 526, 532, 535, 536, 537, 542, 544, 545, 547, 548, 549, 552, 566, 568, 576, 577, 579, 580, 592, 593, 595, 598, 599, 600, 602, 610, 611, 615, 616, 619, 623, 625, 630, 631, 645, 650, 651, 662, 665, 670, 675, 678, 684, 686, 689, 691, 694, 696, 712, 715, 734, 739, 748, 752, 754, 761, 765, 769, 783, 784, 786, 789, 790, 791, 795, 799, 804, 806, 815, 818, 821, 829, 840, 841, 848, 850, 861, 863, 868, 869, 874, 876, 877, 878, 879, 884, 891, 892, 893, 894, 898, 905, 906, 912, 913, 914, 918, 928, 934, 935, 939, 940, 942, 953, 954, 965, 966, 971, 982, 984, 992, 994, 996, 1005, 1006, 1021, 1026, 1039, 1049, 1063, 1076, 1077, 1212, 1213, 1218, 1221, 1224, 1227, 1232, 1234, 1237, 1239, 1242, 1244, 1247, 1249, 1251, 1253, 1260, 1262, 1265, 1267, 1268, 1269, 1271, 1278, 1283, 1291, 1304, 1318, 1320, 1322, 1326, 1333, 1336, 1337, 1338, 1340, 1357, ]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "missing = set(table_subjects) - set(df2.index)\n", "print missing" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([312, 73, 259])\n" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "new_index = sorted(set(df2.index).intersection(table_subjects))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "df3 = df2.loc[new_index]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "cols2 = [\"_\".join((m_s[1],m_s[0])) for m_s in df3.columns]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "df3.columns = cols2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "df4=df3.sort(axis=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "df4.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>3rd-Ventricle_area</th>\n", " <th>3rd-Ventricle_d1</th>\n", " <th>3rd-Ventricle_d2</th>\n", " <th>3rd-Ventricle_d3</th>\n", " <th>4th-Ventricle_area</th>\n", " <th>4th-Ventricle_d1</th>\n", " <th>4th-Ventricle_d2</th>\n", " <th>4th-Ventricle_d3</th>\n", " <th>5th-Ventricle_area</th>\n", " <th>5th-Ventricle_d1</th>\n", " <th>...</th>\n", " <th>ctx-rh-transversetemporal_d2</th>\n", " <th>ctx-rh-transversetemporal_d3</th>\n", " <th>ctx-rh-unknown_area</th>\n", " <th>ctx-rh-unknown_d1</th>\n", " <th>ctx-rh-unknown_d2</th>\n", " <th>ctx-rh-unknown_d3</th>\n", " <th>non-WM-hypointensities_area</th>\n", " <th>non-WM-hypointensities_d1</th>\n", " <th>non-WM-hypointensities_d2</th>\n", " <th>non-WM-hypointensities_d3</th>\n", " </tr>\n", " <tr>\n", " <th>subject</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8 </th>\n", " <td> 1042.746860</td>\n", " <td> 36.276714</td>\n", " <td> 17.819852</td>\n", " <td> 6.320717</td>\n", " <td> 2324.878508</td>\n", " <td> 49.889879</td>\n", " <td> 26.155790</td>\n", " <td> 21.261250</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td> 12.586128</td>\n", " <td> 9.730251</td>\n", " <td> 468.230734</td>\n", " <td> 89.336443</td>\n", " <td> 66.987532</td>\n", " <td> 29.612856</td>\n", " <td> 6.132118</td>\n", " <td> 91.831367</td>\n", " <td> 48.206247</td>\n", " <td> 31.402270</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 641.619491</td>\n", " <td> 33.376639</td>\n", " <td> 17.140485</td>\n", " <td> 5.425703</td>\n", " <td> 1403.124541</td>\n", " <td> 43.208795</td>\n", " <td> 20.090662</td>\n", " <td> 16.680234</td>\n", " <td> 0.035986</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td> 13.389130</td>\n", " <td> 9.157265</td>\n", " <td> 491.930440</td>\n", " <td> 75.610846</td>\n", " <td> 57.675523</td>\n", " <td> 29.442350</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 394.184189</td>\n", " <td> 28.178006</td>\n", " <td> 17.457000</td>\n", " <td> 4.612694</td>\n", " <td> 997.982595</td>\n", " <td> 39.937451</td>\n", " <td> 23.283802</td>\n", " <td> 15.212459</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td> 15.531115</td>\n", " <td> 7.816032</td>\n", " <td> 689.650087</td>\n", " <td> 66.820655</td>\n", " <td> 59.974526</td>\n", " <td> 25.034827</td>\n", " <td> 9.535910</td>\n", " <td> 53.497664</td>\n", " <td> 49.886065</td>\n", " <td> 24.233473</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 353.600531</td>\n", " <td> 28.792360</td>\n", " <td> 15.420484</td>\n", " <td> 5.069910</td>\n", " <td> 993.396872</td>\n", " <td> 43.278170</td>\n", " <td> 21.165607</td>\n", " <td> 12.541365</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td> 12.197054</td>\n", " <td> 11.192667</td>\n", " <td> 965.982823</td>\n", " <td> 81.553663</td>\n", " <td> 60.539905</td>\n", " <td> 27.950776</td>\n", " <td> 22.280935</td>\n", " <td> 77.317527</td>\n", " <td> 51.776174</td>\n", " <td> 34.210083</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 1109.025713</td>\n", " <td> 35.185224</td>\n", " <td> 15.608342</td>\n", " <td> 8.507182</td>\n", " <td> 1909.614231</td>\n", " <td> 47.138095</td>\n", " <td> 22.108704</td>\n", " <td> 18.356068</td>\n", " <td> 0.190569</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td> 14.344592</td>\n", " <td> 13.565896</td>\n", " <td> 1177.163048</td>\n", " <td> 74.779676</td>\n", " <td> 59.780500</td>\n", " <td> 29.122445</td>\n", " <td> 1.769736</td>\n", " <td> 72.965745</td>\n", " <td> 41.464603</td>\n", " <td> 8.558598</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 464 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ " 3rd-Ventricle_area 3rd-Ventricle_d1 3rd-Ventricle_d2 \\\n", "subject \n", "8 1042.746860 36.276714 17.819852 \n", "9 641.619491 33.376639 17.140485 \n", "15 394.184189 28.178006 17.457000 \n", "19 353.600531 28.792360 15.420484 \n", "25 1109.025713 35.185224 15.608342 \n", "\n", " 3rd-Ventricle_d3 4th-Ventricle_area 4th-Ventricle_d1 \\\n", "subject \n", "8 6.320717 2324.878508 49.889879 \n", "9 5.425703 1403.124541 43.208795 \n", "15 4.612694 997.982595 39.937451 \n", "19 5.069910 993.396872 43.278170 \n", "25 8.507182 1909.614231 47.138095 \n", "\n", " 4th-Ventricle_d2 4th-Ventricle_d3 5th-Ventricle_area \\\n", "subject \n", "8 26.155790 21.261250 NaN \n", "9 20.090662 16.680234 0.035986 \n", "15 23.283802 15.212459 NaN \n", "19 21.165607 12.541365 NaN \n", "25 22.108704 18.356068 0.190569 \n", "\n", " 5th-Ventricle_d1 ... ctx-rh-transversetemporal_d2 \\\n", "subject ... \n", "8 NaN ... 12.586128 \n", "9 NaN ... 13.389130 \n", "15 NaN ... 15.531115 \n", "19 NaN ... 12.197054 \n", "25 NaN ... 14.344592 \n", "\n", " ctx-rh-transversetemporal_d3 ctx-rh-unknown_area ctx-rh-unknown_d1 \\\n", "subject \n", "8 9.730251 468.230734 89.336443 \n", "9 9.157265 491.930440 75.610846 \n", "15 7.816032 689.650087 66.820655 \n", "19 11.192667 965.982823 81.553663 \n", "25 13.565896 1177.163048 74.779676 \n", "\n", " ctx-rh-unknown_d2 ctx-rh-unknown_d3 non-WM-hypointensities_area \\\n", "subject \n", "8 66.987532 29.612856 6.132118 \n", "9 57.675523 29.442350 NaN \n", "15 59.974526 25.034827 9.535910 \n", "19 60.539905 27.950776 22.280935 \n", "25 59.780500 29.122445 1.769736 \n", "\n", " non-WM-hypointensities_d1 non-WM-hypointensities_d2 \\\n", "subject \n", "8 91.831367 48.206247 \n", "9 NaN NaN \n", "15 53.497664 49.886065 \n", "19 77.317527 51.776174 \n", "25 72.965745 41.464603 \n", "\n", " non-WM-hypointensities_d3 \n", "subject \n", "8 31.402270 \n", "9 NaN \n", "15 24.233473 \n", "19 34.210083 \n", "25 8.558598 \n", "\n", "[5 rows x 464 columns]" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "df4.to_excel(\"descriptors.xlsx\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 } ], "metadata": {} } ] }

mit

willtchiu/TechCrunchStartupAnalysis

Explore_TC_Data.ipynb

2

357213

{ "cells": [ { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from collections import Counter\n", "from collections import defaultdict\n", "from os import listdir\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import nltk\n", "import plotly.plotly as py" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.read_csv('techcrunch_posts.csv')\n", "orgs = pd.read_csv('organizations.csv')\n", "people = pd.read_csv('people.csv')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cat_start_data = data[data['category'] == 'Startups']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TODO:\n", "- Create time series graph of \"hype/PR\"\n", "- Extract company names and group them with their respective posts\n", "- Create sentiment analysis on each post within the company group\n", "- Graph the sentiment analysis against outcome (if exists) \"funded/exits/failures\"" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df = pd.DataFrame(cat_start_data)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def extract_nouns(content, thresh, draw_plot):\n", " if content == \"\":\n", " print (\"Content is empty...aborting\")\n", " return\n", " is_noun = lambda pos: pos[:2] == 'NN'\n", " tokenized = nltk.word_tokenize(content)\n", " nouns = [word for (word, pos) in nltk.pos_tag(tokenized) if is_noun(pos)]\n", " noun_counts = Counter(nouns)\n", " nc_thresh = thresh\n", " nc_df = pd.DataFrame.from_dict(noun_counts, orient='index')\n", " #nc_df = nc_df.sort(columns=0, ascending=False).iloc[0:thresh]\n", " nc_df = nc_df.sort_values(by=0, axis=0, ascending=False).iloc[0:thresh]\n", " #nc_df = nc_df[nc_df[0] >= thresh]\n", " if draw_plot: nc_df.plot(kind='bar');\n", " return nc_df" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Content is empty...aborting\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x129a42f60>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x144bb8160>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x171bb7eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generalize, examples of startup features articles contents\n", "df1 = extract_nouns(cat_start_data.loc[4]['content'], 5, True)\n", "df2 = extract_nouns(cat_start_data.loc[428]['content'], 5, True)\n", "df3 = extract_nouns(cat_start_data.iloc[1456]['content'], 5, True)\n", "test = extract_nouns(\"\", 5, True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Use either crunchbase or angelist API \n", "# to match nouns with company names" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_companies(nouns):\n", " comp_names = [name for name in nouns.index.values if (orgs['name'].isin([name]).sum() == 1)]\n", " return comp_names" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "Used to create a dictionary that's indexed by a company and accumulates\n", "a table of posts referencing the company\n", "\n", "Input: int thresh - the top 'thresh' noun references to\n", "\"\"\"\n", "def create_posts_index(thresh):\n", " # Flag for drawing noun freq plot of post\n", " draw_plot = False\n", " comp_dict = defaultdict(pd.DataFrame)\n", " empty_content = []\n", " \n", " for index, rows in data.iterrows():\n", " try:\n", " nouns = extract_nouns(rows['content'], thresh, draw_plot)\n", " except (TypeError, KeyError) as e:\n", " print(e, \" occured at index: \", index)\n", " #print(\"Recorded: post indexed at \", index, \" has an error with its content\")\n", " empty_content.append(index)\n", " comp_names = get_companies(nouns)\n", " for comp in comp_names:\n", " comp_dict[comp] = comp_dict[comp].append(data.loc[index])\n", " \n", " return comp_dict, empty_content" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "expected string or bytes-like object occured at index: 823\n", "expected string or bytes-like object occured at index: 1427\n", "expected string or bytes-like object occured at index: 1456\n", "0 occured at index: 2163\n", "0 occured at index: 2219\n", "expected string or bytes-like object occured at index: 2511\n", "expected string or bytes-like object occured at index: 2546\n", "0 occured at index: 2806\n", "0 occured at index: 2831\n", "expected string or bytes-like object occured at index: 2978\n", "expected string or bytes-like object occured at index: 3028\n", "expected string or bytes-like object occured at index: 3285\n", "expected string or bytes-like object occured at index: 3591\n", "expected string or bytes-like object occured at index: 3675\n", "expected string or bytes-like object occured at index: 3712\n", "expected string or bytes-like object occured at index: 3762\n", "expected string or bytes-like object occured at index: 3780\n", "expected string or bytes-like object occured at index: 3837\n", "expected string or bytes-like object occured at index: 3838\n", "0 occured at index: 3866\n", "expected string or bytes-like object occured at index: 3959\n", "expected string or bytes-like object occured at index: 4163\n", "0 occured at index: 4556\n", "expected string or bytes-like object occured at index: 4604\n", "expected string or bytes-like object occured at index: 5199\n", "expected string or bytes-like object occured at index: 5205\n", "expected string or bytes-like object occured at index: 5277\n", "expected string or bytes-like object occured at index: 5314\n", "0 occured at index: 5360\n", "expected string or bytes-like object occured at index: 5414\n", "expected string or bytes-like object occured at index: 5430\n", "expected string or bytes-like object occured at index: 5497\n", "expected string or bytes-like object occured at index: 5511\n", "expected string or bytes-like object occured at index: 5729\n", "expected string or bytes-like object occured at index: 5956\n", "0 occured at index: 6060\n", "expected string or bytes-like object occured at index: 6111\n", "expected string or bytes-like object occured at index: 7004\n", "expected string or bytes-like object occured at index: 7069\n", "0 occured at index: 7380\n", "expected string or bytes-like object occured at index: 7447\n", "0 occured at index: 7504\n", "expected string or bytes-like object occured at index: 7572\n", "expected string or bytes-like object occured at index: 7678\n", "0 occured at index: 7680\n", "0 occured at index: 8010\n", "expected string or bytes-like object occured at index: 8147\n", "expected string or bytes-like object occured at index: 8471\n", "0 occured at index: 8577\n", "expected string or bytes-like object occured at index: 8593\n", "expected string or bytes-like object occured at index: 8685\n", "0 occured at index: 8925\n", "0 occured at index: 9700\n", "expected string or bytes-like object occured at index: 9979\n", "expected string or bytes-like object occured at index: 10095\n", "0 occured at index: 10794\n", "expected string or bytes-like object occured at index: 10935\n", "expected string or bytes-like object occured at index: 10981\n", "expected string or bytes-like object occured at index: 11031\n", "0 occured at index: 12499\n", "expected string or bytes-like object occured at index: 13202\n", "expected string or bytes-like object occured at index: 13374\n", "0 occured at index: 14431\n", "0 occured at index: 17165\n", "expected string or bytes-like object occured at index: 17688\n", "0 occured at index: 20422\n", "0 occured at index: 22752\n", "expected string or bytes-like object occured at index: 25143\n", "expected string or bytes-like object occured at index: 26921\n", "expected string or bytes-like object occured at index: 34414\n", "expected string or bytes-like object occured at index: 34517\n", "expected string or bytes-like object occured at index: 37325\n", "expected string or bytes-like object occured at index: 37340\n" ] } ], "source": [ "[company_post_dict, empty_content] = create_posts_index(5)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Writing the company_post_dict to file for easy load" ] }, { "cell_type": "code", "execution_count": 314, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def find_csv_filenames(path_to_companies, ext=\".csv\"):\n", " filenames = listdir(path_to_companies)\n", " return [filename for filename in filenames if filename.endswith(ext)]" ] }, { "cell_type": "code", "execution_count": 315, "metadata": {}, "outputs": [], "source": [ "def create_safe_filename(filename):\n", " # / --> _\n", " # . --> +\n", " name_acc = filename\n", " if '/' in filename:\n", " name_acc = name_acc.replace('/', '_')\n", " if '.' in filename:\n", " name_acc = name_acc.replace('.', \"+\")\n", " return name_acc" ] }, { "cell_type": "code", "execution_count": 316, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def revert_safe_filename(filename):\n", " name_acc = filename\n", " if '_' in filename:\n", " name_acc = name_acc.replace('_', '/')\n", " if '+' in filename:\n", " name_acc = name_acc.replace('+', '.')\n", " return name_acc" ] }, { "cell_type": "code", "execution_count": 317, "metadata": {}, "outputs": [], "source": [ "def save_all_companies(company_post_dict, path_to_data_folder):\n", " for comp, posts in company_post_dict.items():\n", " safe_comp_name = create_safe_filename(comp)\n", " print (safe_comp_name)\n", " posts.to_csv(path_to_data_folder+safe_comp_name+\".csv\", sep=',')" ] }, { "cell_type": "code", "execution_count": 318, "metadata": {}, "outputs": [], "source": [ "def load_all_companies(path_to_companies):\n", " comp_dict = defaultdict(pd.DataFrame)\n", " filenames = find_csv_filenames(path_to_companies)\n", " for filename in filenames:\n", " comp_key = filename.split(\".\")[0]\n", " print (comp_key)\n", " reverted_name = revert_safe_filename(comp_key)\n", " comp_dict[reverted_name] = pd.read_csv(path_to_companies+filename, index_col=0)\n", " return comp_dict" ] }, { "cell_type": "code", "execution_count": 319, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Propstack\n", "News+me\n", "BrightPoint\n", "GenieDB\n", "Hadopi\n", "Stratos\n", "Scoutmob\n", "Placemeter\n", "Dremel\n", "Axiata\n", "Djump\n", "OnePageCRM\n", "Framebench\n", "RapidMiner\n", "Parallels\n", "TapSense\n", "Nuzzel\n", "Benchmark\n", "Blackphone\n", "Themer\n", "Telegram\n", "MovieLaLa\n", "Pitch\n", "Sensorberg\n", "Ossia\n", "BillGuard\n", "Miradore\n", "Porch\n", "StartupBus\n", "CapRally\n", "Algolia\n", "BevSpot\n", "Netflix\n", "Founder2be\n", "Shippable\n", "Regalii\n", "Matchbook\n", "uMake\n", "AppSurfer\n", "Yotpo\n", "Makeshift\n", "Hotspots+io\n", "SalesforceIQ\n", "Nestivity\n", "Harris\n", "HarperCollins\n", "cloudControl\n", "Atmotube\n", "SproutCore\n", "Zymergen\n", "M7\n", "Dwolla\n", "Breather\n", "Hinge\n", "ipsy\n", "Letterboxd\n", "Kaplan\n", "WhatRunsWhere\n", "WatchDox\n", "ScoreBeyond\n", "Glow\n", "Rapid7\n", "FreeCharge\n", "Your\n", "Kabbee\n", "Costa\n", "Poynt\n", "Gamify\n", "MediaSpike\n", "Mandarin\n", "Spotify\n", "Lawdingo\n", "GateGuru\n", "SugarSync\n", "LMS\n", "GrowSumo\n", "Toothpick\n", "LookFlow\n", "Zeemi+tv\n", "Exec\n", "AfterCollege\n", "Peeple\n", "SimpliVity\n", "Quik\n", "CloudBees\n", "HipChat\n", "Fontacto\n", "GetSet\n", "Torque\n", "Pantry\n", "MariaDB\n", "Tailwind\n", "Odyssey\n", "Sullivan\n", "Explorer\n", "FarFaria\n", "Apica\n", "Rosetta\n", "LifeLock\n", "Freshplum\n", "AirMap\n", "Bark\n", "TrustedCompany+com\n", "Yammer\n", "NewVoiceMedia\n", "Mann\n", "AAA\n", "Wakie\n", "WEVR\n", "Parachut\n", "Streak\n", "Ondango\n", "Carmudi\n", "Evertale\n", "AptDeco\n", "Fab\n", "Tailor\n", "Lingo\n", "Stacklist\n", "Block\n", "Procured\n", "Ambassador\n", "HackerOne\n", "Glance\n", "KlickNation\n", "Avvo\n", "Microduino\n", "JigoCity\n", "Acceleprise\n", "Pure\n", "Lendio\n", "Medikly\n", "Doccaster\n", "Moonshark\n", "Exploreka\n", "NimbusBase\n", "Giftly\n", "MicrobeScope\n", "Smule\n", "Vital\n", "Accenture\n", "Brandcast\n", "Hmmm\n", "Botify\n", "MongoDB\n", "FiftyThree\n", "Rentecarlo\n", "Wooplr\n", "Espresa\n", "AMRA\n", "DogSync\n", "Popset\n", "Lumenus\n", "Handy\n", "FitStar\n", "PayDragon\n", "Telesocial\n", "TenderTree\n", "MyHeritage\n", "Hooks\n", "HireArt\n", "Qualcomm\n", "Evernote\n", "Initial\n", "TableGrabber\n", "Meerkat\n", "Polo\n", "BitGo\n", "ClipClock\n", "Flat\n", "Palaround\n", "Wonpy\n", "VouchedFor\n", "GoInstant\n", "Collabspot\n", "Hungama\n", "NetPlenish\n", "Domo\n", "Cucumbertown\n", "Deezer\n", "TouchBistro\n", "LeCab\n", "Ultimaker\n", "Pornhub\n", "Doozton\n", "Planday\n", "Bill+com\n", "Independent\n", "Bitstrips\n", "Instabeat\n", "Wallarm\n", "Vitra\n", "Atari\n", "Shutl\n", "DataSift\n", "comScore\n", "Marvel\n", "Heetch\n", "Superfeedr\n", "Involver\n", "Cylindo\n", "Atomico\n", "Cor\n", "StartupDigest\n", "Peekster\n", "Sneeky\n", "Chitika\n", "MIUI\n", "Genius\n", "LaunchDarkly\n", "Dexplora\n", "Reserverr\n", "Grades+io\n", "Korner\n", "ROLI\n", "Krablr\n", "Variety\n", "Cardwheel\n", "Swrve\n", "Twentify\n", "DealAngel\n", "Ward\n", "Wayra\n", "Screenhero\n", "Jarvis\n", "DropGifts\n", "Belly\n", "Babyhuddle\n", "Versly\n", "Nimbuzz\n", "Flipboard\n", "AppCrawlr\n", "Rocketmine\n", "Perk+com\n", "SnapPages\n", "ShopSocially\n", "Main\n", "PayU\n", "Trent\n", "AgileZen\n", "Lydia\n", "Airware\n", "Tail\n", "Tyler\n", "Casio\n", "Splunk\n", "Connectify\n", "Stratasys\n", "Pusher\n", "Kickpay\n", "Lebara\n", "Twice\n", "Boostable\n", "Sky\n", "Zortrax\n", "Nameless+tv\n", "Marakana\n", "Spectre\n", "CoVenture\n", "Shyp\n", "SmartHires\n", "Twigtale\n", "Photoshop\n", "Robinhood\n", "Milyoni\n", "CenturyLink\n", "Tink\n", "Wantworthy\n", "Wranx\n", "FTC\n", "NerdWallet\n", "mySupermarket\n", "OneID\n", "Rounds\n", "Your+MD\n", "ShopFully\n", "Binksty\n", "AngelScholars\n", "Practo\n", "Genome\n", "Backstage\n", "Tinggal\n", "Buzzcar\n", "Refinery29\n", "Solinea\n", "Vyte+in\n", "BiteHunter\n", "Starship\n", "Entrepreneur\n", "Findables\n", "App+io\n", "Stepes\n", "Nielsen\n", "Attribution\n", "TaDaweb\n", "BlueCrew\n", "eero\n", "Brillen+de\n", "Jimmyjane\n", "Insensi\n", "Boticca\n", "ReBoard\n", "InfluxData\n", "Toggle\n", "Inside+com\n", "Islands\n", "BloomThat\n", "Apkudo\n", "Whitepages\n", "BroadMap\n", "Craves\n", "Connectifier\n", "Socialspiel\n", "ThriveTracker\n", "Steller\n", "Hightail\n", "Whisk\n", "Sketchfab\n", "Spin\n", "EPS\n", "Ubooly\n", "Birch\n", "Wize\n", "Apple\n", "conichi\n", "TradeHero\n", "Libertas\n", "Bitsbox\n", "Mondaine\n", "Mercer\n", "market\n", "NodePrime\n", "RealDirect\n", "Rumgr\n", "Tellem\n", "Scringo\n", "UserEvents\n", "Yunmake\n", "Vlix\n", "Eventful\n", "RentSocial\n", "Sifteo\n", "DeNA\n", "Trestle\n", "WorldRemit\n", "Deekit\n", "Rewardli\n", "Echobox\n", "CrowdCompass\n", "CareZone\n", "Tuition+io\n", "Archos\n", "Bango\n", "Friedman\n", "mark\n", "Tutum\n", "Thermodo\n", "Munchery\n", "Storify\n", "SmartAsset\n", "Synata\n", "Ethnio\n", "Pixable\n", "Foodspotting\n", "ResearchGate\n", "DTN\n", "PoachIt\n", "Allset\n", "SleepSense\n", "Flurry\n", "Wibiya\n", "Baydin\n", "Ibotta\n", "YourTrove\n", "Cloudscaling\n", "SizeUp\n", "PinReach\n", "Rockmelt\n", "Revel\n", "LastPass\n", "Chango\n", "Fullstack\n", "ParAccel\n", "Ofcom\n", "Tackk\n", "Crowdery\n", "Bkstg\n", "Panasonic\n", "Flickr\n", "Uprising\n", "Omnicharge\n", "ICOMP\n", "FoodChéri\n", "Formation\n", "BigID\n", "Dataflow\n", "Cloakroom\n", "Punchfork\n", "Modomoto\n", "Viewfinder\n", "ecoATM\n", "Scroll\n", "Harlequin\n", "Telepathic\n", "Glassbreakers\n", "Echofon\n", "SlideIdea\n", "HomeLight\n", "Grovemade\n", "Accomable\n", "GlobalSign\n", "Zynstra\n", "TigerText\n", "Toonimo\n", "Canva\n", "Musaic\n", "Pirq\n", "Mongoose\n", "Mapstr\n", "Comma\n", "Osmeta\n", "Gorilla\n", "Asseta\n", "Tastemates\n", "Bookboard\n", "Sensegon\n", "Teller\n", "RefME\n", "Mojo\n", "Purch\n", "42Floors\n", "Tindie\n", "GB\n", "Tokopedia\n", "BranchOut\n", "SocialPoint\n", "Zype\n", "Illumio\n", "Solair\n", "Laundrapp\n", "Checkthis\n", "SketchDeck\n", "Evans\n", "EverQuest\n", "ownCloud\n", "CardSpring\n", "Soundwave\n", "Olly\n", "Distributed\n", "InsightSquared\n", "Koding\n", "KupiVIP\n", "tray+io\n", "F6S\n", "colors\n", "Moments\n", "USD\n", "Digi+me\n", "Kidaptive\n", "Datto\n", "Seeqnce\n", "Portfolium\n", "Wiley\n", "Skit\n", "Sodisco\n", "Liberio\n", "Xirrus\n", "Buzztime\n", "Viv\n", "PBS\n", "Weissman\n", "Onehub\n", "VivaKi\n", "Wheelz\n", "Giggem\n", "Bilna\n", "WorldMate\n", "Knoala\n", "Anglr\n", "Populis\n", "Kite+io\n", "AutoBot\n", "Expression\n", "Clari\n", "EverythingMe\n", "Salary+com\n", "Memloom\n", "MuleSoft\n", "MOL\n", "MyPermissions\n", "Appoxee\n", "GrubHub\n", "HiMom\n", "RocketSpace\n", "Piano\n", "NASDAQ\n", "Norby\n", "MyHealthTeams\n", "VSCO\n", "Omixy\n", "Walmart\n", "Swarmly\n", "Peatix\n", "Late\n", "Cray\n", "Signpost\n", "Hamilton\n", "Rentmatic\n", "Ubisoft\n", "Airbnb\n", "People+ai\n", "Virtual\n", "Clinkle\n", "BrightEdge\n", "Zumbox\n", "McAfee\n", "MileWise\n", "Robotbase\n", "TabbedOut\n", "Colingo\n", "SmartNotify\n", "Musixmatch\n", "MassChallenge\n", "Elop\n", "Nimbula\n", "Bidzy\n", "MindMeister\n", "Spartan\n", "HighQ\n", "HyTrust\n", "Salesfusion\n", "Shypmate\n", "Ravi\n", "Triblio\n", "StatusPage\n", "Challenges\n", "iSwapp\n", "Deepak\n", "Blek\n", "Thinknum\n", "Mobcaster\n", "Posse\n", "Creative\n", "Pizza+de\n", "Coliloquy\n", "Acunote\n", "Zoobean\n", "Clutter\n", "FullContact\n", "Teewe\n", "Tinder\n", "GSG\n", "Gogolook\n", "Formspring\n", "WillCall\n", "PROskore\n", "Livefyre\n", "Zoobe\n", "Technopolis\n", "Saga\n", "Selective\n", "Pyxis\n", "Cheezburger\n", "Famo+us\n", "Crowdtap\n", "Grover\n", "ModiFace\n", "Mytonomy\n", "Virb\n", "OkCupid\n", "Xamarin\n", "LaunchHouse\n", "Glassmap\n", "Behavox\n", "NASA\n", "Trulioo\n", "Aorato\n", "Ninebot\n", "SendHub\n", "Selfycart\n", "Cocoon\n", "Central\n", "AdBlock\n", "Graphicly\n", "Ridejoy\n", "Inkling\n", "Okanjo\n", "Ezetap\n", "Totlol\n", "Nima\n", "Struq\n", "Faraday\n", "Seedling\n", "CatchFree\n", "Siftery\n", "Guildery\n", "Vibease\n", "AirConsole\n", "Adictik\n", "Shared\n", "Rhapsody\n", "Punch\n", "Bandzoogle\n", "Koemei\n", "Solidoodle\n", "JFDI+Asia\n", "Tintri\n", "Lancope\n", "Laptop\n", "Panda\n", "HiGear\n", "StarStreet\n", "Bookmate\n", "Kiosked\n", "Kickboard\n", "Bloop\n", "Normal\n", "CashStar\n", "Legacy\n", "ABI\n", "REI\n", "Yamli\n", "Hullabalu\n", "Campless\n", "Mobspire\n", "Leetchi\n", "Penxy\n", "CoffeeTable\n", "Pins\n", "Pypestream\n", "Input\n", "Inadco\n", "VIPKID\n", "meets\n", "ReskillUSA\n", "Topick\n", "Fandalism\n", "MediaPass\n", "Notes\n", "Taggstar\n", "Slack\n", "Dexter\n", "lastminute+com\n", "Android\n", "HomeMe\n", "Kudoso\n", "GradFly\n", "Contour\n", "Custom\n", "Figma\n", "U\n", "Wallapop\n", "Tagg\n", "WebKit\n", "Vouch\n", "Monsieur\n", "Trusted\n", "Sundance\n", "ScaleArc\n", "Quora\n", "Bezar\n", "Midokura\n", "Tracky\n", "Pulsate\n", "Relevance\n", "BridgeU\n", "Compose\n", "MediaMath\n", "Adventr\n", "Yoyo\n", "Distimo\n", "Pijon\n", "Tyba\n", "GroSocial\n", "Viber\n", "BuySellAds\n", "CrowdStrike\n", "ZURB\n", "Amiad\n", "Kingdon\n", "ShapeUp\n", "Board\n", "Quip\n", "SnipSnap\n", "Brightpearl\n", "Quiqup\n", "Hiri\n", "Posthaven\n", "Stephens\n", "Twigmore\n", "Fujitsu\n", "Z2\n", "Skills\n", "WebRTC\n", "Makers\n", "Popcorn\n", "Shopzilla\n", "K12\n", "Cazena\n", "CIT\n", "Smigin\n", "IOC\n", "Kitchensurfing\n", "Recurly\n", "iversity\n", "Exiles\n", "Ensygnia\n", "BeHere\n", "InCrowd\n", "Sparta\n", "Livrada\n", "Xeround\n", "Terrafugia\n", "Fanout\n", "Gametime\n", "Weilos\n", "Synkio\n", "Memolane\n", "Solum\n", "MacroFab\n", "MoneySavingExpert\n", "Perpetually\n", "Journy\n", "CIA\n", "Inkl\n", "O2\n", "Docker\n", "HeavenHR\n", "Doximity\n", "Apigee\n", "EVRYTHNG\n", "Shopmox\n", "Zuli\n", "Madison\n", "Wappwolf\n", "Supahands\n", "Iris\n", "Voyat\n", "Spot+IM\n", "Hudson\n", "Playsino\n", "TastemakerX\n", "Infusionsoft\n", "ElasticBox\n", "Intern\n", "Tron-Club\n", "Encoding+com\n", "Livestar\n", "Reputation+com\n", "Vine\n", "Airy\n", "BigStash\n", "Zoopla\n", "AMA\n", "Travel+ru\n", "Zimride\n", "EDM\n", "Alcatel-Lucent\n", "Referly\n", "Food52\n", "Corona\n", "OpenTV\n", "Medifund\n", "GoMiles\n", "Gigit\n", "Crimson\n", "KidoZen\n", "Pinster\n", "KinderTown\n", "CustomInk\n", "Mixpanel\n", "Celery\n", "Glyph\n", "Verisart\n", "edX\n", "BookDoc\n", "Knewton\n", "MobLabs\n", "QSAlpha\n", "Wavee\n", "Slice\n", "Elevatr\n", "Flypay\n", "Smiirl\n", "Verbase\n", "Moolaguides\n", "Xperia\n", "Curbside\n", "CodeHS\n", "Nomiku\n", "LUUV\n", "SportStream\n", "CoverHound\n", "Ploom\n", "Mimo\n", "Ringadoc\n", "TYLT\n", "Knowmia\n", "Daniel\n", "NGI\n", "Signal\n", "Bunkr\n", "ModoPayments\n", "Teams\n", "Hewlett-Packard\n", "Plink\n", "Sylaps\n", "Wingsplay\n", "Earmark\n", "Expedia\n", "Lesson\n", "Flipd\n", "Jimdo\n", "Lighter\n", "StyleCard\n", "Kana\n", "AppLovin\n", "SavingStar\n", "FOVE\n", "Tango\n", "iAdvize\n", "Watch\n", "Enthuse\n", "HowAboutWe\n", "MTM\n", "Cylance\n", "Clifton\n", "PX\n", "Appsee\n", "Alto\n", "Extole\n", "Bright+com\n", "Function\n", "Rapportive\n", "DigitalOcean\n", "xAd\n", "Digg\n", "TestChameleon\n", "iContact\n", "Operator\n", "GetScale\n", "Invenias\n", "Strata\n", "DoubleRecall\n", "Bitcovery\n", "Machine\n", "Prss\n", "KidAdmit\n", "Chipworks\n", "CAC\n", "Afero\n", "BuildZoom\n", "Roposo\n", "CRM\n", "Quantcast\n", "Modington\n", "Zap\n", "Theranos\n", "Stik\n", "ShopLogic\n", "Roadie\n", "Singtel\n", "GFG\n", "BPM\n", "VoiceBunny\n", "Yapp\n", "Grind\n", "Wooga\n", "Yoics\n", "Conferize\n", "MySQL\n", "Qalendra\n", "Zillabyte\n", "BioBots\n", "Code+org\n", "Appurify\n", "Open\n", "Meetey\n", "Set\n", "Chartio\n", "Mojiva\n", "Taleo\n", "Bounty\n", "Shotclock\n", "Audiogalaxy\n", "Brickstream\n", "Reflektion\n", "Reverb\n", "Lodgeo\n", "LaunchGram\n", "Nature\n", "OZON\n", "DramaFever\n", "StoryBox\n", "Welkio\n", "Jumio\n", "Oppo\n", "Panzura\n", "Vrv\n", "Sync+ME\n", "Accompany\n", "Skimlinks\n", "GoGoVan\n", "Netagio\n", "Convoy\n", "Triptease\n", "Brand\n", "Breakout\n", "Hippo\n", "Lover+ly\n", "Gate\n", "JPEGmini\n", "Ollie\n", "Adyen\n", "Streamroot\n", "Sywork\n", "Wandoujia\n", "Coursera\n", "ReelSurfer\n", "Woopra\n", "Feed\n", "PAPER\n", "Kleverbeast\n", "ShowMe\n", "Boombotix\n", "Expa\n", "TechLaunch\n", "Datahero\n", "Hotaru\n", "Citymapper\n", "Miramax\n", "Alley\n", "Flic\n", "SlimPay\n", "CoverItLive\n", "Evri\n", "NIST\n", "MakeGood\n", "Catawiki\n", "Reuters\n", "Intro\n", "Brainient\n", "IOVOX\n", "SilverPush\n", "Volo\n", "ComplyAdvantage\n", "Seenth+is\n", "DistroKid\n", "Pertino\n", "Qplay\n", "Fuze\n", "SynapSense\n", "Shuttlecook\n", "Juicero\n", "MediaCore\n", "Hitachi\n", "CatFi\n", "Dharma\n", "LoopPay\n", "Head\n", "Speek\n", "GetFeedback\n", "Taxi\n", "Bambuser\n", "ALOHA\n", "RadiumOne\n", "Healy\n", "Visa\n", "Shopa\n", "BloomNation\n", "Skout\n", "Plan\n", "Tissot\n", "UnboundID\n", "Naya\n", "Expion\n", "Beat\n", "Apptimize\n", "Rackspace\n", "Speakaboos\n", "MySmartPrice\n", "Between\n", "Omlet\n", "Odeo\n", "MyVR\n", "Egg\n", "TrendKite\n", "Pluralsight\n", "Virtuo\n", "Accelerator\n", "Overnight\n", "Hudl\n", "LaunchPoint\n", "Qubit\n", "TalkTo\n", "Metamarkets\n", "GCE\n", "LightSpeed\n", "OpenFeint\n", "Analogue\n", "Comet\n", "Selequity\n", "Squarespace\n", "Mantaphrase\n", "Gyft\n", "Pitchbox\n", "GroupAhead\n", "Ousta\n", "Carte\n", "Pixelapse\n", "Trunk\n", "Armor\n", "StyleCaster\n", "Applause\n", "Seconds\n", "BeachMint\n", "biNu\n", "Pathbrite\n", "KKR\n", "iPrice\n", "GRID\n", "Carnegie\n", "Down\n", "Pop\n", "Drimmit\n", "ChaCha\n", "Sqrrl\n", "Iconicfuture\n", "TakeLessons\n", "Moovweb\n", "ThriveHive\n", "Bose\n", "Aurelius\n", "NativeX\n", "Banjo\n", "Sanbolic\n", "Syapse\n", "Skillshare\n", "Chartcube\n", "Shopcade\n", "Band\n", "VideoBlocks\n", "BrandProject\n", "Progressly\n", "BlackJet\n", "eduClipper\n", "SoftLayer\n", "Metromile\n", "Selphee\n", "Final\n", "Nimbl\n", "Lawson\n", "Dinein+co+uk\n", "Things\n", "Exogen\n", "SeatGeek\n", "CNIL\n", "ink\n", "GatherContent\n", "SmarTots\n", "Timbre\n", "Hathway\n", "Stauffer\n", "Deepomatic\n", "Nikon\n", "FITiST\n", "Givit\n", "Assist\n", "Trusk\n", "FireEye\n", "Zenprise\n", "Anthony\n", "RankBoards\n", "FatFractal\n", "Selerity\n", "Chicisimo\n", "Realtidbits\n", "Wadi\n", "vLine\n", "Smarsh\n", "VAT\n", "Razer\n", "IndoorAtlas\n", "SIRUM\n", "Wag\n", "Weightless\n", "Stickam\n", "Chiizu\n", "Boutine\n", "Pryte\n", "GLG\n", "Xunlei\n", "BeMyEye\n", "Ericsson\n", "Carsome\n", "EasilyDo\n", "Pluto\n", "Latch\n", "Keepsy\n", "Passworks\n", "CipherCloud\n", "TourPal\n", "doctape\n", "Zazzle\n", "StumbleUpon\n", "Holden\n", "APP\n", "HeartThis\n", "textPlus\n", "Zhaopin\n", "PPI\n", "Marino\n", "Newswire\n", "Breakthrough\n", "UP\n", "Doctolib\n", "BuzzTale\n", "Tonsser\n", "Horizons\n", "FeedHenry\n", "Mandiant\n", "MedStartr\n", "Susie\n", "DMARC\n", "Sooqini\n", "Viddsee\n", "Plumbr\n", "Virtustream\n", "Kickbooster\n", "PeerStreet\n", "AdMob\n", "RecruitLoop\n", "Prysm\n", "TellApart\n", "MOCACARE\n", "Ambition\n", "Nirvanix\n", "SteelHouse\n", "WPP\n", "Lumens\n", "Bluefin\n", "Burstly\n", "Traity\n", "Scentbird\n", "MyMiniLife\n", "Truphone\n", "Seven\n", "Fitbit\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Lavabit\n", "Uniplaces\n", "Affirm\n", "TripAdvisor\n", "AppFog\n", "Gobstopper\n", "CollegeBudget\n", "Bebo\n", "MSN\n", "Joyable\n", "Magenta\n", "Xigo\n", "Hound\n", "Menulog\n", "BitWall\n", "Viadeo\n", "SyndicateRoom\n", "Olery\n", "Nextpeer\n", "VideoSelfie\n", "Force\n", "iwoca\n", "Tiiny\n", "Friendsy\n", "myTomorrows\n", "Impermium\n", "Blippy\n", "LogMeIn\n", "iFetch\n", "Givey\n", "Airlike\n", "SimpleHoney\n", "Tastemade\n", "PEN\n", "Gaikai\n", "Altiscale\n", "Muve\n", "PushPage\n", "DueDil\n", "BufferBox\n", "NumberFour\n", "Shark\n", "Nexon\n", "Carousel\n", "Z\n", "Georama\n", "Nokia\n", "PRX\n", "AppCertain\n", "THB\n", "GMT\n", "Fleex\n", "Browning\n", "Newton\n", "JustBook\n", "Pick\n", "BarkBox\n", "Teleza\n", "Sourcebits\n", "Neyya\n", "Compute\n", "Moglue\n", "Moment+me\n", "Osmo\n", "Monday52\n", "Hunter\n", "Drivemode\n", "grasp\n", "Espacio\n", "Ayannah\n", "eGood\n", "YesGraph\n", "Top10\n", "Sonicbids\n", "Politix\n", "mBank\n", "Blottr\n", "ContainerShip\n", "SnappyLabs\n", "Lendingkart\n", "Orange\n", "Luxe\n", "Interactive\n", "Divshot\n", "Detour\n", "TuneUp\n", "Flips\n", "Glose\n", "ARTIK\n", "Linea\n", "NewzSocial\n", "Genera\n", "Evidence+com\n", "Wonga\n", "Target\n", "SlickFlick\n", "DuckDuckGo\n", "Weekly\n", "Esri\n", "Cal\n", "NAD\n", "JustWatch\n", "UserVoice\n", "Neuron\n", "Outfit7\n", "BrightRoll\n", "SpaceX\n", "Flixster\n", "SocialMart\n", "Keywee\n", "StoryDesk\n", "Tapstream\n", "Visier\n", "Spinlister\n", "Netbiscuits\n", "Appboy\n", "Zipmark\n", "Cedexis\n", "Alfred\n", "Sidebark\n", "Rothschild\n", "SiteScout\n", "SweetLabs\n", "shopkick\n", "Reliance\n", "MyHealthPal\n", "Mensch\n", "BucketListly\n", "Deja\n", "Onfleet\n", "Digitsole\n", "Prim\n", "x\n", "Moped\n", "Schibsted\n", "Oliba\n", "Kitematic\n", "Displair\n", "egg\n", "Homejoy\n", "SocialCrunch\n", "GENWI\n", "Duolingo\n", "Riney\n", "Lane\n", "Palringo\n", "Nimble\n", "Hamburg\n", "InvoiceASAP\n", "Logikcull\n", "YouView\n", "Mailbox\n", "Sigma\n", "Monstro\n", "Luka\n", "Poppin\n", "EQAL\n", "Moveline\n", "Kinder\n", "Socialbakers\n", "Fanmode\n", "OKDOTHIS\n", "TappIn\n", "Habit\n", "Catchbox\n", "Yohann\n", "MyWidz\n", "Kickstarter\n", "Kodak\n", "Kurrenci\n", "LIVE\n", "Weebly\n", "BetaBait\n", "Modbook\n", "Calxeda\n", "Conspire\n", "Mogul\n", "Flytenow\n", "Fintech\n", "Rondee\n", "Ascribe\n", "Meizu\n", "BugSense\n", "Hartz\n", "Roadtrippers\n", "Philly\n", "Qardio\n", "Radical+FM\n", "Marketwired\n", "Omise\n", "Polls\n", "Traxo\n", "Gabriel\n", "WearYouWant\n", "MyEnergy\n", "Breadcrumb\n", "Blockfeed\n", "Eko\n", "Jeeran\n", "Duo\n", "Bigcolors\n", "MoneyLion\n", "Svpply\n", "EyeIn\n", "Fundly\n", "BentoBox\n", "Flite\n", "Totsy\n", "Springer\n", "FiveStars\n", "CEM\n", "SMASHD\n", "TalkBin\n", "Localytics\n", "Retale\n", "Garmin\n", "Kifi\n", "TapCommerce\n", "Tastebud\n", "Dysonics\n", "Jinn\n", "Cliptamatic\n", "Greta\n", "MinHash\n", "Incase\n", "TerrAvion\n", "Stuart\n", "Idea+me\n", "Edelman\n", "Panono\n", "LightFreq\n", "ProtonMail\n", "Parse\n", "Sharetribe\n", "Watchsend\n", "Kash\n", "AltspaceVR\n", "Zidisha\n", "Zoom\n", "Metrekare\n", "Tribesports\n", "Genee\n", "Glympse\n", "ZeroCater\n", "HTC\n", "Shoot\n", "Lex\n", "Wickr\n", "Neemware\n", "Mobcrush\n", "OnePlus\n", "Minube\n", "Wochit\n", "Hooray\n", "Boden\n", "Location\n", "SoSocio\n", "Boombox\n", "DataXu\n", "Likemind\n", "VINA\n", "Nvidia\n", "SocialCode\n", "Broadcom\n", "Trademob\n", "Fuelling\n", "MTN\n", "Issuu\n", "Infobip\n", "Amplfy\n", "Foodmento\n", "Lenka\n", "LoungeUp\n", "Bell\n", "Fluance\n", "Boston\n", "SkinVision\n", "Evie\n", "Unilever\n", "DreamCheaper\n", "Jybe\n", "Phonotonic\n", "Lifesum\n", "TestObject\n", "Moneytree\n", "DogVacay\n", "Contently\n", "Supersonic\n", "OpenStreetMap\n", "Niice\n", "Amino\n", "Savings+com\n", "Wallace\n", "Wrapp\n", "Proud\n", "Tuurnt\n", "Seclore\n", "Wiivv\n", "Voyage\n", "FishBrain\n", "Luvocracy\n", "Moonfruit\n", "Plane\n", "Leadbolt\n", "Weekdone\n", "Tedemis\n", "Drizly\n", "AdStage\n", "Privacy\n", "Delhivery\n", "ChatWork\n", "Microsystems\n", "Getable\n", "OpenRent\n", "Glopho\n", "HelloMD\n", "ShipBob\n", "Manpacks\n", "Kareo\n", "RoboEarth\n", "TCV\n", "RSA\n", "Fan\n", "NeuCoin\n", "Starbucks\n", "Auth0\n", "Buffalo\n", "Converser\n", "mokono\n", "PrePlay\n", "SnapUp\n", "MarketMeSuite\n", "Nurph\n", "JUCE\n", "Bowflex\n", "Capptain\n", "Reactor\n", "SimplePrints\n", "Stripe\n", "Prenetics\n", "Zattikka\n", "GetJar\n", "VirtuOz\n", "Snowden\n", "reddit\n", "MLS\n", "LaunchRock\n", "Rivigo\n", "Intercom\n", "AngelPad\n", "Printoo\n", "SumUp\n", "GiftRocket\n", "Siminars\n", "Pinpuff\n", "uBeam\n", "Reachli\n", "Meadow\n", "Sailo\n", "Skift\n", "Provender\n", "Eventifier\n", "PicMix\n", "Greyloft\n", "CorFire\n", "BTC\n", "Chronos\n", "Indochino\n", "timeshel\n", "SoPost\n", "Rally+org\n", "Restaurant-Kritik\n", "Ghost\n", "TiVo\n", "Aclima\n", "NeoGAF\n", "MetricsHub\n", "Streetline\n", "Clipless\n", "TheIceBreak\n", "DailyDeal\n", "ThinkGrid\n", "Runscope\n", "Weathermob\n", "Spangle\n", "BERG\n", "Wantster\n", "Shellhammer\n", "Thinkfuse\n", "StellaService\n", "Jetpac\n", "Buyhandpicked\n", "Jibo\n", "Pixloo\n", "Fleck\n", "ThreatMetrix\n", "Topguest\n", "Roamz\n", "MobileIron\n", "WatchMouse\n", "NSA\n", "LockerDome\n", "MST\n", "MTT\n", "Hostmaker\n", "TrustEgg\n", "Audioair\n", "CellScope\n", "Tello\n", "Neoji\n", "Stamplia\n", "Wikipad\n", "Impossible\n", "Diagnosia\n", "Reiss\n", "Wanderfly\n", "Guides+co\n", "Schrader\n", "Tripod\n", "Loco2\n", "Xeneta\n", "Crashlytics\n", "Emerson\n", "Radionomy\n", "PubNub\n", "Smarkets\n", "Tidemark\n", "Lip\n", "HMD\n", "Esper\n", "EPO\n", "Stubb\n", "Nearpod\n", "BizSlate\n", "Grotech\n", "Scarosso\n", "Talk+co\n", "Eurogamer\n", "Punchbowl\n", "Pingboard\n", "Carvana\n", "Tinitell\n", "Logitech\n", "Qwerky\n", "RCS\n", "Engadget\n", "Luminoso\n", "PathSource\n", "Blinkist\n", "Babbel\n", "Catbird\n", "Stacks\n", "SuperAwesome\n", "Memoir\n", "Supdate\n", "Kingsoft\n", "SeeWhy\n", "Swipp\n", "Move\n", "Jomi\n", "Billboard\n", "GiveMeTap\n", "Anagog\n", "Codacy\n", "JFrog\n", "Tampax\n", "Dropcam\n", "Shinola\n", "EduKart\n", "Causes\n", "Repost\n", "Carousell\n", "Actifio\n", "Allure\n", "StyleSeat\n", "Talk\n", "Kershaw\n", "CoTweet\n", "Hunt\n", "Talent\n", "Article\n", "Do\n", "Datahug\n", "Gravitant\n", "Material\n", "dot429\n", "MercadoLibre\n", "Alignable\n", "MemSQL\n", "mFoundry\n", "Syncplicity\n", "Refresh\n", "Wealthfront\n", "Everpurse\n", "ClearDATA\n", "Swisscom\n", "BlockBeacon\n", "CloudPress\n", "Gaga\n", "TeePublic\n", "Mutualink\n", "Avaamo\n", "Anki\n", "Strayboots\n", "CellSavers\n", "Windward\n", "ODI\n", "Saya\n", "Brewster\n", "Mahalo\n", "Xyo\n", "CloudOn\n", "Spayce\n", "Rose\n", "MobiKwik\n", "SketchUp\n", "ThoughtSTEM\n", "Quinn\n", "Fantastic\n", "Fliplet\n", "SnapEDA\n", "Narrow\n", "Tenor\n", "Network\n", "MyFitnessPal\n", "YourSports\n", "Boundary\n", "Zen\n", "AirPR\n", "Radian6\n", "carpooling+com\n", "Yelp\n", "Sr+Pago\n", "Mintigo\n", "MyLife\n", "Spitz\n", "Napster\n", "RadPad\n", "Catcher\n", "Via\n", "Results\n", "HashTip\n", "Sunstone\n", "Nerve\n", "Gigwalk\n", "YP\n", "Wriggle\n", "Livescribe\n", "Gnip\n", "Inbox\n", "Fiz\n", "Maxim\n", "ClarityRay\n", "Gates\n", "Chegg\n", "Kargo\n", "Mirakl\n", "OneTouch\n", "Saida\n", "nodes\n", "StarOfService\n", "ServiceMesh\n", "Dell\n", "Teradata\n", "Openfund\n", "Gliffy\n", "Meddik\n", "Heek\n", "Pleek\n", "Chartbeat\n", "Spotivate\n", "Loyalize\n", "Creator\n", "getTalent\n", "Meetings+io\n", "Tapsule\n", "Instacart\n", "Foursquare\n", "MyJobCompany\n", "Acano\n", "Trackin\n", "Spotfund\n", "Serverless\n", "Actility\n", "Couple\n", "Wrap\n", "Talkspace\n", "Taptu\n", "FinLeap\n", "Endeca\n", "Ecoisme\n", "Publons\n", "SmoovUp\n", "DATA\n", "AHHHA\n", "Viralheat\n", "Humanoid\n", "RedLaser\n", "Runkeeper\n", "KweekWeek\n", "Monaeo\n", "AllPeers\n", "Felix\n", "Q4\n", "Zentyal\n", "MobileSpan\n", "Slush\n", "Trigger\n", "ShuttleCloud\n", "Fireside\n", "Hoover\n", "Pick1\n", "Goldee\n", "Hilton\n", "Fuel\n", "SocialGuide\n", "Registry\n", "IBM\n", "Archive\n", "Picturesqe\n", "VTech\n", "Vedantu\n", "Care+com\n", "Friendster\n", "Vitrue\n", "PreApps\n", "Scriptlance\n", "Bsecure\n", "Buyou\n", "Trippy\n", "Soldsie\n", "Omniture\n", "AMD\n", "GeoOrbital\n", "FanBread\n", "Bugcrowd\n", "Togethera\n", "Lingua+ly\n", "Phonio\n", "Meldium\n", "Tapastreet\n", "MightyText\n", "Audi\n", "Tint\n", "LeadLedger\n", "Infochimps\n", "Fuzel\n", "Omaze\n", "ReservationHop\n", "Helium\n", "EverySignal\n", "Runa\n", "TrekkSoft\n", "AgLocal\n", "Lootsie\n", "Giphy\n", "Ebates\n", "Metabiota\n", "Medallia\n", "CareerFoundry\n", "Gan\n", "iZotope\n", "Vigilent\n", "Innoz\n", "Tynt\n", "Vortex\n", "LeadSift\n", "Fortumo\n", "AetherPal\n", "WaterO\n", "Teckler\n", "YesVideo\n", "Angle\n", "Vadio\n", "Wiper\n", "Cinemagram\n", "Zenamins\n", "Karmaloop\n", "Earshot\n", "Entitle\n", "Newsweek\n", "WeShould\n", "Bringg\n", "NexTravel\n", "Benchling\n", "Say\n", "IDC\n", "Circa\n", "DSC\n", "what3words\n", "storefront\n", "barcode\n", "Learnist\n", "Pubslush\n", "Vuclip\n", "NetBeez\n", "Booshaka\n", "Zoosk\n", "App+net\n", "iFixit\n", "Ogone\n", "Trax\n", "MikMak\n", "ShoeDazzle\n", "Bannerman\n", "Ravti\n", "Placecast\n", "Picker\n", "Burberry\n", "Vision\n", "DipJar\n", "Tule\n", "NowFloats\n", "Prakash\n", "TiE\n", "Birchbox\n", "Woot\n", "Zendesk\n", "MudWatt\n", "TransferGo\n", "Teespring\n", "Ludei\n", "Freshdesk\n", "Testing\n", "Birst\n", "DesignCrowd\n", "Havenly\n", "TopCoder\n", "Dhingana\n", "MindMixer\n", "Pyne\n", "Jason\n", "Torbit\n", "Gigaom\n", "Klout\n", "CyActive\n", "Omniata\n", "SplashPost\n", "LibraTax\n", "Wiselike\n", "Snupps\n", "Rapchat\n", "WrkRiot\n", "Threadflip\n", "Zapier\n", "fanatix\n", "Yoke\n", "Klook\n", "Doxie\n", "StrongLoop\n", "Hippflow\n", "MovieGlu\n", "Gogoprint\n", "Draft\n", "Vinli\n", "BMW\n", "mParticle\n", "Michigan\n", "MVP\n", "GroupMe\n", "Tune\n", "Noom\n", "Yumist\n", "Legit\n", "TransferWise\n", "WudStay\n", "StopTheHacker\n", "Housing+com\n", "Cut\n", "Verdict\n", "Codie\n", "Valeo\n", "Sponsify\n", "Placester\n", "Christensen\n", "Camu\n", "SCC\n", "Appistry\n", "Health2Sync\n", "WordPress\n", "AltaVista\n", "Jiffy\n", "Reedsy\n", "OrlyAtomics\n", "Simplr\n", "Vivaldi\n", "EQ\n", "Code42\n", "Cooliris\n", "Prete\n", "Tynker\n", "Smarterer\n", "Buster\n", "James\n", "Zofari\n", "AppHarbor\n", "RadioShack\n", "Assistant\n", "Stingray\n", "Medium\n", "Vizify\n", "HSN\n", "ETA\n", "Ribbit\n", "Quantum\n", "Cassandra\n", "Paylib\n", "ZipList\n", "Repixl\n", "Jet\n", "Lish\n", "Ticketmaster\n", "VitalFields\n", "Discovery\n", "SixDoors\n", "MoID\n", "SecondMarket\n", "Placed\n", "Pinvolve\n", "DailyBooth\n", "Premier\n", "StyleTread\n", "Hublished\n", "Evertoon\n", "Macbeth\n", "HyprMX\n", "Stocksy\n", "Qwiki\n", "MapMyFitness\n", "Drync\n", "Flyer\n", "Launch48\n", "Zimbra\n", "Facebook\n", "Divine\n", "Wanova\n", "Divide\n", "Highway1\n", "Sygic\n", "Myntra\n", "Rebelle\n", "AIM\n", "FwdHealth\n", "LaMetric\n", "Neverware\n", "Tour\n", "Meetupcall\n", "GuideSpark\n", "Novauris\n", "marGenius\n", "MobileWorks\n", "Parenthoods\n", "Traxpay\n", "Datapipe\n", "Mobee\n", "Curiator\n", "GiveMeSport\n", "Vinci\n", "Hertz\n", "Flynn\n", "VoodooPC\n", "Spring+me\n", "Vite\n", "Appia\n", "Goalbook\n", "Narrato\n", "Hulbee\n", "Booker\n", "Ember\n", "Alibaba\n", "WorldDesk\n", "Open-Xchange\n", "Bitly\n", "hike\n", "Livspace\n", "BlackBerry\n", "BeyondCore\n", "Revl\n", "Monzo\n", "Qwilr\n", "Cloaq\n", "Dice\n", "Blogger\n", "Rakuten\n", "Sikka\n", "Claranet\n", "BlueKite\n", "ChatGrape\n", "Helpshift\n", "FightMe\n", "Organizer\n", "Coin\n", "WeatherPlanner\n", "Techlist\n", "Existor\n", "Tap\n", "Seiko\n", "Tribeca\n", "Tracks+by\n", "GreyOrange\n", "Snapverse\n", "YouNow\n", "Foster\n", "Evaneos\n", "Jibbigo\n", "Woobox\n", "Esquire\n", "Permira\n", "Geoloqi\n", "Hear\n", "UberConference\n", "Publishizer\n", "Serco\n", "Amen\n", "Snaptee\n", "Hukkster\n", "iZettle\n", "Unface+me\n", "TaxiMonger\n", "CoolaData\n", "Omega\n", "Fitocracy\n", "Azendoo\n", "Cookening\n", "Brave\n", "Blab\n", "Schoology\n", "Aggregift\n", "AppArchitect\n", "Fuhu\n", "Picnik\n", "YieldKit\n", "Commons\n", "Blurtt\n", "busuu\n", "KitSplit\n", "PepperTap\n", "Fondu\n", "Rad\n", "Square\n", "Wisemetrics\n", "Schneider\n", "Buongiorno\n", "SimpleTuition\n", "Storm8\n", "BrightFunnel\n", "SponsorHub\n", "Citysearch\n", "Emily\n", "Stratumn\n", "Memorability\n", "SITA\n", "Robotiky\n", "Kili\n", "Piece\n", "Ive\n", "Bonobos\n", "YouTube\n", "Weplay\n", "retailer\n", "Comparably\n", "Moodle\n", "Kngine\n", "Klarna\n", "Earbits\n", "Cuvva\n", "LetsTransport\n", "Curalate\n", "Eyeota\n", "HotPads\n", "AlertMe\n", "Rypple\n", "AlgoTrim\n", "Rearden\n", "Codenvy\n", "Cloud+com\n", "Web2go\n", "CertiVox\n", "FlowVella\n", "LIV\n", "XO\n", "Viki\n", "Dormi\n", "Favor\n", "Beintoo\n", "Databox\n", "Superb\n", "Hem\n", "Dejamor\n", "Bloc\n", "Watsi\n", "Gibbon\n", "Hotmail\n", "Sears\n", "Magic\n", "Datalogix\n", "TourCommand\n", "Onfido\n", "Fitternity\n", "Science\n", "Kismet\n", "Messenger\n", "Hitpost\n", "Harp\n", "Bawte\n", "EE\n", "NewsCred\n", "Jumpstarter\n", "Line-Up\n", "Ongage\n", "Pursway\n", "Safello\n", "Honeypot\n", "Edmodo\n", "Cooper\n", "Konekt+me\n", "Ticketfly\n", "Slingo\n", "Youku\n", "PCalc\n", "Reader\n", "AppRedeem\n", "Osper\n", "Pager\n", "LINK\n", "Crocodoc\n", "Bubbli\n", "Akeneo\n", "Greenfield\n", "Hasselblad\n", "AdEspresso\n", "Magna\n", "CloudGOO\n", "StormTag\n", "Puzzlephone\n", "Swagbucks\n", "Gradle\n", "Pixboom\n", "Waterworks\n", "Convercent\n", "StyleSeek\n", "SalesGossip\n", "Scholly\n", "Opternative\n", "Inside\n", "freee\n", "ICO\n", "Twitmusic\n", "Rocketrip\n", "LearnKo\n", "Quest\n", "Webflow\n", "Happify\n", "Beast\n", "Slick\n", "Playdemic\n", "inploi\n", "Bitbucket\n", "Formlabs\n", "ClassPager\n", "Paddle8\n", "Grovo\n", "ADTRAN\n", "WeFinance\n", "Cinematique\n", "Galxyz\n", "Voonik\n", "DMCA\n", "Getaround\n", "Newvem\n", "Beddit\n", "Redbox\n", "Monroe\n", "Friday\n", "Monoidics\n", "Hull\n", "Kno\n", "Lasso\n", "Gengo\n", "Cruz\n", "Opening\n", "Emergent\n", "Ele+me\n", "Maluuba\n", "Woisio\n", "Netlify\n", "ADstruc\n", "Milkster\n", "Kony\n", "Boxcar\n", "SparkLabs\n", "Riley\n", "Hipster\n", "VaporChat\n", "Scotiabank\n", "Amobee\n", "Comment\n", "YouTurn\n", "AirPair\n", "Crunchyroll\n", "AOL\n", "UPC\n", "Koozoo\n", "Mixlr\n", "Appreciate\n", "KnowMe\n", "Codeanywhere\n", "Hydroswarm\n", "Origami\n", "SubPac\n", "PayLane\n", "Wiggio\n", "TCG\n", "MindJolt\n", "Appirio\n", "Quartet\n", "Bitstamp\n", "CloudPhysics\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PunchTab\n", "NHS\n", "Cyanogen\n", "ShipStation\n", "Credible\n", "Proto+io\n", "keychain\n", "Modbot\n", "Meetup\n", "Donde\n", "CodinGame\n", "Mobilewalla\n", "Thumbtack\n", "Battlefield\n", "Sqreen\n", "Taplister\n", "tenXer\n", "Tapit\n", "Opbeat\n", "Woollip\n", "TouchTen\n", "LiftDNA\n", "PumpUp\n", "Ahead\n", "Her\n", "Tracxn\n", "PlayHaven\n", "JobVidi\n", "Maker\n", "NewsWhip\n", "Collaaj\n", "TweetDeck\n", "Collect\n", "Pivotal\n", "Saber\n", "Decide\n", "BitPay\n", "Toolbox+com\n", "Industrie\n", "Workshop\n", "LetsWombat\n", "Winamp\n", "Mantis\n", "King+com\n", "Tapvalue\n", "Blackboard\n", "Routific\n", "Frrole\n", "Accounts\n", "Cenzic\n", "HeyLets\n", "Moasis\n", "GoGoGrandparent\n", "Amazon\n", "Infinite+ly\n", "Looksee\n", "Neura\n", "Livongo\n", "ReadyForZero\n", "Samson\n", "Speedo\n", "Audiodraft\n", "Elephanti\n", "Proxy\n", "Luminox\n", "Teal\n", "MixBit\n", "Jajah\n", "Hungryroot\n", "Retrofit\n", "Conversation\n", "Energy\n", "Guys\n", "Haier\n", "FarmLogs\n", "FwdForce\n", "Thanx\n", "JackThreads\n", "DJI\n", "Doorman\n", "Tagwhat\n", "Komprise\n", "Mouawad\n", "Bankons\n", "eReader\n", "Blue\n", "Dish+fm\n", "Kippt\n", "Betfair\n", "Tykoon\n", "Redkite\n", "Discourse\n", "OhMiBod\n", "Gratafy\n", "Roomer\n", "GREE\n", "Get+com\n", "EyeTrackShop\n", "Jobvite\n", "Simplee\n", "Vidme\n", "Memories\n", "TetraScience\n", "Pastebin\n", "JustGiving\n", "Virool\n", "MobStac\n", "DBX\n", "Dashlane\n", "Workspot\n", "LogDog\n", "Infomedia\n", "DataCamp\n", "Perpetu\n", "Locu\n", "Zipments\n", "Dónde\n", "Marblar\n", "Darktrace\n", "Undrip\n", "Inneractive\n", "Everyme\n", "Hamer\n", "Myspace\n", "Atomwise\n", "BlockAvenue\n", "Informatica\n", "Flashgap\n", "GetReal\n", "HomeAway\n", "Taplytics\n", "Naytev\n", "Cloud4Wi\n", "JD+com\n", "Stay+com\n", "ReadWrite\n", "Hibob\n", "BrightContext\n", "Hitwise\n", "Jongla\n", "AppCampus\n", "Joomla\n", "Fastacash\n", "OpenSesame\n", "Twistory\n", "DKSH\n", "VetCloud\n", "Spruceling\n", "Sysomos\n", "Hitlist\n", "Openbucks\n", "Personal\n", "Entelo\n", "Loki\n", "Frame+io\n", "Swatch\n", "Headspace\n", "Screen\n", "BYLINED\n", "Scoop+it\n", "Cardify\n", "Raydiance\n", "Joyent\n", "DigiSynd\n", "Fon\n", "blinkbox\n", "Sproutkin\n", "Smart\n", "Work4\n", "DocuSign\n", "Tracks\n", "Zone\n", "Looklive\n", "Cobone\n", "Mango\n", "Conversocial\n", "Andy\n", "Whirlpool\n", "Darjeelin\n", "Organic\n", "Innov8\n", "Endor\n", "Politwoops\n", "True\n", "TRVL\n", "Nodester\n", "Alive\n", "Rossum\n", "Queue\n", "Rodeo\n", "Hoppit\n", "Youzee\n", "eHarmony\n", "PAM\n", "Junyo\n", "Nymi\n", "Swyft\n", "Adzerk\n", "Plukka\n", "Instapaper\n", "Sansan\n", "FI-WARE\n", "Degreed\n", "TabTale\n", "Roomblocker\n", "Fame\n", "MediaGlu\n", "AppAdvice\n", "OMGPOP\n", "Scientia\n", "Coull\n", "Blendr\n", "ActivePath\n", "Visually\n", "OutStart\n", "DGSE\n", "Jostle\n", "Icertis\n", "Fluent\n", "Concierge\n", "Forget+me\n", "Corner\n", "Mozbii\n", "LSTN\n", "Showpad\n", "RewardLoop\n", "Jobandtalent\n", "Homescreen\n", "MyEdu\n", "BaubleBar\n", "Avancar\n", "Pond5\n", "Tate\n", "Plated\n", "Nest\n", "VidMob\n", "Lyst\n", "Zenoti\n", "Zazzy\n", "Dudek\n", "Cleversafe\n", "MakersKit\n", "Grabio\n", "Canalys\n", "Werkly\n", "Summer\n", "Sush+io\n", "Fetchnotes\n", "PSG\n", "Smith\n", "Demotix\n", "RelateIQ\n", "Fossil\n", "AppSumo\n", "Convies\n", "Tapjoy\n", "SocialTwist\n", "Tripl\n", "Sainsbury’s\n", "Encription\n", "Civo\n", "Magine\n", "StreetEasy\n", "Marsh\n", "Opower\n", "Wantering\n", "Redfin\n", "SEB\n", "HD+\n", "InMobi\n", "fromAtoB\n", "AppStack\n", "News\n", "Beepl\n", "EasyPost\n", "Pause\n", "Start-Up\n", "Techstars\n", "Lineshark\n", "Appysnap\n", "Oatmeal\n", "SkillPages\n", "Ayasdi\n", "Breezeworks\n", "FileThis\n", "Taps\n", "SRCH2\n", "Cortica\n", "Betaworks\n", "VerticalResponse\n", "Moshi\n", "AdYapper\n", "LinkedIn\n", "Tivoli\n", "Reeder\n", "iStopOver\n", "HackFwd\n", "Columbus\n", "Dubsmash\n", "ClearTax\n", "PowaTag\n", "Umeng\n", "Modus\n", "Playlab\n", "Locent\n", "Fly6\n", "Taykey\n", "Quettra\n", "Classroom\n", "Locqus\n", "JibJab\n", "Appknox\n", "Tinyclues\n", "Datanyze\n", "Atheer\n", "Inktank\n", "DXY\n", "PostGhost\n", "IPTV\n", "Minilogs\n", "DynamicOps\n", "Katana\n", "Highlight\n", "Overhead\n", "NSL\n", "Thomson\n", "Go-Jek\n", "Festicket\n", "Night\n", "Zola\n", "Softonic\n", "Rebtel\n", "Outlier\n", "LeadFormix\n", "Flirtey\n", "Carwow\n", "Mailjet\n", "Boop\n", "RedKix\n", "InsideView\n", "TextMe\n", "StraighterLine\n", "PersistIQ\n", "Punchcard\n", "Flud\n", "Freebase\n", "Pinion\n", "Adcade\n", "Whyd\n", "Springpad\n", "Lucky\n", "Sidestep\n", "Litsy\n", "Care24\n", "Uppidy\n", "Crossbar\n", "Dale\n", "Holmes\n", "Rhombus\n", "NYU\n", "Yahoo\n", "UX\n", "Mach\n", "Artiphon\n", "Versa\n", "PlaySay\n", "Geckoboard\n", "Crashpadder\n", "Clickable\n", "Summa\n", "MakeSpace\n", "Aging2+0\n", "BrightBytes\n", "Booktrack\n", "Skymind\n", "Augmedix\n", "Veriflow\n", "Heroku\n", "Rentlord\n", "Dinube\n", "Needle\n", "LocalUncle\n", "Anker\n", "Mobcast\n", "Balanced\n", "Hipmunk\n", "Adidas\n", "Zabosu\n", "Hartmann\n", "T-Mobile\n", "Angel+ai\n", "Workfront\n", "ImpressPages\n", "Gett\n", "Tempdrop\n", "Cabify\n", "Pieceable\n", "mon+ki\n", "Sign2Pay\n", "NewHive\n", "HelloSociety\n", "Whatser\n", "Libratone\n", "FinancialForce\n", "SmartSync\n", "Aviv\n", "GSF\n", "MindRDR\n", "QOOQ\n", "Avis\n", "ice\n", "Freshly\n", "ResolutionTube\n", "BillPay\n", "Lee\n", "Gartner\n", "FiveAI\n", "Showcase\n", "iStoryTime\n", "Urbane\n", "BetterDoctor\n", "CourseTalk\n", "SupaPass\n", "viaCycle\n", "Everpix\n", "Predix\n", "MyOptique\n", "Knotch\n", "Runfaces\n", "SendGrid\n", "Paracosm\n", "Charterhouse\n", "Fastbite\n", "Hortonworks\n", "ManageFlitter\n", "Ruby\n", "Pipedrive\n", "Git\n", "Kinvey\n", "Baby+com+br\n", "CrowdHut\n", "Collins\n", "Profig\n", "LivePerson\n", "Claire\n", "Kinsa\n", "Voxsup\n", "Apportable\n", "Archimedia\n", "Weathernews\n", "Expansys\n", "LiveIntent\n", "Jumptap\n", "Unsilo\n", "Pingdom\n", "OpenTable\n", "Zimperium\n", "Rocana\n", "Volley\n", "LifeTip\n", "Hurley\n", "ZipMatch\n", "Jukely\n", "Aktana\n", "Phosphorus\n", "InstaVet\n", "McMillan\n", "Umoove\n", "Archer\n", "EPC\n", "Netokracija\n", "Efrat\n", "Engrade\n", "Mobile\n", "Appsfire\n", "Cameron\n", "Magzter\n", "LaunchKit\n", "Tonic\n", "Saffronart\n", "Pundit\n", "Platfora\n", "Hyperdrive\n", "Zscaler\n", "providers\n", "PhilterIt\n", "Ringly\n", "DARPA\n", "ZipRecruiter\n", "SocialRent\n", "Aidin\n", "reKiosk\n", "Naritiv\n", "Hiptype\n", "Written\n", "Kenexa\n", "Context\n", "GoEuro\n", "Nextdoor\n", "Chapman\n", "Buffer\n", "CardMunch\n", "Achievers\n", "RocketBank\n", "Ali\n", "Carro\n", "Circles\n", "Huckle\n", "Bookindy\n", "Plivo\n", "Ifeelgoods\n", "Booyah\n", "Jumia\n", "Kiip\n", "Ranker\n", "ShoorK\n", "Aviate\n", "video\n", "Vibe\n", "Delighted\n", "Hawkins\n", "Echo360\n", "Postable\n", "Curtis\n", "Dobango\n", "Markforged\n", "Cubby\n", "Skillbridge\n", "LoveList\n", "TrackDuck\n", "Startups\n", "Twitpic\n", "SoundCloud\n", "III\n", "Xetum\n", "RackWare\n", "Zillow\n", "EyeEm\n", "Applifier\n", "GOQii\n", "Wibbitz\n", "Prowl\n", "Vringo\n", "Flattr\n", "Splurgy\n", "SocialRadar\n", "Zipongo\n", "vArmour\n", "Player+me\n", "Edwin\n", "LIFX\n", "Dropbox\n", "Bubbly\n", "Sojern\n", "Ionic\n", "Reamaze\n", "OnLive\n", "Diaspora\n", "Sutro\n", "Snyk\n", "Braintree\n", "Greenstart\n", "Chomp\n", "TokBox\n", "Blloon\n", "Greatist\n", "Aerospace\n", "VigLink\n", "WishPop\n", "Evomail\n", "Arduino\n", "OpenShift\n", "AB\n", "Viva\n", "PillPack\n", "Rate\n", "Hykoo\n", "Prevent\n", "Opinion\n", "Chartburst\n", "PastBook\n", "Burn\n", "NPR\n", "HappyFresh\n", "HelloFax\n", "Cowbird\n", "Surf\n", "Peerby\n", "Frederick\n", "OrderWithMe\n", "TinyTap\n", "Gameloft\n", "Naylor\n", "Krawd\n", "Phonedeck\n", "Yetang\n", "Lark\n", "DataFox\n", "MyStream\n", "myWebRoom\n", "GuestDriven\n", "Loot\n", "Stupeflix\n", "Glossier\n", "Vandrico\n", "Pro+com\n", "Welltok\n", "Guardian\n", "Hushed\n", "Management\n", "Fanwards\n", "News360\n", "BrandMyMail\n", "BetterLesson\n", "Songdrop\n", "Alphabet\n", "WAYN\n", "Suning\n", "WalletKit\n", "atVenu\n", "EIS\n", "Jolla\n", "Incubate\n", "Libsyn\n", "colourDNA\n", "JumpCloud\n", "Seedcamp\n", "SAM\n", "Insyde\n", "Blup\n", "LaunchBit\n", "Graham\n", "RedMart\n", "HelloFresh\n", "Rebel\n", "RegistryLove\n", "Bizible\n", "Leslie’s\n", "Paper+li\n", "Goodreads\n", "OpenSky\n", "Weaveworks\n", "Venturocket\n", "Zipcar\n", "NDS\n", "Redpoint\n", "Victor\n", "Macro\n", "AirWatch\n", "ArtCling\n", "Transit\n", "PowerPoint\n", "Diffbot\n", "Niche\n", "Postgres\n", "Net1\n", "Lookback\n", "Medine\n", "LiveOps\n", "AppSense\n", "Pixeom\n", "DormChat\n", "Minuum\n", "Munch\n", "Lightricks\n", "Bizdaq\n", "Antidate\n", "Mileways\n", "Notey\n", "doctHERs\n", "Fair\n", "ShopPad\n", "Darkstore\n", "Dillon\n", "TrialPay\n", "Loon\n", "Defender\n", "M4JAM\n", "ClassDojo\n", "Million\n", "Advise+me\n", "Redbeacon\n", "AdMobius\n", "XING\n", "HigherMe\n", "SinglePlatform\n", "Raspberry\n", "Meexo\n", "Barobot\n", "Middle\n", "Mogreet\n", "Mills\n", "Frugalo\n", "Wrapify\n", "Arbor\n", "kununu\n", "Morphlabs\n", "Goldbely\n", "StartWire\n", "Snaptu\n", "Educreations\n", "Udemy\n", "MIKA\n", "Forever\n", "Bantr\n", "CMS\n", "WedPics\n", "RebelMouse\n", "CBC\n", "Comunitee\n", "CNNIC\n", "Addventure\n", "Home\n", "StackEngine\n", "Mt+Gox\n", "NewsiT\n", "Outerwall\n", "Plain\n", "Checkmarx\n", "BigBasket\n", "Medypal\n", "Dickson\n", "Scenery\n", "Appcore\n", "Refresh+io\n", "Wallet\n", "Trapit\n", "Jobspotting\n", "PinClout\n", "Calendo\n", "Ticketbis\n", "LOVEFiLM\n", "FunnyJunk\n", "Glocal\n", "Launcher\n", "York\n", "Digia\n", "Carpenter\n", "Creek\n", "Upstart\n", "FlyCleaners\n", "CWT\n", "Listia\n", "Sweep\n", "Chatterfly\n", "Haiku\n", "Zedge\n", "Expand\n", "SocMetrics\n", "Orami\n", "Alerts\n", "Satago\n", "Winnie\n", "Coffee\n", "Swapt\n", "Alchemy\n", "Voxer\n", "Freespee\n", "Mixbook\n", "My-Apps\n", "Let\n", "BlinkMail\n", "Kitestring\n", "SmartNews\n", "Gigwell\n", "W3i\n", "Tipflare\n", "Triptrotting\n", "MyMusicTaste\n", "MetaLab\n", "Voxy\n", "Breitling\n", "Iotera\n", "Topics\n", "Yext\n", "Ideum\n", "Jirnexu\n", "Implisit\n", "Studios\n", "Bitbar\n", "Orbit\n", "Holvi\n", "CoCoon\n", "Qloo\n", "Temnos\n", "Last+fm\n", "Walgreens\n", "Circl\n", "Smaato\n", "Storycode\n", "Zebra\n", "Clarizen\n", "Readyforce\n", "Emoji\n", "MommyCoach\n", "Froont\n", "PledgeCents\n", "Chatterbox\n", "Phoenix\n", "adBrite\n", "Citelighter\n", "Encore\n", "Cotendo\n", "Pokémon\n", "GamesThatGive\n", "SmartRecruiters\n", "URX\n", "RefreshBox\n", "Gojee\n", "Seriously\n", "Vacatia\n", "Fjuul\n", "DFKI\n", "Cushman\n", "ExactTarget\n", "Couchbase\n", "Knocki\n", "PrecisionHawk\n", "Checkbook\n", "Suiteness\n", "Cronofy\n", "Mobilisafe\n", "Wisely\n", "Spoon\n", "Verifly\n", "Emberlight\n", "Adaptive\n", "Mozy\n", "WebMD\n", "Aircall\n", "Credits\n", "Tutanota\n", "Glimpse\n", "AddThis\n", "Lucent\n", "SocialFlow\n", "Flexport\n", "Orchestra\n", "BetterWorks\n", "BeLuvv\n", "Mindie\n", "Octoly\n", "Cinegif\n", "Merch\n", "Insta\n", "Method\n", "Bloglovin\n", "Spoqa\n", "Zurf\n", "Meterfy\n", "Seahorse\n", "Little\n", "Thefuture+fm\n", "Simplify360\n", "SkySpecs\n", "Acquia\n", "Unmute\n", "Avira\n", "Qlika\n", "Truebill\n", "Sunglass\n", "Slinger\n", "BuildDirect\n", "Postman\n", "eSpark\n", "Aframe\n", "Jetsetter\n", "Acronis\n", "Happtique\n", "OneOps\n", "Wix\n", "PrivacyStar\n", "Weibo\n", "OMGICU\n", "Trilogy\n", "CompStak\n", "AKQA\n", "Pamper\n", "Lifeliqe\n", "OrderUp\n", "Fantasy\n", "ShoreTel\n", "Seedrs\n", "Numecent\n", "Julep\n", "Shopular\n", "needs\n", "Thunder\n", "JustReachOut\n", "CareLedger\n", "CircuitLab\n", "GEMA\n", "Alleyoop\n", "Movi\n", "Mattel\n", "Nebula\n", "QVC\n", "F+ounders\n", "Cotap\n", "Startupi\n", "Socialtext\n", "Zamurai\n", "SinDelantal+Mx\n", "WeTransfer\n", "Playfire\n", "iHeartRadio\n", "Evi\n", "PrimeSense\n", "Egnyte\n", "Investing+com\n", "Motus\n", "Satellogic\n", "Tend+ai\n", "Tabber\n", "Express\n", "YouAppi\n", "Veoh\n", "Individual\n", "Cotopaxi\n", "Hart\n", "Hall\n", "Coconut\n", "Selfie\n", "Alicanto\n", "LetsListen\n", "Kstartup\n", "ShareThis\n", "Disconnect\n", "Appy\n", "Imgur\n", "Talkdesk\n", "MVF\n", "InfoScout\n", "Giftivo\n", "Optimus\n", "Cameo\n", "Lightwave\n", "DailyRounds\n", "HealthLoop\n", "DocSend\n", "Lab42\n", "MyColorScreen\n", "WebEx\n", "Founders\n", "Azimo\n", "ATM\n", "Grabyo\n", "Gradifi\n", "RedditGifts\n", "Catch+com\n", "LaFourchette\n", "ZappRx\n", "Frontback\n", "Ubokia\n", "Rent2Buy\n", "Diamanti\n", "CareSkore\n", "Happn\n", "Penultimate\n", "WeatherSphere\n", "BuyWithMe\n", "WeStore\n", "Hubbl\n", "Skype\n", "CircleUp\n", "menu\n", "Blanc\n", "Seerslab\n", "Dealupa\n", "jEugene\n", "FundersClub\n", "Pixelpipe\n", "Marfeel\n", "Cliptone\n", "Brayola\n", "Wendel\n", "Skedaddle\n", "LovelyHeroku\n", "Sphero\n", "Josephine\n", "Moat\n", "Linode\n", "Over\n", "Niall\n", "Wishpond\n", "Patton\n", "JetSmarter\n", "Coinfloor\n", "Bizo\n", "Peyman\n", "KeepTruckin\n", "Fradio\n", "Slingbox\n", "Jellynote\n", "Bloomfire\n", "Keybase\n", "Jabra\n", "Grindr\n", "GameFounders\n", "Fundable\n", "Flint\n", "Leak\n", "V-Key\n", "Zomato\n", "Noise\n", "Cyvera\n", "Fribi\n", "Junar\n", "Freewheel\n", "AIR\n", "ToutApp\n", "Mophie\n", "CMB\n", "Phononic\n", "Baron\n", "Chance\n", "SteelBrick\n", "Fanbase\n", "Hometeam\n", "Learndot\n", "Honk\n", "Zappos\n", "Paytm\n", "Infratel\n", "Plenummedia\n", "Hobzy\n", "Sundar\n", "Wittlebee\n", "MoPub\n", "Textura\n", "Telegraph\n", "Videology\n", "Cakey\n", "AdStack\n", "Twitch\n", "Overtime\n", "Mambu\n", "DWS\n", "Monotype\n", "Mobileye\n", "Bondsy\n", "PiinPoint\n", "BrewDrop\n", "Gucci\n", "Celly\n", "Snaplay\n", "Grasshopper\n", "CauseRocket\n", "LovePalz\n", "NICE\n", "ServisHero\n", "Mirantis\n", "DreamHost\n", "Juengo\n", "Shuttleworth\n", "Poncho\n", "Iconfinder\n", "Tapkast\n", "Webber\n", "Livemap\n", "Ingenico\n", "Zynga\n", "Dakwak\n", "Leaky\n", "Dotcom\n", "Instabug\n", "Strings\n", "PowerReviews\n", "Brainly\n", "OptimisCorp\n", "Log\n", "StartX\n", "Robocoin\n", "goBalto\n", "Statwing\n", "Quotle\n", "QASymphony\n", "NewsON\n", "Connexity\n", "Hiya\n", "Jasper\n", "Shutterstock\n", "LightUp\n", "Inverse\n", "Touch\n", "Driver\n", "NGDATA\n", "Revolut\n", "Inc+\n", "Flipagram\n", "Kytephone\n", "Medigo\n", "Send\n", "HyPer\n", "Marketo\n", "Property\n", "Appnique\n", "Purism\n", "VMware\n", "Stick\n", "SnapLogic\n", "Moovit\n", "SocialRank\n", "MONOQI\n", "Mattermark\n", "Platform\n", "Piccsy\n", "Lima\n", "Trio\n", "Silvercar\n", "Helpling\n", "Mint\n", "Tomfoolery\n", "Favr+tt\n", "GeoPoll\n", "SocialShield\n", "ExpenseMagic\n", "CultureSphere\n", "Cinder\n", "Novartis\n", "Deeyoon\n", "RetailNext\n", "Urbita\n", "Streetlife\n", "BonitaSoft\n", "ViSenze\n", "Flocations\n", "Webroot\n", "Whipclip\n", "Logan\n", "Openet\n", "Nutrino\n", "Richardson\n", "Meltwater\n", "BioBeats\n", "DataTorrent\n", "Wake\n", "Polycom\n", "SpotlessCity\n", "Self\n", "Instagram\n", "DraftKings\n", "Securifi\n", "Schoola\n", "Nubank\n", "EmployInsight\n", "POP\n", "Filip\n", "Experience\n", "LeanData\n", "Ranger\n", "BAC\n", "Fama\n", "SkyHealth\n", "Rdio\n", "Flybridge\n", "Addvocate\n", "VCNC\n", "taste\n", "RenéSim\n", "Contentful\n", "Tung\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MobiTV\n", "Hivemapper\n", "AppFirst\n", "BloomReach\n", "NoshList\n", "JANDI\n", "Luff\n", "seed\n", "AddSearch\n", "SimilarTech\n", "Adzuna\n", "Cloudike\n", "Talend\n", "Unreel+me\n", "Plugaway\n", "Curioos\n", "Aoliday\n", "E3\n", "PagerDuty\n", "Quikr\n", "GraphOn\n", "OpenSignal\n", "FunderCloud\n", "LoopFuse\n", "KashFlow\n", "Frameri\n", "aCommerce\n", "Gigya\n", "Docurated\n", "gumi\n", "Path+To\n", "Bebop\n", "Endorse+me\n", "Lookup\n", "Yamaha\n", "BlazingDB\n", "Telenor\n", "SharesPost\n", "Vertro\n", "Creandum\n", "Whirlscape\n", "Spreaker\n", "REBBL\n", "Fantoo\n", "MiMedia\n", "Freeman\n", "Percolate\n", "Lifecake\n", "Avon\n", "Moodstocks\n", "Kindly\n", "Snackr\n", "Vingle\n", "Azalead\n", "Workday\n", "Fortify\n", "Deem\n", "Shelf\n", "Travis\n", "Rythm\n", "StackMob\n", "Azumio\n", "InstallMonetizer\n", "SwiftGift\n", "Wit+ai\n", "DataStax\n", "imoji\n", "Strap\n", "Levi’s\n", "AmazonFresh\n", "Ketchuppp\n", "Automatic\n", "GiftCards+com\n", "Ventoura\n", "Quandoo\n", "inMarket\n", "Lumus\n", "Voyagin\n", "Nosto\n", "Diveboard\n", "Ting\n", "CodeCombat\n", "Audible\n", "Captora\n", "Qype\n", "Mozio\n", "Wordeo\n", "SprinkleBit\n", "Quikkly\n", "Glancee\n", "JuicyCanvas\n", "Sony\n", "Postwire\n", "eFounders\n", "IRIS\n", "Sociocast\n", "Washio\n", "Gone\n", "Dispop\n", "Cheerboo\n", "Tutorspree\n", "Embrace\n", "Bucket\n", "Timeline\n", "Luminate\n", "BetaNoodle\n", "Forage\n", "Boxer\n", "Lettuce\n", "Soluto\n", "Gravie\n", "Conyac\n", "CoreOS\n", "Autos\n", "Pixel\n", "Influitive\n", "Appcues\n", "Women+com\n", "Mondo\n", "Shopgate\n", "Airpaper\n", "StartApp\n", "MoonClerk\n", "CommonKey\n", "Salesforce\n", "Kik\n", "Dodge\n", "FreedomPop\n", "Authy\n", "NanoRacks\n", "Landlordology\n", "Terascore\n", "Vilynx\n", "Captain401\n", "Vemory\n", "Glovo\n", "TrueAccord\n", "UrbanStems\n", "Backflip\n", "OpenLabel\n", "ITG\n", "inPulse\n", "DoubleDutch\n", "ValoBox\n", "Internet+org\n", "SportsQuest\n", "Synapsify\n", "iFood\n", "Catch\n", "Watchwith\n", "Shoutlet\n", "Wish\n", "Hatchery\n", "Fullscreen\n", "Matternet\n", "Office\n", "Ezeecube\n", "Ossur\n", "Wind\n", "PeerIndex\n", "Digits\n", "Wufoo\n", "Flipp\n", "Criteo\n", "FF\n", "Todacell\n", "Notabli\n", "Docracy\n", "District\n", "Avast\n", "Spacio\n", "Sumpto\n", "Songbird\n", "Contastic\n", "Tradeshift\n", "Salted\n", "VINCI\n", "Clearpath\n", "Vurb\n", "Stylect\n", "PlaySquare\n", "Gambitious\n", "Curse\n", "Shutterfly\n", "Forsythe\n", "Uber\n", "BlueStacks\n", "Tep\n", "Relcy\n", "Subway\n", "Notch\n", "ParcelBright\n", "Auraslate\n", "DEMO\n", "Archives+com\n", "Graduateland\n", "HWTrek\n", "Piazza\n", "Agogo\n", "Citron\n", "ReTargeter\n", "Identified\n", "Dull\n", "Avid\n", "Zoho\n", "Poptip\n", "CIS\n", "Northzone\n", "Zolt\n", "NightOwl\n", "BigTime\n", "Taskhub\n", "HeadBox\n", "Page365\n", "LearnSprout\n", "Mindshapes\n", "Obi\n", "TrueFacet\n", "Works\n", "CredSimple\n", "Abine\n", "Lamudi\n", "ICANN\n", "Preact\n", "REX\n", "Casper\n", "Guardly\n", "Wagon\n", "Greats\n", "GoPro\n", "TapHeaven\n", "Pulselocker\n", "Chromatik\n", "Glasses\n", "Skitch\n", "Lernstift\n", "Teads\n", "Merienda\n", "Tesco\n", "CouchCommerce\n", "CornerJob\n", "EST\n", "Drippler\n", "GoodRx\n", "CruiseWise\n", "Sharalike\n", "Startups+co\n", "LuckyTrip\n", "Kera\n", "Slidejoy\n", "Pacifica\n", "First\n", "Human\n", "Goxip\n", "Jetbay\n", "Podio\n", "Hired\n", "PicsArt\n", "tracx\n", "ItsOn\n", "StorSimple\n", "Eventbrite\n", "Kapost\n", "FlashFunders\n", "BustedTees\n", "Aceable\n", "Soon\n", "MineralTree\n", "Evercontact\n", "VISR\n", "Kwoller\n", "Tagstand\n", "YourBus\n", "Badgeville\n", "Coles\n", "Moto\n", "Deloitte\n", "Face+com\n", "Turo\n", "Polymer\n", "Payvment\n", "BVP\n", "Loops\n", "Mowbly\n", "CashCashPinoy\n", "FX\n", "Vinomofo\n", "Softcard\n", "Venuelabs\n", "Telecom\n", "RJMetrics\n", "Rescale\n", "NEEO\n", "TripleLift\n", "BlueVia\n", "DWNLD\n", "Biba\n", "Gogoro\n", "Atom\n", "Shopline\n", "Groopt\n", "Petnet\n", "HackerRank\n", "Avegant\n", "Vidcode\n", "Alert+Us\n", "Filter+ly\n", "Space\n", "Kobalt\n", "B4RM4N\n", "HBO\n", "DroneBase\n", "ZMP\n", "Alfresco\n", "Staples\n", "SensioLabs\n", "BlindType\n", "Videdressing\n", "Thirst\n", "Telecast\n", "Riot\n", "FortyCloud\n", "Napwell\n", "Singtrix\n", "YCharts\n", "WikiLeaks\n", "Wynd\n", "GOAT\n", "Sketch\n", "Tealium\n", "Sugru\n", "ProFounder\n", "Bigpoint\n", "Evergram\n", "Molotov\n", "AirHelp\n", "Rolex\n", "Eligible\n", "Rust\n", "Cozy\n", "AsthmaMD\n", "BlueTalon\n", "appLOUD\n", "Thingsee\n", "EveryMove\n", "doo\n", "Xero\n", "DefinedCrowd\n", "Esplorio\n", "Equation\n", "Moltin\n", "Webflakes\n", "VisualGraph\n", "Dove\n", "BuzzFeed\n", "Blispay\n", "Famous\n", "inDinero\n", "Cellrox\n", "Underdog\n", "eeGeo\n", "SaneBox\n", "Solid\n", "Songza\n", "Source\n", "Zemanta\n", "DoctorsElite\n", "Occipital\n", "Epoch\n", "Civil\n", "Biomeme\n", "Baidu\n", "Spots\n", "Amiga\n", "Goodservice\n", "Logica\n", "Memeoirs\n", "Onyx\n", "Solocam\n", "Farmigo\n", "Sher+ly\n", "Crowdbooster\n", "Nikkei\n", "SourceNinja\n", "SpareMin\n", "Withings\n", "Happy\n", "HOOQ\n", "Wolf\n", "Sprinklr\n", "Julpan\n", "FWD+us\n", "HelloSign\n", "CreativeLive\n", "BitTorrent\n", "Pozitron\n", "Canvsly\n", "Ola\n", "Eyegroove\n", "M2M\n", "Prize\n", "WANdisco\n", "AcuityAds\n", "TextMaster\n", "Chaatz\n", "UCP\n", "Level\n", "DoorDash\n", "HopSkipDrive\n", "Verelo\n", "LittleThings\n", "BrandAds\n", "Omni3D\n", "Coub\n", "Carriage\n", "Clipmarks\n", "ip+access\n", "Swiggy\n", "Wajam\n", "Amulyte\n", "MobilyTrip\n", "Squawka\n", "Mixmax\n", "FriendFinder\n", "Timeular\n", "Yieldify\n", "Checkr\n", "Parature\n", "Syncapse\n", "Hoodline\n", "Inuvo\n", "Grockit\n", "iSuppli\n", "Feels\n", "CardBlanc\n", "MasteryConnect\n", "ModCloth\n", "TerraTalk\n", "Scott\n", "Sophos\n", "RadioPublic\n", "Knotable\n", "StatMuse\n", "SAY\n", "Wellth\n", "StartUp\n", "JamCam\n", "ShopSavvy\n", "Looxcie\n", "OneGo\n", "Hulu\n", "Nooka\n", "Tweetwall\n", "Yoshi\n", "Offset\n", "Kids\n", "Else\n", "Zaarly\n", "AdQuantic\n", "Bitdefender\n", "Tech+eu\n", "Collection\n", "Pheed\n", "Blavity\n", "Pivot3\n", "Peeks\n", "Hooch\n", "Blooie\n", "SaaS\n", "RogerVoice\n", "Starwood\n", "Zuberance\n", "Vorwerk\n", "Tomcar\n", "Rocketmiles\n", "Careerify\n", "Reagan+com\n", "Enswers\n", "Switchcam\n", "Vanhawks\n", "CloudCar\n", "Discord\n", "Dacuda\n", "Zopper\n", "Applauze\n", "Daimler\n", "BeauCoo\n", "Vettery\n", "CargoX\n", "OMG\n", "Flipkart\n", "Henkel\n", "Idomoo\n", "Aptonomy\n", "Bumble\n", "Friend+ly\n", "Linqia\n", "Mola+com\n", "Blekko\n", "Gorgias\n", "Kabam\n", "Ameyo\n", "Apttus\n", "HSBC\n", "TOTVS\n", "Lollicam\n", "Palm\n", "Motosumo\n", "Tapstack\n", "Snapjoy\n", "Lenstag\n", "Vive\n", "PlanGrid\n", "Cloudability\n", "Magnify\n", "Lumi\n", "Postmaster\n", "JustPark\n", "Pad\n", "Occupy+here\n", "LearnVest\n", "Providence\n", "Erickson\n", "Ruckus\n", "RSVP\n", "JAM\n", "GCHQ\n", "Grapeshot\n", "Trialfire\n", "Digital\n", "Lugg\n", "Wink\n", "TalentSky\n", "Pexip\n", "BugBuster\n", "Fling\n", "GroupLogic\n", "TranServ\n", "Slim\n", "Roomi\n", "Voxel8\n", "Island\n", "Stuffle\n", "Mars\n", "Socialcam\n", "Ubiquisys\n", "Tank\n", "Rolltape\n", "Mohiomap\n", "Roku\n", "Flytrex\n", "ERN\n", "Airtame\n", "Fuel3D\n", "Rubrik\n", "Yobongo\n", "CodeNow\n", "RupeePower\n", "Flowdock\n", "RDS\n", "Identity\n", "Read\n", "ArabNet\n", "Levitas\n", "Fyndiq\n", "Sticky\n", "Everlane\n", "Houzz\n", "Redbooth\n", "Kurio\n", "Kantar\n", "MetaWatch\n", "Tresorit\n", "Griffin\n", "MonkeyParking\n", "SpoonRocket\n", "AlienVault\n", "Quirky\n", "Soundrop\n", "Hipstamatic\n", "ATF\n", "NextSuit\n", "Summify\n", "Spreedly\n", "Standard\n", "CryptoSeal\n", "MessageMe\n", "Wedge\n", "PayDivvy\n", "Livemocha\n", "SurveyMonkey\n", "TapCanvas\n", "Lob\n", "Plizy\n", "Fundera\n", "Clustree\n", "Tapingo\n", "Dataminr\n", "DrinkMate\n", "Shape\n", "Enstratius\n", "Kamino\n", "Restorando\n", "Reco\n", "Freshii\n", "Twiggle\n", "Waze\n", "onefinestay\n", "LesPAC+com\n", "NBA\n", "Awair\n", "DailyLit\n", "Mimi\n", "RevUpNet\n", "Strava\n", "Shotput\n", "S4\n", "ClearFit\n", "Appfuel\n", "Ryzing\n", "Blippar\n", "Toro\n", "Samos\n", "FrameBlast\n", "Google\n", "Talko\n", "Ovatemp\n", "Kin\n", "Lender\n", "Lendix\n", "SalesVu\n", "Tunepics\n", "SundaySky\n", "Ripples\n", "DragonWave\n", "Sandglaz\n", "Life360\n", "Mashable\n", "Knowre\n", "Nifty\n", "Zeplin\n", "CrowdFlik\n", "Everett\n", "Press\n", "Exitround\n", "eGym\n", "Neumann\n", "Bluesmart\n", "AnkerBox\n", "Gravity4\n", "Crosley\n", "IPO\n", "LoopMe\n", "Lambda\n", "Swell\n", "Peekaboo\n", "Me-Mover\n", "Asana\n", "Duetto\n", "Flash\n", "Indix\n", "Argus\n", "Bloom+fm\n", "theAudience\n", "Supercell\n", "Flowtab\n", "Suite\n", "Arro\n", "SmashFly\n", "Synchronica\n", "Zingaya\n", "PayStand\n", "icons\n", "UrbanClap\n", "BeeTV\n", "MMS\n", "Flavorpill\n", "Minefold\n", "Deliv\n", "Insurify\n", "Fancred\n", "Backupify\n", "Cola\n", "Taggar\n", "Thumb\n", "Camera+\n", "Shake\n", "Layer\n", "Verge\n", "H2L\n", "Olark\n", "Magento\n", "Shasta\n", "Aereo\n", "Talkwheel\n", "ScribbleLive\n", "Deeplink\n", "Alaris\n", "XYZprinting\n", "Mozilla\n", "Wercker\n", "BuiltWith\n", "BrandYourself\n", "MoEngage\n", "UpLabs\n", "Withlocals\n", "Paktor\n", "LearnStreet\n", "Workable\n", "Korbit\n", "TripIt\n", "Perfect\n", "Quill\n", "Newsle\n", "Glowforge\n", "Cleveron\n", "Grasswire\n", "Retina\n", "MakeTime\n", "Keek\n", "Lavaboom\n", "Mogees\n", "Buddytruk\n", "Snapette\n", "Expertmaker\n", "Facer\n", "Kokoroe\n", "Richard\n", "CoinTent\n", "Qumulo\n", "Atooma\n", "Panaya\n", "Dasher\n", "Chartboost\n", "Singly\n", "CoPromote\n", "VictorOps\n", "Afrostream\n", "Uqora\n", "CompareAsiaGroup\n", "Mertado\n", "Reelio\n", "LyteShot\n", "Brandwatch\n", "SocialBattles\n", "Plyfe\n", "GetGoing\n", "mNectar\n", "conTgo\n", "Lenovo\n", "Fonticons\n", "Outbrain\n", "Yottaa\n", "Boompi\n", "Skillz\n", "FIS\n", "Schramm\n", "Milestone\n", "Vogels\n", "Elastica\n", "Ooyala\n", "Pinspire\n", "Certiport\n", "Mention\n", "Aldridge\n", "StartupYard\n", "Chalkable\n", "Wharton\n", "Ampush\n", "CoFoundersLab\n", "Nomi\n", "Yoga\n", "Brightcove\n", "Craftsvilla\n", "Locket\n", "Partner\n", "WeWork\n", "NPO\n", "Vidyard\n", "Wanderu\n", "PhotoSpotLand\n", "Rollout+io\n", "Sounds\n", "Segway\n", "MetaPack\n", "Foodity\n", "Skyscanner\n", "Nanigans\n", "BlueStripe\n", "AdRoll\n", "Appiterate\n", "Secret+li\n", "Naver\n", "Ybrain\n", "Sendicate\n", "Adnimation\n", "GAIN\n", "Wilogo\n", "Believe+in\n", "Telefonica\n", "Cater2+me\n", "PSafe\n", "Readmill\n", "Tapulous\n", "Hotspots\n", "Tarlton\n", "HubSpot\n", "Codecademy\n", "Miyowa\n", "Pebble\n", "PrivateCore\n", "Nestle\n", "OKpanda\n", "Kwaga\n", "CluckCluck\n", "Trello\n", "Firebase\n", "Clip\n", "Bitcasa\n", "Snips\n", "CityPockets\n", "Humin\n", "NationBuilder\n", "Mindshare\n", "Knok\n", "STI\n", "Beamr\n", "AppDirect\n", "Savored\n", "PeepCode\n", "Wolfe\n", "Lookcraft\n", "Empath\n", "SevenVentures\n", "RedDoorz\n", "Emojiary\n", "Ulmon\n", "Senzari\n", "codeSpark\n", "Fitt\n", "SpaceSplitter\n", "Groopie\n", "Qualtrics\n", "Foodpanda\n", "Innovid\n", "Farm2050\n", "Merriman\n", "Cosy\n", "CustomMade\n", "panels\n", "Concurrent\n", "SeamlessDocs\n", "OneSchool\n", "Neone\n", "Lytro\n", "Blendle\n", "CareZapp\n", "FabFitFun\n", "RealCrowd\n", "Voodoo\n", "Paragraph\n", "Case\n", "Hooked\n", "Intralist\n", "Giffiti\n", "CommonFloor\n", "PodShare\n", "Taulia\n", "Hachi\n", "MyCheck\n", "Apptopia\n", "Goliath\n", "Apsalar\n", "LiveRail\n", "Postini\n", "Quickoffice\n", "ZEFR\n", "Symplified\n", "Aleph\n", "Archant\n", "Odysee\n", "VenueNext\n", "NovoEd\n", "Stayzilla\n", "Glints\n", "CodeGuard\n", "OurMix\n", "iwaku\n", "TuneCore\n", "TravelPerk\n", "Eversnap\n", "Timekiwi\n", "Wrike\n", "Neolane\n", "Audvisor\n", "Peecho\n", "Scheduler\n", "Smartsheet\n", "Stampery\n", "Everwise\n", "FirstLook\n", "SenSprout\n", "Webedia\n", "CSR\n", "Experiences\n", "Bpifrance\n", "Pegatron\n", "Greedy\n", "Walker\n", "Heyday\n", "HushHush\n", "Labor\n", "ConsultingMD\n", "Rooster\n", "Grabr\n", "Tikker\n", "Pedius\n", "BestVendor\n", "Formvertise\n", "Curology\n", "Sightly\n", "HowGood\n", "Chewse\n", "Google_\n", "Fresh\n", "Unigo\n", "Qwaya\n", "SimilarWeb\n", "CEE\n", "Tubular\n", "SportsHero\n", "drop\n", "Knoll\n", "Qosmos\n", "Shaper\n", "Yell\n", "Dodo\n", "Fenix\n", "Modsy\n", "Swype\n", "Roche\n", "ASML\n", "Scripted+com\n", "Gnzo\n", "Balance\n", "PediaPress\n", "Addr\n", "Housebites\n", "Hootsuite\n", "Layar\n", "Cupple\n", "PredictionIO\n", "PAX\n", "Trending\n", "HealthTap\n", "Booktrope\n", "Random\n", "Jetlore\n", "Caviar\n", "Skyera\n", "TRAFI\n", "Hamwells\n", "Bownty\n", "MOX\n", "Jaunt\n", "Binpress\n", "Indisys\n", "eBuddy\n", "LetsLunch\n", "Easy\n", "Opener\n", "FairFleet\n", "VizEat\n", "Fovo\n", "Headout\n", "Maily\n", "AmbyGear\n", "AppHero\n", "Antonio\n", "Code\n", "HUDWAY\n", "PieSync\n", "SirionLabs\n", "Crazy\n", "YogiPlay\n", "Workestra\n", "Nifti\n", "Box\n", "Bond\n", "ThirdLove\n", "Sookasa\n", "Discoverly\n", "ParkWhiz\n", "i2\n", "AngelList\n", "Legend\n", "NHTSA\n", "Donuts\n", "Radar\n", "Citrrus\n", "ROIKOI\n", "Tripda\n", "AlumniFunder\n", "Babu\n", "Sumo\n", "Dyn\n", "LuckyPennie\n", "Navdy\n", "Monument\n", "Ecwid\n", "Maritz\n", "PickTrace\n", "Newsela\n", "Kuapay\n", "GarageBand\n", "Edyn\n", "CloudCheckr\n", "Salonmeister\n", "MeetMe\n", "MakeMeReach\n", "IMAX\n", "Spotcap\n", "NoRedInk\n", "Pops\n", "Tru\n", "Babblr\n", "Arable\n", "JamBase\n", "Affinity\n", "Splacer\n", "Broadcast\n", "Concert\n", "Holographic\n", "Mandriva\n", "Stackla\n", "Tripbirds\n", "HotelTonight\n", "Wild\n", "TheFamily\n", "AdsNative\n", "EMC\n", "Mindsy\n", "NextDocs\n", "AppInTop\n", "Zenefits\n", "NoBroker\n", "AnyPresence\n", "Courier\n", "Coast\n", "ST-Ericsson\n", "Eco\n", "Aquto\n", "Qualys\n", "Spotsetter\n", "Gal\n", "Wikipedia\n", "BeatDeck\n", "Sellfy\n", "GlobalSCAPE\n", "Tawkers\n", "Cleartrip\n", "Astrid\n", "Bync\n", "Pepperdata\n", "SeedPlus\n", "Sunlight\n", "ABS\n", "Hear2Read\n", "Instructure\n", "Snoox\n", "Maybank\n", "LiveNinja\n", "PayPal\n", "Shopify\n", "Quepasa\n", "PHIND\n", "Priceonomics\n", "Fieldwire\n", "ZestFinance\n", "Mei+com\n", "Epocrates\n", "Guesswork\n", "StandWith\n", "Cisco\n", "Siasto\n", "Adelphic\n", "Armarium\n", "Cobook\n", "Skydio\n", "Ultra\n", "PostUp\n", "Distill\n", "Maheswari\n", "Datameer\n", "Syncsort\n", "Pounce\n", "Monitise\n", "Asteroid\n", "Breathometer\n", "DemystData\n", "Out\n", "MUBI\n", "Sensay\n", "Sweeten\n", "Zoute\n", "Change+org\n", "Camera360\n", "Paidy\n", "Shazam\n", "LG\n", "Vertu\n", "SIP\n", "Booking+com\n", "ImpulseSave\n", "Posterous\n", "EngageSciences\n", "Hands\n", "Spiegel\n", "GoBank\n", "HackHands\n", "Blueprint\n", "Path\n", "MightyTV\n", "Narratives\n", "Silverpop\n", "TradingView\n", "Amadeus\n", "Submittable\n", "BRIKA\n", "JobScout\n", "Lanyrd\n", "Tapad\n", "Crowdynews\n", "Moju\n", "InnoGames\n", "Ellen\n", "Nelson\n", "Underwood\n", "VideoElephant\n", "Nexus\n", "AT\n", "Taylor\n", "Matt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MetaMind\n", "Unruly\n", "Giftiki\n", "Locca\n", "Pressfolios\n", "Lexity\n", "Graduway\n", "Echograph\n", "Kaggle\n", "Vobi\n", "Crushpath\n", "Drupal\n", "Polyvore\n", "Triller\n", "DailyWorth\n", "SAP\n", "Maxthon\n", "Housejoy\n", "Tyro\n", "Apphance\n", "Fanatics\n", "BioDigital\n", "Jewelbots\n", "Quri\n", "Vinebox\n", "eShares\n", "BBVA\n", "Coupang\n", "SoftBank\n", "Gleam\n", "Spredfast\n", "Koality\n", "Seamless\n", "Insight\n", "GetLinks\n", "Academe\n", "AnchorFree\n", "Anywhere\n", "Eyeview\n", "Vizury\n", "FoundersCard\n", "Povio\n", "MoPho\n", "Gizmox\n", "PeopleBrowsr\n", "Roca\n", "Spectrm\n", "PromoJam\n", "Animoto\n", "Loosecubes\n", "Gumhouse\n", "Plaid\n", "Kepler\n", "Morrisons\n", "Reactions\n", "Lucey\n", "Impraise\n", "International\n", "Secusmart\n", "AVG\n", "Wileyfox\n", "Fotolia\n", "Inspirato\n", "Dreamit\n", "Muzik\n", "PicMonkey\n", "Geeksphone\n", "Dymant\n", "Futuremark\n", "EPIX\n", "BlockTrail\n", "Emburse\n", "Noke\n", "Bump\n", "Photomyne\n", "Sapho\n", "Wonderloop\n", "Clustrix\n", "DigitalGenius\n", "Makerbase\n", "CloudSpokes\n", "iQiyi\n", "Blockchain\n", "Odnoklassniki\n", "Loggly\n", "Famigo\n", "SittingAround\n", "SquareOne\n", "Yield\n", "MindSnacks\n", "Playlists+net\n", "Cvent\n", "Highrise\n", "Liftopia\n", "Axway\n", "UPS\n", "Metrics\n", "Chute\n", "SigOpt\n", "Clippet\n", "Vaavud\n", "Phonvert\n", "Legentas\n", "Haptik\n", "Hotspot\n", "Intel\n", "Affectiva\n", "LegbaCore\n", "RakNet\n", "Metail\n", "Judicata\n", "Fatmap\n", "Azullo\n", "Widdit\n", "Lamoda\n", "Transcoder\n", "Foodzy\n", "Ideal\n", "Systems\n", "Auxmoney\n", "Engagor\n", "Floobits\n", "Got\n", "Newsana\n", "Inspirock\n", "Yodlee\n", "Ginger+io\n", "Fortune\n", "iHeart\n", "Taboola\n", "Grooveshark\n", "Thinkful\n", "Ellation\n", "Rafter\n", "Fandango\n", "Spoonflower\n", "Workflow\n", "Eventjoy\n", "Ping\n", "Paltalk\n", "Babylon\n", "Spinnakr\n", "SkyGiraffe\n", "Accel-KKR\n", "reps\n", "FastSpring\n", "Umano\n", "Ethan\n", "Mingleton\n", "FilterEasy\n", "Cemmerce\n", "ZigBee\n", "Revelator\n", "Rainmaker\n", "PredictSpring\n", "Retrace\n", "Massdrop\n", "Photoful\n", "CanvasPop\n", "PLUMgrid\n", "Velti\n", "Reebee\n", "Sex+com\n", "MetaWorld\n", "LocalOn\n", "Tradedoubler\n", "Vello\n", "CatchThatBus\n", "Tizen\n", "Schaft\n", "RT\n", "Sparkcentral\n", "Philo\n", "Truecaller\n", "Bubble\n", "CIO\n", "Trooly\n", "OfficeDrop\n", "Intuit\n", "SimplyInsured\n", "ClassPass\n", "GoingOn\n", "TapEngage\n", "Wallmob\n", "Vivendi\n", "Wayfair\n", "Badoo\n", "Pirate3D\n", "Spotwag\n", "Netizine\n", "Skycure\n", "Tethercell\n", "Shorts\n", "Sanders\n", "Witness\n", "Onesheet\n", "Clarity\n", "Apigy\n", "TurningArt\n", "Rutube\n", "Uptake\n", "Radoop\n", "Postmates\n", "Ascend\n", "Ektron\n", "TED\n", "Wayin\n", "Boxful\n", "ScaleBase\n", "Sokikom\n", "Conceivable\n", "Powhow\n", "Paribus\n", "Blackstone\n", "Boxee\n", "Inmarsat\n", "Everyplay\n", "Unbabel\n", "FlashSoft\n", "ShopperTrak\n", "OneLouder\n", "YouLike\n", "Scrumpt\n", "Netatmo\n", "Caskers\n", "AdColony\n", "Fictiv\n", "Elance\n", "Mode\n", "Blip\n", "Devialet\n", "Photobucket\n", "ClipMine\n", "Glitter\n", "BagThat\n", "WinBeta\n", "CollabFinder\n", "eMarketer\n", "DoxOut\n", "GoodData\n", "Kara\n", "Kitchenbowl\n", "Sisense\n", "Rockpack\n", "Dynamics\n", "Gild\n", "MavenSay\n", "PlayerScale\n", "Posmetrics\n", "Trellie\n", "Thistle\n", "Cloze\n", "Baker\n", "Fandeavor\n", "Flipps\n", "TalentBin\n", "Unii\n", "InnoSpring\n", "Sensinode\n", "Pixate\n", "Emotient\n", "Postcard\n", "Movebubble\n", "edocr\n", "Contextly\n", "Everloop\n", "Skriware\n", "GoldieBlox\n", "DLD\n", "Bob\n", "PETA\n", "Perion\n", "WeHostels\n", "Zopa\n", "Spraffl\n", "Thurst\n", "SlideMail\n", "Skycatch\n", "ParStream\n", "Chirpify\n", "Futurelytics\n", "Tattoodo\n", "Bitdeli\n", "Bluebox\n", "Mapillary\n", "Ironclad\n", "Synergyse\n", "Airwallex\n", "Jirafe\n", "Errplane\n", "Flypad\n", "MartJack\n", "Thorn\n", "XYZE\n", "Tastebuds\n", "TVSmiles\n", "FoundationDB\n", "HealthKit\n", "Zeo\n", "Trifacta\n", "TreSensa\n", "Emergence\n", "Mercari\n", "Tropo\n", "CrowdTwist\n", "Houston\n", "Hughes\n", "MobileDevHQ\n", "MyState\n", "Bloomberg\n", "Deliveroo\n", "Runtastic\n", "Moven\n", "Loopcam\n", "Xola\n", "cisimple\n", "Fanplayr\n", "Pearson\n", "AutoLotto\n", "Zoomdata\n", "Workshare\n", "Muzy\n", "NSI\n", "Imagine\n", "Bullhorn\n", "Hickies\n", "Zizoo\n", "Kogan\n", "Currents\n", "Opera\n", "Feedspot\n", "Vita\n", "Crusher\n", "Pharmly\n", "QuickPay\n", "YouEye\n", "Gousto\n", "Borro\n", "Aetna\n", "AfterShip\n", "UCWeb\n", "Nutanix\n", "BEUC\n", "Stanley\n", "Ozlo\n", "Johnson\n", "Grofers\n", "batteryPOP\n", "Valassis\n", "Clay\n", "KidZui\n", "Telefónica\n", "PostRocket\n", "SnapApp\n", "Pascal\n", "MicroEval\n", "Synacor\n", "Comptel\n", "LokLok\n", "PandaWhale\n", "Kickscout\n", "TestFlight\n", "Avaz\n", "Forbes\n", "Follow\n", "HappyFunCorp\n", "Shipwire\n", "ArtCorgi\n", "WellPath\n", "AppGlu\n", "OpenDNS\n", "Mitro\n", "Architizer\n", "Upverter\n", "EdSurge\n", "AliveCor\n", "SeatID\n", "Planspot\n", "Bugsnag\n", "Songkick\n", "Mindwork\n", "Mailgun\n", "NowThis\n", "Appthority\n", "Caspida\n", "CommonBond\n", "RocketClub\n", "Garantia\n", "MOG\n", "Chosen\n", "Cloth\n", "Technorati\n", "Nuomi\n", "Sensel\n", "Gig\n", "Fender\n", "AroundMe\n", "EatStreet\n", "Picattoo\n", "Lully\n", "Gigstart\n", "Print+io\n", "Citi\n", "Spritz\n", "Swill\n", "Wax\n", "Stonesoft\n", "Insightpool\n", "Doublie\n", "Swapbox\n", "GlassUp\n", "InVision\n", "HouseFix\n", "OmniVirt\n", "Geofeedia\n", "Lobster\n", "Camarilla\n", "Natasha\n", "Ushi\n", "Parrot\n", "Safaricom\n", "Blipfoto\n", "SeedCloud\n", "ConsenSys\n", "Lipman\n", "CourseSmart\n", "Itseez\n", "Thismoment\n", "Eaze\n", "SIM\n", "Pushbullet\n", "Anniversary\n", "Telerik\n", "Engagio\n", "Kobo\n", "Antuit\n", "Pluralis\n", "Flomio\n", "Monese\n", "Birdback\n", "Dynatrace\n", "Interview\n", "Taco\n", "Cody\n", "Fantuition\n", "Stamper\n", "Openera\n", "GameFly\n", "SkyBell\n", "Trendrr\n", "GumGum\n", "Vert\n", "Shaka\n", "Gobi\n", "Crate\n", "Meograph\n", "LeWeb\n", "PhoneFactor\n", "Planted\n", "Banters\n", "Metaresolver\n", "Jobr\n", "Loveflutter\n", "Stevie\n", "DroneDeploy\n", "Mix\n", "Fish\n", "Beacons\n", "Iconic\n", "Interviewed\n", "UberMedia\n", "Flyfit\n", "FeeX\n", "PetHub\n", "Burton\n", "Druva\n", "Campalyst\n", "Ancera\n", "Diary+com\n", "CallWave\n", "Zeekit\n", "Bloomspot\n", "Floored\n", "Carbyn\n", "CGI\n", "Zhenai\n", "Ling\n", "Mail\n", "DataGravity\n", "Puzzle\n", "YouNoodle\n", "AppGyver\n", "Tinkergarten\n", "Hike\n", "Globevestor\n", "KDDI\n", "Livestream\n", "Gruveo\n", "Knozen\n", "Zappli\n", "Prompt+ly\n", "ThousandEyes\n", "Kinnek\n", "CloudEndure\n", "Hyperink\n", "Lybrate\n", "Brighter\n", "Clipboard\n", "PTT\n", "Base79\n", "Humble\n", "Ziptask\n", "ProBoards\n", "WeMesh\n", "Seelio\n", "GymGroups\n", "PureVPN\n", "WeLab\n", "Jitterbit\n", "Piqora\n", "Memorado\n", "Spendesk\n", "Acorns\n", "GroundLink\n", "Radian\n", "Zuora\n", "ASUS\n", "Mhelpdesk\n", "Kazaana\n", "Poshmark\n", "SFR\n", "Tor\n", "Nova\n", "Alpine\n", "Gunosy\n", "Lookout\n", "Cable\n", "DataRank\n", "Vevo\n", "Parklet\n", "Quixey\n", "LeanMarket\n", "Priest\n", "Pathwright\n", "Whitetruffle\n", "Tipbit\n", "Gozaik\n", "Globant\n", "StudyRoom\n", "DelFly\n", "Revolution\n", "PayNearMe\n", "Voxel\n", "Looklist\n", "Celonis\n", "MSNBC\n", "Lore\n", "Percentil\n", "Awesome\n", "Razorpay\n", "Tigerlabs\n", "WhipTail\n", "FastPay\n", "BetterCloud\n", "NomadCast\n", "Industry\n", "Stringr\n", "Almond\n", "Sonian\n", "Boostcase\n", "HoneyBook\n", "BFF\n", "Stichy\n", "Cyan\n", "Leftronic\n", "Beepi\n", "Bizdom\n", "Musical+ly\n", "Rixty\n", "Socar\n", "Schuler\n", "Flyby\n", "ESPN\n", "Quarterly\n", "CSG\n", "Burpple\n", "NextVR\n", "Shopwave\n", "Corgi\n", "Control\n", "GIFs\n", "Mingle\n", "Spotbros\n", "Zilingo\n", "Keurig\n", "PrepWork\n", "Vimeo\n", "ARM\n", "Sincerely\n", "Jeffrey\n", "Axtria\n", "Healint\n", "Bizzabo\n", "Airobotics\n", "Eventioz\n", "Playmatics\n", "Madvertise\n", "PivotDesk\n", "Tagboard\n", "Freeview\n", "Fin\n", "Milk\n", "Promote\n", "CGTrader\n", "Soundtracker\n", "Puppet\n", "InMotion\n", "Splash+FM\n", "Moxtra\n", "Well\n", "Jordan\n", "ArmorText\n", "InvisibleHand\n", "Module\n", "GitHub\n", "Zuoyebang\n", "MasterCard\n", "PrePay\n", "PlaceIQ\n", "Duracell\n", "NeonMob\n", "Blockai\n", "Aviary\n", "Nintendo\n", "Everyday+me\n", "Line\n", "Quartzy\n", "Metaio\n", "Combinator\n", "Pingpad\n", "Tilt\n", "KFit\n", "Cuff\n", "Spawnsong\n", "Zeetings\n", "Glide\n", "Baltimore\n", "PrestaShop\n", "Air\n", "CarHero\n", "Videopixie\n", "AdSemble\n", "Krossover\n", "Pavlok\n", "Ableton\n", "Dekko\n", "Legimi\n", "OpenX\n", "Youmiam\n", "Loudie\n", "Credo\n", "A\n", "GigLocator\n", "PageCloud\n", "Servy\n", "Vodafone\n", "ShoCard\n", "PicnicHealth\n", "Tripshare\n", "Capography\n", "M-Files\n", "Escrow+com\n", "SurfEasy\n", "Kentik\n", "SQFT\n", "ThankView\n", "BigDoor\n", "Sitedrop\n", "Reebonz\n", "Porsche\n", "Thirdshelf\n", "Grokr\n", "Procera\n", "Kitchenbug\n", "HitBliss\n", "Reverb+com\n", "SimpleCitizen\n", "WonderLuk\n", "Shine\n", "RocketFrog\n", "Accellion\n", "Ustream\n", "Paperspace\n", "Agrilyst\n", "Pintrips\n", "CollabNet\n", "Disqus\n", "Amiigo\n", "Laugh+ly\n", "Gotham\n", "Canviz\n", "Calm\n", "Monkey\n", "Tudou\n", "Chef\n", "FileMaker\n", "Protag\n", "Blocks\n", "Mangrove\n", "Ask+com\n", "Greenwood\n", "Ellis\n", "FitBark\n", "Beeswax\n", "Bromium\n", "Yemeksepeti\n", "TouchCast\n", "HouseTrip\n", "b8ta\n", "SNCF\n", "Windy\n", "Class\n", "Manilla\n", "Tinychat\n", "Cards\n", "Bathys\n", "Call9\n", "Toptal\n", "Behance\n", "Shoppable\n", "Celmatix\n", "ARCEP\n", "Snapguide\n", "FreeWavz\n", "SoundFocus\n", "Ebyline\n", "Motorola\n", "Sharethrough\n", "Destiny\n", "Yesware\n", "Carvoyant\n", "SlideRocket\n", "Tapglue\n", "JoyTunes\n", "Koudai\n", "Mustbin\n", "Betterment\n", "AppLabs\n", "VoloMetrix\n", "Fulcrum\n", "Anaplan\n", "Beamery\n", "Stipple\n", "SE\n", "TAG\n", "Mikme\n", "Setster\n", "Satechi\n", "Telogis\n", "AppFormix\n", "Tamr\n", "Longaccess\n", "Mitch\n", "Canonical\n", "SevenFifty\n", "Chicago\n", "EvntLive\n", "Apester\n", "Sonalight\n", "Playtox\n", "FiscalNote\n", "Startupbootcamp\n", "TrueStart\n", "Meritful\n", "Silp\n", "Print\n", "Nagios\n", "Auto+ru\n", "Nosh\n", "Shpock\n", "Creatrs\n", "Drivy\n", "Scribit\n", "Cloudyn\n", "Fleksy\n", "ISKN\n", "Studypool\n", "LookSee\n", "Bellabox\n", "Triposo\n", "CBS\n", "TrendSpottr\n", "Ohlala\n", "Viewbix\n", "StockTwits\n", "AppLift\n", "Lionside\n", "Kidizen\n", "Ometria\n", "Confide\n", "KitchMe\n", "Ribbon\n", "About\n", "Fuzmo\n", "OnSwipe\n", "USM\n", "Black\n", "Funzio\n", "Bulletin\n", "Kay\n", "Marketplace\n", "Hotlist\n", "Teleborder\n", "Cone\n", "Sosh\n", "Greenhouse\n", "Toronto\n", "Vanitee\n", "Costello\n", "Taskworld\n", "Yozio\n", "Lumosity\n", "Visual\n", "Hometalk\n", "ShopLocket\n", "Ctrip\n", "Acompli\n", "Moff\n", "Olapic\n", "Akimbo\n", "AnyRoad\n", "Daric\n", "Amiato\n", "Trunkt\n", "Jukedeck\n", "Cizoo\n", "HashiCorp\n", "Kander\n", "Paintzen\n", "DG\n", "Tonight\n", "iRobot\n", "OYO\n", "Zite\n", "Nestio\n", "Dingo\n", "Parcel\n", "Pathmapp\n", "Klarismo\n", "Simplilearn\n", "Memo\n", "PipelineDB\n", "Grouper\n", "Tanner\n", "Typeform\n", "LEGO\n", "BikeTag\n", "WiMP\n", "waves\n", "HGST\n", "LivingSocial\n", "Melodigram\n", "Rockbot\n", "SolarCity\n", "Quattrocento\n", "Chipolo\n", "Percival\n", "EdReach\n", "Stocard\n", "DCM\n", "Snapdeal\n", "Homee\n", "Finale\n", "Worldpay\n", "Aislelabs\n", "Applicasa\n", "ViVu\n", "Stitcher\n", "TuneWiki\n", "BlockScore\n", "WireOver\n", "Capital\n", "Blurb\n", "Button\n", "Jake\n", "ProfitBricks\n", "EyeVerify\n", "about+me\n", "Voicegem\n", "Nuji\n", "Contactually\n", "Sqord\n", "Eliademy\n", "Predictive\n", "OneClass\n", "GetYourGuide\n", "HERE\n", "G5\n", "SimpleLegal\n", "Zinc\n", "Playdek\n", "Misen\n", "Fiksu\n", "TrakInvest\n", "GroupTalent\n", "Storyful\n", "Cruise+me\n", "Meesho\n", "People+\n", "OMsignal\n", "Pioneer\n", "Atlassian\n", "Hansoft\n", "Airpush\n", "WaHome\n", "DHL\n", "Urbanspoon\n", "Ask+fm\n", "thredUP\n", "Guesty\n", "Svyaznoy\n", "Onavo\n", "GV\n", "Stylight\n", "Techy\n", "Evite\n", "ShareGrid\n", "earthmine\n", "Printhug\n", "Foodini\n", "Tote\n", "Carspring\n", "Elmo\n", "HireVue\n", "Blockspring\n", "Groupnotes\n", "Cybozu\n", "Lock8\n", "Buccaneer\n", "MobiCart\n", "Flatchat\n", "TapFame\n", "Wisembly\n", "SOASTA\n", "Byline\n", "Trackbuster\n", "Ariba\n", "Mocana\n", "Nexmo\n", "TechCrunch\n", "Talkz\n", "Please\n", "Libboo\n", "Boni\n", "Navmii\n", "Dedrone\n", "IAC\n", "Lyft\n", "PaeDae\n", "Yardsale\n", "Experts\n", "Udacity\n", "Tagged\n", "PonoMusic\n", "HDI\n", "Wikets\n", "Clarivoy\n", "Urbee\n", "Tippr\n", "Vkontakte\n", "LevelUp\n", "BookingBug\n", "Nordstrom\n", "Cube26\n", "Trulia\n", "Acer\n", "Jink\n", "Stir\n", "Washbox\n", "Compendium\n", "Soundsgood\n", "MakerBot\n", "Webrazzi\n", "Looksery\n", "EverTrue\n", "SkyPhrase\n", "Robocat\n", "Gem\n", "Sense360\n", "Wikia\n", "Marco\n", "Cloudera\n", "Arch\n", "Etsy\n", "Voxeet\n", "Stage\n", "Unison\n", "gamesGRABR\n", "Workbench\n", "Binocular\n", "Food\n", "FOBO\n", "Appcelerator\n", "Modern\n", "Batch\n", "Brightkite\n", "Trustpilot\n", "La-La\n", "Sculpt\n", "eBay\n", "BodeTree\n", "Soulmix\n", "Immersit\n", "Boatbound\n", "Collections\n", "Remitly\n", "Moo+do\n", "PureWrist\n", "Kroll\n", "Streamweaver\n", "Invoice2go\n", "SnapKeys\n", "Tile\n", "MeARKET\n", "Wheretoget\n", "Codementor\n", "Venmo\n", "Virtru\n", "Votizen\n", "Influenster\n", "Jusp\n", "Filabot\n", "Engodo\n", "Yondr\n", "Klash\n", "Fusion-io\n", "Motilo\n", "PernixData\n", "Verious\n", "AlchemyAPI\n", "Heyzap\n", "Findster\n", "WellBiome\n", "PasswordBox\n", "Xpire\n", "Crowdpark\n", "Plex\n", "gdgt\n", "Parliament\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "RTI\n", "Xiaomi\n", "TuneIn\n", "Olson\n", "XPRIZE\n", "Donut\n", "HelloSoda\n", "Pattern\n", "Foundation\n", "TappingStone\n", "Stamped\n", "Tout\n", "Floret\n", "UpTo\n", "JVP\n", "IPG\n", "Storybricks\n", "Guvera\n", "EVA\n", "Okta\n", "TradeGecko\n", "Telstra\n", "Farfetch\n", "Zello\n", "Instavest\n", "Lion\n", "QR\n", "Wantful\n", "Spoolee\n", "Lolapps\n", "ExoClick\n", "Curatum\n", "Workato\n", "Tectonic\n", "XSEDE\n", "LeFeed\n", "VHX\n", "SnapKnot\n", "indoo+rs\n", "PMA\n", "Picks\n", "AppsBuilder\n", "CrestaTech\n", "Cloudant\n", "Indie\n", "Decorist\n", "TSMC\n", "Pickie\n", "PARC\n", "Shuddle\n", "Immediately\n", "Upworthy\n", "Likelii\n", "LeKiosk\n", "Autodesk\n", "Sportradar\n", "Gradberry\n", "Seesmic\n", "Soompi\n", "GazeHawk\n", "Boxever\n", "Sprightly\n", "Recombine\n", "Yeti\n", "Kryptnostic\n", "Tictail\n", "Seesaw\n", "Issue\n", "Vungle\n", "Nickelodeon\n", "Zumper\n", "Nearby\n", "Made\n", "GroupSpaces\n", "Roomorama\n", "Woozworld\n", "VitaPortal\n", "WearToday\n", "Batterii\n", "FarEye\n", "FindIt\n", "Jolicloud\n", "SIMI\n", "vrAse\n", "NodeFly\n", "Baarzo\n", "Soli\n", "Remus\n", "Minted\n", "Velostrata\n", "Bud\n", "TrackR\n", "Guild\n", "Dyson\n", "PCI\n", "Stypi\n", "Request\n", "PadMapper\n", "Online\n", "StudyBlue\n", "Canal+\n", "Terminal\n", "SocialChorus\n", "Pressly\n", "Promoboxx\n", "Glassdoor\n", "Scosche\n", "Aarki\n", "StartupHighway\n", "Gaana\n", "Bellabeat\n", "Symantec\n", "Trustev\n", "Caterspot\n", "Gobble\n", "Modi\n", "Treasure\n", "Adly\n", "Tapdaq\n", "LEAGUE\n", "M+dot\n", "CentOS\n", "Noodle\n", "NetSuite\n", "Threes\n", "Skimbox\n", "Action\n", "Coinsetter\n", "Zen99\n", "zVentures\n", "Konami\n", "Sidestage\n", "Socrative\n", "Qriously\n", "Peapod\n", "Bishop\n", "Hipcamp\n", "Mink\n", "Ubo\n", "Montblanc\n", "Digimarc\n", "TaskRabbit\n", "Type\n", "Crowd\n", "wydr\n", "HackerEarth\n", "Demandware\n", "CarTrade\n", "ViSalus\n", "Coursmos\n", "Mobage\n", "Fingo\n", "Mendeley\n", "Neumob\n", "Hollar\n", "GemShare\n", "Clinton\n", "Attachments+me\n", "Dandy\n", "AdVine\n", "Galaxia\n", "IndieGameStand\n", "Vitacost\n", "Printful\n", "Neptune\n", "LegalZoom\n", "Madefire\n", "Kiva\n", "Playbasis\n", "Forrst\n", "R\\GA\n", "Swytch\n", "Depop\n", "Harman\n", "BlaBlaCar\n", "Scandy\n", "LS9\n", "ThingLink\n", "Everplaces\n", "Picfair\n", "Flag\n", "Adform\n", "Outdoorsy\n", "Xfers\n", "VMFive\n", "Confluence\n", "Jaguar\n", "Bernstein\n", "Liberty\n", "Caltrain\n", "BET\n", "Dropost+it\n", "Joint\n", "Fabrily\n", "ZenDeals\n", "Malwarebytes\n", "Kahuna\n", "Spindle\n", "Desti\n", "InsideSales+com\n", "Gigster\n", "Supple\n", "WalkMe\n", "Will\n", "Goodsie\n", "Consumr\n", "Draper\n", "Avado\n", "Alexa\n", "Viking\n", "Hexadite\n", "Oolu\n", "Pinweel\n", "AppThwack\n", "Mindjet\n", "CrowdOptic\n", "PageLever\n", "GO1\n", "Jobbatical\n", "Ringya\n", "Schools\n", "Rhinobird\n", "FriendFeed\n", "StickNFind\n", "KinoPoisk\n", "BarEye\n", "Elf\n", "Powerset\n", "Savioke\n", "Teabox\n", "Jean\n", "Ubimo\n", "Nowait\n", "Ocean\n", "Aspire\n", "Media+net\n", "SparkReel\n", "Storylane\n", "Hadoop\n", "Pinterest\n", "KiteDesk\n", "Peoplefluent\n", "Randstad\n", "Netskope\n", "MoWeather\n", "Monetate\n", "Summly\n", "IFTTT\n", "OpenStack\n", "Coach\n", "Bosch\n", "GMV\n", "EveryLodge\n", "Structure\n", "Aviso\n", "Verbling\n", "moosify\n", "WebGL\n", "CapLinked\n", "Mashape\n", "Noggin\n", "Near+in\n", "Waterstones\n", "Binge\n", "Sorted\n", "Turck\n", "Expensify\n", "Estimize\n", "Shapeways\n", "Commonred\n", "AirPooler\n", "Zirtual\n", "Humans\n", "Opuss\n", "Oscar\n", "Mobincube\n", "Beestar\n", "Linqapp\n", "Maxis\n", "Nets\n", "OneSky\n", "Feed+fm\n", "Automattic\n", "Siri\n", "BDC\n", "Gowalla\n", "Aperto\n", "Passport\n", "Zui\n", "Voter\n", "SumAll\n", "ShipHawk\n", "K1\n", "Betable\n", "Unified\n", "Meusu\n", "Taobao\n", "inspiration\n", "Sharewall\n", "Oculus\n", "Coinbase\n", "Softcover\n", "HelloWallet\n", "Neto\n", "Basket\n", "Snapcart\n", "Wifarer\n", "Whittl\n", "Appear\n", "tibbr\n", "Epson\n", "Opinsy\n", "Bullet\n", "Sold\n", "Slidebox\n", "Swap+com\n", "LendUp\n", "Ding\n", "Moss\n", "Tracy\n", "WayUp\n", "MarkaVIP\n", "Vinaya\n", "Ahalogy\n", "Woven\n", "Tinybop\n", "SwiftKey\n", "Instabridge\n", "Forgacs\n", "Hagen\n", "Avenue\n", "CareMessage\n", "BlockCorp\n", "Docphin\n", "M+Gemi\n", "Stone\n", "Linio\n", "Kymeta\n", "Hubub\n", "Solvate\n", "Presence\n", "Academia\n", "Roadster\n", "Integral\n", "StrikeAd\n", "hi5\n", "Station\n", "Scribd\n", "N26\n", "XEED\n", "Clearbit\n", "Frontier\n", "Dogecoin\n", "Wandera\n", "Playdom\n", "Branson\n", "Veenome\n", "Voddler\n", "Zula\n", "Twilio\n", "BenchPrep\n", "Larky\n", "DigitalGlobe\n", "Reissued\n", "Localmind\n", "Next\n", "MyTime\n", "EquipmentShare\n", "Vastrm\n", "Adafruit\n", "Streem\n", "Visualead\n", "Eden\n", "Hua\n", "Confluent\n", "Steinberg\n", "BumeBox\n", "Experfy\n", "FanBridge\n", "Wavecell\n", "Deliverd\n", "Byliner\n", "Movinga\n", "Mesosphere\n", "Bazaarvoice\n", "Schiller\n", "Ifinity\n", "StackPath\n", "Deeper\n", "Estify\n", "Gant\n", "Commerce\n", "Bitcellar\n", "Rickshaw\n", "Loewe\n", "Sevenhugs\n", "Virtusize\n", "SocialPandas\n", "Prefundia\n", "Peer39\n", "Mouthee\n", "Mission\n", "Cubic\n", "Ronin\n", "Appixia\n", "Connector\n", "WhatsApp\n", "Pack\n", "Growth\n", "Quizlet\n", "ManageIQ\n", "Backplane\n", "Pickingo\n", "Curacity\n", "Bankjoy\n", "Ador\n", "OrderAhead\n", "GlobeIn\n", "Meituan-Dianping\n", "Saavn\n", "GoCardless\n", "Kazam\n", "Shopmium\n", "DPS\n", "Unmetric\n", "StubHub\n", "GoDaddy\n", "RingCentral\n", "Kamcord\n", "Xfire\n", "Narvar\n", "Xi3\n", "Qik\n", "HealthKeep\n", "Parchment\n", "AlterGeo\n", "Gumroad\n", "Shaker\n", "Foodily\n", "Stamplay\n", "Venture\n", "iFit\n", "Eatigo\n", "Fileboard\n", "MoveInsure\n", "Fancy\n", "Sonatype\n", "Airtime\n", "Owlin\n", "Branch8\n", "Palace\n", "WebOS\n", "Prior\n", "Frichti\n", "AddToAny\n", "RealScout\n", "Zoomingo\n", "Staffjoy\n", "Anturis\n", "OTTO\n", "Callblock\n", "Somo\n", "Peel\n", "Nestor\n", "Zayo\n", "Microsoft\n", "Oodle\n", "Bindo\n", "GetApp\n", "Teleca\n", "Blogmutt\n", "Viacom\n", "Field\n", "numberFire\n", "Pearltrees\n", "Insync\n", "CAPS\n", "Capitan\n", "Avaya\n", "Thomvest\n", "UpDesk\n", "Mobento\n", "AMADAS\n", "Roombeats\n", "Mueller\n", "Gimmie\n", "Fosbury\n", "Ping+it\n", "Stamp+it\n", "ScienceLogic\n", "Infosys\n", "CallApp\n", "Ubuntu\n", "Kabbage\n", "HealthSouk\n", "Ride\n", "Civic\n", "Macmillan\n", "KeyMe\n", "Effektif\n", "MyNextRun\n", "Marriott\n", "Coraid\n", "CANDDi\n", "Doblet\n", "Weotta\n", "Seatwave\n", "Delta\n", "Tag\n", "GoPollGo\n", "Ellevest\n", "LOLA\n", "FounderDating\n", "Hopster\n", "AppDynamics\n", "Mashery\n", "Spool\n", "Jawbone\n", "TextTeaser\n", "GlassesGroupGlobal\n", "Hopper\n", "Z4\n", "Venio\n", "iControl\n", "Femsplain\n", "Brit\n", "Mavrck\n", "DoubleClick\n", "Meebo\n", "JolieBox\n", "Lindsay\n", "TTS\n", "Healthrageous\n", "Eumakh\n", "Glooko\n", "Sparks\n", "Mashburn\n", "Producteev\n", "Playfish\n", "Synthesio\n", "Labster\n", "Metadata\n", "Shoptiques\n", "Kraft\n", "SeedInvest\n", "GrabCAD\n", "Appier\n", "Tingbot\n", "SiteTagger\n", "Plus\n", "Levebee\n", "Locationary\n", "IDNow\n", "Fingerprint\n", "OUYA\n", "Timehop\n", "Groupon\n", "TodayTix\n", "Save\n", "Apprenda\n", "Narrative\n", "Prezi\n", "Pagevamp\n", "Peakon\n", "cPulse\n", "Hybrid\n", "Monica+Andy\n", "YPlan\n", "Handpick\n", "Jelastic\n", "Adioso\n", "ROOBO\n", "Paydiant\n", "Neptune+io\n", "Treatwell\n", "Kochava\n", "Bidgely\n", "Gymdeck\n", "Pi\n", "OrangeScape\n", "PHP\n", "Movidius\n", "Yarly\n", "Onyara\n", "Stardoll\n", "Vostu\n", "CloudSolar\n", "Hellofood\n", "BuzzMyVideos\n", "Dailymotion\n", "Tame\n", "Bottlenose\n", "PiCloud\n", "Coupons+com\n", "Chargifi\n", "StoreDot\n", "Wurldtech\n", "Voz+io\n", "Cybereason\n", "Mapbox\n", "Backblaze\n", "FameBit\n", "Sawyer\n", "CloudLock\n", "Vroom\n", "STABiLGO\n", "Buttercoin\n", "Sebastian\n", "VideoAmp\n", "Lara\n", "Synology\n", "Snowball\n", "Sherpaa\n", "Parlio\n", "Nike\n", "Cymmetria\n", "Pinshape\n", "Watchup\n", "Chalkfly\n", "Neat\n", "iBeat\n", "MOVE\n", "Skim\n", "Homesnap\n", "Synaptics\n", "Margo\n", "Dreem\n", "Gluster\n", "Allre\n", "Crater\n", "Swiftype\n", "Trade\n", "Fitbay\n", "Favado\n", "Salorix\n", "Voltus\n", "State\n", "Finish\n", "Compilr\n", "Sharecare\n", "One\n", "Wowo\n", "Encap\n", "Zeel\n", "MTailor\n", "Vertical\n", "Hublo\n", "Trainline\n", "Duarte\n", "FamilyLeaf\n", "GoSquared\n", "DropTask\n", "Looker\n", "Roambi\n", "Agorize\n", "Ostrich\n", "YouMagine\n", "SPSS\n", "Surge\n", "Descomplica\n", "Vue\n", "SugarCRM\n", "Sailthru\n", "SanDisk\n", "F-Secure\n", "Shadow\n", "Spartoo\n", "Watchville\n", "Francis\n", "Tonara\n", "VideoSlam\n", "isocket\n", "Craigslist\n", "Cleanly\n", "Dache\n", "SimpleReach\n", "Lumoid\n", "Scopely\n", "Wattpad\n", "FounderFuel\n", "Endgame\n", "Barley\n", "Oink\n", "Axonix\n", "Yo\n", "Henderson\n", "Hat\n", "SMS\n", "Want\n", "Mixer\n", "Clementine\n", "MindMeld\n", "Apperian\n", "Price\n", "Paidpiper\n", "WePay\n", "NapTime\n", "Aardvark\n", "Gidsy\n", "Flybrix\n", "CREO\n", "Gliimpse\n", "PlateJoy\n", "Impending\n", "SessionM\n", "Tableau\n", "Samba\n", "Public\n", "Privalia\n", "Getonic\n", "Documentum\n", "Stickers\n", "Transifex\n", "Imagination\n", "Ansa\n", "Pley\n", "Barlow\n", "Quantifind\n", "AngelHack\n", "OneTwoTrip\n", "Shapr\n", "letgo\n", "Sonos\n", "ShortForm\n", "AuraVisor\n", "Elepath\n", "TripleMint\n", "hoppr\n", "Beanstock\n", "Desk+com\n", "Outernet\n", "Woojer\n", "zero\n", "Trusteer\n", "Casabu\n", "Smava\n", "Gemnote\n", "Dude\n", "Roomlia\n", "LoveThis\n", "Indiegogo\n", "Markerly\n", "startup\n", "Codex\n", "Protect\n", "Mailbird\n", "Pintics\n", "Valkee\n", "CumuLogic\n", "Appsecute\n", "Lakestar\n", "vox+io\n", "TrueVault\n", "InstaEDU\n", "PowerInbox\n", "SteelSeries\n", "Trim\n", "Fiberead\n", "FanDuel\n", "Canon\n", "HelloTech\n", "Maderight\n", "Sina\n", "KYA\n", "Blast\n", "Bertelsmann\n", "R_GA\n", "Houdini\n", "DueCourse\n", "Boomi\n", "HotelQuickly\n", "Sensoria\n", "Mezi\n", "Seculert\n", "Silver\n", "Betterfly\n", "Shoobs\n", "Prizeo\n", "Larsen\n", "OpenBrand\n", "Publicize\n", "Tide\n", "Doodle+ly\n", "Transcriptic\n", "Coderwall\n", "Endaga\n", "CREXi\n", "Daydream\n", "Comcast\n", "Reserve\n", "Ringblingz\n", "MAZ\n", "Fox\n", "Plympton\n", "GoTenna\n", "TacoCopter\n" ] } ], "source": [ "save_all_companies(company_post_dict, \"./companies/\")" ] }, { "cell_type": "code", "execution_count": 320, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "42Floors\n", "A\n", "AAA\n", "Aardvark\n", "Aarki\n", "AB\n", "ABI\n", "Abine\n", "Ableton\n", "about+me\n", "About\n", "ABS\n", "Academe\n", "Academia\n", "Acano\n", "Accel-KKR\n", "Acceleprise\n", "Accelerator\n", "Accellion\n", "Accenture\n", "Accomable\n", "Accompany\n", "Accounts\n", "Aceable\n", "Acer\n", "Achievers\n", "Aclima\n", "aCommerce\n", "Acompli\n", "Acorns\n", "Acquia\n", "Acronis\n", "Actifio\n", "Actility\n", "Action\n", "ActivePath\n", "AcuityAds\n", "Acunote\n", "Adafruit\n", "Adaptive\n", "AdBlock\n", "adBrite\n", "Adcade\n", "AdColony\n", "Addr\n", "AddSearch\n", "AddThis\n", "AddToAny\n", "Addventure\n", "Addvocate\n", "Adelphic\n", "AdEspresso\n", "Adform\n", "Adictik\n", "Adidas\n", "Adioso\n", "Adly\n", "AdMob\n", "AdMobius\n", "Adnimation\n", "Ador\n", "AdQuantic\n", "AdRoll\n", "AdSemble\n", "AdsNative\n", "AdStack\n", "AdStage\n", "ADstruc\n", "ADTRAN\n", "Adventr\n", "AdVine\n", "Advise+me\n", "AdYapper\n", "Adyen\n", "Adzerk\n", "Adzuna\n", "Aereo\n", "Aerospace\n", "AetherPal\n", "Aetna\n", "Afero\n", "Affectiva\n", "Affinity\n", "Affirm\n", "Aframe\n", "Afrostream\n", "AfterCollege\n", "AfterShip\n", "Aggregift\n", "AgileZen\n", "Aging2+0\n", "AgLocal\n", "Agogo\n", "Agorize\n", "Agrilyst\n", "Ahalogy\n", "Ahead\n", "AHHHA\n", "Aidin\n", "AIM\n", "AIR\n", "Airbnb\n", "Aircall\n", "AirConsole\n", "AirHelp\n", "Airlike\n", "AirMap\n", "Airobotics\n", "AirPair\n", "Airpaper\n", "AirPooler\n", "AirPR\n", "Airpush\n", "Airtame\n", "Airtime\n", "Airwallex\n", "Airware\n", "AirWatch\n", "Airy\n", "Aislelabs\n", "Akeneo\n", "Akimbo\n", "AKQA\n", "Aktana\n", "Alaris\n", "Alcatel-Lucent\n", "Alchemy\n", "AlchemyAPI\n", "Aldridge\n", "Aleph\n", "Alert+Us\n", "AlertMe\n", "Alerts\n", "Alexa\n", "Alfred\n", "Alfresco\n", "Algolia\n", "AlgoTrim\n", "Ali\n", "Alibaba\n", "Alicanto\n", "AlienVault\n", "Alignable\n", "Alive\n", "AliveCor\n", "Alley\n", "Alleyoop\n", "AllPeers\n", "Allre\n", "Allset\n", "Allure\n", "Almond\n", "ALOHA\n", "Alphabet\n", "Alpine\n", "AltaVista\n", "AlterGeo\n", "Altiscale\n", "Alto\n", "AltspaceVR\n", "AlumniFunder\n", "AMA\n", "AMADAS\n", "Amadeus\n", "Amazon\n", "AmazonFresh\n", "Ambassador\n", "Ambition\n", "AmbyGear\n", "AMD\n", "Amen\n", "Ameyo\n", "Amiad\n", "Amiato\n", "Amiga\n", "Amiigo\n", "Amino\n", "Amobee\n", "Amplfy\n", "Ampush\n", "AMRA\n", "Amulyte\n", "Anagog\n", "Analogue\n", "Anaplan\n", "Ancera\n", "AnchorFree\n", "Android\n", "Andy\n", "Angel+ai\n", "AngelHack\n", "AngelList\n", "AngelPad\n", "AngelScholars\n", "Angle\n", "Anglr\n", "Animoto\n", "Anker\n", "AnkerBox\n", "Anki\n", "Anniversary\n", "Ansa\n", "Anthony\n", "Antidate\n", "Antonio\n", "Antuit\n", "Anturis\n", "AnyPresence\n", "AnyRoad\n", "Anywhere\n", "AOL\n", "Aoliday\n", "Aorato\n", "Aperto\n", "Apester\n", "Apica\n", "Apigee\n", "Apigy\n", "Apkudo\n", "App+io\n", "App+net\n", "APP\n", "AppAdvice\n", "AppArchitect\n", "Appboy\n", "AppCampus\n", "Appcelerator\n", "AppCertain\n", "Appcore\n", "AppCrawlr\n", "Appcues\n", "AppDirect\n", "AppDynamics\n", "Appear\n", "Apperian\n", "AppFirst\n", "AppFog\n", "AppFormix\n", "Appfuel\n", "AppGlu\n", "AppGyver\n", "Apphance\n", "AppHarbor\n", "AppHero\n", "Appia\n", "Appier\n", "AppInTop\n", "Appirio\n", "Appistry\n", "Appiterate\n", "Appixia\n", "Appknox\n", "AppLabs\n", "Applause\n", "Applauze\n", "Apple\n", "Applicasa\n", "Applifier\n", "AppLift\n", "appLOUD\n", "AppLovin\n", "Appnique\n", "Apportable\n", "Appoxee\n", "Appreciate\n", "AppRedeem\n", "Apprenda\n", "AppsBuilder\n", "Appsecute\n", "Appsee\n", "AppSense\n", "Appsfire\n", "AppStack\n", "AppSumo\n", "AppSurfer\n", "Appthority\n", "AppThwack\n", "Apptimize\n", "Apptopia\n", "Appurify\n", "Appy\n", "Appysnap\n", "Apsalar\n", "AptDeco\n", "Aptonomy\n", "Apttus\n", "Aquto\n", "Arable\n", "ArabNet\n", "Arbor\n", "ARCEP\n", "Arch\n", "Archant\n", "Archer\n", "Archimedia\n", "Architizer\n", "Archive\n", "Archives+com\n", "Archos\n", "Arduino\n", "Argus\n", "Ariba\n", "ARM\n", "Armarium\n", "Armor\n", "ArmorText\n", "AroundMe\n", "Arro\n", "ArtCling\n", "ArtCorgi\n", "Article\n", "ARTIK\n", "Artiphon\n", "Asana\n", "Ascend\n", "Ascribe\n", "Ask+com\n", "Ask+fm\n", "ASML\n", "Aspire\n", "Asseta\n", "Assist\n", "Assistant\n", "Asteroid\n", "AsthmaMD\n", "Astrid\n", "ASUS\n", "AT\n", "Atari\n", "ATF\n", "Atheer\n", "Atlassian\n", "ATM\n", "Atmotube\n", "Atom\n", "Atomico\n", "Atomwise\n", "Atooma\n", "Attachments+me\n", "Attribution\n", "atVenu\n", "Audi\n", "Audible\n", "Audioair\n", "Audiodraft\n", "Audiogalaxy\n", "Audvisor\n", "Augmedix\n", "Auraslate\n", "AuraVisor\n", "Aurelius\n", "Auth0\n", "Authy\n", "Auto+ru\n", "AutoBot\n", "Autodesk\n", "AutoLotto\n", "Automatic\n", "Automattic\n", "Autos\n", "Auxmoney\n", "Avaamo\n", "Avado\n", "Avancar\n", "Avast\n", "Avaya\n", "Avaz\n", "Avegant\n", "Avenue\n", "AVG\n", "Aviary\n", "Aviate\n", "Avid\n", "Avira\n", "Avis\n", "Aviso\n", "Aviv\n", "Avon\n", "Avvo\n", "Awair\n", "Awesome\n", "Axiata\n", "Axonix\n", "Axtria\n", "Axway\n", "Ayannah\n", "Ayasdi\n", "Azalead\n", "Azendoo\n", "Azimo\n", "Azullo\n", "Azumio\n", "B4RM4N\n", "b8ta\n", "Baarzo\n", "Babbel\n", "Babblr\n", "Babu\n", "Baby+com+br\n", "Babyhuddle\n", "Babylon\n", "BAC\n", "Backblaze\n", "Backflip\n", "Backplane\n", "Backstage\n", "Backupify\n", "Badgeville\n", "Badoo\n", "BagThat\n", "Baidu\n", "Baker\n", "Balance\n", "Balanced\n", "Baltimore\n", "Bambuser\n", "Band\n", "Bandzoogle\n", "Bango\n", "Banjo\n", "Bankjoy\n", "Bankons\n", "Bannerman\n", "Banters\n", "Bantr\n", "barcode\n", "BarEye\n", "Bark\n", "BarkBox\n", "Barley\n", "Barlow\n", "Barobot\n", "Baron\n", "Base79\n", "Basket\n", "Batch\n", "Bathys\n", "Batterii\n", "batteryPOP\n", "Battlefield\n", "BaubleBar\n", "Bawte\n", "Baydin\n", "Bazaarvoice\n", "BBVA\n", "BDC\n", "BeachMint\n", "Beacons\n", "Beamery\n", "Beamr\n", "Beanstock\n", "Beast\n", "Beat\n", "BeatDeck\n", "BeauCoo\n", "Bebo\n", "Bebop\n", "Beddit\n", "Beepi\n", "Beepl\n", "Beestar\n", "Beeswax\n", "BeeTV\n", "Behance\n", "Behavox\n", "BeHere\n", "Beintoo\n", "Believe+in\n", "Bell\n", "Bellabeat\n", "Bellabox\n", "Belly\n", "BeLuvv\n", "BeMyEye\n", "Benchling\n", "Benchmark\n", "BenchPrep\n", "BentoBox\n", "BERG\n", "Bernstein\n", "Bertelsmann\n", "BestVendor\n", "BET\n", "BetaBait\n", "Betable\n", "BetaNoodle\n", "Betaworks\n", "Betfair\n", "BetterCloud\n", "BetterDoctor\n", "Betterfly\n", "BetterLesson\n", "Betterment\n", "BetterWorks\n", "Between\n", "BEUC\n", "BevSpot\n", "BeyondCore\n", "Bezar\n", "BFF\n", "Biba\n", "Bidgely\n", "Bidzy\n", "BigBasket\n", "Bigcolors\n", "BigDoor\n", "BigID\n", "Bigpoint\n", "BigStash\n", "BigTime\n", "BikeTag\n", "Bill+com\n", "Billboard\n", "BillGuard\n", "BillPay\n", "Bilna\n", "Bindo\n", "Binge\n", "Binksty\n", "Binocular\n", "Binpress\n", "biNu\n", "BioBeats\n", "BioBots\n", "BioDigital\n", "Biomeme\n", "Birch\n", "Birchbox\n", "Birdback\n", "Birst\n", "Bishop\n", "Bitbar\n", "Bitbucket\n", "Bitcasa\n", "Bitcellar\n", "Bitcovery\n", "Bitdefender\n", "Bitdeli\n", "BiteHunter\n", "BitGo\n", "Bitly\n", "BitPay\n", "Bitsbox\n", "Bitstamp\n", "Bitstrips\n", "BitTorrent\n", "BitWall\n", "Bizdaq\n", "Bizdom\n", "Bizible\n", "Bizo\n", "BizSlate\n", "Bizzabo\n", "Bkstg\n", "Blab\n", "BlaBlaCar\n", "Black\n", "BlackBerry\n", "Blackboard\n", "BlackJet\n", "Blackphone\n", "Blackstone\n", "Blanc\n", "Blast\n", "Blavity\n", "BlazingDB\n", "Blek\n", "Blekko\n", "Blendle\n", "Blendr\n", "BlindType\n", "blinkbox\n", "Blinkist\n", "BlinkMail\n", "Blip\n", "Blipfoto\n", "Blippar\n", "Blippy\n", "Blispay\n", "Blloon\n", "Bloc\n", "Block\n", "Blockai\n", "BlockAvenue\n", "BlockBeacon\n", "Blockchain\n", "BlockCorp\n", "Blockfeed\n", "Blocks\n", "BlockScore\n", "Blockspring\n", "BlockTrail\n", "Blogger\n", "Bloglovin\n", "Blogmutt\n", "Blooie\n", "Bloom+fm\n", "Bloomberg\n", "Bloomfire\n", "BloomNation\n", "BloomReach\n", "Bloomspot\n", "BloomThat\n", "Bloop\n", "Blottr\n", "Blue\n", "Bluebox\n", "BlueCrew\n", "Bluefin\n", "BlueKite\n", "Blueprint\n", "Bluesmart\n", "BlueStacks\n", "BlueStripe\n", "BlueTalon\n", "BlueVia\n", "Blup\n", "Blurb\n", "Blurtt\n", "BMW\n", "Board\n", "Boatbound\n", "Bob\n", "Boden\n", "BodeTree\n", "Bond\n", "Bondsy\n", "Boni\n", "BonitaSoft\n", "Bonobos\n", "Bookboard\n", "BookDoc\n", "Booker\n", "Bookindy\n", "Booking+com\n", "BookingBug\n", "Bookmate\n", "Booktrack\n", "Booktrope\n", "Boombotix\n", "Boombox\n", "Boomi\n", "Boompi\n", "Boop\n", "Booshaka\n", "Boostable\n", "Boostcase\n", "Booyah\n", "Borro\n", "Bosch\n", "Bose\n", "Boston\n", "Boticca\n", "Botify\n", "Bottlenose\n", "Boundary\n", "Bounty\n", "Boutine\n", "Bowflex\n", "Bownty\n", "Box\n", "Boxcar\n", "Boxee\n", "Boxer\n", "Boxever\n", "Boxful\n", "Bpifrance\n", "BPM\n", "Brainient\n", "Brainly\n", "Braintree\n", "Branch8\n", "BranchOut\n", "Brand\n", "BrandAds\n", "Brandcast\n", "BrandMyMail\n", "BrandProject\n", "Brandwatch\n", "BrandYourself\n", "Branson\n", "Brave\n", "Brayola\n", "Breadcrumb\n", "Breakout\n", "Breakthrough\n", "Breather\n", "Breathometer\n", "Breezeworks\n", "Breitling\n", "BrewDrop\n", "Brewster\n", "Brickstream\n", "BridgeU\n", "Bright+com\n", "BrightBytes\n", "BrightContext\n", "Brightcove\n", "BrightEdge\n", "Brighter\n", "BrightFunnel\n", "Brightkite\n", "Brightpearl\n", "BrightPoint\n", "BrightRoll\n", "BRIKA\n", "Brillen+de\n", "Bringg\n", "Brit\n", "Broadcast\n", "Broadcom\n", "BroadMap\n", "Bromium\n", "Browning\n", "Bsecure\n", "BTC\n", "Bubble\n", "Bubbli\n", "Bubbly\n", "Buccaneer\n", "Bucket\n", "BucketListly\n", "Bud\n", "Buddytruk\n", "Buffalo\n", "Buffer\n", "BufferBox\n", "BugBuster\n", "Bugcrowd\n", "BugSense\n", "Bugsnag\n", "BuildDirect\n", "BuildZoom\n", "BuiltWith\n", "Bullet\n", "Bulletin\n", "Bullhorn\n", "Bumble\n", "BumeBox\n", "Bump\n", "Bunkr\n", "Buongiorno\n", "Burberry\n", "Burn\n", "Burpple\n", "Burstly\n", "Burton\n", "BustedTees\n", "Buster\n", "busuu\n", "Buttercoin\n", "Button\n", "Buyhandpicked\n", "Buyou\n", "BuySellAds\n", "BuyWithMe\n", "Buzzcar\n", "BuzzFeed\n", "BuzzMyVideos\n", "BuzzTale\n", "Buzztime\n", "BVP\n", "Byline\n", "BYLINED\n", "Byliner\n", "Bync\n", "Cabify\n", "Cable\n", "CAC\n", "Cakey\n", "Cal\n", "Calendo\n", "Call9\n", "CallApp\n", "Callblock\n", "CallWave\n", "Calm\n", "Caltrain\n", "Calxeda\n", "Camarilla\n", "Cameo\n", "Camera+\n", "Camera360\n", "Cameron\n", "Campalyst\n", "Campless\n", "Camu\n", "Canal+\n", "Canalys\n", "CANDDi\n", "Canon\n", "Canonical\n", "Canva\n", "CanvasPop\n", "Canviz\n", "Canvsly\n", "Capital\n", "Capitan\n", "CapLinked\n", "Capography\n", "Capptain\n", "CapRally\n", "CAPS\n", "Captain401\n", "Captora\n", "Carbyn\n", "CardBlanc\n", "Cardify\n", "CardMunch\n", "Cards\n", "CardSpring\n", "Cardwheel\n", "Care+com\n", "Care24\n", "CareerFoundry\n", "Careerify\n", "CareLedger\n", "CareMessage\n", "CareSkore\n", "CareZapp\n", "CareZone\n", "CargoX\n", "CarHero\n", "Carmudi\n", "Carnegie\n", "Carousel\n", "Carousell\n", "Carpenter\n", "carpooling+com\n", "Carriage\n", "Carro\n", "Carsome\n", "Carspring\n", "Carte\n", "CarTrade\n", "Carvana\n", "Carvoyant\n", "Carwow\n", "Casabu\n", "Case\n", "CashCashPinoy\n", "CashStar\n", "Casio\n", "Caskers\n", "Casper\n", "Caspida\n", "Cassandra\n", "Catawiki\n", "Catbird\n", "Catch+com\n", "Catch\n", "Catchbox\n", "Catcher\n", "CatchFree\n", "CatchThatBus\n", "Cater2+me\n", "Caterspot\n", "CatFi\n", "CauseRocket\n", "Causes\n", "Caviar\n", "Cazena\n", "CBC\n", "CBS\n", "Cedexis\n", "CEE\n", "Celery\n", "Cellrox\n", "CellSavers\n", "CellScope\n", "Celly\n", "Celmatix\n", "Celonis\n", "CEM\n", "Cemmerce\n", "CentOS\n", "Central\n", "CenturyLink\n", "Cenzic\n", "Certiport\n", "CertiVox\n", "CGI\n", "CGTrader\n", "Chaatz\n", "ChaCha\n", "Chalkable\n", "Chalkfly\n", "Challenges\n", "Chance\n", "Change+org\n", "Chango\n", "Chapman\n", "Chargifi\n", "Chartbeat\n", "Chartboost\n", "Chartburst\n", "Chartcube\n", "Charterhouse\n", "Chartio\n", "ChatGrape\n", "Chatterbox\n", "Chatterfly\n", "ChatWork\n", "Checkbook\n", "Checkmarx\n", "Checkr\n", "Checkthis\n", "Cheerboo\n", "Cheezburger\n", "Chef\n", "Chegg\n", "Chewse\n", "Chicago\n", "Chicisimo\n", "Chiizu\n", "Chipolo\n", "Chipworks\n", "Chirpify\n", "Chitika\n", "Chomp\n", "Chosen\n", "Christensen\n", "Chromatik\n", "Chronos\n", "Chute\n", "CIA\n", "Cinder\n", "Cinegif\n", "Cinemagram\n", "Cinematique\n", "CIO\n", "CipherCloud\n", "Circa\n", "Circl\n", "Circles\n", "CircleUp\n", "CircuitLab\n", "CIS\n", "Cisco\n", "cisimple\n", "CIT\n", "Citelighter\n", "Citi\n", "Citron\n", "Citrrus\n", "Citymapper\n", "CityPockets\n", "Citysearch\n", "Civic\n", "Civil\n", "Civo\n", "Cizoo\n", "Claire\n", "Claranet\n", "Clari\n", "Clarity\n", "ClarityRay\n", "Clarivoy\n", "Clarizen\n", "Class\n", "ClassDojo\n", "ClassPager\n", "ClassPass\n", "Classroom\n", "Clay\n", "Cleanly\n", "Clearbit\n", "ClearDATA\n", "ClearFit\n", "Clearpath\n", "ClearTax\n", "Cleartrip\n", "Clementine\n", "Cleveron\n", "Cleversafe\n", "Clickable\n", "Clifton\n", "Clinkle\n", "Clinton\n", "Clip\n", "Clipboard\n", "ClipClock\n", "Clipless\n", "Clipmarks\n", "ClipMine\n", "Clippet\n", "Cliptamatic\n", "Cliptone\n", "Cloakroom\n", "Cloaq\n", "Cloth\n", "Cloud+com\n", "Cloud4Wi\n", "Cloudability\n", "Cloudant\n", "CloudBees\n", "CloudCar\n", "CloudCheckr\n", "cloudControl\n", "CloudEndure\n", "Cloudera\n", "CloudGOO\n", "Cloudike\n", "CloudLock\n", "CloudOn\n", "CloudPhysics\n", "CloudPress\n", "Cloudscaling\n", "CloudSolar\n", "CloudSpokes\n", "Cloudyn\n", "Cloze\n", "CluckCluck\n", "Clustree\n", "Clustrix\n", "Clutter\n", "CMB\n", "CMS\n", "CNIL\n", "CNNIC\n", "Coach\n", "Coast\n", "Cobone\n", "Cobook\n", "Coconut\n", "Cocoon\n", "Codacy\n", "Code+org\n", "Code\n", "Code42\n", "Codeanywhere\n", "Codecademy\n", "CodeCombat\n", "CodeGuard\n", "CodeHS\n", "Codementor\n", "CodeNow\n", "Codenvy\n", "Coderwall\n", "codeSpark\n", "Codex\n", "Codie\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CodinGame\n", "Cody\n", "Coffee\n", "CoffeeTable\n", "CoFoundersLab\n", "Coin\n", "Coinbase\n", "Coinfloor\n", "Coinsetter\n", "CoinTent\n", "Cola\n", "Coles\n", "Coliloquy\n", "Colingo\n", "Collaaj\n", "CollabFinder\n", "CollabNet\n", "Collabspot\n", "Collect\n", "Collection\n", "Collections\n", "CollegeBudget\n", "Collins\n", "colors\n", "colourDNA\n", "Columbus\n", "Combinator\n", "Comcast\n", "Comet\n", "Comma\n", "Comment\n", "Commerce\n", "CommonBond\n", "CommonFloor\n", "CommonKey\n", "Commonred\n", "Commons\n", "Comparably\n", "CompareAsiaGroup\n", "Compendium\n", "Compilr\n", "ComplyAdvantage\n", "Compose\n", "CompStak\n", "Comptel\n", "Compute\n", "comScore\n", "Comunitee\n", "Conceivable\n", "Concert\n", "Concierge\n", "Concurrent\n", "Cone\n", "Conferize\n", "Confide\n", "Confluence\n", "Confluent\n", "conichi\n", "Connectifier\n", "Connectify\n", "Connector\n", "Connexity\n", "ConsenSys\n", "Conspire\n", "ConsultingMD\n", "Consumr\n", "Contactually\n", "ContainerShip\n", "Contastic\n", "Contentful\n", "Contently\n", "Context\n", "Contextly\n", "conTgo\n", "Contour\n", "Control\n", "Convercent\n", "Conversation\n", "Converser\n", "Conversocial\n", "Convies\n", "Convoy\n", "Conyac\n", "Cookening\n", "CoolaData\n", "Cooliris\n", "Cooper\n", "CoPromote\n", "Cor\n", "Coraid\n", "CoreOS\n", "CorFire\n", "Corgi\n", "Corner\n", "CornerJob\n", "Corona\n", "Cortica\n", "Costa\n", "Costello\n", "Cosy\n", "Cotap\n", "Cotendo\n", "Cotopaxi\n", "CoTweet\n", "Coub\n", "Couchbase\n", "CouchCommerce\n", "Coull\n", "Coupang\n", "Couple\n", "Coupons+com\n", "Courier\n", "Coursera\n", "CourseSmart\n", "CourseTalk\n", "Coursmos\n", "CoVenture\n", "CoverHound\n", "CoverItLive\n", "Cowbird\n", "Cozy\n", "cPulse\n", "Craftsvilla\n", "Craigslist\n", "Crashlytics\n", "Crashpadder\n", "Crate\n", "Crater\n", "Craves\n", "Cray\n", "Crazy\n", "Creandum\n", "Creative\n", "CreativeLive\n", "Creator\n", "Creatrs\n", "Credible\n", "Credits\n", "Credo\n", "CredSimple\n", "Creek\n", "CREO\n", "CrestaTech\n", "CREXi\n", "Crimson\n", "Criteo\n", "CRM\n", "Crocodoc\n", "Cronofy\n", "Crosley\n", "Crossbar\n", "Crowd\n", "Crowdbooster\n", "CrowdCompass\n", "Crowdery\n", "CrowdFlik\n", "CrowdHut\n", "CrowdOptic\n", "Crowdpark\n", "CrowdStrike\n", "Crowdtap\n", "CrowdTwist\n", "Crowdynews\n", "Cruise+me\n", "CruiseWise\n", "Crunchyroll\n", "Crusher\n", "Crushpath\n", "Cruz\n", "CryptoSeal\n", "CSG\n", "CSR\n", "Ctrip\n", "Cubby\n", "Cube26\n", "Cubic\n", "Cucumbertown\n", "Cuff\n", "CultureSphere\n", "CumuLogic\n", "Cupple\n", "Curacity\n", "Curalate\n", "Curatum\n", "Curbside\n", "Curiator\n", "Curioos\n", "Curology\n", "Currents\n", "Curse\n", "Curtis\n", "Cushman\n", "Custom\n", "CustomInk\n", "CustomMade\n", "Cut\n", "Cuvva\n", "Cvent\n", "CWT\n", "CyActive\n", "Cyan\n", "Cyanogen\n", "Cybereason\n", "Cybozu\n", "Cylance\n", "Cylindo\n", "Cymmetria\n", "Cyvera\n", "Dache\n", "Dacuda\n", "DailyBooth\n", "DailyDeal\n", "DailyLit\n", "Dailymotion\n", "DailyRounds\n", "DailyWorth\n", "Daimler\n", "Dakwak\n", "Dale\n", "Dandy\n", "Daniel\n", "Daric\n", "Darjeelin\n", "Darkstore\n", "Darktrace\n", "DARPA\n", "Dasher\n", "Dashlane\n", "DATA\n", "Databox\n", "DataCamp\n", "Dataflow\n", "DataFox\n", "DataGravity\n", "Datahero\n", "Datahug\n", "Datalogix\n", "Datameer\n", "Dataminr\n", "Datanyze\n", "Datapipe\n", "DataRank\n", "DataSift\n", "DataStax\n", "DataTorrent\n", "DataXu\n", "Datto\n", "Daydream\n", "DBX\n", "DCM\n", "DealAngel\n", "Dealupa\n", "Decide\n", "Decorist\n", "Dedrone\n", "Deekit\n", "Deem\n", "Deepak\n", "Deeper\n", "Deeplink\n", "Deepomatic\n", "Deeyoon\n", "Deezer\n", "Defender\n", "DefinedCrowd\n", "Degreed\n", "Deja\n", "Dejamor\n", "Dekko\n", "DelFly\n", "Delhivery\n", "Delighted\n", "Deliv\n", "Deliverd\n", "Deliveroo\n", "Dell\n", "Deloitte\n", "Delta\n", "Demandware\n", "DEMO\n", "Demotix\n", "DemystData\n", "DeNA\n", "Depop\n", "Descomplica\n", "DesignCrowd\n", "Desk+com\n", "Desti\n", "Destiny\n", "Detour\n", "Devialet\n", "Dexplora\n", "Dexter\n", "DFKI\n", "DG\n", "DGSE\n", "Dharma\n", "Dhingana\n", "DHL\n", "Diagnosia\n", "Diamanti\n", "Diary+com\n", "Diaspora\n", "Dice\n", "Dickson\n", "Diffbot\n", "Digg\n", "Digi+me\n", "Digia\n", "Digimarc\n", "DigiSynd\n", "Digital\n", "DigitalGenius\n", "DigitalGlobe\n", "DigitalOcean\n", "Digits\n", "Digitsole\n", "Dillon\n", "Dinein+co+uk\n", "Ding\n", "Dingo\n", "Dinube\n", "DipJar\n", "Disconnect\n", "Discord\n", "Discourse\n", "Discoverly\n", "Discovery\n", "Dish+fm\n", "Displair\n", "Dispop\n", "Disqus\n", "Distill\n", "Distimo\n", "Distributed\n", "District\n", "DistroKid\n", "Diveboard\n", "Divide\n", "Divine\n", "Divshot\n", "DJI\n", "Djump\n", "DKSH\n", "DLD\n", "DMARC\n", "DMCA\n", "Do\n", "Dobango\n", "Doblet\n", "Doccaster\n", "Docker\n", "Docphin\n", "Docracy\n", "DocSend\n", "doctape\n", "doctHERs\n", "Doctolib\n", "DoctorsElite\n", "Documentum\n", "Docurated\n", "DocuSign\n", "Dodge\n", "Dodo\n", "Dogecoin\n", "DogSync\n", "DogVacay\n", "Domo\n", "Donde\n", "Donut\n", "Donuts\n", "doo\n", "Doodle+ly\n", "DoorDash\n", "Doorman\n", "Doozton\n", "DormChat\n", "Dormi\n", "dot429\n", "Dotcom\n", "DoubleClick\n", "DoubleDutch\n", "DoubleRecall\n", "Doublie\n", "Dove\n", "Down\n", "Doxie\n", "Doximity\n", "DoxOut\n", "Dónde\n", "DPS\n", "Draft\n", "DraftKings\n", "DragonWave\n", "DramaFever\n", "Draper\n", "DreamCheaper\n", "DreamHost\n", "Dreamit\n", "Dreem\n", "Dremel\n", "Drimmit\n", "DrinkMate\n", "Drippler\n", "Drivemode\n", "Driver\n", "Drivy\n", "Drizly\n", "DroneBase\n", "DroneDeploy\n", "drop\n", "Dropbox\n", "Dropcam\n", "DropGifts\n", "Dropost+it\n", "DropTask\n", "Drupal\n", "Druva\n", "Drync\n", "DSC\n", "DTN\n", "Duarte\n", "Dubsmash\n", "DuckDuckGo\n", "Dude\n", "Dudek\n", "DueCourse\n", "DueDil\n", "Duetto\n", "Dull\n", "Duo\n", "Duolingo\n", "Duracell\n", "DWNLD\n", "Dwolla\n", "DWS\n", "DXY\n", "Dymant\n", "Dyn\n", "DynamicOps\n", "Dynamics\n", "Dynatrace\n", "Dyson\n", "Dysonics\n", "E3\n", "Earbits\n", "Earmark\n", "Earshot\n", "earthmine\n", "EasilyDo\n", "Easy\n", "EasyPost\n", "Eatigo\n", "EatStreet\n", "Eaze\n", "Ebates\n", "eBay\n", "eBuddy\n", "Ebyline\n", "Echo360\n", "Echobox\n", "Echofon\n", "Echograph\n", "Eco\n", "ecoATM\n", "Ecoisme\n", "Ecwid\n", "Edelman\n", "Eden\n", "EDM\n", "Edmodo\n", "edocr\n", "EdReach\n", "EdSurge\n", "eduClipper\n", "Educreations\n", "EduKart\n", "Edwin\n", "edX\n", "Edyn\n", "EE\n", "eeGeo\n", "eero\n", "Effektif\n", "eFounders\n", "Efrat\n", "Egg\n", "Egnyte\n", "eGood\n", "eGym\n", "eHarmony\n", "EIS\n", "Eko\n", "Ektron\n", "Elance\n", "Elastica\n", "ElasticBox\n", "Ele+me\n", "Elepath\n", "Elephanti\n", "Elevatr\n", "Elf\n", "Eliademy\n", "Eligible\n", "Ellation\n", "Ellen\n", "Ellevest\n", "Ellis\n", "Elmo\n", "Elop\n", "Else\n", "eMarketer\n", "Ember\n", "Emberlight\n", "Embrace\n", "Emburse\n", "EMC\n", "Emergence\n", "Emergent\n", "Emerson\n", "Emily\n", "Emoji\n", "Emojiary\n", "Emotient\n", "Empath\n", "EmployInsight\n", "Encap\n", "Encoding+com\n", "Encore\n", "Encription\n", "Endaga\n", "Endeca\n", "Endgame\n", "Endor\n", "Endorse+me\n", "Energy\n", "Engadget\n", "EngageSciences\n", "Engagio\n", "Engagor\n", "Engodo\n", "Engrade\n", "Enstratius\n", "Enswers\n", "Ensygnia\n", "Entelo\n", "Enthuse\n", "Entitle\n", "Entrepreneur\n", "EPC\n", "EPIX\n", "EPO\n", "Epoch\n", "Epocrates\n", "EPS\n", "Epson\n", "EQ\n", "EQAL\n", "Equation\n", "EquipmentShare\n", "eReader\n", "Erickson\n", "Ericsson\n", "ERN\n", "Errplane\n", "Escrow+com\n", "eShares\n", "Espacio\n", "eSpark\n", "Esper\n", "Esplorio\n", "ESPN\n", "Espresa\n", "Esquire\n", "Esri\n", "EST\n", "Estify\n", "Estimize\n", "ETA\n", "Ethan\n", "Ethnio\n", "Etsy\n", "Eumakh\n", "Eurogamer\n", "EVA\n", "Evaneos\n", "Evans\n", "Eventbrite\n", "Eventful\n", "Eventifier\n", "Eventioz\n", "Eventjoy\n", "Evercontact\n", "Everett\n", "Evergram\n", "Everlane\n", "Everloop\n", "Evernote\n", "Everpix\n", "Everplaces\n", "Everpurse\n", "EverQuest\n", "Eversnap\n", "Evertale\n", "Evertoon\n", "EverTrue\n", "Everwise\n", "Everyday+me\n", "EveryLodge\n", "Everyme\n", "EveryMove\n", "Everyplay\n", "EverySignal\n", "EverythingMe\n", "Evi\n", "Evidence+com\n", "Evie\n", "Evite\n", "EvntLive\n", "Evomail\n", "Evri\n", "EVRYTHNG\n", "ExactTarget\n", "Exec\n", "Exiles\n", "Existor\n", "Exitround\n", "ExoClick\n", "Exogen\n", "Expa\n", "Expand\n", "Expansys\n", "Expedia\n", "ExpenseMagic\n", "Expensify\n", "Experfy\n", "Experience\n", "Experiences\n", "Expertmaker\n", "Experts\n", "Expion\n", "Exploreka\n", "Explorer\n", "Express\n", "Expression\n", "Extole\n", "EyeEm\n", "Eyegroove\n", "EyeIn\n", "Eyeota\n", "EyeTrackShop\n", "EyeVerify\n", "Eyeview\n", "Ezeecube\n", "Ezetap\n", "F+ounders\n", "F-Secure\n", "F6S\n", "Fab\n", "FabFitFun\n", "Fabrily\n", "Face+com\n", "Facebook\n", "Facer\n", "Fair\n", "FairFleet\n", "Fama\n", "Fame\n", "FameBit\n", "Famigo\n", "FamilyLeaf\n", "Famo+us\n", "Famous\n", "Fan\n", "Fanatics\n", "fanatix\n", "Fanbase\n", "FanBread\n", "FanBridge\n", "Fancred\n", "Fancy\n", "Fandalism\n", "Fandango\n", "Fandeavor\n", "FanDuel\n", "Fanmode\n", "Fanout\n", "Fanplayr\n", "Fantastic\n", "Fantasy\n", "Fantoo\n", "Fantuition\n", "Fanwards\n", "Faraday\n", "FarEye\n", "FarFaria\n", "Farfetch\n", "Farm2050\n", "Farmigo\n", "FarmLogs\n", "Fastacash\n", "Fastbite\n", "FastPay\n", "FastSpring\n", "FatFractal\n", "Fatmap\n", "Favado\n", "Favor\n", "Favr+tt\n", "Feed+fm\n", "Feed\n", "FeedHenry\n", "Feedspot\n", "Feels\n", "FeeX\n", "Felix\n", "Femsplain\n", "Fender\n", "Fenix\n", "Festicket\n", "Fetchnotes\n", "FF\n", "FI-WARE\n", "Fiberead\n", "Fictiv\n", "Field\n", "Fieldwire\n", "FiftyThree\n", "FightMe\n", "Figma\n", "Fiksu\n", "Filabot\n", "Fileboard\n", "FileMaker\n", "FileThis\n", "Filip\n", "Filter+ly\n", "FilterEasy\n", "Fin\n", "Final\n", "Finale\n", "FinancialForce\n", "Findables\n", "FindIt\n", "Findster\n", "Fingerprint\n", "Fingo\n", "Finish\n", "FinLeap\n", "Fintech\n", "Firebase\n", "FireEye\n", "Fireside\n", "First\n", "FirstLook\n", "FIS\n", "FiscalNote\n", "Fish\n", "FishBrain\n", "FitBark\n", "Fitbay\n", "Fitbit\n", "FITiST\n", "Fitocracy\n", "FitStar\n", "Fitt\n", "Fitternity\n", "FiveAI\n", "FiveStars\n", "Fiz\n", "Fjuul\n", "Flag\n", "Flash\n", "FlashFunders\n", "Flashgap\n", "FlashSoft\n", "Flat\n", "Flatchat\n", "Flattr\n", "Flavorpill\n", "Fleck\n", "Fleex\n", "Fleksy\n", "Flexport\n", "Flic\n", "Flickr\n", "Fling\n", "Flint\n", "Flipagram\n", "Flipboard\n", "Flipd\n", "Flipkart\n", "Fliplet\n", "Flipp\n", "Flipps\n", "Flips\n", "Flirtey\n", "Flite\n", "Flixster\n", "Flocations\n", "Flomio\n", "Floobits\n", "Floored\n", "Floret\n", "Flowdock\n", "Flowtab\n", "FlowVella\n", "Fluance\n", "Flud\n", "Fluent\n", "Flurry\n", "Fly6\n", "Flybridge\n", "Flybrix\n", "Flyby\n", "FlyCleaners\n", "Flyer\n", "Flyfit\n", "Flynn\n", "Flypad\n", "Flypay\n", "Flytenow\n", "Flytrex\n", "FOBO\n", "Follow\n", "Fon\n", "Fondu\n", "Fontacto\n", "Fonticons\n", "Food\n", "Food52\n", "FoodChéri\n", "Foodily\n", "Foodini\n", "Foodity\n", "Foodmento\n", "Foodpanda\n", "Foodspotting\n", "Foodzy\n", "Forage\n", "Forbes\n", "Force\n", "Forever\n", "Forgacs\n", "Forget+me\n", "Formation\n", "Formlabs\n", "Formspring\n", "Formvertise\n", "Forrst\n", "Forsythe\n", "Fortify\n", "Fortumo\n", "Fortune\n", "FortyCloud\n", "Fosbury\n", "Fossil\n", "Foster\n", "Fotolia\n", "Foundation\n", "FoundationDB\n", "Founder2be\n", "FounderDating\n", "FounderFuel\n", "Founders\n", "FoundersCard\n", "Foursquare\n", "FOVE\n", "Fovo\n", "Fox\n", "Fradio\n", "Frame+io\n", "Framebench\n", "FrameBlast\n", "Frameri\n", "Francis\n", "Frederick\n", "Freebase\n", "FreeCharge\n", "FreedomPop\n", "freee\n", "Freeman\n", "Freespee\n", "Freeview\n", "FreeWavz\n", "Freewheel\n", "Fresh\n", "Freshdesk\n", "Freshii\n", "Freshly\n", "Freshplum\n", "Fribi\n", "Frichti\n", "Friday\n", "Friedman\n", "Friend+ly\n", "FriendFeed\n", "FriendFinder\n", "Friendster\n", "Friendsy\n", "fromAtoB\n", "Frontback\n", "Frontier\n", "Froont\n", "Frrole\n", "Frugalo\n", "FTC\n", "Fuel\n", "Fuel3D\n", "Fuelling\n", "Fuhu\n", "Fujitsu\n", "Fulcrum\n", "FullContact\n", "Fullscreen\n", "Fullstack\n", "Function\n", "Fundable\n", "Fundera\n", "FunderCloud\n", "FundersClub\n", "Fundly\n", "FunnyJunk\n", "Funzio\n", "Fusion-io\n", "Futurelytics\n", "Futuremark\n", "Fuze\n", "Fuzel\n", "Fuzmo\n", "FWD+us\n", "FwdForce\n", "FwdHealth\n", "FX\n", "Fyndiq\n", "G5\n", "Gaana\n", "Gabriel\n", "Gaga\n", "Gaikai\n", "GAIN\n", "Gal\n", "Galaxia\n", "Galxyz\n", "Gambitious\n", "GameFly\n", "GameFounders\n", "Gameloft\n", "gamesGRABR\n", "GamesThatGive\n", "Gametime\n", "Gamify\n", "Gan\n", "Gant\n", "GarageBand\n", "Garantia\n", "Garmin\n", "Gartner\n", "Gate\n", "GateGuru\n", "Gates\n", "GatherContent\n", "GazeHawk\n", "GB\n", "GCE\n", "GCHQ\n", "gdgt\n", "Geckoboard\n", "Geeksphone\n", "Gem\n", "GEMA\n", "Gemnote\n", "GemShare\n", "Genee\n", "Genera\n", "Gengo\n", "GenieDB\n", "Genius\n", "Genome\n", "GENWI\n", "Geofeedia\n", "Geoloqi\n", "GeoOrbital\n", "GeoPoll\n", "Georama\n", "Get+com\n", "Getable\n", "GetApp\n", "Getaround\n", "GetFeedback\n", "GetGoing\n", "GetJar\n", "GetLinks\n", "Getonic\n", "GetReal\n", "GetScale\n", "GetSet\n", "Gett\n", "getTalent\n", "GetYourGuide\n", "GFG\n", "Ghost\n", "Gibbon\n", "Gidsy\n", "Giffiti\n", "GIFs\n", "GiftCards+com\n", "Giftiki\n", "Giftivo\n", "Giftly\n", "GiftRocket\n", "Gig\n", "Gigaom\n", "Giggem\n", "Gigit\n", "GigLocator\n", "Gigstart\n", "Gigster\n", "Gigwalk\n", "Gigwell\n", "Gigya\n", "Gild\n", "Gimmie\n", "Ginger+io\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Giphy\n", "Git\n", "GitHub\n", "GiveMeSport\n", "GiveMeTap\n", "Givey\n", "Givit\n", "Gizmox\n", "Glance\n", "Glancee\n", "Glassbreakers\n", "Glassdoor\n", "Glasses\n", "GlassesGroupGlobal\n", "Glassmap\n", "GlassUp\n", "Gleam\n", "GLG\n", "Glide\n", "Gliffy\n", "Gliimpse\n", "Glimpse\n", "Glints\n", "Glitter\n", "GlobalSCAPE\n", "GlobalSign\n", "Globant\n", "GlobeIn\n", "Globevestor\n", "Glocal\n", "Glooko\n", "Glopho\n", "Glose\n", "Glossier\n", "Glovo\n", "Glow\n", "Glowforge\n", "Gluster\n", "Glympse\n", "Glyph\n", "GMT\n", "GMV\n", "Gnip\n", "Gnzo\n", "Go-Jek\n", "GO1\n", "Goalbook\n", "GOAT\n", "goBalto\n", "GoBank\n", "Gobble\n", "Gobi\n", "Gobstopper\n", "GoCardless\n", "GoDaddy\n", "GoEuro\n", "GoGoGrandparent\n", "Gogolook\n", "Gogoprint\n", "Gogoro\n", "GoGoVan\n", "GoingOn\n", "GoInstant\n", "Gojee\n", "Goldbely\n", "Goldee\n", "GoldieBlox\n", "Goliath\n", "GoMiles\n", "Gone\n", "GoodData\n", "Goodreads\n", "GoodRx\n", "Goodservice\n", "Goodsie\n", "Google\n", "Google_\n", "GoPollGo\n", "GoPro\n", "GOQii\n", "Gorgias\n", "Gorilla\n", "GoSquared\n", "Got\n", "GoTenna\n", "Gotham\n", "Gousto\n", "Gowalla\n", "Goxip\n", "Gozaik\n", "GrabCAD\n", "Grabio\n", "Grabr\n", "Grabyo\n", "Gradberry\n", "Grades+io\n", "GradFly\n", "Gradifi\n", "Gradle\n", "Graduateland\n", "Graduway\n", "Graham\n", "Grapeshot\n", "Graphicly\n", "GraphOn\n", "grasp\n", "Grasshopper\n", "Grasswire\n", "Gratafy\n", "Gravie\n", "Gravitant\n", "Gravity4\n", "Greatist\n", "Greats\n", "GREE\n", "Greedy\n", "Greenfield\n", "Greenhouse\n", "Greenstart\n", "Greenwood\n", "Greta\n", "Greyloft\n", "GreyOrange\n", "GRID\n", "Griffin\n", "Grind\n", "Grindr\n", "Grockit\n", "Grofers\n", "Grokr\n", "Groopie\n", "Groopt\n", "Grooveshark\n", "GroSocial\n", "Grotech\n", "GroundLink\n", "GroupAhead\n", "Grouper\n", "GroupLogic\n", "GroupMe\n", "Groupnotes\n", "Groupon\n", "GroupSpaces\n", "GroupTalent\n", "Grovemade\n", "Grover\n", "Grovo\n", "GrowSumo\n", "Growth\n", "GrubHub\n", "Gruveo\n", "GSF\n", "GSG\n", "Guardian\n", "Guardly\n", "Gucci\n", "Guesswork\n", "GuestDriven\n", "Guesty\n", "Guides+co\n", "GuideSpark\n", "Guild\n", "Guildery\n", "GumGum\n", "Gumhouse\n", "gumi\n", "Gumroad\n", "Gunosy\n", "Guvera\n", "Guys\n", "GV\n", "Gyft\n", "Gymdeck\n", "GymGroups\n", "H2L\n", "Habit\n", "Hachi\n", "HackerEarth\n", "HackerOne\n", "HackerRank\n", "HackFwd\n", "HackHands\n", "Hadoop\n", "Hadopi\n", "Hagen\n", "Haier\n", "Haiku\n", "Hall\n", "Hamburg\n", "Hamer\n", "Hamilton\n", "Hamwells\n", "Handpick\n", "Hands\n", "Handy\n", "Hansoft\n", "Happify\n", "Happn\n", "Happtique\n", "Happy\n", "HappyFresh\n", "HappyFunCorp\n", "Haptik\n", "Harlequin\n", "Harman\n", "Harp\n", "HarperCollins\n", "Harris\n", "Hart\n", "Hartmann\n", "Hartz\n", "HashiCorp\n", "HashTip\n", "Hasselblad\n", "Hat\n", "Hatchery\n", "Hathway\n", "Havenly\n", "Hawkins\n", "HBO\n", "HD+\n", "HDI\n", "Head\n", "HeadBox\n", "Headout\n", "Headspace\n", "Healint\n", "Health2Sync\n", "HealthKeep\n", "HealthKit\n", "HealthLoop\n", "Healthrageous\n", "HealthSouk\n", "HealthTap\n", "Healy\n", "Hear\n", "Hear2Read\n", "HeartThis\n", "HeavenHR\n", "Heek\n", "Heetch\n", "Helium\n", "HelloFax\n", "Hellofood\n", "HelloFresh\n", "HelloMD\n", "HelloSign\n", "HelloSociety\n", "HelloSoda\n", "HelloTech\n", "HelloWallet\n", "Helpling\n", "Helpshift\n", "Hem\n", "Henderson\n", "Henkel\n", "Her\n", "HERE\n", "Heroku\n", "Hertz\n", "Hewlett-Packard\n", "Hexadite\n", "Heyday\n", "HeyLets\n", "Heyzap\n", "HGST\n", "hi5\n", "Hibob\n", "Hickies\n", "HiGear\n", "HigherMe\n", "Highlight\n", "HighQ\n", "Highrise\n", "Hightail\n", "Highway1\n", "hike\n", "Hilton\n", "HiMom\n", "Hinge\n", "Hipcamp\n", "HipChat\n", "Hipmunk\n", "Hippflow\n", "Hippo\n", "Hipstamatic\n", "Hipster\n", "Hiptype\n", "HireArt\n", "Hired\n", "HireVue\n", "Hiri\n", "Hitachi\n", "HitBliss\n", "Hitlist\n", "Hitpost\n", "Hitwise\n", "Hivemapper\n", "Hiya\n", "HMD\n", "Hmmm\n", "Hobzy\n", "Holden\n", "Hollar\n", "Holmes\n", "Holographic\n", "Holvi\n", "Home\n", "HomeAway\n", "Homee\n", "Homejoy\n", "HomeLight\n", "HomeMe\n", "Homescreen\n", "Homesnap\n", "Hometalk\n", "Hometeam\n", "HoneyBook\n", "Honeypot\n", "Honk\n", "Hooch\n", "Hoodline\n", "Hooked\n", "Hooks\n", "HOOQ\n", "Hooray\n", "Hootsuite\n", "Hoover\n", "Hopper\n", "Hoppit\n", "hoppr\n", "HopSkipDrive\n", "Hopster\n", "Horizons\n", "Hortonworks\n", "Hostmaker\n", "Hotaru\n", "HotelQuickly\n", "HotelTonight\n", "Hotlist\n", "Hotmail\n", "HotPads\n", "Hotspot\n", "Hotspots+io\n", "Hotspots\n", "Houdini\n", "Hound\n", "Housebites\n", "HouseFix\n", "Housejoy\n", "HouseTrip\n", "Housing+com\n", "Houston\n", "Houzz\n", "HowAboutWe\n", "HowGood\n", "HSBC\n", "HSN\n", "HTC\n", "Hua\n", "Hubbl\n", "Hublished\n", "Hublo\n", "HubSpot\n", "Hubub\n", "Huckle\n", "Hudl\n", "Hudson\n", "HUDWAY\n", "Hughes\n", "Hukkster\n", "Hulbee\n", "Hull\n", "Hullabalu\n", "Hulu\n", "Human\n", "Humanoid\n", "Humans\n", "Humble\n", "Humin\n", "Hungama\n", "Hungryroot\n", "Hunt\n", "Hunter\n", "Hurley\n", "Hushed\n", "HushHush\n", "HWTrek\n", "Hybrid\n", "Hydroswarm\n", "Hykoo\n", "HyPer\n", "Hyperdrive\n", "Hyperink\n", "HyprMX\n", "HyTrust\n", "i2\n", "IAC\n", "iAdvize\n", "iBeat\n", "IBM\n", "Ibotta\n", "ICANN\n", "ice\n", "Icertis\n", "ICO\n", "ICOMP\n", "Iconfinder\n", "Iconic\n", "Iconicfuture\n", "icons\n", "iContact\n", "iControl\n", "IDC\n", "Idea+me\n", "Ideal\n", "Identified\n", "Identity\n", "Ideum\n", "IDNow\n", "Idomoo\n", "Ifeelgoods\n", "iFetch\n", "Ifinity\n", "iFit\n", "iFixit\n", "iFood\n", "IFTTT\n", "iHeart\n", "iHeartRadio\n", "III\n", "Illumio\n", "Imagination\n", "Imagine\n", "IMAX\n", "Imgur\n", "Immediately\n", "Immersit\n", "imoji\n", "Impending\n", "Impermium\n", "Implisit\n", "Impossible\n", "Impraise\n", "ImpressPages\n", "ImpulseSave\n", "Inadco\n", "Inbox\n", "Inc+\n", "Incase\n", "InCrowd\n", "Incubate\n", "Independent\n", "Indie\n", "IndieGameStand\n", "Indiegogo\n", "inDinero\n", "Indisys\n", "Individual\n", "Indix\n", "Indochino\n", "indoo+rs\n", "IndoorAtlas\n", "Industrie\n", "Industry\n", "Infinite+ly\n", "Influenster\n", "Influitive\n", "InfluxData\n", "Infobip\n", "Infochimps\n", "Infomedia\n", "Informatica\n", "InfoScout\n", "Infosys\n", "Infratel\n", "Infusionsoft\n", "Ingenico\n", "Initial\n", "ink\n", "Inkl\n", "Inkling\n", "Inktank\n", "inMarket\n", "Inmarsat\n", "InMobi\n", "InMotion\n", "Inneractive\n", "InnoGames\n", "InnoSpring\n", "Innov8\n", "Innovid\n", "Innoz\n", "inploi\n", "inPulse\n", "Input\n", "Insensi\n", "Inside+com\n", "Inside\n", "InsideSales+com\n", "InsideView\n", "Insight\n", "Insightpool\n", "InsightSquared\n", "inspiration\n", "Inspirato\n", "Inspirock\n", "Insta\n", "Instabeat\n", "Instabridge\n", "Instabug\n", "Instacart\n", "InstaEDU\n", "Instagram\n", "InstallMonetizer\n", "Instapaper\n", "Instavest\n", "InstaVet\n", "Instructure\n", "Insurify\n", "Insyde\n", "Insync\n", "Integral\n", "Intel\n", "Interactive\n", "Intercom\n", "Intern\n", "International\n", "Internet+org\n", "Interview\n", "Interviewed\n", "Intralist\n", "Intro\n", "Intuit\n", "Inuvo\n", "Invenias\n", "Inverse\n", "Investing+com\n", "InvisibleHand\n", "InVision\n", "Invoice2go\n", "InvoiceASAP\n", "Involver\n", "IOC\n", "Ionic\n", "Iotera\n", "IOVOX\n", "ip+access\n", "IPG\n", "IPO\n", "iPrice\n", "ipsy\n", "IPTV\n", "iQiyi\n", "Iris\n", "iRobot\n", "Ironclad\n", "ISKN\n", "Island\n", "Islands\n", "isocket\n", "Issue\n", "Issuu\n", "iStopOver\n", "iStoryTime\n", "iSuppli\n", "iSwapp\n", "ITG\n", "Itseez\n", "ItsOn\n", "Ive\n", "iversity\n", "iwaku\n", "iwoca\n", "iZettle\n", "iZotope\n", "Jabra\n", "JackThreads\n", "Jaguar\n", "Jajah\n", "Jake\n", "JAM\n", "JamBase\n", "JamCam\n", "James\n", "JANDI\n", "Jarvis\n", "Jason\n", "Jasper\n", "Jaunt\n", "Jawbone\n", "JD+com\n", "Jean\n", "Jeeran\n", "Jeffrey\n", "Jelastic\n", "Jellynote\n", "Jet\n", "Jetbay\n", "Jetlore\n", "Jetpac\n", "Jetsetter\n", "JetSmarter\n", "jEugene\n", "Jewelbots\n", "JFDI+Asia\n", "JFrog\n", "Jibbigo\n", "JibJab\n", "Jibo\n", "Jiffy\n", "JigoCity\n", "Jimdo\n", "Jimmyjane\n", "Jink\n", "Jinn\n", "Jirafe\n", "Jirnexu\n", "Jitterbit\n", "Jobandtalent\n", "Jobbatical\n", "Jobr\n", "JobScout\n", "Jobspotting\n", "JobVidi\n", "Jobvite\n", "Johnson\n", "Joint\n", "Jolicloud\n", "JolieBox\n", "Jolla\n", "Jomi\n", "Jongla\n", "Joomla\n", "Jordan\n", "Josephine\n", "Jostle\n", "Journy\n", "Joyable\n", "Joyent\n", "JoyTunes\n", "JPEGmini\n", "JUCE\n", "Judicata\n", "Juengo\n", "Juicero\n", "JuicyCanvas\n", "Jukedeck\n", "Jukely\n", "Julep\n", "Julpan\n", "Jumia\n", "Jumio\n", "JumpCloud\n", "Jumpstarter\n", "Jumptap\n", "Junar\n", "Junyo\n", "Jusp\n", "JustBook\n", "JustGiving\n", "JustPark\n", "JustReachOut\n", "JustWatch\n", "JVP\n", "Jybe\n", "K1\n", "K12\n", "Kabam\n", "Kabbage\n", "Kabbee\n", "Kaggle\n", "Kahuna\n", "Kamcord\n", "Kamino\n", "Kana\n", "Kander\n", "Kantar\n", "Kaplan\n", "Kapost\n", "Kara\n", "Kareo\n", "Kargo\n", "Karmaloop\n", "Kash\n", "KashFlow\n", "Katana\n", "Kay\n", "Kazaana\n", "Kazam\n", "KDDI\n", "Keek\n", "Keepsy\n", "KeepTruckin\n", "Kenexa\n", "Kentik\n", "Kepler\n", "Kera\n", "Kershaw\n", "Ketchuppp\n", "Keurig\n", "Keybase\n", "keychain\n", "KeyMe\n", "Keywee\n", "KFit\n", "Kickboard\n", "Kickbooster\n", "Kickpay\n", "Kickscout\n", "Kickstarter\n", "KidAdmit\n", "Kidaptive\n", "Kidizen\n", "KidoZen\n", "Kids\n", "KidZui\n", "Kifi\n", "Kiip\n", "Kik\n", "Kili\n", "Kin\n", "Kinder\n", "KinderTown\n", "Kindly\n", "King+com\n", "Kingdon\n", "Kingsoft\n", "Kinnek\n", "KinoPoisk\n", "Kinsa\n", "Kinvey\n", "Kiosked\n", "Kippt\n", "Kismet\n", "Kitchenbowl\n", "Kitchenbug\n", "Kitchensurfing\n", "KitchMe\n", "Kite+io\n", "KiteDesk\n", "Kitematic\n", "Kitestring\n", "KitSplit\n", "Kiva\n", "KKR\n", "Klarismo\n", "Klarna\n", "Klash\n", "Kleverbeast\n", "KlickNation\n", "Klook\n", "Klout\n", "Knewton\n", "Kngine\n", "Kno\n", "Knoala\n", "Knocki\n", "Knok\n", "Knoll\n", "Knotable\n", "Knotch\n", "KnowMe\n", "Knowmia\n", "Knowre\n", "Knozen\n", "Koality\n", "Kobalt\n", "Kobo\n", "Kochava\n", "Kodak\n", "Koding\n", "Koemei\n", "Kogan\n", "Kokoroe\n", "Komprise\n", "Konami\n", "Konekt+me\n", "Kony\n", "Koozoo\n", "Korbit\n", "Korner\n", "Koudai\n", "Krablr\n", "Kraft\n", "Krawd\n", "Kroll\n", "Krossover\n", "Kryptnostic\n", "Kstartup\n", "Kuapay\n", "Kudoso\n", "kununu\n", "KupiVIP\n", "Kurio\n", "Kurrenci\n", "Kwaga\n", "KweekWeek\n", "Kwoller\n", "KYA\n", "Kymeta\n", "Kytephone\n", "La-La\n", "Lab42\n", "Labor\n", "Labster\n", "LaFourchette\n", "Lakestar\n", "Lambda\n", "LaMetric\n", "Lamoda\n", "Lamudi\n", "Lancope\n", "Landlordology\n", "Lane\n", "Lanyrd\n", "Laptop\n", "Lara\n", "Lark\n", "Larky\n", "Larsen\n", "Lasso\n", "Last+fm\n", "lastminute+com\n", "LastPass\n", "Latch\n", "Late\n", "Laugh+ly\n", "Launch48\n", "LaunchBit\n", "LaunchDarkly\n", "Launcher\n", "LaunchGram\n", "LaunchHouse\n", "LaunchKit\n", "LaunchPoint\n", "LaunchRock\n", "Laundrapp\n", "Lavabit\n", "Lavaboom\n", "Lawdingo\n", "Lawson\n", "Layar\n", "Layer\n", "Leadbolt\n", "LeadFormix\n", "LeadLedger\n", "LeadSift\n", "LEAGUE\n", "Leak\n", "Leaky\n", "LeanData\n", "LeanMarket\n", "Learndot\n", "Learnist\n", "LearnKo\n", "LearnSprout\n", "LearnStreet\n", "LearnVest\n", "Lebara\n", "LeCab\n", "Lee\n", "Leetchi\n", "LeFeed\n", "Leftronic\n", "Legacy\n", "LegalZoom\n", "LegbaCore\n", "Legend\n", "Legentas\n", "Legimi\n", "Legit\n", "LEGO\n", "LeKiosk\n", "Lender\n", "Lendingkart\n", "Lendio\n", "Lendix\n", "LendUp\n", "Lenka\n", "Lenovo\n", "Lenstag\n", "Lernstift\n", "Leslie’s\n", "LesPAC+com\n", "Lesson\n", "Let\n", "letgo\n", "LetsListen\n", "LetsLunch\n", "LetsTransport\n", "LetsWombat\n", "Letterboxd\n", "Lettuce\n", "Levebee\n", "Level\n", "LevelUp\n", "Levitas\n", "Levi’s\n", "LeWeb\n", "Lex\n", "Lexity\n", "LG\n", "Libboo\n", "Liberio\n", "Libertas\n", "Liberty\n", "LibraTax\n", "Libratone\n", "Libsyn\n", "Life360\n", "Lifecake\n", "Lifeliqe\n", "LifeLock\n", "Lifesum\n", "LifeTip\n", "LiftDNA\n", "Liftopia\n", "LIFX\n", "Lighter\n", "LightFreq\n", "Lightricks\n", "LightSpeed\n", "LightUp\n", "Lightwave\n", "Likelii\n", "Likemind\n", "Lima\n", "Lindsay\n", "Line-Up\n", "Line\n", "Linea\n", "Lineshark\n", "Ling\n", "Lingo\n", "Lingua+ly\n", "Linio\n", "LINK\n", "LinkedIn\n", "Linode\n", "Linqapp\n", "Linqia\n", "Lion\n", "Lionside\n", "Lip\n", "Lipman\n", "Lish\n", "Listia\n", "Litsy\n", "Little\n", "LittleThings\n", "LIV\n", "LIVE\n", "Livefyre\n", "LiveIntent\n", "Livemap\n", "Livemocha\n", "LiveNinja\n", "LiveOps\n", "LivePerson\n", "LiveRail\n", "Livescribe\n", "Livestar\n", "Livestream\n", "LivingSocial\n", "Livongo\n", "Livrada\n", "Livspace\n", "LMS\n", "Lob\n", "Lobster\n", "Localmind\n", "LocalOn\n", "LocalUncle\n", "Localytics\n", "Location\n", "Locationary\n", "Locca\n", "Locent\n", "Lock8\n", "LockerDome\n", "Locket\n", "Loco2\n", "Locqus\n", "Locu\n", "Lodgeo\n", "Loewe\n", "Log\n", "Logan\n", "LogDog\n", "Loggly\n", "Logica\n", "Logikcull\n", "Logitech\n", "LogMeIn\n", "Loki\n", "LokLok\n", "LOLA\n", "Lolapps\n", "Lollicam\n", "Longaccess\n", "Lookback\n", "Lookcraft\n", "Looker\n", "LookFlow\n", "Looklist\n", "Looklive\n", "Lookout\n", "Looksee\n", "Looksery\n", "Lookup\n", "Loon\n", "Loopcam\n", "LoopFuse\n", "LoopMe\n", "LoopPay\n", "Loops\n", "Loosecubes\n", "Loot\n", "Lootsie\n", "Looxcie\n", "Lore\n", "Loudie\n", "LoungeUp\n", "LOVEFiLM\n", "Loveflutter\n", "LoveList\n", "LovelyHeroku\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "LovePalz\n", "Lover+ly\n", "LoveThis\n", "Loyalize\n", "LS9\n", "LSTN\n", "Lucent\n", "Lucey\n", "Lucky\n", "LuckyPennie\n", "LuckyTrip\n", "Ludei\n", "Luff\n", "Lugg\n", "Luka\n", "Lully\n", "Lumens\n", "Lumenus\n", "Lumi\n", "Luminate\n", "Luminoso\n", "Luminox\n", "Lumoid\n", "Lumosity\n", "Lumus\n", "LUUV\n", "Luvocracy\n", "Luxe\n", "Lybrate\n", "Lydia\n", "Lyft\n", "Lyst\n", "LyteShot\n", "Lytro\n", "M+dot\n", "M+Gemi\n", "M-Files\n", "M2M\n", "M4JAM\n", "M7\n", "Macbeth\n", "Mach\n", "Machine\n", "Macmillan\n", "Macro\n", "MacroFab\n", "Made\n", "Madefire\n", "Maderight\n", "Madison\n", "Madvertise\n", "Magenta\n", "Magento\n", "Magic\n", "Magine\n", "Magna\n", "Magnify\n", "Magzter\n", "Mahalo\n", "Maheswari\n", "Mail\n", "Mailbird\n", "Mailbox\n", "Mailgun\n", "Mailjet\n", "Maily\n", "Main\n", "MakeGood\n", "MakeMeReach\n", "Maker\n", "Makerbase\n", "MakerBot\n", "Makers\n", "MakersKit\n", "Makeshift\n", "MakeSpace\n", "MakeTime\n", "Maluuba\n", "Malwarebytes\n", "Mambu\n", "ManageFlitter\n", "ManageIQ\n", "Management\n", "Mandarin\n", "Mandiant\n", "Mandriva\n", "Mango\n", "Mangrove\n", "Manilla\n", "Mann\n", "Manpacks\n", "Mantaphrase\n", "Mantis\n", "Mapbox\n", "Mapillary\n", "MapMyFitness\n", "Mapstr\n", "Marakana\n", "Marblar\n", "Marco\n", "Marfeel\n", "marGenius\n", "Margo\n", "MariaDB\n", "Marino\n", "Maritz\n", "mark\n", "MarkaVIP\n", "Markerly\n", "market\n", "MarketMeSuite\n", "Marketo\n", "Marketplace\n", "Marketwired\n", "Markforged\n", "Marriott\n", "Mars\n", "Marsh\n", "MartJack\n", "Marvel\n", "Mashable\n", "Mashape\n", "Mashburn\n", "Mashery\n", "MassChallenge\n", "Massdrop\n", "MasterCard\n", "MasteryConnect\n", "Matchbook\n", "Material\n", "Matt\n", "Mattel\n", "Mattermark\n", "Matternet\n", "MavenSay\n", "Mavrck\n", "Maxim\n", "Maxis\n", "Maxthon\n", "Maybank\n", "MAZ\n", "mBank\n", "McAfee\n", "McMillan\n", "Me-Mover\n", "Meadow\n", "MeARKET\n", "Medallia\n", "Meddik\n", "Media+net\n", "MediaCore\n", "MediaGlu\n", "MediaMath\n", "MediaPass\n", "MediaSpike\n", "Medifund\n", "Medigo\n", "Medikly\n", "Medine\n", "Medium\n", "MedStartr\n", "Medypal\n", "Meebo\n", "Meerkat\n", "Meesho\n", "Meetey\n", "Meetings+io\n", "MeetMe\n", "meets\n", "Meetup\n", "Meetupcall\n", "Meexo\n", "Mei+com\n", "Meituan-Dianping\n", "Meizu\n", "Meldium\n", "Melodigram\n", "Meltwater\n", "Memeoirs\n", "Memloom\n", "Memo\n", "Memoir\n", "Memolane\n", "Memorability\n", "Memorado\n", "Memories\n", "MemSQL\n", "Mendeley\n", "Mensch\n", "Mention\n", "menu\n", "Menulog\n", "Meograph\n", "MercadoLibre\n", "Mercari\n", "Mercer\n", "Merch\n", "Merienda\n", "Meritful\n", "Merriman\n", "Mertado\n", "Mesosphere\n", "MessageMe\n", "Messenger\n", "Metabiota\n", "Metadata\n", "Metail\n", "Metaio\n", "MetaLab\n", "Metamarkets\n", "MetaMind\n", "MetaPack\n", "Metaresolver\n", "MetaWatch\n", "MetaWorld\n", "Meterfy\n", "Method\n", "Metrekare\n", "Metrics\n", "MetricsHub\n", "Metromile\n", "Meusu\n", "Mezi\n", "mFoundry\n", "Mhelpdesk\n", "Michigan\n", "MicrobeScope\n", "Microduino\n", "MicroEval\n", "Microsoft\n", "Microsystems\n", "Middle\n", "Midokura\n", "MightyText\n", "MightyTV\n", "MIKA\n", "MikMak\n", "Mikme\n", "Milestone\n", "Mileways\n", "MileWise\n", "Milk\n", "Milkster\n", "Million\n", "Mills\n", "Milyoni\n", "MiMedia\n", "Mimi\n", "Mimo\n", "Mindie\n", "Mindjet\n", "MindJolt\n", "MindMeister\n", "MindMeld\n", "MindMixer\n", "MindRDR\n", "Mindshapes\n", "Mindshare\n", "MindSnacks\n", "Mindsy\n", "Mindwork\n", "Minefold\n", "MineralTree\n", "Mingle\n", "Mingleton\n", "MinHash\n", "Minilogs\n", "Mink\n", "Mint\n", "Minted\n", "Mintigo\n", "Minube\n", "Minuum\n", "Miradore\n", "Mirakl\n", "Miramax\n", "Mirantis\n", "Misen\n", "Mission\n", "Mitch\n", "Mitro\n", "MIUI\n", "Mix\n", "MixBit\n", "Mixbook\n", "Mixer\n", "Mixlr\n", "Mixmax\n", "Mixpanel\n", "Miyowa\n", "MLS\n", "MMS\n", "mNectar\n", "Moasis\n", "Moat\n", "Mobage\n", "Mobcast\n", "Mobcaster\n", "Mobcrush\n", "Mobee\n", "Mobento\n", "MobiCart\n", "MobiKwik\n", "Mobile\n", "MobileDevHQ\n", "MobileIron\n", "MobileSpan\n", "Mobilewalla\n", "MobileWorks\n", "Mobileye\n", "Mobilisafe\n", "MobilyTrip\n", "Mobincube\n", "MobiTV\n", "MobLabs\n", "Mobspire\n", "MobStac\n", "MOCACARE\n", "Mocana\n", "Modbook\n", "Modbot\n", "ModCloth\n", "Mode\n", "Modern\n", "Modi\n", "ModiFace\n", "Modington\n", "Modomoto\n", "ModoPayments\n", "Modsy\n", "Module\n", "Modus\n", "MoEngage\n", "Moff\n", "MOG\n", "Mogees\n", "Moglue\n", "Mogreet\n", "Mogul\n", "Mohiomap\n", "MoID\n", "Mojiva\n", "Mojo\n", "Moju\n", "mokono\n", "MOL\n", "Mola+com\n", "Molotov\n", "Moltin\n", "Moment+me\n", "Moments\n", "MommyCoach\n", "mon+ki\n", "Monaeo\n", "Mondaine\n", "Monday52\n", "Mondo\n", "Monese\n", "Monetate\n", "MoneyLion\n", "MoneySavingExpert\n", "Moneytree\n", "MongoDB\n", "Mongoose\n", "Monica+Andy\n", "Monitise\n", "Monkey\n", "MonkeyParking\n", "Monoidics\n", "MONOQI\n", "Monotype\n", "Monroe\n", "Monsieur\n", "Monstro\n", "Montblanc\n", "Monument\n", "Monzo\n", "Moo+do\n", "Moodle\n", "Moodstocks\n", "Moolaguides\n", "MoonClerk\n", "Moonfruit\n", "Moonshark\n", "moosify\n", "Moovit\n", "Moovweb\n", "Moped\n", "Mophie\n", "MoPho\n", "MoPub\n", "Morphlabs\n", "Morrisons\n", "Moshi\n", "Moss\n", "Motilo\n", "Moto\n", "Motorola\n", "Motosumo\n", "Motus\n", "Mouawad\n", "Mouthee\n", "Move\n", "Movebubble\n", "MoveInsure\n", "Moveline\n", "Moven\n", "Movi\n", "Movidius\n", "MovieGlu\n", "MovieLaLa\n", "Movinga\n", "Mowbly\n", "MoWeather\n", "MOX\n", "Moxtra\n", "Mozbii\n", "Mozilla\n", "Mozio\n", "Mozy\n", "mParticle\n", "MSN\n", "MSNBC\n", "MST\n", "Mt+Gox\n", "MTailor\n", "MTM\n", "MTN\n", "MTT\n", "MUBI\n", "MudWatt\n", "Mueller\n", "MuleSoft\n", "Munch\n", "Munchery\n", "Musaic\n", "Musical+ly\n", "Musixmatch\n", "Mustbin\n", "Mutualink\n", "Muve\n", "Muzik\n", "Muzy\n", "MVF\n", "MVP\n", "My-Apps\n", "MyCheck\n", "MyColorScreen\n", "MyEdu\n", "MyEnergy\n", "MyFitnessPal\n", "MyHealthPal\n", "MyHealthTeams\n", "MyHeritage\n", "MyJobCompany\n", "MyLife\n", "MyMiniLife\n", "MyMusicTaste\n", "MyNextRun\n", "Myntra\n", "MyOptique\n", "MyPermissions\n", "MySmartPrice\n", "Myspace\n", "MySQL\n", "MyState\n", "MyStream\n", "mySupermarket\n", "MyTime\n", "myTomorrows\n", "Mytonomy\n", "MyVR\n", "myWebRoom\n", "MyWidz\n", "N26\n", "NAD\n", "Nagios\n", "Nameless+tv\n", "Nanigans\n", "NanoRacks\n", "Napster\n", "NapTime\n", "Napwell\n", "Naritiv\n", "Narrative\n", "Narratives\n", "Narrato\n", "Narrow\n", "Narvar\n", "NASA\n", "NASDAQ\n", "Natasha\n", "NationBuilder\n", "NativeX\n", "Nature\n", "Navdy\n", "Naver\n", "Navmii\n", "Naya\n", "Naylor\n", "Naytev\n", "NBA\n", "NDS\n", "Near+in\n", "Nearby\n", "Nearpod\n", "Neat\n", "Nebula\n", "Needle\n", "needs\n", "Neemware\n", "NEEO\n", "Nelson\n", "NeoGAF\n", "Neoji\n", "Neolane\n", "Neone\n", "NeonMob\n", "Neptune+io\n", "Neptune\n", "NerdWallet\n", "Nerve\n", "Nest\n", "Nestio\n", "Nestivity\n", "Nestle\n", "Nestor\n", "Net1\n", "Netagio\n", "Netatmo\n", "NetBeez\n", "Netbiscuits\n", "Netflix\n", "Netizine\n", "Netlify\n", "Neto\n", "Netokracija\n", "NetPlenish\n", "Nets\n", "Netskope\n", "NetSuite\n", "Network\n", "NeuCoin\n", "Neumann\n", "Neumob\n", "Neura\n", "Neuron\n", "Neverware\n", "NewHive\n", "News+me\n", "News\n", "News360\n", "Newsana\n", "NewsCred\n", "Newsela\n", "NewsiT\n", "Newsle\n", "NewsON\n", "Newsweek\n", "NewsWhip\n", "Newswire\n", "Newton\n", "Newvem\n", "NewVoiceMedia\n", "NewzSocial\n", "Nexmo\n", "Nexon\n", "Next\n", "NextDocs\n", "Nextdoor\n", "Nextpeer\n", "NexTravel\n", "NextSuit\n", "NextVR\n", "Nexus\n", "Neyya\n", "NGDATA\n", "NGI\n", "NHS\n", "NHTSA\n", "Niall\n", "NICE\n", "Niche\n", "Nickelodeon\n", "Nielsen\n", "Nifti\n", "Nifty\n", "Night\n", "NightOwl\n", "Niice\n", "Nike\n", "Nikkei\n", "Nikon\n", "Nima\n", "Nimbl\n", "Nimble\n", "Nimbula\n", "NimbusBase\n", "Nimbuzz\n", "Ninebot\n", "Nintendo\n", "Nirvanix\n", "NIST\n", "NoBroker\n", "NodeFly\n", "NodePrime\n", "nodes\n", "Nodester\n", "Noggin\n", "Noise\n", "Noke\n", "Nokia\n", "NomadCast\n", "Nomi\n", "Nomiku\n", "Noodle\n", "Nooka\n", "Noom\n", "Norby\n", "Nordstrom\n", "NoRedInk\n", "Normal\n", "Northzone\n", "Nosh\n", "NoshList\n", "Nosto\n", "Notabli\n", "Notch\n", "Notes\n", "Notey\n", "Nova\n", "Novartis\n", "Novauris\n", "NovoEd\n", "Nowait\n", "NowFloats\n", "NowThis\n", "NPO\n", "NPR\n", "NSA\n", "NSI\n", "NSL\n", "Nubank\n", "Nuji\n", "numberFire\n", "NumberFour\n", "Numecent\n", "Nuomi\n", "Nurph\n", "Nutanix\n", "Nutrino\n", "Nuzzel\n", "Nvidia\n", "Nymi\n", "NYU\n", "O2\n", "Oatmeal\n", "Obi\n", "Occipital\n", "Occupy+here\n", "Ocean\n", "Octoly\n", "Oculus\n", "Odeo\n", "ODI\n", "Odnoklassniki\n", "Odysee\n", "Odyssey\n", "Ofcom\n", "Office\n", "OfficeDrop\n", "Offset\n", "Ogone\n", "Ohlala\n", "OhMiBod\n", "Oink\n", "Okanjo\n", "OkCupid\n", "OKDOTHIS\n", "OKpanda\n", "Okta\n", "Ola\n", "Olapic\n", "Olark\n", "Olery\n", "Oliba\n", "Ollie\n", "Olly\n", "Olson\n", "Omaze\n", "Omega\n", "Ometria\n", "OMG\n", "OMGICU\n", "OMGPOP\n", "Omise\n", "Omixy\n", "Omlet\n", "Omni3D\n", "Omniata\n", "Omnicharge\n", "Omniture\n", "OmniVirt\n", "OMsignal\n", "Onavo\n", "Ondango\n", "One\n", "OneClass\n", "onefinestay\n", "OneGo\n", "Onehub\n", "OneID\n", "OneLouder\n", "OneOps\n", "OnePageCRM\n", "OnePlus\n", "OneSchool\n", "Onesheet\n", "OneSky\n", "OneTouch\n", "OneTwoTrip\n", "Onfido\n", "Onfleet\n", "Ongage\n", "Online\n", "OnLive\n", "OnSwipe\n", "Onyara\n", "Onyx\n", "Oodle\n", "Oolu\n", "Ooyala\n", "Opbeat\n", "Open-Xchange\n", "Open\n", "OpenBrand\n", "Openbucks\n", "OpenDNS\n", "Opener\n", "Openera\n", "Openet\n", "OpenFeint\n", "Openfund\n", "Opening\n", "OpenLabel\n", "OpenRent\n", "OpenSesame\n", "OpenShift\n", "OpenSignal\n", "OpenSky\n", "OpenStack\n", "OpenStreetMap\n", "OpenTable\n", "OpenTV\n", "OpenX\n", "Opera\n", "Operator\n", "Opinion\n", "Opinsy\n", "Opower\n", "Oppo\n", "Opternative\n", "OptimisCorp\n", "Optimus\n", "Opuss\n", "Orami\n", "Orange\n", "OrangeScape\n", "Orbit\n", "Orchestra\n", "OrderAhead\n", "OrderUp\n", "OrderWithMe\n", "Organic\n", "Organizer\n", "Origami\n", "OrlyAtomics\n", "Oscar\n", "Osmeta\n", "Osmo\n", "Osper\n", "Ossia\n", "Ossur\n", "Ostrich\n", "OTTO\n", "OurMix\n", "Ousta\n", "Out\n", "Outbrain\n", "Outdoorsy\n", "Outernet\n", "Outerwall\n", "Outfit7\n", "Outlier\n", "OutStart\n", "OUYA\n", "Ovatemp\n", "Over\n", "Overhead\n", "Overnight\n", "Overtime\n", "Owlin\n", "ownCloud\n", "OYO\n", "Ozlo\n", "OZON\n", "Pacifica\n", "Pack\n", "Pad\n", "Paddle8\n", "PadMapper\n", "PaeDae\n", "Page365\n", "PageCloud\n", "PageLever\n", "Pager\n", "PagerDuty\n", "Pagevamp\n", "Paidpiper\n", "Paidy\n", "Paintzen\n", "Paktor\n", "Palace\n", "Palaround\n", "Palm\n", "Palringo\n", "Paltalk\n", "PAM\n", "Pamper\n", "Panasonic\n", "Panaya\n", "Panda\n", "PandaWhale\n", "panels\n", "Panono\n", "Pantry\n", "Panzura\n", "Paper+li\n", "PAPER\n", "Paperspace\n", "ParAccel\n", "Parachut\n", "Paracosm\n", "Paragraph\n", "Parallels\n", "Parature\n", "PARC\n", "Parcel\n", "ParcelBright\n", "Parchment\n", "Parenthoods\n", "Paribus\n", "Parklet\n", "ParkWhiz\n", "Parliament\n", "Parlio\n", "Parrot\n", "Parse\n", "ParStream\n", "Partner\n", "Pascal\n", "Passport\n", "PasswordBox\n", "Passworks\n", "PastBook\n", "Pastebin\n", "Path+To\n", "Path\n", "Pathbrite\n", "Pathmapp\n", "PathSource\n", "Pathwright\n", "Pattern\n", "Patton\n", "Pause\n", "Pavlok\n", "PAX\n", "Paydiant\n", "PayDivvy\n", "PayDragon\n", "PayLane\n", "Paylib\n", "PayNearMe\n", "PayPal\n", "PayStand\n", "Paytm\n", "PayU\n", "Payvment\n", "PBS\n", "PCalc\n", "PCI\n", "Peakon\n", "Peapod\n", "Pearltrees\n", "Pearson\n", "Peatix\n", "Pebble\n", "PediaPress\n", "Pedius\n", "Peecho\n", "Peekaboo\n", "Peeks\n", "Peekster\n", "Peel\n", "PeepCode\n", "Peeple\n", "Peer39\n", "Peerby\n", "PeerIndex\n", "PeerStreet\n", "Pegatron\n", "PEN\n", "Penultimate\n", "Penxy\n", "People+\n", "People+ai\n", "PeopleBrowsr\n", "Peoplefluent\n", "Pepperdata\n", "PepperTap\n", "Percentil\n", "Percival\n", "Percolate\n", "Perfect\n", "Perion\n", "Perk+com\n", "Permira\n", "PernixData\n", "Perpetu\n", "Perpetually\n", "PersistIQ\n", "Personal\n", "Pertino\n", "PETA\n", "PetHub\n", "Petnet\n", "Pexip\n", "Peyman\n", "Pharmly\n", "Pheed\n", "Philly\n", "Philo\n", "PhilterIt\n", "PHIND\n", "Phoenix\n", "Phonedeck\n", "PhoneFactor\n", "Phonio\n", "Phononic\n", "Phonotonic\n", "Phonvert\n", "Phosphorus\n", "Photobucket\n", "Photoful\n", "Photomyne\n", "Photoshop\n", "PhotoSpotLand\n", "PHP\n", "Pi\n", "Piano\n", "Piazza\n", "Picattoo\n", "Piccsy\n", "Picfair\n", "Pick\n", "Pick1\n", "Picker\n", "Pickie\n", "Pickingo\n", "Picks\n", "PickTrace\n", "PiCloud\n", "PicMix\n", "PicMonkey\n", "PicnicHealth\n", "Picnik\n", "PicsArt\n", "Picturesqe\n", "Piece\n", "Pieceable\n", "PieSync\n", "PiinPoint\n", "Pijon\n", "PillPack\n", "PinClout\n", "Ping+it\n", "Ping\n", "Pingboard\n", "Pingdom\n", "Pingpad\n", "Pinion\n", "Pinpuff\n", "PinReach\n", "Pins\n", "Pinshape\n", "Pinspire\n", "Pinster\n", "Pinterest\n", "Pintics\n", "Pintrips\n", "Pinvolve\n", "Pinweel\n", "Pioneer\n", "Pipedrive\n", "PipelineDB\n", "Piqora\n", "Pirate3D\n", "Pirq\n", "Pitch\n", "Pitchbox\n", "Pivot3\n", "Pivotal\n", "PivotDesk\n", "Pixable\n", "Pixate\n", "Pixboom\n", "Pixel\n", "Pixelapse\n", "Pixelpipe\n", "Pixeom\n", "Pixloo\n", "Pizza+de\n", "Placecast\n", "Placed\n", "PlaceIQ\n", "Placemeter\n", "Placester\n", "Plaid\n", "Plain\n", "Plan\n", "Planday\n", "Plane\n", "PlanGrid\n", "Planspot\n", "Planted\n", "Plated\n", "PlateJoy\n", "Platfora\n", "Platform\n", "Playbasis\n", "Playdek\n", "Playdemic\n", "Playdom\n", "Player+me\n", "PlayerScale\n", "Playfire\n", "Playfish\n", "PlayHaven\n", "Playlab\n", "Playlists+net\n", "Playmatics\n", "PlaySay\n", "Playsino\n", "PlaySquare\n", "Playtox\n", "Please\n", "PledgeCents\n", "Pleek\n", "Plenummedia\n", "Plex\n", "Pley\n", "Plink\n", "Plivo\n", "Plizy\n", "Ploom\n", "Plugaway\n", "Plukka\n", "Plumbr\n", "PLUMgrid\n", "Pluralis\n", "Pluralsight\n", "Plus\n", "Pluto\n", "Plyfe\n", "Plympton\n", "PMA\n", "PoachIt\n", "Podio\n", "PodShare\n", "Pokémon\n", "Politix\n", "Politwoops\n", "Polls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Polo\n", "Polycom\n", "Polymer\n", "Polyvore\n", "Poncho\n", "Pond5\n", "PonoMusic\n", "Pop\n", "Popcorn\n", "Poppin\n", "Pops\n", "Popset\n", "Poptip\n", "Populis\n", "Porch\n", "Pornhub\n", "Porsche\n", "Portfolium\n", "Poshmark\n", "Posmetrics\n", "Posse\n", "Postable\n", "Postcard\n", "Posterous\n", "PostGhost\n", "Postgres\n", "Posthaven\n", "Postini\n", "Postman\n", "Postmaster\n", "Postmates\n", "PostRocket\n", "PostUp\n", "Postwire\n", "Pounce\n", "Povio\n", "PowaTag\n", "PowerInbox\n", "PowerPoint\n", "PowerReviews\n", "Powerset\n", "Powhow\n", "Poynt\n", "Pozitron\n", "PPI\n", "Practo\n", "Prakash\n", "Preact\n", "PreApps\n", "PrecisionHawk\n", "PredictionIO\n", "Predictive\n", "PredictSpring\n", "Predix\n", "Prefundia\n", "Premier\n", "Prenetics\n", "PrePay\n", "PrePlay\n", "PrepWork\n", "Presence\n", "Press\n", "Pressfolios\n", "Pressly\n", "PrestaShop\n", "Prete\n", "Prevent\n", "Prezi\n", "Price\n", "Priceonomics\n", "Priest\n", "Prim\n", "PrimeSense\n", "Print+io\n", "Print\n", "Printful\n", "Printhug\n", "Printoo\n", "Prior\n", "Privacy\n", "PrivacyStar\n", "Privalia\n", "PrivateCore\n", "Prize\n", "Prizeo\n", "Pro+com\n", "ProBoards\n", "Procera\n", "Procured\n", "Producteev\n", "Profig\n", "ProfitBricks\n", "ProFounder\n", "Progressly\n", "Promoboxx\n", "PromoJam\n", "Promote\n", "Prompt+ly\n", "Property\n", "Propstack\n", "PROskore\n", "Protag\n", "Protect\n", "Proto+io\n", "ProtonMail\n", "Proud\n", "Provender\n", "Providence\n", "providers\n", "Prowl\n", "Proxy\n", "Prss\n", "PRX\n", "Prysm\n", "Pryte\n", "PSafe\n", "PSG\n", "PTT\n", "Public\n", "Publicize\n", "Publishizer\n", "Publons\n", "PubNub\n", "Pubslush\n", "Pulsate\n", "Pulselocker\n", "PumpUp\n", "Punch\n", "Punchbowl\n", "Punchcard\n", "Punchfork\n", "PunchTab\n", "Pundit\n", "Puppet\n", "Purch\n", "Pure\n", "PureVPN\n", "PureWrist\n", "Purism\n", "Pursway\n", "Pushbullet\n", "Pusher\n", "PushPage\n", "Puzzle\n", "Puzzlephone\n", "PX\n", "Pyne\n", "Pypestream\n", "Pyxis\n", "Q4\n", "Qalendra\n", "Qardio\n", "QASymphony\n", "Qik\n", "Qlika\n", "Qloo\n", "QOOQ\n", "Qosmos\n", "Qplay\n", "QR\n", "Qriously\n", "QSAlpha\n", "Qualcomm\n", "Qualtrics\n", "Qualys\n", "Quandoo\n", "Quantcast\n", "Quantifind\n", "Quantum\n", "Quarterly\n", "Quartet\n", "Quartzy\n", "Quattrocento\n", "Qubit\n", "Quepasa\n", "Quest\n", "Quettra\n", "Queue\n", "Quickoffice\n", "QuickPay\n", "Quik\n", "Quikkly\n", "Quikr\n", "Quill\n", "Quinn\n", "Quip\n", "Quiqup\n", "Quirky\n", "Quixey\n", "Quizlet\n", "Qumulo\n", "Quora\n", "Quotle\n", "Quri\n", "QVC\n", "Qwaya\n", "Qwerky\n", "Qwiki\n", "Qwilr\n", "Qype\n", "R\\GA\n", "R_GA\n", "Rackspace\n", "RackWare\n", "Rad\n", "Radar\n", "Radian\n", "Radian6\n", "Radical+FM\n", "Radionomy\n", "RadioPublic\n", "RadioShack\n", "RadiumOne\n", "Radoop\n", "RadPad\n", "Rafter\n", "Rainmaker\n", "RakNet\n", "Rakuten\n", "Rally+org\n", "Random\n", "Randstad\n", "Ranger\n", "RankBoards\n", "Ranker\n", "Rapchat\n", "Rapid7\n", "RapidMiner\n", "Rapportive\n", "Raspberry\n", "Rate\n", "Ravi\n", "Ravti\n", "Raydiance\n", "Razer\n", "Razorpay\n", "RCS\n", "Rdio\n", "RDS\n", "Reachli\n", "Reactions\n", "Reactor\n", "Read\n", "Reader\n", "Readmill\n", "ReadWrite\n", "Readyforce\n", "ReadyForZero\n", "Reagan+com\n", "RealCrowd\n", "RealDirect\n", "RealScout\n", "Realtidbits\n", "Reamaze\n", "Rearden\n", "REBBL\n", "Rebel\n", "Rebelle\n", "RebelMouse\n", "ReBoard\n", "Rebtel\n", "Reco\n", "Recombine\n", "RecruitLoop\n", "Recurly\n", "Redbeacon\n", "Redbooth\n", "Redbox\n", "reddit\n", "RedditGifts\n", "RedDoorz\n", "Redfin\n", "Redkite\n", "RedKix\n", "RedLaser\n", "RedMart\n", "Redpoint\n", "Reebee\n", "Reebonz\n", "Reeder\n", "Reedsy\n", "Reelio\n", "ReelSurfer\n", "Referly\n", "Refinery29\n", "Reflektion\n", "RefME\n", "Refresh+io\n", "Refresh\n", "RefreshBox\n", "Regalii\n", "Registry\n", "RegistryLove\n", "REI\n", "Reiss\n", "Reissued\n", "reKiosk\n", "RelateIQ\n", "Relcy\n", "Relevance\n", "Reliance\n", "Remitly\n", "Remus\n", "RenéSim\n", "Rent2Buy\n", "Rentecarlo\n", "Rentlord\n", "Rentmatic\n", "RentSocial\n", "Repixl\n", "Repost\n", "reps\n", "Reputation+com\n", "Request\n", "Rescale\n", "ResearchGate\n", "ReservationHop\n", "Reserve\n", "Reserverr\n", "ReskillUSA\n", "ResolutionTube\n", "Restaurant-Kritik\n", "Restorando\n", "Results\n", "retailer\n", "RetailNext\n", "Retale\n", "ReTargeter\n", "Retina\n", "Retrace\n", "Retrofit\n", "Reuters\n", "Revel\n", "Revelator\n", "Reverb+com\n", "Reverb\n", "Revl\n", "Revolut\n", "Revolution\n", "RevUpNet\n", "Rewardli\n", "RewardLoop\n", "REX\n", "Rhapsody\n", "Rhinobird\n", "Rhombus\n", "Ribbit\n", "Ribbon\n", "Richard\n", "Richardson\n", "Rickshaw\n", "Ride\n", "Ridejoy\n", "Riley\n", "Riney\n", "Ringadoc\n", "Ringblingz\n", "RingCentral\n", "Ringly\n", "Ringya\n", "Riot\n", "Ripples\n", "Rivigo\n", "Rixty\n", "RJMetrics\n", "Roadie\n", "Roadster\n", "Roadtrippers\n", "Roambi\n", "Roamz\n", "Robinhood\n", "Robocat\n", "Robocoin\n", "RoboEarth\n", "Robotbase\n", "Robotiky\n", "Roca\n", "Rocana\n", "Roche\n", "Rockbot\n", "RocketBank\n", "RocketClub\n", "RocketFrog\n", "Rocketmiles\n", "Rocketmine\n", "Rocketrip\n", "RocketSpace\n", "Rockmelt\n", "Rockpack\n", "Rodeo\n", "RogerVoice\n", "ROIKOI\n", "Roku\n", "Rolex\n", "ROLI\n", "Rollout+io\n", "Rolltape\n", "Rondee\n", "Ronin\n", "ROOBO\n", "Roombeats\n", "Roomblocker\n", "Roomer\n", "Roomi\n", "Roomlia\n", "Roomorama\n", "Rooster\n", "Roposo\n", "Rose\n", "Rosetta\n", "Rossum\n", "Rothschild\n", "Rounds\n", "Routific\n", "RSA\n", "RSVP\n", "RT\n", "RTI\n", "Rubrik\n", "Ruby\n", "Ruckus\n", "Rumgr\n", "Runa\n", "Runfaces\n", "Runkeeper\n", "Runscope\n", "Runtastic\n", "RupeePower\n", "Rust\n", "Rutube\n", "Rypple\n", "Rythm\n", "Ryzing\n", "S4\n", "SaaS\n", "Saavn\n", "Saber\n", "Safaricom\n", "Safello\n", "Saffronart\n", "Saga\n", "Saida\n", "Sailo\n", "Sailthru\n", "Sainsbury’s\n", "Salary+com\n", "Salesforce\n", "SalesforceIQ\n", "Salesfusion\n", "SalesGossip\n", "SalesVu\n", "Salonmeister\n", "Salorix\n", "Salted\n", "SAM\n", "Samba\n", "Samos\n", "Samson\n", "Sanbolic\n", "Sanders\n", "Sandglaz\n", "SanDisk\n", "SaneBox\n", "Sansan\n", "SAP\n", "Sapho\n", "Satago\n", "Satechi\n", "Satellogic\n", "Save\n", "Savings+com\n", "SavingStar\n", "Savioke\n", "Savored\n", "Sawyer\n", "Say\n", "Saya\n", "ScaleArc\n", "ScaleBase\n", "Scandy\n", "Scarosso\n", "SCC\n", "Scenery\n", "Scentbird\n", "Schaft\n", "Scheduler\n", "Schibsted\n", "Schiller\n", "Schneider\n", "Scholly\n", "Schoola\n", "Schoology\n", "Schools\n", "Schrader\n", "Schramm\n", "Schuler\n", "Science\n", "ScienceLogic\n", "Scientia\n", "Scoop+it\n", "Scopely\n", "ScoreBeyond\n", "Scosche\n", "Scotiabank\n", "Scott\n", "Scoutmob\n", "Screen\n", "Screenhero\n", "ScribbleLive\n", "Scribd\n", "Scribit\n", "Scringo\n", "Scripted+com\n", "Scriptlance\n", "Scroll\n", "Scrumpt\n", "Sculpt\n", "SE\n", "Seahorse\n", "Seamless\n", "SeamlessDocs\n", "Sears\n", "SeatGeek\n", "SeatID\n", "Seatwave\n", "SEB\n", "Sebastian\n", "Seclore\n", "SecondMarket\n", "Seconds\n", "Secret+li\n", "Seculert\n", "Securifi\n", "Secusmart\n", "seed\n", "Seedcamp\n", "SeedCloud\n", "SeedInvest\n", "Seedling\n", "SeedPlus\n", "Seedrs\n", "Seelio\n", "Seenth+is\n", "Seeqnce\n", "Seerslab\n", "Seesaw\n", "Seesmic\n", "SeeWhy\n", "Segway\n", "Seiko\n", "Selective\n", "Selequity\n", "Selerity\n", "Self\n", "Selfie\n", "Selfycart\n", "Sellfy\n", "Selphee\n", "Send\n", "SendGrid\n", "SendHub\n", "Sendicate\n", "Sensay\n", "Sense360\n", "Sensegon\n", "Sensel\n", "Sensinode\n", "SensioLabs\n", "Sensorberg\n", "Sensoria\n", "SenSprout\n", "Senzari\n", "Serco\n", "Seriously\n", "Serverless\n", "ServiceMesh\n", "ServisHero\n", "Servy\n", "SessionM\n", "Set\n", "Setster\n", "Seven\n", "SevenFifty\n", "Sevenhugs\n", "SevenVentures\n", "Sex+com\n", "SFR\n", "Shadow\n", "Shaka\n", "Shake\n", "Shaker\n", "Shape\n", "Shaper\n", "ShapeUp\n", "Shapeways\n", "Shapr\n", "Sharalike\n", "Sharecare\n", "Shared\n", "ShareGrid\n", "SharesPost\n", "ShareThis\n", "Sharethrough\n", "Sharetribe\n", "Sharewall\n", "Shark\n", "Shasta\n", "Shazam\n", "Shelf\n", "Shellhammer\n", "Sher+ly\n", "Sherpaa\n", "Shine\n", "Shinola\n", "ShipBob\n", "ShipHawk\n", "Shippable\n", "ShipStation\n", "Shipwire\n", "ShoCard\n", "ShoeDazzle\n", "Shoobs\n", "ShoorK\n", "Shoot\n", "Shopa\n", "Shopcade\n", "ShopFully\n", "Shopgate\n", "Shopify\n", "shopkick\n", "Shopline\n", "ShopLocket\n", "ShopLogic\n", "Shopmium\n", "Shopmox\n", "Shoppable\n", "ShopPad\n", "ShopperTrak\n", "ShopSavvy\n", "ShopSocially\n", "Shoptiques\n", "Shopular\n", "Shopwave\n", "Shopzilla\n", "ShoreTel\n", "ShortForm\n", "Shorts\n", "Shotclock\n", "Shotput\n", "Shoutlet\n", "Showcase\n", "ShowMe\n", "Showpad\n", "Shpock\n", "Shuddle\n", "Shutl\n", "Shutterfly\n", "Shutterstock\n", "ShuttleCloud\n", "Shuttlecook\n", "Shuttleworth\n", "Shyp\n", "Shypmate\n", "Siasto\n", "Sidebark\n", "Sidestage\n", "Sidestep\n", "Sifteo\n", "Siftery\n", "Sightly\n", "Sigma\n", "Sign2Pay\n", "Signal\n", "Signpost\n", "SigOpt\n", "Sikka\n", "Silp\n", "Silver\n", "Silvercar\n", "Silverpop\n", "SilverPush\n", "SIM\n", "SIMI\n", "SimilarTech\n", "SimilarWeb\n", "Siminars\n", "SimpleCitizen\n", "Simplee\n", "SimpleHoney\n", "SimpleLegal\n", "SimplePrints\n", "SimpleReach\n", "SimpleTuition\n", "Simplify360\n", "Simplilearn\n", "SimpliVity\n", "Simplr\n", "SimplyInsured\n", "Sina\n", "Sincerely\n", "SinDelantal+Mx\n", "SinglePlatform\n", "Singly\n", "Singtel\n", "Singtrix\n", "SIP\n", "Siri\n", "SirionLabs\n", "SIRUM\n", "Sisense\n", "SITA\n", "Sitedrop\n", "SiteScout\n", "SiteTagger\n", "SittingAround\n", "SixDoors\n", "SizeUp\n", "Skedaddle\n", "Sketch\n", "SketchDeck\n", "Sketchfab\n", "SketchUp\n", "Skift\n", "Skillbridge\n", "SkillPages\n", "Skills\n", "Skillshare\n", "Skillz\n", "Skim\n", "Skimbox\n", "Skimlinks\n", "SkinVision\n", "Skit\n", "Skitch\n", "Skout\n", "Skriware\n", "Sky\n", "SkyBell\n", "Skycatch\n", "Skycure\n", "Skydio\n", "Skyera\n", "SkyGiraffe\n", "SkyHealth\n", "Skymind\n", "Skype\n", "SkyPhrase\n", "Skyscanner\n", "SkySpecs\n", "Slack\n", "SleepSense\n", "Slice\n", "Slick\n", "SlickFlick\n", "Slidebox\n", "SlideIdea\n", "Slidejoy\n", "SlideMail\n", "SlideRocket\n", "Slim\n", "SlimPay\n", "Slingbox\n", "Slinger\n", "Slingo\n", "Slush\n", "Smaato\n", "Smarkets\n", "Smarsh\n", "Smart\n", "SmartAsset\n", "Smarterer\n", "SmartHires\n", "SmartNews\n", "SmartNotify\n", "SmarTots\n", "SmartRecruiters\n", "Smartsheet\n", "SmartSync\n", "SMASHD\n", "SmashFly\n", "Smava\n", "Smigin\n", "Smiirl\n", "Smith\n", "SmoovUp\n", "SMS\n", "Smule\n", "Snackr\n", "SnapApp\n", "Snapcart\n", "Snapdeal\n", "SnapEDA\n", "Snapette\n", "Snapguide\n", "Snapjoy\n", "SnapKeys\n", "SnapKnot\n", "Snaplay\n", "SnapLogic\n", "SnapPages\n", "SnappyLabs\n", "Snaptee\n", "Snaptu\n", "SnapUp\n", "Snapverse\n", "SNCF\n", "Sneeky\n", "Snips\n", "SnipSnap\n", "Snoox\n", "Snowball\n", "Snowden\n", "Snupps\n", "Snyk\n", "SOASTA\n", "Socar\n", "Socialbakers\n", "SocialBattles\n", "Socialcam\n", "SocialChorus\n", "SocialCode\n", "SocialCrunch\n", "SocialFlow\n", "SocialGuide\n", "SocialMart\n", "SocialPandas\n", "SocialPoint\n", "SocialRadar\n", "SocialRank\n", "SocialRent\n", "SocialShield\n", "Socialspiel\n", "Socialtext\n", "SocialTwist\n", "Sociocast\n", "SocMetrics\n", "Socrative\n", "Sodisco\n", "SoftBank\n", "Softcard\n", "Softcover\n", "SoftLayer\n", "Softonic\n", "Sojern\n", "Sokikom\n", "Solair\n", "SolarCity\n", "Sold\n", "Soldsie\n", "Soli\n", "Solid\n", "Solidoodle\n", "Solinea\n", "Solocam\n", "Solum\n", "Soluto\n", "Solvate\n", "Somo\n", "Sonalight\n", "Sonatype\n", "Songbird\n", "Songdrop\n", "Songkick\n", "Songza\n", "Sonian\n", "Sonicbids\n", "Sonos\n", "Sony\n", "Sookasa\n", "Soompi\n", "Soon\n", "Sooqini\n", "Sophos\n", "SoPost\n", "Sorted\n", "Sosh\n", "SoSocio\n", "Soulmix\n", "SoundCloud\n", "SoundFocus\n", "Soundrop\n", "Sounds\n", "Soundsgood\n", "Soundtracker\n", "Soundwave\n", "Source\n", "Sourcebits\n", "SourceNinja\n", "Space\n", "SpaceSplitter\n", "SpaceX\n", "Spacio\n", "Spangle\n", "SpareMin\n", "Sparkcentral\n", "SparkLabs\n", "SparkReel\n", "Sparks\n", "Sparta\n", "Spartan\n", "Spartoo\n", "Spawnsong\n", "Spayce\n", "Speakaboos\n", "Spectre\n", "Spectrm\n", "Speedo\n", "Speek\n", "Spendesk\n", "Sphero\n", "Spiegel\n", "Spin\n", "Spindle\n", "Spinlister\n", "Spinnakr\n", "Spitz\n", "Splacer\n", "Splash+FM\n", "SplashPost\n", "Splunk\n", "Splurgy\n", "Sponsify\n", "SponsorHub\n", "Spool\n", "Spoolee\n", "Spoon\n", "Spoonflower\n", "SpoonRocket\n", "Spoqa\n", "Sportradar\n", "SportsHero\n", "SportsQuest\n", "SportStream\n", "Spot+IM\n", "Spotbros\n", "Spotcap\n", "Spotfund\n", "Spotify\n", "Spotivate\n", "SpotlessCity\n", "Spots\n", "Spotsetter\n", "Spotwag\n", "Spraffl\n", "Spreaker\n", "Spredfast\n", "Spreedly\n", "Sprightly\n", "Spring+me\n", "Springer\n", "Springpad\n", "SprinkleBit\n", "Sprinklr\n", "Spritz\n", "SproutCore\n", "Sproutkin\n", "Spruceling\n", "SPSS\n", "SQFT\n", "Sqord\n", "Sqreen\n", "Sqrrl\n", "Square\n", "SquareOne\n", "Squarespace\n", "Squawka\n", "Sr+Pago\n", "SRCH2\n", "ST-Ericsson\n", "STABiLGO\n", "StackEngine\n", "Stackla\n", "Stacklist\n", "StackMob\n", "StackPath\n", "Stacks\n", "Staffjoy\n", "Stage\n", "Stamp+it\n", "Stamped\n", "Stamper\n", "Stampery\n", "Stamplay\n", "Stamplia\n", "Standard\n", "StandWith\n", "Stanley\n", "Staples\n", "Starbucks\n", "Stardoll\n", "StarOfService\n", "Starship\n", "StarStreet\n", "Start-Up\n", "StartApp\n", "StartUp\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Startupbootcamp\n", "StartupBus\n", "StartupDigest\n", "StartupHighway\n", "Startupi\n", "Startups+co\n", "Startups\n", "StartupYard\n", "StartWire\n", "StartX\n", "Starwood\n", "State\n", "Station\n", "StatMuse\n", "StatusPage\n", "Statwing\n", "Stauffer\n", "Stay+com\n", "Stayzilla\n", "SteelBrick\n", "SteelHouse\n", "SteelSeries\n", "Steinberg\n", "StellaService\n", "Steller\n", "Stepes\n", "Stephens\n", "Stevie\n", "STI\n", "Stichy\n", "Stick\n", "Stickam\n", "Stickers\n", "StickNFind\n", "Sticky\n", "Stik\n", "Stingray\n", "Stipple\n", "Stir\n", "Stitcher\n", "Stocard\n", "Stocksy\n", "StockTwits\n", "Stone\n", "Stonesoft\n", "StopTheHacker\n", "StoreDot\n", "storefront\n", "Storify\n", "Storm8\n", "StormTag\n", "StorSimple\n", "StoryBox\n", "Storybricks\n", "Storycode\n", "StoryDesk\n", "Storyful\n", "Storylane\n", "StraighterLine\n", "Strap\n", "Strata\n", "Stratasys\n", "Stratos\n", "Stratumn\n", "Strava\n", "Strayboots\n", "Streak\n", "Streamroot\n", "Streamweaver\n", "Streem\n", "StreetEasy\n", "Streetlife\n", "Streetline\n", "StrikeAd\n", "Stringr\n", "Strings\n", "Stripe\n", "StrongLoop\n", "Structure\n", "Struq\n", "Stuart\n", "Stubb\n", "StubHub\n", "Studios\n", "StudyBlue\n", "Studypool\n", "StudyRoom\n", "Stuffle\n", "StumbleUpon\n", "Stupeflix\n", "StyleCard\n", "StyleCaster\n", "Stylect\n", "StyleSeat\n", "StyleSeek\n", "StyleTread\n", "Stylight\n", "Stypi\n", "Submittable\n", "SubPac\n", "Subway\n", "SugarCRM\n", "SugarSync\n", "Sugru\n", "Suite\n", "Suiteness\n", "Sullivan\n", "SumAll\n", "Summa\n", "Summer\n", "Summify\n", "Summly\n", "Sumo\n", "Sumpto\n", "SumUp\n", "Sundance\n", "Sundar\n", "SundaySky\n", "Sunglass\n", "Suning\n", "Sunlight\n", "Sunstone\n", "Supahands\n", "SupaPass\n", "Supdate\n", "SuperAwesome\n", "Superb\n", "Supercell\n", "Superfeedr\n", "Supersonic\n", "Supple\n", "Surf\n", "SurfEasy\n", "Surge\n", "SurveyMonkey\n", "Sush+io\n", "Susie\n", "Sutro\n", "Svpply\n", "Svyaznoy\n", "Swagbucks\n", "Swap+com\n", "Swapbox\n", "Swapt\n", "Swarmly\n", "Swatch\n", "Sweep\n", "Sweeten\n", "SweetLabs\n", "Swell\n", "SwiftGift\n", "SwiftKey\n", "Swiftype\n", "Swiggy\n", "Swill\n", "Swipp\n", "Swisscom\n", "Switchcam\n", "Swrve\n", "Swyft\n", "Swype\n", "Swytch\n", "Syapse\n", "Sygic\n", "Sylaps\n", "Symantec\n", "Symplified\n", "Synacor\n", "SynapSense\n", "Synapsify\n", "Synaptics\n", "Synata\n", "Sync+ME\n", "Syncapse\n", "Synchronica\n", "Syncplicity\n", "Syncsort\n", "SyndicateRoom\n", "Synergyse\n", "Synkio\n", "Synology\n", "Synthesio\n", "Sysomos\n", "Systems\n", "Sywork\n", "T-Mobile\n", "TabbedOut\n", "Tabber\n", "Tableau\n", "TableGrabber\n", "Taboola\n", "TabTale\n", "Tackk\n", "Taco\n", "TacoCopter\n", "TaDaweb\n", "TAG\n", "Tagboard\n", "Tagg\n", "Taggar\n", "Tagged\n", "Taggstar\n", "Tagstand\n", "Tagwhat\n", "Tail\n", "Tailor\n", "Tailwind\n", "TakeLessons\n", "Talend\n", "Talent\n", "TalentBin\n", "TalentSky\n", "Taleo\n", "Talk+co\n", "Talk\n", "TalkBin\n", "Talkdesk\n", "Talko\n", "Talkspace\n", "TalkTo\n", "Talkwheel\n", "Talkz\n", "Tame\n", "Tampax\n", "Tamr\n", "Tango\n", "Tank\n", "Tanner\n", "Taobao\n", "Tap\n", "Tapad\n", "Tapastreet\n", "TapCanvas\n", "TapCommerce\n", "Tapdaq\n", "TapEngage\n", "TapFame\n", "Tapglue\n", "TapHeaven\n", "Tapingo\n", "Tapit\n", "Tapjoy\n", "Tapkast\n", "Taplister\n", "Taplytics\n", "TappIn\n", "TappingStone\n", "Taps\n", "TapSense\n", "Tapstack\n", "Tapstream\n", "Tapsule\n", "Taptu\n", "Tapulous\n", "Tapvalue\n", "Target\n", "Tarlton\n", "Taskhub\n", "TaskRabbit\n", "Taskworld\n", "taste\n", "Tastebud\n", "Tastebuds\n", "Tastemade\n", "TastemakerX\n", "Tastemates\n", "Tate\n", "Tattoodo\n", "Taulia\n", "Tawkers\n", "Taxi\n", "TaxiMonger\n", "Taykey\n", "Taylor\n", "TCG\n", "TCV\n", "Teabox\n", "Teads\n", "Teal\n", "Tealium\n", "Teams\n", "Tech+eu\n", "TechCrunch\n", "TechLaunch\n", "Techlist\n", "Technopolis\n", "Technorati\n", "Techstars\n", "Techy\n", "Teckler\n", "Tectonic\n", "TED\n", "Tedemis\n", "TeePublic\n", "Teespring\n", "Teewe\n", "Teleborder\n", "Teleca\n", "Telecast\n", "Telecom\n", "Telefonica\n", "Telefónica\n", "Telegram\n", "Telegraph\n", "Telenor\n", "Telepathic\n", "Telerik\n", "Telesocial\n", "Teleza\n", "TellApart\n", "Tellem\n", "Teller\n", "Tello\n", "Telogis\n", "Telstra\n", "Temnos\n", "Tempdrop\n", "Tend+ai\n", "TenderTree\n", "Tenor\n", "tenXer\n", "Tep\n", "Teradata\n", "Terascore\n", "Terminal\n", "Terrafugia\n", "TerraTalk\n", "TerrAvion\n", "Tesco\n", "TestChameleon\n", "TestFlight\n", "Testing\n", "TestObject\n", "Tethercell\n", "TetraScience\n", "TextMaster\n", "TextMe\n", "textPlus\n", "TextTeaser\n", "Textura\n", "ThankView\n", "Thanx\n", "THB\n", "theAudience\n", "TheFamily\n", "Thefuture+fm\n", "TheIceBreak\n", "Themer\n", "Theranos\n", "Thermodo\n", "ThingLink\n", "Things\n", "Thingsee\n", "Thinkful\n", "Thinkfuse\n", "ThinkGrid\n", "Thinknum\n", "ThirdLove\n", "Thirdshelf\n", "Thirst\n", "Thismoment\n", "Thistle\n", "Thomson\n", "Thomvest\n", "Thorn\n", "ThoughtSTEM\n", "ThousandEyes\n", "Threadflip\n", "ThreatMetrix\n", "thredUP\n", "Threes\n", "ThriveHive\n", "ThriveTracker\n", "Thumb\n", "Thumbtack\n", "Thunder\n", "Thurst\n", "tibbr\n", "Ticketbis\n", "Ticketfly\n", "Ticketmaster\n", "Tictail\n", "Tide\n", "Tidemark\n", "TiE\n", "Tigerlabs\n", "TigerText\n", "Tiiny\n", "Tikker\n", "Tile\n", "Tilt\n", "Timbre\n", "Timehop\n", "Timekiwi\n", "Timeline\n", "timeshel\n", "Timeular\n", "Tinder\n", "Tindie\n", "Ting\n", "Tingbot\n", "Tinggal\n", "Tinitell\n", "Tink\n", "Tinkergarten\n", "Tint\n", "Tintri\n", "Tinybop\n", "Tinychat\n", "Tinyclues\n", "TinyTap\n", "Tipbit\n", "Tipflare\n", "Tippr\n", "Tissot\n", "TiVo\n", "Tivoli\n", "Tizen\n", "Todacell\n", "TodayTix\n", "Togethera\n", "Toggle\n", "TokBox\n", "Tokopedia\n", "Tomcar\n", "Tomfoolery\n", "Tonara\n", "Tonic\n", "Tonight\n", "Tonsser\n", "Toolbox+com\n", "Toonimo\n", "Toothpick\n", "Top10\n", "TopCoder\n", "Topguest\n", "Topick\n", "Topics\n", "Toptal\n", "Tor\n", "Torbit\n", "Toro\n", "Toronto\n", "Torque\n", "Tote\n", "Totlol\n", "Totsy\n", "TOTVS\n", "Touch\n", "TouchBistro\n", "TouchCast\n", "TouchTen\n", "Tour\n", "TourCommand\n", "TourPal\n", "Tout\n", "ToutApp\n", "Trackbuster\n", "TrackDuck\n", "Trackin\n", "TrackR\n", "Tracks+by\n", "Tracks\n", "Tracky\n", "tracx\n", "Tracxn\n", "Tracy\n", "Trade\n", "Tradedoubler\n", "TradeGecko\n", "TradeHero\n", "Trademob\n", "Tradeshift\n", "TradingView\n", "TRAFI\n", "Trainline\n", "Traity\n", "TrakInvest\n", "Transcoder\n", "Transcriptic\n", "TranServ\n", "TransferGo\n", "TransferWise\n", "Transifex\n", "Transit\n", "Trapit\n", "Travel+ru\n", "TravelPerk\n", "Travis\n", "Trax\n", "Traxo\n", "Traxpay\n", "tray+io\n", "Treasure\n", "Treatwell\n", "TrekkSoft\n", "Trellie\n", "Trello\n", "Trending\n", "TrendKite\n", "Trendrr\n", "TrendSpottr\n", "Trent\n", "TreSensa\n", "Tresorit\n", "Trestle\n", "Trialfire\n", "TrialPay\n", "Tribeca\n", "Tribesports\n", "Triblio\n", "Trifacta\n", "Trigger\n", "Triller\n", "Trilogy\n", "Trim\n", "Trio\n", "TripAdvisor\n", "Tripbirds\n", "Tripda\n", "TripIt\n", "Tripl\n", "TripleLift\n", "TripleMint\n", "Tripod\n", "Triposo\n", "Trippy\n", "Tripshare\n", "Triptease\n", "Triptrotting\n", "Tron-Club\n", "Trooly\n", "Tropo\n", "Tru\n", "True\n", "TrueAccord\n", "Truebill\n", "Truecaller\n", "TrueFacet\n", "TrueStart\n", "TrueVault\n", "Trulia\n", "Trulioo\n", "Trunk\n", "Trunkt\n", "Truphone\n", "Trusk\n", "Trusted\n", "TrustedCompany+com\n", "Trusteer\n", "TrustEgg\n", "Trustev\n", "Trustpilot\n", "TRVL\n", "TSMC\n", "TTS\n", "Tubular\n", "Tudou\n", "Tuition+io\n", "Tule\n", "Tune\n", "TuneCore\n", "TuneIn\n", "Tunepics\n", "TuneUp\n", "TuneWiki\n", "Tung\n", "Turck\n", "TurningArt\n", "Turo\n", "Tutanota\n", "Tutorspree\n", "Tutum\n", "Tuurnt\n", "TVSmiles\n", "TweetDeck\n", "Tweetwall\n", "Twentify\n", "Twice\n", "Twiggle\n", "Twigmore\n", "Twigtale\n", "Twilio\n", "Twistory\n", "Twitch\n", "Twitmusic\n", "Twitpic\n", "Tyba\n", "Tykoon\n", "Tyler\n", "TYLT\n", "Tynker\n", "Tynt\n", "Type\n", "Typeform\n", "Tyro\n", "U\n", "uBeam\n", "Uber\n", "UberConference\n", "UberMedia\n", "Ubimo\n", "Ubiquisys\n", "Ubisoft\n", "Ubo\n", "Ubokia\n", "Ubooly\n", "Ubuntu\n", "UCP\n", "UCWeb\n", "Udacity\n", "Udemy\n", "Ulmon\n", "Ultimaker\n", "Ultra\n", "uMake\n", "Umano\n", "Umeng\n", "Umoove\n", "Unbabel\n", "UnboundID\n", "Underdog\n", "Underwood\n", "Undrip\n", "Unface+me\n", "Unified\n", "Unigo\n", "Unii\n", "Unilever\n", "Uniplaces\n", "Unison\n", "Unmetric\n", "Unmute\n", "Unreel+me\n", "Unruly\n", "Unsilo\n", "UP\n", "UPC\n", "UpDesk\n", "UpLabs\n", "Uppidy\n", "Uprising\n", "UPS\n", "Upstart\n", "Uptake\n", "UpTo\n", "Upverter\n", "Upworthy\n", "Uqora\n", "UrbanClap\n", "Urbane\n", "Urbanspoon\n", "UrbanStems\n", "Urbee\n", "Urbita\n", "URX\n", "USD\n", "UserEvents\n", "UserVoice\n", "Ushi\n", "USM\n", "Ustream\n", "UX\n", "V-Key\n", "Vaavud\n", "Vacatia\n", "Vadio\n", "Valassis\n", "Valeo\n", "Valkee\n", "ValoBox\n", "Vandrico\n", "Vanhawks\n", "Vanitee\n", "VaporChat\n", "Variety\n", "vArmour\n", "Vastrm\n", "VAT\n", "VCNC\n", "Vedantu\n", "Veenome\n", "Vello\n", "Velostrata\n", "Velti\n", "Vemory\n", "Venio\n", "Venmo\n", "Ventoura\n", "Venture\n", "Venturocket\n", "Venuelabs\n", "VenueNext\n", "Veoh\n", "Verbase\n", "Verbling\n", "Verdict\n", "Verelo\n", "Verge\n", "Veriflow\n", "Verifly\n", "Verious\n", "Verisart\n", "Versa\n", "Versly\n", "Vert\n", "Vertical\n", "VerticalResponse\n", "Vertro\n", "Vertu\n", "VetCloud\n", "Vettery\n", "Vevo\n", "VHX\n", "Via\n", "Viacom\n", "viaCycle\n", "Viadeo\n", "Vibe\n", "Vibease\n", "Viber\n", "Victor\n", "VictorOps\n", "Vidcode\n", "Viddsee\n", "Videdressing\n", "video\n", "VideoAmp\n", "VideoBlocks\n", "VideoElephant\n", "Videology\n", "Videopixie\n", "VideoSelfie\n", "VideoSlam\n", "Vidme\n", "VidMob\n", "Vidyard\n", "Viewbix\n", "Viewfinder\n", "Vigilent\n", "VigLink\n", "Viki\n", "Viking\n", "Vilynx\n", "Vimeo\n", "VINA\n", "Vinaya\n", "Vinci\n", "Vine\n", "Vinebox\n", "Vingle\n", "Vinli\n", "Vinomofo\n", "VIPKID\n", "Viralheat\n", "Virb\n", "Virool\n", "Virtru\n", "Virtual\n", "Virtuo\n", "VirtuOz\n", "Virtusize\n", "Virtustream\n", "Visa\n", "ViSalus\n", "ViSenze\n", "Visier\n", "Vision\n", "VISR\n", "Visual\n", "Visualead\n", "VisualGraph\n", "Visually\n", "Vita\n", "Vitacost\n", "Vital\n", "VitalFields\n", "VitaPortal\n", "Vite\n", "Vitra\n", "Vitrue\n", "Viv\n", "Viva\n", "VivaKi\n", "Vivaldi\n", "Vive\n", "Vivendi\n", "ViVu\n", "VizEat\n", "Vizify\n", "Vizury\n", "Vkontakte\n", "vLine\n", "Vlix\n", "VMFive\n", "VMware\n", "Vobi\n", "Vodafone\n", "Voddler\n", "Vogels\n", "VoiceBunny\n", "Voicegem\n", "Volley\n", "Volo\n", "VoloMetrix\n", "Voltus\n", "Voodoo\n", "VoodooPC\n", "Voonik\n", "Vortex\n", "Vorwerk\n", "Vostu\n", "Voter\n", "Votizen\n", "Vouch\n", "VouchedFor\n", "vox+io\n", "Voxeet\n", "Voxel\n", "Voxel8\n", "Voxer\n", "Voxsup\n", "Voxy\n", "Voyage\n", "Voyagin\n", "Voyat\n", "Voz+io\n", "vrAse\n", "Vringo\n", "Vroom\n", "Vrv\n", "VSCO\n", "VTech\n", "Vuclip\n", "Vue\n", "Vungle\n", "Vurb\n", "Vyte+in\n", "W3i\n", "Wadi\n", "Wag\n", "Wagon\n", "WaHome\n", "Wajam\n", "Wake\n", "Wakie\n", "Walgreens\n", "Walker\n", "WalkMe\n", "Wallace\n", "Wallapop\n", "Wallarm\n", "Wallet\n", "WalletKit\n", "Wallmob\n", "Walmart\n", "Wandera\n", "Wanderfly\n", "Wanderu\n", "WANdisco\n", "Wandoujia\n", "Wanova\n", "Want\n", "Wantering\n", "Wantful\n", "Wantster\n", "Wantworthy\n", "Wappwolf\n", "Ward\n", "Washbox\n", "Washio\n", "Watch\n", "WatchDox\n", "WatchMouse\n", "Watchsend\n", "Watchup\n", "Watchville\n", "Watchwith\n", "WaterO\n", "Waterstones\n", "Waterworks\n", "Watsi\n", "Wattpad\n", "Wavecell\n", "Wavee\n", "waves\n", "Wax\n", "Wayfair\n", "Wayin\n", "WAYN\n", "Wayra\n", "WayUp\n", "Waze\n", "Wealthfront\n", "WearToday\n", "WearYouWant\n", "Weathermob\n", "Weathernews\n", "WeatherPlanner\n", "WeatherSphere\n", "Weaveworks\n", "Web2go\n", "Webber\n", "Webedia\n", "WebEx\n", "Webflakes\n", "Webflow\n", "WebGL\n", "WebKit\n", "WebMD\n", "WebOS\n", "Webrazzi\n", "Webroot\n", "WebRTC\n", "Wedge\n", "WedPics\n", "Weebly\n", "Weekdone\n", "Weekly\n", "WeFinance\n", "WeHostels\n", "Weibo\n", "Weightless\n", "Weilos\n", "Weissman\n", "WeLab\n", "Welkio\n", "Well\n", "WellBiome\n", "WellPath\n", "Wellth\n", "Welltok\n", "WeMesh\n", "Wendel\n", "Weotta\n", "WePay\n", "Weplay\n", "Wercker\n", "Werkly\n", "WeShould\n", "WeStore\n", "WeTransfer\n", "WEVR\n", "WeWork\n", "Wharton\n", "what3words\n", "WhatRunsWhere\n", "WhatsApp\n", "Whatser\n", "Wheelz\n", "Wheretoget\n", "Whipclip\n", "WhipTail\n", "Whirlpool\n", "Whirlscape\n", "Whisk\n", "Whitepages\n", "Whitetruffle\n", "Whittl\n", "Whyd\n", "Wibbitz\n", "Wibiya\n", "Wickr\n", "Widdit\n", "Wifarer\n", "Wiggio\n", "Wiivv\n", "Wikets\n", "Wikia\n", "WikiLeaks\n", "Wikipad\n", "Wikipedia\n", "Wild\n", "Wiley\n", "Wileyfox\n", "Will\n", "WillCall\n", "Wilogo\n", "WiMP\n", "Winamp\n", "WinBeta\n", "Wind\n", "Windward\n", "Windy\n", "Wingsplay\n", "Wink\n", "Winnie\n", "Wiper\n", "WireOver\n", "Wiselike\n", "Wisely\n", "Wisembly\n", "Wisemetrics\n", "Wish\n", "Wishpond\n", "WishPop\n", "Wit+ai\n", "Withings\n", "Withlocals\n", "Witness\n", "Wittlebee\n", "Wix\n", "Wize\n", "Wochit\n", "Woisio\n", "Wolf\n", "Wolfe\n", "Women+com\n", "Wonderloop\n", "WonderLuk\n", "Wonga\n", "Wonpy\n", "Woobox\n", "Wooga\n", "Woojer\n", "Woollip\n", "Wooplr\n", "Woopra\n", "Woot\n", "Woozworld\n", "Wordeo\n", "WordPress\n", "Work4\n", "Workable\n", "Workato\n", "Workbench\n", "Workday\n", "Workestra\n", "Workflow\n", "Workfront\n", "Works\n", "Workshare\n", "Workshop\n", "Workspot\n", "WorldDesk\n", "WorldMate\n", "Worldpay\n", "WorldRemit\n", "Woven\n", "Wowo\n", "WPP\n", "Wranx\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Wrap\n", "Wrapify\n", "Wrapp\n", "Wriggle\n", "Wrike\n", "Written\n", "WrkRiot\n", "WudStay\n", "Wufoo\n", "Wurldtech\n", "wydr\n", "Wynd\n", "x\n", "xAd\n", "Xamarin\n", "XEED\n", "Xeneta\n", "Xero\n", "Xeround\n", "Xetum\n", "Xfers\n", "Xfire\n", "Xi3\n", "Xiaomi\n", "Xigo\n", "XING\n", "Xirrus\n", "XO\n", "Xola\n", "Xperia\n", "Xpire\n", "XPRIZE\n", "XSEDE\n", "Xunlei\n", "Xyo\n", "XYZE\n", "XYZprinting\n", "Yahoo\n", "Yamaha\n", "Yamli\n", "Yammer\n", "Yapp\n", "Yardsale\n", "Yarly\n", "Ybrain\n", "YCharts\n", "Yell\n", "Yelp\n", "Yemeksepeti\n", "YesGraph\n", "YesVideo\n", "Yesware\n", "Yetang\n", "Yeti\n", "Yext\n", "Yield\n", "Yieldify\n", "YieldKit\n", "Yo\n", "Yobongo\n", "Yodlee\n", "Yoga\n", "YogiPlay\n", "Yohann\n", "Yoics\n", "Yoke\n", "Yondr\n", "York\n", "Yoshi\n", "Yotpo\n", "Yottaa\n", "YouAppi\n", "YouEye\n", "Youku\n", "YouLike\n", "YouMagine\n", "Youmiam\n", "YouNoodle\n", "YouNow\n", "Your+MD\n", "Your\n", "YourBus\n", "YourSports\n", "YourTrove\n", "YouTube\n", "YouTurn\n", "YouView\n", "Youzee\n", "Yoyo\n", "Yozio\n", "YP\n", "YPlan\n", "Yumist\n", "Yunmake\n", "Z\n", "Z2\n", "Z4\n", "Zaarly\n", "Zabosu\n", "Zamurai\n", "Zap\n", "Zapier\n", "Zappli\n", "Zappos\n", "ZappRx\n", "Zattikka\n", "Zayo\n", "Zazzle\n", "Zazzy\n", "Zebra\n", "Zedge\n", "Zeekit\n", "Zeel\n", "Zeemi+tv\n", "Zeetings\n", "ZEFR\n", "Zello\n", "Zemanta\n", "Zen\n", "Zen99\n", "Zenamins\n", "ZenDeals\n", "Zendesk\n", "Zenefits\n", "Zenoti\n", "Zenprise\n", "Zentyal\n", "Zeo\n", "Zeplin\n", "zero\n", "ZeroCater\n", "ZestFinance\n", "Zhaopin\n", "Zhenai\n", "Zidisha\n", "ZigBee\n", "Zilingo\n", "Zillabyte\n", "Zillow\n", "Zimbra\n", "Zimperium\n", "Zimride\n", "Zinc\n", "Zingaya\n", "Zipcar\n", "ZipList\n", "Zipmark\n", "ZipMatch\n", "Zipments\n", "Zipongo\n", "ZipRecruiter\n", "Ziptask\n", "Zirtual\n", "Zite\n", "Zizoo\n", "ZMP\n", "Zofari\n", "Zoho\n", "Zola\n", "Zolt\n", "Zomato\n", "Zone\n", "Zoobe\n", "Zoobean\n", "Zoom\n", "Zoomdata\n", "Zoomingo\n", "Zoopla\n", "Zoosk\n", "Zopa\n", "Zopper\n", "Zortrax\n", "Zoute\n", "Zscaler\n", "Zuberance\n", "Zui\n", "Zula\n", "Zuli\n", "Zumbox\n", "Zumper\n", "Zuora\n", "Zuoyebang\n", "ZURB\n", "Zurf\n", "zVentures\n", "Zymergen\n", "Zynga\n", "Zynstra\n", "Zype\n" ] } ], "source": [ "dic = load_all_companies(\"./companies/\")" ] }, { "cell_type": "code", "execution_count": 321, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>authors</th>\n", " <th>category</th>\n", " <th>content</th>\n", " <th>date</th>\n", " <th>id</th>\n", " <th>img_src</th>\n", " <th>section</th>\n", " <th>tags</th>\n", " <th>title</th>\n", " <th>topics</th>\n", " 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<td>box</td>\n", " <td>https://techcrunch.com/2016/09/07/box-introduc...</td>\n", " </tr>\n", " <tr>\n", " <th>298</th>\n", " <td>Darrell Etherington</td>\n", " <td>Cloud</td>\n", " <td>Sure there’s some kind of fruit-related event ...</td>\n", " <td>2016-09-07</td>\n", " <td>1381251.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>enterprise/</td>\n", " <td>presentation-software,cloud-applications</td>\n", " <td>Box teams up with Google for Docs and Springbo...</td>\n", " <td>google,box</td>\n", " <td>https://techcrunch.com/2016/09/07/box-teams-up...</td>\n", " </tr>\n", " <tr>\n", " <th>349</th>\n", " <td>Josh Constine</td>\n", " <td>Apps</td>\n", " <td>If you combined the fastest and slowest types ...</td>\n", " <td>2016-09-26</td>\n", " <td>1392519.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>NaN</td>\n", " <td>If Google buys Twitter, there’s a perfect spot...</td>\n", " <td>youtube,google,twitter</td>\n", " <td>https://techcrunch.com/2016/09/26/youtweet/</td>\n", " </tr>\n", " <tr>\n", " <th>351</th>\n", " <td>Darrell Etherington</td>\n", " <td>Apps</td>\n", " <td>Google is planning hybrid devices that run bot...</td>\n", " <td>2016-09-26</td>\n", " <td>1392324.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>NaN</td>\n", " <td>Google said to debut Android/Chrome OS hybrid ...</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/09/26/google-said-...</td>\n", " </tr>\n", " <tr>\n", " <th>357</th>\n", " <td>Darrell Etherington</td>\n", " <td>Apps</td>\n", " <td>Google is planning hybrid devices that run bot...</td>\n", " <td>2016-09-26</td>\n", " <td>1392324.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>gadgets/</td>\n", " <td>NaN</td>\n", " <td>Google said to debut Android/Chrome OS hybrid ...</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/09/26/google-said-...</td>\n", " </tr>\n", " <tr>\n", " <th>414</th>\n", " <td>Jon Russell</td>\n", " <td>Apps</td>\n", " <td>There was once a time when Uber’s big advantag...</td>\n", " <td>2016-08-08</td>\n", " <td>1366113.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>asia/</td>\n", " <td>southeast-asia,go-jek,grab</td>\n", " <td>Google Maps adds rides with Uber rivals Grab a...</td>\n", " <td>uber</td>\n", " <td>https://techcrunch.com/2016/08/08/google-maps-...</td>\n", " </tr>\n", " <tr>\n", " <th>445</th>\n", " <td>Ingrid Lunden</td>\n", " <td>Advertising Tech</td>\n", " <td>Google has been focusing for years on finding ...</td>\n", " <td>2016-09-26</td>\n", " <td>1392141.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>advertising</td>\n", " <td>Google embraces the log-in, leaving cookies be...</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/09/26/google-ads/</td>\n", " </tr>\n", " <tr>\n", " <th>462</th>\n", " <td>Darrell Etherington</td>\n", " <td>Gadgets</td>\n", " <td>Google will build upon its OnHub strategy with...</td>\n", " <td>2016-09-23</td>\n", " <td>1391675.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>gadgets/</td>\n", " <td>routers,google-wifi</td>\n", " <td>$129 Google WiFi router that can team up with ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2016/09/23/129-google-w...</td>\n", " </tr>\n", " <tr>\n", " <th>508</th>\n", " <td>Fitz Tepper</td>\n", " <td>Advertising Tech</td>\n", " <td>If there is one threat to Google Search, it’s ...</td>\n", " <td>2016-09-06</td>\n", " <td>1380464.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>social/</td>\n", " <td>search-results,photo-sharing</td>\n", " <td>Google is launching Shop the Look to let you s...</td>\n", " <td>instagram,google</td>\n", " <td>https://techcrunch.com/2016/09/06/google-is-la...</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>Frederic Lardinois</td>\n", " <td>Apps</td>\n", " <td>Google first announced Allo and Duo, its new m...</td>\n", " <td>2016-09-20</td>\n", " <td>1389531.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>google-hangouts,google-duo,google-allo</td>\n", " <td>Allo brings Google’s smarts to messaging</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/09/20/allo-brings-...</td>\n", " </tr>\n", " <tr>\n", " <th>560</th>\n", " <td>Frederic Lardinois</td>\n", " <td>Apps</td>\n", " <td>One of Google Photos‘ standout features has lo...</td>\n", " <td>2016-09-19</td>\n", " <td>1388289.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>digital-media,business,world-wide-web</td>\n", " <td>Google Photos ups its movie and sharing game</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/09/19/google-photo...</td>\n", " </tr>\n", " <tr>\n", " <th>562</th>\n", " <td>Sarah Perez</td>\n", " <td>Apps</td>\n", " <td>Google today is launching a new mobile applica...</td>\n", " <td>2016-09-19</td>\n", " <td>1388444.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>apps,mobile,travel</td>\n", " <td>Google launches a personalized travel planner,...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2016/09/19/google-launc...</td>\n", " </tr>\n", " <tr>\n", " <th>564</th>\n", " <td>Brian Heater</td>\n", " <td>Mobile</td>\n", " <td>This isn’t so much an acquisition as a welcomi...</td>\n", " <td>2016-09-16</td>\n", " <td>1388058.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>urban-engines</td>\n", " <td>Google Maps picks up mapping analytics and vis...</td>\n", " <td>google-maps,google</td>\n", " <td>https://techcrunch.com/2016/09/16/urban-engine...</td>\n", " </tr>\n", " <tr>\n", " <th>592</th>\n", " <td>Ron Miller</td>\n", " <td>Cloud</td>\n", " <td>Workday announced a 7-year cloud infrastructur...</td>\n", " <td>2016-08-15</td>\n", " <td>1368938.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>enterprise/</td>\n", " <td>iaas,workday,softlayer,ibm</td>\n", " <td>Workday gives IBM big win with seven-year clou...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2016/08/15/workday-give...</td>\n", " </tr>\n", " <tr>\n", " <th>595</th>\n", " <td>Frederic Lardinois</td>\n", " <td>Disrupt SF 2016</td>\n", " <td>The cloud services that Google uses to power i...</td>\n", " <td>2016-08-11</td>\n", " <td>1366527.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>enterprise/</td>\n", " <td>enterprise,disrupt-sf-2016,diane-greene</td>\n", " <td>Google’s cloud chief Diane Greene will join us...</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/08/11/googles-clou...</td>\n", " </tr>\n", " <tr>\n", " <th>672</th>\n", " <td>Anthony Ha</td>\n", " <td>Advertising Tech</td>\n", " <td>Google is announcing a couple of upgrades to i...</td>\n", " <td>2016-09-14</td>\n", " <td>1386700.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>mobile/</td>\n", " <td>NaN</td>\n", " <td>Google introduces new app and video ad capabil...</td>\n", " <td>alphabet,google</td>\n", " <td>https://techcrunch.com/2016/09/14/google-intro...</td>\n", " </tr>\n", " <tr>\n", " <th>704</th>\n", " <td>Ingrid Lunden</td>\n", " <td>Cloud</td>\n", " <td>Google today announced another acquisition tha...</td>\n", " <td>2016-08-08</td>\n", " <td>1365556.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>enterprise/</td>\n", " <td>enterprise-software,cloud,orbitera</td>\n", " <td>Google buys Orbitera, a platform for cloud mar...</td>\n", " <td>google</td>\n", " <td>https://techcrunch.com/2016/08/08/google-buys-...</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>37719</th>\n", " <td>Rip Empson</td>\n", " <td>Social</td>\n", " <td>Forget Yammer, forget Asana. In April, Y Combi...</td>\n", " <td>2012-06-26</td>\n", " <td>578025.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>y-combinator,siasto</td>\n", " <td>Amidst Yammers &amp; Asanas, YC Alum Siasto Finds ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/06/26/siasto-growi...</td>\n", " </tr>\n", " <tr>\n", " <th>37785</th>\n", " <td>Sarah Perez</td>\n", " <td>Mobile</td>\n", " <td>App Annie, the app store analytics and market ...</td>\n", " <td>2012-06-20</td>\n", " <td>574486.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>google-play,app-annie,apps</td>\n", " <td>At Last! App Store Analytics Firm App Annie Ad...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/06/20/at-last-app-...</td>\n", " </tr>\n", " <tr>\n", " <th>37921</th>\n", " <td>Rip Empson</td>\n", " <td>Startups</td>\n", " <td>The annual Google Science Fair is an effort to...</td>\n", " <td>2012-06-07</td>\n", " <td>567316.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>science,google-science-fair,google</td>\n", " <td>Meet The Top 15 Finalists In Google’s Annual S...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/06/07/meet-the-top...</td>\n", " </tr>\n", " <tr>\n", " <th>37949</th>\n", " <td>Eric Eldon</td>\n", " <td>Startups</td>\n", " <td>Meebo, the seven year-old chat service that mo...</td>\n", " <td>2012-06-04</td>\n", " <td>565787.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>More On Meebo: Price Is Around $100M, Product ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/06/04/more-on-meeb...</td>\n", " </tr>\n", " <tr>\n", " <th>38026</th>\n", " <td>Anthony Ha</td>\n", " <td>Startups</td>\n", " <td>There’s a lot of talk these days about what yo...</td>\n", " <td>2012-05-24</td>\n", " <td>561047.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>Predictive Startup Recorded Future Raises $12M...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/05/24/recorded-fut...</td>\n", " </tr>\n", " <tr>\n", " <th>38031</th>\n", " <td>Rip Empson</td>\n", " <td>Mobile</td>\n", " <td>Just over a week ago, the jury began deliberat...</td>\n", " <td>2012-05-23</td>\n", " <td>560513.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>patents</td>\n", " <td>The Verdict Is In: Google Did NOT Infringe On ...</td>\n", " <td>android,google,oracle</td>\n", " <td>https://techcrunch.com/2012/05/23/the-verdict-...</td>\n", " </tr>\n", " <tr>\n", " <th>38147</th>\n", " <td>Mike Butcher</td>\n", " <td>Europe</td>\n", " <td>[The article below was written on the incorrec...</td>\n", " <td>2012-05-13</td>\n", " <td>551159.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>tropo,twilio,adinsight,iovox</td>\n", " <td>How The Future Of Web Advertising Is Linked To...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/05/13/hold-the-pho...</td>\n", " </tr>\n", " <tr>\n", " <th>38169</th>\n", " <td>Anthony Ha</td>\n", " <td>Startups</td>\n", " <td>When Google Drive launched at the end of April...</td>\n", " <td>2012-05-10</td>\n", " <td>549581.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>HelloFax: With 51K Installs, We’re The Top Goo...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/05/10/hellofax-wit...</td>\n", " </tr>\n", " <tr>\n", " <th>38173</th>\n", " <td>Rip Empson</td>\n", " <td>Enterprise</td>\n", " <td>About six years ago, Google launched Apps for ...</td>\n", " <td>2012-05-10</td>\n", " <td>549367.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>google-apps,google,flashpanel,bettercloud</td>\n", " <td>BetterCloud Nabs $2.2M From Angels To Bring Be...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/05/10/bettercloud-...</td>\n", " </tr>\n", " <tr>\n", " <th>38174</th>\n", " <td>Ingrid Lunden</td>\n", " <td>Social</td>\n", " <td>Anybeat, a social network that launched last y...</td>\n", " <td>2012-05-09</td>\n", " <td>549625.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>google,social-networks,anybeat</td>\n", " <td>‘Anonymous’ Social Network Anybeat Is Getting ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/05/09/anonymous-so...</td>\n", " </tr>\n", " <tr>\n", " <th>38269</th>\n", " <td>Anthony Ha</td>\n", " <td>Startups</td>\n", " <td>BrandYourself, a startup offering a cheap and ...</td>\n", " <td>2012-05-01</td>\n", " <td>544381.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>Now BrandYourself Users Can See The Companies ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/05/01/now-brandyou...</td>\n", " </tr>\n", " <tr>\n", " <th>38308</th>\n", " <td>Rip Empson</td>\n", " <td>Apps</td>\n", " <td>At this time of year, we usually find ourselve...</td>\n", " <td>2012-04-26</td>\n", " <td>542138.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>trimble,sketchup,google</td>\n", " <td>A Rare Sale: Despite 30 Million Activations In...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/26/sketchup-goe...</td>\n", " </tr>\n", " <tr>\n", " <th>38421</th>\n", " <td>John Biggs</td>\n", " <td>Startups</td>\n", " <td>If you needed any more proof that Google Drive...</td>\n", " <td>2012-04-16</td>\n", " <td>536329.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>google-drive,google</td>\n", " <td>Google Drive Lives: Google Drive App Found</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/16/google-drive...</td>\n", " </tr>\n", " <tr>\n", " <th>38476</th>\n", " <td>Rip Empson</td>\n", " <td>Apps</td>\n", " <td>Web apps look a lot different today than they ...</td>\n", " <td>2012-04-11</td>\n", " <td>533781.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>meteor-com,meteor</td>\n", " <td>Meteor: Etherpad Founder &amp; Other Rockstars Tea...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/11/meteor-launch/</td>\n", " </tr>\n", " <tr>\n", " <th>38513</th>\n", " <td>NaN</td>\n", " <td>Social</td>\n", " <td>Editor’s Note: Ian Lurie is CEO of Portent Inc...</td>\n", " <td>2012-04-08</td>\n", " <td>532080.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>sergey-brin,larry-page,google</td>\n", " <td>How The IPO Ruined Google</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/08/how-the-ipo-...</td>\n", " </tr>\n", " <tr>\n", " <th>38514</th>\n", " <td>Derek Andersen</td>\n", " <td>Startups</td>\n", " <td>Editor’s note: Derek Andersen is founder of St...</td>\n", " <td>2012-04-08</td>\n", " <td>532060.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>Pinterest’s Unlikely Journey To Top Of The Sta...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/08/pinterest-st...</td>\n", " </tr>\n", " <tr>\n", " <th>38534</th>\n", " <td>Sarah Perez</td>\n", " <td>Startups</td>\n", " <td>DudaMobile, the DIY mobile website maker, fres...</td>\n", " <td>2012-04-05</td>\n", " <td>530973.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>dudamobile,smb,business,google</td>\n", " <td>Google’s GoMo Expands, Adds DIY Mobile Website...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/05/googles-gomo...</td>\n", " </tr>\n", " <tr>\n", " <th>38541</th>\n", " <td>Rip Empson</td>\n", " <td>Social</td>\n", " <td>Back in November, we reported that Sean Garret...</td>\n", " <td>2012-04-04</td>\n", " <td>530777.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>gabriel-stricker,twitter,google</td>\n", " <td>Twitter Nabs Googler Gabriel Stricker As Comms VP</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/04/twitter-new-...</td>\n", " </tr>\n", " <tr>\n", " <th>38586</th>\n", " <td>NaN</td>\n", " <td>Apps</td>\n", " <td>Happy April 1st, Everybody! It’s that very spe...</td>\n", " <td>2012-04-01</td>\n", " <td>528633.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>april-fools</td>\n", " <td>April Fools 2012: We Ruin Every (Tech-Related)...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/01/april-fools-...</td>\n", " </tr>\n", " <tr>\n", " <th>38590</th>\n", " <td>Ingrid Lunden</td>\n", " <td>Europe</td>\n", " <td>London’s claim to being the hub for tech start...</td>\n", " <td>2012-04-01</td>\n", " <td>528688.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>Fragmentation? Open Source? Buzzwords For Andr...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/04/01/fragmentatio...</td>\n", " </tr>\n", " <tr>\n", " <th>38627</th>\n", " <td>Alexia Tsotsis</td>\n", " <td>Apps</td>\n", " <td>Mobile web and ad optimization startup AppStac...</td>\n", " <td>2012-03-27</td>\n", " <td>526395.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>appstack</td>\n", " <td>Mobile Ad Optimization Startup AppStack Raises...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/27/appstack-rai...</td>\n", " </tr>\n", " <tr>\n", " <th>38662</th>\n", " <td>Josh Constine</td>\n", " <td>Social</td>\n", " <td>Google and Facebook can’t help you find which ...</td>\n", " <td>2012-03-25</td>\n", " <td>525232.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>ark,ycombinator,battlefield</td>\n", " <td>Find Everyone You Can’t Google Or Facebook Wit...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/25/ark-people-s...</td>\n", " </tr>\n", " <tr>\n", " <th>38677</th>\n", " <td>Rip Empson</td>\n", " <td>Apps</td>\n", " <td>In the age of endless sharing, super cookies, ...</td>\n", " <td>2012-03-22</td>\n", " <td>524441.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>disconnect,disconnect-me,brian-kennish</td>\n", " <td>Disconnect: Ex-Googlers Raise Funding To Stop ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/22/disconnect-m...</td>\n", " </tr>\n", " <tr>\n", " <th>38742</th>\n", " <td>Kim-Mai Cutler</td>\n", " <td>eCommerce</td>\n", " <td>More people leaving Google Wallet means more f...</td>\n", " <td>2012-03-16</td>\n", " <td>521647.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>Google Wallet’s Founding Engineer, Product Lea...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/16/google-walle...</td>\n", " </tr>\n", " <tr>\n", " <th>38760</th>\n", " <td>Alexia Tsotsis</td>\n", " <td>Social</td>\n", " <td>AllThingsD is reporting that super-founder Kev...</td>\n", " <td>2012-03-15</td>\n", " <td>520847.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>milk</td>\n", " <td>Winning A Bidding War With Facebook, Google Pi...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/15/winning-a-bi...</td>\n", " </tr>\n", " <tr>\n", " <th>38775</th>\n", " <td>Sarah Perez</td>\n", " <td>eCommerce</td>\n", " <td>Today, Google is announcing a partnership with...</td>\n", " <td>2012-03-14</td>\n", " <td>520181.0</td>\n", " <td>https://tctechcrunch.files.wordpress.com/2011/...</td>\n", " <td>startups/</td>\n", " <td>e-commerce,google-offers,signpost,local-commerce</td>\n", " <td>Google Offers Partners With Signpost, The “AdS...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/14/google-offer...</td>\n", " </tr>\n", " <tr>\n", " <th>38798</th>\n", " <td>Rip Empson</td>\n", " <td>Startups</td>\n", " <td>As a founding partner at Y Combinator, Paul Gr...</td>\n", " <td>2012-03-10</td>\n", " <td>518873.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>startups,paul-graham</td>\n", " <td>Paul Graham Wants You To Build A New Search En...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/03/10/paul-grahams...</td>\n", " </tr>\n", " <tr>\n", " <th>39007</th>\n", " <td>Jon Orlin</td>\n", " <td>Apps</td>\n", " <td>The mundane Address Book was big news this wee...</td>\n", " <td>2012-02-17</td>\n", " <td>498499.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>address-book,kaspars-dancis,codo,cobook</td>\n", " <td>Cobook, A Slick Address Book App That Doesn’t ...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/02/17/cobook-a-sli...</td>\n", " </tr>\n", " <tr>\n", " <th>39023</th>\n", " <td>Anthony Ha</td>\n", " <td>Enterprise</td>\n", " <td>Startup Podio says it just addressed one of th...</td>\n", " <td>2012-02-16</td>\n", " <td>497979.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>NaN</td>\n", " <td>Podio Plugs Google Docs Into Its Collaboration...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/02/16/podio-google...</td>\n", " </tr>\n", " <tr>\n", " <th>39059</th>\n", " <td>Sarah Perez</td>\n", " <td>Enterprise</td>\n", " <td>Austin-based cloud apps startup Spanning, whic...</td>\n", " <td>2012-02-13</td>\n", " <td>496404.0</td>\n", " <td>https://tctechcrunch2011.files.wordpress.com/2...</td>\n", " <td>startups/</td>\n", " <td>spanning,enterprise,google-apps</td>\n", " <td>Google Apps Backup Service Spanning Gets Sexy:...</td>\n", " <td>NaN</td>\n", " <td>https://techcrunch.com/2012/02/13/google-apps-...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1497 rows × 11 columns</p>\n", "</div>" ], "text/plain": [ " authors category \\\n", "23 Devin Coldewey Artificial Intelligence \n", "26 Frederic Lardinois Apps \n", "30 Sarah Perez Mobile \n", "39 Devin Coldewey Artificial Intelligence \n", "67 John Mannes Media \n", "130 Darrell Etherington Gadgets \n", "160 Jon Russell Asia \n", "195 Darrell Etherington Artificial Intelligence \n", "196 Brian Heater Gadgets \n", "208 Josh Constine Apps \n", "217 Natasha Lomas Apps \n", "258 Natasha Lomas Apps \n", "271 Romain Dillet Gadgets \n", "296 Ron Miller Cloud \n", "298 Darrell Etherington Cloud \n", "349 Josh Constine Apps \n", "351 Darrell Etherington Apps \n", "357 Darrell Etherington Apps \n", "414 Jon Russell Apps \n", "445 Ingrid Lunden Advertising Tech \n", "462 Darrell Etherington Gadgets \n", "508 Fitz Tepper Advertising Tech \n", "556 Frederic Lardinois Apps \n", "560 Frederic Lardinois Apps \n", "562 Sarah Perez Apps \n", "564 Brian Heater Mobile \n", "592 Ron Miller Cloud \n", "595 Frederic Lardinois Disrupt SF 2016 \n", "672 Anthony Ha Advertising Tech \n", "704 Ingrid Lunden Cloud \n", "... ... ... \n", "37719 Rip Empson Social \n", "37785 Sarah Perez Mobile \n", "37921 Rip Empson Startups \n", "37949 Eric Eldon Startups \n", "38026 Anthony Ha Startups \n", "38031 Rip Empson Mobile \n", "38147 Mike Butcher Europe \n", "38169 Anthony Ha Startups \n", "38173 Rip Empson Enterprise \n", "38174 Ingrid Lunden Social \n", "38269 Anthony Ha Startups \n", "38308 Rip Empson Apps \n", "38421 John Biggs Startups \n", "38476 Rip Empson Apps \n", "38513 NaN Social \n", "38514 Derek Andersen Startups \n", "38534 Sarah Perez Startups \n", "38541 Rip Empson Social \n", "38586 NaN Apps \n", "38590 Ingrid Lunden Europe \n", "38627 Alexia Tsotsis Apps \n", "38662 Josh Constine Social \n", "38677 Rip Empson Apps \n", "38742 Kim-Mai Cutler eCommerce \n", "38760 Alexia Tsotsis Social \n", "38775 Sarah Perez eCommerce \n", "38798 Rip Empson Startups \n", "39007 Jon Orlin Apps \n", "39023 Anthony Ha Enterprise \n", "39059 Sarah Perez Enterprise \n", "\n", " content date \\\n", "23 Want Google Assistant, but don’t want to spend... 2016-10-12 \n", "26 With Android Experiments, Google is giving its... 2016-10-12 \n", "30 Apple has unveiled its own official measuremen... 2016-10-11 \n", "39 Want Google Assistant, but don’t want to spend... 2016-10-12 \n", "67 Today Google added a new “fact-check” tag to i... 2016-10-13 \n", "130 Google’s new hardware is getting a Google-owne... 2016-10-07 \n", "160 Google today announced a collection of updates... 2016-09-27 \n", "195 Google unveiled a lot of new devices today, bu... 2016-10-04 \n", "196 Sometimes it’s best to just sit back and feign... 2016-10-04 \n", "208 If you combined the fastest and slowest types ... 2016-09-26 \n", "217 Alphabet (née Google) has been given yet anoth... 2016-10-03 \n", "258 Alphabet (née Google) has been given yet anoth... 2016-10-03 \n", "271 Google is set to announce a bunch of new produ... 2016-10-03 \n", "296 Box has always been known as the irritant in t... 2016-09-07 \n", "298 Sure there’s some kind of fruit-related event ... 2016-09-07 \n", "349 If you combined the fastest and slowest types ... 2016-09-26 \n", "351 Google is planning hybrid devices that run bot... 2016-09-26 \n", "357 Google is planning hybrid devices that run bot... 2016-09-26 \n", "414 There was once a time when Uber’s big advantag... 2016-08-08 \n", "445 Google has been focusing for years on finding ... 2016-09-26 \n", "462 Google will build upon its OnHub strategy with... 2016-09-23 \n", "508 If there is one threat to Google Search, it’s ... 2016-09-06 \n", "556 Google first announced Allo and Duo, its new m... 2016-09-20 \n", "560 One of Google Photos‘ standout features has lo... 2016-09-19 \n", "562 Google today is launching a new mobile applica... 2016-09-19 \n", "564 This isn’t so much an acquisition as a welcomi... 2016-09-16 \n", "592 Workday announced a 7-year cloud infrastructur... 2016-08-15 \n", "595 The cloud services that Google uses to power i... 2016-08-11 \n", "672 Google is announcing a couple of upgrades to i... 2016-09-14 \n", "704 Google today announced another acquisition tha... 2016-08-08 \n", "... ... ... \n", "37719 Forget Yammer, forget Asana. In April, Y Combi... 2012-06-26 \n", "37785 App Annie, the app store analytics and market ... 2012-06-20 \n", "37921 The annual Google Science Fair is an effort to... 2012-06-07 \n", "37949 Meebo, the seven year-old chat service that mo... 2012-06-04 \n", "38026 There’s a lot of talk these days about what yo... 2012-05-24 \n", "38031 Just over a week ago, the jury began deliberat... 2012-05-23 \n", "38147 [The article below was written on the incorrec... 2012-05-13 \n", "38169 When Google Drive launched at the end of April... 2012-05-10 \n", "38173 About six years ago, Google launched Apps for ... 2012-05-10 \n", "38174 Anybeat, a social network that launched last y... 2012-05-09 \n", "38269 BrandYourself, a startup offering a cheap and ... 2012-05-01 \n", "38308 At this time of year, we usually find ourselve... 2012-04-26 \n", "38421 If you needed any more proof that Google Drive... 2012-04-16 \n", "38476 Web apps look a lot different today than they ... 2012-04-11 \n", "38513 Editor’s Note: Ian Lurie is CEO of Portent Inc... 2012-04-08 \n", "38514 Editor’s note: Derek Andersen is founder of St... 2012-04-08 \n", "38534 DudaMobile, the DIY mobile website maker, fres... 2012-04-05 \n", "38541 Back in November, we reported that Sean Garret... 2012-04-04 \n", "38586 Happy April 1st, Everybody! It’s that very spe... 2012-04-01 \n", "38590 London’s claim to being the hub for tech start... 2012-04-01 \n", "38627 Mobile web and ad optimization startup AppStac... 2012-03-27 \n", "38662 Google and Facebook can’t help you find which ... 2012-03-25 \n", "38677 In the age of endless sharing, super cookies, ... 2012-03-22 \n", "38742 More people leaving Google Wallet means more f... 2012-03-16 \n", "38760 AllThingsD is reporting that super-founder Kev... 2012-03-15 \n", "38775 Today, Google is announcing a partnership with... 2012-03-14 \n", "38798 As a founding partner at Y Combinator, Paul Gr... 2012-03-10 \n", "39007 The mundane Address Book was big news this wee... 2012-02-17 \n", "39023 Startup Podio says it just addressed one of th... 2012-02-16 \n", "39059 Austin-based cloud apps startup Spanning, whic... 2012-02-13 \n", "\n", " id img_src \\\n", "23 1401019.0 https://tctechcrunch2011.files.wordpress.com/2... \n", "26 1400778.0 https://tctechcrunch2011.files.wordpress.com/2... 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https://tctechcrunch2011.files.wordpress.com/2... \n", "39059 496404.0 https://tctechcrunch2011.files.wordpress.com/2... \n", "\n", " section tags \\\n", "23 mobile/ google-assistant \n", "26 mobile/ app \n", "30 mobile/ NaN \n", "39 gadgets/ google-assistant \n", "67 social/ blog,google-search,world-wide-web,digital-media \n", "130 gadgets/ NaN \n", "160 asia/ india,connectivity,emerging-markets \n", "195 gadgets/ google-pixel,smartphones \n", "196 gadgets/ googlepixel,googlehardware,googlehardware2016 \n", "208 social/ NaN \n", "217 europe/ european-competition-commission,smartphones \n", "258 mobile/ european-competition-commission,smartphones \n", "271 gadgets/ leak,google-pixel,pixel \n", "296 enterprise/ content-management,diane-greene,aaron-levie \n", "298 enterprise/ presentation-software,cloud-applications \n", "349 mobile/ NaN \n", "351 mobile/ NaN \n", "357 gadgets/ NaN \n", "414 asia/ southeast-asia,go-jek,grab \n", "445 mobile/ advertising \n", "462 gadgets/ routers,google-wifi \n", "508 social/ search-results,photo-sharing \n", "556 mobile/ google-hangouts,google-duo,google-allo \n", "560 mobile/ digital-media,business,world-wide-web \n", "562 mobile/ apps,mobile,travel \n", "564 mobile/ urban-engines \n", "592 enterprise/ iaas,workday,softlayer,ibm \n", "595 enterprise/ enterprise,disrupt-sf-2016,diane-greene \n", "672 mobile/ NaN \n", "704 enterprise/ enterprise-software,cloud,orbitera \n", "... ... ... \n", "37719 startups/ y-combinator,siasto \n", "37785 startups/ google-play,app-annie,apps \n", "37921 startups/ science,google-science-fair,google \n", "37949 startups/ NaN \n", "38026 startups/ NaN \n", "38031 startups/ patents \n", "38147 startups/ tropo,twilio,adinsight,iovox \n", "38169 startups/ NaN \n", "38173 startups/ google-apps,google,flashpanel,bettercloud \n", "38174 startups/ google,social-networks,anybeat \n", "38269 startups/ NaN \n", "38308 startups/ trimble,sketchup,google \n", "38421 startups/ google-drive,google \n", "38476 startups/ meteor-com,meteor \n", "38513 startups/ sergey-brin,larry-page,google \n", "38514 startups/ NaN \n", "38534 startups/ dudamobile,smb,business,google \n", "38541 startups/ gabriel-stricker,twitter,google \n", "38586 startups/ april-fools \n", "38590 startups/ NaN \n", "38627 startups/ appstack \n", "38662 startups/ ark,ycombinator,battlefield \n", "38677 startups/ disconnect,disconnect-me,brian-kennish \n", "38742 startups/ NaN \n", "38760 startups/ milk \n", "38775 startups/ e-commerce,google-offers,signpost,local-commerce \n", "38798 startups/ startups,paul-graham \n", "39007 startups/ address-book,kaspars-dancis,codo,cobook \n", "39023 startups/ NaN \n", "39059 startups/ spanning,enterprise,google-apps \n", "\n", " title \\\n", "23 Add Google Assistant to your phone by tweaking... \n", "26 Google’s new Sprayscape app is purposely imper... \n", "30 According to Apple’s official figures, iOS 10 ... \n", "39 Add Google Assistant to your phone by tweaking... \n", "67 Google starts highlighting fact-checks in News \n", "130 Google’s self-made hardware is getting a pop-u... \n", "160 Google expands its initiative to provide free ... \n", "195 Google’s new smartphones are about Google, not... \n", "196 Here’s the Google Pixel \n", "208 If Google buys Twitter, there’s a perfect spot... \n", "217 Google gets more time to respond to EU antitru... \n", "258 Google gets more time to respond to EU antitru... \n", "271 Here’s what the Google Pixel could look like \n", "296 Box introduces “New Box” at BoxWorks \n", "298 Box teams up with Google for Docs and Springbo... \n", "349 If Google buys Twitter, there’s a perfect spot... \n", "351 Google said to debut Android/Chrome OS hybrid ... \n", "357 Google said to debut Android/Chrome OS hybrid ... \n", "414 Google Maps adds rides with Uber rivals Grab a... \n", "445 Google embraces the log-in, leaving cookies be... \n", "462 $129 Google WiFi router that can team up with ... \n", "508 Google is launching Shop the Look to let you s... \n", "556 Allo brings Google’s smarts to messaging \n", "560 Google Photos ups its movie and sharing game \n", "562 Google launches a personalized travel planner,... \n", "564 Google Maps picks up mapping analytics and vis... \n", "592 Workday gives IBM big win with seven-year clou... \n", "595 Google’s cloud chief Diane Greene will join us... \n", "672 Google introduces new app and video ad capabil... \n", "704 Google buys Orbitera, a platform for cloud mar... \n", "... ... \n", "37719 Amidst Yammers & Asanas, YC Alum Siasto Finds ... \n", "37785 At Last! App Store Analytics Firm App Annie Ad... \n", "37921 Meet The Top 15 Finalists In Google’s Annual S... \n", "37949 More On Meebo: Price Is Around $100M, Product ... \n", "38026 Predictive Startup Recorded Future Raises $12M... \n", "38031 The Verdict Is In: Google Did NOT Infringe On ... \n", "38147 How The Future Of Web Advertising Is Linked To... \n", "38169 HelloFax: With 51K Installs, We’re The Top Goo... \n", "38173 BetterCloud Nabs $2.2M From Angels To Bring Be... \n", "38174 ‘Anonymous’ Social Network Anybeat Is Getting ... \n", "38269 Now BrandYourself Users Can See The Companies ... \n", "38308 A Rare Sale: Despite 30 Million Activations In... \n", "38421 Google Drive Lives: Google Drive App Found \n", "38476 Meteor: Etherpad Founder & Other Rockstars Tea... \n", "38513 How The IPO Ruined Google \n", "38514 Pinterest’s Unlikely Journey To Top Of The Sta... \n", "38534 Google’s GoMo Expands, Adds DIY Mobile Website... \n", "38541 Twitter Nabs Googler Gabriel Stricker As Comms VP \n", "38586 April Fools 2012: We Ruin Every (Tech-Related)... \n", "38590 Fragmentation? Open Source? Buzzwords For Andr... \n", "38627 Mobile Ad Optimization Startup AppStack Raises... \n", "38662 Find Everyone You Can’t Google Or Facebook Wit... \n", "38677 Disconnect: Ex-Googlers Raise Funding To Stop ... \n", "38742 Google Wallet’s Founding Engineer, Product Lea... \n", "38760 Winning A Bidding War With Facebook, Google Pi... \n", "38775 Google Offers Partners With Signpost, The “AdS... \n", "38798 Paul Graham Wants You To Build A New Search En... \n", "39007 Cobook, A Slick Address Book App That Doesn’t ... \n", "39023 Podio Plugs Google Docs Into Its Collaboration... \n", "39059 Google Apps Backup Service Spanning Gets Sexy:... \n", "\n", " topics \\\n", "23 android \n", "26 google \n", "30 apple \n", "39 android \n", "67 NaN \n", "130 google \n", "160 google \n", "195 google \n", "196 NaN \n", "208 youtube,google,twitter \n", "217 android,google \n", "258 android,google \n", "271 google \n", "296 box \n", "298 google,box \n", "349 youtube,google,twitter \n", "351 google \n", "357 google \n", "414 uber \n", "445 google \n", "462 NaN \n", "508 instagram,google \n", "556 google \n", "560 google \n", "562 NaN \n", "564 google-maps,google \n", "592 NaN \n", "595 google \n", "672 alphabet,google \n", "704 google \n", "... ... \n", "37719 NaN \n", "37785 NaN \n", "37921 NaN \n", "37949 NaN \n", "38026 NaN \n", "38031 android,google,oracle \n", "38147 NaN \n", "38169 NaN \n", "38173 NaN \n", "38174 NaN \n", "38269 NaN \n", "38308 NaN \n", "38421 NaN \n", "38476 NaN \n", "38513 NaN \n", "38514 NaN \n", "38534 NaN \n", "38541 NaN \n", "38586 NaN \n", "38590 NaN \n", "38627 NaN \n", "38662 NaN \n", "38677 NaN \n", "38742 NaN \n", "38760 NaN \n", "38775 NaN \n", "38798 NaN \n", "39007 NaN \n", "39023 NaN \n", "39059 NaN \n", "\n", " url \n", "23 https://techcrunch.com/2016/10/12/add-google-a... \n", "26 https://techcrunch.com/2016/10/12/spray-and-pray/ \n", "30 https://techcrunch.com/2016/10/11/according-to... \n", "39 https://techcrunch.com/2016/10/12/add-google-a... \n", "67 https://techcrunch.com/2016/10/13/google-helps... \n", "130 https://techcrunch.com/2016/10/07/googles-self... \n", "160 https://techcrunch.com/2016/09/27/google-stati... \n", "195 https://techcrunch.com/2016/10/04/googles-new-... \n", "196 https://techcrunch.com/2016/10/04/google-pixel/ \n", "208 https://techcrunch.com/2016/09/26/youtweet/ \n", "217 https://techcrunch.com/2016/10/03/google-gets-... \n", "258 https://techcrunch.com/2016/10/03/google-gets-... \n", "271 https://techcrunch.com/2016/10/03/heres-what-t... \n", "296 https://techcrunch.com/2016/09/07/box-introduc... \n", "298 https://techcrunch.com/2016/09/07/box-teams-up... \n", "349 https://techcrunch.com/2016/09/26/youtweet/ \n", "351 https://techcrunch.com/2016/09/26/google-said-... \n", "357 https://techcrunch.com/2016/09/26/google-said-... \n", "414 https://techcrunch.com/2016/08/08/google-maps-... \n", "445 https://techcrunch.com/2016/09/26/google-ads/ \n", "462 https://techcrunch.com/2016/09/23/129-google-w... \n", "508 https://techcrunch.com/2016/09/06/google-is-la... \n", "556 https://techcrunch.com/2016/09/20/allo-brings-... \n", "560 https://techcrunch.com/2016/09/19/google-photo... \n", "562 https://techcrunch.com/2016/09/19/google-launc... \n", "564 https://techcrunch.com/2016/09/16/urban-engine... \n", "592 https://techcrunch.com/2016/08/15/workday-give... \n", "595 https://techcrunch.com/2016/08/11/googles-clou... \n", "672 https://techcrunch.com/2016/09/14/google-intro... \n", "704 https://techcrunch.com/2016/08/08/google-buys-... \n", "... ... \n", "37719 https://techcrunch.com/2012/06/26/siasto-growi... \n", "37785 https://techcrunch.com/2012/06/20/at-last-app-... \n", "37921 https://techcrunch.com/2012/06/07/meet-the-top... \n", "37949 https://techcrunch.com/2012/06/04/more-on-meeb... \n", "38026 https://techcrunch.com/2012/05/24/recorded-fut... \n", "38031 https://techcrunch.com/2012/05/23/the-verdict-... \n", "38147 https://techcrunch.com/2012/05/13/hold-the-pho... \n", "38169 https://techcrunch.com/2012/05/10/hellofax-wit... \n", "38173 https://techcrunch.com/2012/05/10/bettercloud-... \n", "38174 https://techcrunch.com/2012/05/09/anonymous-so... \n", "38269 https://techcrunch.com/2012/05/01/now-brandyou... \n", "38308 https://techcrunch.com/2012/04/26/sketchup-goe... \n", "38421 https://techcrunch.com/2012/04/16/google-drive... \n", "38476 https://techcrunch.com/2012/04/11/meteor-launch/ \n", "38513 https://techcrunch.com/2012/04/08/how-the-ipo-... \n", "38514 https://techcrunch.com/2012/04/08/pinterest-st... \n", "38534 https://techcrunch.com/2012/04/05/googles-gomo... \n", "38541 https://techcrunch.com/2012/04/04/twitter-new-... \n", "38586 https://techcrunch.com/2012/04/01/april-fools-... \n", "38590 https://techcrunch.com/2012/04/01/fragmentatio... \n", "38627 https://techcrunch.com/2012/03/27/appstack-rai... \n", "38662 https://techcrunch.com/2012/03/25/ark-people-s... \n", "38677 https://techcrunch.com/2012/03/22/disconnect-m... \n", "38742 https://techcrunch.com/2012/03/16/google-walle... \n", "38760 https://techcrunch.com/2012/03/15/winning-a-bi... \n", "38775 https://techcrunch.com/2012/03/14/google-offer... \n", "38798 https://techcrunch.com/2012/03/10/paul-grahams... \n", "39007 https://techcrunch.com/2012/02/17/cobook-a-sli... \n", "39023 https://techcrunch.com/2012/02/16/podio-google... \n", "39059 https://techcrunch.com/2012/02/13/google-apps-... \n", "\n", "[1497 rows x 11 columns]" ] }, "execution_count": 321, "metadata": {}, "output_type": "execute_result" } ], "source": [ "company_post_dict['Google']" ] }, { "cell_type": "code", "execution_count": 322, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Not found in dic: FoodChéri\n", "Not found in dic: egg\n", "Not found in dic: Dónde\n", "Not found in dic: HD+\n", "Not found in dic: CoCoon\n", "Not found in dic: Pokémon\n", "Not found in dic: POP\n", "Not found in dic: RenéSim\n", "Not found in dic: IRIS\n", "Not found in dic: VINCI\n", "Not found in dic: SAY\n", "Not found in dic: Camera+\n", "Not found in dic: Telefónica\n", "Not found in dic: Hike\n", "Not found in dic: Air\n", "Not found in dic: LookSee\n", "Not found in dic: People+\n", "Not found in dic: Canal+\n", "Not found in dic: Tag\n", "Not found in dic: Monica+Andy\n", "Not found in dic: MOVE\n", "Not found in dic: startup\n" ] } ], "source": [ "# Error checking against generated company lists\n", "for key in company_post_dict:\n", " if key in dic:\n", " pass\n", " else:\n", " print (\"Not found in dic: \" + key)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Indexing to startup table: orgs[orgs['name'].isin(['BBB'])]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# def company_pr_freq(name, )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

tbphu/fachkurs_bachelor

PythonIntro/games/20171122_RPS_class.ipynb

3

2373

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Rock, Paper, Scissors\n", "#### Jorin Diemer, Jens Hahn - WS 2017" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "class RockPaperScissors:\n", " def __init__(self):\n", " self.rules = {'rock': {'paper': False, 'scissors': True},\n", " 'paper': {'rock': True, 'scissors': False},\n", " 'scissors': {'rock': False, 'paper': True}}\n", " self.score = [0,0]\n", " \n", " def computer_choice(self):\n", " choice = np.random.choice(list(self.rules))\n", " return choice\n", "\n", " def user_choice(self):\n", " choice = None\n", " while not choice in self.rules:\n", " choice = input('Choose \"rock\", \"paper\", or \"scissors\":')\n", " return choice\n", "\n", " def evaluate(self):\n", " human_choice = self.user_choice()\n", " pc_choice = self.computer_choice()\n", " if human_choice == pc_choice:\n", " print('Tied game.')\n", " elif self.rules[human_choice][pc_choice]:\n", " print('You won!')\n", " self.score[0] += 1\n", " else:\n", " print('You lost!')\n", " self.score[1] += 1\n", " \n", " def start(self, rounds=5):\n", " while self.score[0] < rounds//2 + 1 and self.score[1] < rounds//2 + 1:\n", " self.evaluate()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "game = RockPaperScissors()\n", "game.start()" ] }, { "cell_type": "raw", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

NeuroDataDesign/pan-synapse

pipeline_1/background/basicReg.md.ipynb

1

1687380

apache-2.0

dolphyin/cs194-16-data_manatees

format_311.ipynb

2

54518

{ "metadata": { "name": "", "signature": "sha256:bf7d390a7374589a3b4e618f725ab14fbed1831db6fa920c83cb4156059ccdd9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook takes in the raw 311 data (from datasf.org) and converts it to a (feature vector, bin) dataframe, for joining with the 911 dataset." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Notebook for converting 311 data into format of [tuple, list of 311 reports that occured at that tuple\n", "DATA_PATH = \"data/data_311.csv\" # path of amazon machine\n", "import pandas as pd\n", "data_311 = pd.read_csv(DATA_PATH, na_values=['-'])\n", "data_311 = data_311.where((pd.notnull(data_311)), None)\n", "data_311 = data_311[['Opened', 'Category', 'Request Type', 'Supervisor District', 'Point']]\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Opened</th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>Point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 11/29/2014 12:57:11 AM</td>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> (37.782620592996, -122.416286644263)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 11/29/2014 12:32:49 AM</td>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> (37.771787999, -122.4712056)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 11/29/2014 12:22:50 AM</td>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 11/29/2014 12:21:33 AM</td>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> (37.7899262286022, -122.394276396696)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 11/29/2014 12:16:54 AM</td>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " Opened Category \\\n", "0 11/29/2014 12:57:11 AM SFHA Requests \n", "1 11/29/2014 12:32:49 AM Rec and Park Requests \n", "2 11/29/2014 12:22:50 AM SFHA Requests \n", "3 11/29/2014 12:21:33 AM Streetlights \n", "4 11/29/2014 12:16:54 AM SFHA Requests \n", "\n", " Request Type Supervisor District \\\n", "0 SFHA Priority - Emergency 6 \n", "1 Park - Neighborhood_Services_Area 1 \n", "2 SFHA Priority - Emergency 5 \n", "3 Streetlight - Light_Burnt_Out 6 \n", "4 SFHA Priority - Emergency 5 \n", "\n", " Point \n", "0 (37.782620592996, -122.416286644263) \n", "1 (37.771787999, -122.4712056) \n", "2 (37.777584951111, -122.440874464132) \n", "3 (37.7899262286022, -122.394276396696) \n", "4 (37.777584951111, -122.440874464132) " ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "#drop all the blank rows at the end of the 311 dataset\n", "data_311 = data_311.dropna(thresh=5)\n", "data_311.tail()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Opened</th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>Point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1098661</th>\n", " <td> 07/01/2008 03:03:00 AM</td>\n", " <td> Street and Sidewalk Cleaning</td>\n", " <td> Sidewalk_Cleaning</td>\n", " <td> 9</td>\n", " <td> (37.7406417656081, -122.423090064246)</td>\n", " </tr>\n", " <tr>\n", " <th>1098662</th>\n", " <td> 07/01/2008 02:07:00 AM</td>\n", " <td> Abandoned Vehicle</td>\n", " <td> Abandoned Vehicle - Car4door</td>\n", " <td> 5</td>\n", " <td> (37.781800341, -122.428537476)</td>\n", " </tr>\n", " <tr>\n", " <th>1098663</th>\n", " <td> 07/01/2008 01:56:00 AM</td>\n", " <td> Tree Maintenance</td>\n", " <td> Trees - Damaged_Tree</td>\n", " <td> 5</td>\n", " <td> (37.7643657242198, -122.458814894064)</td>\n", " </tr>\n", " <tr>\n", " <th>1098664</th>\n", " <td> 07/01/2008 12:26:00 AM</td>\n", " <td> Street and Sidewalk Cleaning</td>\n", " <td> Illegal_Dumping</td>\n", " <td> 4</td>\n", " <td> (37.7617471571397, -122.475924197088)</td>\n", " </tr>\n", " <tr>\n", " <th>1098665</th>\n", " <td> 07/01/2008 12:13:00 AM</td>\n", " <td> Street and Sidewalk Cleaning</td>\n", " <td> Sidewalk_Cleaning</td>\n", " <td> 3</td>\n", " <td> (37.800869062548, -122.406192162738)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " Opened Category \\\n", "1098661 07/01/2008 03:03:00 AM Street and Sidewalk Cleaning \n", "1098662 07/01/2008 02:07:00 AM Abandoned Vehicle \n", "1098663 07/01/2008 01:56:00 AM Tree Maintenance \n", "1098664 07/01/2008 12:26:00 AM Street and Sidewalk Cleaning \n", "1098665 07/01/2008 12:13:00 AM Street and Sidewalk Cleaning \n", "\n", " Request Type Supervisor District \\\n", "1098661 Sidewalk_Cleaning 9 \n", "1098662 Abandoned Vehicle - Car4door 5 \n", "1098663 Trees - Damaged_Tree 5 \n", "1098664 Illegal_Dumping 4 \n", "1098665 Sidewalk_Cleaning 3 \n", "\n", " Point \n", "1098661 (37.7406417656081, -122.423090064246) \n", "1098662 (37.781800341, -122.428537476) \n", "1098663 (37.7643657242198, -122.458814894064) \n", "1098664 (37.7617471571397, -122.475924197088) \n", "1098665 (37.800869062548, -122.406192162738) " ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#convert the time the 311 report was opened to a python datetime\n", "data_311['DateTime'] = pd.to_datetime(data_311['Opened'])\n", "data_311 = data_311.drop('Opened', 1)\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>Point</th>\n", " <th>DateTime</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> (37.782620592996, -122.416286644263)</td>\n", " <td>2014-11-29 00:57:11</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> (37.771787999, -122.4712056)</td>\n", " <td>2014-11-29 00:32:49</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:22:50</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> (37.7899262286022, -122.394276396696)</td>\n", " <td>2014-11-29 00:21:33</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:16:54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " Category Request Type \\\n", "0 SFHA Requests SFHA Priority - Emergency \n", "1 Rec and Park Requests Park - Neighborhood_Services_Area \n", "2 SFHA Requests SFHA Priority - Emergency \n", "3 Streetlights Streetlight - Light_Burnt_Out \n", "4 SFHA Requests SFHA Priority - Emergency \n", "\n", " Supervisor District Point \\\n", "0 6 (37.782620592996, -122.416286644263) \n", "1 1 (37.771787999, -122.4712056) \n", "2 5 (37.777584951111, -122.440874464132) \n", "3 6 (37.7899262286022, -122.394276396696) \n", "4 5 (37.777584951111, -122.440874464132) \n", "\n", " DateTime \n", "0 2014-11-29 00:57:11 \n", "1 2014-11-29 00:32:49 \n", "2 2014-11-29 00:22:50 \n", "3 2014-11-29 00:21:33 \n", "4 2014-11-29 00:16:54 " ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "def create_bins(min_pt, max_pt, n):\n", " \"\"\"\n", " Creates n equally spaced bins between min_pt and max_pt\n", " \n", " @params min_pt float min value\n", " @params max_pt float max value\n", " @params n number of bins to create\n", " @return np.array bin values\n", " \"\"\"\n", " return np.linspace(min_pt, max_pt, n)\n", "\n", "import bisect\n", "def get_bin(bins, val):\n", " \"\"\"\n", " Determines which bin the input val falls into. Bins are represented \n", " by an increasing np.array. Val is assigned to the highest bin whose \n", " value is less than val. (e.g. for bins [0.0, 0.5, 1.0], 0.25 would \n", " be assigned to bin 0.0, 0.75 would be assigned to 0.5)\n", " \n", " @params bins np.array of increasing values\n", " @params val float to bin\n", " @return bin that val belongs to\n", " \"\"\"\n", " index = bisect.bisect_right(bins, val)-1 #bisect_left returns 2 with [0.0, 0.5, 1.0] and 0.75 as input\n", " return bins[index]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "mintime = data_311['DateTime'].min()\n", "maxtime = data_311['DateTime'].max()\n", "\n", "print mintime\n", "print maxtime" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2008-07-01 00:13:00\n", "2014-11-29 00:57:11\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "sec_range = (((maxtime-mintime).total_seconds())/5000)\n", "#using 5000 time bins, each time bin is this many hours\n", "print sec_range/3600" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "11.2417472778\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "#make time bin for 311 dataset\n", "#subtracting 2 datetimes creates a tdelta object\n", "bins = create_bins(0, (maxtime-mintime).total_seconds(), 5000)\n", "data_311['TimeBin'] = data_311.apply(lambda row: get_bin(bins, (row['DateTime']-mintime).total_seconds()), axis = 1)\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>Point</th>\n", " <th>DateTime</th>\n", " <th>TimeBin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> (37.782620592996, -122.416286644263)</td>\n", " <td>2014-11-29 00:57:11</td>\n", " <td> 2.023515e+08</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> (37.771787999, -122.4712056)</td>\n", " <td>2014-11-29 00:32:49</td>\n", " <td> 2.023110e+08</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:22:50</td>\n", " <td> 2.023110e+08</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> (37.7899262286022, -122.394276396696)</td>\n", " <td>2014-11-29 00:21:33</td>\n", " <td> 2.023110e+08</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:16:54</td>\n", " <td> 2.023110e+08</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " Category Request Type \\\n", "0 SFHA Requests SFHA Priority - Emergency \n", "1 Rec and Park Requests Park - Neighborhood_Services_Area \n", "2 SFHA Requests SFHA Priority - Emergency \n", "3 Streetlights Streetlight - Light_Burnt_Out \n", "4 SFHA Requests SFHA Priority - Emergency \n", "\n", " Supervisor District Point \\\n", "0 6 (37.782620592996, -122.416286644263) \n", "1 1 (37.771787999, -122.4712056) \n", "2 5 (37.777584951111, -122.440874464132) \n", "3 6 (37.7899262286022, -122.394276396696) \n", "4 5 (37.777584951111, -122.440874464132) \n", "\n", " DateTime TimeBin \n", "0 2014-11-29 00:57:11 2.023515e+08 \n", "1 2014-11-29 00:32:49 2.023110e+08 \n", "2 2014-11-29 00:22:50 2.023110e+08 \n", "3 2014-11-29 00:21:33 2.023110e+08 \n", "4 2014-11-29 00:16:54 2.023110e+08 " ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "print bins" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0.00000000e+00 4.04783859e+04 8.09567718e+04 ..., 2.02270494e+08\n", " 2.02310973e+08 2.02351451e+08]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#separate the point tuple into x and y columns\n", "data_311['X'] = data_311.apply(lambda row: float(row['Point'].strip(')(').split(',')[1]), axis=1)\n", "data_311['Y'] = data_311.apply(lambda row: float(row['Point'].strip(')(').split(',')[0]), axis=1)\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>Point</th>\n", " <th>DateTime</th>\n", " <th>TimeBin</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> (37.782620592996, -122.416286644263)</td>\n", " <td>2014-11-29 00:57:11</td>\n", " <td> 2.023515e+08</td>\n", " <td>-122.416287</td>\n", " <td> 37.782621</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> (37.771787999, -122.4712056)</td>\n", " <td>2014-11-29 00:32:49</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.471206</td>\n", " <td> 37.771788</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:22:50</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.440874</td>\n", " <td> 37.777585</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> (37.7899262286022, -122.394276396696)</td>\n", " <td>2014-11-29 00:21:33</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.394276</td>\n", " <td> 37.789926</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:16:54</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.440874</td>\n", " <td> 37.777585</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " Category Request Type \\\n", "0 SFHA Requests SFHA Priority - Emergency \n", "1 Rec and Park Requests Park - Neighborhood_Services_Area \n", "2 SFHA Requests SFHA Priority - Emergency \n", "3 Streetlights Streetlight - Light_Burnt_Out \n", "4 SFHA Requests SFHA Priority - Emergency \n", "\n", " Supervisor District Point \\\n", "0 6 (37.782620592996, -122.416286644263) \n", "1 1 (37.771787999, -122.4712056) \n", "2 5 (37.777584951111, -122.440874464132) \n", "3 6 (37.7899262286022, -122.394276396696) \n", "4 5 (37.777584951111, -122.440874464132) \n", "\n", " DateTime TimeBin X Y \n", "0 2014-11-29 00:57:11 2.023515e+08 -122.416287 37.782621 \n", "1 2014-11-29 00:32:49 2.023110e+08 -122.471206 37.771788 \n", "2 2014-11-29 00:22:50 2.023110e+08 -122.440874 37.777585 \n", "3 2014-11-29 00:21:33 2.023110e+08 -122.394276 37.789926 \n", "4 2014-11-29 00:16:54 2.023110e+08 -122.440874 37.777585 " ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# filter out all data that are in the top/bottom 1% of X or Y locations.\n", "# This was done because we noticed there were some outliers in the X and Y data.\n", "#min_x_loc = np.percentile(data_311['X'],1) #without filter it was -122.514436\n", "#max_x_loc = np.percentile(data_311['X'],99) #without filter -119.850760\n", "#min_y_loc = np.percentile(data_311['Y'],1) #without filter 37.624394\n", "#max_y_loc = np.percentile(data_311['Y'],99) #without filter37.881603\n", "#orig_len = len(data_311)\n", "#print \"done\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# find the min and max locations. These values will be hard-coded into format_911.\n", "# hard code these (like we do in the 911 data) to get rid of hidden precision\n", "min_x_loc = -122.505716\n", "max_x_loc = -122.384338\n", "min_y_loc = 37.710652\n", "max_y_loc = 37.803545\n", "print \"min x location: %f\" % min_x_loc #min x location: -122.505716\n", "print \"max x location: %f\" % max_x_loc #max x location: -122.384338\n", "print \"min y location: %f\" % min_y_loc #min y location: 37.710652\n", "print \"max y location: %f\" % max_y_loc #max y location: 37.803545" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "min x location: -122.505716\n", "max x location: -122.384338\n", "min y location: 37.710652\n", "max y location: 37.803545\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "#print \"original length : \" + str(orig_len)\n", "#print \"original length * 96% : \" + str(orig_len*.96)\n", "data_311 = data_311[data_311['X']>=min_x_loc]\n", "data_311 = data_311[data_311['X']<=max_x_loc]\n", "data_311 = data_311[data_311['Y']>=min_y_loc]\n", "data_311 = data_311[data_311['Y']<=max_y_loc]\n", "print \"after length : \" + str(len(data_311)) #should go down about 4% (actually .98*.98)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "after length : 1055187\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "#temp. don't cut off extremes\n", "#min_x_loc = -122.514436\n", "#max_x_loc = -119.850760\n", "#min_y_loc = 37.624394\n", "#max_y_loc = 37.881603\n", "#print \"done\"\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "#create xbins and ybins\n", "xbins = create_bins(min_x_loc, max_x_loc, 10)\n", "ybins = create_bins(min_y_loc, max_y_loc, 10)\n", "data_311['XBin'] = data_311.apply(lambda row: get_bin(xbins, row['X']), axis = 1)\n", "data_311['YBin'] = data_311.apply(lambda row: get_bin(ybins, row['Y']), axis = 1)\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>Point</th>\n", " <th>DateTime</th>\n", " <th>TimeBin</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>XBin</th>\n", " <th>YBin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> (37.782620592996, -122.416286644263)</td>\n", " <td>2014-11-29 00:57:11</td>\n", " <td> 2.023515e+08</td>\n", " <td>-122.416287</td>\n", " <td> 37.782621</td>\n", " <td>-122.424797</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> (37.771787999, -122.4712056)</td>\n", " <td>2014-11-29 00:32:49</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.471206</td>\n", " <td> 37.771788</td>\n", " <td>-122.478743</td>\n", " <td> 37.762259</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:22:50</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.440874</td>\n", " <td> 37.777585</td>\n", " <td>-122.451770</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> (37.7899262286022, -122.394276396696)</td>\n", " <td>2014-11-29 00:21:33</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.394276</td>\n", " <td> 37.789926</td>\n", " <td>-122.397824</td>\n", " <td> 37.782902</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> (37.777584951111, -122.440874464132)</td>\n", " <td>2014-11-29 00:16:54</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.440874</td>\n", " <td> 37.777585</td>\n", " <td>-122.451770</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ " Category Request Type \\\n", "0 SFHA Requests SFHA Priority - Emergency \n", "1 Rec and Park Requests Park - Neighborhood_Services_Area \n", "2 SFHA Requests SFHA Priority - Emergency \n", "3 Streetlights Streetlight - Light_Burnt_Out \n", "4 SFHA Requests SFHA Priority - Emergency \n", "\n", " Supervisor District Point \\\n", "0 6 (37.782620592996, -122.416286644263) \n", "1 1 (37.771787999, -122.4712056) \n", "2 5 (37.777584951111, -122.440874464132) \n", "3 6 (37.7899262286022, -122.394276396696) \n", "4 5 (37.777584951111, -122.440874464132) \n", "\n", " DateTime TimeBin X Y XBin \\\n", "0 2014-11-29 00:57:11 2.023515e+08 -122.416287 37.782621 -122.424797 \n", "1 2014-11-29 00:32:49 2.023110e+08 -122.471206 37.771788 -122.478743 \n", "2 2014-11-29 00:22:50 2.023110e+08 -122.440874 37.777585 -122.451770 \n", "3 2014-11-29 00:21:33 2.023110e+08 -122.394276 37.789926 -122.397824 \n", "4 2014-11-29 00:16:54 2.023110e+08 -122.440874 37.777585 -122.451770 \n", "\n", " YBin \n", "0 37.772581 \n", "1 37.762259 \n", "2 37.772581 \n", "3 37.782902 \n", "4 37.772581 " ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "#we should have timebin, xbin, and ybin at this point. \n", "#Get rid of DateTime, point, X, and Y (the columns that we used to generate these bins).\n", "data_311 = data_311[['Category','Request Type', 'Supervisor District', 'TimeBin','XBin','YBin']]\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>TimeBin</th>\n", " <th>XBin</th>\n", " <th>YBin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> 2.023515e+08</td>\n", " <td>-122.424797</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.478743</td>\n", " <td> 37.762259</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.451770</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.397824</td>\n", " <td> 37.782902</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.451770</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ " Category Request Type \\\n", "0 SFHA Requests SFHA Priority - Emergency \n", "1 Rec and Park Requests Park - Neighborhood_Services_Area \n", "2 SFHA Requests SFHA Priority - Emergency \n", "3 Streetlights Streetlight - Light_Burnt_Out \n", "4 SFHA Requests SFHA Priority - Emergency \n", "\n", " Supervisor District TimeBin XBin YBin \n", "0 6 2.023515e+08 -122.424797 37.772581 \n", "1 1 2.023110e+08 -122.478743 37.762259 \n", "2 5 2.023110e+08 -122.451770 37.772581 \n", "3 6 2.023110e+08 -122.397824 37.782902 \n", "4 5 2.023110e+08 -122.451770 37.772581 " ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "#store into csv as checkpoint\n", "data_311.to_csv('intermediate_311.csv', index_label=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "DATA_PATH = \"intermediate_311.csv\" # path of amazon machine\n", "import pandas as pd\n", "data_311 = pd.read_csv(DATA_PATH, na_values=['-'])\n", "data_311.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Category</th>\n", " <th>Request Type</th>\n", " <th>Supervisor District</th>\n", " <th>TimeBin</th>\n", " <th>XBin</th>\n", " <th>YBin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 6</td>\n", " <td> 2.023515e+08</td>\n", " <td>-122.424797</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Rec and Park Requests</td>\n", " <td> Park - Neighborhood_Services_Area</td>\n", " <td> 1</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.478743</td>\n", " <td> 37.762259</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.451770</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Streetlights</td>\n", " <td> Streetlight - Light_Burnt_Out</td>\n", " <td> 6</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.397824</td>\n", " <td> 37.782902</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> SFHA Requests</td>\n", " <td> SFHA Priority - Emergency</td>\n", " <td> 5</td>\n", " <td> 2.023110e+08</td>\n", " <td>-122.451770</td>\n", " <td> 37.772581</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " Category Request Type \\\n", "0 SFHA Requests SFHA Priority - Emergency \n", "1 Rec and Park Requests Park - Neighborhood_Services_Area \n", "2 SFHA Requests SFHA Priority - Emergency \n", "3 Streetlights Streetlight - Light_Burnt_Out \n", "4 SFHA Requests SFHA Priority - Emergency \n", "\n", " Supervisor District TimeBin XBin YBin \n", "0 6 2.023515e+08 -122.424797 37.772581 \n", "1 1 2.023110e+08 -122.478743 37.762259 \n", "2 5 2.023110e+08 -122.451770 37.772581 \n", "3 6 2.023110e+08 -122.397824 37.782902 \n", "4 5 2.023110e+08 -122.451770 37.772581 " ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "#aggregate 311 reports by tuple\n", "agg_dict_311 = {}\n", "for index, row in data_311.iterrows():\n", " tupe = (row['TimeBin'], row['XBin'], row['YBin'])\n", " if tupe not in agg_dict_311:\n", " agg_dict_311[tupe] = []\n", " agg_dict_311[tupe].append(row.to_dict())\n", "print len(agg_dict_311)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "249147\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get all possible labels for certain categorical data, by looping through the data.\n", "Category_set = set()\n", "Request_Type_set = set()\n", "#Request_Details_set = set()\n", "Supervisor_District_set = set()\n", "#Source_set = set()\n", "ct=0\n", "for row_tuple in data_311.iterrows():\n", " ct+=1\n", " if ct>1:\n", " break\n", " row = row_tuple[1] # a pandas Series\n", " print row\n", " Category = row['Category']\n", " Request_Type = row['Request Type']\n", " #Request_Details = row['Request Details']\n", " Supervisor_District = row['Supervisor District']\n", " #Source = row['Source']\n", " Category_set.add(Category)\n", " Request_Type_set.add(Request_Type)\n", " #Request_Details_set.add(Request_Details)\n", " Supervisor_District_set.add(Supervisor_District)\n", " #Source_set.add(Source)\n", "print \"done\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Category SFHA Requests\n", "Request Type SFHA Priority - Emergency\n", "Supervisor District 6\n", "TimeBin 2.023515e+08\n", "XBin -122.4248\n", "YBin 37.77258\n", "Name: 0, dtype: object\n", "done\n" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(Category_set)\n", "#print len(Request_Type_set)\n", "#print len(Request_Details_set)\n", "print len(Supervisor_District_set)\n", "#print len(Source_set)\n", "print Category_set\n", "print Supervisor_District_set\n", "#print Source_set" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "27\n", "12\n", "set(['Streetlights', 'Abandoned Vehicle', 'Interdepartmental Request', 'MUNI Feedback', 'Graffiti Private Property', 'DPW Volunteer Programs', 'Tree Maintenance', 'Rec and Park Requests', 'Residential Building Request', 'Blocked Street or SideWalk', 'Sidewalk or Curb', 'Temporary Sign Request', 'Sewer Issues', '311 External Request', 'Litter Receptacles', 'Catch Basin Maintenance', 'Street Defects', 'Street and Sidewalk Cleaning', 'Color Curb', 'SFHA Requests', 'Construction Zone Permits', 'Graffiti Public Property', 'Illegal Postings', 'Damaged Property', 'Unpermitted Cab Complaint', 'General Requests', 'Sign Repair'])\n", "set([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0])\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# Generate a feature vector given a list of dictionaries. Each dictionary represents one 311 report.\n", "# Regularization should also go here\n", " # @param reports_311: a list of dictionaries. Each dictionary is of the form\n", " # {'Category': string, 'Request Details': string, 'Request Type': string, 'Source': string, 'Supervisor District':string,\n", " # 'TimeBin': float, 'XBin':float, 'YBin':float}\n", "\n", "#create map dictionaries that will map each one of the categories from the category sets to an index in feature_vector.\n", "category_map = {}\n", "supervisor_map = {}\n", "source_map = {}\n", "index = 0\n", "for cat in Category_set:\n", " category_map[cat] = index\n", " index += 1 \n", "for cat in Supervisor_District_set:\n", " supervisor_map[cat] = index\n", " index += 1\n", "#for cat in Source_set:\n", "# source_map[cat] = index\n", "# index += 1\n", " \n", "def generate_feature_vector(reports_311):\n", " # simple feature vector that is just sum of counts of 311 reports of each category \n", " # from (Category, Supervisor District, and Source)\n", " feature_vector = []\n", " for i in xrange(index):\n", " feature_vector.append(0)\n", " for report in reports_311:\n", " # the first 27 features correspond to the number of 311 reports from each category from Category set\n", " # the next 12 features correspond to the number of 311 reports from each category from Supervisor_District set\n", " # the next 9 features correspond to the number of 311 reports from each category from Source set\n", " feature_vector[category_map[report[\"Category\"]]]+=1\n", " feature_vector[supervisor_map[report[\"Supervisor District\"]]]+=1\n", " #feature_vector[source_map[report[\"Source\"]]]+=1\n", " return feature_vector\n", "print \"done\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "done\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "print category_map\n", "print supervisor_map" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'Streetlights': 0, 'Abandoned Vehicle': 1, 'Interdepartmental Request': 2, 'MUNI Feedback': 3, 'Graffiti Private Property': 4, 'DPW Volunteer Programs': 5, 'Tree Maintenance': 6, 'Construction Zone Permits': 20, 'Residential Building Request': 8, 'Blocked Street or SideWalk': 9, 'Sidewalk or Curb': 10, 'Temporary Sign Request': 11, 'Sewer Issues': 12, '311 External Request': 13, 'Litter Receptacles': 14, 'Catch Basin Maintenance': 15, 'Street Defects': 16, 'Street and Sidewalk Cleaning': 17, 'Color Curb': 18, 'SFHA Requests': 19, 'Rec and Park Requests': 7, 'Graffiti Public Property': 21, 'Illegal Postings': 22, 'Damaged Property': 23, 'Unpermitted Cab Complaint': 24, 'General Requests': 25, 'Sign Repair': 26}\n", "{0.0: 27, 1.0: 28, 2.0: 29, 3.0: 30, 4.0: 31, 5.0: 32, 6.0: 33, 7.0: 34, 8.0: 35, 9.0: 36, 10.0: 37, 11.0: 38}\n" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "agg_dict_311_2 = {}\n", "agg_dict_311_2['feature-311'] = []\n", "agg_dict_311_2['tuple'] = []\n", "for key in agg_dict_311:\n", " val = agg_dict_311[key]\n", " feature_vector_311 = generate_feature_vector(val)\n", " agg_dict_311_2['tuple'].append(key)\n", " agg_dict_311_2['feature-311'].append(feature_vector_311)\n", "print \"done\" " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "done\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "len(agg_dict_311_2['feature-311'])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "249147" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "df_311_final = pd.DataFrame(agg_dict_311_2)\n", "df_311_final.head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>feature-311</th>\n", " <th>tuple</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n", " <td> (6274149.81096, -122.424797333, 37.7725806667)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, ...</td>\n", " <td> (68448950.5183, -122.451770222, 37.7622592222)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n", " <td> (180695514.556, -122.505716, 37.7519377778)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...</td>\n", " <td> (198060742.097, -122.505716, 37.7209734444)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> [0, 0, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n", " <td> (166811428.2, -122.424797333, 37.7622592222)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ " feature-311 \\\n", "0 [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ... \n", "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, ... \n", "2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ... \n", "3 [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ... \n", "4 [0, 0, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ... \n", "\n", " tuple \n", "0 (6274149.81096, -122.424797333, 37.7725806667) \n", "1 (68448950.5183, -122.451770222, 37.7622592222) \n", "2 (180695514.556, -122.505716, 37.7519377778) \n", "3 (198060742.097, -122.505716, 37.7209734444) \n", "4 (166811428.2, -122.424797333, 37.7622592222) " ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "print df_311_final.iloc[0][0]\n", "print len(df_311_final.iloc[0][0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0]\n", "39\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "df_311_final.to_csv('df_311_ready_to_join.csv', index_label=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "t0 = [x[0] for x in df_311_final['tuple']]\n", "t0s = set(t0)\n", "print len(t0s)\n", "\n", "t1 = [x[1] for x in df_311_final['tuple']]\n", "t1s = set(t1)\n", "print len(t1s)\n", "\n", "t2 = [x[2] for x in df_311_final['tuple']]\n", "t2s = set(t2)\n", "print len(t2s)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4997\n", "9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 29 } ], "metadata": {} } ] }

apache-2.0

rishuatgithub/MLPy

torch/PYTORCH_NOTEBOOKS/02-ANN-Artificial-Neural-Networks/01-Linear-Regression-with-PyTorch.ipynb

1

64667

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"../Pierian-Data-Logo.PNG\">\n", "<br>\n", "<strong><center>Copyright 2019. Created by Jose Marcial Portilla.</center></strong>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression with PyTorch\n", "In this section we'll use PyTorch's machine learning model to progressively develop a best-fit line for a given set of data points. Like most linear regression algorithms, we're seeking to minimize the error between our model and the actual data, using a <em>loss function</em> like mean-squared-error.\n", "\n", "<img src='../Images/linear-regression-residuals.png' width='400' style=\"display: inline-block\"><br>\n", "\n", "Image source: <a href='https://commons.wikimedia.org/wiki/File:Residuals_for_Linear_Regression_Fit.png'>https://commons.wikimedia.org/wiki/File:Residuals_for_Linear_Regression_Fit.png</a>\n", "\n", "To start, we'll develop a collection of data points that appear random, but that fit a known linear equation $y = 2x+1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform standard imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn # we'll use this a lot going forward!\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a column matrix of X values\n", "We can create tensors right away rather than convert from NumPy arrays." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = torch.linspace(1,50,50).reshape(-1,1)\n", "\n", "# Equivalent to\n", "# X = torch.unsqueeze(torch.linspace(1,50,50), dim=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a \"random\" array of error values\n", "We want 50 random integer values that collectively cancel each other out." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.)\n" ] } ], "source": [ "torch.manual_seed(71) # to obtain reproducible results\n", "e = torch.randint(-8,9,(50,1),dtype=torch.float)\n", "print(e.sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a column matrix of y values\n", "Here we'll set our own parameters of $\\mathrm {weight} = 2,\\; \\mathrm {bias} = 1$, plus the error amount.<br><strong><tt>y</tt></strong> will have the same shape as <strong><tt>X</tt></strong> and <strong><tt>e</tt></strong>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([50, 1])\n" ] } ], "source": [ "y = 2*X + 1 + e\n", "print(y.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the results\n", "We have to convert tensors to NumPy arrays just for plotting." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X.numpy(), y.numpy())\n", "plt.ylabel('y')\n", "plt.xlabel('x');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that when we created tensor $X$, we did <em>not</em> pass <tt>requires_grad=True</tt>. This means that $y$ doesn't have a gradient function, and <tt>y.backward()</tt> won't work. Since PyTorch is not tracking operations, it doesn't know the relationship between $X$ and $y$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple linear model\n", "As a quick demonstration we'll show how the built-in <tt>nn.Linear()</tt> model preselects weight and bias values at random." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter containing:\n", "tensor([[0.1060]], requires_grad=True)\n", "Parameter containing:\n", "tensor([0.9638], requires_grad=True)\n" ] } ], "source": [ "torch.manual_seed(59)\n", "\n", "model = nn.Linear(in_features=1, out_features=1)\n", "print(model.weight)\n", "print(model.bias)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Without seeing any data, the model sets a random weight of 0.1060 and a bias of 0.9638." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model classes\n", "PyTorch lets us define models as object classes that can store multiple model layers. In upcoming sections we'll set up several neural network layers, and determine how each layer should perform its forward pass to the next layer. For now, though, we only need a single <tt>linear</tt> layer." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Model(nn.Module):\n", " def __init__(self, in_features, out_features):\n", " super().__init__()\n", " self.linear = nn.Linear(in_features, out_features)\n", " \n", " def forward(self, x):\n", " y_pred = self.linear(x)\n", " return y_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><strong>NOTE:</strong> The \"Linear\" model layer used here doesn't really refer to linear regression. Instead, it describes the type of neural network layer employed. Linear layers are also called \"fully connected\" or \"dense\" layers. Going forward our models may contain linear layers, convolutional layers, and more.</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When <tt>Model</tt> is instantiated, we need to pass in the size (dimensions) of the incoming and outgoing features. For our purposes we'll use (1,1).<br>As above, we can see the initial hyperparameters." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model(\n", " (linear): Linear(in_features=1, out_features=1, bias=True)\n", ")\n", "Weight: 0.10597813129425049\n", "Bias: 0.9637961387634277\n" ] } ], "source": [ "torch.manual_seed(59)\n", "model = Model(1, 1)\n", "print(model)\n", "print('Weight:', model.linear.weight.item())\n", "print('Bias: ', model.linear.bias.item())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As models become more complex, it may be better to iterate over all the model parameters:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear.weight \t 0.10597813129425049\n", "linear.bias \t 0.9637961387634277\n" ] } ], "source": [ "for name, param in model.named_parameters():\n", " print(name, '\\t', param.item())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><strong>NOTE:</strong> In the above example we had our Model class accept arguments for the number of input and output features.<br>For simplicity we can hardcode them into the Model:\n", " \n", "<tt><font color=black>\n", "class Model(torch.nn.Module):<br>\n", "&nbsp;&nbsp;&nbsp;&nbsp;def \\_\\_init\\_\\_(self):<br>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;super().\\_\\_init\\_\\_()<br>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;self.linear = Linear(1,1)<br><br>\n", "model = Model()\n", "</font></tt><br><br>\n", "\n", "Alternatively we can use default arguments:\n", "\n", "<tt><font color=black>\n", "class Model(torch.nn.Module):<br>\n", "&nbsp;&nbsp;&nbsp;&nbsp;def \\_\\_init\\_\\_(self, in_dim=1, out_dim=1):<br>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;super().\\_\\_init\\_\\_()<br>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;self.linear = Linear(in_dim,out_dim)<br><br>\n", "model = Model()<br>\n", "<em>\\# or</em><br>\n", "model = Model(i,o)</font></tt>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see the result when we pass a tensor into the model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([1.1758], grad_fn=<AddBackward0>)\n" ] } ], "source": [ "x = torch.tensor([2.0])\n", "print(model.forward(x)) # equivalent to print(model(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is confirmed with $f(x) = (0.1060)(2.0)+(0.9638) = 1.1758$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the initial model\n", "We can plot the untrained model against our dataset to get an idea of our starting point." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 50.]\n" ] } ], "source": [ "x1 = np.array([X.min(),X.max()])\n", "print(x1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial weight: 0.10597813, Initial bias: 0.96379614\n", "\n", "[1.0697743 6.2627025]\n" ] } ], "source": [ "w1,b1 = model.linear.weight.item(), model.linear.bias.item()\n", "print(f'Initial weight: {w1:.8f}, Initial bias: {b1:.8f}')\n", "print()\n", "\n", "y1 = x1*w1 + b1\n", "print(y1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X.numpy(), y.numpy())\n", "plt.plot(x1,y1,'r')\n", "plt.title('Initial Model')\n", "plt.ylabel('y')\n", "plt.xlabel('x');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set the loss function\n", "We could write our own function to apply a Mean Squared Error (MSE) that follows<br>\n", "\n", "$\\begin{split}MSE &= \\frac {1} {n} \\sum_{i=1}^n {(y_i - \\hat y_i)}^2 \\\\\n", "&= \\frac {1} {n} \\sum_{i=1}^n {(y_i - (wx_i + b))}^2\\end{split}$<br>\n", "\n", "Fortunately PyTorch has it built in.<br>\n", "<em>By convention, you'll see the variable name \"criterion\" used, but feel free to use something like \"linear_loss_func\" if that's clearer.</em>" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "criterion = nn.MSELoss()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set the optimization\n", "Here we'll use <a href='https://en.wikipedia.org/wiki/Stochastic_gradient_descent'>Stochastic Gradient Descent</a> (SGD) with an applied <a href='https://en.wikipedia.org/wiki/Learning_rate'>learning rate</a> (lr) of 0.001. Recall that the learning rate tells the optimizer how much to adjust each parameter on the next round of calculations. Too large a step and we run the risk of overshooting the minimum, causing the algorithm to diverge. Too small and it will take a long time to converge.\n", "\n", "For more complicated (multivariate) data, you might also consider passing optional <a href='https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Momentum'><tt>momentum</tt></a> and <a href='https://en.wikipedia.org/wiki/Tikhonov_regularization'><tt>weight_decay</tt></a> arguments. Momentum allows the algorithm to \"roll over\" small bumps to avoid local minima that can cause convergence too soon. Weight decay (also called an L2 penalty) applies to biases.\n", "\n", "For more information, see <a href='https://pytorch.org/docs/stable/optim.html'><strong><tt>torch.optim</tt></strong></a>" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = torch.optim.SGD(model.parameters(), lr = 0.001)\n", "\n", "# You'll sometimes see this as\n", "# optimizer = torch.optim.SGD(model.parameters(), lr = 1e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model\n", "An <em>epoch</em> is a single pass through the entire dataset. We want to pick a sufficiently large number of epochs to reach a plateau close to our known parameters of $\\mathrm {weight} = 2,\\; \\mathrm {bias} = 1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><strong>Let's walk through the steps we're about to take:</strong><br>\n", "\n", "1. Set a reasonably large number of passes<br>\n", "<tt><font color=black>epochs = 50</font></tt><br>\n", "2. Create a list to store loss values. This will let us view our progress afterward.<br>\n", "<tt><font color=black>losses = []</font></tt><br>\n", "<tt><font color=black>for i in range(epochs):</font></tt><br>\n", "3. Bump \"i\" so that the printed report starts at 1<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;i+=1</font></tt><br>\n", "4. Create a prediction set by running \"X\" through the current model parameters<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;y_pred = model.forward(X)</font></tt><br>\n", "5. Calculate the loss<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;loss = criterion(y_pred, y)</font></tt><br>\n", "6. Add the loss value to our tracking list<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;losses.append(loss)</font></tt><br>\n", "7. Print the current line of results<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;print(f'epoch: {i:2} loss: {loss.item():10.8f}')</font></tt><br>\n", "8. Gradients accumulate with every backprop. To prevent compounding we need to reset the stored gradient for each new epoch.<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;optimizer.zero_grad()</font></tt><br>\n", "9. Now we can backprop<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;loss.backward()</font></tt><br>\n", "10. Finally, we can update the hyperparameters of our model<br>\n", "<tt><font color=black>&nbsp;&nbsp;&nbsp;&nbsp;optimizer.step()</font></tt>\n", "</div>" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch: 1 loss: 3057.21679688 weight: 0.10597813 bias: 0.96379614\n", "epoch: 2 loss: 1588.53100586 weight: 3.33490038 bias: 1.06046367\n", "epoch: 3 loss: 830.30010986 weight: 1.01483274 bias: 0.99226278\n", "epoch: 4 loss: 438.85241699 weight: 2.68179965 bias: 1.04252183\n", "epoch: 5 loss: 236.76152039 weight: 1.48402119 bias: 1.00766504\n", "epoch: 6 loss: 132.42912292 weight: 2.34460592 bias: 1.03396463\n", "epoch: 7 loss: 78.56572723 weight: 1.72622538 bias: 1.01632178\n", "epoch: 8 loss: 50.75775909 weight: 2.17050409 bias: 1.03025162\n", "epoch: 9 loss: 36.40123367 weight: 1.85124576 bias: 1.02149546\n", "epoch: 10 loss: 28.98922729 weight: 2.08060074 bias: 1.02903891\n", "epoch: 11 loss: 25.16238213 weight: 1.91576838 bias: 1.02487016\n", "epoch: 12 loss: 23.18647385 weight: 2.03416562 bias: 1.02911627\n", "epoch: 13 loss: 22.16612816 weight: 1.94905841 bias: 1.02731562\n", "epoch: 14 loss: 21.63911057 weight: 2.01017213 bias: 1.02985907\n", "epoch: 15 loss: 21.36677170 weight: 1.96622372 bias: 1.02928054\n", "epoch: 16 loss: 21.22591782 weight: 1.99776423 bias: 1.03094459\n", "epoch: 17 loss: 21.15294647 weight: 1.97506487 bias: 1.03099668\n", "epoch: 18 loss: 21.11501122 weight: 1.99133754 bias: 1.03220642\n", "epoch: 19 loss: 21.09517670 weight: 1.97960854 bias: 1.03258383\n", "epoch: 20 loss: 21.08468437 weight: 1.98799884 bias: 1.03355861\n", "epoch: 21 loss: 21.07901382 weight: 1.98193336 bias: 1.03410351\n", "epoch: 22 loss: 21.07583046 weight: 1.98625445 bias: 1.03495669\n", "epoch: 23 loss: 21.07393837 weight: 1.98311269 bias: 1.03558779\n", "epoch: 24 loss: 21.07269859 weight: 1.98533309 bias: 1.03637791\n", "epoch: 25 loss: 21.07181931 weight: 1.98370099 bias: 1.03705311\n", "epoch: 26 loss: 21.07110596 weight: 1.98483658 bias: 1.03781021\n", "epoch: 27 loss: 21.07048416 weight: 1.98398376 bias: 1.03850794\n", "epoch: 28 loss: 21.06991386 weight: 1.98455977 bias: 1.03924775\n", "epoch: 29 loss: 21.06936646 weight: 1.98410904 bias: 1.03995669\n", "epoch: 30 loss: 21.06883621 weight: 1.98439610 bias: 1.04068720\n", "epoch: 31 loss: 21.06830788 weight: 1.98415291 bias: 1.04140162\n", "epoch: 32 loss: 21.06778145 weight: 1.98429084 bias: 1.04212701\n", "epoch: 33 loss: 21.06726265 weight: 1.98415494 bias: 1.04284394\n", "epoch: 34 loss: 21.06674004 weight: 1.98421574 bias: 1.04356635\n", "epoch: 35 loss: 21.06622314 weight: 1.98413551 bias: 1.04428422\n", "epoch: 36 loss: 21.06570625 weight: 1.98415649 bias: 1.04500473\n", "epoch: 37 loss: 21.06518936 weight: 1.98410451 bias: 1.04572272\n", "epoch: 38 loss: 21.06466866 weight: 1.98410523 bias: 1.04644191\n", "epoch: 39 loss: 21.06415749 weight: 1.98406804 bias: 1.04715967\n", "epoch: 40 loss: 21.06363869 weight: 1.98405814 bias: 1.04787791\n", "epoch: 41 loss: 21.06312370 weight: 1.98402870 bias: 1.04859519\n", "epoch: 42 loss: 21.06260681 weight: 1.98401320 bias: 1.04931259\n", "epoch: 43 loss: 21.06209564 weight: 1.98398757 bias: 1.05002928\n", "epoch: 44 loss: 21.06157875 weight: 1.98396957 bias: 1.05074584\n", "epoch: 45 loss: 21.06106949 weight: 1.98394585 bias: 1.05146194\n", "epoch: 46 loss: 21.06055450 weight: 1.98392630 bias: 1.05217779\n", "epoch: 47 loss: 21.06004143 weight: 1.98390377 bias: 1.05289316\n", "epoch: 48 loss: 21.05953217 weight: 1.98388338 bias: 1.05360830\n", "epoch: 49 loss: 21.05901527 weight: 1.98386145 bias: 1.05432308\n", "epoch: 50 loss: 21.05850983 weight: 1.98384094 bias: 1.05503750\n" ] } ], "source": [ "epochs = 50\n", "losses = []\n", "\n", "for i in range(epochs):\n", " i+=1\n", " y_pred = model.forward(X)\n", " loss = criterion(y_pred, y)\n", " losses.append(loss)\n", " print(f'epoch: {i:2} loss: {loss.item():10.8f} weight: {model.linear.weight.item():10.8f} \\\n", "bias: {model.linear.bias.item():10.8f}') \n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the loss values\n", "Let's see how loss changed over time" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEKCAYAAAAFJbKyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHP5JREFUeJzt3XuU3GWd5/H3p2/p6pDudJMmE9PBdGIGxTMYMCbMoDsBBaLrEVx1FtZLVGbjrHhGHfeCnp3DjDPs0TmjOO4IKw6RMKMiXlizbnacyICM45gbRgQCkxCQtIlJQ0Lul7589496Oql0qitdnaqu7qrP65w6VfXUU1Xfnxb59O/3/H7Po4jAzMxstOoqXYCZmU0uDg4zMyuKg8PMzIri4DAzs6I4OMzMrCgODjMzK4qDw8zMiuLgMDOzojg4zMysKA2VLqAcZsyYEXPnzq10GWZmk8qmTZteiIjOs/WryuCYO3cuGzdurHQZZmaTiqRfjqafD1WZmVlRHBxmZlYUB4eZmRXFwWFmZkVxcJiZWVEcHGZmVhQHh5mZFcXBkePgsT5uX/uvbN7xUqVLMTObsMoWHJKaJa2X9HNJT0j609TeLWmdpK2SvimpKbVPSc+3pdfn5nzWJ1P705KuLVfNA4PBXz24lUd/ua9cX2FmNumVc4/jOHBVRLwGWAgsk3Q58Fng9ohYAOwDbkr9bwL2RcQrgNtTPyRdDNwAvBpYBtwhqb4cBU9rbgRg/9G+cny8mVlVKFtwRNah9LQx3QK4Cvh2al8FXJ8eX5eek15/oySl9vsi4nhEPAtsAxaXo+b6OjFtSoODw8ysgLKOcUiql7QZ2AOsBZ4BXoqI/tSlB5idHs8GdgCk1/cD5+e253lPybVmGjlwzMFhZjaSsgZHRAxExEKgi+xewqvydUv3GuG1kdpPI2mFpI2SNvb29o61ZNoyjRzwHoeZ2YjG5ayqiHgJeBi4HJguaWhW3i5gZ3rcA8wBSK+3AXtz2/O8J/c77oqIRRGxqLPzrLMCj6g108CBo/1n72hmVqPKeVZVp6Tp6XEGeBOwBXgIeGfqthz4Xnq8Oj0nvf6PERGp/YZ01lU3sABYX6662zKNHuMwMyugnOtxzAJWpTOg6oD7I+L7kp4E7pP058DPgLtT/7uBv5W0jeyexg0AEfGEpPuBJ4F+4OaIGChX0Q4OM7PCyhYcEfEYcGme9u3kOSsqIo4B7xrhs24Dbit1jfm0Nntw3MysEF85PkxbppEjJwboGxisdClmZhOSg2OY1owvAjQzK8TBMUxbCg6fkmtmlp+DY5g273GYmRXk4BimNZM9X8DBYWaWn4NjmJOHqo75IkAzs3wcHMN4cNzMrDAHxzCtzR4cNzMrxMExTHNjPVMa6hwcZmYjcHDk0eppR8zMRuTgyKPNa3KYmY3IwZGHJzo0MxuZgyOP1mYvH2tmNhIHRx7ZVQB9HYeZWT4Ojjx8qMrMbGQOjjxaM40cPNbH4OAZS5ubmdU8B0cebZlGBgMOnfDhKjOz4RwceQxdPb7/iA9XmZkN5+DIo/XkRIcODjOz4RwceXhNDjOzkTk48hhak8PzVZmZncnBkcep5WM9OG5mNpyDIw8fqjIzG5mDI4+pTQ3UyYPjZmb5lC04JM2R9JCkLZKekPTR1P4nkn4laXO6vSXnPZ+UtE3S05KuzWlfltq2SbqlXDUPqauTp1Y3MxtBQxk/ux/4REQ8KmkasEnS2vTa7RHxl7mdJV0M3AC8GngZ8ENJv5le/hJwNdADbJC0OiKeLGPtnnbEzGwEZQuOiNgF7EqPD0raAswu8JbrgPsi4jjwrKRtwOL02raI2A4g6b7Ut6zB0drc6LOqzMzyGJcxDklzgUuBdanpI5Iek7RSUntqmw3syHlbT2obqb2svMdhZpZf2YND0nnAd4CPRcQB4E5gPrCQ7B7J54a65nl7FGgf/j0rJG2UtLG3t/ec627NeE0OM7N8yhockhrJhsbXIuK7ABGxOyIGImIQ+AqnDkf1AHNy3t4F7CzQfpqIuCsiFkXEos7OznOuPbt8rK/jMDMbrpxnVQm4G9gSEZ/PaZ+V0+3twOPp8WrgBklTJHUDC4D1wAZggaRuSU1kB9BXl6vuIT6ryswsv3KeVXUF8F7gF5I2p7ZPATdKWkj2cNNzwIcAIuIJSfeTHfTuB26OiAEASR8BfgDUAysj4oky1g1kB8dP9A9yrG+A5sb6cn+dmdmkUc6zqn5M/vGJNQXecxtwW572NYXeVw6nph3pc3CYmeXwleMj8LQjZmb5OThG4DU5zMzyc3CMwHscZmb5OThG0NqcHf5xcJiZnc7BMQKvyWFmlp+DYwStPlRlZpaXg2MEjfV1tDTVe6JDM7NhHBwFeKJDM7MzOTgKcHCYmZ3JwVFAa3Ojr+MwMxvGwVFAdqJDn1VlZpbLwVFAW8arAJqZDefgKKA10+DgMDMbxsFRQFumkYPH+xkYPGPBQTOzmuXgKKC1OXsR4EEPkJuZneTgKMATHZqZncnBUYCDw8zsTA6OAlo90aGZ2RkcHAV4j8PM7EwOjgIcHGZmZ3JwFNCayS7m5GlHzMxOcXAUkGmsp7Fe3uMwM8vh4ChAUnaiQweHmdlJZQsOSXMkPSRpi6QnJH00tXdIWitpa7pvT+2S9EVJ2yQ9JumynM9anvpvlbS8XDXn46nVzcxOV849jn7gExHxKuBy4GZJFwO3AA9GxALgwfQc4M3AgnRbAdwJ2aABbgWWAIuBW4fCZjy0OjjMzE5TtuCIiF0R8Wh6fBDYAswGrgNWpW6rgOvT4+uAeyPrp8B0SbOAa4G1EbE3IvYBa4Fl5ap7uNZMIweO+ToOM7Mh4zLGIWkucCmwDpgZEbsgGy7ABanbbGBHztt6UttI7ePCU6ubmZ2u7MEh6TzgO8DHIuJAoa552qJA+/DvWSFpo6SNvb29Yys2j7ZMgw9VmZnlKGtwSGokGxpfi4jvpubd6RAU6X5Pau8B5uS8vQvYWaD9NBFxV0QsiohFnZ2dJduGobOqIjy1upkZlPesKgF3A1si4vM5L60Ghs6MWg58L6f9fensqsuB/elQ1g+AayS1p0Hxa1LbuGjLNNI/GBw5MTBeX2lmNqE1lPGzrwDeC/xC0ubU9ingM8D9km4CngfelV5bA7wF2AYcAT4AEBF7Jf0ZsCH1+3RE7C1j3adpzZl2ZOqUcv7PZWY2OZTtX8KI+DH5xycA3pinfwA3j/BZK4GVpatu9IbmqzpwrI+XkalECWZmE4qvHD+LkxMdHvEAuZkZODjOamj5WF/LYWaW5eA4C0+tbmZ2OgfHWTg4zMxO5+A4i/Oa05ocDg4zM8DBcVb1dWJas68eNzMb4uAYBc9XZWZ2ioNjFFqbG718rJlZ4uAYBS/mZGZ2ioNjFFozDRw46us4zMzAwTEq3uMwMzvFwTEKDg4zs1McHKPQ2tzI0b4BTvQPVroUM7OKc3CMQlvLqRlyzcxqnYNjFDztiJnZKQ6OUTg5Q66Dw8xsdMEhab6kKenxUkl/KGl6eUubOFq9x2FmdtJo9zi+AwxIegXZdcS7ga+XraoJpi2TJjr0mhxmZqMOjsGI6AfeDnwhIj4OzCpfWROL9zjMzE4ZbXD0SboRWA58P7U1lqekicdjHGZmp4w2OD4A/DZwW0Q8K6kb+LvylTWxNDfWM6WhzsFhZgY0jKZTRDwJ/CGApHZgWkR8ppyFTTRtmUZeOuLgMDMb7VlVD0tqldQB/Bz4qqTPl7e0iWVmazO7Dx6rdBlmZhU32kNVbRFxAPh3wFcj4rXAm8pX1sTT1Z5hx94jlS7DzKziRhscDZJmAb/HqcHxgiStlLRH0uM5bX8i6VeSNqfbW3Je+6SkbZKelnRtTvuy1LZN0i2jrLfkutoz9Ow7SkRUqgQzswlhtMHxaeAHwDMRsUHSPGDrWd5zD7AsT/vtEbEw3dYASLoYuAF4dXrPHZLqJdUDXwLeDFwM3Jj6jruu9haO9w/ywqETlfh6M7MJY7SD498CvpXzfDvwjrO85xFJc0dZx3XAfRFxHHhW0jZgcXptW/o+JN2X+j45ys8tma72DAA9+47QOW3KeH+9mdmEMdrB8S5JD6RDT7slfUdS1xi/8yOSHkuHstpT22xgR06fntQ2Uvu4m9PRki1g39FKfL2Z2YQx2kNVXwVWAy8j+w/3/0ltxboTmA8sBHYBn0vtytM3CrSfQdIKSRslbezt7R1DaYXNnp7d49ixzwPkZlbbRhscnRHx1YjoT7d7gM5ivywidkfEQEQMAl/h1OGoHmBOTtcuYGeB9nyffVdELIqIRZ2dRZd2VlOnNNAxtcl7HGZW80YbHC9Ies/QgLWk9wAvFvtl6cysIW8Hhs64Wg3cIGlKuip9AbAe2AAskNQtqYnsAPrqYr+3VIbOrDIzq2WjGhwHPgj8NXA72UNFPyE7DcmIJH0DWArMkNQD3AoslbQwfcZzwIcAIuIJSfeTHfTuB26OiIH0OR8he0ZXPbAyIp4oYvtKak57C1t+faBSX29mNiGM9qyq54G35bZJ+hjwhQLvuTFP890F+t8G3JanfQ2wZjR1lltXe4YfbtlNRCDlG34xM6t+57IC4B+VrIpJoqs9w/H+QXoPHq90KWZmFXMuwVFzf3J3tWdPyd3hcQ4zq2HnEhw1N/dG7kWAZma1quAYh6SD5A8IAZmyVDSBDe1x+MwqM6tlBYMjIqaNVyGTQaapnhnnNXmPw8xq2rkcqqpJs9tbvMdhZjXNwVEkXwRoZrXOwVGkrvYMv9p3lMHBmjs3wMwMcHAUbU57CycGBuk95Gs5zKw2OTiKNHRKrpeRNbNa5eAokk/JNbNa5+Aoki8CNLNa5+AoUnNjPZ3TpniPw8xqloNjDHxKrpnVMgfHGHS1t3gJWTOrWQ6OMehqz7DzpaMM+FoOM6tBDo4x6GrP0DcQ7Dl4rNKlmJmNOwfHGMzxKblmVsMcHGPgU3LNrJY5OMbgZdOHrh73HoeZ1R4Hxxg0N9ZzwbQp3uMws5rk4BgjX8thZrXKwTFGczq8oJOZ1SYHxxj5Wg4zq1VlCw5JKyXtkfR4TluHpLWStqb79tQuSV+UtE3SY5Iuy3nP8tR/q6Tl5aq3WF3tLfQPBr8+4Gs5zKy2lHOP4x5g2bC2W4AHI2IB8GB6DvBmYEG6rQDuhGzQALcCS4DFwK1DYVNpJ0/J9bocZlZjyhYcEfEIsHdY83XAqvR4FXB9Tvu9kfVTYLqkWcC1wNqI2BsR+4C1nBlGFeGLAM2sVo33GMfMiNgFkO4vSO2zgR05/XpS20jtZ5C0QtJGSRt7e3tLXvhws6Y3Izk4zKz2TJTBceVpiwLtZzZG3BURiyJiUWdnZ0mLy2dKQz0zpzV7llwzqznjHRy70yEo0v2e1N4DzMnp1wXsLNA+IWSv5XBwmFltGe/gWA0MnRm1HPheTvv70tlVlwP706GsHwDXSGpPg+LXpLYJwRcBmlktKufpuN8A/gW4SFKPpJuAzwBXS9oKXJ2eA6wBtgPbgK8AHwaIiL3AnwEb0u3TqW1CmNPRwq79x+gfGKx0KWZm46ahXB8cETeO8NIb8/QN4OYRPmclsLKEpZVMV3uGgXQtR1c6y8rMrNpNlMHxSWkoLDxLrpnVEgfHOfC6HGZWixwc52BWW8bXcphZzXFwnIOmhjpmtTY7OMyspjg4zlFXe4sPVZlZTXFwnKPuGVN5evdBBj29upnVCAfHOXpddwcvHeljW++hSpdiZjYuHBznaEl3BwDrtr9Y4UrMzMaHg+McdbVnmNXWzLpnJ8wF7WZmZeXgOEeSWNLdwbpn95K9AN7MrLo5OEpgcff59B48znMv+uwqM6t+Do4SWJzGOdY/63EOM6t+Do4SmN85lRnnNbFuu8c5zKz6OThKQBKL0ziHmVm1c3CUyJLu8/nVS0d9FbmZVT0HR4mcGufwXoeZVTcHR4lcNHMabZlGB4eZVT0HR4nU1YnXzfU4h5lVPwdHCS3p7uDZFw6z58CxSpdiZlY2Do4SWjIvzVvlvQ4zq2IOjhK6eFYrU5vqPc5hZlXNwVFCDfV1vHZuh4PDzKqag6PElnR38PTug+w9fKLSpZiZlUVFgkPSc5J+IWmzpI2prUPSWklb0317apekL0raJukxSZdVoubRGlqfY8Nz3usws+pUyT2OKyNiYUQsSs9vAR6MiAXAg+k5wJuBBem2Arhz3Cstwm91tTGloc7zVplZ1ZpIh6quA1alx6uA63Pa742snwLTJc2qRIGjMaWhnssubGf9c54p18yqU6WCI4B/kLRJ0orUNjMidgGk+wtS+2xgR857e1LbhLW4u4Mndx7gwLG+SpdiZlZylQqOKyLiMrKHoW6W9G8K9FWetjOW2pO0QtJGSRt7e3tLVeeYLJnXwWDApuf2VbQOM7NyqEhwRMTOdL8HeABYDOweOgSV7vek7j3AnJy3dwE783zmXRGxKCIWdXZ2lrP8s7p0TjuN9fKFgGZWlcY9OCRNlTRt6DFwDfA4sBpYnrotB76XHq8G3pfOrroc2D90SGuiyjTVc0nXdK8IaGZVqaEC3zkTeEDS0Pd/PSL+XtIG4H5JNwHPA+9K/dcAbwG2AUeAD4x/ycVb0t3BXY9sZ9/hE7RPbap0OWZmJTPuwRER24HX5Gl/EXhjnvYAbh6H0krq7ZfO5o6Hn+GenzzHx6/+zUqXY2ZWMhPpdNyqsmDmNK6+eCb3/OQ5Dh/vr3Q5ZmYl4+Aoow8vnc/+o318Y/3zlS7FzKxkHBxldOmF7fz2vPP5yj9t53j/QKXLMTMrCQdHmX34yvnsPnCcBx79VaVLMTMrCQdHmb3+FTP4rdltfPmR7QwMnnHdopnZpOPgKDNJ/Kel83n2hcP8v8cn9OUnZmaj4uAYB9e++jeYN2Mqdzz0DNmzi83MJi8HxziorxN/8LvzeXLXAR7Z+kKlyzEzOycOjnFy/aWzmdXWzB0Pbat0KWZm58TBMU6aGur4/TfMY92ze9n0S8+aa2aTl4NjHN24eA7tLY3c+bD3Osxs8nJwjKOWpgbe/zvd/HDLHtZt98y5ZjY5OTjG2fuvmMu8GVP5j/du5KlfH6h0OWZmRXNwjLO2TCOrPriYTFM977t7PTv2Hql0SWZmRXFwVMCcjhbu/eASjvUN8L6V63nh0PFKl2RmNmoOjgq56DemsfL9r2PX/qO8/6vrOXisr9IlmZmNioOjghbN7eCOd1/Gll0H+dDfbvIMumY2KTg4KuyqV87kL95xCT955kU+/s3NngjRzCa8Sqw5bsO847Vd7Dtygj//v1voPfgv/PFbL+aSrumVLsvMLC/vcUwQv/+GefzFOy5he+9h3vbX/8wffXMzu/YfrXRZZmZncHBMIL/3ujk8/F+W8ge/O5/vP7aLK//yYT6/9l85csJrlpvZxKFqnOZ70aJFsXHjxkqXcU527D3CZ//+Kb7/2C5mtk7hptd3c9UrZzK/cyqSKl2emVUhSZsiYtFZ+zk4JrZNv9zL/1jz1MmJES/saOHKizq58pUXcPm882lurK9whWZWLRwcVRIcQ3r2HeHhp3t56Kk9/PMzL3Csb5BMYz2vfXk78zunMq/zPLpnTKV7xlRmT89QV+e9EjMrTtUFh6RlwF8B9cDfRMRnRupbjcGR61jfAD/d/iIPPbWHn+14ie29hzl0/NQ4yJSGOi7saKFjahMdU5ton9pER0v2vr2lkZamBlqa6sk01ZNprKe5Mfu4qb6OxnrRWF9HQ71orKtzAJnVkNEGx6Q4HVdSPfAl4GqgB9ggaXVEPFnZyiqjubGepRddwNKLLgAgIug9dJztvYd59oXDbO89xPN7j7DvcB9b9xxi3+ET7DtygrFcIlInaKiro64O6iXq6kSdRH26l7J9xNDjbNBI2RtkXzvZlj53aJzmZCwVyKeRXvJYjxVSq7+OV85q5X/eeGlZv2NSBAewGNgWEdsBJN0HXAfUZHAMJ4kLpjVzwbRmLp93ft4+g4PBwWP97D1ygiMn+jnWN8DRE4Mc7RvgyIl+jp4YoG9gkL6BoH8we599PsjAIAxGMDAYDEYwOBgMRDAwCBBEZF+PgCD7mBRSQ1kVETmPz3xtJCO+Mjl2lK1CooZ/IHPaM2X/jskSHLOBHTnPe4AluR0krQBWAFx44YXjV9kkUVcn2loaaWtprHQpZjbJTZbrOPLtdZ72J0VE3BURiyJiUWdn5ziVZWZWeyZLcPQAc3KedwE7K1SLmVlNmyzBsQFYIKlbUhNwA7C6wjWZmdWkSTHGERH9kj4C/IDs6bgrI+KJCpdlZlaTJkVwAETEGmBNpeswM6t1k+VQlZmZTRAODjMzK4qDw8zMijJp5qoqhqRe4Jfn8BEzgBdKVM5k4u2uLd7u2jKa7X55RJz1QriqDI5zJWnjaCb6qjbe7tri7a4tpdxuH6oyM7OiODjMzKwoDo787qp0ARXi7a4t3u7aUrLt9hiHmZkVxXscZmZWFAdHDknLJD0taZukWypdTzlJWilpj6THc9o6JK2VtDXdt1eyxlKTNEfSQ5K2SHpC0kdTe7Vvd7Ok9ZJ+nrb7T1N7t6R1abu/mSYQrTqS6iX9TNL30/Na2e7nJP1C0mZJG1NbSX7rDo4kZ3naNwMXAzdKuriyVZXVPcCyYW23AA9GxALgwfS8mvQDn4iIVwGXAzen/4+rfbuPA1dFxGuAhcAySZcDnwVuT9u9D7ipgjWW00eBLTnPa2W7Aa6MiIU5p+GW5Lfu4Djl5PK0EXECGFqetipFxCPA3mHN1wGr0uNVwPXjWlSZRcSuiHg0PT5I9h+T2VT/dkdEHEpPG9MtgKuAb6f2qttuAEldwL8F/iY9FzWw3QWU5Lfu4Dgl3/K0sytUS6XMjIhdkP1HFrigwvWUjaS5wKXAOmpgu9Phms3AHmAt8AzwUkT0py7V+nv/AvBfgcH0/HxqY7sh+8fBP0jalJbWhhL91ifNtOrj4KzL01p1kHQe8B3gYxFxIPtHaHWLiAFgoaTpwAPAq/J1G9+qykvSW4E9EbFJ0tKh5jxdq2q7c1wRETslXQCslfRUqT7YexyneHla2C1pFkC631PhekpOUiPZ0PhaRHw3NVf9dg+JiJeAh8mO8UyXNPTHYzX+3q8A3ibpObKHnq8iuwdS7dsNQETsTPd7yP6xsJgS/dYdHKd4edrs9i5Pj5cD36tgLSWXjm/fDWyJiM/nvFTt292Z9jSQlAHeRHZ85yHgnalb1W13RHwyIroiYi7Z/57/MSLeTZVvN4CkqZKmDT0GrgEep0S/dV8AmEPSW8j+RTK0PO1tFS6pbCR9A1hKdsbM3cCtwP8G7gcuBJ4H3hURwwfQJy1Jrwf+CfgFp455f4rsOEc1b/clZAdC68n+sXh/RHxa0jyyf4l3AD8D3hMRxytXafmkQ1X/OSLeWgvbnbbxgfS0Afh6RNwm6XxK8Ft3cJiZWVF8qMrMzIri4DAzs6I4OMzMrCgODjMzK4qDw8zMiuLgMJtgJC0dmsnVbCJycJiZWVEcHGZjJOk9aZ2LzZK+nCYSPCTpc5IelfSgpM7Ud6Gkn0p6TNIDQ+sgSHqFpB+mtTIelTQ/ffx5kr4t6SlJX1MtTKhlk4aDw2wMJL0K+PdkJ5JbCAwA7wamAo9GxGXAj8hekQ9wL/DfIuISsleuD7V/DfhSWivjd4Bdqf1S4GNk14aZR3beJbMJwbPjmo3NG4HXAhvSzkCG7IRxg8A3U5+/A74rqQ2YHhE/Su2rgG+luYRmR8QDABFxDCB93vqI6EnPNwNzgR+Xf7PMzs7BYTY2AlZFxCdPa5T+eFi/QnP6FDr8lDt30gD+b9UmEB+qMhubB4F3prUOhtZyfjnZ/6aGZl79D8CPI2I/sE/SG1L7e4EfRcQBoEfS9ekzpkhqGdetMBsD/xVjNgYR8aSk/052hbU6oA+4GTgMvFrSJmA/2XEQyE5h/b9SMGwHPpDa3wt8WdKn02e8axw3w2xMPDuuWQlJOhQR51W6DrNy8qEqMzMrivc4zMysKN7jMDOzojg4zMysKA4OMzMrioPDzMyK4uAwM7OiODjMzKwo/x+qPI4nShESqAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(range(epochs), losses)\n", "plt.ylabel('Loss')\n", "plt.xlabel('epoch');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the result\n", "Now we'll derive <tt>y1</tt> from the new model to plot the most recent best-fit line." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current weight: 1.98381913, Current bias: 1.05575156\n", "\n", "[ 1. 50.]\n", "[ 3.0395708 100.246704 ]\n" ] } ], "source": [ "w1,b1 = model.linear.weight.item(), model.linear.bias.item()\n", "print(f'Current weight: {w1:.8f}, Current bias: {b1:.8f}')\n", "print()\n", "\n", "y1 = x1*w1 + b1\n", "print(x1)\n", "print(y1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X.numpy(), y.numpy())\n", "plt.plot(x1,y1,'r')\n", "plt.title('Current Model')\n", "plt.ylabel('y')\n", "plt.xlabel('x');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Great job!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

mdlai/odscon-sf-2015

01 - Introduction to Scikit-learn.ipynb

2

10103

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Get some data to play with" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "digits.keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "digits.images.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(digits.images[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook\n", "\n", "plt.matshow(digits.images[0], cmap=plt.cm.Greys)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "digits.data.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "digits.target.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "digits.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Data is always a numpy array (or sparse matrix) of shape (n_samples, n_features)**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split the data to get going" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(digits.data,\n", " digits.target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Really Simple API\n", "-------------------\n", "0) Import your model class" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.svm import LinearSVC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) Instantiate an object and set the parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "svm = LinearSVC(C=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) Fit the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "svm.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) Apply / evaluate" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(svm.predict(X_train))\n", "print(y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "svm.score(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "svm.score(X_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And again\n", "---------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rf = RandomForestClassifier(n_estimators=50)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rf.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%load from github" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pylab as pl\n", "from matplotlib.colors import ListedColormap\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import make_moons, make_circles, make_classification\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.lda import LDA\n", "from sklearn.qda import QDA\n", "\n", "h = .02 # step size in the mesh\n", "\n", "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Decision Tree\",\n", " \"Random Forest\", \"AdaBoost\", \"Naive Bayes\", \"LDA\", \"QDA\"]\n", "classifiers = [\n", " KNeighborsClassifier(3),\n", " SVC(kernel=\"linear\", C=0.025),\n", " SVC(gamma=2, C=1),\n", " DecisionTreeClassifier(max_depth=5),\n", " RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n", " AdaBoostClassifier(),\n", " GaussianNB(),\n", " LDA(),\n", " QDA()]\n", "\n", "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n", " random_state=1, n_clusters_per_class=1)\n", "rng = np.random.RandomState(2)\n", "X += 2 * rng.uniform(size=X.shape)\n", "linearly_separable = (X, y)\n", "\n", "datasets = [make_moons(noise=0.3, random_state=0),\n", " make_circles(noise=0.2, factor=0.5, random_state=1),\n", " linearly_separable\n", " ]\n", "\n", "figure = pl.figure(figsize=(27, 9))\n", "i = 1\n", "# iterate over datasets\n", "for ds in datasets:\n", " # preprocess dataset, split into training and test part\n", " X, y = ds\n", " X = StandardScaler().fit_transform(X)\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n", "\n", " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", " # just plot the dataset first\n", " cm = pl.cm.RdBu\n", " cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", " ax = pl.subplot(len(datasets), len(classifiers) + 1, i)\n", " # Plot the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " i += 1\n", "\n", " # iterate over classifiers\n", " for name, clf in zip(names, classifiers):\n", " ax = pl.subplot(len(datasets), len(classifiers) + 1, i)\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test)\n", "\n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, m_max]x[y_min, y_max].\n", " if hasattr(clf, \"decision_function\"):\n", " Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n", " else:\n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n", "\n", " # Plot also the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n", " alpha=0.6)\n", "\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " ax.set_title(name)\n", " ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n", " size=15, horizontalalignment='right')\n", " i += 1\n", "\n", "figure.subplots_adjust(left=.02, right=.98)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

mjbommar/cscs-530-w2016

notebooks/basic-stats/001-storing-model-results.ipynb

2

13821

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## CSCS530 Winter 2015\n", "#### Complex Systems 530 - Computer Modeling of Complex Systems (Winter 2015)\n", "\n", " * Course ID: CMPLXSYS 530\n", " * Course Title: Computer Modeling of Complex Systems\n", " * Term: Winter 2015\n", " * Schedule: Wednesdays and Friday, 1:00-2:30PM ET\n", " * Location: 120 West Hall (http://www.lsa.umich.edu/cscs/research/computerlab)\n", " * Teachers: [Mike Bommarito](https://www.linkedin.com/in/bommarito) and [Sarah Cherng](https://www.linkedin.com/pub/sarah-cherng/35/1b7/316)\n", "\n", "#### [View this repository on NBViewer](http://nbviewer.ipython.org/github/mjbommar/cscs-530-w2015/tree/master/)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Storing Model Results\n", "\n", " In this notebook, we'll learn a common pattern for storing the results of a model run. Specifically, we'll:\n", " \n", " * create a __model results__ folder to store all output\n", " * create a __per-run__ results folder to store output for a single model run\n", " * learn to save our model parameters\n", " * learn to save figures\n", " * learn to save tabular data\n", " \n", " We'll do this using the basic HIV model.\n", " \n", " __N.B.__: We won't be dealing with RNG seeds in this notebook. However, please see the supplemental notebook for instruction on properly setting, using, and recording the RNG seed.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Local Imports\n", "\n", " In the first import section, we use an import from _our own module_. We took the imports and class definitions (``Model``, ``Person``) from our notebooks and pasted them into a ``.py`` file, creating a module. Please review the ``hiv_model.py`` file to understand how this works. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Imports\n", "from hiv_model import Model, Person" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "# Imports\n", "import datetime\n", "import os\n", "import time\n", "\n", "# Scientific computing imports\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import networkx\n", "import pandas\n", "import seaborn; seaborn.set()\n", "\n", "# Import widget methods\n", "from IPython.html.widgets import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing our model output functions\n", "\n", " In this section, we'll define our model output functions. These will manage:\n", " \n", " * creating output directories\n", " * creating output CSV files\n", " * creating output figures\n", " \n", " We'll create one sample model, run it, and then test our methods." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create our test model\n", "m = Model(grid_size=10, num_people=10)\n", "for t in xrange(10):\n", " m.step()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Now, we'll define our methods to store a model's output\n", "\n", "def store_model_parameters(model, run_output_path):\n", " \"\"\"\n", " Store model parameters from a model to the run output path.\n", " \"\"\"\n", " # Create parameters dictionary\n", " model_parameters = {\"grid_size\": model.grid_size,\n", " \"num_people\": model.num_people,\n", " \"min_subsidy\": model.min_subsidy,\n", " \"max_subsidy\": model.max_subsidy,\n", " \"min_condom_budget\": model.min_condom_budget,\n", " \"max_condom_budget\": model.max_condom_budget,\n", " \"condom_cost\": model.condom_cost,\n", " \"min_prob_hookup\": model.min_prob_hookup,\n", " \"max_prob_hookup\": model.max_prob_hookup,\n", " \"prob_transmit\": model.prob_transmit,\n", " \"prob_transmit_condom\": model.prob_transmit_condom,\n", " }\n", " # Convert to dataframe and save\n", " model_parameters_df = pandas.DataFrame(model_parameters.items(),\n", " columns=[\"parameter\", \"value\"])\n", " model_parameters_df.to_csv(os.path.join(run_output_path, \"parameters.csv\"))\n", " \n", "\n", "def store_model_csv(model, run_output_path):\n", " \"\"\"\n", " Store CSV data from a model to the run output path.\n", " \"\"\"\n", " # Create interaction dataframe\n", " try:\n", " interaction_df = pandas.DataFrame(model.history_interactions,\n", " columns=[\"time\", \"agent_a\", \"agent_b\", \"use_condom\", \"is_transmission\"])\n", " except ValueError:\n", " # Sometimes, we have no interactions in \"sparse\" parameter configurations.\n", " interaction_df = pandas.DataFrame(columns=[\"time\", \"agent_a\", \"agent_b\", \"use_condom\", \"is_transmission\"])\n", " \n", " # Create time series data frame\n", " tsdata_df = pandas.DataFrame(model.history_num_infected,\n", " columns=[\"num_infected\"])\n", " tsdata_df[\"num_interactions\"] = model.history_num_interactions\n", " tsdata_df[\"num_interactions_condoms\"] = model.history_num_interactions_condoms\n", " \n", " # Save the dataframes\n", " interaction_df.to_csv(os.path.join(run_output_path, \"interactions.csv\"))\n", " tsdata_df.to_csv(os.path.join(run_output_path, \"timeseries.csv\"))\n", "\n", " \n", "def store_model_figures(model, run_output_path):\n", " \"\"\"\n", " Store figures data from a model to the run output path.\n", " \"\"\"\n", " # Plot time series of infections and interactions.\n", " f = plt.figure(figsize=(10, 8))\n", " \n", " # Create our top panel\n", " plt.subplot(211)\n", " plt.plot(model.history_num_infected)\n", " plt.legend((\"Number of infections\"), loc=\"best\")\n", " \n", " # Create our bottom panel and add the legend\n", " plt.subplot(212)\n", " plt.plot(numpy.array(model.history_num_interactions) - numpy.array(model.history_num_interactions_condoms))\n", " plt.plot(model.history_num_interactions_condoms)\n", " plt.legend((\"Number of interactions without condoms\",\n", " \"Number of interactions with condoms\"),\n", " loc=\"best\")\n", " plt.tight_layout()\n", " \n", " # Save\n", " plt.savefig(os.path.join(run_output_path, \"infections_interactions.png\"))\n", " \n", " # Next, plot the initial and final space timesteps.\n", " \n", " # Get colormap\n", " cmap = seaborn.cubehelix_palette(light=1, as_cmap=True)\n", "\n", " # Plot initial step.\n", " f = plt.figure(figsize=(10, 10))\n", " plt.title(\"Infected space at t={0}\".format(0))\n", " plt.pcolor(model.get_space_infected(0), vmin=-1, vmax=1, cmap=cmap)\n", " ax = f.gca()\n", " ax.set_aspect(1./ax.get_data_ratio()) \n", " plt.tight_layout()\n", " plt.colorbar()\n", " \n", " # Save\n", " plt.savefig(os.path.join(run_output_path, \"space_initial.png\"))\n", " \n", " # Plot final step\n", " plt.title(\"Infected space at t={0}\".format(model.t-1))\n", " plt.pcolor(model.get_space_infected(model.t-1), vmin=-1, vmax=1, cmap=cmap)\n", " ax = f.gca()\n", " ax.set_aspect(1./ax.get_data_ratio()) \n", " plt.tight_layout()\n", " plt.colorbar()\n", " \n", " # Save\n", " plt.savefig(os.path.join(run_output_path, \"space_final.png\")) \n", " \n", "\n", "def store_model(model, output_path=\"output\"):\n", " \"\"\"\n", " Store a model to the model output path.\n", " \"\"\"\n", " # First, we need to make sure the directory exists.\n", " try:\n", " os.makedirs(output_path)\n", " except:\n", " pass\n", " \n", " \"\"\"\n", " Next, we need to create a unique timestamp for the model.\n", " We'll do that using a timestamp of the form: YYYYMMDD-Run#\n", " \n", " We then need to create that directory too.\n", " \"\"\"\n", " timestamp_suffix = time.strftime(\"%Y%m%d\")\n", " \n", " run_id = 0\n", " run_output_path = os.path.join(output_path,\n", " \"run-{0}-{1}\".format(timestamp_suffix,\n", " run_id))\n", " # Get a unique run #\n", " while os.path.exists(run_output_path):\n", " run_id += 1\n", " run_output_path = os.path.join(output_path,\n", " \"run-{0}-{1}\".format(timestamp_suffix,\n", " run_id)) \n", "\n", " try:\n", " os.makedirs(run_output_path)\n", " except:\n", " pass\n", " \n", " \"\"\"\n", " Finally, we need to store data and figures to the path.\n", " \"\"\"\n", " store_model_parameters(model, run_output_path)\n", " store_model_csv(model, run_output_path)\n", " store_model_figures(model, run_output_path)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Finally, test our output method with the model.\n", "store_model(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running our parameter sweep" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running 10 samples for subsidy value 0.0, prob_hookup value 0.1\n", "Running 10 samples for subsidy value 0.0, prob_hookup value 0.5\n", "Running 10 samples for subsidy value 0.0, prob_hookup value 0.9\n", "Running 10 samples for subsidy value 0.33, prob_hookup value 0.1\n", "Running 10 samples for subsidy value 0.33, prob_hookup value 0.5\n", "Running 10 samples for subsidy value 0.33, prob_hookup value 0.9\n", "Running 10 samples for subsidy value 0.66, prob_hookup value 0.1\n", "Running 10 samples for subsidy value 0.66, prob_hookup value 0.5\n", "Running 10 samples for subsidy value 0.66, prob_hookup value 0.9\n", "Running 10 samples for subsidy value 1.0, prob_hookup value 0.1\n", "Running 10 samples for subsidy value 1.0, prob_hookup value 0.5\n", "Running 10 samples for subsidy value 1.0, prob_hookup value 0.9\n" ] } ], "source": [ "# Set number of samples per value and steps per sample\n", "num_samples = 10\n", "num_steps = 100\n", "\n", "# Set basic model parameters\n", "grid_size = 10\n", "num_people =10\n", "\n", "# Set subsidy values to \"sweep\" over\n", "subsidy_sweep_values = [0.0, 0.33, 0.66, 1.0]\n", "prob_hookup_values = [0.1, 0.5, 0.9]\n", "subsidy_sweep_output = []\n", "\n", "# Iterate over subsidy\n", "for subsidy_value in subsidy_sweep_values:\n", " # Iterate over prob_hookup\n", " for prob_hookup_value in prob_hookup_values:\n", " print(\"Running {0} samples for subsidy value {1}, prob_hookup value {2}\"\\\n", " .format(num_samples, subsidy_value, prob_hookup_value))\n", " for n in xrange(num_samples):\n", " # Output info\n", " m = Model(grid_size=grid_size,\n", " num_people=num_people,\n", " min_condom_budget=0.0,\n", " max_condom_budget=1.0,\n", " min_prob_hookup=prob_hookup_value-0.1,\n", " max_prob_hookup=prob_hookup_value+0.1,\n", " min_subsidy=subsidy_value,\n", " max_subsidy=subsidy_value)\n", "\n", " # Run the model for num-steps\n", " for t in xrange(num_steps):\n", " m.step()\n", "\n", " # Output our model\n", " store_model(m)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-2-clause

CrowdTruth/CrowdTruth-core

tutorial/notebooks/.ipynb_checkpoints/Highlighting Task - Event Extraction-checkpoint.ipynb

1

121796

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Highlighting Task - Event Extraction from Text\n", "\n", "In this tutorial, we will show how *dimensionality reduction* can be applied over *both the media units and the annotations* of a crowdsourcing task, and how this impacts the results of the CrowdTruth quality metrics. We start with an *open-ended extraction task*, where the crowd was asked to highlight words or phrases in a text that refer to events or actions. The task was executed on [FigureEight](https://www.figure-eight.com/). For more crowdsourcing annotation task examples, click [here](https://raw.githubusercontent.com/CrowdTruth-core/tutorial/getting_started.md).\n", "\n", "To replicate this experiment, the code used to design and implement this crowdsourcing annotation template is available here: [template](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/Event-Text-Highlight/template.html), [css](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/Event-Text-Highlight/template.css), [javascript](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/Event-Text-Highlight/template.js).\n", "\n", "This is how the task looked like to the workers:\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sample dataset for this task is available in [this file](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/data/event-text-highlight.csv), containing raw output from the crowd on FigureEight. Download the file and place it in a folder named `data` that has the same root as this notebook. The answers from the crowd are stored in the `tagged_events` column." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [\"income fell\"]\n", "1 [\"net income fell\"]\n", "2 [\"reported third-quarter net income fell 5.9 %...\n", "3 [\"reported third-quarter net income fell\"]\n", "4 [\"reported third-quarter\"]\n", "5 [\"reported\",\"fell\",\"year-ago\"]\n", "6 [\"reported\",\"fell\"]\n", "7 [\"reported\",\"fell\"]\n", "8 [\"reported\",\"fell\"]\n", "9 [\"reported\",\"fell\"]\n", "10 [\"reported\",\"fell\"]\n", "11 [\"reported\",\"fell\"]\n", "12 [\"reported\",\"income fell\"]\n", "13 [\"reported\",\"income\",\"fell\"]\n", "14 [\"reported\",\"third-quarter\",\"net\",\"income\",\"fe...\n", "15 [\"reported\"]\n", "16 [\"reported\"]\n", "17 [\"Separately\",\"reported third-quarter net inco...\n", "18 [\"Separately\",\"reported\"]\n", "19 [\"third-quarter\"]\n", "20 [\"held\",\"20\",\"years\",\"anti-abortion movement\"]\n", "21 [\"held\",\"anti-abortion\",\"movement\",\"said\"]\n", "22 [\"jobs he has held over the past 20 years have...\n", "23 [\"jobs he has held\"]\n", "24 [\"jobs\",\"anti-abortion movement\",\"news release\"]\n", "25 [\"jobs\",\"anti-abortion movement\"]\n", "26 [\"jobs\",\"he\",\"has\",\"held\",\"over\",\"the\",\"past\",...\n", "27 [\"Justice\",\"Department\",\"said\"]\n", "28 [\"purports to be a devout Roman Catholic\"]\n", "29 [\"purports\",\"has held over\",\"anti-abortion\",\"m...\n", "Name: tagged_events, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "test_data = pd.read_csv(\"../data/event-text-highlight.csv\")\n", "test_data[\"tagged_events\"][0:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the diverse behavior of the crowd workers. While most annotated each word individually, the worker on *row 2* annotated a chunk of the sentence together in one word phrase. Also, when no answer was picked by the worker, the value in the cell is `[NONE]`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A basic pre-processing configuration\n", "\n", "Our basic pre-processing configuration attempts to normalize the different ways of performing the crowd annotations.\n", "\n", "We set `remove_empty_rows = False` to keep the empty rows from the crowd. This configuration option will set all empty cell values to correspond to a *NONE* token in the annotation vector.\n", "\n", "We build the annotation vector to have one component for each word in the sentence. To do this, we break up multiple-word annotations into a list of single words in the `processJudgments` call:\n", "\n", "```\n", "judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace(' ',self.annotation_separator))\n", "```\n", "\n", "The final configuration class `Config` is this:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import crowdtruth\n", "from crowdtruth.configuration import DefaultConfig\n", "\n", "class Config(DefaultConfig):\n", " inputColumns = [\"doc_id\", \"sentence_id\", \"events\", \"events_count\", \"original_sententce\", \"processed_sentence\", \"tokens\"]\n", " outputColumns = [\"tagged_events\"]\n", " open_ended_task = True\n", " annotation_separator = \",\"\n", "\n", " remove_empty_rows = False\n", " \n", " def processJudgments(self, judgments):\n", " # build annotation vector just from words\n", " judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace(' ',self.annotation_separator))\n", "\n", " # normalize vector elements\n", " judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace('[',''))\n", " judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace(']',''))\n", " judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace('\"',''))\n", " judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace(',,,',','))\n", " judgments[self.outputColumns[0]] = judgments[self.outputColumns[0]].apply(\n", " lambda x: str(x).replace(',,',','))\n", " return judgments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can pre-process the data and run the CrowdTruth metrics:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "data_with_stopwords, config_with_stopwords = crowdtruth.load(\n", " file = \"../data/event-text-highlight.csv\",\n", " config = Config()\n", ")\n", "\n", "processed_results_with_stopwords = crowdtruth.run(\n", " data_with_stopwords,\n", " config_with_stopwords\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing stopwords from Media Units and Annotations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more complex dimensionality reduction technique involves removing the stopwords from both the *media units* and the crowd *annotations*. Stopwords (i.e. words that are very common in the English language) do not usually contain much useful information. Also, the behavior of the crowds w.r.t them is inconsistent - some workers omit them, some annotate them.\n", "\n", "The first step is to build a function that removes stopwords from strings. We will use the `stopwords` corpus in the `nltk` package to get the list of words. We want to build a function that can be reused for both the text in the media units and in the annotations column. Also, we need to be careful about omitting punctuation.\n", "\n", "The function `remove_stop_words` does all of these things:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import nltk\n", "from nltk.corpus import stopwords\n", "import string\n", "\n", "stopword_set = set(stopwords.words('english'))\n", "stopword_set.update(['s'])\n", "\n", "def remove_stop_words(words_string, sep):\n", " '''\n", " words_string: string containing all words\n", " sep: separator character for the words in words_string\n", " '''\n", " words_list = words_string.split(sep)\n", " corrected_words_list = \"\"\n", " for word in words_list:\n", " if word not in stopword_set:\n", " if corrected_words_list != \"\":\n", " corrected_words_list += sep\n", " corrected_words_list += word\n", " return corrected_words_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the new configuration class `ConfigDimRed`, we apply the function we just built to both the column that contains the media unit text (`inputColumns[2]`), and the column containing the crowd annotations (`outputColumns[0]`):" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "class ConfigDimRed(Config):\n", " def processJudgments(self, judgments):\n", " judgments = Config.processJudgments(self, judgments)\n", " \n", " # remove stopwords from input sentence\n", " for idx in range(len(judgments[self.inputColumns[2]])):\n", " judgments.at[idx, self.inputColumns[2]] = remove_stop_words(\n", " judgments[self.inputColumns[2]][idx], \" \")\n", " \n", " for idx in range(len(judgments[self.outputColumns[0]])):\n", " judgments.at[idx, self.outputColumns[0]] = remove_stop_words(\n", " judgments[self.outputColumns[0]][idx], self.annotation_separator)\n", " if judgments[self.outputColumns[0]][idx] == \"\":\n", " judgments.at[idx, self.outputColumns[0]] = self.none_token\n", " return judgments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can pre-process the data and run the CrowdTruth metrics:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "data_without_stopwords, config_without_stopwords = crowdtruth.load(\n", " file = \"../data/event-text-highlight.csv\",\n", " config = ConfigDimRed()\n", ")\n", "\n", "processed_results_without_stopwords = crowdtruth.run(\n", " data_without_stopwords,\n", " config_without_stopwords\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effect on CrowdTruth metrics\n", "\n", "Finally, we can compare the effect of the stopword removal on the CrowdTruth *sentence quality score*." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,u'without stopwords')" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a2051ac50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(\n", " processed_results_with_stopwords[\"units\"][\"uqs\"],\n", " processed_results_without_stopwords[\"units\"][\"uqs\"],\n", ")\n", "plt.plot([0, 1], [0, 1], 'red', linewidth=1)\n", "plt.title(\"Sentence Quality Score\")\n", "plt.xlabel(\"with stopwords\")\n", "plt.ylabel(\"without stopwords\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The red line in the plot runs through the diagonal. All sentences above the line have a higher *sentence quality score* when the stopwords were removed.\n", "\n", "The plot shows that removing the stopwords improved the quality for a majority of the sentences. Surprisingly though, some sentences decreased in quality. This effect can be understood when plotting the *worker quality scores*." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,u'without stopwords')" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a215064d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(\n", " processed_results_with_stopwords[\"workers\"][\"wqs\"],\n", " processed_results_without_stopwords[\"workers\"][\"wqs\"],\n", ")\n", "plt.plot([0, 0.8], [0, 0.8], 'red', linewidth=1)\n", "plt.title(\"Worker Quality Score\")\n", "plt.xlabel(\"with stopwords\")\n", "plt.ylabel(\"without stopwords\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The quality of the majority of workers also has increased in the configuration where we removed the stopwords. However, because of the inter-linked nature of the CrowdTruth quality metrics, the annotations of these workers now has a greater weight when calculating the *sentence quality score*. So the stopword removal process had the effect of removing some of the noise in the annotations and therefore increasing the quality scores, but also of *amplifying the true ambiguity in the sentences*." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>duration</th>\n", " <th>input.doc_id</th>\n", " <th>input.events</th>\n", " <th>input.events_count</th>\n", " <th>input.processed_sentence</th>\n", " <th>input.sentence_id</th>\n", " <th>input.tokens</th>\n", " <th>job</th>\n", " <th>output.tagged_events</th>\n", " 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"1893181947 1 \n", "1893181948 3 \n", "1893181949 2 \n", "1893181950 2 \n", "1893181951 2 \n", "1893181952 1 \n", "1893181953 3 \n", "1893181954 3 \n", "1893181955 4 \n", "1893181956 3 \n", "1893181957 9 \n", "1893181958 6 \n", "\n", " input.processed_sentence \\\n", "unit \n", "1893181917 Separately , Esselte Business Systems reported... \n", "1893181918 Kopp purports to be a devout Roman Catholic , ... \n", "1893181919 `` We as Christians have a responsibility to p... \n", "1893181920 Slepian 's death was among the first topics ra... \n", "1893181921 `` It 's possible that New York politics has n... \n", "1893181922 Slepian , 52 , an obstetrician and gynecologis... \n", "1893181923 A jogger observed Kopp 's car at 6 a.m. near S... \n", "1893181924 But some other parties and social organization... \n", "1893181925 `` Extending membership to these three democra... \n", "1893181926 The U.S. military buildup in Saudi Arabia cont... \n", "1893181927 The Iraqi ambassador to Venezuela said on Tues... \n", "1893181928 Bush told a news conference on Tuesday that th... \n", "1893181929 After a two-hour meeting at his Kennebunkport ... \n", "1893181930 Replied State Department deputy spokesman Rich... \n", "1893181931 Meanwhile , Egypt 's official Middle East News... \n", "1893181932 The top commander of a Cambodian resistance fo... \n", "1893181933 The economy created jobs at a surprisingly rob... \n", "1893181934 The police and prosecutors said they had ident... \n", "1893181935 By a 6-3 vote , the court upheld a discriminat... \n", "1893181936 One GOP source , reporting on a call from the ... \n", "1893181937 Iraq 's Saddam Hussein , his options for endin... \n", "1893181938 Over the weekend , Pentagon officials confirme... \n", "1893181939 Despite the early indications of success , the... \n", "1893181940 The lights are out and the heat is off and tho... \n", "1893181941 The White House said President Bush has approv... \n", "1893181942 For NPR news , I 'm Auncil Martinez reporting . \n", "1893181943 More heavy gunfire in the Serbian province of ... \n", "1893181944 Officials said the president himself met with ... \n", "1893181945 Women have become the sole support of their fa... \n", "1893181946 Yet , the Taliban have forbidden them to work . \n", "1893181947 The president responded , \" Everything , every... \n", "1893181948 A powerful ice storm continues to maintain its... \n", "1893181949 Previously , watch imports were denied such du... \n", "1893181950 They expect him to cut costs throughout the or... \n", "1893181951 He declined to discuss other terms of the issue . \n", "1893181952 Primerica closed at $ 28.25 , down 50 cents . \n", "1893181953 Both sides are in talks to settle the dispute . \n", "1893181954 DPC said it could n't comment on the suit . \n", "1893181955 Nashua announced the Reiss request after the m... \n", "1893181956 The transaction is expected to close around ye... \n", "1893181957 Based on physical evidence _ including a rifle... \n", "1893181958 The New York Times said in an editorial on Sat... \n", "\n", " input.sentence_id input.tokens job \\\n", "unit \n", "1893181917 11 39 ../data/event-text-highlight \n", "1893181918 14 39 ../data/event-text-highlight \n", "1893181919 14 39 ../data/event-text-highlight \n", "1893181920 16 39 ../data/event-text-highlight \n", "1893181921 43 39 ../data/event-text-highlight \n", "1893181922 17 39 ../data/event-text-highlight \n", "1893181923 20 39 ../data/event-text-highlight \n", "1893181924 10 39 ../data/event-text-highlight \n", "1893181925 12 39 ../data/event-text-highlight \n", "1893181926 11 38 ../data/event-text-highlight \n", "1893181927 32 38 ../data/event-text-highlight \n", "1893181928 56 38 ../data/event-text-highlight \n", "1893181929 4 38 ../data/event-text-highlight \n", "1893181930 25 38 ../data/event-text-highlight \n", "1893181931 64 38 ../data/event-text-highlight \n", "1893181932 6 38 ../data/event-text-highlight \n", "1893181933 8 38 ../data/event-text-highlight \n", "1893181934 5 38 ../data/event-text-highlight \n", "1893181935 4 38 ../data/event-text-highlight \n", "1893181936 7 38 ../data/event-text-highlight \n", "1893181937 3 38 ../data/event-text-highlight \n", "1893181938 60 38 ../data/event-text-highlight \n", "1893181939 16 38 ../data/event-text-highlight \n", "1893181940 1 38 ../data/event-text-highlight \n", "1893181941 4 38 ../data/event-text-highlight \n", "1893181942 10 10 ../data/event-text-highlight \n", "1893181943 2 10 ../data/event-text-highlight \n", "1893181944 13 10 ../data/event-text-highlight \n", "1893181945 14 10 ../data/event-text-highlight \n", "1893181946 15 10 ../data/event-text-highlight \n", "1893181947 14 10 ../data/event-text-highlight \n", "1893181948 2 10 ../data/event-text-highlight \n", "1893181949 6 10 ../data/event-text-highlight \n", "1893181950 13 10 ../data/event-text-highlight \n", "1893181951 7 10 ../data/event-text-highlight \n", "1893181952 7 10 ../data/event-text-highlight \n", "1893181953 9 10 ../data/event-text-highlight \n", "1893181954 7 10 ../data/event-text-highlight \n", "1893181955 13 10 ../data/event-text-highlight \n", "1893181956 6 10 ../data/event-text-highlight \n", "1893181957 9 55 ../data/event-text-highlight \n", "1893181958 3 54 ../data/event-text-highlight \n", "\n", " output.tagged_events \\\n", "unit \n", "1893181917 {u'cents': 2, u'share': 2, u'period': 2, u'in'... \n", "1893181918 {u'': 2, u'Catholic': 3, u'anti-abortion': 9, ... \n", "1893181919 {u'a': 3, u'protect': 24, u'said': 9, u'from':... \n", "1893181920 {u'among': 1, u'raised': 12, u'followed': 11, ... \n", "1893181921 {u'own': 1, u'willingness': 4, u'steppingstone... \n", "1893181922 {u'and': 4, u'shot': 18, u'an': 1, u'through':... \n", "1893181923 {u'': 2, u'plate': 1, u'number': 1, u'observed... \n", "1893181924 {u'and': 2, u'burdens': 5, u'some': 1, u'acces... \n", "1893181925 {u'century': 2, u'three': 1, u'''': 1, u'disas... \n", "1893181926 {u'kingdom': 3, u'force': 4, u'guard': 14, u'a... \n", "1893181927 {u'and': 1, u'ambassador': 1, u'Kuwait': 1, u'... \n", "1893181928 {u'and': 2, u'on': 1, u'force': 4, u'it': 1, u... \n", "1893181929 {u'Kuwait': 3, u'Bush': 3, u'at': 1, u'home': ... \n", "1893181930 {u'Replied': 12, u'Boucher': 3, u'rhetoric': 4... \n", "1893181931 {u'large-scale': 7, u'Agency': 2, u'Thursday':... \n", "1893181932 {u'and': 2, u'force': 2, u'almost': 1, u'recov... \n", "1893181933 {u'financial': 7, u'caused': 2, u'surprisingly... \n", "1893181934 {u'and': 1, u'find': 5, u'linking': 3, u'in': ... \n", "1893181935 {u'the': 1, u'citizenship': 4, u'unmarried': 4... \n", "1893181936 {u'and': 1, u'help': 5, u'is': 4, u'need': 7, ... \n", "1893181937 {u'Gulf': 5, u'increasingly': 3, u'ending': 7,... \n", "1893181938 {u'and': 1, u'within': 2, u'powerful': 1, u'of... \n", "1893181939 {u'indications': 5, u'tough': 2, u'is': 2, u'd... \n", "1893181940 {u'and': 5, u'Canada': 2, u'people': 1, u'is':... \n", "1893181941 {u'produced': 9, u'Islands': 1, u'said': 7, u'... \n", "1893181942 {u'I': 1, u'reporting': 16, u'Martinez': 1, u'... \n", "1893181943 {u'heavy': 8, u'province': 2, u'NONE': 1, u'gu... \n", "1893181944 {u'NONE': 1, u'himself': 2, u'said': 10, u'met... \n", "1893181945 {u'NONE': 2, u'support': 14, u'sole': 6, u'hav... \n", "1893181946 {u'them': 4, u'forbidden': 17, u'work': 7, u'N... \n", "1893181947 {u'president': 3, u'NONE': 1, u'responded': 17} \n", "1893181948 {u'grip': 7, u'NONE': 1, u'continues': 6, u'po... \n", "1893181949 {u'NONE': 1, u'imports': 7, u'were': 2, u'watc... \n", "1893181950 {u'NONE': 2, u'cut': 11, u'to': 2, u'costs': 9... \n", "1893181951 {u'NONE': 2, u'terms': 1, u'of': 1, u'to': 2, ... \n", "1893181952 {u'NONE': 1, u'Primerica': 2, u'$': 2, u'cents... \n", "1893181953 {u'NONE': 1, u'to': 2, u'settle': 13, u'in': 4... \n", "1893181954 {u'comment': 15, u'NONE': 1, u'said': 12, u'co... \n", "1893181955 {u'NONE': 1, u'Nashua': 1, u'after': 1, u'requ... \n", "1893181956 {u'NONE': 2, u'transaction': 11, u'end': 4, u'... \n", "1893181957 {u'': 52, u'el': 1, u'his': 1, u'en': 2, u'Bas... \n", "1893181958 {u'Court': 1, u'in': 3, u'it': 3, u'years': 3,... \n", "\n", " output.tagged_events.annotations \\\n", "unit \n", "1893181917 79 \n", "1893181918 128 \n", "1893181919 94 \n", "1893181920 119 \n", "1893181921 102 \n", "1893181922 117 \n", "1893181923 112 \n", "1893181924 120 \n", "1893181925 128 \n", "1893181926 148 \n", "1893181927 104 \n", "1893181928 128 \n", "1893181929 108 \n", "1893181930 80 \n", "1893181931 111 \n", "1893181932 157 \n", "1893181933 125 \n", "1893181934 102 \n", "1893181935 104 \n", "1893181936 100 \n", "1893181937 122 \n", "1893181938 99 \n", "1893181939 128 \n", "1893181940 125 \n", "1893181941 83 \n", "1893181942 24 \n", "1893181943 26 \n", "1893181944 43 \n", "1893181945 36 \n", "1893181946 38 \n", "1893181947 21 \n", "1893181948 48 \n", "1893181949 36 \n", "1893181950 45 \n", "1893181951 38 \n", "1893181952 38 \n", "1893181953 48 \n", "1893181954 54 \n", "1893181955 54 \n", "1893181956 55 \n", "1893181957 281 \n", "1893181958 154 \n", "\n", " output.tagged_events.unique_annotations worker uqs \\\n", "unit \n", "1893181917 24 20 0.608170 \n", "1893181918 29 20 0.334970 \n", "1893181919 20 20 0.576942 \n", "1893181920 22 20 0.551484 \n", "1893181921 29 20 0.477072 \n", "1893181922 30 20 0.573296 \n", "1893181923 33 20 0.657856 \n", "1893181924 27 20 0.373122 \n", "1893181925 29 20 0.431015 \n", "1893181926 34 20 0.528953 \n", "1893181927 25 20 0.450525 \n", "1893181928 33 20 0.402053 \n", "1893181929 29 20 0.475422 \n", "1893181930 21 20 0.379997 \n", "1893181931 24 20 0.532090 \n", "1893181932 35 20 0.539572 \n", "1893181933 32 20 0.463548 \n", "1893181934 26 20 0.461942 \n", "1893181935 26 20 0.446112 \n", "1893181936 23 20 0.448201 \n", "1893181937 29 20 0.339267 \n", "1893181938 32 20 0.554168 \n", "1893181939 28 20 0.452174 \n", "1893181940 31 20 0.336470 \n", "1893181941 21 20 0.478675 \n", "1893181942 7 20 0.848991 \n", "1893181943 4 20 0.716488 \n", "1893181944 9 20 0.574096 \n", "1893181945 6 20 0.566965 \n", "1893181946 6 20 0.679360 \n", "1893181947 3 20 0.865400 \n", "1893181948 10 20 0.478889 \n", "1893181949 9 20 0.553471 \n", "1893181950 9 20 0.579558 \n", "1893181951 9 20 0.677483 \n", "1893181952 9 20 0.679813 \n", "1893181953 7 20 0.769411 \n", "1893181954 8 20 0.629596 \n", "1893181955 10 20 0.652620 \n", "1893181956 11 20 0.623260 \n", "1893181957 69 20 0.521132 \n", "1893181958 41 20 0.433716 \n", "\n", " unit_annotation_score uqs_initial \\\n", "unit \n", "1893181917 {u'cents': 0.0297850757146, u'share': 0.029785... 0.519530 \n", "1893181918 {u'': 0.157986305381, u'Catholic': 0.026908423... 0.259531 \n", "1893181919 {u'a': 0.0704736341848, u'protect': 1.47222699... 0.401958 \n", "1893181920 {u'among': 0.0200950204081, u'raised': 0.67052... 0.406153 \n", "1893181921 {u'own': 0.0221168623273, u'willingness': 0.25... 0.358854 \n", "1893181922 {u'and': 0.1014974899, u'shot': 0.990028551248... 0.431335 \n", "1893181923 {u'': 0.0396913152867, u'plate': 0.04870870448... 0.492269 \n", "1893181924 {u'and': 0.0825392628047, u'burdens': 0.344247... 0.290667 \n", "1893181925 {u'century': 0.0685543621321, u'three': 0.0487... 0.328617 \n", "1893181926 {u'kingdom': 0.0994574494386, u'force': 0.1399... 0.400969 \n", "1893181927 {u'and': 0.0325544025466, u'ambassador': 0.020... 0.342065 \n", "1893181928 {u'and': 0.14169536593, u'on': 0.0209061159915... 0.336127 \n", "1893181929 {u'Kuwait': 0.0748202295946, u'Bush': 0.064393... 0.360409 \n", "1893181930 {u'Replied': 0.659090232546, u'Boucher': 0.061... 0.305077 \n", "1893181931 {u'large-scale': 0.273694670853, u'Agency': 0.... 0.435103 \n", "1893181932 {u'and': 0.0830114770935, u'force': 0.06061338... 0.428880 \n", "1893181933 {u'financial': 0.356021526749, u'caused': 0.08... 0.408510 \n", "1893181934 {u'and': 0.0203175077078, u'find': 0.268739659... 0.405740 \n", "1893181935 {u'the': 0.0199130729834, u'citizenship': 0.13... 0.382814 \n", "1893181936 {u'and': 0.0199130729834, u'help': 0.258170521... 0.392595 \n", "1893181937 {u'Gulf': 0.180771506315, u'increasingly': 0.0... 0.258450 \n", "1893181938 {u'and': 0.0194958678635, u'within': 0.0485962... 0.464803 \n", "1893181939 {u'indications': 0.271023781306, u'tough': 0.0... 0.330208 \n", "1893181940 {u'and': 0.170866699369, u'Canada': 0.01983097... 0.296920 \n", "1893181941 {u'produced': 0.491280127077, u'said': 0.32528... 0.402183 \n", "1893181942 {u'I': 0.0297608786779, u'reporting': 0.937529... 0.604289 \n", "1893181943 {u'heavy': 0.340689482465, u'province': 0.0293... 0.524574 \n", "1893181944 {u'NONE': 0.0087320973195, u'himself': 0.08564... 0.418177 \n", "1893181945 {u'NONE': 0.0213007712472, u'support': 0.75809... 0.464811 \n", "1893181946 {u'them': 0.160573478252, u'forbidden': 0.9670... 0.535963 \n", "1893181947 {u'president': 0.0950187394689, u'NONE': 0.009... 0.703831 \n", "1893181948 {u'grip': 0.516808569107, u'NONE': 7.967463463... 0.337153 \n", "1893181949 {u'NONE': 7.96746346345e-07, u'imports': 0.374... 0.383764 \n", "1893181950 {u'NONE': 0.0101591382774, u'cut': 0.829219536... 0.330809 \n", "1893181951 {u'NONE': 0.00980459272858, u'terms': 0.066722... 0.441666 \n", "1893181952 {u'NONE': 8.17248045606e-07, u'Primerica': 0.1... 0.463861 \n", "1893181953 {u'NONE': 8.60151861794e-07, u'to': 0.15607993... 0.509102 \n", "1893181954 {u'comment': 0.900546927647, u'NONE': 8.545539... 0.454137 \n", "1893181955 {u'NONE': 2.06151867342e-06, u'Nashua': 0.0107... 0.424066 \n", "1893181956 {u'NONE': 0.0104003935425, u'transaction': 0.7... 0.418970 \n", "1893181957 {u'': 0.000116692296731, u'el': 2.24408262943e... 0.283726 \n", "1893181958 {u'Court': 0.0605714503761, u'in': 0.127326102... 0.224615 \n", "\n", " unit_annotation_score_initial \n", "unit \n", "1893181917 {u'cents': 0.1, u'share': 0.1, u'period': 0.1,... \n", "1893181918 {u'': 0.1, u'Catholic': 0.15, u'anti-abortion'... \n", "1893181919 {u'a': 0.15, u'protect': 1.2, u'said': 0.45, u... \n", "1893181920 {u'among': 0.05, u'raised': 0.6, u'followed': ... \n", "1893181921 {u'own': 0.05, u'willingness': 0.2, u'stepping... \n", "1893181922 {u'and': 0.2, u'shot': 0.9, u'an': 0.05, u'thr... \n", "1893181923 {u'': 0.1, u'plate': 0.05, u'number': 0.05, u'... \n", "1893181924 {u'and': 0.1, u'burdens': 0.25, u'some': 0.05,... \n", "1893181925 {u'century': 0.1, u'three': 0.05, u'''': 0.05,... \n", "1893181926 {u'kingdom': 0.15, u'force': 0.2, u'guard': 0.... \n", "1893181927 {u'and': 0.05, u'ambassador': 0.05, u'Kuwait':... \n", "1893181928 {u'and': 0.1, u'on': 0.05, u'force': 0.2, u'it... \n", "1893181929 {u'Kuwait': 0.15, u'Bush': 0.15, u'at': 0.05, ... \n", "1893181930 {u'Replied': 0.6, u'Boucher': 0.15, u'rhetoric... \n", "1893181931 {u'large-scale': 0.35, u'Agency': 0.1, u'Thurs... \n", "1893181932 {u'and': 0.1, u'force': 0.1, u'almost': 0.05, ... \n", "1893181933 {u'financial': 0.35, u'caused': 0.1, u'surpris... \n", "1893181934 {u'and': 0.05, u'find': 0.25, u'linking': 0.15... \n", "1893181935 {u'the': 0.05, u'citizenship': 0.2, u'unmarrie... \n", "1893181936 {u'and': 0.05, u'help': 0.25, u'is': 0.2, u'ne... \n", "1893181937 {u'Gulf': 0.25, u'increasingly': 0.15, u'endin... \n", "1893181938 {u'and': 0.05, u'within': 0.1, u'powerful': 0.... \n", "1893181939 {u'indications': 0.25, u'tough': 0.1, u'is': 0... \n", "1893181940 {u'and': 0.25, u'Canada': 0.1, u'people': 0.05... \n", "1893181941 {u'produced': 0.45, u'said': 0.35, u'for': 0.1... \n", "1893181942 {u'I': 0.05, u'reporting': 0.8, u'Martinez': 0... \n", "1893181943 {u'heavy': 0.4, u'province': 0.1, u'NONE': 0.0... \n", "1893181944 {u'NONE': 0.05, u'himself': 0.1, u'said': 0.5,... \n", "1893181945 {u'NONE': 0.1, u'support': 0.7, u'sole': 0.3, ... \n", "1893181946 {u'them': 0.2, u'forbidden': 0.85, u'work': 0.... \n", "1893181947 {u'president': 0.15, u'NONE': 0.05, u'responde... \n", "1893181948 {u'grip': 0.35, u'NONE': 0.05, u'continues': 0... \n", "1893181949 {u'NONE': 0.05, u'imports': 0.35, u'were': 0.1... \n", "1893181950 {u'NONE': 0.1, u'cut': 0.55, u'to': 0.1, u'cos... \n", "1893181951 {u'NONE': 0.1, u'terms': 0.05, u'of': 0.05, u'... \n", "1893181952 {u'NONE': 0.05, u'Primerica': 0.1, u'$': 0.1, ... \n", "1893181953 {u'NONE': 0.05, u'to': 0.1, u'settle': 0.65, u... \n", "1893181954 {u'comment': 0.75, u'NONE': 0.05, u'said': 0.6... \n", "1893181955 {u'NONE': 0.05, u'Nashua': 0.05, u'after': 0.0... \n", "1893181956 {u'NONE': 0.1, u'transaction': 0.55, u'end': 0... \n", "1893181957 {u'': 2.6, u'el': 0.05, u'his': 0.05, u'en': 0... \n", "1893181958 {u'Court': 0.05, u'in': 0.15, u'it': 0.15, u'y... " ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_with_stopwords[\"units\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

poldrack/fmri-analysis-vm

analysis/connectivity/PPI.ipynb

1

198370

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we will work through a simulation of psychophysiological interaction" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os,sys\n", "import numpy\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "sys.path.insert(0,'../')\n", "from utils.mkdesign import create_design_singlecondition\n", "from nipy.modalities.fmri.hemodynamic_models import spm_hrf,compute_regressor\n", "from utils.make_data import make_continuous_data\n", "from statsmodels.tsa.arima_process import arma_generate_sample\n", "import scipy.stats\n", "import seaborn as sns\n", "sns.set_style(\"white\")\n", "\n", "results_dir = os.path.abspath(\"../results\")\n", "if not os.path.exists(results_dir):\n", " os.mkdir(results_dir)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the data generated using the DCM forward model. In this model, there should be a significant PPI between roi 0 and rois 2 and 4 (see the B matrix in the DCM notebook)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "../utils/mkdesign.py:25: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " design[b:b+blocklength]=1\n" ] } ], "source": [ "dcmdata=numpy.load(os.path.join(results_dir,'dcmdata.npz'))\n", "data_conv=dcmdata['data']\n", "# downsample to 1 second TR\n", "data=data_conv[range(0,data_conv.shape[0],100)]\n", "ntp=data.shape[0]\n", "\n", "# create a blocked design\n", "d,design=create_design_singlecondition(blockiness=1.0,deslength=ntp,blocklength=20,offset=20)\n", "\n", "regressor,_=compute_regressor(design,'spm',numpy.arange(0,ntp))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-50, 300)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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04RQjGp1cQTzU3EtXaxdqwFgxcQ4mcCtS25KVFPwNR9ETdgWrJ9HKuC/7Ppvh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\n2hBjs2PHDjQ3N+Pll1+e7VWZt4y0jWk/PjcqKyvx2muvwefz4c0338RDDz2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f26341b8f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(data.shape[1]):\n", " plt.plot(data[:,i], label=\"ROI %d\"%i)\n", "plt.legend(loc=\"best\")\n", "plt.xlim([-50,300])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up the PPI model, using ROI 0 as the seed" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ROI 0: 3.86593321294 6.80322958119e-05\n", "ROI 1: 1.01708139751 0.154972654455\n", "ROI 2: 1.42428415275 0.077708801814\n", "ROI 3: 1.80274877169 0.0362223520829\n", "ROI 4: 9.01167903117 0.0\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2602a550b8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IAAAAC1x3whqPVnuZAwkmvXsP2xEAAB0cjTBnrouw2rKDXuZAgmlpqLcdAT5Kz+lpOwIA\nBA7DkQAAABa47oRVvf+RlzmQYLpEamxHgI+yz2m0HQFAEgozHunIdRFWsZs5YUGScZAiLEj61FGE\nAYDfWKICAAB4IiQ6YU5cF2FlFce8zIEE02JsJ4Cfjn3SYDsCAASO6yLs4BGGp4KktpHhqSA5UMWX\nLADtjylhztxPzK+r8zIHEsyx+hO2I8BHxxp4voMkP6+P7QgICCbmO2OJCgAAAAvcL9bawJyRIGmM\nNtuOAB9V1R21HQG+ohMGJALXRVh9c5OXOZBgMlLSbEcAACCpsUQFAADwRIg5YY5cF2FGrFkQJFkZ\nGbYjwEdZ6Z1tRwCQhKjBnLkuwlhwLVg6p6XbjgAfZaV3sh0BAAKH4UgAAOAJhiOduS7CWOsjWDJS\nqc+DJCs903YEAAgc15+0qeEUL3MgwTAHMFhSwiwZCKD9henfOHJdhKWlUIQFSUMz64QFSVM0ajsC\nAAQOX38BAAAsiGNOGPVakHAB72BpiLIYM4D2x8R8Z3EMR1KEBUkdRVigMBwJAP7jFDgAAOAJGmHO\nXBdhKQxHBkpTC52RIOFsWABeYHkrZ+6LME5hDxRj+FAOEq6IAQD+c3/ZIt6jA4WiO1hYggaAF5iY\n74xPWgAAAAtcd8K4jE2wcIWEYElP4fUNAH5z/c7bo3MnL3MgwdQ1sm5UkPTM6mw7AnxU28DrG/5g\nNNIZw5EAAAAWuO6E9e2d7WUOABYV9M+1HQE+2vLuAdsREBBMzHfmugg7q393L3MgwWSkMycsSAZ9\ntY/tCPARRRiQGJiNCwAAPEEjzJnrIixvEMMVQZLRJc12BPgoZ2hf2xHgq222AyAgWDHfmesiLHsA\nwxVBkt6Vs2GDJLN3L9sRACBwXBdhnfpQhAVJSqdM2xHgo5RMlqgAAL+xRAUAAIAFrjth4fQML3Mg\nwWTm9bYdAQDQwTElzBlnR6JtvHKCxRjbCQAkIdYJc0YRBgAAktqqVav01FNPKRKJKD8/X7Nnz9aI\nESPa3LekpERr165VKBSS+dwX1HPOOUfr16+XJK1du1YlJSWt9snIyNCOHTviykURBkDGtNiOACAJ\nJUIj7JVXXtFDDz2kBx54QMOHD9fKlSs1bdo0bdy4UTk5OSftP2vWLM2cOTP2c3Nzs6644gp94xvf\naLVf165dtWnTplgR9vd0/SjCADAcCSBprVixQpMmTVJxcbEkac6cOXr99de1Zs0a3XTTTSftn5WV\npaysrNjPv/nNb1RTU6OJEye22i8UCrVZxMWDIgyATAtFGID2Z3tOWFNTk3bu3Kmbb745ti0UCmn0\n6NHavn27q2O8/PLLuuiii9Tnb5bqqqur08UXX6yWlhYNGTJEd911l84+++y48rFEBQAASErV1dWK\nRqPq2bNnq+25ubmKRCKnvP2hQ4f05ptv6qqrrmq1vX///nrwwQe1dOlSPfLII2ppadHkyZP18ccf\nx5WPThgAAEAb1q5dq+zsbF1yySWtthcWFqqwsLDVz0VFRXrxxRc1Y8YM18enCAMAAJ6wPTG/R48e\nSklJOanrVVVVdVJ3rC2//OUvVVxcrNRU53IpNTVVBQUF2r9/f1z5GI4EAABJKS0tTUOHDlVpaWls\nmzFGpaWlGjlypONtt27dqrKyMl155ZWnvJ+Wlhbt2rVLvXrFdx1eOmEAFAonwHnkAJJO2HYrTNIN\nN9ygkpISDRs2LLZERX19fexsx0WLFunQoUNauHBhq9u9/PLLOvfcczVw4MCTjvn444+rsLBQffv2\nVU1NjZYvX67KykpXBdvnUYQBAABPJEANpqKiIlVXV2vJkiWKRCIqKCjQ8uXLY8tLRCIRVVZWtrrN\n8ePH9Zvf/EazZs1q85jHjh3Tfffdp0gkouzsbA0bNkwvvPBCmwWbk5Ax7hYIajxWFdeBAXQcJtps\nOwJ89MC/Pmo7Anw0b+N8a/e97ntL2v2YxY+5n/ie6OiEAUiMr6sAko7tdcISHUUYAIVCnKMDAH6j\nCANAJwwALKAIAwAAnuD7nTPGIAAAACygEwYAADzBxHxndMIAAAAscN0Jazxa7WUOJJj07j1sRwAA\ndHA0wpy5LsJqyw56mQOJxrTYTgAfpWZl244AIAkxHOmM4UgAAAALXHfCqt7/yMscSDCNn9TZjgAf\ndR1wuu0IABA4rouwit3MCQuSzh/X2o4AH/WuqbcdAQACx3URVlZxzMscACw6euSE7QgAkhBTwpyx\nThgAAPAEE/OduS7CDh6p8TIHEkxtY6PtCPBRWYRONwD4zf3E/DomagdJdR1zwoLkWAOv7yAZ2vtM\n2xEQEDTCnLFEBQAAgAXuF2ttaPAyBxIMnZFgOVjzse0I8BGdMPglTCvMkesirL65ycscACyqb2KJ\nCgDtjxrMmesizMh4mQMJplNahu0I8FFmWqbtCAAQOMwJAwAAsMB1JywkeopBkp1BZyRIumdyAW8A\n8JvrIozJdcGSmZZmOwJ81DW9i+0IAJIQi7U6Y8V8AADgCWowZ66LsNRwipc5kGBSQkwXDJKMVDqf\nAOA310VYWgpFWJCkhPn6EiSZFGEAPBDis8QR7Q4AAAAL4piYT70WJAw/B0tmarrtCACSEHPCnMUx\nHEkRFiSd0jhnI0iaWyjCAMBvrj9pmagdLF0y+FAOEk4jD5b6pmbbEQCIJSoAAIBH+ILnzH0nLEwn\nLEgy0pgTFiS8UQYLnTAgMbi/bBHv0YHCZaqCJTWF5xtA+6N2cMZwJAAA8ARddmeui7CMVOq1IGlu\nabEdAT7ifRIA/Oe6surRuZOXOQBY1K1zhu0I8NHHn9TajoCA4AueM2bbAwAAWOC6E9a3d7aXOZBg\njtc12o4AH/Xv1812BPhoV+UR2xEAKI4i7Kz+3b3MgQRztOqE7Qjw0VeG97YdAX76n722EyAoGI90\n5LoIyxuU62UOJJjOh47bjgAfdcs/03YEAAgcTnkEAACeYIkKZ66LsOwBfbzMgQSTnn3UdgT4KDMv\nz3YEAAgc10VYpz4UYUES5gLegZKS2dl2BABJiEaYM4YjAQCAJ0JhqjAnrouwcDqLOQZJRg4nYgAA\n4CU6YWgTRTcAAN6iCAMg0xK1HQEAAociDAAAeIKJ+c4owgBIxthOACAJsU6YM4owADItFGEA4DeK\nMAAA4AkaYc4owgCwlg8AWEARBgAAPMGcMGdh2wEAAACCiE4YACZuAIAFFGEAAMATfL9zRhEGQKEQ\nMxMAwG8UYQAAwBNMzHdGEQaAMQMA3qDJ7oiHBwAAwAI6YQAAwBMMRzqjEwYAAGABnTC0qaWpyXYE\n+CiclmY7AgAEDkUYAADwBKORzlwXYY1Hq73MgQQTTk2xHQE+CqfyfQwA/Ob6nbe27KCXOZBg0rK7\n2I4AH3VKS7cdAUASYmK+M9dFWNX7H3mZAwkmq1eW7QjwEZ0wAPAfZ0cCAABPhELt/+/vsWrVKl18\n8cUaMWKErrrqKr3zzjtfuO8f//hH5efnt/pXUFCgqqqqVvu9+uqruuyyyzRixAhdccUVeuONN+LO\n5frrb8Vu5oQFSe7xRtsR4CNjjO0IAJJRAgxHvvLKK3rooYf0wAMPaPjw4Vq5cqWmTZumjRs3Kicn\np83bhEIhbdq0SV26/HVqTm5ubuy/3377bc2cOVMzZ87UhAkTtH79et12221at26dzj77bNfZXBdh\nZRXHXB8UHV9Tc4vtCPBRw4lm2xEAwBMrVqzQpEmTVFxcLEmaM2eOXn/9da1Zs0Y33XTTF94uJydH\nWVltT8159tlnNW7cON14442SpDvuuEN/+MMf9Nxzz+n+++93nY3hSAAAkJSampq0c+dOXXTRRbFt\noVBIo0eP1vbt27/wdsYYffvb39bYsWM1depUvf32261+v337do0ePbrVtrFjxzoesy2uO2EHj9TE\ndWB0bOGw/RYy/HPkaL3tCADQ7qqrqxWNRtWzZ89W23Nzc7V37942b5OXl6e5c+dq2LBhamxs1OrV\nqzVlyhS99NJLKigokCQdPny4zWNGIpG48rk/O7KuLq4Do2PrfIwV1IOkrpErJABof6EO+IW+f//+\n6t+/f+znwsJClZeXa8WKFVq4cGG73pf7dcIaGtr1jpHYjp6gMxIkHx1jzmeQ9O6abTsCAsL2vPwe\nPXooJSXlpA5VVVXVSZ0sJ8OHD281JJmXl/eljykxJwwAACSptLQ0DR06VKWlpbFtxhiVlpZq5MiR\nro/z/vvvq1evXrGfCwsLWx1TkrZs2aLCwsK48rnuhNU3M1wRJDV0PgPlcN0ntiPAR3TC4JdEWDH/\nhhtuUElJiYYNGxZboqK+vl4TJ06UJC1atEiHDh2KDTWuXLlSZ555ps455xw1NDRo9erV2rp1q55+\n+unYMadMmaLrrrtOzzzzjCZMmKANGzZo586dmjdvXlzZXBdhRqwjFCT1TRTdQXK88YTtCADgiaKi\nIlVXV2vJkiWKRCIqKCjQ8uXLY2uERSIRVVZWxvZvamrSwoULdejQIWVmZmrw4MFasWKFzj///Ng+\nI0eO1KJFi7R48WItXrxY/fr109KlS+NaI0ySQsblKo3XXvjduA6Mji0rPdN2BPho95EK2xHgo1Ff\nGWQ7Anw0b+N8a/f97hO/aPdjDrvl6nY/pi2uO2Eh2W8pwj/RFhZrDRJWzAcA/7kuwsIJMK4L/7Tw\noRwoiTBvAwCCxnURBgAAEBe+4DlyXYSlhlO8zIEEw4kYwZISYrUaAPCb6yIsLYUiLEiaolHbEeCj\n9BSukACg/XXEFfP9FMecML4pB0k4xMT8IKHTDcALjEY6o7ICAACwII7hSOq1IGloZk5YkKTQCQPg\nBVphjlwXYUzcBZJXGkUYAPiOygoAAMAC952wMPVakLA4b7AwMR+AF/goceb+skU8kIGSlsI6vgAA\neIlPWgAA4AnWCXPmugjLSKVeC5JGFmsNlExe3wDgO9fvvD06d/IyBwCLeH0Hy5HaE7YjICBCzGVy\n5LoI69s728scSDBpVZyIEST5Z+XYjgAfvbGj3HYEBAU1mCM+aQEAACxw3Qk7q393L3MgwXTuxByh\nIBn41dNtR4CP6IQBicH1J23eoFwvcyDBZPVkjlCQ5AztZzsCfLXVdgAAYokKAADgESbmO3NdhGUP\n6ONlDiSYrv1YoiJIMnufZjsCgCREEebMdRHWqQ9FWJCE0zNsRwAAIKm5LsL4UAaSl2mh8wnAA6zB\n4IiHBwAAwAIm5gOQaW62HQFAEmJOmDOKMAAMRwKABQxHAgAAWEAnDIAU4vsYgPbHcKQzijAACoVT\nbEcAgMChCAMAAN6gEeaIIgwAAHgiFKYKc0IRBoA3SgCwgCIMAHPCAHiDifmOOCUKAADAAjphAPi2\nCgAWUIQBAABP8P3OGcORAAAAFtAJQ5tMlAs6B0kohbcCAO2PFfOd0QkDAACwgK+/AADAG6xB6Igi\nDG1ieAoA8GUxHOmM4UgAAAALXLc7Go9We5kDCSa9W3fbEeAnvq0CgO/ohAEAAFjguhNWW3bQyxxI\nMC29621HgI/Sc3rajgAgGdFkd+S6CKt6/yMvcyDBZNecsB0BPurav8V2BABJiIn5zhiOBAAAsMB1\nJ6xiNxPzg6SxttF2BPgo2sgVEgC0vxDrhDlyXYSVVRzzMgcSTH0DH8pBUne8yXYEAAgc10XYwSM1\nXuZAgmlqZo5QkByqqrMdAUAyYk6YI5ZFBwAAnmBivjP3Z0fW8U05SKItxnYE+Ki2kTmAQZKRyvdv\nIBG4XyesocHLHEgwYRZ3CZTKmqO2I8BHA3N72Y4AQCxRAQAAYIXrTlh9M2dPBUlmWprtCPDR8UYW\n5wXgAQZVHLkuwoyYIwQkK17fALzAOmHOGI4EAACwwHUnLERPMVBSwtTnQRIO8XwD8ABLVDhyXYSF\neSADhbMjgyU9hTmAAOA310VYajjFyxxIMCywFyzpKawbBaD98VnijDEIAAAAC1x//U1LoRMGJKs0\nOt0A4Ls45oTRNAuSqOEC3gCAL4klKhxRWQEAAFgQx3Ak9VqQ1DdxhYQgaYw2244AIAkxMd+Z6yIs\nheHIQKlt5ILtQUIRBsAT1GCOqKwAAAAscN8JYwX1QGmIMhwZJI083wA8wHCkM/eXLeJxDJQmhqcC\nhQt4A0hPuk4lAAAWfElEQVRmq1at0lNPPaVIJKL8/HzNnj1bI0aMaHPfzZs36/nnn9d7772nxsZG\nnXPOObr99ts1duzY2D5r165VSUmJQqGQjPn0/TMjI0M7duyIK5frIiwjlRW1g4QlSYIlIyXddgQA\n8MQrr7yihx56SA888ICGDx+ulStXatq0adq4caNycnJO2n/btm0aM2aM7rrrLmVnZ2vNmjWaPn26\nXn75ZeXn58f269q1qzZt2hQrwv6erh+VFQAA8EYCrBO2YsUKTZo0ScXFxZKkOXPm6PXXX9eaNWt0\n0003nbT/D3/4w1Y/33nnnXrttdf029/+tlURFgqF2izi4uG6COvRudOXuiN0LN0yO9uOAB/17JJl\nOwIAtLumpibt3LlTN998c2xbKBTS6NGjtX37dlfHMMaotrZW3bp1a7W9rq5OF198sVpaWjRkyBDd\nddddOvvss+PK57oI69s7O64Do2OrbWi0HQE+GjGgl+0I8NG2XZW2IyAgbE/Mr66uVjQaVc+ePVtt\nz83N1d69e10dY/ny5aqrq9Nll10W29a/f389+OCDGjx4sI4fP67ly5dr8uTJ2rBhg3r37u06H8OR\nAAAAbVi/fr2WLl2qJ554otXQY2FhoQoLC1v9XFRUpBdffFEzZsxwfXzXRdhZ/bu7Pig6vpQEGMeH\nf4aOOsN2BPiIThh8Y7kT1qNHD6WkpCgSibTaXlVVdVJ37G9t2LBBP/rRj/Too49q1KhRjvumpqaq\noKBA+/fvjyuf6yIsb1BuXAdGx5bWiSZpkOQM62s7Anz1lu0ACAjbw5FpaWkaOnSoSktLdckll0j6\ndI5XaWmprrvuui+83a9//WvNnj1bixcv1vjx4095Py0tLdq1a5e+9rWvxZXP9Sdt9oA+cR0YHVta\nVqbtCPBRp9Pcz2EAgI7khhtuUElJiYYNGxZboqK+vl4TJ06UJC1atEiHDh3SwoULJX06BFlSUqJZ\ns2Zp+PDhsS5aZmamsrI+PYnp8ccfV2Fhofr27auamhotX75clZWVuvLKK+PKRrsDAAAkraKiIlVX\nV2vJkiWKRCIqKCjQ8uXLY3O8IpGIKiv/OkS/evVqRaNRzZ07V3Pnzo1tLy4u1oIFCyRJx44d0333\n3adIJKLs7GwNGzZML7zwggYOHBhXtpD5bJWxU6iPVMR1YHRsjdVHbEeAjzJ70+kOkrlXLbIdAT6a\nt3G+tfs+tOV37X7MXmNOPTzYUbjuhIXTM7zMgQSTnuM8YREAgFPiJC9HDEcCAABP2J6Yn+gowtCm\ncFqa7QgAACQ1ijAAMi1R2xEAJCM6YY7CtgMAAAAEEZ0wAJK7k6QBIC4hJuY7ohMGAABgAZ0wAMzb\nAAALKMIAAIA3+ILniOFIAAAAC+iEAQAAT7BYqzOKMAAA4A2KMEcUYQAUCjEzAQD8RhEGAAA8wTph\nzvj6CwAAYAFFGAAAgAUMRwJg8iwAb/De4ohOGAAAgAV0wgAAgDfohDmiCAMAAJ5gsVZnrouwxqPV\nXuZAgknv1t12BPiJN0oA8B2dMAAA4A3WCXPkugirLTvoZQ4kmGjPOtsR4KOMnj1tRwCAwHFdhFW9\n/5GXOZBgsnodtx0BPspuabEdAQACx3URVrGbOWFB0jVCJyxImhuabUcAkIS4Lq0zHh0AAAALXHfC\nyiqOeZkDCSb9cIrtCPDRsep62xEAJCPOvHbkugg7eKTGyxxIMNEWYzsCfHSwitc3APiNJSoAAIAn\nWKzVmfuzI+uYqB0kDc1M1A6ST+p5fQfJgJw82xEQFKwT5sj9OmENDV7mQIKpbeL5DpIDxw7ZjgAf\nUYQBiYGzIwEAACxw3Qmrb27yMgcSTGOU4cggOd7A4rwA4DfXRZgRZ8sFSVqYJSqChMmzALzAe4sz\n10VYSDyQQZKewomzQdIpLdN2BADJiCLMEXPCAAAALHDd7ghTzQZKKsORgZKRkmE7AoBkxLUjHbku\nwvhQDhaK7mDJSE23HQEAAoeJPwAAwBMhFmt15LoIS0uhExYkYVrIgdIpleFIAPBbHHPC+FAOkk5p\nabYjwEfdM7vYjgAAgcNwJAAA8Abzix3FMRxJJyxIumYwPBUknIgBAP5zXYSlMBwZKJ3TGY4MkhQm\nzwZKQ3PUdgQEBCvmO3NfhIUpwoIklc5noBhx4k2QUITBNzRwHPHoAAAAWOD+2pF0FAPFGC7YDgD4\nclgnzJnrIiwjlRMpg6S5pcV2BPgoJN4oAcBvDEcCAABY4Lq91aNzJy9zALCoexeWJAmSY/UNtiMg\nKJjL5Mh1Eda3d7aXOZBgTtQ3244AH53Vr5vtCPDRnkNHbUcAoDiKsLP6d/cyBxLMidpG2xHgo76F\np9mOAD+V7rGdAAHBOmHOmBMGAABggetOWN6gXC9zIME0n2iyHQE+6l7Qz3YE+OoPtgMgKFis1ZHr\nIix7QB8vcyDBmCgragdJRm6e7QgAkhHrhDmiRAUAALDAdSesUx86YUHCivnBEk7jgu0A4DfXRVg4\nnXWEAAAA2gvXIgIAAJ5giQpnFGEAJIafAXiBsyMdUYQBkDFcsB0A/EYRBgAAPMFwpDP6hAAAABbQ\nCQMAAN5gTpgjijAACoVTbEcAgMChRAUAALCAThjaxpIFwcLkWQAeCHHtSEcUYWhTtOGE7QjwUUpm\nZ9sRAMAzq1at0lNPPaVIJKL8/HzNnj1bI0aM+ML9t27dqoULF+qDDz7Q6aefrunTp+uf//mfW+3z\n6quvasmSJTp48KDOOuss3X333ZowYUJcuRiOBAAA3giF2v9fnF555RU99NBDmjFjhtauXav8/HxN\nmzZNR44caXP/AwcOaPr06Ro1apR+9atfacqUKZo9e7a2bNkS2+ftt9/WzJkzddVVV2ndunW65JJL\ndNttt2n37t1xZaMIQ5uMMfwL0D8A8EIoFG73f/FasWKFJk2apOLiYg0cOFBz5sxRZmam1qxZ0+b+\nzz//vM4880zdc889GjBggK655hpdeumlWrFiRWyfZ599VuPGjdONN96oAQMG6I477tDQoUP13HPP\nxZWN4Ui0KZyabjsCAABfSlNTk3bu3Kmbb745ti0UCmn06NHavn17m7fZsWOHRo8e3Wrb2LFjtWDB\ngtjP27dv14033njSPq+99lpc+SjC0KZwWprtCACAjs7yST/V1dWKRqPq2bNnq+25ubnau3dvm7c5\nfPiwcnNzT9r/+PHjamxsVHp6ug4fPtzmMSORSFz5XBdh6dm5p94JAJDw5m2cbzsCAoLawRlzwgAA\nQFLq0aOHUlJSTupQVVVVndTJ+kxeXp6qqqpO2j8rK0vp6emxfeI55hehCAMAAEkpLS1NQ4cOVWlp\naWybMUalpaUaOXJkm7cpLCxstb8kbdmyRYWFhXHt4wZFGAAASFo33HCDXnrpJa1bt04ffvih/uM/\n/kP19fWaOHGiJGnRokX6wQ9+ENt/8uTJKi8v18MPP6w9e/Zo1apV2rRpU6uJ+FOmTNGbb76pZ555\nRnv27NFjjz2mnTt36tprr40rW8hwfjoAAEhin1+staCgQLNnz9bw4cMlSSUlJTp48KB+/vOfx/bf\ntm2bFixYoN27d+u0007Trbf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"text/plain": [ "<matplotlib.figure.Figure at 0x7f26008e6ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seed=0\n", "X=numpy.vstack((regressor[:,0],data[:,seed],regressor[:,0]*data[:,seed],numpy.ones(data.shape[0]))).T\n", "hat_mtx=numpy.linalg.inv(X.T.dot(X)).dot(X.T)\n", "\n", "for i in range(data.shape[1]):\n", " beta_hat=hat_mtx.dot(data[:,i])\n", " resid=data[:,i] - X.dot(beta_hat)\n", " sigma2hat=(resid.dot(resid))/(X.shape[0] - X.shape[1])\n", " c=numpy.array([0,0,1,0]) # contrast for PPI\n", " t=c.dot(beta_hat)/numpy.sqrt(c.dot(numpy.linalg.inv(X.T.dot(X)).dot(c))*sigma2hat)\n", " print ('ROI %d:'%i, t, 1.0 - scipy.stats.t.cdf(t,X.shape[0] - X.shape[1]))\n", " \n", "import seaborn as sns\n", "sns.heatmap(X, vmin=0, xticklabels=[\"task\", \"seed\", \"task*seed\", \"mean\"], \n", " yticklabels=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the relation between the ROIs as a function of the task" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f2602955d68>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QGY0GeXs/q5deelCBgXebBdzExJu0f79lCG/YsK2qVzff0jpvd7+89w0IyGBT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\n1rFjx5SZmamHH35Y7du3V1JSUm09FQAAANQRV0UPsy+WLl2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2602a515c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "on_tp=numpy.where(regressor>0.9)[0]\n", "off_tp=numpy.where(regressor<0.01)[0]\n", "roinum=4\n", "\n", "plt.scatter(data[on_tp,0],data[on_tp,roinum], label=\"task ON\")\n", "fit = numpy.polyfit(data[on_tp,0],data[on_tp,roinum],1)\n", "plt.plot(data[on_tp,0],data[on_tp,0]*fit[0] +fit[1])\n", "plt.scatter(data[off_tp,0],data[off_tp,roinum],color='red', label=\"task OFF\")\n", "fit = numpy.polyfit(data[off_tp,0],data[off_tp,roinum],1)\n", "plt.plot(data[off_tp,0],data[off_tp,0]*fit[0] +fit[1],color='red')\n", "plt.xlabel(\"activation in ROI 0\")\n", "plt.ylabel(\"activation in ROI %d\"%roinum)\n", "plt.legend(loc=\"best\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

intel-analytics/BigDL

python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb

2

12193

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "<a href=\"https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "id": "fDBPZ0_rfBmU" }, "source": [ "##### Copyright 2016 The BigDL Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xBWVU_bhfkY7" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "#" ] }, { "cell_type": "markdown", "metadata": { "id": "voMBntim9bMf" }, "source": [ "## **Environment Preparation**" ] }, { "cell_type": "markdown", "metadata": { "id": "I_OS4HKJMNpv" }, "source": [ "**Install bigdl-chronos**\n", "\n", "You can install the latest pre-release version using `pip install --pre --upgrade bigdl-chronos[all]`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3qfT8CaC51hI" }, "outputs": [], "source": [ "# Install latest pre-release version of bigdl-chronos\n", "# Installing bigdl-chronos from pip will automatically install pyspark, bigdl, and their dependencies.\n", "!pip install --pre --upgrade bigdl-chronos\n", "!pip uninstall -y torchtext # uninstall torchtext to avoid version conflict\n", "exit() # refresh the pkg you just install" ] }, { "cell_type": "markdown", "metadata": { "id": "l-l4vel5N3qP" }, "source": [ "## **Unsupervised Anomaly Detection using Chronos Anomaly Detector**\n", "\n", "Anomaly detection detects data points in data that does not fit well with the rest of data. In this quickstart we demonstrate how to do anomaly detection for 1-D data using Chronos's Anomaly Detector." ] }, { "cell_type": "markdown", "metadata": { "id": "TsT-0y8w-6N5" }, "source": [ "## **Step 0: Prepare dataset**\n", "For demonstration, we use the publicly available real time traffic data from the Twin Cities Metro area in Minnesota, collected by the Minnesota Department of Transportation. Detailed information can be found [here](https://github.com/numenta/NAB/blob/master/data/realTraffic/speed_7578.csv)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "devxHuDW-0Mb" }, "outputs": [], "source": [ "# download the dataset\n", "!wget https://raw.githubusercontent.com/numenta/NAB/master/data/realTraffic/speed_7578.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Data Preprocessing\n", "Now we need to do data cleaning and preprocessing on the raw data. Note that this part could vary for different dataset. \n", "\n", "For the machine_usage data, the pre-processing contains 2 parts: <br>\n", "1. Change the time interval from irregular to 5 minutes.<br>\n", "2. Check missing values and handle missing data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LVpFKkCX_3WF" }, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.read_csv(\"speed_7578.csv\", parse_dates=[\"timestamp\"])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from bigdl.chronos.data import TSDataset\n", "\n", "tsdata = TSDataset.from_pandas(df, dt_col=\"timestamp\", target_col=\"value\")\n", "df = tsdata.resample(\"5min\")\\\n", " .impute(mode=\"linear\")\\\n", " .to_pandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Use Chronos Anomaly Detector\n", "DBScanDetector uses DBSCAN clustering for anomaly detection. The DBSCAN algorithm tries to cluster the points and label the points that do not belong to any clusters as -1. It thus detects outliers detection in the input time series. DBScanDetector assigns anomaly score 1 to anomaly samples, and 0 to normal samples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from bigdl.chronos.detector.anomaly import DBScanDetector\n", "\n", "ad = DBScanDetector(eps=0.3, min_samples=6)\n", "ad.fit(df['value'].to_numpy())\n", "anomaly_indexes = ad.anomaly_indexes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Plot the result\n", "Draw anomalies(red) in line chart." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(16,6))\n", "plt.plot(df.timestamp, df.value, label='value')\n", "plt.scatter(df.timestamp[anomaly_indexes],\n", " df.value[anomaly_indexes],\n", " color='red', label='anomalies value')\n", "\n", "plt.title('the anomalies value')\n", "plt.xlabel('datetime')\n", "plt.legend(loc='upper left')\n", "plt.show()" ] } ], "metadata": { "colab": { "collapsed_sections": [], "include_colab_link": true, "name": "chronos_autots_nyc_taxi.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

TEAMDSS/KBO_Player_Salary

defense_crawling.ipynb

1

9330

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import package\n", "import pandas as pd\n", "import time\n", "import pickle\n", "from selenium import webdriver\n", "import glob\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "api_delay_term = 3" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# crawling_defense\n", "def crawling_defense(season_id, team_id):\n", " \"\"\"\n", " season_id = 0 ~ 14\n", " team_id = 1 ~ 10\n", " ------------------------------------------------------------------------------------\n", " <season_id>\n", " 0 : 2002 ~ 14 : 2016\n", " \n", " <team_id> ==> It can be different from several season.\n", " 1 : Nexen heroes\n", " 2 : Doosan\n", " 3 : Lotte\n", " 4 : Samsung\n", " 5 : Hanhwa\n", " 6 : KIA\n", " 7 : KT\n", " 8 : LG twins\n", " 9 : NC dinos\n", " 10 : SK wyberns\n", " \"\"\"\n", " driver = webdriver.Firefox()\n", " url = \"http://www.koreabaseball.com/Record/Player/Defense/Basic.aspx\"\n", " driver.get(url)\n", " \n", " # click season\n", " driver.find_element_by_css_selector('#cphContainer_cphContents_ddlSeason_ddlSeason').\\\n", " find_elements_by_css_selector('option')[season_id].click()\n", " time.sleep(api_delay_term)\n", " \n", " # click team\n", " driver.find_element_by_css_selector('#cphContainer_cphContents_ddlTeam_ddlTeam').\\\n", " find_elements_by_css_selector('option')[team_id].click()\n", " time.sleep(api_delay_term)\n", " \n", " # get page number\n", " page_elements = driver.find_elements_by_css_selector(\".paging02 a\")\n", " page_number = len(page_elements)\n", " if page_number == 1:\n", " page_number = page_number\n", " \n", " if page_number > 1:\n", " page_number = page_number -2\n", " \n", " # make empty dataframe\n", " defense_df = pd.DataFrame(columns=[\n", " 'rank', 'name', 'team', 'POS', 'G', 'GS', 'IP', 'E', 'PKO', 'PO',\n", " 'A', 'DP', 'FPCT', 'PB', 'SB', 'CS', 'CS%'\n", " ])\n", " \n", " # if having one page\n", " if page_number == 1:\n", " elements = driver.find_elements_by_css_selector(\".record_result tr\")\n", " elements = elements[1:len(elements)+1]\n", " \n", " for element in elements:\n", " tmp_dict = {\n", " 'rank' : element.find_elements_by_css_selector('td')[0].text,\n", " 'name' : element.find_elements_by_css_selector('td')[1].text,\n", " 'team' : element.find_elements_by_css_selector('td')[2].text,\n", " 'POS' : element.find_elements_by_css_selector('td')[3].text,\n", " 'G' : element.find_elements_by_css_selector('td')[4].text,\n", " 'GS' : element.find_elements_by_css_selector('td')[5].text,\n", " 'IP' : element.find_elements_by_css_selector('td')[6].text,\n", " 'E' : element.find_elements_by_css_selector('td')[7].text,\n", " 'PKO' : element.find_elements_by_css_selector('td')[8].text,\n", " 'PO' : element.find_elements_by_css_selector('td')[9].text,\n", " 'A' : element.find_elements_by_css_selector('td')[10].text,\n", " 'DP' : element.find_elements_by_css_selector('td')[11].text,\n", " 'FPCT' : element.find_elements_by_css_selector('td')[12].text,\n", " 'PB' : element.find_elements_by_css_selector('td')[13].text,\n", " 'SB' : element.find_elements_by_css_selector('td')[14].text,\n", " 'CS' : element.find_elements_by_css_selector('td')[15].text,\n", " 'CS%' : element.find_elements_by_css_selector('td')[16].text,\n", " }\n", " defense_df.loc[len(defense_df)] = tmp_dict\n", " \n", " # if having other more pages\n", " if page_number > 1:\n", " for page in range(1, page_number+1):\n", " driver.find_element_by_css_selector('#cphContainer_cphContents_ucPager_btnNo' + str(page)).click()\n", " time.sleep(api_delay_term)\n", " \n", " elements = driver.find_elements_by_css_selector(\".record_result tr\")\n", " elements = elements[1:len(elements)+1]\n", " \n", " for element in elements:\n", " tmp_dict = {\n", " 'rank' : element.find_elements_by_css_selector('td')[0].text,\n", " 'name' : element.find_elements_by_css_selector('td')[1].text,\n", " 'team' : element.find_elements_by_css_selector('td')[2].text,\n", " 'POS' : element.find_elements_by_css_selector('td')[3].text,\n", " 'G' : element.find_elements_by_css_selector('td')[4].text,\n", " 'GS' : element.find_elements_by_css_selector('td')[5].text,\n", " 'IP' : element.find_elements_by_css_selector('td')[6].text,\n", " 'E' : element.find_elements_by_css_selector('td')[7].text,\n", " 'PKO' : element.find_elements_by_css_selector('td')[8].text,\n", " 'PO' : element.find_elements_by_css_selector('td')[9].text,\n", " 'A' : element.find_elements_by_css_selector('td')[10].text,\n", " 'DP' : element.find_elements_by_css_selector('td')[11].text,\n", " 'FPCT' : element.find_elements_by_css_selector('td')[12].text,\n", " 'PB' : element.find_elements_by_css_selector('td')[13].text,\n", " 'SB' : element.find_elements_by_css_selector('td')[14].text,\n", " 'CS' : element.find_elements_by_css_selector('td')[15].text,\n", " 'CS%' : element.find_elements_by_css_selector('td')[16].text,\n", " }\n", " defense_df.loc[len(defense_df)] = tmp_dict\n", "\n", " return defense_df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nexen_defense_df = crawling_defense(13, 1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nexen_defense_df.to_csv(\"nexen_defense.csv\",encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "440.5892791748047" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start_time = time.time()\n", "doosan_defense_df = crawling_defense(13,2)\n", "lotte_defense_df = crawling_defense(13,3)\n", "samsung_defense_df = crawling_defense(13,4)\n", "hanhwa_defense_df = crawling_defense(13,5)\n", "KIA_defense_df = crawling_defense(13,6)\n", "KT_defense_df = crawling_defense(13,7)\n", "LG_defense_df = crawling_defense(13,8)\n", "NC_defense_df = crawling_defense(13,9)\n", "SK_defense_df = crawling_defense(13,10)\n", "end_time = time.time()\n", "\n", "end_time-start_time" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "doosan_defense_df.to_csv(\"doosan_defense.csv\",encoding='utf-8')\n", "lotte_defense_df.to_csv(\"lotte_defense.csv\",encoding='utf-8')\n", "samsung_defense_df.to_csv(\"samsung_defense.csv\",encoding='utf-8')\n", "hanhwa_defense_df.to_csv(\"hanhwa_defense.csv\",encoding='utf-8')\n", "KIA_defense_df.to_csv(\"KIA_defense.csv\",encoding='utf-8')\n", "KT_defense_df.to_csv(\"KT_defense.csv\",encoding='utf-8')\n", "LG_defense_df.to_csv(\"LG_defense.csv\",encoding='utf-8')\n", "NC_defense_df.to_csv(\"NC_defense.csv\",encoding='utf-8')\n", "SK_defense_df.to_csv(\"SK_defense.csv\",encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

sanket-patil/speech-vectors

notebooks/urban_sounds.ipynb

1

966

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "parent_dir = '../data/UrbanSound8K/audio'\n", "tr_sub_dirs = ['fold1','fold2']\n", "ts_sub_dirs = ['fold3']\n", "tr_features, tr_labels = parse_audio_files(parent_dir, tr_sub_dirs)\n", "ts_features, ts_labels = parse_audio_files(parent_dir, ts_sub_dirs)\n", "\n", "tr_labels = one_hot_encode(tr_labels)\n", "ts_labels = one_hot_encode(ts_labels)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

JackKelly/neuralnilm_prototype

notebooks/experiment_027.ipynb

2

91149

{ "metadata": { "name": "", "signature": "sha256:6bd262373db9812afeb1d1d373e0e042f6b0b6dcadca3257be6a88eaa84b4377" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function, division\n", "import matplotlib\n", "matplotlib.use('nbagg') # interactive plots in iPython. New in matplotlib v1.4\n", "# %matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "from nilmtk import DataSet, MeterGroup\n", "import pandas as pd\n", "import numpy as np\n", "from time import time" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/dist-packages/bottleneck/__init__.py:13: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility\n", " from .func import (nansum, nanmax, nanmin, nanmean, nanstd, nanvar, median,\n", "/usr/local/lib/python2.7/dist-packages/bottleneck/__init__.py:19: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility\n", " from .move import (move_sum, move_nansum,\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from pybrain.supervised import RPropMinusTrainer\n", "from pybrain.datasets import SequentialDataSet\n", "from pybrain.structure import RecurrentNetwork, FullConnection\n", "from pybrain.structure.modules import LSTMLayer, BiasUnit, LinearLayer, TanhLayer, SigmoidLayer" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "CONFIG = dict(\n", " EPOCHS_PER_CYCLE = 5,\n", " CYCLES = 6,\n", " HIDDEN_LAYERS = [50, 50],\n", " PEEPHOLES = True,\n", " TRAINERCLASS = RPropMinusTrainer,\n", " # instead, you may also try\n", " # TRAINERCLASS = BackpropTrainer(net, dataset=trndata, verbose=True, \n", " # momentum=0.9, learningrate=0.00001)\n", " INPUTS = [], #, 'hour of day (int)', 'outside temperature', 'is business day (-1, 1)'\n", " EXPERIMENT_NUMBER = 27\n", ")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Load dataset\n", "dataset = DataSet('/data/mine/vadeec/merged/ukdale.h5')\n", "dataset.set_window(\"2014-01-01\", \"2014-01-07\")\n", "elec = dataset.buildings[1].elec" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Select top-5 meters identified in UK-DALE paper\n", "# APPLIANCES = ['kettle', 'dish washer', 'HTPC', 'washer dryer', 'fridge freezer']\n", "APPLIANCES = ['kettle', 'toaster']\n", "selected_meters = [elec[appliance] for appliance in APPLIANCES]\n", "selected_meters.append(elec.mains())\n", "selected = MeterGroup(selected_meters)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "df = selected.dataframe_of_meters()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Use human-readable column names\n", "df.columns = selected.get_labels(df.columns)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "mains = (df['Toaster'] + df['Kettle']).fillna(0).diff().dropna()\n", "appliances = df['Toaster'].fillna(0).diff().dropna()\n", "del df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Constrain outputs to [-1,1] because we're using TanH\n", "maximum = appliances.abs().max()\n", "appliances /= maximum\n", "mains_same_scale_as_appliances = mains / maximum\n", "\n", "# standardise input\n", "mains = (mains - mains.mean()) / mains.std()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "ax = mains.plot()\n", "ax = appliances.plot(ax=ax)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.focus_on_mousover = false;\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this.root.attr('style', 'display: inline-block');\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " fig.waiting = false;\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", " canvas_div.resizable({ resize: mpl.debounce_resize(\n", " function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n", " , 50)});\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both;');\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", " function mouse_event_fn(event) {\n", " return fig.mouse_event(event, event['data']);\n", " }\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keydown('key_release', canvas_keyboard_event);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (this.focus_on_mouseover && name === 'motion_notify')\n", " {\n", " this.canvas.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", " /* Don't fire events just when a modifier is changed. Modifiers are\n", " sent along with other keys. */\n", " if (event.keyCode >= 16 && event.keyCode <= 20) {\n", " return;\n", " }\n", "\n", " value = '';\n", " if (event.ctrlKey) {\n", " value += \"ctrl+\";\n", " }\n", " if (event.altKey) {\n", " value += \"alt+\";\n", " }\n", " value += String.fromCharCode(event.keyCode).toLowerCase();\n", "\n", " this.send_message(name, {key: value});\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " var format_dropdown = this.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " this.ondownload(this, format);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "mpl.debounce_event = function(func, time){\n", " var timer;\n", " return function(event){\n", " clearTimeout(timer);\n", " timer = setTimeout(function(){ func(event); }, time);\n", " };\n", "}\n", "\n", "mpl.debounce_resize = function(func, time){\n", " var timer;\n", " return function(event, ui){\n", " clearTimeout(timer);\n", " timer = setTimeout(function(){ func(event, ui); }, time);\n", " };\n", "}\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " function() { },\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", " // Disable right mouse context menu.\n", " $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.send_message('closing', {});\n", " fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Close figure', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type == 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (cell.output_area.outputs[j]['text/html'] == html_output) {\n", " var output = cell.output_area.outputs[j];\n", " return [cell, output, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x7f943f539190>" ] }, { "html": [ "<img 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\">" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x7f94398c6f90>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Build PyBrain dataset\n", "N_OUTPUTS = 1\n", "N_INPUTS = 1\n", "N = len(mains)\n", "ds = SequentialDataSet(N_INPUTS, N_OUTPUTS)\n", "ds.newSequence()\n", "ds.setField('input', pd.DataFrame(mains).values)\n", "ds.setField('target', pd.DataFrame(appliances).values)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "ds.getSequence(0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/dist-packages/PyBrain-0.3.3-py2.7.egg/pybrain/datasets/sequential.py:45: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " return self.getField(field)[seq[index]:]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "[array([[ 1.63132217e-07],\n", " [ 1.63132217e-07],\n", " [ 1.63132217e-07],\n", " ..., \n", " [ 1.63132217e-07],\n", " [ 1.63132217e-07],\n", " [ 1.63132217e-07]]), array([[ 0.],\n", " [ 0.],\n", " [ 0.],\n", " ..., \n", " [ 0.],\n", " [ 0.],\n", " [ 0.]])]" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# Build network\n", "net = RecurrentNetwork()\n", "\n", "def lstm_layer_name(i):\n", " return 'LSTM{:d}'.format(i)\n", "\n", "# Add modules\n", "net.addInputModule(LinearLayer(dim=ds.indim, name='in'))\n", "net.addOutputModule(TanhLayer(dim=ds.outdim, name='out'))\n", "net.addModule(TanhLayer(10, name='tanh_input')) \n", "net.addModule(TanhLayer(10, name='tanh_output')) \n", "for i, n_cells in enumerate(CONFIG['HIDDEN_LAYERS']):\n", " net.addModule(LSTMLayer(n_cells, name=lstm_layer_name(i+1), peepholes=CONFIG['PEEPHOLES'])) \n", "\n", "# Bias\n", "bias = BiasUnit()\n", "net.addModule(bias)\n", "\n", "#c_output_bias = FullConnection(bias, net['out'], name='c_output_bias')\n", "#c_output_bias._setParameters(np.zeros(1))\n", "#net.addConnection(c_output_bias)\n", "\n", "c_tanh_input_bias = FullConnection(bias, net['tanh_input'], name='c_tanh_input_bias')\n", "c_tanh_input_bias._params = np.random.uniform(-0.1, 0.1, size=c_tanh_input_bias.paramdim)\n", "net.addConnection(c_tanh_input_bias)\n", "\n", "c_tanh_output_bias = FullConnection(bias, net['tanh_output'], name='c_tanh_output_bias')\n", "c_tanh_output_bias._params = np.random.uniform(-0.1, 0.1, size=c_tanh_output_bias.paramdim)\n", "net.addConnection(c_tanh_output_bias)\n", "\n", "forwards_connection = FullConnection(net['in'], net['tanh_input'], name='c_in_to_tanh')\n", "forwards_connection._params = np.random.uniform(-0.2, 0.2, size=forwards_connection.paramdim)\n", "net.addConnection(forwards_connection)\n", "\n", "# Add other connections\n", "n_hidden_layers = len(CONFIG['HIDDEN_LAYERS'])\n", "prev_layer_name = 'tanh_input'\n", "for i in range(n_hidden_layers):\n", " hidden_layer_i = i + 1\n", " layer_name = lstm_layer_name(hidden_layer_i)\n", " \n", " recurrent_connection = FullConnection(net[layer_name], net[layer_name], name='c_' + layer_name + '_to_' + layer_name)\n", " recurrent_connection._params = np.random.uniform(-0.05, 0.05, size=recurrent_connection.paramdim)\n", " net.addRecurrentConnection(recurrent_connection)\n", " \n", " #bias_connection = FullConnection(bias, net[layer_name], name='c_' + layer_name + '_bias')\n", " #bias_connection._params = np.zeros(bias_connection.paramdim)\n", " #net.addConnection(bias_connection)\n", " \n", " forwards_connection = FullConnection(net[prev_layer_name], net[layer_name], name='c_' + prev_layer_name + '_to_' + layer_name)\n", " forwards_connection._params = np.random.uniform(-0.2, 0.2, size=forwards_connection.paramdim)\n", " net.addConnection(forwards_connection)\n", " prev_layer_name = layer_name\n", " \n", "layer_name = lstm_layer_name(n_hidden_layers)\n", "connect_to_out = FullConnection(net[layer_name], net['tanh_output'], name='c_' + layer_name + '_to_tanh_out')\n", "connect_to_out._params = np.random.uniform(-0.2, 0.2, size=connect_to_out.paramdim)\n", "net.addConnection(connect_to_out)\n", "\n", "connect_to_out = FullConnection(net['tanh_output'], net['out'], name='c_tanh_to_out')\n", "connect_to_out._params = np.random.uniform(-0.2, 0.2, size=connect_to_out.paramdim)\n", "net.addConnection(connect_to_out)\n", "\n", "net.sortModules()\n", "print(net)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "RecurrentNetwork-8\n", " Modules:\n", " [<BiasUnit 'BiasUnit-7'>, <LinearLayer 'in'>, <TanhLayer 'tanh_input'>, <LSTMLayer 'LSTM1'>, <LSTMLayer 'LSTM2'>, <TanhLayer 'tanh_output'>, <TanhLayer 'out'>]\n", " Connections:\n", " [<FullConnection 'c_LSTM1_to_LSTM2': 'LSTM1' -> 'LSTM2'>, <FullConnection 'c_LSTM2_to_tanh_out': 'LSTM2' -> 'tanh_output'>, <FullConnection 'c_in_to_tanh': 'in' -> 'tanh_input'>, <FullConnection 'c_tanh_input_bias': 'BiasUnit-7' -> 'tanh_input'>, <FullConnection 'c_tanh_input_to_LSTM1': 'tanh_input' -> 'LSTM1'>, <FullConnection 'c_tanh_output_bias': 'BiasUnit-7' -> 'tanh_output'>, <FullConnection 'c_tanh_to_out': 'tanh_output' -> 'out'>]\n", " Recurrent Connections:\n", " [<FullConnection 'c_LSTM1_to_LSTM1': 'LSTM1' -> 'LSTM1'>, <FullConnection 'c_LSTM2_to_LSTM2': 'LSTM2' -> 'LSTM2'>]\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# define a training method\n", "trainer = CONFIG['TRAINERCLASS'](net, dataset=ds, verbose=True, delta0=0.001)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# carry out the training\n", "net.reset()\n", "# train_errors = []\n", "t0 = time()\n", "EPOCHS = CONFIG['EPOCHS_PER_CYCLE'] * CONFIG['CYCLES']\n", "# trainer.trainUntilConvergence(maxEpochs=EPOCHS, verbose=True)\n", "# start_time = time()\n", "print(\"Starting training with\", EPOCHS, \"epochs...\")\n", "for i in xrange(CONFIG['CYCLES']):\n", " trainer.trainEpochs(CONFIG['EPOCHS_PER_CYCLE'])\n", "# train_errors.append(trainer.testOnData())\n", " # epoch = (i+1) * CONFIG['EPOCHS_PER_CYCLE']\n", " # seconds_elapsed = time() - start_time\n", " # seconds_per_epoch = seconds_elapsed / epoch\n", " # seconds_remaining = (EPOCHS - epoch) * seconds_per_epoch\n", " # td_elapsed = timedelta(seconds=seconds_elapsed)\n", " # td_elapsed_str = str(td_elapsed).split('.')[0]\n", " # eta = (datetime.now() + timedelta(seconds=seconds_remaining)).time()\n", " # eta = eta.strftime(\"%H:%M:%S\")\n", " # print(\"\\r epoch = {}/{} error = {} elapsed = {} ETA = {}\"\n", " # .format(epoch, EPOCHS, train_errors[-1], td_elapsed_str, eta),\n", " # end=\"\")\n", " # stdout.flush()\n", "print(\"Finished training. total seconds =\", time() - t0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Starting training with 30 epochs...\n", "epoch 30 total error 0.00014874 avg weight 0.16229" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 31 total error 0.00014686 avg weight 0.16954" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 32 total error 0.00014537 avg weight 0.17074" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 33 total error 0.00014352 avg weight 0.17974" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 34 total error 0.00014177 avg weight 0.18547" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 35 total error 0.00013974 avg weight 0.19769" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 36 total error 0.00013765 avg weight 0.20793" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 37 total error 0.00013669 avg weight 0.22592" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 38 total error 0.00013648 avg weight 0.22912" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 39 total error 0.00013385 avg weight 0.24714" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 40 total error 0.00013122 avg weight 0.26037" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 41 total error 0.00012792 avg weight 0.28771" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 42 total error 0.00012395 avg weight 0.31065" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 43 total error 0.00011931 avg weight 0.33982" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 44 total error 0.00011386 avg weight 0.35532" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 45 total error 0.00010802 avg weight 0.38739" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 46 total error 0.00010454 avg weight 0.4019" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 47 total error 0.00010307 avg weight 0.42146" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 48 total error 9.9174e-05 avg weight 0.44001" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch 49 total error 9.4455e-05 avg weight 0.47119" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-23-b36b80f7b18a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Starting training with\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mEPOCHS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"epochs...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCONFIG\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'CYCLES'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrainEpochs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCONFIG\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'EPOCHS_PER_CYCLE'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;31m# train_errors.append(trainer.testOnData())\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;31m# epoch = (i+1) * CONFIG['EPOCHS_PER_CYCLE']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/PyBrain-0.3.3-py2.7.egg/pybrain/supervised/trainers/trainer.pyc\u001b[0m in \u001b[0;36mtrainEpochs\u001b[1;34m(self, epochs, *args, **kwargs)\u001b[0m\n\u001b[0;32m 35\u001b[0m Additional arguments are passed on to the train method.\"\"\"\n\u001b[0;32m 36\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdummy\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m 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44\u001b[1;33m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_calcDerivs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mponderation\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/PyBrain-0.3.3-py2.7.egg/pybrain/supervised/trainers/backprop.pyc\u001b[0m in \u001b[0;36m_calcDerivs\u001b[1;34m(self, seq)\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[1;32mfor\u001b[0m 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result.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputbuffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moffset\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minpt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 51\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforget\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;32mreturn\u001b[0m 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\u001b[0;36m_forwardImplementation\u001b[1;34m(self, inbuf, outbuf)\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0moffset\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecurrentConns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 93\u001b[1;33m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moffset\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[1;32min\u001b[0m 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\u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'reshape'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 221\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "# Disaggregate!\n", "START = \"2014-01-01\"\n", "END = \"2014-01-03\"\n", "print(\"Starting disaggregation...\")\n", "net.reset()\n", "estimates = pd.Series(index=appliances[START:END].index)\n", "for date, mains_value in mains[START:END].iteritems():\n", " estimates[date] = net.activate(mains_value)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "estimates.plot()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mains[START:END].plot()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "appliances[START:END].plot()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = estimates[START:END].cumsum().plot(label='estimates')\n", "ax = mains_same_scale_as_appliances[START:END].cumsum().plot(ax=ax, label='aggregate')\n", "ax = appliances[START:END].cumsum().plot(ax=ax)\n", "plt.legend()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "estimates.cumsum().to_hdf('neuronilm_estimates_{:03d}.hdf'.format(CONFIG['EXPERIMENT_NUMBER']), 'df')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 } ], "metadata": {} } ] }

mit

jaewoosong/torch-tutorial-korean

todo/DQN_Training_iTorch.ipynb

1

34667

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Q-Network Training\n", "\n", "In this worksheet we are going to go over training a Reinforcement Learning (RL) agent trained on ATARI 2600 games. We will use the famous game of pong as a our training test bed.\n", "\n", "One can download the code required to train the DQN agent at https://sites.google.com/a/deepmind.com/dqn/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "We first start by setting up a function to update our lua path so that DQN related classes can be loaded." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "require 'cutorch'\n", "require 'cunn'\n", "require 'alewrap'\n", "\n", "torch.setdefaulttensortype('torch.FloatTensor')\n", "torch.setnumthreads(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Game Environment\n", "We create an RL environment that wraps an ATARI game so that we can train an agent.\n", "The agent will have no knowledge of what game it is playing." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Playing:\tpong\t\n" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- Create the Game Object\n", "game_options = {\n", " -- name of the game to play (you need the ROM file for this game)\n", " env='pong',\n", " -- directory where the ROMS are stored\n", " game_path='/home/ubuntu/torch/install/share/lua/5.1/dqn/roms/',\n", " -- we want get RGB frames\n", " env_params = {useRGB = true},\n", " -- we will repeat each action 4 times\n", " actrep = 4,\n", " -- for every new episode, play null actions a random number of time [0,30]\n", " random_starts = 30,\n", " -- use gpu\n", " gpu = 1,\n", " -- have some info logs\n", " verbose = 2\n", "}\n", "game_env = alewrap.GameEnvironment(game_options)\n", "game_actions = game_env:getActions()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{\n", " 1 : 0\n", " 2 : 3\n", " 3 : 4\n", "}\n", "\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- These are the valid actions for this game. The total possible action set is in (0-17)\n", "print(game_actions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Agent\n", "We create an agent that can **learn to play** any game" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Creating Agent Network from dqn.convnet_atari3\t\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "nn.Sequential {\n", " [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> output]\n", " (1): nn.Reshape(4x84x84)\n", " (2): nn.SpatialConvolution(in: 4, out: 32, kW: 8, kH: 8, dW: 4, dH: 4, padding: 1)\n", " (3): nn.Rectifier\n", " (4): nn.SpatialConvolution(in: 32, out: 64, kW: 4, kH: 4, dW: 2, dH: 2)\n", " (5): nn.Rectifier\n", " (6): nn.SpatialConvolution(in: 64, out: 64, kW: 3, kH: 3)\n", " (7): nn.Rectifier\n", " (8): nn.Reshape(3136)\n", " (9): nn.Linear(3136 -> 512)\n", " (10): nn.Rectifier\n", " (11): nn.Linear(512 -> 3)\n", "}\n", "{\n", " gradInput : CudaTensor - empty\n", " modules : \n", " {\n", " 1 : \n", " nn.Reshape(4x84x84)\n", " {\n", " nelement : 28224\n", " _input : CudaTensor - empty\n", " output : CudaTensor - size: 1x4x84x84\n", " gradInput : CudaTensor - empty\n", " size : LongStorage - size: 3\n", " _gradOutput : CudaTensor - empty\n", " batchsize : LongStorage - size: 4\n", " }\n", " 2 : \n", " nn.SpatialConvolution(in: 4, out: 32, kW: 8, kH: 8, dW: 4, dH: 4, padding: 1)\n", " {\n", " dH : 4\n", " dW : 4\n", " nOutputPlane : 32\n", " output : CudaTensor - size: 1x32x20x20\n", " gradInput : CudaTensor - empty\n", " " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "finput : CudaTensor - empty\n", " fgradInput : CudaTensor - empty\n", " gradBias : CudaTensor - size: 32\n", " weight : CudaTensor - size: 32x4x8x8\n", " bias : CudaTensor - size: 32\n", " gradWeight : CudaTensor - size: 32x4x8x8\n", " padding : 1\n", " nInputPlane : 4\n", " kW : 8\n", " kH : 8\n", " }\n", " 3 : \n", " nn.Rectifier\n", " {\n", " gradInput : CudaTensor - empty\n", " output : CudaTensor - size: 1x32x20x20\n", " }\n", " 4 : \n", " nn.SpatialConvolution(in: 32, out: 64, kW: 4, kH: 4, dW: 2, dH: 2)\n", " {\n", " dH : 2\n", " dW : 2\n", " nOutputPlane : 64\n", " output : CudaTensor - size: 1x64x9x9\n", " gradInput : CudaTensor - empty\n", " finput : CudaTensor - empty\n", " fgradInput : CudaTensor - empty\n", " gradBias : CudaTensor - size: 64\n", " weight : CudaTensor - size: 64x32x4x4\n", " bias : CudaTensor - size: 64\n", " gradWeight : CudaTensor - size: 64x32x4x4\n", " padding : 0\n", " nInputPlane : 32\n", " kW : 4\n", " kH : 4\n", " }\n", " 5 : \n", " nn.Rectifier\n", " {\n", " gradInput : CudaTensor - empty\n", " output : CudaTensor - size: 1x64x9x9\n", " }\n", " 6 : \n", " nn.SpatialConvolution(in: 64, out: 64, kW: 3, kH: 3)\n", " {\n", " dH : 1\n", " dW : 1\n", " nOutputPlane : 64\n", " output : CudaTensor - size: 1x64x7x7\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ " gradInput : CudaTensor - empty\n", " finput : CudaTensor - empty\n", " fgradInput : CudaTensor - empty\n", " gradBias : CudaTensor - size: 64\n", " weight : CudaTensor - size: 64x64x3x3\n", " bias : CudaTensor - size: 64\n", " gradWeight : CudaTensor - size: 64x64x3x3\n", " padding : 0\n", " nInputPlane : 64\n", " kW : 3\n", " kH : 3\n", " }\n", " 7 : \n", " nn.Rectifier\n", " {\n", " gradInput : CudaTensor - empty\n", " output : CudaTensor - size: 1x64x7x7\n", " }\n", " 8 : \n", " nn.Reshape(3136)\n", " {\n", " nelement : 3136\n", " _input : CudaTensor - empty\n", " output : CudaTensor - empty\n", " gradInput : CudaTensor - empty\n", " size : LongStorage - size: 1\n", " _gradOutput : CudaTensor - empty\n", " batchsize : LongStorage - size: 2\n", " }\n", " 9 : \n", " nn.Linear(3136 -> 512)\n", " {\n", " gradBias : CudaTensor - size: 512\n", " weight : CudaTensor - size: 512x3136\n", " bias : CudaTensor - size: 512\n", " gradInput : CudaTensor - empty\n", " gradWeight : CudaTensor - size: 512x3136\n", " output : CudaTensor - empty\n", " }\n", " 10 : \n", " nn.Rectifier\n", " {\n", " gradInput : CudaTensor - empty\n", " output : CudaTensor - empty\n", " }\n", " 11 : \n", " nn.Linear(512 -> 3)\n", " {\n", " gradBias : CudaTensor - size: 3\n", " weight : CudaTensor - size: 3x512\n", " bias : CudaTensor - size: 3\n", " gradInput : CudaTensor - empty\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ " gradWeight : CudaTensor - size: 3x512\n", " output : CudaTensor - empty\n", " }\n", " }\n", " output : CudaTensor - empty\n", "}\n", "Convolutional layers flattened output size:\t3136\t\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "require 'dqn'\n", "\n", "agent_params = {\n", " -- The agent only knows about actions it can take in the environment\n", " actions = game_actions,\n", " -- we will use gpu\n", " gpu = 1,\n", " -- we will print info\n", " verbose = 2,\n", " -- learning rate for SGD\n", " lr=0.00025,\n", " -- Random exploration ratio, start from 100% exploration\n", " ep=1,\n", " -- Drop down to 10% exploration\n", " ep_end=0.1,\n", " -- Linear decay over 1M steps\n", " ep_endt=1000000,\n", " -- Discount factor \\gamma for Q-Learning\n", " discount=0.99,\n", " -- Number of frames to input into convolutional net\n", " hist_len=4,\n", " -- Learning starts after a delay of 50K actions, we do not want to overfit onto early experience\n", " learn_start=50000,\n", " -- We will store last 1M transitions\n", " replay_memory=1000000,\n", " -- We will update every 4 actions\n", " update_freq=4,\n", " -- Will update only once\n", " n_replay=1,\n", " -- Network spec\n", " network= \"dqn.convnet_atari3\",\n", " -- pre-processing spec (just scale down to grayscale 84x84)\n", " preproc=\"dqn.net_downsample_2x_full_y\",\n", " -- size of inputs after rescale (84*84)\n", " state_dim=7056,\n", " -- size of minibatch for SGD\n", " minibatch_size=32,\n", " -- we will scale reward values to limit to 1,-1\n", " rescale_r=1,\n", " -- we use Y channel\n", " ncols=1,\n", " -- buffer on GPU\n", " bufferSize=512,\n", " -- set of validation transitions to track training progress\n", " valid_size=500,\n", " -- update target Q network every 10K updates\n", " target_q=10000,\n", " -- we will clip errors that go into DQN\n", " clip_delta=1,\n", " -- clip reward between -1,1\n", " min_reward=-1,\n", " -- clip reward between -1,1\n", " max_reward=1\n", "}\n", "agent = dqn.NeuralQLearner(agent_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logs to Track\n", "We will log the following quantities to track training" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "-- The following quantities are collected during evaluation of the agent\n", "\n", "-- How many times did the agent end up in reward states\n", "reward_counts = {}\n", "-- How many episodes did the agent finish\n", "episode_counts = {}\n", "-- How long does it take to learn/test\n", "time_history = { 0 }\n", "-- The reward agent gets during testing\n", "reward_history = {}\n", "\n", "-- The following are training measures collected by agent during training\n", "\n", "-- Maximum q-value during training\n", "qmax_history = {}\n", "-- Value of the validation states\n", "v_history = {}\n", "-- TD error over the validation states\n", "td_history = {}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What does the input to agent look like?\n", "The current state of the game can be grabbed by getState() fucnction call on the environment." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{\n", " reward : 0\n", " screen : CudaTensor - size: 1x3x210x160\n", " terminal : false\n", "}\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "screen, reward, terminal = game_env:getState()\n", "print({screen = screen, reward = reward, terminal = terminal})\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What does the game look like?\n", "We can play some random actions and look at the screens. We should be able to play animations with ipython!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Console does not support images" ] }, "metadata": { "image/png": { "height": 1272, "width": 972 } }, "output_type": "display_data" } ], "source": [ "local screens = {}\n", "for i=1,36 do\n", " local screen, reward, terminal = game_env:step(game_actions[torch.random(3)])\n", " table.insert(screens, screen[1]:clone())\n", "end\n", "itorch.image(screens)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "Now we will train the agent to play the game" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "-- Training options\n", "opt = {\n", " -- number of evaluation steps\n", " eval_steps = 125000,\n", " -- frequency of evaluation\n", " eval_freq = 250000,\n", " -- total number of training steps\n", " steps = 50000000,\n", " -- frequency of progress reporting\n", " prog_freq = 10000,\n", " -- frequency to save agent on disk\n", " save_freq = 125000,\n", " -- filename for saved agent\n", " name = 'Itorch_DQN3_0_1_pong_FULL_Y',\n", " -- we want to use random starts of up to 30 nil steps\n", " random_starts = 30,\n", " -- we repeat every action 4 times\n", " actrep = 4,\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Iteration ..\t0\t\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "local total_reward\n", "local nrewards\n", "local nepisodes\n", "local episode_reward\n", "\n", "local learn_start = agent.learn_start\n", "local start_time = sys.clock()\n", "local step = 0\n", "\n", "print(\"Iteration ..\", step)\n", "while step < opt.steps do\n", " step = step + 1\n", " local action_index = agent:perceive(reward, screen, terminal)\n", "\n", " -- game over? get next game! \n", " if not terminal then\n", " screen, reward, terminal = game_env:step(game_actions[action_index], true)\n", " else\n", " if opt.random_starts > 0 then\n", " screen, reward, terminal = game_env:nextRandomGame()\n", " else\n", " screen, reward, terminal = game_env:newGame()\n", " end\n", " end\n", "\n", " if step % opt.prog_freq == 0 then\n", " assert(step==agent.numSteps, 'trainer step: ' .. step ..\n", " ' & agent.numSteps: ' .. agent.numSteps)\n", " print(\"Training Steps: \", step)\n", " agent:report()\n", " collectgarbage()\n", " end\n", "\n", " if step%1000 == 0 then collectgarbage() end\n", "\n", " if step % opt.eval_freq == 0 and step > learn_start then\n", "\n", " print('Evaluating')\n", " screen, reward, terminal = game_env:newGame()\n", "\n", " total_reward = 0\n", " nrewards = 0\n", " nepisodes = 0\n", " episode_reward = 0\n", "\n", " local eval_time = sys.clock()\n", " for estep=1,opt.eval_steps do\n", " local action_index = agent:perceive(reward, screen, terminal, true, 0.05)\n", " -- Play game in test mode (episodes don't end when losing a life) \n", " screen, reward, terminal = game_env:step(game_actions[action_index])\n", "\n", " if estep%1000 == 0 then collectgarbage() end\n", "\n", " -- record every reward \n", " episode_reward = episode_reward + reward\n", " if reward ~= 0 then\n", " nrewards = nrewards + 1\n", " end\n", "\n", " if terminal then\n", " total_reward = total_reward + episode_reward\n", " episode_reward = 0\n", " nepisodes = nepisodes + 1\n", " screen, reward, terminal = game_env:nextRandomGame()\n", " end\n", " end\n", "\n", " eval_time = sys.clock() - eval_time\n", " start_time = start_time + eval_time\n", " agent:compute_validation_statistics()\n", " local ind = #reward_history+1\n", " total_reward = total_reward/math.max(1, nepisodes)\n", "\n", " if #reward_history == 0 or total_reward > torch.Tensor(reward_history):max() then\n", " agent.best_network = agent.network:clone()\n", " end\n", "\n", " if agent.v_avg then\n", " v_history[ind] = agent.v_avg\n", " td_history[ind] = agent.tderr_avg\n", " qmax_history[ind] = agent.q_max\n", " end\n", " print(\"V\", v_history[ind], \"TD error\", td_history[ind], \"Qmax\", qmax_history[ind])\n", "\n", " reward_history[ind] = total_reward\n", " reward_counts[ind] = nrewards\n", " episode_counts[ind] = nepisodes\n", " time_history[ind+1] = sys.clock() - start_time\n", "\n", " local time_dif = time_history[ind+1] - time_history[ind]\n", "\n", " local training_rate = opt.actrep*opt.eval_freq/time_dif\n", "\n", " print(string.format(\n", " '\\nSteps: %d (frames: %d), reward: %.2f, epsilon: %.2f, lr: %G, ' ..\n", " 'training time: %ds, training rate: %dfps, testing time: %ds, ' ..\n", " 'testing rate: %dfps, num. ep.: %d, num. rewards: %d',\n", " step, step*opt.actrep, total_reward, agent.ep, agent.lr, time_dif,\n", " training_rate, eval_time, opt.actrep*opt.eval_steps/eval_time,\n", " nepisodes, nrewards))\n", " end\n", "\n", " if step % opt.save_freq == 0 or step == opt.steps then\n", " local filename = opt.name\n", " torch.save(filename .. \".t7\", {\n", " reward_history = reward_history,\n", " reward_counts = reward_counts,\n", " episode_counts = episode_counts,\n", " time_history = time_history,\n", " v_history = v_history,\n", " td_history = td_history,\n", " qmax_history = qmax_history,\n", " opt = opt})\n", "-- we are not going to bother saving agents for now.\n", "-- torch.save(filename .. \"_agent.t7\", {\n", "-- agent = agent,\n", "-- best_model = agent.best_network,\n", "-- })\n", " print('Saved:', filename .. '.t7')\n", " io.flush()\n", " collectgarbage()\n", " end\n", "end\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "iTorch", "language": "lua", "name": "itorch" }, "language_info": { "name": "lua", "version": "5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

KristofferC/ConstLab.jl

examples/mises_mixed_hardening.ipynb

1

1146036

mit

anhquan0412/deeplearning_fastai

deeplearning1/nbs/lesson4.ipynb

1

311797

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Can not use cuDNN on context None: Disabled by dnn.enabled flag\n", "Mapped name None to device cuda0: GeForce GTX 1060 3GB (0000:01:00.0)\n", "Using TensorFlow backend.\n" ] } ], "source": [ "from __future__ import division, print_function\n", "%matplotlib inline\n", "from importlib import reload # Python 3\n", "import utils; reload(utils)\n", "from utils import *\n", "from keras.layers.merge import dot, add, concatenate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "path = \"data/ml-latest-small/\" # from https://grouplens.org/datasets/movielens/\n", "#path = \"data/ml-20m/\"\n", "model_path = path + 'models/'\n", "if not os.path.exists(model_path): os.mkdir(model_path)\n", "\n", "batch_size=64\n", "#batch_size=1" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Set up data" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We're working with the movielens data, which contains one rating per row, like this:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>userId</th>\n", " <th>movieId</th>\n", " <th>rating</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>31</td>\n", " <td>2.5</td>\n", " <td>1260759144</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1029</td>\n", " <td>3.0</td>\n", " <td>1260759179</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1061</td>\n", " <td>3.0</td>\n", " <td>1260759182</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1129</td>\n", " <td>2.0</td>\n", " <td>1260759185</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1172</td>\n", " <td>4.0</td>\n", " <td>1260759205</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " userId movieId rating timestamp\n", "0 1 31 2.5 1260759144\n", "1 1 1029 3.0 1260759179\n", "2 1 1061 3.0 1260759182\n", "3 1 1129 2.0 1260759185\n", "4 1 1172 4.0 1260759205" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings = pd.read_csv(path+'ratings.csv')\n", "ratings.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "100004" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ratings)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Just for display purposes, let's read in the movie names too." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "users = ratings.userId.unique()\n", "movies = ratings.movieId.unique()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "# userId and movieId become ditionary elements with values ranging from 0 to max len \n", "userid2idx = {o:i for i,o in enumerate(users)}\n", "movieid2idx = {o:i for i,o in enumerate(movies)}" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We update the movie and user ids so that they are contiguous integers, which we want when using embeddings." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "ratings.movieId = ratings.movieId.apply(lambda x: movieid2idx[x])\n", "ratings.userId = ratings.userId.apply(lambda x: userid2idx[x])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(0, 670, 0, 9065)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_min, user_max, movie_min, movie_max = (ratings.userId.min(), \n", " ratings.userId.max(), ratings.movieId.min(), ratings.movieId.max())\n", "user_min, user_max, movie_min, movie_max" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(671, 9066)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_users = ratings.userId.nunique()\n", "n_movies = ratings.movieId.nunique()\n", "n_users, n_movies" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "This is the number of latent factors in each embedding." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "n_factors = 50" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "np.random.seed = 42" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Randomly split into training and validation." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "msk = np.random.rand(len(ratings)) < 0.8\n", "trn = ratings[msk]\n", "val = ratings[~msk]" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Create subset for Excel" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We create a crosstab of the most popular movies and most movie-addicted users which we'll copy into Excel for creating a simple example. This isn't necessary for any of the modeling below however." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "g=ratings.groupby('userId')['rating'].count()\n", "topUsers=g.sort_values(ascending=False)[:15]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "g=ratings.groupby('movieId')['rating'].count()\n", "topMovies=g.sort_values(ascending=False)[:15]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "top_r = ratings.join(topUsers, rsuffix='_r', how='inner', on='userId')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "top_r = top_r.join(topMovies, rsuffix='_r', how='inner', on='movieId')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>movieId</th>\n", " <th>27</th>\n", " <th>49</th>\n", " <th>57</th>\n", " <th>72</th>\n", " <th>79</th>\n", " <th>89</th>\n", " <th>92</th>\n", " <th>99</th>\n", " <th>143</th>\n", " <th>179</th>\n", " <th>180</th>\n", " <th>197</th>\n", " <th>402</th>\n", " <th>417</th>\n", " <th>505</th>\n", " </tr>\n", " <tr>\n", " <th>userId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>14</th>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>1.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>4.5</td>\n", " <td>5.0</td>\n", " <td>4.5</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.5</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>211</th>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>4.5</td>\n", " <td>4.0</td>\n", " 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<td>4.5</td>\n", " <td>4.5</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>379</th>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>451</th>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " <td>3.5</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>467</th>\n", " <td>3.0</td>\n", " <td>3.5</td>\n", " <td>3.0</td>\n", " <td>2.5</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>3.5</td>\n", " <td>3.5</td>\n", " <td>3.0</td>\n", " <td>3.5</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>508</th>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>4.5</td>\n", " <td>3.0</td>\n", " <td>4.5</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>NaN</td>\n", " <td>2.5</td>\n", " <td>2.0</td>\n", " <td>3.5</td>\n", " <td>3.5</td>\n", " <td>3.5</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>579</th>\n", " <td>4.5</td>\n", " <td>4.5</td>\n", " <td>3.5</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>4.5</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>3.5</td>\n", " <td>3.0</td>\n", " <td>4.5</td>\n", " <td>4.0</td>\n", " <td>4.5</td>\n", " </tr>\n", " <tr>\n", " <th>623</th>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "movieId 27 49 57 72 79 89 92 99 143 179 180 197 402 417 \\\n", "userId \n", "14 3.0 5.0 1.0 3.0 4.0 4.0 5.0 2.0 5.0 5.0 4.0 5.0 5.0 2.0 \n", "29 5.0 5.0 5.0 4.0 5.0 4.0 4.0 5.0 4.0 4.0 5.0 5.0 3.0 4.0 \n", "72 4.0 5.0 5.0 4.0 5.0 3.0 4.5 5.0 4.5 5.0 5.0 5.0 4.5 5.0 \n", "211 5.0 4.0 4.0 3.0 5.0 3.0 4.0 4.5 4.0 NaN 3.0 3.0 5.0 3.0 \n", "212 2.5 NaN 2.0 5.0 NaN 4.0 2.5 NaN 5.0 5.0 3.0 3.0 4.0 3.0 \n", "293 3.0 NaN 4.0 4.0 4.0 3.0 NaN 3.0 4.0 4.0 4.5 4.0 4.5 4.0 \n", "310 3.0 3.0 5.0 4.5 5.0 4.5 2.0 4.5 4.0 3.0 4.5 4.5 4.0 3.0 \n", "379 5.0 5.0 5.0 4.0 NaN 4.0 5.0 4.0 4.0 4.0 NaN 3.0 5.0 4.0 \n", "451 4.0 5.0 4.0 5.0 4.0 4.0 5.0 5.0 4.0 4.0 4.0 4.0 2.0 3.5 \n", "467 3.0 3.5 3.0 2.5 NaN NaN 3.0 3.5 3.5 3.0 3.5 3.0 3.0 4.0 \n", "508 5.0 5.0 4.0 3.0 5.0 2.0 4.0 4.0 5.0 5.0 5.0 3.0 4.5 3.0 \n", "546 NaN 5.0 2.0 3.0 5.0 NaN 5.0 5.0 NaN 2.5 2.0 3.5 3.5 3.5 \n", "563 1.0 5.0 3.0 5.0 4.0 5.0 5.0 NaN 2.0 5.0 5.0 3.0 3.0 4.0 \n", "579 4.5 4.5 3.5 3.0 4.0 4.5 4.0 4.0 4.0 4.0 3.5 3.0 4.5 4.0 \n", "623 NaN 5.0 3.0 3.0 NaN 3.0 5.0 NaN 5.0 5.0 5.0 5.0 2.0 5.0 \n", "\n", "movieId 505 \n", "userId \n", "14 5.0 \n", "29 5.0 \n", "72 4.0 \n", "211 NaN \n", "212 2.0 \n", "293 NaN \n", "310 4.0 \n", "379 4.0 \n", "451 5.0 \n", "467 4.0 \n", "508 4.5 \n", "546 5.0 \n", "563 5.0 \n", "579 4.5 \n", "623 4.0 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(top_r.userId, top_r.movieId, top_r.rating, aggfunc=np.sum)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "heading_collapsed": true }, "source": [ "## Dot product" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The most basic model is a dot product of a movie embedding and a user embedding. Let's see how well that works:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "user_in = Input(shape=(1,), dtype='int64', name='user_in')\n", "u = Embedding(input_dim=n_users, output_dim=n_factors, input_length=1, embeddings_regularizer=l2(1e-4))(user_in)\n", "movie_in = Input(shape=(1,), dtype='int64', name='movie_in')\n", "m = Embedding(input_dim=n_movies, output_dim=n_factors, input_length=1, embeddings_regularizer=l2(1e-4))(movie_in)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = dot([u, m], axes=2)\n", "x = Flatten()(x)\n", "model = Model([user_in, movie_in], x)\n", "model.compile(Adam(0.001), loss='mse')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "user_in (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "movie_in (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "embedding_1 (Embedding) (None, 1, 50) 33550 user_in[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_2 (Embedding) (None, 1, 50) 453300 movie_in[0][0] \n", "____________________________________________________________________________________________________\n", "dot_1 (Dot) (None, 1, 1) 0 embedding_1[0][0] \n", " embedding_2[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 1) 0 dot_1[0][0] \n", "====================================================================================================\n", "Total params: 486,850\n", "Trainable params: 486,850\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'user_in:0' shape=(?, 1) dtype=int64>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_in" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80057 samples, validate on 19947 samples\n", "Epoch 1/1\n", "80057/80057 [==============================] - 82s - loss: 9.8353 - val_loss: 4.2363\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x2522457de80>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=1, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.01" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/3\n", "80292/80292 [==============================] - 2s - loss: 2.7601 - val_loss: 2.7221\n", "Epoch 2/3\n", "80292/80292 [==============================] - 2s - loss: 2.3373 - val_loss: 2.5846\n", "Epoch 3/3\n", "80292/80292 [==============================] - 2s - loss: 2.2085 - val_loss: 2.5560\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f68035bdcf8>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=3, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.001" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/6\n", "80292/80292 [==============================] - 2s - loss: 2.1578 - val_loss: 2.5482\n", "Epoch 2/6\n", "80292/80292 [==============================] - 2s - loss: 2.1298 - val_loss: 2.5491\n", "Epoch 3/6\n", "80292/80292 [==============================] - 2s - loss: 2.1063 - val_loss: 2.5560\n", "Epoch 4/6\n", "80292/80292 [==============================] - 2s - loss: 2.0855 - val_loss: 2.5582\n", "Epoch 5/6\n", "80292/80292 [==============================] - 2s - loss: 2.0624 - val_loss: 2.5702\n", "Epoch 6/6\n", "80292/80292 [==============================] - 2s - loss: 2.0423 - val_loss: 2.5764\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f6802eb65c0>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=6, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The [best benchmarks](http://www.librec.net/example.html) are a bit over 0.9, so this model doesn't seem to be working that well..." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Bias" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The problem is likely to be that we don't have bias terms - that is, a single bias for each user and each movie representing how positive or negative each user is, and how good each movie is. We can add that easily by simply creating an embedding with one output for each movie and each user, and adding it to our output." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "def embedding_input(name, n_in, n_out, reg):\n", " inp = Input(shape=(1,), dtype='int64', name=name)\n", " return inp, Embedding(input_dim=n_in, output_dim=n_out, input_length=1, embeddings_regularizer=l2(reg))(inp)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4)\n", "movie_in, m = embedding_input('movie_in', n_movies, n_factors, 1e-4)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "def create_bias(inp, n_in):\n", " x = Embedding(input_dim=n_in, output_dim=1, input_length=1)(inp)\n", " return Flatten()(x)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "ub = create_bias(user_in, n_users)\n", "mb = create_bias(movie_in, n_movies)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "x = dot([u, m], axes=2)\n", "x = Flatten()(x)\n", "x = add([x, ub])\n", "x = add([x, mb])\n", "model = Model([user_in, movie_in], x)\n", "model.compile(Adam(0.001), loss='mse')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "user_in (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "movie_in (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "embedding_3 (Embedding) (None, 1, 50) 33550 user_in[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_4 (Embedding) (None, 1, 50) 453300 movie_in[0][0] \n", "____________________________________________________________________________________________________\n", "dot_2 (Dot) (None, 1, 1) 0 embedding_3[0][0] \n", " embedding_4[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_5 (Embedding) (None, 1, 1) 671 user_in[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_4 (Flatten) (None, 1) 0 dot_2[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_2 (Flatten) (None, 1) 0 embedding_5[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_6 (Embedding) (None, 1, 1) 9066 movie_in[0][0] \n", "____________________________________________________________________________________________________\n", "add_1 (Add) (None, 1) 0 flatten_4[0][0] \n", " flatten_2[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_3 (Flatten) (None, 1) 0 embedding_6[0][0] \n", "____________________________________________________________________________________________________\n", "add_2 (Add) (None, 1) 0 add_1[0][0] \n", " flatten_3[0][0] \n", "====================================================================================================\n", "Total params: 496,587\n", "Trainable params: 496,587\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/1\n", "80292/80292 [==============================] - 2s - loss: 8.7433 - val_loss: 3.4836\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f6801faa208>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=1, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.01" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/6\n", "80292/80292 [==============================] - 2s - loss: 2.5806 - val_loss: 2.3029\n", "Epoch 2/6\n", "80292/80292 [==============================] - 2s - loss: 1.9956 - val_loss: 2.1075\n", "Epoch 3/6\n", "80292/80292 [==============================] - 2s - loss: 1.8357 - val_loss: 2.0133\n", "Epoch 4/6\n", "80292/80292 [==============================] - 2s - loss: 1.7409 - val_loss: 1.9403\n", "Epoch 5/6\n", "80292/80292 [==============================] - 2s - loss: 1.6631 - val_loss: 1.8701\n", "Epoch 6/6\n", "80292/80292 [==============================] - 2s - loss: 1.5887 - val_loss: 1.8054\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f6802ed4eb8>" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=6, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.001" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/10\n", "80292/80292 [==============================] - 2s - loss: 1.5190 - val_loss: 1.7389\n", "Epoch 2/10\n", "80292/80292 [==============================] - 2s - loss: 1.4496 - val_loss: 1.6736\n", "Epoch 3/10\n", "80292/80292 [==============================] - 2s - loss: 1.3808 - val_loss: 1.6142\n", "Epoch 4/10\n", "80292/80292 [==============================] - 2s - loss: 1.3144 - val_loss: 1.5591\n", "Epoch 5/10\n", "80292/80292 [==============================] - 2s - loss: 1.2491 - val_loss: 1.5088\n", "Epoch 6/10\n", "80292/80292 [==============================] - 2s - loss: 1.1878 - val_loss: 1.4578\n", "Epoch 7/10\n", "80292/80292 [==============================] - 2s - loss: 1.1288 - val_loss: 1.4163\n", "Epoch 8/10\n", "80292/80292 [==============================] - 2s - loss: 1.0732 - val_loss: 1.3694\n", "Epoch 9/10\n", "80292/80292 [==============================] - 2s - loss: 1.0205 - val_loss: 1.3295\n", "Epoch 10/10\n", "80292/80292 [==============================] - 2s - loss: 0.9703 - val_loss: 1.2908\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f6801faa320>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=10, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/5\n", "80292/80292 [==============================] - 2s - loss: 0.9233 - val_loss: 1.2580\n", "Epoch 2/5\n", "80292/80292 [==============================] - 2s - loss: 0.8800 - val_loss: 1.2260\n", "Epoch 3/5\n", "80292/80292 [==============================] - 2s - loss: 0.8389 - val_loss: 1.1969\n", "Epoch 4/5\n", "80292/80292 [==============================] - 2s - loss: 0.8020 - val_loss: 1.1698\n", "Epoch 5/5\n", "80292/80292 [==============================] - 2s - loss: 0.7677 - val_loss: 1.1452\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f6802ed4a90>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=batch_size, epochs=5, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "This result is quite a bit better than the best benchmarks that we could find with a quick google search - so looks like a great approach!" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.save_weights(model_path+'bias.h5')" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.load_weights(model_path+'bias.h5')" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can use the model to generate predictions by passing a pair of ints - a user id and a movie id. For instance, this predicts that user #3 would really enjoy movie #6." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 4.9892]], dtype=float32)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([np.array([3]), np.array([6])])" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Analyze results" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "To make the analysis of the factors more interesting, we'll restrict it to the top 2000 most popular movies." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "g=ratings.groupby('movieId')['rating'].count()\n", "topMovies=g.sort_values(ascending=False)[:2000]\n", "topMovies = np.array(topMovies.index)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "First, we'll look at the movie bias term. We create a 'model' - which in keras is simply a way of associating one or more inputs with one more more outputs, using the functional API. Here, our input is the movie id (a single id), and the output is the movie bias (a single float)." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "get_movie_bias = Model(movie_in, mb)\n", "movie_bias = get_movie_bias.predict(topMovies)\n", "movie_ratings = [(b[0], movie_names()[movies[i]]) for i,b in zip(topMovies,movie_bias)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Now we can look at the top and bottom rated movies. These ratings are corrected for different levels of reviewer sentiment, as well as different types of movies that different reviewers watch." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.50823206, 'Battlefield Earth (2000)'),\n", " (-0.13802283, 'Speed 2: Cruise Control (1997)'),\n", " (-0.10757273, '2 Fast 2 Furious (Fast and the Furious 2, The) (2003)'),\n", " (-0.099033587, 'Little Nicky (2000)'),\n", " (-0.087039262, 'Super Mario Bros. (1993)'),\n", " (-0.049692307, 'Jaws 3-D (1983)'),\n", " (0.067072563, 'Godzilla (1998)'),\n", " (0.074751392, 'Police Academy 6: City Under Siege (1989)'),\n", " (0.075012401, 'Police Academy 5: Assignment: Miami Beach (1988)'),\n", " (0.077604346, 'Blade: Trinity (2004)'),\n", " (0.098121516, 'Batman & Robin (1997)'),\n", " (0.10278502, 'Haunting, The (1999)'),\n", " (0.11563323, 'Blair Witch Project, The (1999)'),\n", " (0.12438908, 'Howard the Duck (1986)'),\n", " (0.12522519, 'Lost in Space (1998)')]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_ratings, key=itemgetter(0))[:15]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(1.4108304, 'Tom Jones (1963)'),\n", " (1.3752841, 'Porco Rosso (Crimson Pig) (Kurenai no buta) (1992)'),\n", " (1.3736762, 'Letters from Iwo Jima (2006)'),\n", " (1.3715636, 'Avengers: Age of Ultron (2015)'),\n", " (1.3444295, \"Howl's Moving Castle (Hauru no ugoku shiro) (2004)\"),\n", " (1.3339735, 'Argo (2012)'),\n", " (1.328054, 'Shawshank Redemption, The (1994)'),\n", " (1.3223944,\n", " 'Fog of War: Eleven Lessons from the Life of Robert S. McNamara, The (2003)'),\n", " (1.3075876, 'My Neighbor Totoro (Tonari no Totoro) (1988)'),\n", " (1.29387, 'Paprika (Papurika) (2006)'),\n", " (1.288847, 'Modern Times (1936)'),\n", " (1.2749821, 'General, The (1926)'),\n", " (1.2747028, 'Harry Potter and the Deathly Hallows: Part 2 (2011)'),\n", " (1.2723743,\n", " \"Monty Python's And Now for Something Completely Different (1971)\"),\n", " (1.2655969, 'The Revenant (2015)')]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_ratings, key=itemgetter(0), reverse=True)[:15]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can now do the same thing for the embeddings." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(2000, 50)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_movie_emb = Model(movie_in, m)\n", "movie_emb = np.squeeze(get_movie_emb.predict([topMovies]))\n", "movie_emb.shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2000, 1, 50)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_movie_emb.predict([topMovies]).shape" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Because it's hard to interpret 50 embeddings, we use [PCA](https://plot.ly/ipython-notebooks/principal-component-analysis/) to simplify them down to just 3 vectors. " ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=3)\n", "movie_pca = pca.fit(movie_emb.T).components_" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "fac0 = movie_pca[0]" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_comp = [(f, movie_names()[movies[i]]) for f,i in zip(fac0, topMovies)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Here's the 1st component. It seems to be 'critically acclaimed' or 'classic'." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(0.011226126320379693, 'RoboCop 3 (1993)'),\n", " (0.0078716698259611688, 'Police Academy 5: Assignment: Miami Beach (1988)'),\n", " (0.0077262310347635081, 'Mission to Mars (2000)'),\n", " (0.0064721869975817269, 'Battlefield Earth (2000)'),\n", " (0.0062127231326777098, 'Barb Wire (1996)'),\n", " (0.0058832830348050065, 'Mighty Morphin Power Rangers: The Movie (1995)'),\n", " (0.0057819443702678492, 'X-Men Origins: Wolverine (2009)'),\n", " (0.0057817441730701391, 'Bio-Dome (1996)'),\n", " (0.005781389310885462, 'Howard the Duck (1986)'),\n", " (0.0054670725098811285, 'Lolita (1997)')]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.052890492005052581, 'Wallace & Gromit: A Close Shave (1995)'),\n", " (-0.050841010663626743, 'Star Wars: Episode IV - A New Hope (1977)'),\n", " (-0.049944742190885363, 'Amadeus (1984)'),\n", " (-0.049246174546123275,\n", " \"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\"),\n", " (-0.049152467014311127, 'Run Lola Run (Lola rennt) (1998)'),\n", " (-0.049128126059456503, 'Star Wars: Episode VI - Return of the Jedi (1983)'),\n", " (-0.049019741276832583, 'Seven (a.k.a. Se7en) (1995)'),\n", " (-0.048763816592434478,\n", " 'Lord of the Rings: The Fellowship of the Ring, The (2001)'),\n", " (-0.048522566455089379, \"Schindler's List (1993)\"),\n", " (-0.048490031070226335, \"Ferris Bueller's Day Off (1986)\")]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0))[:10]" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "fac1 = movie_pca[1]" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_comp = [(f, movie_names()[movies[i]]) for f,i in zip(fac1, topMovies)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The 2nd is 'hollywood blockbuster'." ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(0.089763794425230248, 'Independence Day (a.k.a. ID4) (1996)'),\n", " (0.082481808992957262, 'Stargate (1994)'),\n", " (0.075202916964643554, 'Ace Ventura: Pet Detective (1994)'),\n", " (0.075096380784319031, 'Armageddon (1998)'),\n", " (0.073007693041213545, 'Rock, The (1996)'),\n", " (0.072833267497441609, 'Speed (1994)'),\n", " (0.071935710919232093, 'Titanic (1997)'),\n", " (0.070593224049008141, 'Star Wars: Episode I - The Phantom Menace (1999)'),\n", " (0.070216856704080149, 'Happy Gilmore (1996)'),\n", " (0.067770050911144394, 'Die Hard: With a Vengeance (1995)')]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.065291630296936576, 'Brokeback Mountain (2005)'),\n", " (-0.062228361401071283, 'Annie Hall (1977)'),\n", " (-0.05711136955146863, 'Apocalypse Now (1979)'),\n", " (-0.055546273177881127, 'Vertigo (1958)'),\n", " (-0.053861524618477798, 'City Lights (1931)'),\n", " (-0.051540528501327648, 'Harold and Maude (1971)'),\n", " (-0.050212882067681805, 'Manhattan (1979)'),\n", " (-0.049323688257642717, 'Heavenly Creatures (1994)'),\n", " (-0.04888208810926576, '8 1/2 (8½) (1963)'),\n", " (-0.047568694007240744, 'Gosford Park (2001)')]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0))[:10]" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "fac2 = movie_pca[2]" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_comp = [(f, movie_names()[movies[i]]) for f,i in zip(fac2, topMovies)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The 3rd is 'violent vs happy'." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(0.12771136594581653, 'Lord of the Rings: The Two Towers, The (2002)'),\n", " (0.12416130751199246,\n", " 'Lord of the Rings: The Fellowship of the Ring, The (2001)'),\n", " (0.10774460999231848, 'Dumb & Dumber (Dumb and Dumber) (1994)'),\n", " (0.097855138307821166, 'Matrix, The (1999)'),\n", " (0.096519093444194196, 'Star Wars: Episode VI - Return of the Jedi (1983)'),\n", " (0.096028597633532206,\n", " 'Lord of the Rings: The Return of the King, The (2003)'),\n", " (0.093372233415449646, 'Seven (a.k.a. Se7en) (1995)'),\n", " (0.092204737242516085, 'Fight Club (1999)'),\n", " (0.088830550133604566, 'Dark Knight, The (2008)'),\n", " (0.084454786769486812,\n", " 'Star Wars: Episode V - The Empire Strikes Back (1980)')]" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.081268064072003571, 'Sabrina (1995)'),\n", " (-0.072882174081468354, 'Bend It Like Beckham (2002)'),\n", " (-0.069120924379602863, 'Pay It Forward (2000)'),\n", " (-0.066709931261842326, 'Legally Blonde (2001)'),\n", " (-0.065476502200492157, 'Postman, The (Postino, Il) (1994)'),\n", " (-0.065083000473015226, 'Sense and Sensibility (1995)'),\n", " (-0.062875264063149472, \"Romy and Michele's High School Reunion (1997)\"),\n", " (-0.059339590424728771, 'Beverly Hills Cop III (1994)'),\n", " (-0.05781742852876845, 'Sliding Doors (1998)'),\n", " (-0.056878951867276789, \"Singin' in the Rain (1952)\")]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0))[:10]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can draw a picture to see how various movies appear on the map of these components. This picture shows the 1st and 3rd components." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "# The following would be for Python 2 only\n", "# reload(sys)\n", "# sys.setdefaultencoding('utf8')" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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m709lbOs2ZJeW8GbNbPPxkhIAQt08+GjIaO5ZuZink1ZRbZp08vHjvcGjrO2k\nnShkw/GjONrb8+/+wymoKOfZDas4VlLMv/oPt9YLqhljelGhwq+IiIjIOVL4lfMqIaAVz/Tsb33s\nbH/yR25Pfh5lVVVMXbaA0/YvoqK6mlC32ss/u5y2vPgUi2HQ0du30b5v7NCFv6z7mW/3p9KnVQgj\nwqPOunHV6ePNLy9neupOblq2gK8umkCwm3uj556yIzeLw0WF9Jj5Sa3ykspK0goLmtZGThZbs4/z\n/q5frjutNk1Kq6o4XlpiDW5dfOu+NqebFNWem5bOZ+Tcr+gXHMqg4HAGhoTX2jSqycyTc8unziyu\nrOCNbRtZlpHG8ZJiKs1qyqqqiDntfbm0TQceXfMTj8f1xsHOjtkH9tS5zvZMBr9ibM1UVs8MblP0\nDw7jkbhePL1hFY+tXY6jnT13dokj6fgR7GqGfbykmCfWLefiyLaMa92GosoK/rVtA/euXMwnQ8di\nZxiYmBgG/LPPEDwcHQH4v/i+3LxsAVklxfjXvL9ONZ+X0qqqlnniIiIiIn9gCr/SomZtSuelhSlk\n5JXg0aaQqCCXemdczZog9fbAEYS41Z7ls5yxoZWLfd0fU0d7e+v1lA25Ozae8ZFtWZ6Rxooj6by5\nfRNPJvRjSpuYBs9xsVhqjbezzwASvv6UGXuTubdrgjU0nr7EuKK6ulYb1aZJBx8/Xu07pE77Xk1c\nZluNyZ+6xNWZ8QasS2dPjbcxnX39WTzhclZkprP6aDqPrPmJDj5+fDRkdLMD8J6CPADrzsMvbFrL\nz5mHeSSuF63dPXGxWHhkzU+1Xo/BIeE4WywsTNuPh6MjheVljG/dtsE+ojy8KKwo52hxUb2zv3vz\n82hbMwN9LnycnNiZm/2rzr2xQyw3xHThWEkxXo5OpBcV8s8t6wmreV2mpe7Exd6h1jXLL/UZwqDZ\n09mYdZSEgFYEOLsS5OJmDb6A9YuZjOIia/jNLysFar/nIiIiIvLr6D6/0mJmbUrnsW+2kZ5XggkU\nl1eRfKSQWZvS69Rt4+WNo509GcUnaO3hVevPmTO/5yLSw4vrYrrw7qCRTI5uz8x9Kc063zAMDAxK\nK0/uwnsqhBwvKbbW2XVGiOrs48+hwgJ8nJzrPDfvJoaYTj5+7CvIr3N+aw+vOl8OnI27gyOjIqJ4\nKrE/7w4ayZqjGRxs4gz0KVXV1XySsp0Id0/rjPvG40eZGNWOkeFRdPDxo5WrG4fOaNdiZ8clUe34\net9uvt6Rws2rAAAgAElEQVS3m4vCImsFvjONDI/Cwc6O93dtrXNsUdoBDp4oYHxkm2aNvT4dffzZ\nX5hHtVnfldJnZxgGQa5uOFsszD24j2BXNzrXLOUurazEzq72FwvWL01q+usREMSxkiKKKiqsdQ7U\nXIsdetoKg9T8XBzs7Gh/llUOIiIiInJ2mvmVFvPSwhRKKmovz6wyTV5amMLEuNBa5e4OjtzUMZYX\nN63FNE0SA1tRXFnJ5qxj2BkGl7ftcE5jKa2s5IXNaxkVHkWomwfZpSVsPH6UrvUsoT5deVWVNdgW\nlJfxeepOiisrrLsCO1ssdPcL5L1dW4jw8KSwvJxXtqyv1cb4yLZ8kLyNO5cv4s9d4wl2dedI8QkW\nHz7IFe06NmnH57u69OD2nxYS4ubO6Igo7O3sSM3LZWv2sVozimfzUfI2Apxd6ejji8XOju8O7sXd\nwYFWDVxTe0pOaQlV1dUUV1aQkpfDxynb2ZWbzbuDRlpn3CM9vFiUdoBhoa2x2Nnx5vaNlFXXXZ57\naZsY3tu1FTvggyGjG+032M2dR+J68dyG1TjY2TEpuj3O9hZWHUnnpc3rGBMRzegmXDN9Nr2Cgimr\nqiIlL6fWPaczik6QX15GelEh8MsXGxHuntaNqN7ftZUBwWHYGQY/pB3gvV1beK3fUOvrMigkgo9T\ntvPG9o0nlz1XVPDKlqSTAbnmGvVxrdvw1o5NPLZ2OXfH9qCgvIznNq5hZHgUfs6/bN6WdPwI8QGt\nmrRJmoiIiIg0Tv+jkhaTkVfSrPJ7Y+Pxd3bhw+RtPJm0EncHRzp6+9a7029z2RkGBeXlPLZ2OcdK\nivF2cmZISPhZbxmz6mgG/Wd9AYCbxYFoT29e7z+MXqfdHunvvQbwxLqfmbJwFhHunvwtoR9XL55r\nPe5isTBt2Fj+uWU996xYTGFFOYEurvQKDGny7sIDgsN4Z9BI3tqxiQ+Tt2Jv2BHp6cUlNbeBaio3\niwMfJG/lQGEBBidnlN8bNOqsYWrsvK8BcLU4EOrmTu+gYF7oPci65Bng0R69eGLtz1z941w8HR25\nPqYLZfVcmxru7mndsbpXE+4RfG37zkS4e/L+rq1M35NMZXU1kR6e3B3bg2vbd27W82+Ij5MzI8Ii\nmXNgT63w+69tG/h2f6r18cQF3wLw6dAx1p+B5Rlp/GfHZsqrq+jg7cubAy5iUEi49Zw+rUL4Z98h\nfLBrKx/s2oqTvYXufgG8P3iUdfdpNwcHPhoymmc3rGbKwll4OjoxPKw1D3RLrDXOuQf38efYHi3y\nnEVERET+6AzzVy77u5ASEhLMpKSkCz0MOUO/fywhvZ6gG+rtwspHh16AEcn/ijHfz2R8ZBvu6Bx3\noYdilZqfy3WLv2fR+Mtwd2h4KfaFsiz9EC9uXsec0Zc0e6m7iIiI2D7DMDaYpplwocfxe6L/UUmL\neWhkDC4OtXfQdXGw56GRDW8wJbYtp7SEL1J3kl5UyOVtO17o4dTSzsuHR+J6cfhE4YUeSr2Kqyp5\nvtdABV8RERGRFqJlz9JiTl3Xe2q35xBvFx4aGVPnel/54+jz7TR8nJx5KrH//+SOxRObuYz8t9SU\n+0GLiIiISNNp2bOIiIiIiMjvjJY9N5/W04mIiIiIiIjNU/gVERERERERm6fwKyIiIiIiIjZP4VdE\nRERERERsnsKviJwXgX+z8N2Orxt8fKHkleTS6cUQ9ufsvdBDqdfNMy7nrZWvXOhhiIiIiNgchV8R\n+VW2Zmyk1ZOOjH1/QJPqb3vwMCNixp3nUZ3da8ufZ3i70UT5trGWPTHvPi56pxfhz7gR/2qbes+b\nvf0rhrwdT+tnPejxSjRvrHi5Tp2vt0631un8Uih3fH0dRwuP1KpTWFrA4/PuJfblcMKedqXn6zHM\n3v6V9fgDg//Caz8/T0Fpfgs9YxEREREBhV8R+ZU+3/ghNybeQfKxHew+vuus9YM8WuFkcfoNRtaw\n4vJipm38kKt63FirvNqs5vJu13JZt2vrPW9x6nxu//oaro2fyk93buGFcf/mnTWv88HaN6111h5a\nyV3fXM/l3a5l+V1b+eSKr9l9fBd3fv1LmxVVFVz66Uj2Zafy3qXTWXX3Tv418QMifCKtdToFxdLa\nJ5qvtkxr2ScvIiIi8gen8CsizVZSUcI326ZzbcJUxneazLSNH571nDOXPWcWpHPrV1fR7nl/2j3v\nz1Wfj2dfdqr1+ItLn2Lgm934dtsMEl9rT9Rz3lw3/RKyi7KsdXYe3cbkjy8i+u8+RD7nxeC3erBi\n/9IGx7A4dT6GYdArol+t8ufHvs7U3n8i2q9dved9tWUaI9qP46aedxDpG81F7cdyT/9H+PeKlzh1\nr/SktDWEeIZxe997ae0TRUJ4b6b2uosN6eus7Uzf9DFZxVl8euW39G7dnwifSHq37k9caGKt/kbG\njOPb7f8962sqIiIiIk2n8Csizfbdzq8J82pNp6BYLu12NV9t+ZyKqoomn19cXsykj4fjZHFm1o1L\nmDd1BUEewUz5ZCTF5cXWeofyDjBr+5d8fMVMvrxuPtszN/P3xf9nPX7HzGsJ9Ahm4S2rWXL7Bh4a\n8lecLM4N9rvm4Aq6BvfAMIxmPd+yyjKcz2jX2cGFjILDpOUdBKBnRF+OnshkYcp3mKZJdlEW326b\nwfB2o63nzE+eTc/wvjw27x46vxRK/zdieXHpU3Veux6hiWxKX09JRUmzxikiIiIiDVP4FZFm+2Lj\nh1za7WoA+kYOwsXBlQXJc5p8/qztMzBNk39N/IDOrbrSLqADL49/m6LyEyza/b21XlV1Jf+e9CGd\nW3UlMbwP18ZP5ef9S6zH0/IPMqjNcNoFdCDary1jO04kMbxPg/2m5R+klUdIs5/vkLYjWJAyh6V7\nfqC6upq9Wbt5e9WrABw9kQlAYngf3pnyBXd8fR2hT7vQ8cVWmJj8e9JH1nYO5u7nu50zqayu4Iur\n5/DI0Kf4JOldnv3x8Vr9BXmEUFFVwZHCjGaPVURERETqZ7nQAxCR35d92XtYe2gl/5n8OQCGYTA5\n9kqmbfyQ8Z0nN6mNLRkbOZS3n6i/e9cqL6ko5sBpuzCHebXG09nL+riVZwhZRcesj2/vcy/3z76V\nLzd/yoCooYzrdAntAjo02G9pRQkBbkFNGuPpro2fyoGcvVw//RIqqivwcPLkll5389Kyp7EzTn6H\nmHJsJ4/Pu4f7Bz7BkLYjOHoik6d+eJQHv7uDNy/5GDh5bbG/WyCvTHgHezt7uoXEk1uczV8XPMCT\nI160zki7OLhYxysiIiIiLUPhV0TOatamdF5amEJGXgmObl9QVV1F3KtR1uOnrntNz08j1Cv8rO1V\nm9V0adWdd6bU3dTJx8XX+ncHe4daxwwMqs1q6+OHh/yNKV2vYnHqApbu+YGXf3qGl8a9VWdDq1N8\nXf3JL8096/jOZBgGfx3xD54Y/hzHThzBzzWAn/cvBqC1TzQAr//8AnGhifyp/4MAdKYrrg5uTPhw\nME8Me5YQrzCC3FthsXfA3s7e2nb7gA4UVxSTXZyFv1sAALklOQD41TwWERERkXOnZc8i0qhZm9J5\n7JttpOeVUE0VuRU/4Fp9PX8ZPJ8lt29gye0bWHrHRjoFdWX6po+b1GbX4Dj25+zBz9WfaL+2tf74\nuPqevYHTRPu145bed/PFNd9xVdxNfL7xgwbrxgZ3b9LO1A2xt7Mn2DMUR4sj32ybQUJ4b2tgLako\nrhVqT9UHrIG9Z0RfDuTspbr6lwC/NysVVwdX/Fz9rWXJR3cQ7BlKoHvzZ6lFREREpH4KvyLSqJcW\nplBSUQVAlV0SUIBZMZwvVkPHoC7WPxO7XMZ/N31inQVuzOSuVxHgFsR10yex6sBPHMzdz+oDy/nr\nggdr7fjcmJKKEh6Zezcr9y/jUO4BNhxey7pDK4kJ6NTgOUPajmD38V3kFGfXKt+XvYdtmZs5UphB\neVU52zI3sy1zM+WV5QBkF2Xx0br/sPv4LrZlbuaJeffx3Y6ZPDvqFWsbI2PGsSB5Dh+t+w8Hcvax\n9tBKnph3H12DexDmHQHADYm3k1uSwxPz72NPVgpL9izkxWVPcUPi7bU24VpzaAVD2oxo0usgIiIi\nIk2jZc8i0qiMvF+uO62yW4SdGYuBZ61ygAmdp/Dsj4+zbO8ihrRtPLi5Oroy+6alPLvocW6ecTl5\nJfm08ghmYJuheDn7NGlc9oY9+aW5/HnWzRwtzMTH1Y8R7cfy5IgXGzynU1AscaGJfLttBjf3utNa\nfv+cW1l1YLn18bD/JACQdO8e6z14v9zyGU8tegRMk/jw3nx742J6hPW0nnNF3PWcKCvkw3Vv8eQP\nD+Hh5EX/qCH89aLnrXVCvcL58tr5/HXhgwx9O55A91ZcGXcD9w98wlqntKKUebtmMePaeU16HURE\nGjJr3XqiAgPpFtn6Qg9FROR/gtGUWZr/NQkJCWZSUtKFHobIH0K/fywhPa/uxkuh3i6sfHToObf/\n9Fdf4+vhzp9GjTzntppiSeoCnph/Pyv+tK3OMuX6HMnL45p/vcmbU28kJqT5O0U31wdr32JByhy+\num7BObWzMiWFdxctJjM3j+FdY3n44vEtNML6Xf36G1ycmMBlfXuf135+bb8PfPI5I7t3ZUS3rue9\nrwth+NPP8dcplzCwU8cLPZQm234ojdfnLSAtK4tO4WG8cv2157W/+z/5jKjAAO4ePeq89nO2fu96\n/0Mu79eXgR0b3pzvlC0HDvLK3Hl8eOdt2Ns1b7He7PVJLNq6jZevuwZnB4ezn9AMT3/1NR3DQrm0\nz9k/C4ezc7j340/5+K7bcXdu+FZ0F0pz3g+R/zWGYWwwTTPhQo/j90QzvyLSqIdGxvDYN9usS58B\nXBzseWhkTIPnvDj7O/KLi3nuyssbbfvbtesBuHPkb7fEd2i7UdyUnUpGwWFu/NfnjdYd0a0r1w0a\ncN7GMvzp56x/d3F0JNzPF79W2Tw/5vVzbvufc75ndFx3JvVMwNnR8ZzbO5s3p974m/Tza6zZncrx\nggKGxXa50ENptqtff4Oj+fkNHu/aOuK8hsbT+3eyWAj28WFizwTGxfc457bfWvgDbYICee7Ky3F2\nbNlwVp8nL5uCpZkB8ny4ZkB//rPoR/p3iMHuLPccf/fHxVw9oJ81+C7cvIV/z1/I3McebvS85PQM\nvtuwkX+eh+ALcM3AATzwyWeMjut+1kD74ZKlTEjoYa1XXlnJa9/PJzXzCIeysujcwBcfs9cnMXt9\nEkfy8gn08uSq/v1qfXlVWVXF9BWr+GHrVrIKCgn392PqsKH0bNum3nF8sWIlHy5ZxsWJ8bW+AGnO\n+yEiv38KvyLSqIlxoQDW3Z5DvF14aGSMtfxcTOqVyKReiefcTnPd0vtuAL68/x5r2Zrdqbwyd16t\nMkeLhROlped1LPePG0Pv9u04UVrKl6vW8MPmo1zZ3a3euhVVVTjYn322+kRpKQUlJSS2icbf07Ol\nh1wvb7f6x/y/4Nt16xnRrWuzZ87+F7w59Uaqa1Zo7T1ylMe++C9v3HwjgV4n31dLE34eztW1A/sz\nPiGekvJyFm7eymvfz8fN2ZkhneteX19VXY2dYdS6hr0h6Tm5TEhMsD6X883TxeU36edserZryytz\n57EudQ+927drsN6OtMOkZWUz6FfM5ncIDeH92289l2E2KjookGAfbxZv287FiQ1POh3LL2Blym5u\nGzHcWlZVXY2jxZ6LE+NZt2dvvb9j5yRt4L0fl3D/+LF0CA0hJT2DV+bOw8PZmT4x7QH4aOlPLNq6\nlfvHjSUiwJ+kvft48suZvH7j9bQLblWrvZ2H05m3cRPRQYF1+mrq+yEitkHhV0TOamJcaIuE3VNO\nlJby7o+LWZW8m7LKStq1asVtI4bVWlb8865kPvlpOenZOXi7uTIuvgdX9e9n/U/11a+/wei47hwv\nKGDp9h24OjkxqVcil/ft0+Rx+Lq7W/9+albi9LJTYwU4ml/AB4uXsSMtjSBvb+4aeRHxbaKt9Q4e\nP847ixaz7VAaThYLcVGR3DHyojrtncnd2Rlfd3d83d25d+xolm7fwardqXQKD7POoMdGhDNrXRKV\nVVXMfPA+CktKeGvhIlbvTqW8spLO4WHcNXIEkYEBbD5wkAc/PTmj/eBnJ28l9fJ119A9sjU70g7z\nweKlpGRk4O7iTJ/27bll+FDcnJwA2HrwEO/+uJgDx45jZ2dHuJ8vD04YR1RgICdKS3lj/kKS9u6j\nqKwMPw8PJvVMZHLvntb34/QlwUfz83lrwQ9s3H8AgPjoKO4aNYKAmjD+ybLl/LwrmasH9OPDpcvI\nKyomLiqSB8aPxcvVFTg5e/XR0mWkZh6hsqqKqKBAbhs+jE7hYU1+j/OKiti4bz+3Dh9Wq3zm6rUs\n3LKVzNxc3Jyd6Nm2DbddNLxZyzJ/3LqNf81bwKOTLqZvzX/IT1dVXc2rc+ex+cABck4UEeDpwZi4\nOC7t27vJM0ynf6lw3LWgpsy13p+rgpISnv7qa9bt2Yu3mxs3DB7I8K6x1uNZBQX8Z9FikvbuA6BT\nWCh3jhxBmF/jO6y7ODpZ+7tp6GB+2rmLVckpDOncyfo+XtqnF5//vIKjefnMfuRB7O3seH/xEpZs\n30lRaSltWgVx20XDiY0It15KAPDynLm8PGcuD00Yx8ju3c76Odp39BhvL1xESkYG1aZJiK8Pd464\niO5RkVRWVfGfRT/y885kCkpK8HZzZWiXLtwy/OTlGWcuP27scwS/zLI+ffmlvLVwEUfy8ogJCeHB\nCeMI9jl5j/KMnFze/mERyekZlJSXE+bnyw2DBzUaouzt7OjZrg1Ld+xstN6S7duJi4rEqRkzt6Zp\n8uWqNczduJHswhOE+PpwRd8+tX4O9h09xts/LGJH2mGcLBb6xLTnzpEXWX/2T/3eiY+OYsaq1ZRV\nVNIvpj13jxlVaxa5T/v2LNm+o9Hwu2zHTiIDAwjy+uV+7S6Ojtw7dgwA+48dqzf8/rh1G2N6xDG0\nS2cAQnx8SMnI5L+rVlvD749bt3FFv77W13BCQjwb9+1n5pq1PDbpYmtbJ0pLef7bWTw4fhyfLv+5\nTl9NfT9ExDb8/r4GF5HfNdM0eWL6DLIKCnn2ysv4z603E9s6nIc+nUZ2YSEAuzMyeWbmNwzoEMN7\nt9/C1GFDmb5iFbPW177W/+u164gKDODtW2/m8n59eO/HJexMO2w9/uLs77j69TdaZNwfLVnGpJ4J\nvHPbLcSEBPPsN7MoKa/ZDbqwkPs+/oyowEDeuPlGXrz2KkrKy/nrf7+yzto1hcXeHnt7e6qqflli\nvvXgIfYdPcbzV1/Bi9debX1eyekZPH35pbxx8404Ozjw2BfTKauooHN4GB/ccXLG52+XTubL+++h\nc3gY+44e45HPv6BPTDveue0Wnrx0CnuPHOXlOXOBk0HtrzO+IjY8nHdvu4U3br6BS3r1xM44+c/E\nR0t/Yv+xYzx75WV8fNcdPDhhHP6eHvU+j2rT5K8zviK3qIiXr7ual6+7mqzCQv4246tau4Efyctj\n2Y6dPHXZFF64+kr2HDnCh0uWWY+XlJczvGssr95wHW9MvZG2rYJ4fPoM8ouLm/yabk9Lw8FisQaa\nUwzD4M6RF/H+Hbfy+KSJJKdn8sb8hU1u95u163hjwQ88e+Xl9QZfOPmz7u/hwV8mX8KHd97GjUMG\n88WKlSzcvKXJ/TTH58tX0DemPe/cNpXBnTvx8py51iXLpRUVPPDpNBwtFl65/hr+ddP1+Hm48/Dn\n0yitqGhWP44WC5Wn3a7rSF4eS7bv4K9TLuGd26biaLHw3o9LWLZjJw+OH8t/bp1KVGAgj02bTnZh\nIQGennx5/z04Ozhw58iL+PL+exjcuVOTPkd//3YWvh7uvDH1Rt65bSrXDRqAo+Xk9/jfrlvPyuQU\nnpg8iY//dAd/mXwJ4f5+DT6Pxj5Hp1RUVTF95SoenDCWf910PSfKSnnt+/nW4yXl5fRs24YXrrmK\nd26byoCOHXjyy5kcyspq9DXsEBLC1oMHG62z7VAa7UOCG61zpo+WLmP+5s3cPXoUH9xxG1f268tr\n389nze5U63gfmzYdF0dH3rj5Rp68bAo70g5bfw+c3vf+Y8d58Zqr+cvkSaxITuGbtetq1YmpmZEt\na+TnZ9uhQ7QPbt5zgJOv+6n39RRHi4WU9Awqa34/ltdTx8nBge2H0mqVvTp3HgM7dqR7VGSD/TXl\n/RAR26DwKyK/qc0HDrL3yFH+dulkOoSGEurry41DBhPs482PW7cDMHPNWrq2juD6wYMI8/NjWGwX\nLu3TmxkrV9dqKyE6iok9Ewn19WVSz0RCfX2sM41wchY3xKdpu0efzeTePekT054wP19uHjqYwpIS\n9hw5CsB3SRtpExTELcOH0jrAn+igIB6ZOIHkjAx2Z2Q0qf3yyko+X76C4rIy4qIireWOFot19jU6\nKJDD2Tms3p3KfePG0LV1BNFBgTwycQJFZeUs3rYdB3t762yhh4sLvu7uONjb8+XqNQzu3IlL+/Qm\nzM+XjmGh3DN2FD/vSia3qIiisjJOlJbSu307Qnx9iPD3Z1hsF1oHnLz/8LH8fNoGt6JDaChB3l50\nj2zd4HLMTfv2s//oMR6/ZCIxISHEhITw+CUTSc08Uuv9qaqu5qGLxxMdFESn8DDG9ohj02nH46Ii\nuahrLK0D/Inw9+dPo0biaLFn/Z69TX3bOJpXgLeba50lz5N79yQuKpJW3t50i2zNLcOH8tPOXU36\nsuKjpcv4YsUqXrr2arq2jmiwnsXenhuGDKJDaAitvL0Z3LkT4+J7sGT7jiaPvzmGd41leNfYms/U\nIOzt7Nh28BAAS7fvAEwemjCO6KAgIvz9uXfsGErKK6zB6GyqqqtZuHkL+48dq/UzWlFVxaMTJ9Au\nOJiowEDKKyv5LmkDU4cNpXf7drQO8OfesaPxcXdj9voN2NvZWWdy3ZxOzio7OTg06XN0LC+f+Ogo\nIvz9CfX1pX+HDtaVAEfz8wnz9SM2IpwgLy86h4cxqnu3ep/L2T5Hpz/nP48eRYfQUKKDgrisT2+2\nHjxo/RKnTasgxifEEx0USKivL1cP6E/b4FYs35nc6Gvp5+FBVkEhVad9iXCmo3n5+HnU/wVTfUrK\ny5m5Zh0PjBtLz7ZtCPbxZlhsF8b06M6cpA0ALNm+g5KKCh6dOIHooJO7QN83bgwrklNIz8mxtuXm\n5Mi9Y0fTOsCfhDbRDOzUsdZnE8Dfw53K6mqyC080OKZj+fn4ezS++qU+CdHRLNi8heT0DEzTJCUj\ng/mbNlNZXW398iuhTTTfrF1HWlY21abJhr37WLErmZwTv4zn+42byMjN5cYhgxrtrynvh4jYBi17\nFpHf1O7MTMoqKpj88qu1yssrK8nIzQXgUFYWvdq1rXW8S0Q4ny3/maKyMusy3agzrt/yc/cgr7jI\n+njqsCEtNu7owF/6OvUf0ryik33tzsxk66FDjHu+7m2WMnLz6BDa8JLxf8yaw4uzv6O8shI3Jydu\nu2gYPU977pEBAbVmNw5lZWFnGHQK+6VNd2dnogIDOHi84dmm1MxMMnJyWbZjZ51jmTm5dAoPY2S3\nrjw6bTpxUZH0iIpiQKcO1uWK4+N78PTMb0jNPEJ8VBS927dr8PYph7Ky8fNwp5W3t7UsxMcHPw8P\nDh3PIj46CoAgb69ay4z9PDysrylAblERHy/9ic0HDpJbVER1dTXllZUcyy9o8Hmeqayyos7sEMCm\n/QeYvmIVh7KyKCoro7q6moqqKnJOnMC/kcDx7bp1FJeV8+bUm866XBjgu6QNzN+0maP5BZRVVFBV\nXU3gaUtAW9Lp1zPa29nh5eZKXtHJoJCaeYTM3DzG/+OlWueUVVRYP3cN+WjpMj79abn1mvPL+vSu\nteFVgKcHPqctw87MzaWyupou4eG1xtMpLKzRGdGmfI4m9+7FP7/7nh+2bCUuKpIBHTsQ4X/yC5qR\n3bry8OfTueHNt4mPjqZn2zb0bNe23iXmTf0cOdjb15o99nN3p6KqisLSUjxdXCgpL+ez/2fvvqOj\nqNoADv82vfdOEkIKCSEhhN6LdCnSpCmKioIVUQHhUywgKCI2pEgRBBSk9947hJIQSmjpvfe+8/2x\nsLAkNMWG73NOzmFn7szcmdld9p1773v3H+TYlatkFRRQUVlJWUVFtWNLb2dsYICC5nvP9C6J4soq\nKqp9795NbHoGZRUVjP9luc7ySrUaZxvNey4uPQNvJyfMbnyHAtT1cEdPpSI2PYMadpr3tKeDo84D\nIwdLCy4l6j7IMzLQdIEurbh7y29pRQWGD3EONz3bphVZhQWM+kkzd7ythTmdQ+qx4shR7f18vUsn\nZmzawkuz5wLgZmdLl/ohbLvRsyI+I5OFe/bxzbDn7js2/kHuhxDi8SDBrxDiL3Xzh8zXw56rsu72\nH2R3c/vPWIM7pypSwZ81fZu+/q0fgjfHHd88lqIoNPXzZUSnDlW2s71PIqhXOj5BY18fzIyNqy37\nMFlw75VkSFEUuoXW147Rvd3NYG/MUz3p27QJJ69d48jlyyzcu49PBvSn8Y0gYtmoNzhx9RpnomP4\n368raBtYhzEPO4XSbVW88/6pQKflddq6DWQXFvJq54642NhgaKDPmCXLKL+tW/j9WJuZUVCsO6Yw\nNSeX//26gicb1GdYuzZYmZlyJTmFz9as03apvJsgDw9OXrvO3sjzDL1PJvC95y8wa/tORnTqQF0P\nd8yMjVl/8hSHL0U9cP0fxp2ZjFWotNdTrSj4ujjzv359qmxneZ9EUP2aNaVbaAjGhobYW1hUeZ+Z\nGD6aYOFBPkfPt2tDh+AgTl69xslr11iy/yCjunejW2h9/FxdWfbW64Rdu87p6Bimrd+Ij7MzXwwd\n8lBZfG8/vypJ0u747M/duZuwa9d4pVNH3O1sMTY05It1G6iovHcLYl5JCUYGBvcMtKzMTMkvrjrN\n3Kh2Ac0AACAASURBVN3crNOkQQOqJBGr8l1ZDZ3vVv07Owaqqny33qybzY0x+tWxNq36+XsQxoaG\njOnVk9HdnyS7sBA7Cws2nz6DmZER1jfeCzbm5nw68GnKKirIKyrC3tKS+bv3asdjX0hIILeoSBsc\ng+ZzcC42jo1hp9k0fqz24cKD3A8hxONBgl8hxCOx7kyiNiN0TYtkfByqD2T9XFzILihEpVLdtUuy\np4MD528buwuaOUEdrSwfKED+q/m5urD/wkWcra0fOvuunYWFtrXlQXg6OKBWFC4kJGq73BaWlhKd\nln7XLp4Avi4uxKSn3/dYPi7O+Lg4M6hlC8Yv+5UdEedofGPqEGszMzrVC6ZTvWAa+/owZfVaRnXv\nVqV1ytPBnsz8AlJycrStv0nZ2WTm51PzRivdg4iMT+D1rp21SWiyCwrIukcXy+r4ujiTU1REblGR\nNpFWVHIyFZWVvNq5kza4OXb56gPtz8/VlX7NmjJu6S+oVJopX+5a/7h46tSoQe8mtzKaJ9+nlfXP\n4ufqwt7I81ibmT30XKtWpqYP9R51tbXFUF+fyPh43Ow0n/FKtZoLCQnaBEZ3q+ODfI7c7e1wt7ej\nT9PGfLN5K1vPnKVbaH1A8wCtTWAd2gTWoUtIPd5cuIikrCzc7XXH/v7ez9GdIuPj6VQvWDtHrKYH\nS06V490pJi0NPxeXe5bxdXG579jh29V0dMBQX5/U3Fydbum383R0YNvZcIpKS7XfpefjE1ArCp6O\nD/7ZBIhJT8fBUrfV/06+rs7EPsQ53MlAX1+bJG9f5AWa1var8iDDyMAABysrKiorOXjxknY4RssA\n/ypjpr/csIkadnYMadVCJ3P+g9wPIcTjQcb8CiH+sHVnEhm/5hyJOcUoQFFZBZdTcpi3L4KrKSna\nv5ScHBp416KuhwcTV6zkxJWrJGfncCE+gcX79mvHJz7dvCkRsXEs3neAhMxMdp+LZNWx4wx4iEzO\nAPN372XMz8v+hDPW1atxIwpLSpm8ei0XExJJys7m1PVoZmzaTFFp6SM9lru9HS38a/PN5i2cu5EM\n6/O16zE3NrpnYDGoZXOiEpP4ZvMWriSnkJiVxbHLV/h60xYAkrNzmLdrD+fjE0jNyeVsdAzX09K0\nweqivfs5fCmKhMwsYtMzOHTxEq62ttV2y2zgXYtazk5MXbOeqKQkopKSmLpmPX6uLnf9UX63c90d\nEUlsejqXEpOYvHrdQz9c8HVxwcbcXCcJjrudLWpFYc3xEyRna5I1rb4jmU9GXh4v/DCHQ5eqjt0M\nqOHGF88OZuXR4yw9cEi7fN2Jk7zwwxyd+l9JSeHElaskZGax9MBBwm+8x286dOkSL/wwh4y8B+/K\n/Xt0CA7C1sKcictXEh4TS3J2DhGxcczZsYuEzKz77+AhmBoZ0bNRA+bv3sPxK1eJTc/g2y1byS4o\npFejhnfd7n6fo9Lycr7bso2zMbGk5ORwMSGRyPh47bj0VUePsyfyPLHpGSRmZbHnRhb46qb7+r2f\noyr7sbPj0KXLXElO1iSmW7uesoqK+253Li6exr7e9yzTyMe7SvIm0LTw3v69ejUlhei0NMyMjXm6\neTPm7tzN1jNnSczK4mpKChvDTrHp1GlA8z4wudE6fT01jYjYOL7ZvIVWAf4P9YDj5jk08rnPOXh7\nczEhscpY2tj0dK6mpJBbVExJWZn2PG5KyMxkZ8Q5EjKzuJSYyOTVa4lOT+elJ9ppy1xMSOTgxUsk\nZWdzLjaO8cuWo1YUBrbU/D+h6cbupPNnYmiIlalm+e0t/A9yP4QQjwdp+RVC/GFfbo+iuFy3u6ix\nXgErDmxkxYFby1rXCeCjp/sxZchAftq7nxmbtpBTWIithTl1PTzoVK8eoGld+7B/XxbvP8Cvhw5j\na2HOoJbN6X2PKTWqk1VQcN/xjI+Cg6Ul37zwPAv27GX8L8spq6jAydqKht7ev2u82/2M6dWDWdt3\n8uGKldopWqYOGXzPKVG8nZ2ZMWwoP+3dz7uLl6BWFFxsbWjl7w+AiaEBiVlZfLpqNXlFxdiYm9Mh\nKIhBN35IGhros3DvPlKyczAyMKCOew0mDRpQ7bFUKhWfDnyaH7bt4L0bDx8a1PLijW5dHmj+15ve\n69mDrzdt4dV5C7G3tOC5tm0eKtMzaLqtdq0fwu5zkbQM8Ndei9e7dGb5kSP8tHc/dd3dGdGpA5NX\nr9VuV6FWE5+ZSWFJ9Q8vAmrU4ItnBzNu6a+AZoxiblEx8ZmZ2jI9GjbgWkoqU9auR1EUWtcJ4Olm\nTbVjEgEKS0qJz8zUyZ78ZzAxNOTr54cyf/deJq1ac2O6KgtCvGpiafpwLcEPYngHzfRCX27YpJ3q\naOozg++ZwOlBPkf5JSV8uX4jWQUFWJma0rS2LyM6aeaQNTU24rcjR0nMykaF5sHH1CGDdKbnud3v\n+Rzd6dXOnZi+cROjFy3BwsSEvk2b3Df4zcjL40J8gs50PNXpGBzE/F17iElL18lWXlpRwcgfF+iU\ntTI1Zc2Yd3ihfVtsLcxZefQ4323ZhpmxMT7Ozgy8MQWZiaEhU58ZzOztO3ljwU8YGRjQ4sZURw+j\nrKKCQ5ei+PyZwfcs18TPF2NDA05evaYzjdCEX1ZoM5ED2vPZNfF/AFSqFVYdO05CRib6+vrU96rJ\ndy88r5NHoKyigp/27ic5OxtTIyOa+Pkyrk+vh+7Z8KD3QwjxeFD9WePj/kyNGjVSwsLC7l9QCPGX\nqPX+Zqr7JlEB0Z93/6urI4SO7MJCXpo1lx+Gv6gdDyjE32Xuzt0UlpbwTo/7fzfO27WH3KIi3uvV\n4y+o2YNbfzKMI1GX+eLZIfctuzHsFAcuXOLL5575C2r28B7mfgjxT6NSqU4pivJwLQP/cdLtWQjx\nh7nZVJ8w527Lhfgr2Zqb816vHqTd1tIkxN/FxtyMF9rde+qdm4a0bomrrc0/bgoefT093uja5YHK\nPtkglNBaXhSUPHziq7/Cw9wPIcS/n7T8CiH+sJtjfm/v+mxqqM/UvsH0Dr37ND9CCCGEEOL3kZbf\nhydjfoUQf9jNAPdmtmc3G1PGdPGXwFcIIYQQQvxjSPArhHgkeofWkGBXCCGEEEL8Y8mYXyGEEEII\nIYQQjz0JfoUQQgghhBBCPPYk+BVCCCGEEEII8diT4FcIIYQQQgghxGNPgl8hhBBCCCGEEI89CX6F\nEEIIIYQQQjz2JPgVQgghhBBCCPHYeyTBr0ql6qpSqaJUKtVVlUr1fjXrjVUq1Yob64+rVCqv29bV\nU6lUR1Uq1XmVSnVOpVKZPIo6CSGEEEIIIYQQN/3h4FelUukDPwDdgEBgsEqlCryj2EtAtqIovsDX\nwBc3tjUAlgIjFUWpC7QDyv9onYQQQgghhBBCiNs9ipbfJsBVRVGuK4pSBiwHnrqjzFPA4hv/XgV0\nUKlUKqAzEKEoSjiAoiiZiqJUPoI6CSGEEEIIIYQQWo8i+K0BxN/2OuHGsmrLKIpSAeQC9kBtQFGp\nVNtVKtVplUo19hHURwghhBBCCCGE0GHwDzh+K6AxUATsVqlUpxRF2X1nQZVK9QrwCoCnp+dfWkkh\nhBBCCCGEEP9uj6LlNxHwuO21+41l1Za5Mc7XGshE00p8QFGUDEVRioAtQIPqDqIoyo+KojRSFKWR\no6PjI6i2EEIIIYQQQoj/ikcR/J4E/FQqVS2VSmUEDAI23FFmA/D8jX/3B/YoiqIA24FglUpldiMo\nbgtceAR1EkIIIYQQQgghtP5wt2dFUSpUKtUbaAJZfWChoijnVSrVp0CYoigbgAXAEpVKdRXIQhMg\noyhKtkqlmoEmgFaALYqibP6jdRJCCCGEEEIIIW6n0jTA/rs0atRICQsL+7urIYQQQgghhBB/ixu5\nkhr93fX4N3kU3Z6FEEIIIYQQQoh/NAl+hRBCCCGEEEI89iT4FUIIIYQQQgjx2JPgVwghhBBCCCHE\nY0+CXyGEEEIIIYQQjz0JfoUQQgghhBBCPPYk+BVCCCGEEEII8diT4FcIIYQQQgghxGNPgl8hhBBC\nCCGEEI89CX6FEEIIIYQQQjz2JPgVQgghhBBCCPHYk+BXCCGEEEIIIcRjT4JfIYQQQgghhBCPPQl+\nhRBCCCGEEEI89iT4FUIIIYQQQgjx2JPgVwghhBBCCCHEY0+CXyGEEEIIIYQQjz0JfoUQQgghhBBC\nPPYk+BVCCCGEEEII8diT4FcIIYQQQgghxGNPgl8hxH9S/RYerNnwy99djT8kNy+H5h38iYuP/rur\nUq23xgxjwc8z/+5qCCGEEEIAEvwKUcWcBV/T95kOhLbypGl7P0aMGszlqxd0yiiKwndzPqdVp0CC\nm7nx7PCeXLl2UadMbl4O730wkgata9KgdU3e+2Akefm52vWlpSWMm/g6PQe0IrCxE88O7/mH6z5y\n1BACGjpw+NjeByp/Iuwwz43oTZP2vtRrXoMOPRvw7oRXKCjI+8N1+SuEnwtj5KghNG7rTd0mLnTp\n05Tv535BaWnJ3121v8ScBTNo26ojnh61tMsmT3ufvkOeIKipK+2fDKl2uy071tJrYBvqNa9Bu271\nmL/4uyplysrL+HbWFJ7oXp+6TVxo2y2Yn3+Zq1Nm8S9z6NKnKcHN3GjdpS4fTx1DYVGBdv3rr4xl\nzoIZ5Of/O95PQgghhHi8SfArxB1OnDrEMwNeZMWibfz843r09Q0YNrIvObnZ2jLzFn3HT0tm8eG4\nz1m9dBf2dg68MLIfBYX52jLvjH+ZCxfDWTBzJQtmruTCxXDGfDBSu75SXYmxkTHPDBxO21ad/3C9\n09JTOHriAMOeeZXf1i65b/mr1y7x0htPE+AXyJIfN7Bp5WE+/d9XWFpYUVZW9ofr82fbvW8rQ17s\njo2NLYvmrGXHupO8+cpYVqz+mRde7UdZ+T//HP6I4uIiVq5dQv/eQ3WWq9UKvXsOonePgdVut//Q\nTt6d8AoD+z3PppWH+WjCdBYtncOS5fN0yo1+fzgHjuxh0odfs33dCb6b9hP+tetq12/cuopp33zM\nqy+9w9Y1x5g2aTYHDu3ks2njtWX8/QLxqOHF+i2/PcIzF0IIIYT4nRRF+df9NWzYUBHir1JQmK/4\nN7BXdu/bqiiKoqjVaqVFxwBl1rzp2jLFxUVK/RYeyq8rf1IURVGuXLuk+NW3VcLOHNWWOXn6qOJX\n31a5Fn25yjE+njpGeealHn+onrPnz1Bef2eokpAYpwQ1dVWysjPvWf6npbOUVp0D71nm2MmDil99\nW2XP/m1KzwGtlbpNXJTeg9sp586f0ZbJys5U3h73ktKqc6AS1NRV6da3mbJq3VKd/ajVamX+4u+V\njj0bKoGNnZVWnQOVL7/9RLs+OTVRGTX2RaVhay+lYWsvZfgbA5TomKt3rVdRUaHSpJ2vMvLtZ6qs\ni7xwVqkdaqfMW/StdllM7DXlmZd6KHWbuCidn2qs7Nm/TQlp7q6sXr9MURRFGfpyL+XjqWN09pOf\nn6sEN3NTtu3aoCiKomzbtUHp3r+lEtDISQlo6qwEd/FV9kccVqZvn6+8u/hjZeSoIUqjNrWU4GZu\nSufeTZSNW1dp9/Xez58oXZ9rV+35xSfGKrVD7ZSIyNM6x1++epHSuJ2PUlpWWu012LpjndKoTS1F\nrVbrLB+z8nPlhz1LlPmLv1PadatXZbvR7w9XXh39rPZ1Sm660nxEG6V5xwDtvg4e2a2EtvJUMrMy\ntOXyiguUQXNHKYnZqYqiaN6zQ17srrPvb2ZNUZ7s11xn2fdzvlAGDuta7Tk8SvMOrFB+2Lv0/gWF\nEEKIxwQQpvwDYrN/05/B3x18C/FPV1hYgFqtxsrKBoD4xFjSM1Jp2by9toyJiSmNGjTndPgJBvUf\nxtmIk5ibWdAgpKm2TMP6TTEzNedM+Am8vfwe6NgJSXE80b0+n38yk769hty1nKIorF6/jDFvf0wN\nNw9CghqyfvMKhj3z6l23cbB3Iis7k2MnD9Kscet71uPzryfywZgpODu5MnPuNEaMGsyuDacwNTWj\ntKyEDP1SQvv05L0eIzlyfB8TJ7+Dq4s7LZq2BeCr7yfx68qFjH93Mo0btCArO4MLUecor6xgyJy3\nuPjbJrq06sbS+ZswNDRkwc8zGTayD1vXHMPU1KxKfQ4e3UN2TiYvP/8mAEk5aaw4uYnTcefJKc7H\nyt2VeSvm0KLTkwS4ePP6u0OxsrTht8XbKS4p4rNpEygrL9Xub0Cf5/jk8zGMf2cSRkbGAGzatgYz\nU3OeaNOV9IxU3nl/OP2feYmKUE9GtXuelLgY/Jy9aFi7Pm+9O4zKinJ+/nEDFhaWRMdc0e67uLiI\nPYt+xrVWrWrPz93Nk5bN2rF6/TKC64Zqt1u9bhm9uw/AyNCo2nuyZvcaKiyNUalUOss/7PE6+nr6\n/Lp8frXblZWVYnzjHG9S6euTnpFKYnI87m6e7Nq7heDABvy0dBbrNi3HxNgU+1o1Ce3YATcbJ0Dz\nfl65finPfTWSHGM1ZpX6FB+9QNtWnXT2rWdrwZmIk/T6dji2lrb0DHmC/g276ZTZGL6bjeF7SM3L\nwNHSjkFNetCxTkvt+rGrvuBcYlSVc/G0c2Pu0MkA9G/YlRcXv0+f0E64WjtVe+5CCCGE+G+T4FeI\n+/jsy/HU8Q8mtF5jADIyUgFwsNP9ge1g70hqWjIA6Zlp2Nra6wQmKpUKOzsH0jPSHvjYBgYG1PLy\nw8LC6p7lTpw6TG5eNu1aa7pP9+4xkEXLZt8z+O3WqTeHju7huVeewt7OkXpBDWjaqBW9uw/Ezs5B\np+zrL79H6xYdAJj6yUzadAli49ZVDOj7HC5ObtRv24684nw83b3wdB/GsRMH2bRtNS2atqWwqIBF\ny2bzv/em0L/3swDU9PQmNKQJBy6foDIxEyMDQ9oNGEjAjW61kz74muYdarP34Hae7NynSt1jYq8B\n4OPtz+XUaMavmY6HnStvtB+Kh50b32R+yubNq5i9fxkDvVpz9XoUezadxc3VHYAJY6Yw5MUntfvr\n3KEHn04bx449m+jRtR8Aq9YvpXePgRgaGpKWnkJ5RTk169YlJr6cbi26QItb9UlLS6Jzh57U8Q8C\nwKNGTe26zdvXoADtnr77+Q3o8xwfTHqb8e9OxtjYhKvXozh7LozJE7+56/3LykjDwMy0ynJLE4u7\nbgPQqsUTTJn+Pw4d3UOLpu2Ii48m5/xVANLTU3B38yQ+MYZTZ49hZGTEzOmLycjOYNTEERhVAD1f\nA6BH135sDNvBgV9XoygKarWap7oPZMyoj7XHOhkTwZoLe1HUaiZ2Gona1JBvdy/CyMCIXiGa99Om\niL0sPLyKUR2ex9/Fm6iUaL7bvQgLY3OaedcHNAF9eWWldr/lleW8umwibfwaa5fZmFnRwLMumyP2\nMbz1gHteAyGEEEL8N0nwK8Q9TJn+P06dOc4vP21BX1//Lz++i5Mb29cev2+5lWuX0K1Tb20rYdeO\nvfj0i3GEnwsjJLhRtdvo6+vz+Sc/8Pbr/+PYiYOcPRfGgp9nMmfBDJYt2ISfTx1t2fr1bgUZ5mYW\n1PYL5Op1TUtcZWUlp/fs5vKZ02z9+nvKysooLy+jSSNNy93V61GUlZXSvEmbKnXYfv4glqUqinJy\nGfvyYD40uNXKWVxSRFx8zD3PW1EUZuxciIu1I189PQF9PU0aAytTC/T19ZnadwyrV/+Ms5MrpYYw\nfs2XXEi6iqHKAJVKRemNccFGRsZ4BNVl2rypVDhb8fOOX4iMPE2dzk9QUl5KQO0gavj68cWE0ZjV\ncKTFyTC8guryy+sz+WrHAqzr+DB7/lccPLybxo1akmkBUSUpmBgYo38hkcLsbBZO/ICln3yiqTcK\nxSXFTF87ix+vbMfd2hn09NixZxM9u/VnztLvMXawpdAI3l4+iZjMRDztXHmrwzB8nWoSkXCJK8nR\nGFqY0e3bFwF4pmkvnm3Wm7GrvsDLvgY323b3XDrKujM7SchOxsjAiCC32vTrO5RXRz9LRUU5ZmYW\nWPi4k332Eno3rp9arUalUjFjyjwsLa04eCUM95aNObZlDxmZaTjYO3Ei7DDn9u3nkwlfkaZfwrYT\nOzlx6hDfzZ7KqNcmaI598Sj1a9Uljt1YGZlRu1YgAxp1Z2XYFnrWewKVSsWeS0foWrcN7fybAeBq\n7cSV1GhWhm3RBr93BvR7Lh2ltLyMznV1eyw0867PoiNrJPgVQgghRLUk4ZUQdzFl+gQ2b1/D4h/X\n4enupV3u4OAMQEaWbgtuRmY6DvaadY72TmRnZ6IZjqGhKApZWRk4OjzaLpl5+bls372RFWsWU6eR\nI3UaOdK4nQ8lJcUPlPjKxcmN3j0G8vH4L9my+igqlR7zFz/49DQLfp5JxIH91GnZgkVz17F++X46\ntn+S8vLye26XmpdBRGIUrlaOBNQOolafTsyc9Svrl+9n/fL97Fh3kkH9h1W7rVdNHwD2nz5AbGYi\n/Rt01Qa+oAm4vTx9sDDWdJlWFIUP1n2FqaEJ3wz6kAndX0NBE3zfFNC4CSnR0URejSSgwgY//7pc\nLEhk/dld6Ovrs3HJHoa9PRYbZ2ccstVcWr6Ri1GRAPg0CGXPpjP0fWoIe0/vZ+0P3xOQZ8rUvu+R\nU5yPsb0N/UeN1p5bj9deo+0rw5g86jNmP/spnYPboO/hwJKVC6ioqODA3m1Y+dVk0ZHVvNCyP98P\n/ghLEwumbfsRRVGo4+qLv6cflFewbPjXLBv+Nf0adK32WpVXVvBss9788MwnfNJrFPmlhRR42XD2\ncDx7t4Szfu1hjB1sAfCo4QWAo4MLzk6uWFpqehycT7pMgG8gAEkpCQB8PeszenTtq2n9d/fE3seL\nd974kHmLv6eiokJ7bHWp5n1gZ6vpTWBsYERGQTZp+ZnaMkYGhjp1NjIw4nJqNBWVFdWe07bIAzSq\nGYSjpZ3Ocn9nbzILsknKefDeFUIIIYT475DgVwhg3ZlEWn6+h1rvb6bl53t4YcybbNq2hsVz1+FT\nq7ZOWY8aNXF0cObwsX3aZaWlJYSdOUqDkCaApqW0sKiAM+EntGXOhJ+gqLiQ0BtlHpUNW1ZiZ2vP\nhhUHtMHV+uX7mfTh12zZsZai4sIH3pe1lQ1Ojs4U3TZdDWimFLqpqLiQK1cv4uOtuS6nzh6jZp1A\naoXUI9A/GE+PWkTf6JYM4FOrNkZGxhw9cUBnnzsvHKKBRyChwY2IT4ihZZ0mRGTHUNPTW/tnY21b\nbT1bNW+PjY0dy375EQAPO1ftuvMXwzl64gC9nuyvPX5qWgp52dm812U4tRzcUXLyQVG4khZDUo6m\nG7udiwsWzg6YpRaxd88Wnh/wMq39GnM2XjPNlYWJOX4BQXg2bcj6X/fh7OjKlh1rtcd1ca7BUz0H\nod/Il94Dn+Pg7m14ObgzqMPTlOUVYGJuTk1PbwytLDiTeY3PhkygRUATXK2d6BXSgVZPdCX87Al+\n+W0BxcVFWNSqwXPN+xDiUQcPO1eGNO1FfHYyGQXZGOobUMu7NqU5ediZW2Nnbo2pkUm116pL3dY0\nqVUPV2sn/F28eaP9UCKTLpNVlIuLkxuGhkYURCcQUKeetrt7g/pNSEtP0U5blJaXiapYM0a6hqsH\nACUlxejp6faG0NfT13ng06BmXU5FnsTO3hE7OwcSslNYc3o7AFmFOQA09Axix/lDRKVEoygKl1Oj\n2X7+ABXqSvJKdN+HAAnZKZxLjKJrUNsq6+zMNePyU/Myqr0WQgghhPhvk27P4j9v3ZlExq85R3G5\nZkxhypHZpCcf4ZXR32NtZUP6jTG+ZmbmmJtZoFKpeH7ISOYsnIG3lx+1avowa95XmJta0KObZryo\nr7c/rVt0YOJn7zDpg68BmPjZO7Rv00Un2dXVa5coqygnOzuToqJCLkSdAyDQP1hTl7Qknh/Rh3ff\n/JDOT/Sotv6r1i2lS4de1L7RMneTV01fpn39EVu2r9WOtb3d8lWLuBh1jk5PdMfTvRalZSWs27SC\nqCsXeHnYKJ2ys+ZNx87WHidHF2b++CWGhkb07KYJLr08fTix4RiesXFci77MkuXzSEiKJdC/HgAW\n5pY8P2QEX33/KUZGRpqEVzmZLFkxnwmvTqSJRxALf/6BE6vWUubtQFfvZmRmpLFr3xYG939B28p7\nOzNTcyZ/+A2jxr6AWU4NrjW+hFmlPqfDj/PF1xNpWL8Zzw3RTCvVomk77JydyTh0hpheVyktKWbK\n9A8w0DdAT6UiLisJNxtNi31Ak6Ys+Pl7DA0MebJLH9ZG7OZSynXORpzkyPH9lNmaUppfwO59W0lJ\nTcLX25+r5HNq6zYOOAShb2lGYVoG0fFx+Hj7A9Cv52C+mjuF7YsX0bFGKAmlWRSlpPPUG92xreOL\nsbUloGkBtffw4ItvPqJF6w4kGBlSy8Fde872NwK73OJ8HC3tqBMcytpfF5Gdk4Wtza0W0IKsbNJK\nFIzS8ikvL2f70R1sObeXLFUpheXFlJeUknslmtPnT+Fh68LSlQsojEnk5RmTtfvo2a0/s+ZNZ/xH\nb/DmyHEkXr9G+NYddO3YC3s7RwDat+nCT0tnERRYnywKyYlL5Jt1U2jfujMGBpr/WroFtWV+6ZfE\n25vT8/tXMDMypXf9jiw9vl47Hn5w055kFeXy7sopKIqCrZkVHeq0ZNWprajQTeYFmlZfO3NrmtSq\nV2Wd8Y0W5LKKx3uaKyGEEEL8PhL8iv+8L7dHaQNfAP343QD8+OVwfvzyVrk3RozlrZHvA/DysLco\nKS3m08/HkpuXQ0hQQxbOXoWFuaW2/Iyp85j0xThefF0TJHZo242J70/TOfbLbw4kMTle+7r3IE1r\n1uUzWQBUVFQQHXOFgoK8aut+/mI4Fy5FMHHcF1XWGRka8UTbbjfmgq0a/NYLasDp8BN8NOU90tJT\nMDUxpaanN9MmzabXk0/rlH3vrY/4fMaHXI+5ip9PAHO/+5Udl3L4cvtxktLrYWW9g71Ll3F6a8Fm\n9gAAIABJREFU3Sb69hpMr25Pa8cEA7z75kSsLG34Yd50UlOTsLS2QV3DlmnbNC23NK9NQlgkhTsv\n0mNrC1yc3GjauJU2w3Z1Oj/Rg2nTF/DhV+MYN/ZlysvKcHfz5Ok+Qxnxwtva8c96eno89dIrrP95\nAU8P7YSbizvvvzOJdya8fGNPtwKsWkHBXNy1j66demFhbolKpRmfa2Fhxamzxzl97iRFhQV8XuME\nr738Hk91H8BXOxagKAqTvnifpJQEKvWgdouOfHzjXpuamtHy2cHEHD7BW2NfIC8/F0wM6dCqM68O\nGavTur3bdROTPhtDh049WBy1E/3bWlZvBotqRQ1ADU8vTB3t2Lx9Dc8OHK4td2bzVjLjbr2n3nxt\nEACzF6yhtpc/8SnxjNg2mDGjh6FCRWDdENy6tsL/RrIu0IzrXjRnLZO+GEe/ZzuiZ2SIZ506TPn4\ne22Z14a/h0ql4ttZU0lMiUfP2Ij+3QYy+o0PtGXKykqJuRDJvJkrqenjh7WppbYl3cVKE0QbGxjx\nTqcXeeuJ58guysPO3IatkfsxNTLB2uzW5wk0Dwh2XTxM16A2OtfmpvwSTS+HO7cTQgghhAAJfoUg\nKadY53VZF804WRUQ/Xn3ardRqVS8NfJ9bTBcHWsrG6Z/Nveex967Jfye693dPLWBcHXq1gm55/ov\nJ8++67rAgHpMmzTrnse/KTSkCRt/O6R9rdNabmiOWfPu2BiUMbrja/QOrVFlez09PUa8+DYjXnwb\ngM82z0JRFJ5t9tStQi/B+vBdJOek8Xm/sQ9Urx5te7Ip/gQG+gZ8N2iizrhfgILSIiyMzQgJqE9Y\nt9bsfmk6ZkaaDMlLftvFuNXT8Lyty3RZSQklpcVVHhb4evuz4IeVrDq1jY3hu1n84pc66xs92Y1P\nnnqb4rISBsx9k4GdXsLFyQ2AkvJSUktzaD9gIO92fomE7BRe/nkCw/uOIdAjQGc/xfkFeHn6EBgU\nClE773nuBnoGOIQGsuTXHxnc/wVtQrbWQ4fgZV+D19o/y5XUGN5a/ik/DfsCF2tNsBmXlYR797Z8\n0W8s9dwDSM3LYNhPVa+3t5cfP81eA8CqU9vYdfGwzsMdAwMD3hwxjjdHjNNel4/vuC4r1y0lJKgh\nDW/r6r8/6jh1XH2wMdPNYG6gb6Adw7v/8nGaeoWgp9K9n0evnSavuIAudaufmismMxEDPX287N2r\nXS+EEEKI/zYZ8yv+89xsqk4Xc6/lomprOQBKOV/tOMq19Djt382xl9O3z2P69nkA5BTlcSz6LB0D\nW+Dl4K7z16VuayISorQJi346vIr3V+sGVLdTqVS80+lFknPTeG/lVI5fP0tSThrRGQmsDNvKhDWa\nbdv7N8PY0IjpO+YTnZHAucQovtu9mJY+DXGzcaa8vJyi/Dwidu+hjn89GtZv9ruui6mRCZ3rtmbh\n4ZWcjj1PbGYiX+9cSOWN1loAd1sX2vs3Y8bOhRy8EkZybhrhMef5YcN85i/5nueGjHigYzlbOWDk\nak+bTt25EnuZktvmLb7J0dIOQ30DNobvJjk3jRPR4fx8dG01e7u3hjWDiM9KIq9YdwxuUk4q19Lj\nyCrMoUJdob3v5TcSVZWrK2nduy9xWUlcS49jzv5fOHgljBFtBmv3kZCdwu6LR0jMTiUq5TpTt84h\nNjORYS37VanH1sj91Peoc9d5fM8nXaauW21MDI2rXS8eD2MP9mdW+P/+7mr8LiN3dWDpxa/+kmM9\nyHXaGfsbfTbUvmcZ8c90Ju0Qr+xsR6VSef/Cf7HSymKe3dqIaznn/+6qCFGFtPyK/7wxXfx1xvwC\nmBrqM6aL/99Yq3+2O1vLAcyMM4AtvPHLFu2ylr4N+aD766Tl32qd3nPpKIb6BjTwDKqyD39nbxwt\n7dhx/iDDWvYjqzCX5Nx7Z+71d/Hm+0EfseLkJr7fu4TcG11n/Zy9eLWdpgXXxNCYyb3fZe7+X3l7\n+SSMDAxp5h3KyLaaIOx0+HGWTJ6EpZ0dX896+ODwdi+3HkhpeSmTNs/E+MZ8tncGpu90epHlJzex\n8NBvZBRkk3H4LNlXY2jevD2D+g3jQsrV+x4n0M2XJ4PbcehKGIc2fqmd6uh2NmZWvNt5OIuOrGZj\nxB5qOXjwSptBfLBuxkOdUy0Hd2o7e7P/8nF63pifF+CbXYs4l3ire/sbv3wMwKIXpuFs5UDf3kP4\neMN3bFw+6UaWah++6D8Wfxdv7TZqRc2aMztIzE5BX0+fEPcAZgyYgLOV7lzTyblphMdf4v1ud384\nsC/qeJVrIP4dvjo1ml1xKwHQVxlgYWhNTavatKrRnW5ez2Cgdysj+IdN56GvMrzbrh74eHmlWXzS\nYrHO8svZ4Yza151FnY/ibO7xh47xKGyJXsqP5z5hZY/zGOpphnKUq8t4elMgLmY1mdNxt7ZsUkE0\nL+1szZSWywl1alXlOj2/vRk9vYfR32/kX34er+7uSG3bEEY3qBr4n0zZw8SjzzGv4wHcLb2r2VpU\nZ0HkZAYFvIW+StPrJ6M4mfmRk7mac46kgmg61xzI2w2m62xTri5jedR37IlfS2ZxCh6WvrxYdwIN\nnW8lECwsz+fnC9M4mryd3NJMfG3qMbLeJ/jZ3sqzMC3sTfbG6/5fGWjXmK/aapYZ65vSz28EC89P\n4bOWy/6sSyDE76K6PTPnv0WjRo2UsLCw+xcU4gGtO5PIl9ujSMopxs3GlDFd/Kvtvis0Wn6+h8Rq\nAuAaNqYcfv+Jv6FG4s8WFnOOOft/Ye7Qz6p0L/8nOBEdzvxDvzH7mU+rHQ8s/tm+OjWazOIU3mv0\nLWqlktzSLMLTD7Pi8ve4mnvxeasVmBiYPdLj/VnBb7m6TBuo3mnkrg60qvEkz9Z594H2lZB/nZd3\nteHL1msIctAMH4jMOM6UE6+SX57Dkq4nsTG2B2Br9DJmR0xkVY/zGOlXzf5eXfC7M/Y3ZoV/wNpe\nlx/2NB/Kuqvz+fnil/zS7UyV+zj5+CvklmbyZZvVf2odHifnMo7xybEX+aXbae29TiqIYcP1hfhY\nB7M5ejFeVgFVgt955z5lf8J63gqdhruFD2Gpe1kQOZlv2m2klrUmaebk4y8Tn3+NN+pPwc7Eid1x\nq9l4fRFzO+7FzkTT62Za2Jvkl2UzusGtB6mGeoZYGt3KYZFTmsnQrY2Y3WEX7pZVE1eKR0OlUp1S\nFKXR312PfxNp+RUC6B1aQ4LdhyCt5f89jbyC6ZnzBBkFWVVaZf8JSspLeafTixL4/osZ6hlpf1w7\nmLriY1OXBs5teHNPN1ZemcXQOu8Bmu68Xlb+vBbyGaAJNn++8CV749dSUJ6Dp6U/zweOoaFzuz9c\np0qlku/OjCU8/QjZJWk4mLrS1WsI/fxGasek3wyk6zo0YcO1n6hQl7O8ezg5pRl8e3osp9P2Y2Ps\nwJCA0Q99fHdLb+xNnInIOKINfsMzjlDfqSWphfGcyzhK6xqamQAiMo5Qx66BNhi6/TqNPdiftKIE\nFkROZkGkJrP71j4J2uOcSTvE3IiPSCmKw9+2PqMbfIWLuedd67Xmyo/sjPuN5MJYLAytaOTcnuFB\nH2JhZF1t+Q6e/Vh4fioHEjfSueZA7fKc0kyOJ+9kVINbw1ti8i6xIHIykRknMNY3JdSpNa8Ef4St\niSZvwbSwNympKCLIvimrr86lrLKEFm5deS1kMsb6muFKReUFzAwfz9Gk7ZgamNPH92XC0w/jYOqq\nDQjLKkv5+cI09iWso6A8j5pWtRkW+D6hTpqcAhXqcr478z4RGZp772jqRtdaz9DPd4Q2AeGD1EWt\nqFl9ZQ7bYn4hrTgJG2N7Ono+zfOBmlwL6UVJzIv8lDNpmnnnA+0aMaLeJ7hZeN31+u+LX0eoY2ud\nhxxuFl6MrPcpAPsT1lW73Z74NQwJeJsmLpoePL0sXuBM2kHWXJ3Huw2/priikKNJ2/mo+U8EO2iG\n/zwXOIZjyTvYEr1E56GNoZ6x9vNaHRtjewLsGrAvYd0DP+wR4q/wz3t8L4T4x+sdWoOpfYOpYWOK\nCk2L79S+wf/ZBwh7v/2B0789vq0We7/7gZjjJ3mqfqd/XOB75cAhDs6dT5vaTQhwkdaFx42XVQAN\nndtxOHHrXcvMOPUO5zKOMa7xTGZ32E1Hz/58fPQFrude+MPHVxQ19iYujG8ymx877uP5wHGsuDyT\nnbErdMqdyzxGdO5FJrdcytRWywFNUJxUGMOUVsv5sNkCdsevIrUoXme7r06N5vnt984xUM+xBRHp\nR7SvI9KPUM+hOcEOzQm/fXnGMeo5tqh2Hx82nYeDqStDAt5mWbfTLOt2WruuXF3Gb5dnMrrBdL5u\nu56C8jy+Pzv+nnVSqfQYUe8T5nbYw9jGM4nKPsvsiA/vWt7SyJbmrl3Yccd12xO3GmMDU1q5aZJL\nZhQnM/ZAf3ysg/iu/RamtPqVwvI8Jh0frjOHeHj6ERILrvN5qxWMazyTg4mb2XhtkXb9j+c+5nzm\nSSY2W8jUVsu5nB3OxazTOsf+6tTbXMgK4/3Gs5jdYRft3fsw8ejzxORdAkCtVOJk5qa9988Gvscv\nl75hV9wqnf3cry4LIz/jt8s/MMj/LeZ22MP4xrOxN3EBoLiikLGHnsbUwJxprVcyo+16rI3t+d/h\nIZRWVu1ddVNk5gmdbsgPqlxdhpGebk4EI30TzmeeADQBvxo1htWWOamzLCLjKIM2hzB8R2u+OzOO\nnNLMKserbVufcxnHHrqeQvyZpOVXCPG7PGhr+YklvxJzQvOfpkpPD1NrK1zrBhLc80mMzB5dN8a/\nU4vhw9DTf/gWx8gt27iwdQeguTaGJsZYOjvjFhSIb9vWGBr//YmbkiIvUJydg2fjhtpl1w4fJe7U\naXISEikvLqH7xx9gbm+ns112fAIR6zeRFReHSqWHe/16hPR9SuecUqMuE7l5G7lJyRgYGVGzaSOC\nezypvZZpV65yee9+smLjKC8uwcLRAb92bfBu3lS7D+/mzbi4fRfpV6/j6CvjBR9HnpZ+nE0/WO26\npIIY9iesZ1GXYziZab6Pevm8wNn0Q2yJXsob9afcdb9hafuqJHtSUOu8NtAz5LnAMdrXzuYeXM05\nx76E9XTxupW4zVDPmNENvsJIX/P+Tsi/TljqXqa3WUtd+8YAvNvwG17crhuc2pk44Wpe857nH+LQ\nglnhH1BWWQooXMw6zajQaTia1mBuxEcAxOdfJasklRCHltXuw9LIFj2VPqYGFlVa6yqVCl4P+Uzb\nNbWf3wi+Pv0eiqJoWzjv1Mf31vRqzuYevBT0Pz459hLvNvymSpb2m7p6DWbC4cEk5F/Xju3dEbuC\ntu5PYWKgaSXddH0xfrYhDKt7ayaFdxp+zeAtIVzLjcTXJhgACyNrXqv/GfoqfTwsfWnl9iRn0w/R\nv/arFJbnsStuFeMazyTUqRUAoxt8xdBtjbX7TMi/zsHETSzpehJ7U00g2tt3OGfSDrI1ehmvhkzC\nSN9Ep8XS2dyDK9kR7E9YR6eat6YDvF9d1l//iddDJtOp5gBA00IbaK/ppbo3fi0GKgPeDp2uvdaj\nQqcxcEs9wlL20rLGk9Vey7SiBOxNnKtddy8NnNqw5uo8ghya4mruxem0/RxL3o7qxj2zNLKhtm0I\nv0Z9g6elLzbGDuyJX8vl7LN4WPpp99PY+Qna1OiJs5kHKYVx/HxxGhMODeLb9pt1uvzbmzhzuCih\nSj2E+DtJ8CuE+NM5+9emyXNDUCrV5KWkcvKX5ZQVFdP8haF/W50UtRoFzTRMf5Sxufk911dWVKBv\nUP3XraWTE+1GvQYKlBUVkXHtOhd37ib62Anav/0GplZW1W73V7my/wBeTZvoXKfKsjJcAvypERzE\n2TXrq2xTnJvL/pmzcQ8NIfTpvlSUlHBm9TpOLv2VFi8NAyAnIZGDc+YR0LEDTYYOpjgnl1MrVqGo\nFer36QVAxvUYrN1cCej4BCZWVqRcvMSp5SvRNzSgZiNNMK5vaIBnowZc2X9Qgt/HlILC7fNx3+5a\nbiQKCiN2tddZXq4uI8Sx+kDwpmD7prwVqjtHekxeFJOOD9dZtjl6CdtjfiW1KIGyyhIqlAqcTXUf\n/HlZ+WsDX4D4/CvooYe/bX3tMmczd+xMdQOWF+reu4UVIMSxJWXqUi5lnUJBwdrYDjeLWtiZOJNc\nGEtWSRrh6Ucw1jclwC70vvu7k6Gesc6YTHsTZyrUZRSU5+iM4bzd2fTD/BY1k7j8KxRV5KNWKqlQ\nl5FdkqYNJu9U37EVLmae7IhdzotBE7iUdZrY/CjeaXhr3OiVnHNEpB+pNgN1cmGsNvitaVlbm+gJ\nwM7EWdvSn1QQQ6VSgb/trWthZmiB523B29WcCBQUhu9so3OMcnUZDZxuLdt4fRE7Y3/Tufd3Pqy4\nV11i8y5ToS6jvmOraq/J1ZxzJBXG0Hej7pCh0spikgtjq90GoExdWu247vt5LWQy35wew8s726JS\n6VHDvBYdaw5gb/wabZlxjWYy4/S7PLutEXoqfWrbhNDWvZe2RRygvUcf7b9rWdfB1yaYYdubcSp1\nH81cO2vXGembUFZZ8tD1FOLPJMGvEOJPp2dgoA3izGxt8AitT8xx3S5UZcXFRKzbSGJEJJXl5dh6\n1CCkz1PYeXpo159ZuYaUi1GUl5Rgam2FX9vW1G7f9oG2jz52gjMr19D8xecIX7+J/NQ0Qvr0ImLd\nRnp+9rFOABuxYTPJ5y/QZbymxSfhbATnt2wjPz0dYwtLfFo1p07njton9Xu//QFrVxcaDNBMz7Pp\no0l4NW1MUXYOieEROPv70+Kl56u9Nip9Pe21MbW2wtrVBbfgILZPmUbE+k00HToEgMryCiI2bCTu\n1BnKi0uwcXcjpHcvHH1uBXxJkRcIX7uewqxs7Gp64tu6JccWLdG2zN7vGt6pJL+A1KgrhDzVU2f5\nzfJZcfHVbUZS5AVQ6dFgQH9t0NxwUH92TJ1Ofno6lo6OxJ0+i5WLC0HduwJg6ehIyFM9OfrTYup2\n64yhiQmBXTrq7Ne3dUvSrlwl4WyENvgFqBFcl/0/zKGirAwDo+oTDYl/r7j8K7jeZfypWlGjQsW3\n7TZjoKf7k+Z+wYGxviluFrV0lhWU5+m83p+wgbkRHzM86AMC7RthZmDBxuuLOZK8TaeciX71vVhU\ndwnaH4aLuSdOZu5EZBxFQdGOxTQxMMPXJpiIjKOcyzhKXfsmOlmxH9TtgdvtdVbfJSFqalECHx15\njq5eQxha5z0sjWy5mnuOL06+ToW6/K7HUalUdKo5gM3RS3i+7ji2xy7H2zqQ2rYh2jKKoqapa0de\nrDuhyva2t7VY33meKpWqSqv9vahRo6fS5/v2W6u0VN8cq7snfg3zz03i5eCJBNg1wMzAgvXXFnIy\ndY9O+T9SF0VR42cTzNhG31dZd7cHDwBWRrbkl+U80DFuZ2PswMfNf6KssoS8smzsTVyYHzkJF7Nb\nny83i1pMb7OG4opCiisKsTNxYvLxl3Exu3sPBUczN+xMnUksiNZZnl+eg7Wx3V22EuLvIcGvEOIv\nVZCRScrFSzrdhBVF4dCc+RiamNBqxEsYmZsRczyMfd/PotsH4zG1tiJy01Zyk5JpNeIlTKwsKczM\norSg4IG3B00L7IVtO2k06GmMLcwxtrTk0s49xJ8Jx7dVC+2+4k6dxre1ptUoKy6eowsXU6dLJ2o2\nbkBWbDynlq/E0MQEv7at73qel/fuJ7BLJ+qMGQ0PmVTf1NoKz8YNiD0ehqJWo9LTI2L9RuLPnKXx\nkEFYONgTtWcfB2f9SLeJEzC1tqIwK5sjC37Ct3UrvFs2JzcpmfC1uq2y97qG1cm4fh19A32s3Fwf\nqv7qigr09PV0Wov1DTU/EDOuRWPp6Ii6mtZwfUNDKssryI5PwMnPt9p9V5SUYGpjo7PM1tMDpVJN\nZnQMzv4yZ+m/xe1Z9r0DkvF1qfpBicm7xKnUfQz2f6vaffhYB6GgkF2adt+W3t/jfOYJ/G3r08vn\nBe2ye7XI3eRh6YsaNVHZZ7VdXNOKEskqTv1d9QhxaKEd39vB89Yc2PUcmxOefpiIjKP08X35nvsw\nVBmifgRzwl7JDqdCXc4r9T7WBs4nUnY90Ladag5g2cUZHEzcxP6EDQwLHKez3scmiGPJO3A280Bf\n7/f9RHWz8EJfZcDl7LParvDFFYXE5V+hppXm+8HXOhi1UklOaQZBDk2r3c/5zJPUsW9ED+9bDy6T\nCmMeqi41rWpjoGfE2fRDdDUfUmW9j00wh5K2YG3sgLmh5QPv18e6LnH5Vx6qLrcz0jfBwdSVcnUZ\nh5O28oRH3yplTA3MMTUwJ680m9NpBxkR/PFd95ddkk5WSVqVLvWxeVH4WAf/7noK8WeQhFdCiD9d\nysVLrHn3fVa/M5Ytn3xGXkoq/h1vTYmUdvkqOQmJNH/peey9amLp6Ehwj25Y2NsTe1IzrVlRVjY2\nHu7Ye9XE3M4OJz9fPELrP/D2oOnqHPp0Xxy8a2Hp5ISRqSmeDUOJO3lKWybjerRmjGvDBoAmiHX0\n9SGoe1csnZyo2bghtTu049Iu3af/d3L09SGg4xNYOjpi6eT40NfMysWF8pISSgsLqSgt5dqhI9Tr\n1QO3oECsXJxpOOhpjK0suXrwEADXDh3G3N6e+n2fwsrZCY/QELxb6o4vvNc1rE5RVjbGFpYP3TXc\nqbYfpQWFXNyxm8qKCsqKiji3YTMAJXmaljWXOgFkxsYRezIMdWUlRTk5nN+mGf9ckptX7X6TIs+T\nGnUF7xa6CYIMjIwwNDWhMCur2u3EP8+6M4mMX3OOxJxiFKCorJKo1Cx+ORlBZnEK13MvsObKj4w7\n+DS+NsH0u8vctO6W3rT36MOMU+9wMHETyYWxXM4OZ9WVORxO3FLtNg+jhoU313IjOZmyh8SC6/xy\n6ZsHSuDjbulDI+d2fH92HBczT3Et5zwzTo2u0hr90/mpvH9o4F32cks9xxZcyj7Dpewz1HNorl0e\n7NCM/QkbyCnNIMSh+mRXNzmZe3A+4wQZxcnklv7+z0oNi1qoUbPu6nxSCuPYF7+OddfmP9C2jqZu\nNHBuyw9nJ1CpLtfpPgua8dp5ZVl8fvJ1orLOkFwYy+m0A3xz+r17JoC6nbmhFR09+7Mg8jPOph8m\nNu8y35zWZAq/2artaeVHmxq9mH7qbQ4lbialMI6o7LOsvDyLI0maVn13C2+uZEdwKnUfiQXXWXrx\nKy5kPtw0m+aGVvTyHsaCyM/YGbuSpIIYLmWdZkv0UkDzIMPSyIZPj71IZMZxUgrjiMg4ytyIj+/5\nkKWhczsu3JGACuBaznmu5ZynuKKQ/LIcruWcJy7vVpB8MfMUh5O2klwYy7mMY3xw+Bn0Vfo6n6+T\nKXsIS91LcmEsp1L3M+7Q09SyCqCjZ38ACsvzWBA5mYtZp0gtjCc8/TCfHHsBBxMXnS7PiqJwPvME\njR5B1nUhHiVp+RVC/OkcfbxpOPhpKsvLuX7kGIXpmfi1u9Vqmh0fT0V5ORvGT9TZrrKigoKMDAB8\nWrXgyMLFZMfF4xzgj1tQoLZ18EG2B01SKRt33bF6NRs35PK+AxRmZWFuZ0dc2GkcfX0ws9W0Lual\npOJaN1D3fLy9ubB1B+XFJRiaVt+10s7j980RqqXtcqiiICMTdWUlDt63umjq6elh71WTvGRNS1J+\nahp2nrpdQ+29dF/f6xpWp7K8HH3Dh/9vwtrVhSZDBxO+ZgORm7ag0tfDr21rTCwt4UZXcZc6/oT0\n7snp39ZwYuly9AwMCOzSiYxr17VlbpdxPZpji5cS2r8P9l5Vu9/pGxpSWXb3Lpfin+XL7VE6U6UB\nmFpdZEnCkyxL1MfC0IqaVv48E/AO3Wo9c9d5cwHeaTCD5VHfsTByChnFyTeS9tQnJODeweCDeLLW\ns1zPPc+0sDdRUGjp1o2+fq9UyVpcfb2+5tszY3n/0ACsjO14JmB0lYy4WSVpD9SSHOLQggp1GQ6m\nrjpdtevaN6GssgQzA0t875P9d2idd/n+zPu8uKMV5f9n7yyjo7rWBvxMZiITd1cgBsHd3SlavNAC\nhZZCDanRUkqBYqVCWyhuxSW4S4DgkEBcCEmIuyfj34+BCUMChdv2tr3fedbKWjl7tp/JyXn3a2qZ\nXqqjl8HHqj5vN/qKPfG/sCV6KYF2LXgz6Au+uTn1hdr38RrNrezzdHEfjIWRvhWHg9SVbzsFszHq\nGz6/8hpyVRUOpm40d+yMRPTiJt1TGs5jZfgnzLv6BqYSc4bWm0JBVQ6GT/hlz2rxPTtif2B95MJH\n3xsb/G2a0PSRz29/n/E8KI7hm5vvIEJEB9d+DK43ifMPD7zwPAAmBX2OhZENv8WuIL8yCxsTB13w\nK6nEjOWd9rMh8hsWXJ9CubIUexNnGju0w9zw2fEeunu8yqaoxaSV3tf5a6vUSqaf761X70rmCVzM\nvNjQKxQAmbqKzVFLyKp4iFRiSivnHnzc8mc9rXO5ooTN0UvJq8zE0tiWjm79GRc4W6eJNxCJuV8c\nzemUPZQrSrCVOtHYvj2ft16LVFLtPhSVfwOZspJ2rn1far8EBP5qRJpn+HT8k2nRooXm1q2XO30T\nEBD4e7ixdQey8nI6vl0dROb8jz9rtan9tP6eMafPknDhIl0/mF6jvaGJiVZoAmRlZWRGx5ITl0Ba\n+F3cmzam1WujX6j9Y5/fod8urlHnxMIleLVsjn/3rhyaM49GgwboIgqfWvKtNjr1gOp/4Nmx8YT8\nvJohyxZhaGJSq89vvU4dCOjetcZYTxJ57ARp4ffo89lHNT67s2c/KTduMXjJAoozszi1eDn95n6G\nuUN1qqFrm7ehUiho/+YEQtduQGJsQuvx1aZ12XHxhPy0Wi8a87P2sDbuh14l8shxBn0zv9bPC1If\ncmbZd7VGe35MVUkpYmMjRMCB2Z/RZsI4PW2zRqOhqqQEQ6mUioJCTixcQo9ZH2DrVS2F4mRBAAAg\nAElEQVS4595P4tLqtQT16/NM/+R9Mz6i5djReDZ/+YA/Av99fD45Wqs3gAh4sLj/f3s6Av/jyFVV\njDvRitH+7zG43pu/3+BfwNqIr6lUltUI2vZPYf61SQTaNme43zt/91T+pxGJRLc1Gk2Lv3se/yYE\nza+AgMCfzpO+fK9rMmlgp6+1adC3N5dWraFu+7ZIrayw8XCnqrQMkcgAc3u7Z/ZrbG6Od6sWeLdq\ngXP9AK5t3kbzkcNfuP2z8GzRnJRbd7BycUEll+HRpDoAi6WzE/lJ+kE8cpOSkFpbY2jy8tE2X4TK\n4hJSb93BrXFDRAbaNRlIxOQlPdAJv2q1mvzkFJ15toWTIxkRUXr9FKSk1uj7WXtYm4bXxsMdWVkZ\nsrIyjM3N/6O1mFhqDy6Srl7HwNAQJ3/9qKYikQiplRUAqbfvYGpjjbWHu+7z3MT7XFq9jgb9ej9T\n8C3LzUOlUGLzRDuBfzau1lLSi2qasbpaS/+G2Qj8r5FQeI/08gf4WTemQlnKrrifUKhldHQb8HdP\n7U9jtP97HHmwBbVG/cz0Un8XMlUlvtaNGFR30t89FQGBGvyz/loEBP6h/HryZ9p90kyvbMWhpTSb\nUR/PyQ7sCd3xH/e94tBSenz57MBJ/zZq8+WLzSwhOCxdV8fRtx6Wzs5EnzgNaFMh2ft4E7pmPZlR\nMZTl5ZP3IJnIoyfITUwCIPLocdLvRlCak0tJVjbpdyMwt7NFbCh5ofYAKo0Kz8kOFJTqmx56tWxG\nSVY2kUeP4xLUQM+U2b9bF3IT7xN57ASlOTmk3LxN/LkQAnrU1Op+d2gZszbVHpjnWWhUaipLSqgs\nLqE4M4v7oVc5u+IHjMxMaThQqwGTGBtTt0M77h06QmZUNCVZ2dzZtRdZSakuMFfdDu0oy8sj/MAh\nSrJzSAu/x/3Qq9pBRJBXkst7771CRGhIrXtYG9bubhhbmJN3X1/4rywpoTAtndKcHABKsrIoTEtH\nVl6uq5MQcomC1IeU5uSQcPEyYXv2P8rtXC3cxJ45R1FGBsWZWUSdOEXs6XM0HTZE52Ock5DIxVVr\nqduhLZ4tmmn3qaSEqlL9IF2595Mws7f7j3yrBf4eZvf2R2qoH2VYaihmdm//Z7QQEHhxNGjYl7Ca\naed68enlUZTKC1nacd8z0zD9GzE3smKU/7v/OMEXtBGzRwe8r5f+S0Dgn4Kg+RX4nyG3JIdfjv/I\n2XunyCzIwFxqgbejDwNbDmFE+9GYmfxnmqvaiEuP4fvDy1gzdRPN6rbAUvpiuVg9Jzuw6u319G8+\nkBXjVjLg3WpT2sfXfq2e7YP5ZxF1KYboS7EM/0wbbKTdJ81Iy9emrTExkuJp78WE7pMZ22n8S/f9\npC+fRpWLRnYZlWFDlp2MY3DTan9bv26dufnbTgJ6dsPM1pa3ohexpOksbuzYTfJDS0zMHmLmYc7g\nVlprHgOJhIgjxyjPL0BsKMHW24sOb2nN19LyH/JW9CL6SJqQsuo+5phQRhUP1LmcOBhGvqaMz5pP\n41k6YTNbW+zr+JB3P4kGj1LvPMbGw522E18n6tgJYk+dxdjCgoCe3ajXST9vY6W8krWXfuH43PNE\n/LQVgOvxV/j11C9EpNwluyiLb9/4keHt9U2MS3NyODxnHmo0yDQKKk00NGzbiVb9B2FoYsLDvFTa\nf9ocMQb0lzQjd1UOUoxI1xRg06GxLpK1ma0t7Sa9Qfj+g8ScP0+KKoeAjp0gtBCxxBB7S1v8PRtw\nJziYeNWxGntYGwYGBvi0aUXKIy30Y+5fvkL08VO660urtcFuWo4dhU+bVgAUpDwk6thJlHIZFo6O\nNB81HO9W+pZZWdGxxJw6g1qpxMrNlfaTJ+LSIFD3efK1m6jkcuLOXiDu7AVduamtDQO++kJ3nXo7\njDpt9YNgCfyzefwseGwh4motZXZvf71nhIDAf4qfTWNWdj3+d09DQEDgH4gg/Ar8T/AwL5WhS/pj\nYWLBrEGfEuBeHxNDE+Iz4th5eRs25rYMbj3s9zt6QZJztJqw3k376XK9Pk12cg7b5+7GpZ4zo+a+\n+rt9vrVyIsZmf54ZrVwpx0hSM0iMSqkidM9V+k6tDoxhqbDmfbtxWFRZUZZfjtpVxqdbZ2IhtWBg\nS62ALK+UE7rvGom3kqgoqcDRy4Gu4zrhXMdJ18+KcSupucs9uCuFjEcmjuXFFVzaGUpK5ENkFXU4\nse4S3cZ3RoYS49a+9G8+kLBTd9m1eyc+zZx0Zr71e/ekfu+eta7V1daN0OXhuuutFzaxO3Q7h+ec\n4sNHZWbGZtxLuQvLN9XaR7da/IUf496kEe5Nnh1Mpuv70/jp6Hc09mmGl4M3Xo8Es3MRp/F3DWBY\n2xF8uKFm/w369ubzuz8gEon4cuQCLKSWrD29is03FnN2wAAMH63t1vJIvXYnw47x0/aPudRjgf4+\nBDXgcPoFrsZFcC7iDD8q2mNoYoKxhfbgp8fo1xmwoCc3l0dgbfbsHJJP4te1MycWLqUsL19nUh7U\nr4/OX/tZPOl7/Cy6vPd8X7BW40bTalzt/siPKc7IpCg9nbYTX/6gRuDvZXBTN0HYFRAQEBD4ryII\nvwL/E8z5bTYGIgOOfH4aU+PqaIOeDl70aNyLJwO7peenMW/nHC7HhADQsX4Xvhq1CBdbV12dVSdW\nsu70Kspl5fRpOgBPh+rosisOLeX7w8sA8JqizWmXujaXuw/CWBq8iMjUeyiUcvobvErzli3Ji8gn\nP72AV1b2AGDqaq0PzAfoRyY+G3+KpcGLyC/JpX1gJ5aO/w5bi2pd5e7Q7aw++TMPc1NwtXVjXJcJ\nTOw+RWci6jnZga/HLCY05hIhUecZ1+UNPh/+VY29ir+RiMRIgkdg9UunWCPB0FJCx4HtubL3GoGB\nARyu2MOpsOMMbDmE5JwHbFy4GYoNCDE+hZ2bDUMcRrN3cTCvLx6Lha057T5pxquvjGLblXAqysNA\n4oeTLJdBitHEqzajyk9hxNJ2DKl8DZFIxMAP+mFsaszt42HsXRyMRFMdyTOgnT92W51QF71YQD6x\ngRhHq2oh3NzEvEbZk0SnRbH0wEJi02PwdfFj8bhvaehV7ed7K/EGSw4s4G5yOFamVvRs3IdPh83F\nQvrsPIzBN/YzuuNremXdGvakW0OtwD5z47s12jzITuJO0i1OzD1PfY8gABaNXUbzWQ04eGM/ozuO\nq3Udx+8cpUNgZ73vJcCFAzs5FrqTn6ZvoCAqEfndZHzbtNUd0Pi7BeJo7czxO0cY3XHcM9fyJCYW\nFrQcO5KKwsL/yJ/6r6ayuITW48ZgJBV8RQUEBAQEBASezz/PUUBA4CUpLCsgJOo847tO1BN8n+Tx\ny79arebNn8eRV5rLzlnB7JwVTHZRFm/+Ml4nIB++Gczy4G/4cODHHPv8HHWd67Lu9CpdX2/1eocl\n41cAcGt5pE4rV1ZVxtA2w9n70WEOzD6OU4k7P8d+i1dTDyJDojk8R2smumT8ihqavLT8VBJXpjG/\n41K2fbiHqNQIlu9cwtGfTvDzW2v4btJPXN9wh3c7zeDs/FA+HzGfy3uvsPL9X4i9Gs/6mZt5p+pj\nwrdF0tm3K6fmhTC+y0RAa9I84wmtY+zVeOo08dEbv9AoD+PmBgS280dipD0TMzY0QaHSpo4pLSvB\nqtCObmM6s+nrLXRp05Uv781CamvCvbMRLJw9H4VcwbpLq2jXqD4a+0+oNB9IXcloCkR55NmNYMEb\nZ1j26o9kJmbR/fXOuNR1xtbFhh5vdEUpV+KvCuLEfm1eTqm5CflGOSiT/5po9Ev2L+CToV9w7POz\n2JjZ8P66qbr7H5sWzWvfD6dn4z6cnHueNVM3Ef0w8rm+vEXlhSRkxtHI+9k5c2tDrpQB2r1+jIGB\nAUYSI24mXK+1zalzxwmNuciYTvrCa1lVGacuH2CcuAPXf1hLH0ljDP1daTT4Fb16TXyaci3+6kvN\n061h0HNTIj2mvKyc7+Ytp6So9jy9fwXOgf44Bwb818YTEBAQEBAQ+PciaH4F/vUk5zxAo9FQ10n/\n5bzV7EaUVBYDMKT1cL4Zt5zLsReJSYvm0qKbeNhrU6n8OHk1nea04nLMRTrW78yGs2sY1m4kr3V+\nHYB3+8/gSlwoKY9Mnc1MzLE01UanfVIj1z6wOmhV9OVYHF0cqJCXUeJYQNrZDDqMaAuApalVDU2e\nSqX1kXW388Cvbj1GtxtHUXAl4jZiRswZypjvhzHc/jXyjpTi0tEVTwcv4nwTqbgnI+56AgPf78+A\nr3oy3GA8jg898Orprevb08EbR+vq8TLiMwho4/fM/dSgITo1gtj0aBqbN2Xh7PmgBqnGhtunb5GV\nlsHoAeM5c/cUxblFpMdn6tq29mvHTxM/JzgsnRXHovHLjuWaOIx3u09nfPvG5D7U5twVG1U/ekQG\nIsSGYtzK9fPiFhjl4pzt8sx51sbdm+Ec2X2Iu1V3KJIV8f1X3+Li4Uq3ft1xcHbU1Zs16BPaBWh9\ndt9/ZRbDlgwgqzATF1tXVp/8mVdaDGZKr2qT3IVjl9L3627kleRib1kzqFJ6fhoajQYnq5cLplLX\n2Rc3W3eWHljAkvHfYWpsxrrTq8kszCCnOLtGfZVSxdqjq7E2taFXY62/eG5WDhdPhbA27BdEcgP6\nDJpKp15dtP7lzQYilkiQVckIOXmBuMhYUrNSKRQXkPEwHVePau2/XCbn/PGzxEXGUlleiaWNFc3a\nNKd1J60vbWVFJRdPXeBBfBLFhcVIzUzxDfSlc5+umJqZAmBmbkbD5o24eOoCA0YMfKm9EBAQEBAQ\nEBD4qxE0vwL/s+z96DAn5p6niXczZIoqABIz43GydtYJvgBeDt44WTmTkBkHQEJmPM3r6Afmefq6\nNvJKcvlk60w6z2nNxvUbOJK7j/ySPAqMczE0lnD/zoNntnWz00/RYpFnjUqtoveUHhjYQFxpNN+m\nLSC3IIdB7/YnYLoXodEXQSOiz5QeOHjak2mQhk0jS1Kj0vT62jlzP58M1fqgVpXLkFXIMbOpqSFf\nFryIgOleJGTEcy7iLG/1nk4Ht874+Pow+ZO3UVrLySsoYvG1r2kzpwmy+0o0eSLKi6oj/Dby0mo+\nBzd146e2LpgYGBAjvktnf63gaetig4WdBZd3X6WyrAqVUsWNI7cpKyjDDH2T4ipxBZpyXhpDQ0M6\n9OiEpbUlIyaORiFXsGvDDlRKla5OgHt93e+PBda80lwAIlLucuD6XgKme+l+hi7RpsdIyU2udcyq\nR98vY8OXi2xpKDHk13c2kZKTTKMP/PCf5snV2Mt0CequM2d/ksi7EUSW32NExzEYSrRm4gqFgqiy\ne5QbldHHtfb8qEf3HiYp/j4DRw2iRZtWiI0M2L5mGyXF1Rra04dPkRiTwMBRg3lr9ju079aB88fO\nEnH7HgClJaWUFpfSrX8PJs98m0GjB5P6IJXg3/brjdWoZRMiwyKorKiZxkZAQEBAQEBA4O9E0PwK\n/OvxdvRBJBKRmJWgV/7YH9LE6MV8AUXUHrjqRZmxcTp5Jbl83OsL4jYkM/GjN5i4biwKtYKm7fyJ\nDIl6ZluJ2FDvWpGrwkJtxU+Tf0Wj0fCO/BOMlcZo0PB+11n4d69H9Ml40sIyMDatFrikVlIKSp5t\ncqpUKLXjPZViBODNnm8zqsNYzi6/RP/W/Wg3rA2Hdx5ELJHw/cll3DK+yRCzMYxLn4oGDaUmxVQ4\nlWJnYAtqbR+mxloN4LljZ7gbHEOVYQWVVHDz0nUauAUhMZQw8P1+nFp3llVT1wIa1CYqzB3M0eTr\nmzirRCpUcjW/LvuFwoJCrKytaNa2Ba06tEZk8Jx7JQJjE2MMDAxw9XClVafW7Nm4i/zcPF2VA1v3\n4+3uQ58hfXUm8SEnLxAtiUajUTOqw1jsc5ywsbfF2MSI6PAoRCIR2RHZaHw0uvF/WvQDTVo1JSE9\nHoCfV/xI324DaNulnW6sqsoqzh49g0Ku4Pj+o1Tdk9F9QC9cPbQ+5pocDd3kvVk87TvOnzhLZVYl\nl0zP07xezQOXPed2Uq4qY3SHat9iVw83ikwKSS1MYUXBEkQpINqnnd+0Xyezrs5qWha2Zdi4EXjV\n9UZ2tQovN29sNLbcuXqLLn26AZCe/JCgZo3wrqc1ibe2tebuzTDSU9Np2LwRjs6OvPr6CN24tva2\ndO/fg10bdyCrkmFsov0eOjo7YmFpQWxEDE1b66cHExAQEBAQEBD4OxE0vwL/WoLD0mm/+BzNFlzF\n2KQRv55aQ3lV2XPb1HPxI7soi4d5qbqylNxksouz8HXV5pf0dfHjTtJtvXZPX9c2l5CoK0Tlt+Jg\ncCkatYYTC84xLOMN2GvCzcO3SYl4iI3IFpVa9dy+ADQayBfn8NrCUYxbNJqT9vtR96hgwrJxdHql\nE96OdbA2s8HYqKamUfMcN1mpuQmItBrgp7Exs8XbsQ4SAwk8FcH6ZuJ1enfsy8R5r+PTyxVpoIhD\nFjsQacDKoWaaJ0WpCk0ldBqpNQW/n5DI5bOXAHDycaT5iEbIXIppN6EVExaNR6QxoFhUqNeHgVyM\nXFNFp95deHv2O3R/pSdXz4dy6+rN392/x1RVVhEVpvWvNhDXFPhrI8izEfEZcdiZ2JMbn4OLpSvv\nfzCTkcPHEH41jOi7+ocYNy5dp37dIMyNzbH3s+fc0TOkJWvTRmk0GnZt2EFpcQkSiYSOvbrg4ePF\nb79uobSkVNeHUqkk/NIdXh05gj5v9CMmPYpeTfryNCHJ52jo2pg6znX1yj8aModTX4Yw2XsqC3su\n48Tc8wB8Pvwrlr72PRq1RnfgEZceQ5BnIySGEh4+eKjrw93Hk4SYeEqKtK4CackPyc7Ipq6//lhP\nIpPJkIglGBrqH964eriRmpTyu3stICAgICAgIPDfRBB+Bf6VBIel8+n+CNKLKtEAMuOxlFTK6fxF\nFw5e3098RhxJWfc5eH0/MWlRiA20L/4dAzsT6F6f99a9zd3kcO4mh/P+uqkEeTaifYBWUJvQfTL7\nru5i+8WtPMi+z0/Hvif8wbOF38dz0Rg4QdVV3AvyuGYp4oJrCPvMt0BPGeMWjsbe045WRh0JjblU\nqz/nkxjZS7BUWSM1l2LjZM2UoW+zOnQl++7tJKMsjbj0GKIfRlJQlv+7ezXq26Es3v81AGKJGDtX\nWwrSC15wp+F+XCKKPAXbjm/i409nEpZ4mxvSq2jkGkwKzKjbrE6NNpJKY6wcLGnVpSUmRlJUzkpu\n3rpGSYVWK33j0nUatWxMm25tMFCJqciv5L5BnF4fFjIrxHYiAhvVx9rWBr/6/rTr1p47V249d74K\nuYLzJ85RXFjMt3OXEh0ehW99P+wd7V9ovVP7vkt4chjBqXtRWMjwaupFWMZtttzbgHddbx4k6puv\n+/jVoXWHNnRs0JkCST429rYkP6oTExNNZMo9ArsGohFBsbwIh/r2iCxERD4yJ76cFEKa7CFBXRoS\nUxzF9M2T6d20L50adNUbJyn9PqnyZAY1q5lMytnGRRvJ2dgJDytP/N20uXJdbF3xdffDzcud0LOX\nyM7NJiLlHh6GXqSnpFFWWn1Y1HtQH5xcnFi58Ae++XgBW1dtpmu/7vjWr90/vKqyipCTF2jSuhkG\nYv1/JeaW5hQXFr3QfgsICAgICAgI/LcQzJ4F/pUsOxlHpaJagyoSO6Kx+ooK9UmWH/yGzMIMJGIJ\n9Zz9GNdlAm90e1NbTyRi3bStfLnzM0YtHwxAh8DOzB/9jc78dWDLIaTmprAseBGV8gp6Nu7Dmz2n\nsvfKzufORWQ+CZ/iM5ioVYQrNmNqMAh7xyyw0mDvYYd/Gz+KjhexJ3Yjba7sYDqfPXN9pr7GVF6u\n4OB3R2g3rA39/Aci6iHh6uGrrN77M1UmlfQQ96ee+Pej3KbmJuNqU53GybuRJ+nxGbToX22SKtIY\noCrQkJOSi1KhoqK4gpyUXOTlCjx9vPip+xq+37yCiJx7VMoqaFzYnGHy8ShM5TToFMjh49XjKWQK\noi5FY+gk5ueFP9LWuANH7h6kXF3GzZ+vsaDTcvJTCvD3DyTxdhIXtl2kbvM6pEYm0ZSmgDZqsJPa\nldSKByyd8w0A18uucLPiGu/YffDc9RoaGtK6bRseXE2k77D+XA+5Sr9hA353nx4T6N6APbMPMWPl\ndDYmrWXTV+vxdPCiT9N+mBtaUFGm74js6KINJjam43hmbHqX9+rMpLysAoArEZfZVbiNXd9sA2DF\noSWsOLQEP6NA6tdtAEBBRQHnK09z8ucjOFo5MaztCN4bMLPGvHaF/oaRyIjuDWrPdfw8Bo0azJE9\nh/hkwUxMVCZUJlXRoEkQmenVwcpuht4gLSWN4RNGYmVtTeqDFM4eOY21jTV1A/SDycllcnZv3IGF\npQXd+/eoMZ7E0BDFIxN7AQEBAQEBAYF/CoLwK/CvJKOoZjAdkYE1lQYjubSo9qA/j3Gzc2fdtC3P\nrTO93wdM76cvZM0Y+JHu9/7NB5K6NldvLiKJJ0H0I91Yg9x2AQoVhC+cp2vj16oel3ddYcvbe/Fu\n6MmKcSt1/c4Y+JHuGmBklzGUN63g8q4rHFl5HHmlDDNrc/o1HMjiUUuQWki5sv86CTcSdW1S1+YS\ndTGGcxdD9OZ9ZfEdveuGXRqw9fOdVJZVac2ggWOzzrF+xma2HdMK+PfOFXPvXCQVxiIsGpgR5NuQ\n2a98xuXdVygrKEOpVuJYz54RM19FLNFq1be8sYvARvW5tD8UhVxJg9b+BDYLYLzJG8RHx3P2yGnm\nzJ7LnZN3IdeQa9tuY25jTv0OAbQZ3BLLM7uJvKPVhmbdz8YcCwa+Phmvet4AJO9Oom1ZB6ZMnPrc\ne4cIPhw6mw+HzgagrKSM4O37eO3t12nr357UtblsW71FZx/uYe9J6tpcgrfvR1alNQdv7N2EN+pN\nxsHZkT5Dqs2PD+88qJczGkD8KDBV56BueNh5crcgHCcXbZTq+o5BzPD4lPHvvFFjmo99ZAcGDcE4\n0YSPFn763GV9NHQO6lugUbxY+qfH308AG3tbxk19g10LfuPTDnMZ3eU19m/bi7WtNaANmnX++FmG\nvjYcv/pa838nVyeyM7K5FnJVT/iVy+TsXL8dgJETRyMxrPlvpKqiUhcBWkBAQEBAQEDgn4Ig/Ar8\nK3G1lpJeiwDsav1iwa3+qrkcsXPWK38Sa0crZmx9V3f95O+1XZtZmdJ7Sk2t2mPaDW1Nu6Gt9coa\ndAqkQafA587X1tWWes3qcPfMPdoMbgVo/XZnbH1XZ8L9WKveWZ2MaVYJwWHpDG7ti39rXzRqDcvn\nLsGliYNesK3HGNoZYBSgofug7rqy4sJi3e/NejfmXsJtHJwd6f9qtUY2I7U6SnV0SBwGthpKikuw\ntbdFo9FwO/kGO2bsx9be9rnre5rWndpw49I1YiNiCGio3RtTc1M9k1+AnIxsrB4Jg/8p34xbzppN\n1Tmhnd2cKS8rQyQSYWNn84f6FkvEODg6kJedS71A35dun1eSS/8WAxnVeSyVFZUkxd2n2yOtrVql\nRq1SY/BUIDEDkUhP2JdVybSCr0bDqDfHYmRsVOtYudk5ePh4vfQcBQQEBAQEBAT+SgSfX4F/JbN7\n+yN9KmKx1FDM7N7+/6/n8qJ0HNUeI2lNweVpc3IA1Gp+OB5BWUkZedm5nAw+jlwuf6YvqK2DLaUl\nJUTeiaAwv5DbV24RHR6pV6dVh9ZE3LpL2PU7FOTmE3ruMump6QAoFSocPO3oOLw91y5c4frFaxTk\n5nN4xmnK0soIPXf5pdZqbGJMk1ZNuXgqBI1aK8h51/Xhfmwi8VFx5OfkcfrQSb20P/8pge4NaGbX\nUnft41sHD28P9mzaRWJsAkUFhaQlPyTk5IX/KCBUHf+6PEx+qFemUqrISs8iKz0LpVJJWWk5WelZ\nFORV+3Xfj0ukKKOI0a1e40FCEttWb8HO0Z7GLbWpqYxNjPGs48X5Y2dJuZ9MUUEhd2+GE3H7Hv5B\nWtN6WZWMHWu3UVVZxSsjB6GQKygrKaOspEwvjZRCriAzLfO5gbIEBAQEBAQEBP4OBM2vwL+SwU3d\nAK2wllFUiau1lNm9/XXl/1/n8qJY2lvQrHeTGuW1mZO7U4p74Q1++PoGRsZG2DnaM+y14XjV9a61\nb7/6/rTt3I7Th06iUCio41eXTr26cOLAMbYuusS4zzpSv0kDCgsKuXD8HAqFAr/6/rTu1IZ7t+4i\nMRTrNNJSUylXQ65w/vhZDA0NsXdyoEX7lrWO+zxadmjNzcs3iLobSVDThjRu1YSczGyO7D4EQPN2\nLfFvEEBFRUWt7ecM2UWLnnWQ/I7SefGkQ5j7VODwyABAJBIxcuIYQk6e59jeI5SXlWNmbo6HtweN\nmjd66XU0ad2Mdd/9SmVFJVJTrWVBaUkp679fo6tTmH+bsGu38azjxbiprwNawfXY7hPIZBWYWZoR\n0DCQLn26In4iAvaQscM4f/wswdsPUFVRiZWNFZ17d9Htd1Z6pu6AYtXSn/Xm9drb43Xfh/ioOKys\nrfCsI2h+BQSeRUpaEhPeH8TBzaFYmNeMmP93M/advkwYNY0enV48XoKAgIDAvwHR0/5r/wZatGih\nuXXr+RFfBQQEXp72i8/Vak7uZi0l9JNuNcrXfn6O0EPaHLdiiQGmFka41bOlRc86dBkWqJdPuKy4\nCrHEAKlZ7aayL8Laz89RVlTFhz/10yt/EJXDV6P3s+z4GBzc/vwXycfC75B3ni14h19MYfuSUBYf\nGqWLfnxhbzTXjieSEptHZam81vklR+ey5/trJEXlYmAgokWPOoye3Q4T0+r0QUmROez94ToPonOx\n9cvH3MyakW8NpE5DJ12diNCHBK+6RXpiARIjMb5NnBk5ow3O3o/8euUqZvf9jalLe+Lf3OXP3B49\nNvy4jlYdWxPUtOFfNobAv5eCojxWbV7O5etnySvIwcLMkno+AUwYNZ22LToD0J+BIw8AACAASURB\nVHdMS0YNnsjrI37Hv/+/yKrNyzlz8Qj71l/4U/qb9dVk6vkE8PZ4bXA7mbyKBd99TGxCBA9SE2gc\n1JL1K/bXaLczeCO7Dm4gIysNZ0c33hz7Hq/0qs6/rVAq2LB9JYdP7SYnLwtvj7q8P3kO7VvVfH4D\nrN/+IyvXf8PIQRP49L1FuvKQK6dYvnoeBzddxsBAMBIUEPinIhKJbms0mhZ/9zz+TQhPNAEBAR3/\niQl3gzZufH9uPMuPj2HWrwNo0tmL4F9useiNg8gqFLp65lYmf0jw/atRPm3u/ZKc/i2CDoP89dL+\nyCqVBLV1Z/Dbtf9fKswpZ9mUIzi4WzJ321BmrupP+v0C1n1+XlenqkLBt1OPYu1gxhfbhvD6ByOR\nmpmw/O2jVJbLAchNK+GH90/g18yZr3a/yuw1A5DLlKyYdkzXj6GRmDZ9fTm9PeIPrfN5lJeVE9gw\nkAZNgv6yMQT+3cya9yZRsWHMm7WCg5sv8+PCrbRv1Y3iksLfb/ySKBTyP73PP4OsnHQuhJ5gUO+R\nujK1So2xkTEjB0+gQ+vutbbbfWgzP6xdwJRxM9i3/gJTX5/FNz9+RsiVU7o6P29Ywp7Dm/lo+gL2\nbwjh1VfGM+PLScQm1Py7vxd9m31Ht+FXp36Nzzq07k5FRRmXb5z7E1YsICAg8M9BMHsWEBDQ8Z+Y\ncEuMxFjbayP72jiZ4xVgT1BbD74cuZdjG8MZMk2rLf1m4kHc69ky7jNtPmWlQsX+n25y9WgC5SUy\n3OraMHR6Kxq29/jD61Cr1Gycf5GY6+kU51dg62RG56GB9HmjiS6o02Mtsl8zF85sj0SpULEy5A1K\n8ivZ+FUIkVcfYmkrZdAzBNcnKSmoJPpaGiNntNEr7z1Oa9r8ICqn1nZ3L6YgMhAxfk5HndD8+ued\n+OLVPWSnFuPkaUXmg0LKi2UMeacFDu6WgA02doOYfXw7WclF+DRwJDk6F5VSzfD3W+v6GTCpKUve\nPExpYSUWNloT6aZdvFj21hFklQqMpYa1zumPYGZuRtuu7f/0fgX+NygpK+ZOxHVWL91F62ba54Cr\nkwdBAdUuGJNmDCUzO43vfp3Pd7/OByD8bCZFxQUsXjmHOxHXKS4pxM3Fk/EjpjK4zyi9tnU8fZGa\nmHL41G5cnD3Y/ssJUh7eZ/6KWUTEhOHi5M6sd77io/lT+OTdRQzqoxVAf1i7kHOXj5OVk46tjT29\nOg/knQmzMTYy4eCJXfy65VsAmnTXWk18Nft7BvUZSWlZCd+tmc/50BPIZFUE+jZkxttf0sC/plvJ\nY05eOERdnwBcnNx1ZVKpKZ9/uBSAhKQYSstrxiA4cnovQ/uPpW+3IQC4u3oRFRfOxl0/0bldLwCO\nntnLhFHT6dRGG8xuxMDXuX7nIlv2rGbRZ9XuCqVlJXy2aBpfzfqO1Vu/rTGWWCymQ+vunDh3QNeX\ngICAwP8CgvArICCgx+Cmbn/YX9nd15aG7T24dSZJJ/w+zbovzpPzsIS3F3fHxtmce5dS+f7d43y5\nYyie/vZ/aHy1WoONoxnvLO+JhY0JDyJz2Dj/ImbWJnQeWh0NO+5WJlJzI2au6q+Larzui3PkZZbx\n0ZoBGJkYsn1ZKHkZpc8dLyEsC4mRGPd6LxeJWiFXIZYY6GmLjUy0j+X4sEycPK1w9rbGwsaEiwdi\nGfhWcwBC9sVg52KOW13teD5BjoglBoTsj6Xz0ABkVUouH4rDJ8hBJ/gCeDdwQKXScP9uNvXbuPOi\nzFt1GkszE2aM1wosn3x/HC8Xa6aObPtS6xX483n63rwoE77Yw4DOAQzr8d8zTzeVmmEqNSPk6ima\nNmyFsZFJjTor5q1nxJQeDOozihEDX9eVy+QyAnwb8saoaZibWnDtzkUWfPcRLo5uOkEa4OiZfQzr\n/xobvg9Go9GgVqv58MuJ2Ns6suWnI8hkVSz7ZW4NrbDURMq82StwtHchKSWehd9/hJGREdMmfEzv\nrgO5nxzLxWunWffIFNnczAKNRsO7c17D3MySHxduxcrCmsOndjNl1nCCN13Gwc6J2giLuE59v5o+\n/6t2XSUlswirZ9jkKRQyjI30I+wbG5sQGRuOQqnAUGKIXC5HIjFiylf7eG9Me4J8nTE2MiEs8oZe\nu6+/m02PTgNo2bR9rcIvQFBAE9b99mPtk/kDrN9/E7lSxdQRbX6/soCAgMCfjCD8CggI/CW41rUh\n+np6rZ/lPCzm+vFElp8Yi52LBQA9RgcRdS2NC3uiGf95p2f2GxH6kLdar9Mrezp0gcRQzNAnhG4H\nN0uSY/K4fjxRT/g1NBYzaX5XDI20pt5ZyUXcu/yQOZsH4dtUq+GZvKAbs/ttf+5a8zJKsbSV6gmx\nL0L9Vm7sXH6VI+vD6DO+EbJKJXu+vw5Aca42+JbUzIhPNgzkxw9OcmR9GAD2rhbM/nWATlB+fP3z\nrFNsXXQJjVqDZ4A9pj3smLfqNPOm9gTAWGqIqbkRub8jzP8ecyZ3Q/ySa/0ryM4vZeLcvXz/0Sv4\nev2xA5P/VU5fTWD17mvs+27cf2W85x2MSMQS5n/0PfNXzGbfkW0E1AuiSVBLenZ+hYaBzQCwsrTB\nwMAAM1Nz7G0ddW2dHFzo22Oc7n6/OmAcN8NCOXEuWE/4dXP2ZObUeQDci89k2lffkZSSiEL6Kt9t\nj6dJgCsTx37MjC9G6c1tyrgZT/ThwaQx77Fl92qmTfgYE2MpUqkZYrFEb043wi4TlxjF+f2RmBhr\nD5mmTfiYkKunOXJ6LxNGTat1jzKy0/Cr2+AldxbatuhC8PEddOvQnwb+jYmOv8uBY9tRKhUUFRfg\nYOdE25ZdWLf9F/wbvkX9uo5cvRXCucvHUChVzPr2KElpBRTkXMXF4gELP/1Jr//w2Ay2HrlDSkYh\nxkaG+DiWkpOXiVKlRCLWPmtuR6ez/VgYKRmFGErEBNZxZNKQlrg5Wen6ORISw+GQGHIKynCwMWNk\nn8Z0b12dK/zVng2ZNG8vg7s1wMXe4qX3QUBAQOCPIAi/AgICfw3PiaWXHJOHRgOfDd6lV65UqAls\n5frcbv2bufDGl531ytISC1j5wUm9snO7o7i4P5b8zFLkVUpUSjV2rvovWm71bHWCL0DGg0JEBiJ8\ngqpfcO1dLbBxMH3unBQypV4/L4pbPVve/LorO5ZfYd/KG4jFInqMaYilnRTRI/NseZWS9XMvULeh\nE2990x21SsOJzXf54f0TzNsxDGNTQ4ryKtgw7wLtX/GjTV9fqsrl7P/lJrG3MvBs7Kg3pqGJGIXs\nj/k3W5jVzO8sIPAi9Og0gI5tenDn3nXuRd8m9OZ5tuxZzfSJn/Dm2Pef2U6lUrF9/yqyH+xgwrtL\nUKkUKJQKWjTWF7IDH2lUj1+K5Zdd13A2z8XWxpF1898gv6iCC7eSuHtfUyOI0+mQI/y2bw0PM5Kp\nqCxHrVajUj//7yQ6/h5Vskq6DtX3cZfLZaRlJD+znUxWVUOD+yJMGfch+YW5vPHeK2g0GmxtHHil\n1wg27foZA5F2PbPfmc+wt8YSeuELWobMxd3Vm4G9R7H/6HbaNfbCzV7J1t9OsmnxSQwl1a4PJWVV\nfLnqNMN7NmLm+E7kF1ew8Of1aDQa5HIZEqmErLxSvv71LAO7BDJzfCcqZQo2Bt/iy19Os+6rVwE4\nejGWjcG3eHdse/y9HYhPzmXl9lDMTY1o3dATACsLE5oFuHLsYiyThr589H4BAQGBP4Ig/AoICLwU\nwWHpOp/gpjHF+FrWNF0ESL9fiKN77ZGXNWoNIhF8uWMYYon+S6iR8fOFSCOpBCdPK72yilKZ3vX1\nE4lsX3qFUTPbUq+xE1JzI87ujOT2uQd69YyltT8CRSLRc+fwNObWJpSXyH6/Yi207e9L2/6+FOdX\nYCw1RASc3HrvkX8vXD2WQG5aCZ9vGazTLL+9pDvvtN/I7XMPaDfAj7M7IzGWShg5o1oQeGtRd96c\nvpOKJ+a1YsslUp0URBfms+WzXVTJlbRt7MnUkW0xMdLuRZVcyS87rxIaloyJsYSBXWoGw3lau3fu\nxn0OnY8mLbsII0MJQb7OTHm1FfbWZoBWA/fpDydY+G5vNh+6TUpGIR4u1rw7uh31PLUa25KyKlbt\nvkbU/WxKy2U421kwtEcQPdv6vtR+JqcXsGbfDWKScjAyFNO6oSdvDW+N2aO81iu2XKKkvIqmAa7s\nOx1Z6x5oNBr2nYnk+OU4CoorcHGw4NWejejWqjp38fZj4Zy6Ek9haSXmUmOaBboy8/XaLRZUajUr\nt1/hXnwmhSWV2Fub0ru9P0O7B+l80F9kXi9yb57kXnwm32/T5sXuP20jAGP6NWFs/6YAKBQqVm4P\nJeT2A0xNDBnUpT7DelabQZdXyll/4CbX7qYiVyip62HHm0NbvbSW/clnRh2qcDJQUlkhw9rCgy7d\n5uLtsZ3VW77l9RFTKSqTU1xaxbYjdzh4ZQsOtuaM6deEpIRjbNm9GmunAUiMnTAwMEZUGYJCqdAb\nS2piSl5hOav3Xqd/pwAsxAoephjgZGeBk50F9es6UVRSxtYtEHU/my0fbmV4dzsWf/8Wlvbd+OKD\nnwgNzyLh/jWi7u3Q9RsRn0lGbrUfbnJ6AccvxWAgNsfBayoOtloNZ4CPI2q1mh0nYpk4d0+t99va\nypbi0iLW7b/BqSsJAPRoUw+1Wv/EsPbv4XQ+/3ApBYW52Ns6se/oNsxMzbGxtgOgoATMHUexb81Q\nlIoKHO2d+WHtQjzdvRnaI4jlv4agVlUwbGIX3ThaIf8aiHYxYlkSRkbGuDpa0qGpM3GREhBp/3YS\nU/NRqdS8Pqg54keHB8N7N+KzH05QXFaFlbkJ524k0ru9H11a1AHAxd6ChJQ89p6K0Am/AK0bebL5\n0G1B+BUQEPivIwi/AgICL0xwWDqf7o+g8lFk5Aq5ktisUoLD0vX8hNMSCoi88pBXJjertR+vAHs0\nGijOqyCw1Z+fDzk+LIu6DR3pMbpaI5PzsGYAmadx8bZBo9aQFJmDbxNtst78zFIKc2vP//sYr0B7\nSgur9IJLvSxWdlrt8sUDsRgaiWnwyCdXXqUE0GmCQSuci0TaQ4THdZ7WZBmIH9V/4n26qlyO3FxE\nhVjFgvd6k1dYzuL1F3BztGJEb63GbP3+m4THZvDZ5G7YWZuy/Vg4kYnZtGv87Ly9SqWKsf2b4O5s\nTUlZFRuDb7F0QwhLZ+inpNp86DZvDG6BraWUNXuvs2zTRVZ/MQSRSIRcqaKehx2v9myIqdSI8NgM\nftpxBQcbM5oEPN8aQLc+mYIvfj6Fn5cD380eQGmFnB+3h/L9tsvMmVyd6iUqMRtbS9Nn7sGWw3cI\nDUtm6og2uDtZEfsghx+3X8Hc1IhWQR6EhiWz/2wkH03ojLerDcWlVcQ+qD2oGWjN8u2sTflkUhes\nzE2IT85j5Y4rWJgZ07ud3wvP62XvTWAdR6a82orNh+6wbt4wAKTG1dq+4PPRjO3XhGE9GnIrOo1f\n91ynfl0nAus4otFomPfLaUylRnw5tQcWpsacvZ7Ipz+eYM3codhaPd8aQjfGU8+MEpmKfGMpM4c2\nJ8DWmO1rDuJQJkalUiKTy/hl53VEIjG92/vy2rAhpGUXAxAWeYOObXpwv6Ap86f1wtvVmrdmHQOs\na4x5KSwZpVLN8F6NiEuoJDcvm5y8LBzttX/TKQ9jUavVAMiVKvYeP4G9jRObf/gJGytTIpKuUVmR\nh79JPYKXrmXwR5MRiw1Bo9aNsXRTCG6ufsTH7uPLqb2QqcyxsZTi6eaIUqXG3aWYYb08ar3fAfWC\nuHL7DiKLOrw3pj3ebjYcuRjL+VtJ1POw043xe99DgJPng+nYpqfu7z/yfjYu9hbYWVsBViiUCs5e\nOkrPzq8A0CioE4433+OXOUN048xd9gEKtRW2Tt0wNKyOyJ+d8wBDE1cSU/No5OeCn5c9YrEBp0Lj\n6dXeD5lcxdlrifh52WNlrj0EVSjVGD2VMcDISEx8Sh5KlRrJowM8Py978osqyMwtwcXhn5fnWEBA\n4H+Xv99pS0BA4F/DspNxupfYx2iUalYER1GYU05qXB4nttxl8aRDeAXa0/f1xrX24+xtTdv+vqz7\n4jw3T90nJ62EB1E5HN8Uzq0zSX94ns5eVqTE5HHvUipZKUUc/PU2cbczf7edi481Ddt7sHn+RRLv\nZpESm8e6z8/rtNEH/ceSsrs69UdpYhoXBn3K3YHv0av0MvFhWXr9FeVVkBKbR1ay9gU+I6mQ2z8c\n4qDfGF2dYzO3Euw7hqzkIs7sjGTbN5cZ/n5rzCy1ZpEN2rpTWa5gy4JLZCQVkp5YwLq55zEQG+gO\nDhp39CIlJpeDq2+RlVJEcnQu6764gLFUgtRC+zKbezUKr5+3IdHA7Mld8HS2plmgGx2aehMelwFA\nZZWCU1fjmTC4Bc3ru+HtasOHr3XA4Hc04b3a+dEyyAMXewv8vR2YNqotUfezySss16v32oBmNPZz\nwcPZmtF9m5CWXUx+UQVVecVc6zydvkGu1PWww8Xegr4d/GnXxIuQ2y/+fbhwK4kqmZKZr3fC282W\nhr7OvDu6HVfCU8jIqT78MDUxZNrotrXuQZVMQfC5KN4b254WDdxxtregS8u69Gnvx9GQGAByCsqw\ntZTSLNANR1tzfL3seeU5WliJ2IBxA5rh5+WAk50FHZv70LeDPyG39Nf2vHk9eW+aBbhwfc02/HMT\n9O6NQiZn44eLOLNuNwCGEjGmJkaIAFsrU2ytTJGXlLJi1IcEJVyhqZc1r3Spj6ujJQO71MfdVsrx\nL5ayYtSHXD4fRlJaAZ+92RV/bwdcHS0Z90oznO0sOHfj/gvfkyefGSJlGRUPVlGed4NfTl7GyUqJ\ne1EKl7Mv06ppR8zNLKhMTKCHeWs0Iensmb2Q+F17cRHL8XKvQ3TsDWQVyZSWpLNuyTwal/gSmOHJ\ntk+/JS2mek4ZOSWYGYu5e+A44b+eYZTdUFbP+orwOze4F32bb1fNQyKWIKpSElSURPsiQ7qJ2nDy\n11/JzXlIXOwp0lKvkVCVRPaDNNJi7mNp6YisqoCY+HsUFueTnVdM3669adKgJd/8MB21LBFLaRV3\no26xZuu3NPCUP/N+t2vRhQcpkQzpXp+OzX3wcLbmrVdbYyIuoqQ4laLiAsrLy9h19DSDOtnrvoc+\nzuBjn86uo+eJiA3j46/fJvFBHO9O+lS39siYOygqYkjLSOHOvWtM+2QMao2aNx75H5tKLTA0dqae\nT4DuR2piioeLE+n5Rpy/mYRKpSavqJzLNy5jbOZHQYk297ujnTkL3u3FtqPhDH5/CyNmbSM5o5Av\np1ZHg24e6MbpqwnEp+Si0WhISMnjZGgCSpWakrIqXT27R4cn2fllL/xdEhAQEPgzEDS/AgICL0xG\nUWWNMtt8GbYHkph56AGmFsa41bNh8NQWdHk1EInhs02YJ83vwuG1d9j93TUKsssxszKmTpDjczXB\nosoKbCJucLLjUSoz8zE0l2Lm7YJpq8aINUpdva7D65Mal8/qT86gAVr0qEOf8Y24GBz7u2t8c0FX\nNs4LYcmbh7GwNmHQ2y0oKai5boCoZTsQS43peeFHjm2L4urRBJp389F9fn53FAdX39ZdfzftOB6y\nDJqqqtWxuWklSGVKPh+2GxcfG17/ohPtX6nWBrr62PDByj4cXH2br8cdQAR4Btgz45d+2DqbA1C/\ntRtvLe7B8U3hHNsYjpGJhDoNnWjQxh2lgXasyIVbiPP2w9nWErGBAZXZhUR8vQnXazG45RRw60EC\ntjPHolSqCaij9RNWK5Qkrz7A4BOnMd5XwdltB2jwqX7gJEVZJVfnbiDj5A3E5ZUUWFtxq3FDMLcg\np7AMexszNBoNTWPjyHztMsElFdg29cV9lvYAoKi0EntPezyGduLoh79woWFD8osrUChUKFVqGvo6\n/+49e8zDrGJ83GwxNanWbgbWccRAJCI1qwhXR62GydPFWme2CVrBMC45F4DUrCLkChVzfz7NkyK/\nUq3GyVa73x2aeXPwfDQT5+6hWaAbzeu70aahJ4bP+b4fuxTLySvx5BSUIZerUKrVOD7q7zHPm1dm\nXqnu3ogMDOg9dTRbP1pGoGt1IKFL2w+jVmvo/Nqg390rhcQI+9JcvTIXeREiI2M0lRU8zCpGplAy\n5uMdenXkShWZub9vRfGYJ58ZGgNjJFJPTDJPUpXyG++Hu9LENIhSMw+WfrEaAF9zDdckdmSUXqG8\nIAf/grrkpWQy4ospJKUmceXWJn5deomWxo3R1LPkfmkKzfw6cmDxGgytHu2/RoN38UMSbsjp/954\nCssLOPzLZvZ9s4p70gRmTv2SmfMmYRSdjSGGjJr/Prv2rifrzgMWf/AhmVYQ2GAod8M2E9CuGWEn\nLuHn24Grt84wZfYISsuK6dv7Q1Zuv4K/9+sYZJ3iy2UzKS4twM7GgSYNWmJh04zNxw/Ver+bNuoI\nSFBUJABa6xgDAxEPYtdQXv7kPYlgzoIf2HBImwJJXpVNVupuVPI8roea0KJxOzavPISbc3V6uMqq\nShLjgxk6cT2mUlM6tO7Ogk9WYmmu7yryNA425nTs0JJVu67y3dZLiDRlFBfdx9FnMI+NTgqKK/jh\nt1C6t65L5xZ1qKxSsO1oGIvXX2DRe30wMBAxqm9jCksqmbX8KBrAxkJK9zZ12Xc6Us+dxOiRKb/8\nD+ZXFxAQEHhZBOFXQEDghXG1lpL+xMtsbGM7Yhvb4WYtJfSTbs9pCZ9u0H8hlxiKGfJOS4a882I+\nX+UPc3ANOYihhZSAGaOxCvRCbGJESfxDknecYd789ji4Wer6nvRVFyZ91UWvjydz9k5eUPt8rexM\n+WBlX72yzsO0EaIP/vDUnJKzcOnVEjMPR3pPseCzIbvITSvR+evWtr6U3ecI/6JaS9VlWCDh4VdY\nd3vKM9ce1NaDoLbPz3/cpm892vStp/OvPFJUSb3kAuraScm/FUtxQhr3m/hSx00b9EstV2Bsa4my\ndzsqTl6ttc/oZTtI2XeB5C5tMK/nTlNbCdcmL8V0eD9w0Zqb3pr5E0mXoygZ2pMO3RvhcTEMp+2n\n+K1de5RKrZlo0c4zBN1Pwn/FdBzrexP7w26ipi7DsGUbXaTueA8P2HCMIW8OwsffDamxIZsP3ab4\nCW3RH+FJ5fXTkapFInSprh5Zw/Ll291xsNEXTh+bbDrYmLPmy6GEx2USHpvB+v032XEsnBWzB2Bi\nXDN/8sXbSazZe4NJQ1oQWMcRUxMjjlyM4erdVL16z5vX01g72dPptYGc3hQM3p6kRsRz9/QVRsyd\nhqHJ7wdTKrR0RJqYiEaj0Qkl0twMxF7eKGOj0aDB2kLK0g/7UVFUQnjwSTJjEgFwelBFYaYfNi4O\nAFzZc4KE63dpPbQndmGXqLyl4ODDaHpNGan3zJBqwM22C7nO/TGztmCEwQPKRUZYZoqxsrQBYNJX\n7zCosJybUWmEx2Zw7V4KgSVx/B979x0dRdUGcPi32fTee6+UkFBCJ3RCld6RJoKiiKBUGwpIURSU\nolJERaqA9C69dwIhhJBGeu89u/v9sWGTJQlFRJDvPudwTnbmnTt3Z2dD3rkt7V48n01ZzhufbaWT\nfhqOXs4EjRukej/RN+4wrOlIAof0YNuBaxTmp9NwYH9c/HxwATy8arHqvTl89+Fqig1lWGlYIskv\nIdzaGzsPZyZN/4Lbpy5zeOVmvAO7kVVUxq+LFhAXGsG2L3/AxKU2tXzHsuKTiu7C8SnZXAmJ50qo\nEWnFzZk+uDlBLbw5eSWSb387XePnLZVKMbJoy8Gj6xnZv+I99Om7jLSsfBZM6sqdqFQ+XLSHeRM7\nP3QfjkNTqoG1hfq9+YCPVyMKZZ+zeNprj70HHlhTvnwTQO/2dcnILuTnDV+RatuHkEQTbMtnZN57\n8g662pq80afid9qUka0Z+ckWQiNTqOtpg462JpOGt2LC0BZk5RRiZqLHgdN30dPVUnWNBsjNV85F\nYGxY/ZwRgiAIz4tIfgVBeGJTO/uojd8D0NOSMrWzz3M/9/WPViLRkNBu71do6lf8wWTgbINdxwC1\nJKEgPpUbs34m9XQwANaB/vjNHoO+XcV4usjfDxH+404KEtLQt7fE+53euA3tpNqfF5XI1Wk/kHHt\nLvoOVtT7tGLNUYDtTsoxlNm3o7mz5A9qTR7ImC/akhQSR9S8AySfvA6AeSMf/D8fjaHbk41bzYtO\n4ubsX8i4Hk5ZXiGGHvbU+XAwdh0rEvf4/ecJ/XYzeVFJSHW1ManlTJMfPuRAXL7a55NfIuNOUi4n\nVh3AoK4X7o0dVROMGThZ4z97DLf2XqP0+FVAOTmNplSDsKgU7CyNuL/tBO7jevJreB7NzE1wHxFI\nyulgUq7cRFbfE1lhMYkHL3KhYUNmvd9L+UdyWz92Hb1K7egYQJm8ZW0/RrCXJ12DmmBiqEvAt++x\nu/4buMdVLIV1uxhqmRjikZqCW0c/FAoF8Sk5GOpXjEF8HCdbEw6fC6egqFTV+hsamYJcocDJpurY\n0Oo425mipSklJSMff5+aPzNtLU2a+DrRxNeJAUF+vD5zE7cjU2hYu2rPhZCIFHxc1btGJ6Y93XJT\nD382AD6tm7Dt90OYXr/EweDzNOreBoda7mrHaWlqIK8mgc7VN4PCdGJvheNcz5uUqDikxQVIHZwo\nu3MbRxsTss7GICsr5eSK37D3dmXw5+8h1ZRyec8xtn75A6O+mYGWjvLzyUnNIOzcdbK862NnpkfK\n7Wuc3ryPqZ1bqu5JfVkppRIN8gyN+bhnPeKXnkRRxx9Q71lhaWZA11Y+dG3lw5b914hedwsdA300\npVIkCjnZ8Ym06tdJ7RgXPx8S7kYD4GUiJQYFl1NKaAwcPb0PXV19jK3NuHziJJtvbqaWVS10DM0o\nkVbcX67+tZCVlmFQmk9UjvI7ZOPuhFwm535oJKCeqDlYm+BgbULPdnVYCyIDNwAAIABJREFUvvEs\nB8/eJaiF92M/bwM9bRxdWmOhc5PcvByMDI1RKBTcjUlVjaV+0vvwYR6OFuw5EYpcrlBNpvY0JBIJ\nFqb6WFlYY2QRSEpROh7l45CLS8qqlPng9cMPaTSlGliaKSe8O3klkia+TmrHxiRkoinVwNXB7Knr\nKAiC8CzEmF9BEJ5Y7wYOzO9bDwdTPSSAg6ke8/vWU5vs6nkozswl+cR13Ed2UUt8K3vQeqWQyzk3\nZiHFadkEbv6CwM1fUJicwfkxC1V/oMXvv8CNT1fj+WZ3Oh5ZjOeY7lz/eBWJhy+pyjg/9isUcjlt\nd8yj0aJ3Cf12C/KSipllu11ZjaGHPV7jetLtymq83+pJvWZ2JH65Ag0dLVr/MZu2O+aha23GqSFf\nUFb4ZLNBlxUUYdOuAa3Wf0aHg9/g0LUZ58d9Te69OACKUjK5+O5iXPq3o9Ox72i9dQ5O/ZRLP1U3\nJlsuV5By4TYubX0xszZ45Ln1dLUIau7F2h2XuRYaT2lRCcev31dLnqS6OhjFJyvLlslBJkeircWe\nE6EkpuVy8VYsqXnF2GRkAFBwPxlZRg7xVlYVZejpYNLQG5uMTNU2B2tjkk1MiDh8hdikLH7Ycp7k\n9KdLENs29kBHW8q3v50kOj6DW+FJLNt4lhb1XVRdnh9HX1eLvh3rsubPSxw6e5eElBwiYtPZd+oO\n+0+HAcq1cw+euUt0fAZJabkcPh+OplQD+xom7nGwNiYiNp3LIXHEp2Szcf91boUnVRtbk4c/m5iE\nTJb8fpoIU2fITEeqpUmLgd2qHGdtYUhJqYxrofFk5xVRXFo+PEAiwcDLk1vHletK3zx2gWJzWyhf\nz9XbxYo67jYs+2YLRSWl+A/oQZpMk33XErBr15rSomIir4aoziOXy+kyfggyAyPydY1wCPAn8vod\n6lnqMqWNC45GWpRoSNFSyBnjZ0kDCy2KC4oIjs1Sq+9Pf5znckgciWm5RMSmE3boBBJNTTwa+WJq\npIuhVDnRm0xTm/zCkorPzcSIgixld2wtWSlIJOw9H8m3v53kTmQ8Xy6ZSXhcGMdPHEKGOe0ad0ZT\nX33SLj0jAyQaGjib6RIRm8Ghs3dJzS5CoqVFclyyKq64pIwVm88RfDeR5PRc7kSlEhKZjHN5b4gn\n+bx7tfMlMc+XG3cziEvOZuXWC6qxtfBk92F1/LxtKSmTERWfobY9JSOPiNh0kjOU36mI2HQiYtMp\nLKr4nbbt8E2i4zOU6/wat2D/mTjeGtBU1RW/sa8jEbHpbNh3nfiUbO7dT2PxutNYmRng6axMkOOT\nszl64R7xKdmERaey8OfjxCRmMbKn+uSHIRHJ1PWwUc1kLgiC8G8Rv3UEQXgqvRs4PPdk92H50Ymg\nUGDorn7efY3HUpqjnInZuW9rGsx/i5TTN8kOjaHz6eUYOCnHrjZZOomDgRNIPR2MdaA/4St34ty3\nDR6jlMmCkbs9mcER3P1hB3adGpNyKpic8Di6nF2BvoMyafP7fDQn+32iOreutRkaUilSA110rZWt\nF9Gb/kKhUNDo2wmqZLzhgrfYW/8Nko5cxvG1lo99r6Z1XDGt46p6XWtifxKPXCZ+73lqvd+fwuRM\nFKVlOHRvhr6j8v2Z1FIuIVLdmGwAo5xcdG3M4Qny7zF9G1NUUsbclUcJNDPH8fJNGvXqBHIFySdv\nkLD/PFrlCZSWoR7mjXzokpHEgXN32HsilMZ5mdRKTSNLT5lYFKUqk5tCHfWuuFoWJuiFVUxCNqir\nP5s2HuDetQh+Xryfjs08advYg9gk9eSosgcrwzxoUdLV1mTOu0Gs3HqRyV/vQUtTSjM/5VJHT2N4\nj4aYGumx/a9bLN98Dn1dLdwdLejfUTl7uKG+NlsP32TNn5cok8lxtjXl47HtVd1DH9a1lQ+RcRl8\ntfYEAC3qu9Cngy+Hz4U/Vb0qfzY62pq81rY2irAikErJTc8mJyUdcwcbVfz2+T8RfyeSDmUy9s27\ny1ljbwa1rhgjbOjtzb1du8jPyuHOmasUuvqq5k6WSODzdzqxevZKCuOz+H3iF4DyWh+TSikrLSU7\nOV1VlrGlGTr6ypnOT16JIrwoA++CbCYu2AXAqA51ebNvEL/svMzBs3e5fCqYJkBQy1qsPVGxBJlc\noUyAUzMLcC1Jwyknke5T3kSn/KHX6z0acnftLb5cdRTH2jEsmKQ+ROEBiUTCnAmd2XE0hIsxBmhZ\njMcwNxJ7aws+mzqaG9v21nid3RzMGdrNgt92X6W4pIzmGlLquVsTXP790dCQkFdQwuJ1p8nIKcDY\nQIfGvk68Wd4d+Ek+774dfMnMKeT7DWcAaNfEg3YB7sSWz24Nj78Pq2NsqEsLfxeOX4pUtdgC/L7n\nGn9duKd6/eBzmf9+F/y87QC4fDuOzQeDKS2T4eZgzqdvdSCgrqPqGH8fe6aOasO2I7fYdvgm2tpS\narlaM/vdIFV3f5lCwZ9HQ4hPzkYq1cDP245FH3bHxkL9u3HicqRqyS1BEIR/k0h+BUF4aT0Yv6p5\nL4b3gYtR6fSptL/NtrkoZHKuzfgRWZGyFSj3Xhx6NmaqxBfAwMUWXRszcsLjsA70Jzc8HtdB6mN+\nLZrUJvHw5YoybM1ViS+AeQMv0Hh0Z5msm5EUxKawq9brattlhcXkxyTXcJS6soIiQhdvIemvKxSl\nZCIvlSEvLsGktnI5G9M6LlgH+nGk42SsW/tj3coPh+7N0bEwqTImO0FP2RKpJStDqqPFB4MCq5xv\nWPcGnP1jv+q1ro4WH45szYcjoTi9J1en/YDGqi1IJHDDxRaXge2J3nxUtcZvwJKJXJ2ynI6bdyKR\namDq645hr0CMbkbg521H+mXlH/O/zB2AfqXxfXo6WjSsba9aM9ZIX4cWjT1ILsjj7YVDnuhaZZY/\n+Ki87I6rgznz3u9S4zEfjKj+GlT+Q1wikdCzbZ0a19Ft7u9C80cs/fQwLU0pk15vxaTXW6ltH9qt\n/lPV68DtFLYnlpGgY469qR4SRRl68ZH0mvImN46c4cCKDQye875q2ZtObw2irLy3glQq5WMrc7JT\nMljz53Y+e7sDth7ObLp5lb3fr8PA1Ii5nw5R7j+kTAz1dbWo425NMkV0mziiSv10DSuuu4ZUOdnU\ng2Q05PhFjq7dxt7lo9WOGdUrgFG9ApCVlfHd8Gk09LKm/8C2qv3jBzYD4Oq+E5zZcpu+n45X68rd\ntUM9wn/V4LPRrfBuVnH9CrJz0TdV3u8GpsYo5HJ8HEz44t0gVcyvUxbg1cgTawtD9E2N0aWMbYsr\nJnArzM1HIZejb2rEsJZeqmv/3fBptGtZm7dbKF9raUqZNrpNlevxwJN83lKpBuP6N2Vc/5ofzDzu\nPqzJ4C7+zPzuAEO6+qNfvr71ByMCq73HKpv/fvUPEiprE+BOmwD3Gvc725qydOajJ1y7eCsWDQ0J\nrRq4PvZ8giAI/zSR/AqC8FKqvD6ovpExcuDwwWAkzf1VLc8GzspWLqne4yf4AdRmG61+/zNVGYVc\njkkdN5osn1xln7Zp9RPUPOzmnF9JPnGdep+MwNDNDqmuDpcnL0VeUr7er1RKy/WfkXH1LiknbxC9\n6SghC9cT+MecGsdka5oaUZKdX9Mpa6RjYULzNTOQFZVQkpmLrq05IfN/x8Cl4sGCoastrbfOoayg\niNLcQvRszLgw/hv0yz8bXStlW2JRarbaw4SitCxVi/kDJVl56Fg8vntyaZmMlIw8th2+hau9GWbG\nf29t5f+Sh9fLTcrII2TjUZzr1sWtQW2s3Rz4dcpCLu86SpPeyqVnjMwfP87Zt11TDv24idbDela7\n39rNkbCzV9EzMkTX4J+7zlJNTSwcbMiIS8a9gXpyd2Xvcc7+cYA+08dWGcMs1dTExs2RmOAwteQ3\nJvguXk2V6yHbuDuiIZUSExxG7VaNAMhNzyI9PoU23srZ2O29XLnw52Fy07MwsjAtLyMMqZYmNm4V\nk8tlJaUhKy3F2q2iBfRl52Jvxpi+jUlKz8Pd0fxFV6eK4uIyJr0eWGWCN0EQhH+D+M0jCMJLqfL4\n1QIdXe7aOdAsNIQlu28+8jgjT0cKkzPJj01RbcuPSaIoORMjL+UfsEZeDqRfUh83l34xFCMvp4oy\nkjIoSEhT7c+8fq9iKuAamNZzJz8mEW1zYwzd7NT+aZtV3yX2YemX7uDcrw0O3ZpjUtsVPTsL8mPU\nxwtKJBIsGvlQe/JA2u1diK6NOXG7ztQ4Jtu2gSe54bFPdP7qSHW10bOzQFEmI37feew6NakSo6mv\ni56NGSVZeaScvI59kLILqL6zDTrWpqScuqGKlRWVkH4xFPNG6hOl5YTFYurrxuOERqbw3vxd5OQV\nMWVU67/9vv5LHh7P3SLrHhoKGb8rlPe0gakx7d/ox7mtB0iLffya1g/Uad2Y8Svn0LBb9dexdqtG\n6JsYsXPRGmJv3yM7JZ240AhOrNtJZmJqtcc8KVf/WsSHqa91fGn3UU5t2EPQW4Mws7MiPyuH/Kwc\nigsqejQ06t6WkBOXuHn0POnxyRz7ZTv5mdn4d2wBgI6+Hr7tmnJqw25iboaREhXH/uXrsXK2w7me\nchkxF38fLBxtObBiPSlRccTcDOPk+t3Ua99M1cUaIP5OJCbWFqqZrf8rOjT1fCkTX4DARm7Ucvtv\nXU9BEF4douVXEISX0sPjV7c2bs7EQ3sZvGkzsX4GmNRxRaIpJSs4guzb0Vi39gfAOtAPk9ouXJq4\nBP/P3wDgxmdrMPV1x6plPQC83+rFhfHfYFrPHZs2/iQfv07sjlM0WzlVVYaRpz2XJ32P36zRyIpK\nCP5iLRLNmtdxBXDq05rwn3ZxfswCan84GH0HSwoT0kg4dAn34UFPNOOzobsdCQcuYBfUBA1NKaFL\ntiArrpiUJuPqXVJOBWPdpj66ViZk3YqiMCENY29lElTdmOyINvWJ3vRXlXNlhSjHWpbmFoBEQlZI\nFBpamhh7Kx8CZFy7S2FSBqZ1XClMyiB08RYUCjne43urykg+fg2FQoGRhwN50Unc+vI3DD0ccBmo\n7FYukUjwHNODsGXbMfJ0wNDNnrDvt6Kpr4tT74pumGWFxWTdjKDu9KGPvUZ+3nZsXzz8sXGvksrf\nB/uiTPxz4vjTpgEJuRX3Rq0WDQm/EMzBHzYwZM4kVVfkR9HQ0EDPuOZeCVo62gyc9R6nN+5hz5Jf\nKSkoxMDMBKe6ns/cElyvfTPWzVhEYV4+eobKydhuHDyNXCZj73e/qcXWad2YLu8o7w2fFg0ozMvn\nwvZD5GflYOFkR58Z4zC2qkj22o7sg4ZUg73f/UZZSSnOvl50fWeoqku4hoYGfaaP5a81W9k063s0\ntbWo1apRlRbwO2evUq9Ds2d6n4IgCMLLQ1LTGoIvs4CAAMXly5dfdDUEQXiOWi44qjZ+FcCosIBe\n924TmJ1CYWIaEk1NjDwdcOjaDPdRXdEyVP4xXhCfyo3P1pB6RtlKbNXKD/85b6ovdbTuIOE/7aIg\nPhV9B6sqSx3lRiZwrXypIz17S+p9OopL7y2h/pwxqsTuSIdJ2HdvTp0PKtbqLErN4tb830k+eoXS\n3AJ0bcyxau6L78fD0TE3Ll/ndw29wtYDVHldEJfClakryLhyFy0TAzzH9CDtfAjaZkYELH6PnPA4\nbs7+haxbkZTm5KNnZ4nbsE5qCenDSrLy2N94LO32LMTYx1m1/cFyTZXpO1rR5dyPAKSeC+H6xyvJ\nv5+Mpr4utu0bUnfG6+jZViQZcbvPELJgPYVJ6WiZGuLQtRl1pw1Fy7hiZmmFQkHo4i1ErT9EaXY+\n5vW98J87VjVRF0DsjlOELtlC0PGlNb6P/2fVfR+AJ1pj+2W297vfsHC0pVm/oMcH/8vSYhPZOncF\noxd/pJrMSxAE4WUikUiuKBSKgMdHCg+I5FcQhJfSw2McQTl+9d9YWulVdGv+OorTc2i06N0XXZVq\nHesxHc8xPXDq8+hJef5fvarfh5y0TO5dDKZht5onkHpRom/cAZTdswVBEF5GIvl9eqLbsyAIL6UH\nf9B/fTCMhKxC7E31mNrZ5z/9h/6L5DOhHxFr96GQyZA8QXfYf1NRWjYO3Zvj2LvV44P/T72q3wdj\nS7OXMvEFkfQKgiC8ikTLryAIgiAIgiAIwn+MaPl9emK2Z0EQBEEQBEEQBOGVJ5JfQRAEQRAEQRAE\n4ZUnkl9BEARBEARBEAThlSeSX0EQBEEQBEEQBOGVJ5JfQRAEQRAEQRAE4ZUnkl9BEARBEARBEATh\nlSeSX0EQBEEQBEEQBOGVJ5JfQRAEQRAEQRAE4ZX3jyS/Eomki0QiCZNIJPckEsmMavbrSCSSzeX7\nL0gkEteH9jtLJJI8iUQy5Z+ojyAIgiAIgiAIgiBU9szJr0QikQLLga5AHWCIRCKp81DYGCBToVB4\nAouBhQ/t/xbY/6x1EQRBEARBEARBEITq/BMtv02AewqFIlKhUJQAm4BeD8X0An4t/3kr0EEikUgA\nJBJJbyAKCPkH6iIIwissOikVnfZDuRIW+aKr8n/jtwMnMO82+plj/k1vLvyR3h99/aKr8Z9y8kYo\ndUd8gEwmf9FVqaK4pBTPwe+J770gCILwzP6J5NcBiK30Oq58W7UxCoWiDMgGLCQSiSEwHfjicSeR\nSCTjJBLJZYlEcjk1NfUfqLYgCC8TnfZDH/nvzYU/vugqqpLvv0smk7No4y78Rk3BtOsobHq+SfO3\nP2bZ9gP/YC3/Pu8hE/l2856nPm5Au+bcWb/kOdSoZn0/XoRex2EcuXzzXz1vZSeu30an/VDSsnMe\nGffgvtHrOIz7yWlq+zJz8zDpMvKFP9SZ+eN6ZgzrjVSq/LMgMT2TEXOXUW/kh+h1HFbt96+0rIwv\nf9tOrWGTMO48koA3Z3Dw4g21mNyCQj5c9htegydi0mUkbSbM4vKdCLWYvMIiJn3/C+4DJ2DSZSS+\nIz7kuz/2qfbraGsxeWAPPlq58Tm8c0EQBOH/ieYLPv/nwGKFQpFX3hBcI4VCsRJYCRAQEKB4/lUT\nBOHfFLN1hernfeeuMf6bVWrb9LS1yczLfy7nLiktQ1vr+f86nPvbNn7aeZglE0cRUMuDgqJirt+L\nrpIQ/dfo6Wijp6P9r50vMT2TY9dCmNivG2v3HaNjQL1/7dzPwsHSnN8OnOCTkf1U2zYeOYO1qQn3\nU57tHiiTyZBqaPC4/0urc+7WXcJiE+nftplqW3FpGRYmRkwd0pM1e45We9ysn/9g/aFT/DBlLLWc\n7Tl8KZiBn33LiaVfUN/LFYC3F63iZuR9Vs94GwcrczYePk3XqfO4/vPXOFiZAzB1xTqOXr3FzzPH\n42pnzengUMZ/sxpLEyOGBQUCMKRjS2b8tJ7bUXHUcXN86vcoCIIgCPDPtPzGA06VXjuWb6s2RiKR\naAImQDrQFPhKIpFEA5OAjyQSyYR/oE6CIPzH2Jqbqv6ZGupX2WZSvg3gfnIaXafOw7TrKPxHT63S\n+hcaHUevmV9h0f0NHPu+zfA5S0nKyFLtf9AtdtHGXbgPnID7IOWvnZLSMj5auRH3gRMw7TqKFuM/\n4dAl9ZasyrLzChg9bwWOfd/GuPNIfIa9z/dba56+YM/Zq4x9rSMD27fA3d4GX3dnXg9qzUfD+6pi\n5HI589Ztx2PQBIw6j6DhmOnsOnNZtf9BK+KWo2fpOGk2Jl1G0mTcTG5G3CckKpY2E2Zh1m007SZ+\nTlRiykPnv0Kztz7CuPNIvIe+z2drNlNSWgZAp8lziElOY+ZPG1St7ZUdvXqLBm9Mw6zbaII+mKtW\n9sPdnuf8spUGb0xjy9Gz1Bo2CYvub9D/02/UWkjLZDKmLF+HTc83sen5JlOWr2PC4jV0mjynxuv3\nwLqDJwlq7Mc7fYLYc/YK6dm51cbNX/cnTv3exrzbaMYu/JHC4hLVvuKSUj5c9htO/ZSfXeC7n3Hm\n5h3V/upadSt3u49OSiXog7kAOPR5+4l6Jwzv3JrfDp5Eoah4fvvL/uMM79K6SuzHKzfiO+JDTLqM\nxHvIRGb+tIGikor6P7jGvx04Qa1hkzDqPIL1h09h13scxSWlamWN/HIZfT9eVGO9Nv11hnYN6qo9\nwHC1tWLxeyMZ0aUNZsaG1R634fAppgx5jW7NGuBub8NbvTrRpWl9lvyxF4DC4hL+PHmRuWMH06Z+\nHTwdbPl0VH887G1YueuIqpzzIeEM6xRI2wZ1cbW14vWg1jSt7cnF0HuqGHNjQ1rU9Wbz0bM1vg9B\nEARBeJx/Ivm9BHhJJBI3iUSiDQwGdj0UswsYWf5zf+CoQilQoVC4KhQKV2AJME+hUCz7B+okCMIr\n7LM1m3m3T2curZpPgI87w+cuJa+wCFC2CnaYNJu6bk6cXjGH/V9/RF5hEf0/+Qa5vGI846kbodyM\nvM/uhdM5sOgjAMZ+9SOnboTy68fvcnXNQl4PCqTvx4sIjoipth6zft7CrahY/vxyCjd/XcTKqW/h\nYGlWY71tzE04eeM2yRnZNcYs3XaAbzfv5ctxQ7iyeiG9WgUwaNZibtyLVoub/cs2Phz8GhdWzsPU\n0IDhc5cyeemvfDFmIGeWz6GopJQPlv6qij906Qaj5q1gfO8grv38FSunjmP7iYt8unozAJu/mIyj\nlTkfj+hLzNYVaq3uxaVlfLVhJyunjuPk0i/IystnwuI1Nb4HgJikVP44dp4/Zn/A3q9mcCM8hs/W\nbFHtX7x5L+sOnuDHKeM4uWw2coX8iRIbhULBL/uPM7RjK1xsrWhS25P1h09XiTt1I5TgiBgOLPqY\nTV9M4sjlm2rdZmeu3MDW4+f5aepbXFg5D183J16bvpDE9MzH1gHAycqCzZ9PAuD6z18Rs3UF37w7\n4pHHdGlan+KSUo5dU05xcT08msiEFPq3aVYl1kBPh5XTxnHjl0V89/5o/jh2jgW/71CLiU5KZdNf\nZ9k4630ur1pAr1aNkcvl7D57RRWTnVfAztOXGdWtbY31OnMzjEY+7k/0visrLi1DV1u9xV9PR5uz\nN8MA5QMOmVyOrrZW1ZhbYarXLer5sPfsVWJT0gFlS/SNiBiCmvirHRdQy4NTwaFPXU9BEARBeOCZ\nk9/yMbwTgINAKLBFoVCESCSS2RKJpGd52BqUY3zvAR8AVZZDEgRBeFIT+3ejR4tGeDnaMXvMIDJy\n8lTJ4cpdR6jn4cK8cUOo7eJAPQ9nfp45nkt3ItTGVOpqa7Fy6lvUdXPC192ZiPhkNh89x/rPJhLo\nXxt3exve6dOZLk3rs2r3X4CyNaz46AZVGfeT02jg5Urj2p642FrRpn4d+rWtmsg88PX418nIzcdl\nwDvUHz2VtxetZMfJi2otgUu27GXywO4M7tASbyc7Zo0eQKt6tVi8Za9aWe8P6EbXZg2o5ezA+wO6\nERoTzzu9g2jboC513BwZ3zuIE9dvq+IX/r6TDwZ1Z2TXtng42NC2QV2+HDeYVbuPoFAoMDc2RKqh\ngaGerqq1/YEymYzvJo6mcW1P6nk4M3lgd07eCFWr98PKZHJWT3+beh7ONKvrzZge7Tl2tWJew2Xb\nDzBlcE/6tG6Cj7M937w7Ahsz0xrLe+DkjVAyc/Pp2qwBAMOCAvll//EqcVINDVZNf5u6bk4ENfbn\ny3FDWL3nL/ILi8gvLGLlriN8OW4w3Zo1oLaLA8smj8HazIQfdxx+bB0ApFINVYuolZlxld4J1dGU\nShkWFMiv5fVdu+8Y/ds2RV9Pp0rsR8P70sLXB1dbK7o2a8C0ob3YfPScWkxJaRlrZ75DA2836ro5\nYaSvx+AOLdWux6a/zmCsr0e38utVnfvJadhZPP7aP6xTgB9Lt+0n7H4CcrmcI5dvsuPUJRLLe1kY\n6evRrI4XC37fQXxqBjKZnA2HT3P+djiJ6RU9MRZPGImfhzOeg9/DoNNwOk6ew5djh9C9eUO189lZ\nmhGTJOb8EARBEP6+f2SQm0Kh2Afse2jbZ5V+LgIGPKaMz/+JugiC8Oqr514x0sK+vKU1NVPZPfXq\n3ShOB9+pdvbhyIQUGtf2BKCumxM6lVqkrodHoVAoqD96qtoxxaVltG3w8OptSuN6dmTIF99x9W4U\nHRrVo3uLhrT2r11jvWu7OnJtzUKu3o3izM0wTgffYejs7+kYUI8d86aSV1hEQnomzX291Y5r4evD\ngQvXa7wGNmYmyvdUaZu1mQn5RcUUFBWjr6vD1fAoLt2JYNHG3aoYuUJBYXEJSRlZ2FnU3GKto6WF\nj7O96rWdhRklpWVk5uZjXkOXWGcbS7Vk0M7ClNQs5WeUnVdAUkYWAbU8VPslEgmNa3kQl5peYz1A\nmTD2a9NUNUa7b+smTPr+Fy6G3qNJ+WervD7OGOrpql43reNFSWkZkQnK7tqlZTKa1/VR7ZdKNWhW\n14vQmIdH7fyzRnZpQ9O3PiIpI4vNR8+yY97UauO2n7jA0m37iYhPJq+wCJlcjkyuPhOzg5U5NuYm\natvG9GhP07c+Ii41HUcrC349cILXO7dGUyqtsU6FJSVVWnCfxDcTRjD+m1XUf2MqEiS429swoksb\nVXIP8PPMd3jr659wHzQBqYYGDbxcGdS+BVfvRqlilv95kHMh4Wyb+yEuNlacCg5lxo/rcbG1onOl\n1l89bW21ruuCIAiC8LRe9IRXgiD8n9txLZ6vD4aRkFWIvakeUzv7PPYYLc2KX10PJviRl7dCyhUK\nujarz4K3h1U57kGSCKCvq97aJlcokEgknPlhLlqa6omCXg2JQZem9Qnf+B0HL9zg2LUQes/8in5t\nmrJq+ts11l1DQ4OAWh4E1PLg/QHd2HD4NKPnr+BU8B0alE8SVJ2H5zGq7ho88rrI5Xwysi992zSt\nUraVqXGN5wXQlKp3Eqoou+ZlcR6+hhKJRK3b+d+RlZfPnycvUlJWxs97j6m2y+Ry1u49ppb8/l0P\nrrNG+Q+VG7dLy8qeuXwfZ3saeLkyYu4ybMxNaVbXm+iHWjMv3A4xWHiRAAAgAElEQVTn9TlL+WRk\nX75+xx8TQ332nL3KjB/Xq8UZ6FZtMfbzcKGBlxvrDpykZ6sAroRFsnbmO4+sk6Wx0d+aTM7K1Jit\ncz6kqKSE9Ow87C3N+HjVJtzsrFUxHg42HFnyGfmFReQUFGJnYcaw2d+rYgqLS/h09SY2zHqfHi0a\nAVDPw5ngiBgWb9mjlvxm5uZh+Zh7VRAEQRAeRSS/giC8MDuuxTNz+00KS2UAxGcVMnP7TXp5/P0J\n3Rt4ubL1+HlcbCzVksHH8fd0RaFQkJyRRdsGdZ/4OEsTY4YFBTIsKJDOTfwZPncZyyaPUWtVfpTa\nLsqV4fIKizA20Mfewoxzt+7SvqGvKubsrTBquzzbDLcNvNwIu5+Ap4NtjTFaWprPnKA+CRNDfWzN\nTbkSFkG7hsprrVAouBwWodbd+mEbj5zBytSYnfOnqW0/HxLO9B/Xs+jd4RiUt/beirpPfmGR6vXF\n0HC0tTRxt1cmXdpampwLCcPDwQZQLkN1PiScwR1aAKiSrKT0LNXDgeB76mO/tcvvL5ns6e7XUV3b\nMu7rlcx/q/pls87euouDpbnaRGhPMyP4G93b8c2mPaRl59LC11ut1b46/l6uhEbHPXH5D9PV1sbB\nypzSsjL+PHmR/m2rPmAx0NPFQE+XzNw8Dl8KZt5bQwDlA4XSMuVM1ZVpaGigkKtf15CouEc+IBIE\nQRCExxHJryAIL8zXB8NUie8DhaUydl5P+ttlvt0riJ/3HmPY7KVMGfIaliZGRCWmsPX4eb4a/zpG\n+nrVHuftZMeQji0Zu/AnFo4fRn0vVzJz8zlx/Tbudtb0bt2kyjFfrP2D+l6u1HF1pEwmZ8epS7jZ\nWdeY+A7+fAnN63rT3NcbGzMTopNS+XT1JmzMTGheV9nVefKgHsz+ZSueDrY08HZj45HTnL55hws/\nzfvb1wTgoxF96PPRIpxtLOnfthlSqQa3o+K4dCdClYS52Fhx+mYYQzpmoKOtiaXJ82tlm9C3C99s\n3oOXox21XBxYveevx3a//mXfcfq0bkJdNye17V6Odsz8aQN/HD/PqK5tAeWY43Ffr+Tj4X1JSM/k\nk1WbeKNbO1UyPO61jny8chMWxka42lnz/dZ9pGRm81avTgB4OtjgZG3BnF+3MXfsYGKSUpn/0IRT\nzjaWSCQS9l+4RvfmDdHT0Vbral2T14Na071FQ0wNDard7+VoS3xaBhuPnKZpHS8OXwpmy1PMcjyo\nfQumrfidlbuPsGzSG4+N7xTgV+246Qfj6HPzC9GQSLhxLxptTU1quyofxFwMvUdCagZ+ni4kpGUy\n59dtyBVyPhz8mqqMQ5duIJcr8HG2JyI+mZk/bcDH2Z6RXdoAYGygT2v/2nyyahOGero421hy6kYo\n6w+dYt449YcDZ27eYdboR46gEgRBEIRHEsmvIAgvTEJWYbXbMwv+/rg+e0szjn0/i09Xb+a16Qso\nKinFydqSjgH10NF6dGvsqmlvseD3HcxcuYH41AzMjQwJqOVR45hfHS0tZq3ZQnRSKrraWjSp7cn2\nL6fUWH6nAD/+OH6ORZt2kZVXgLWpMc19vfnxw3GqsbMT+nYmr7CQj1ZuIDkzG28nezZ9Pgk/D5e/\nfU0Aghr7s2PeVOb//ieLt+xFUyrFy9GW4Z0rltmZNbo/7367htqvT6a4tFRtcq9/2uRB3UnKyGLs\nVz8hkcCILm3o1aoxyZnVz4R97W4U1+9Fs2TiqCr7tLU06dGiIWv3HlMlv4H+tanj6kjQh3MpKCqm\nT+smai2t88YpWx7Hff0TWXkF1Pd0ZffC6arkW0tTk3WfvMfE79bSeOwM/D1dmP3mIPp89LWqDAcr\ncz4b1Y9Za7bw9qJVvB4UyOpHdHl/QCrVeOSDhR4tGvHBoB5MWb6OwuISOgb48dmo/kz8bu1jywbl\nRFP92zZj+8mLamv31mRop5Z8vGpjlTV0m4z7SC1u77mruNhYcnfj9wAUlZQya+0fRCWkYKinQ5em\n9Vk78x21pD4nv5BPVm0iPk35feod2JjZYwap9cpY9+l7fLpqE6O+XE5Gbh7ONpbMGj2Ad/oEqWLO\nh9wlO7+g2m77giAIgvCkJI+arfNlFRAQoLh8+fLjAwVBeKm1XHCU+GoSYAdTPc7MaP8CaiS8SE3G\nzaSFr0+1Ca7wdF6bsRBHS3N+mDL2ieI/XrmRtOxcfpo67jnX7O8Z8vkS6nu5Mn1Y7xddFUEQhJeG\nRCK5olAoAl50Pf5L/ol1fgVBEP6WqZ190NN6aHIpLekTTXol/LfFJKWyes9fhN1PICQqlg+W/crN\nyPtqLdHC08vMzWP3mSscuRzMhH5dnvi46cN64WZnjUz2/Md8P63iklLqeTgzsX+3F10VQRAE4T9O\ntPwKgvBCVTfbc+8GDi+6WsJzFpuSzoi5S7kVFYtcrqC2iwOzRg+gU2O/F121/zTvIRPJyM1nxrBe\nTBnS80VXRxAEQXiORMvv0xPJryAIgiAIgiAIwn+MSH6fnuj2LAiCIAiCIAiCILzyRPIrCIIgCIIg\nCIIgvPJE8isIgiAIgiAIgiC88kTyKwiCIAiCIAiCILzyRPIrCIIgCIIgCIIgvPJE8isIgiAIgiAI\ngiC88kTyKwiCIAiCIAiCILzyRPIrCIIgCIIgCIIgvPJE8isIgiAIgiAIgiC88kTyKwiCIAiCIAiC\nILzyRPIrCIIgCIIgCIIgvPJE8isIgiAIgiAIgiC88kTyKwiCIAiCIAiCILzyRPIrCMJLbe/0bzm9\ndP2Lrka1/nzvSy6s2faiq/Gf9fvCSVw/ufeRMatmjeHOlZP/Uo1enOLCfH758h2y05NfdFWqkJWV\nsm7B+6TERb7oqgiCIAjCM9F80RUQhP93e6Z9w60/j+DXP4hu8yer7Tv21RourNqKR9smDFj1xQuq\n4dNZ0XYkjV5/jaZv9n/mslLCorh75BzvHP8FAFlpGScX/0rkyctk3U9E21Afl6b+tJk6GhN7a9Vx\nZcUlHF24mtA9JygrKsaleX2CPn8XYzsrVUx2QgqHPl/O/fM30NTVoU6PtrSf8SZSbS1VzP0Lwfw1\nfxVp4TEYWlvQbGx/GgztrtrfcsJQNgybhv/ALugaGTzz+/2vKMzL4dKRbdwPu0F+bhY6evqY2zjS\noM1rOHnVe+Jy+r07G01tnedY00fbuXIuCVF3aNyxHwEd+qjtO7RhKRE3L+DbrBOBvUY+97pcObYT\nZx9/TCxsAEhLjOHa8d0kxtylKD8XQ1MLage0pX5gNyQaFc+t05NiObXrV1JiI9DVN6ROk/Y0at8b\niUSiiom4dZFLh7eSnZ6CiYU1TYIG4F63MQAyWRkXD23l/t0b5KSnoK2ri717HZp1GYSRqSUAUk0t\n6rfuzvkDm+j55kfP/VoIgiAIwvMiWn4F4SVgbGfFnf2nKCkoUm2Tl8m4teMvjCsldf9vrqzbhU/n\nluiUJ5alRcUkh0TQYvxgRu1YRr8fZpGTlMqWNz5BXiZTHffXlz9x9+AZen47nWEbF1GcV8DWcZ8j\nlylj5DIZW8fOoiS/kGEbF9Hz2+mEHTzNXwtWqcrIik3ij7Gf4dCwNqN3LqP52wM5POcH7hw4rYqx\n9nHD1MmOkJ1H/6Ur8nI4uP47UuIiadvvTYZ+uIhuIz7E2dufooK8pypHz9AYreec/MplMhQKRY37\nDU0sCLt6Si2mKD+X6NCrGJpYPNe6PVBaUkzopePUDmir2pYaH42egTEdBr7N4MkLadyxH1eO7uDq\nid2qmJKiAnavWYC+oTH93p1Nyx7DuX5yLzdO71fFJMWEc3jjMrzqt2TgxC/xqt+SQxuWknz/HgBl\npSWkJUTTqF0v+r83hy7DPyAvO529a79SfV8AvOq3IDH6LhnJcc//ggiCIAjCcyJafgXhJWDl40Ze\nSjp39p3Er38QAPeOX0RTRxunxr4UZuaqxQdvPcSF1VvJik3C2N6aBkO703hkL1WL0AKvrgR9/i6R\np64QfeYaRraWdJnzHmYu9uz/aAlxV0Iwc3Gg24LJ2Nb1VJUbd/U2JxatJfFmOLomhni1b0rbqW+o\nks/1w6Zh6emMrrEh1zfvRyKR4NunA+2mjUGiocH6YdPIiU/h2MI1HFu4BoAZ4co/xMMOnuH097+T\nERWHvoUpDYZ0o/n4wWotVJXJZTLu7DtF968+VG3TNTJg8K/z1OK6zH6P1d3eJi3iPtY+bhTl5nNj\n6yG6z5+MW6uGALy2aCor2owk+ux13AMbEXX6KqnhMbxz4ldVa3DbaWPY/9ES2kweiY6RAdc27sXQ\n2oKgz94BwNLTmYQbYVxcs41aXVqpzu/Zvimhe47T6PXXnuYj/88qLswnMTqM18bMwNHTFwAjM0us\nnTzU4n5fOAmfRoFkpycTdfsKWtq61A/sRv3W3dVifJt3Um3LTkvi+PbVJMdGYGhqQYtuw6qcPy87\ng3P7NnD/bjAAti5etOwxHFNLWwAuHdlGxK1L1A/sxpWjO8jNTGXMrFVo6ehW+36cffyIun2FhMhQ\nHDzqAHD3+hmsnTx4+M68H3aDK8d3kpEUh0QiwdrRnZY9XsfM2gGAnMxU1n81maBhE7l94ShJMXcx\nMrOkZY/hj2wRvx92A4lEgq2Lt2pb7YA2ajHG5takJUQTeesSjdr1Kq/nWcpKi2k/4G00tbSxsHUi\nKzWB4NP78W/VFYlEQvCZAzi411Ed08jagfiI2wSfOUAn5wno6Orz2pgZaudq03sMm5dMJzM1AQtb\nJwB09Q2xdfEm/MY5mgYNqPG9CIIgCMLLTLT8CsJLwm9AZ4K3HVK9Dt56iHp9O8FDf4Jf37yfE9/+\nQuD7wxl7YCXtZ7zJhZV/cHX9HrW4sz9sok73Nryxezl2vl7snLSA/R8tocGwHozeuQxDG3P2Tv9G\nFZ8SFsXm0R/j2aEZb+xeTt9ln5AcGsm+mYvVyr296xgSqQbDN39Dp1nvcOmXHYTuVY7J7Lv8U4xs\nLWk5YSgTzq5nwlnlWN2kW+HsmDgP76AWjNn7A22njObcT1u4sm5Xjdcj5U4Uxbn52Pl6PfK6FecV\nAKBrbKg6l7y0TJX4grJl3dLDifirtwGIvxaKpYeTWjdo91YNkZWUkhRyrzzmjloZAO6BjUi6FY6s\ntEy1zd7fh4Tgu5QWFT+ynq8KLW1dtLR1ib59lbLSkkfG3ji9HzNrBwZMmEvjjn25cGgLkbcuVRur\nkMs58PsSFAoFfcbPol2/cVz+azvysoprXVpSzK5V85BqatF73Cf0Hf85+kam7F4zn9KSiuufm5FK\n+PWzBA19jwETlfE10dDQxLtBK+5cPqHadufyySrJJ0BpaTF+LbvQ793Z9Br7Mdq6euz79RtkleoI\ncPHQH9RrEcSAifOwcnDn8MbllBYXVSnvgcToO1g5uNX4IOiBkqJCdPQqutcn37+HnasPmlraqm1O\nXvXIz8kkNzNVFePo5atWjpN3PZLuh9d8nuJCAHT09NW22zi5kxAV+sg6CoIgCMLLTCS/gvCSqPta\nW5JuhpMRHU9eagZRJy9Tr1+nKnFnl2+k3bQx1OoaiKmTLV4dmtFs3IAqya9v7w7Uea0t5q4ONB8/\niIL0LNwCG+HdsTnmbo40HTuA1LBoCjKyAbiwehu1u7Wm6Zh+mLs6YF+/Fp2/mEDYwTPkp2epyrXw\ndKb1pBGYuzlSu1trXJr6E33uOgB6pkZIpBpoG+hhaGWOoZU5ABd/3o5Tk3oEvj8cczdH6vZqT5Mx\n/biw8o8ar0dOQgpIJBham9cYIysp5eiC1Xi2b6pKZPNTM5FINdAzN1GL1bc0Iz81UxmTlom+pZna\nfj1zEyRSDfUYC1P1MixMkZfJKMzMUW0ztLZAXlpGXnJ6jfV8lWhIpbQbMI6718/w8+y32L7ic87u\n26DqRluZjZMnjdr1wtTKjrpNO+DdoJVal9zK4u6FkJkST4dB47Gyd8XO1ZuWPV5HLq/oensv+Dyg\noF3/cVjYOWNmbU+bPmMoLS4m5s41VZxcVkaHgeOxcnDDwtYJDan0ke+pdkAbIkMuU1JUQEpcJLmZ\nqbj7NqkS5+HbBA/fJpha2mJh50y7/uPIzUwlJS5CLc6vZRdcazfE1NKWpp0HUlyYR1piTI3nz81K\nR9/ItMb9AKnxUYRdPUXdZh1U2wpys9A3VL/P9YxMyvcpv9cFeVVj9A1NVPsfJisr49y+9bjUblCl\n27e+kRm5mWmPrKcgCIIgvMxEt2dBeEnomhjh3akFwVsPoWtkgHNTP7VJnAAK0rPISUzlwKdLOThr\nmWq7vEwGD41rtPZxU/1sUJ7EWXm7VtlWkJ6FvrkJybfCyYxJIHRfpZl1y8vMup+oiq9cLoChjTkF\nlZLj6qRHxOLRVj2ZcGpUlzNL11Ocm6/qVl1ZWVEJUk2p2uQ+lcnLZOye8jXFOXn0/3HWI8//PGnq\nKlvdyoof3Qr6KvHwbYKLT30So8NIvn+P+3eDuXFqH02CBqi61wLYOHuqHWfr7EVUyOVqy8xMjcfA\n2Fw1yRKg7HpcqTU0NT6KnMxUVn/+ptqxZaUl5GSkqF4bmJijb1SR8N29doYTO35Wve4+air2brVU\nr82sHbCwcyb8xjnSEu/j6d+s2rHI2enJXDy8lZTYCArzc1AoFCgUCnKz0rGrFGdh61xRF2PlQ5bC\nvBxqIistQdPQuMb9makJ7Pt1EX4tO+NRTVL+T5HLZPy1ZQXFhQV0Hf5Blf2aWtrIHtPaLwiCIAgv\nM5H8CsILsONaPF8fDCMhq5ButxKpZaRsmfLrH8Se6d+gra9L4PvDqxz3YFKezrMn4NiwziPPoaFZ\nqbWrPIHQ0NKstEmiVqZCrsB/YBcaj1af9RbA0KaiBahyGeUloZDXPKHQY9XQ1VPPzBhZaRmlhUVo\n6amP15SXydg5eQGpd6MZ+vtC9MwqEgcDKzMUMjmFGdlqLbcFaZk4BdRVxliaEX/ltlqZhRnZKGRy\nDKzMVDEPJ/UF6VloaErVzleUpRyPrf9QS/OrpvI9a2+qx9TOPvRuUA8nr3oEdOjDsW2ruPzXduoH\ndkeq+Xz+a1EoFFjaudBp8LtV9unoG6p+fngGadc6DbGpNCbZwKRqb4JaAa0JOX+EnIxUeoyeVu35\n9/36DYYm5rTp8wYGxmZINKRsXjwduUy923PlluaHv2fV0dU3orgwv9p9mSkJ7Fr9JZ5+zWnWZbDa\nPn0jUwry1FtwC8tbdB8k//qGVWMK8rLVHg6AMvE9vGk5Gcmx9Br7MboGRlXqUlyYh65BzUm6IAiC\nILzsRLdnQfiX7bgWz8ztN4nPKkQBFJTIuJOUw45r8bi0qI9US5OCzBy8OjavcqyBpRmGNhZk3U/E\nzMW+yr9nYVPXg9TwmGrL1dJ98hl5pVpaKGRytW0WHk7EXVVPNmOvhGBka4mOofq4QlV9arsDkHbv\nvtp2WWkZO96fT2pYFEPXLVR1rX7A1tcLDS1Nos5UdIPNSUwlLSIWh/IHBg4NapMWEUtOYqoqJurM\nNaTaWqoJwBwa1FIrQxlzFVtfL6SVHgCkhsdgZGOBwUPdqF8lD9+z8VmFzNx+kx3X4lUx5tYOyOVy\nZGUVLYMPd4VOjr2HmXX196mZlQP5ORnkZVV0H0+JjVRLGq3sXclOT0bXwAgTS1u1f7qVkt+Haevo\nqcVWHiP7gKdfM7LSkjAwMa/SYg3KGaCzUhNo2LYnjp6+mFk7UFpcqNYt+++ytHchIyWhyvaM5Hh2\nrvoSD9+mtOzxepX9Ns6eJEaHqY29jr13CwNjM4zMrFQxceG31I6LC7+FrXPFWHqZrIxDG5eRnnSf\nnm9+VGMX7IykOKzsXf/OWxQEQRCEl4JIfgXhX/b1wTAKS9X/YJbLFXx9MAyJRMIbu1cw/uhaNHWq\n/oEOEDjxdS6s2srFtX+SHhlH6t1obv55hHM/bn6mejUbN5DE4Lsc+HQpSSH3yIxJ4N7RCxz45Pun\nKsfEwZrYyyHkJqWpxhM3GdOP2Is3OVU+23PIzqNc+nk7TcfWvBawvoUpNnU9ibscotomL5OxY+I8\nEm7coefiGSCBvNQM8lIzVBNO6RoZ4N8/iONfrSH6zDWSQu6xZ+oirH3ccG1RHwC3Vg2x8nJhz7Rv\nSAq5R/SZaxz7ag3+g7qoumA3GNKdvOQ0jsz9kbR797mx5QA3tx+hyZh+avWMvXQLt8BGT3WN/msq\n37O6FDOYv3ArvceqfefIyUgh4uYFrp3ci6NHXbR1Kx5mJMfe4+rxXWSlJXH74jHCrp7Gr2XXas/h\n6FkXUyt7/vrjR9ISYkiKCefs3t/R0KhoRfWq3wI9Q2P2r1tMQmQoORkpJETd4eze9WSlJT3Te9TW\n0WPEzKX0G/95tft19AzQNTDi9qVjZKclkRAZyskda9Xq93c5efuRlRJPUX7FrO4ZyXHsWv0lDu61\nadiuJwW5Wap/D3jVb4Gmlg7Htq4kPSmWyFuXuHZiN37lMz0D+LXsTHzkba4e30VmSgJXj+8iITIU\nv5ZdAGWL76ENS0mJvUenwe8ikUhU53l4QrPE6DCcvP2e+f0KgiAIwosiuj0Lwr8sIavwkdtragl9\nwH9gF7T0dLmweisnFq1FU1cHKy9nGj7jUjvWtdwYtuErTi7+jQ3DpqGQyzF1ssOrU9UW6EcJfH84\nBz5byo8d3kBWUsqM8P3Y1vWk9/cfcfr73zn342YMLExpNm4AjYb3fGRZ9Qd15cYfB1RdsXOS0gg/\ncg6AX3q/pxbbbcEH+JVPENbh47eQaErZMWk+ZUUluDT3p8fXU1TdUTWkUvqv+oJDs5bz++ApaOpq\nU/e1drSbPkZVnqmTLQNWzeaveSu5tmEvhjYWdPrkbbVljsqKSwg/fJaBP899qmv0X1P5ni1FkwQs\nCeAuZrlX2LxkHwbGZnj5N6dR+95qx/m36kp64n2uHNuJlrYOTTr1w6Ne9WNWJRoadHl9Esf/XMO2\nFbMwMrWgebehHNm8QhWjpa1D73Gfcv7gJg5uWEpJUQEGxmY4uNdWmwX579LRrfm7J9HQoNOQCZze\nvY7N383E2MKGFt2GcnD9d898XgtbJ6wdPbgXfB7f5sp7OOLmBQrzcrgXfL58oq8K4+f/rqrva2Nm\ncGrnL2xb/hk6evr4t+qGf6uKBwy2Lt50GjyBi4f/4NKRbRib29BpyARV63ZeTgbRt68AsHXZp2rn\nadd/HLUatQaU6wUXFxXU+PkJgiAIwn+B5FHjkF5WAQEBisuXq580RRBedi0XHCW+mgTYwVSPMzPa\nv4AavbzKiktY2Xksr309FafGvo8/4F925ffdhB85x+Bf5j0++D/s79yzD6/hKzza/bAbnN6zjsGT\nv0KjhkneXqSD67/H0t5FbUIzQRAE4cWSSCRXFApFwIuux3/Jy/c/rCC84qZ29kFPS72rpJ6WlKmd\nfV5QjZ7cx59v4Z1Ja/+182nqaNPjqykUZuc+PvgFkGpK6fTZO6rXy386TO+B377AGj2b+IQMfAOm\nc+t2nNr2//I9+2/JzimgddAc7sf9vSWvnH388W3WifzsjH+4ZkqLvtvLvK92/q1jZWWlWNg6qbUo\nC8L/2Lvr+CqrP4Djn7vtrru7GT0YjBzd3RKCgQUICoqEIogCSrcgKKCEIN2MzpFjjGZj3d0d9/n9\nMbhwWQHKD+O8X6+99D7POc85T2zc73NKEAThn0h0exaE/7N+XnYAFcyca0diUiarfz7Beb8HpKbl\nYmqiR2ufmoz5sBPWVlWvA/pXmDZzO/sOXi+3feeW8dSq+ecm1HpZjk3r43cpiHlj1nLnXgwlJaU4\nOVrQv483w4e0fG2tZPW8p7B47nAautr/X8orLi5h01Y/DvsGEhGZjKaWBs6O5vTr7U2/3t5oar66\nP+dVPbMvo0vvucz+5g2aertVn/gZsXFpdO0zDyMjXXz3TcZAX0e5792P1lDDzYppU/pVcYTn9yLH\n+3nDadr41MTR/snM6D8s3M+NmxGEhCZibmbAsQNTy+XzPX6TnzecJjIyBRMTPYYN1ua9t9sq91f2\nO6mjLefahbLu9lf9Q3lv9NpyafbvnIirc9lyae+93Zbu/ebz1putcLA3K5e2Kuoacrw7lp8FXhAE\nQRD+aUTwKwivQT8vu3KBQ0xsGiPeW4WdnQlzZg7BydGM6Jg0lq3yZejbK9my4WPsbMsv0fJXa97U\nnbnfqS6pYmxc9TjkV2nbjkt8v2Af7wxvw9SJfdDR0eTi5WCWrDjCrdtRLPj+zVdafnFJKXKNPz+p\n0Z+qQ3EJH41bx4OgOMaO7kLjhs4YGOhw5140G7ecx9nJ4qUCyRdR0TNblRFTlr7C2kBBQRG//HqG\nz8a9/tbI/IIidu+9xsol76hslxQSfXs25mFoAhcvPyyX77zfA6Z8vY0vv+iDTwsPwiKSmDl7F9pa\nct4c0hKAqV/0LneOI95fhXcjl3LH27f9c4wMn/yumpg8GQdtaqJPy2Y1+GPXZb4YL7qiC4IgCP9N\nIvgVhL+JOfP2oqYm45dVH6KjXTbTs421Cb+s+pCe/Rcwe94+Vi8bCZS1SLm5WmJgoMPO3VdQU5PR\nu2cjJn7aQ9kSWlxcworVxzjke4PMzHzc3Kz4dEwXfFpU3VVVU1MDc/Pya3xWRJIkNmw8y/bdV0hO\nycLR3pz33mlL7x6NAPjiyy0YGOjwzVcDAFi+6ihr159iy4aPaVDfCYCOPb9nwthuyjxPS0jMYN7i\nA7w5pCUTx/dQbh88sDlmZgaM/2IjnTrUo2snT4a/9yMN6zsx6bNeynQ5OQW07TqLubOG0blDvWqv\nyeMWtFVLR7Jq7XEeBMezdMFbtGtdW6VeXXrPBeDzqVuALdjamKi06h0+GsjyVUdJS8+heRN3vp0+\nCBPjJ4HInv3X2LDpHDGxadhYGzNkYHNGDPOptBV70+8X8A8IZ+tv46hX50lLs72dKV061icvr2xW\n3opaKqfN3E56Ri6rlo5U3rPftpxn+67LxCdkYGqiR68ejRLJEhsAACAASURBVCoMIhUKBd8v2M95\nvwesXfkBTo7mxCek88PCA1y+WhbMtWhWgy+/6KPSM2H7rsts2HSO+IQMbKyNef+dtgzq36zCcwNY\n/fMJdu+7RkpqNoaGOrRs5sEP3w2pND3A8CE+bNnqx5uDW2JlWfEay9U9n1WVPW3mdvwDwvAPCGPr\njrJJ1o7un1LhC6jzFx6ADLwaOKts/2py2fjYDZvOVhj8Hjh8g7atazP0jbJJ5Rzszfjg3fas23iG\nYYNbIJPJMNDXweCpVZwCAiOIiU2r8PqYmuqrPGfPatemDstX+YrgVxAEQfjPEsGvIPwNZGbmceFS\nMJ+M6aIMfB/T0dZkyKAWrPzpGJlZecqWnUNHbjBiWCs2r/+YB8FxTPl6G3Vr2dOjW9lyPl9/u4Po\nmFTmzR6GlaUR5/2CGPvZb2zbOI5aHn9NF+blq45y/ORtvp7SD2cnC27ejmTm7F0YGurQtlVtmjR2\nY/PWC8r0166HYWKsx7XrYTSo70RUdAqJiZk0aexa4fGPnbhNcXEp773drty+ju3q4uRoziHfQLp2\n8qR390asXX+KieOfvAA4fuo2Wppy2raq9ULXZMmKI3zxWU8c7c3Q0yu/xvG2jeNo03kWM78eSLtW\ntVFTlyn3xcan43v8FssWvk1+fhGTvvqd5T/68s20siWSdu65wsqfjvPVpD7UqW1PSGgC38zZhYaG\nurK171kHfQNp3tRdJfB9TE1NDX197QrzVWTpj75s33mZSZ/1wruRC2npuTwIKr/GbHFJKV/N+IOH\nIQlsWvcxlhaGKBQKPvl8I1raGqz/aRQA38/fy6dfbOSPjZ8gk8k4cfoO38/fx+TPe9OyeQ38LgUz\ne+5ezM0MaNemTrlyjp+8za+bzzF/zjA83K1JTcvl1u2ocume1aWTJ9euh7Hyp2PMmvFGhWmqez6r\nKnvqF72JiErGxcmSCWPLlgV6uiX1adcDI6hTy065vNDzKioqQeuZ7upa2nISEzOJi0+vMNDetfcq\n7q5W5QJtgCFvraCoqAQ3V0tGvd+xXG+A+vUcSEzKIiomVaV7tiAIgiD8V4gJrwThbyAyOgVJknB1\nsaxwv5urJZIkERWV+tQ2K8aN7oKzkwXdOjegSWNXLl8LASAqJpXDR2+yaO5wvBu54mBvxptDWtLG\npyY7dl+psi5+l4Jp0nq68mf0p+sqTJeXX8TG38/z7fRBtGpZE3s7U3p282Jg/6Zs217WUtbE25Xw\nyGSSU7LILyjizr1o3h3Rhqv+oQBcvR6Gg71ZpeOZI6JS0NfTwtLCsML9rs6WREQmA9Ctsydp6blc\n9Q9T7j/kG0iXTvXR1NR4oWvy8Ued8GnugYO9GaYm+jzr8TZDfW3MzQ1U0pSWKpgz8w1q1rChoacT\ng/o34/K1UOX+n345yeef9qBLJ0/s7Uxp16YOH7zTjm07L1V4jgBRUSmVPhsvIi+vkE2/X2DCuO4M\n6NsERwdzGno6KVseH8vPL2LcZ78SG5fGbz+PVl7/y1dDCA6JZ/7sYdSrY0+9OvbMmz2M+w/iuHy1\n7Nn7ddM5evVoxJtDWuLsZMHwoT707O7Fut/OKo9/7MBUZWAWF5+OhZkBLZt7YGNtQr069pW+BHjW\n55/2YP+hAEJCy6/x+zzPZ1VlG+jrINfQQEdbjrm5AebmBqirV/xPZnx8eqXPaFV8Wnhw+uw9/C4H\no1AoiIhM5rfN5wBITik/yVt2Tj5Hj99iYH/V5YYszA2ZPrU/S+aPYOmCt3B2suD9MT9z/Ua4SjpL\n87I6xsWlv3BdBUEQBOHfQLT8CsI/lIe7tcpnSwtD0tJyALj/IBZJkujzhurMw8VFJTRtUvXY0MZe\nLsycNkD5WUtLXmG60LBECgtLGP3JOniqxaukpBQ7GxOgLDg1NzPgmn8YJiZ6ONib0a2LJz+tO0lx\nSSnXrodV2ur72PO2phkb69GqhQcHj9ygeVN3kpKzuOofyugPOgIvdk3qVtDC+rxsrY1VJmGytDAg\nLb3svqSl55CQmMl33+9m1tw9yjSlpQqqWnZO4q9Zki40LImiohKaN3WvMt3UGduwMDNg/ZpR6Oo8\n6YkQFpGMhYWhSoukg70ZlhYGhIYn0aJZDcIikujfR3XVBa+Gzpw+d6/Csrp08mTzNj+69ZlLy+Ye\ntGpZk/Zt6jzXBF5NGrvSsrkHS1f6snLJu8+ca/XP558p+2kFhcWYmZZ/SVKdQf2bEh2TyqcTf6Ok\nRIGenhYjhvqwau0J1NTKP/cHD99AIUn0eWaIgIuzBS7OFsrPDT2diItLZ8PGszT2ejI2WEtbrqyv\n8Pe0+IemNG05klZtx7yS4+fmpjLv2/qMHLUTF7fne8n0Op06tpB7tw8xbuLp/3vZMybbMmTEWup6\n9qo+sfCPsv6nQTRqMoSGjSvuNfQ6Xbm4gYdBpxkxcuPrrsq/lgh+BeE12nsjtmwG3aQs9ICDfiF0\nal9+PdvQsCRkMhmODk+6Kmo8MwmTTCZD8SiAUigkZDIZ2zaOKzdZU2XB7GPa2nIcHcyrrfvjYG3l\nknexsVZtuX26bt6NXLh6PRRTE32aerthZ2uKibEed+5G4x8QpuxSWhFnR3OycwpITMqscFxnaHgi\n7q5Wys+9engxc/Yupk/tx5GjgVhbGSu//L/INdHRUe16/iIqvC+KJ/cFYPqX/fFq4PTcx3RytCAs\nPKnadGpqsnJhcklJ6XOX81gbn1ocOBTAjZsR+DT3eK481b2iqOwlho21MQd3fcHlayFcvhLCgiUH\nWb32BL//Nk4l8K7MZ590Y9Cby8q1cj7P8/lny37MxFiPrOzy6yBXRyaT8fmnPRg/thspqdmYmugp\nW9Dt7cp3ed659yqdO9TDyKj6Cejq13PgyLGbKtsyM/MAMK2k+zZAbk4qp44v5OGDk2RnJaGtY4il\ndS1atxuLu0fZLNSvMkBLSQph+cI2vD9mD04uT8aJ/7p2MOGhfkyefgs9/Sd/BxfOaYyX9xA6dp38\nl9flRZw6tpAzJ8ovc6avb8HkGTcryFGxUZ8cQa6pU33C1+zPBoY3/P9gz/bPqkwzctTOlzr2y5Sv\nb2CJk0szuvSYhomp4ysrtyK7/5hAXm4aI957PcHO42thaubChCl+KvuCH5xi8/oRaGrq8vXskNdS\nv79a0P0TZGXG4en15CW//+XN3ArcS0LcHQoKsvhs6hVMTB1U8sXF3OLYkTnERd9EpqZOnXo96NZ7\nJlpaT/6ehj48z6lj80mMf4Cmpi4Nvd+gY9epqKs/Cbfu3NzPuVPLSU0JQ1fPjGYtR9Kq3ZMlExs3\nfZNzJ5cTEX4FZ5fK58oQXp4IfgXhNdl7I5Yvd98mv7gUNOWUmJlw/Mh1tndqyODmzsp0+QVFbNtx\nkVYtaz7Xl16A2jVtkSSJ1NScVzYLsJuLFZqaGsTFp9OsSeUtiU0au/HblnOYmRowYpjPo22u7Np7\ntcrxvgCdO9Zn8YojbNh0lqkT+6jsO3H6DlHRqYx/Knhu36YOM9nF2fP3OeQbSI9uDZVB1199TTQ0\n1ClVvFiLrLmZAZYWhkTHptK3V+Pnzteza0OW/ujLnXsx5cb9KhQK8vKK0NfXxsREj+SULJX9QcHx\n2No+aol3sURTU4PLV0Nwcqz8BcfAfk2pU8uO8RM3snzR27R8FAC7OluQnJxFbFyasvU3OiaVpORs\n3B69hHB1tuTGzUgG9nvSNfdGYARuVXTb1tKS07ZVbdq2qs3777ajXdfZzx14e7jb0KdnIxYvP4xc\n/uSftOd9PqsqWy5/vntcq6Yt+w6UX47oeamrqylf7hw+epMGno7lutvfvhNNUHA8Uz7v/VzHfBAc\nj4W5alfskNBENDTUqfFMr5Gnbdv0AcVF+fQdtAgzcxdyc1IID7tEft5f31W6pKQIDQ3Vlwzmlu4Y\nGFgRHnZJGfyWlBQRHemPoZENEWGXlAFXanIYWZnxuLj5/KV1eFnmFm6MHL1LZZua7MVmin86sP83\nq9egD+412ys/79r2Cbo6xnTvO0u5TUfHmPDQi6+sDnK5DhOmXgJJIjkphAO7p7Dl13f5eMJx1NRe\n7wz//28aGtoUFGQSHnoJF7cnw2ACrm3FyNjulfz+vy6XL/xCQ+/BKve4qDgfd4+21KrbFd8D35TL\nk5WZwG8/D6WuZy969Z1DYWEOh/fPYM/2CQx962cAEuLusnn9W7RuP5YBQ5aTlZnAgT1TUChK6dar\n7JjBD06xc+tYevSZhXvN9iQnPWT/zknI5do083kPAA0NLep79ePKhXUi+H1FRPArCK/JgqNBZYHv\nI4U13dHxD2TOjK04zXkDRwdzomNSWb7qKJIE0x7NHPs8nJ0s6Nndi2kztzNpQk9q17IjMyufa9dD\nsbczo3OH8q3LL0pPT4t3R7Rh4bLDSFJZC29eXhE3b0ehpibjjQFlf7SbeLsya+4e4uIzlIFuk8au\nzJy9q8rxvlDWMjdpQk/mLjqAXEODvr0aoa2tyaUrD1m8/DDdOnvStZOnMr2WlpxOHeqzZv0pgoLj\nVWbE/auviZ2tCVeuhdCkkStyTXWVJWaq8vGozvywYB+G+jq09qlFSUkp9x7EkpScxYcj21eY5603\nW3HO7wEfjf2Fj0d1xtvLBX19be4HxfHr5rOM/7gbTb3daObtxrzFBzh99h7OThbs2H2ZhMQMZfD7\nuFvtsh990dRUp3EjVzIz8rj7IIahg1TH/b4xoBmSJDH+i40sW1gWALdoVgMPdxumfL2NqV+UvYz4\nYcE+ateypdmjruMj327LxCmbqVvbjpbNPbhwMYhDR26wdMFbFZ7b3gP+lJQq8KzngK6OFr7Hb6Kh\noY7Tc/Q+eGzsqC70GrgAgBpuVspzre75rK5sO1sT7tyNJjYuDV1dLYwMdSqckdunhQdLVhwhIyMX\n46dmW46KTiEvr4jk5CyKi0uUE4u5uVoil2uQnpHL0RO3aNLYleKiUvYc8OfYyVv8umZ0uTJ27LmC\nk6N5hS9uNv1+HltbU9xdrSguLuHAkRucOnOXJfNVr/n1G+E09nIuN6neY/n5mUSGX+GdD7fhVqM1\nAMYm9tg5NFSmWf/TQDLSYzh2aBbHDpUFKt/NjyMvN41De6cRGXGVvNx0TMwc8WkzmkZNhqrktbCs\ngVxTh8DrOzA2cWD0p0fK1cPFrSXhoRdp13ECADFRAejomtCg0SDCQ/2UwW946EU0NLRxdC7rZh8b\nHcgJ37nEx96mtLQYK5vadOk5HUenJ93wZ0y2pWe/OYSFXCAk6AxNWrxN5+5f4XvwW+7dOkReXjp6\n+mZ4eg2gS49pFV6nyqipaWBgUPlLnsU/NKVh48GkpUbw4K4vmpp6tGw7WqUF/dlW9WuXN3Hx3Boy\nM2LR1NTFxt6TESM3oa6ugUKh4NypZfhf2UxuTipmFq507DqZ2nWfvBCMjQ5k/+4pJCcGY27pTseu\nU8rVKykxmKOHZhEZdhm5XBtX91Z06/NtlefyZ8nlOsjlT1q4NdS10JDrVFrm7cC9nPCdR25OCq41\nWtF30EL09J68KAi4tg2/s6tJT4vCyNiOJs3fpnmrD6pcB14mkynLMzC0ol2nz9m1bRxpKeGYW5a9\nMMvPy+CPTR8R/OAk+gYWdOgyiQaNBiqPcezwHO7fOUJmRhz6BubU9exNhy5lwQw86bbdtuP4Cut/\n6thCAq9vB8qeTUDZJT0x/j5HDnxDVIQ/crk2Net0oUef79DWKXup9bjF2MmlKZfO/0xxSQFNmr9N\np25fcubEYq5d+g2ZTI0WrT+kdftxVd4PNTU1GjQaxA3/bcrgNzc3leD7J/BpO4ZL51XXEY+KuMbx\nIz8QFxOIto4xtep0oXOPaWhrl60Usf6ngVhYeaCtbcj1K5uRydRo0HgQXXpMV96TmwG7uHThF1KS\nQpDLtXF2bUH3Pt9iaGSjLCc56SHHDs0mIvwykqIUS+va9B04Hyub2i9133NzUgkLOU/XntNVtrds\n/SEAsdEV99IIvn8CmUxGr/4/KIPmPgPm8eOSjqSmhGNm7sLtm/uxsKpBhy6TADAzd6FLj6/Zvnk0\n7TtNREtbn5sBO6lZuzNNW74LgKmZE607jOP8mR9p2nKk8mV9rTpd+e3noRQV5aGp+fqWmvy3EsGv\nILwmcRmq3SQlXR3ym3pRGhbFlzP+IC0tBxMTPVr71GLhD29WGSRWZPY3b7B23SkWrzhCQmImRkY6\n1K/j8Je2BH8ypgtmpvr8uvkcs+buQV9Pm5oeNrz3dltlmsfjfo2NdJWtWU0au1JSqqh2vC/A8KE+\nONibsWHTWf7YdYmSEgVOjuZ8PKozwyuYGKl3Dy/2HvCndk1bZWvkY3/lNZk0oSfzlxyk0/7vsbQ0\nUlnqqCqD+jVFV1uTDZvOsvRHX7S15Li5WjFscItK82hqavDzjx+w6fcL7N53jSUrDqOlKcfZyZx+\nvb2VXaj7921CcEgC07/bAcDQwS3o2L4e6Rm5ymNNGNcNQ0Md1qw7RcL3ezAz0y83hvSxwQObI0mo\nBMArFr/NDwv2897oNQA0b1qDryb1Uf6j3bFdXb6c1JdfN59j3qID2NiY8PXUfhXO9AxgYKDD+t/O\nsGjpIUpKSnF1tWLpgrcq7PZbGRtrY4YP8WH9xrMq26t7Pqsr+90RbZg2czt931hMQWFxpUsdebjb\nUL9uWTfjYYOfPJMzZu3CP+DJBGyDhi8DVJdMOnAogEXLDoMk0cDTiQ1rRlG/nmp3u9zcQo4cu8mY\nR+PXn1VcXMqiZYdITMpES0uOu6sVq5aOpM2jWc4fO3I0kI9Hda70Ompq6qGpqceDe8dwdG6q/AL/\ntKFv/8KqJZ1o1GQoTVo8Wde4pKQQG7v6tGo/Fi0tA8IenufA7ikYGdspA2ko+8Lr3WwE74/ZQ2VD\n2V3cWnJo33RKSgrR0NAiPNQPZ9cWOLu24Mj+Gcp04aF+ODg1RkOjbEb2wsIcGjQaRI8+s0BWNnZu\n8/q3mDDZD129J/ftzPHFdOo2la49ZyCTybh8YR337xzhjeGrMTaxJysznpTkJ5PUPe7S/N388rOi\nv6hL59fSqt1Y2nX6jPDQixze9zWmpk7Uqd+jXNrY6Jsc2vsV/Qcvw8mlKQX5mYSFPOmWevnCL/id\nXU3vAXOxtW/AzYBdbNv4AaPH+2JjW4/Cwlw2r38LZ9cWDBiyjKzMBI4cmKFSRnZWIutX96dR02F0\n6zmDUkUxJ3zn8vuvI/lw7IEqg8f/l4z0aO7c3M+wd9ZRVJTHji1jOOk7jz4D5wPgf2ULp44toGff\n2djae5KY8ID9Oyehrq6hbE17Ho+f91JFiXLbmZNL6Nz9Kzp1/5KAa1vZu+NznFyaYWxS1gNHU1OX\nfoMXY2hoQ3JiMAf2TEFDQ0ulG35V9fdpO4aUpIfk5WcwcOgKoKzFu6goj42/vImdY0NGfXKIvLwM\n9u+axN4dnzP07V+Ux44Mv4yhkQ0jR+8kPvYOu7aNIyHuLjZ29Xj/472Eh/hxYM9U3Gq0wdb+yYvi\nijRuMoy1K3vSs++cskDt+i4cnLwxNVMdopMYf5+Nvwyjfecv6DdoIXn5GRzZ/01Z3R61ggLcurGb\n5j7v88HY/STE3WXn1rHY2nni6dW/7DqXFtGh8xeYW7qTl5vGscNz2PH7x2V/GyhrbV23qh+Ozk14\n54NtaOsYEhsdiEIqfen7HhlxFXV1LSyta1W4vzIlpYWoqctVWos1Hj0vURFXMTN3obSkCA0N1b+Z\ncrk2JSUFxMXewsWt5aM0qitIyDW0ycqMJyM9RtnV2tbeE4WihOjI6yp/P4W/iCRJ/7ifxo0bS4Lw\nT9fyh5OS05SD5X5a/nDydVdNEIQXdN7vgdSz/3yppKT0dVelQmfO35N6D1ooFReXVJnuzq2D0vcz\nakvffuksrVnRSzpyYKYUFXldJc2i75tI58+sqrbMPzaPkvZs/1z5ed3qAdLKRR2qzZeaEi5Nn2Qj\nhYVcVObzv7xZKizMlWZOdZSyshIlSZKked81kE6fWFLpcRQKhTTvuwZS4PWdym3TJ9lIB/d8pZLu\n4N5p0vo1b0gKhaLC41y+sE5aNr9VlXU+eXSBNGOynTRrmpvKz/bNo5VpFn3fRNqwdrBKvj3bP5d+\n/rGPSprH1/burUPS7OkeUkF+doVlzp/lJZ06tkhl27rVA6Qdv4+VJEmSrl3aJM2ZXlMqKMhR7g+8\nvvPRtfWTJEmSTvjOk9aveUPlGHm56dL0STZSdGRApec7fZKNdOfmgUr3v6hN696Sdm0bX277yaML\npG+/dJby8zKV286cWCotmdtC+XnhnMbSDf8dKvn8zq2Vli9oU2l5Ade2SbOmuSk/Z6THSmtW9JIW\nzG4kFRcXSpJUdo7HDs9RpikpKZa++8pF5Xl61tWLv6nU7Xnqv2vbeGnTurdUjnPt8uZy9z4sxE+a\nPslGSkkOU+ZbMLuRVFr65Hd69dKu0srFHVWOVd3v69PX4qflPaRrlzdLkiRJKxa2kwKv7yx3rXZu\n/UTas/0zlWPExd6Wpk+ykbKzkyVJKnsO16zopZJmw9rBKn8PnpWUGCxNn2QjZaTHSpIkSceP/CAt\nnOOtvB/Pepn77ndurbRwjnel+2OiAqXpk2yktNQole2J8Q+kb6Y4SGdPLZeKiwulvNx0aevGD6Tp\nk2yksyeXS5IkSQ+DTkszJttKgdd3SiUlxVJmRpz0y6p+0vRJNtLNgN2SJJX9Tn73lYv0MOi0VFpa\nKiUnhUjLFrSWpk+ykSIjrqmU+f2M2pL/lS2V1vUxwF/6G8Rm/6Qf0fIrCK/JpK41n4z5fURHrs6k\nrjVfY60EQXgZrVrWZOjgliQmZWL7aDbpv5P8/CJmf/NGuQnZnlW3fk88anUkMvwK0ZHXCQk+zcVz\na+jYbSptO3xaaT6FopTzp1dy5+Z+srISKC0ppLS0GGdX1R4Ntvb1q62rqZkzRsZ2hIf6Ye/oRUxU\nAH0HLURTUxdbe08iQi9ibVOXnOwklRmLc3JSOHV0PuGhF8nJSUZSlFJcXEBGRuwzdWig8tmr8WB+\n+2UYy+a3wt2jDTVqdaRGzQ7KVs9mPu89VwuiqZkzI97bpLJNU0t1cjEHR9Wx/g5Ojbl/p3zXbwC3\nGm0wNrZnydxmuHu0w82jLXXq9UBLW5+CgmyysxJwdG6iksfRuSkPH5wEyrqMWtnUVpmQx8FJtfy4\n2NtEhl1m9tflx8WnpUZg7+hVzVmXl5Eew8pF7ZSfW3f4tMpnpzpGxnbKrr5Q1kU5NycFKOvGmpkR\nx4Hdkzm450nvG4WilLK4oHJFRXnM/todSZIoLs7Hxq4+w95epzIG3Mq6tvL/1dU10NUzI+dR2QB3\nbx3k0oWfSUuJoKgoF4VCgSSpTjJYVf0rk5z0ECvr2mhpPxn77+DkjUymRnJiMGbmZRM5Wlh5qLRG\n6hlYoKOtOtZfT9+i2vIea9xkGAHXtmFlU5uszHjq1O/BnZv7VdLExd4iLSVCZfvja52WGoG+ftmw\nEWub2ir5DA2tVeoRF3OLMycWEx93l/y8DB53BcnMiMXI2Jb42Ds4OTetcEz+y973kuKCci2vz8PS\nuiYDhizF98C3nPSdh5qaBs193kNf30LZ68ndox1des7g4N6v2LN9AurqmrTtNIHI8CvIZGV/Sxo3\nG05aWgS///oeCkUxWloGNG/1PqePL1KmeUxDrk1JccEL11Wongh+BeE16edlB5SN/Y3LyMfWWIdJ\nXWsqtwuC8M8yYujLT7z0qnXr3KD6RI/I5dq4e7TF3aMt7Tt/zt4dEzlzfBE+bUZXOjmU39nVXDy3\nhu59vsPKphaamnqc8J1b7ku3/DnHr7m4+RARdgln1xbo6pkqv+w7u7YgPPQS+fmZaGrqYe/wJDjb\n/cd4crOT6dZ7JiYmDqhraPLr2sGUlhSpHPvZMXS29p58PvUKIcFnCAu5wJ4/xmNlW5d3Ptj2Qt1+\n1dXlynr+FbS09Rk9/iiR4ZcJfXiO86dXcMJ3LqM+OVwuqFbxnMvDAUiSAo/aHenac0a5ffr6FhXk\nqJ6BoTVjJhxXftbRfbEhO89SV1edjV8mkyFJCgDlf3sPmIeDk3e5vFWRy3UYM+E4Mpka+gYWFY6t\nrKrs6Mjr7Ph9DO06fU6N3u3R1jbkwb1jHD303XMf46U8dX/V1Z45NjLU/kR59Rr25ciBbzh++Hvq\nN+ynMi77MUlS0LjpMFq0/qjcPkOjJ5Ppqak/G2LIlIFpUVEeG9e9iZt7awYOXYGevhl5uWmsW92f\n0tLql2J72fuuq2dKQX7mc6d/mqfXADy9BpCTnYxcUxeZTMbF82sxeapbuE+bUbRs/RHZWYno6BqR\nkRbDiSM/KLuOy2QyuvT4mk7dviQnOwldPTPCQi4AYGqq2r08Py8DXb3/xgR4/28i+BWE16ifl50I\ndgVBeK2US65V8hLOwsoDhaLk0RhcTdTVNZEUqq1bkRFXqVmnMw0bDwLKWoJSk0PR1im/RNnzcHFr\nyYHdU3j44KRK67Gza0sO7/ua/PwMHF2aqgQWUeFX6dF3FjVrdwIgJzuZnOzqlwiDskCzrmcv6nr2\noqH3YH5e2Yu01HDMLf7a2fJjogLKfX48uVJF1NU1cHVvhat7K9p3/oL533kSfP8E3s1HYGBoTVTE\nNZUxgVERV7G0Kpsl3cKyBjeub1eZNCc6UrV8W7v63Ll5AGMT+3JB2stSV9f4S18CVEXfwAIDQ2vS\nUiNeeM1WmUz2p+oZFXENA0Nr2nV6smRSRkbMCx9HXV2uHMf6mIVlDW5c20ZhQY6y9Tc60h9JUmBh\nWeOl61wdbW0D6nr2IvD6jnKTQj1ma1ufpKdan19GSlIIeblpdOr+pXJpqXu3D6uksbGrx82AXRXO\nyP6y993Gth65uank5qaqTJj2IvQNyl4KBVzbioaGFm412qjsl8lkypcAtwL3YGRsi42dao8XNTV1\n5cRetwP34uDUWGWm97TUCEpKCrC1q76njPDiXv9MDRFv2gAAIABJREFUBoIgCIIgvBaPl1yLzchH\nU8qhTtosVu9Yy++nzpCeFsWdWwfwO7MKF/dWyplcjU3siQy/SlZmPLm5qQCYm7sRFnKByPArJCc9\n5NDer0hPj37perm4taSkpBD/K5txeSr4dXRuQnpaFCFBp1W6PAOYWbhy88ZukhKDiY0OZPuWMc8V\n0PmdW8OtG3tITnxIako4t2/sQUvbQPnl9IrfepYvqH7SGYWihOzspHI/T4uJCuDcqRWkJofhf2UL\ngdd30rKCFjSAoHvHuXThF+Jjb5ORHsPtwD0UFuZgblUW/Pi0HcPFcz9x68YeUpJDOXl0PpHhV/Bp\nUzZbeH2v/qipqbN3+2ckJQQREnyWc6eWqZTRtMW7FBZksX3LaKKjAkhLjST04Tn27ZxEYUFOtef8\nd9Ch80QunFnNxXNrSUkKITHhAYHXd3Du1IpXWq6ZhSvZWQncDNhNWmokVy/9xu3AvS98HGNTB5IS\nHpCSFEJubiqlpcV4evVHLtdh9x+fkhh/n4iwy+zfNZk69Xq88hcLvQfMY+o3d7BzqLi3SKv2Y4mN\nvsH+XVOIj71Nako4QfeOs3/X86+1bWRsh4aGFlf8NpCWGknQ/ROcPDZfJU3TFu9QVJTH9i2jiI0O\nJDUlnFs39hAfdwd4uftuY1cPPX1zosKvqmzPzk4iPu4OqSllE90lJwYTH3eHvKeWeLrit564mFuk\nJIdy5eIGDu2dRufuX6Lz1Au+C2dWkRh/n6SEIM6cWMKFMz/So88sZdf03NxUrl76jeTEh8TH3eHw\nvuncvXWQ7r1VewtEhl/BxNQJM4vqJwUVXpxo+RUEQRCE/6inl1wrRosUXHErOcbto1sIPanAwNCG\n+l79adtxvDJPhy6T2L97CkvnlQWo382Po23H8aSnR7Fp3Qjkcm0aeg/G02sAyYnBL1UvYxN7TEyd\nSE+LxPmpIFdLSw9be09iogJwdWulkqf/G4vZt2syPy3rhoGhFe07TyTvUXBeFS0tffzOriY1JRyZ\nTIa1bT3eem+zsrU0NzdNZfbnyqQkh7JgVsNy27/5IQr1R11AW7T+iMT4e5w7tQy5pi4dunyhXLrp\nWdo6hty/48uZE0soLsrH1MyJvoMWKtf+bO7zPkWFORw7PJvcnBTMLNwY+tbPWNvWVV6r4SM3cnD3\nVFYv64q5pTude0zj91/fVZZhaGTNBx/v47jvD2xaN5yS4kKMjG1x92iL+l+0/vGr1rjZcOSauvid\nXc0J3x/QkGtjaeVBs5YjX2m5tep0waftGI4cmEFJcQFuHm3p0GUSB/d8+ULH8W46nIjQi/y0vDtF\nRbnKpY7e/uB3Dh/4hjUreqIh16JWna706PNd9Qf8k+Ry7Qpnen/M2qYO743ew8mj81j/00AUilJM\nzJxUltiqjp6+Gf0HL+WE71yuXvoVK5vadOs1k03r3lSmMTSy4f3Ruzl6aBYb1gwCZFjZ1KLPo2Xt\nXua+q6mp06jJUG7d2EPtet2V269d2siZE4uVnzdvKFsmrv/gJXh5ly2ZGBMdyKnjiygqzMXc0p3e\nA+Yre7o89jDoNOdOLaekpAhr2zoMe2cDHrU6qKQJvL6TY4dmIUkSDk6NGTl6Z7mx9bcC99K42ZsI\nr4asugkB/o68vb0lf3//110NQRAEQfhHc5l6qMIVh2RA+Nye/+/q/Ks9u4avIAj/fzk5Kaxc2JZR\nnx5Rdrn+O0lMeMCvawczftIFlYnSKiOTya5LkvRiA97/40S3Z0EQBEH4j7I1Lj+hTVXbBUEQ/sn0\n9c3p98ZiMtJjq0/8GmRnJTBgyPLnCnyFlyO6PQuCIAjCf5RYck0QhP+aWnW7vu4qVMrdo93rrsK/\nngh+BUEQBOE/Siy59v/z+ZdXq08kCIIgvFIi+BUEQRCE/zCx5JogCILwXyHG/AqCIAiCIAiCIAj/\neiL4FQRBEARBEARBEP71RPArCIIgCIIgCIIg/OuJ4FcQBEEQBEEQBEH41xPBryAIgiAIgiAIgvCv\nJ4JfQRAEQRAEQRAE4V9PBL+CIAiCIAiCIAjCv54IfgVBEARBEARBEIR/PRH8CoIg/AX679/AlvsB\nf9vjvawWW5dzKurh667Gv9rYk7s4HH7/dVejQn6x4bx95HcUkvS6qyIIgiAIf5rG666AIAjC311a\nfh6/3buGX1w4SXk5GGnp4G5szhseDWhp6/y6q/e3o5Akdj28xYHQu0Rmp6MhU6O2qRXDazeixb/4\neqXl59F//wZMtHXY3WckajJZtXn8YsNJzMuhq1NN5ba9IXc4HhlEcHoyOcVF7O79Ljb6hir5gtKS\n+DHQj/tpiajJ1Gjv4ManXq3RlWsCcCjsHrOvnKiwzHVdhlDHzAqAJdfPcislnrCMVEx1dNnTZ6RK\nWh87F36+fZmjEQ/o7lL7ha6HIAiCIPzdiOBXEAShCvE5WXx0Yge6GpqMbtCSGsYWSJLEtcRo5l87\nxd6+773uKr4SxaWlyNXVXyrvNxd9uRwfyZgGLWlm40RhaQlHwh/wxbkDTGzclgE1PCvMp5AkJElC\nXe2f2SnpcPh9fOxcCMlI4Up85HMF+tuDA+npUlvlnAtKimlq7UhrO1eW3ThfLk9yXg6fnN5DB4ca\nTPRuR25xEUsDzjH7ynG+b9UTgI6OHjS3cVLJtzLwArdT4qltaqncppAkerjUJjQjhSsJURXWsadr\nHXYE3xTBryAIgvCPJ4JfQRCEKizwPw3Ahq5DlK1qAM5GpnRzrlVpvoTcbJYEnMU/IRqAJtaOfN64\nDZa6Bso0F+MiWHfnCiEZKWiry6lvbs2cVj3QUi//p9k3/AEL/E8zs0VXNNTU+NrvCEcHjkJDTY3o\n7AwGH9xIP/d6TGnSAYA1ty5xJyWBFR36A3AjKZaVgRcISU9BT65JF+eajG3gowxwPz65C2dDU7Q1\nNDgSfh8bPUPWdx1arh6b7vmz5X4AC9v2pp65Tbn9J6KCORH1kLmte9LW3k25fWxDH4pKS1kacI5W\ndi5Y6hpwKOwei66fZbZPd34MvEBkVjq/dXuTwtIS1ty6SFBaMsWKUtyNzRnn1Yr6T5XXYutypjTp\nwNWEKC7FRWCqrcuH9ZvTzeXJPbmbksB8/9NEZKbhZGjC6AYtmXh2Pz92GEAjK3sAwjNTWRnoR2BS\nLFrqGnhbOTC+UWvMdPQqvbeVORB2j3FePjxMT+FA2N1qg9/0gjyuJUQzrmErle1Da3kBcD81scJ8\nfnERqCFjknc7ZdA8uUl73jryO9HZGTgYGKOtoYG2xpPnqKCkmAux4Qyv3RjZUy3SE73bAbDlfkCl\nwW9rO1cWXz+rPLYgCIIg/FP9M1+vC4Ig/B9kFhZwOT6SgTU8VQLfxww0tSrMp5AkJp87QFpBHis7\nDGBlhwGk5Ocw5fwhpEdjJy/FRTD53AGaWjvya9eh/NhhAF6W9sr9T/sjKJDF18+ysG0fWtu70sDC\nlsLSUh6klQVHN5JiMNbSJiAxVpknIDGGRpZ2ACTl5fD5mX14mFjwW7dhfNWsE8cjg1l986JKOUcj\nHoAEqzsNYkbzLir7JEli+Y3z7Ai+yapOAysMfAGORQThYGCsEvg+Nrx2I4oVCk5Hhyq3FZWWsOHO\nVSY36cDvPUZgrWdAXnER3Zxr8VOnQazrOoQaJhZMPLOfzMJ8leOtv3OFNnaubOz+Jh0dazDn6gkS\ncrMByCsu4otz+3EyNGFDt6GMa9iKlTcuqORPyc9lzIlduBqZsa7LEJa3709eSRGTzx984TGugUmx\nZBXl08LGiW7ONfGLjSC9IK/KPDeT45Grq+NqZPZCZRUrStFQU1NpLX78wuRWclyFeU5GPSS/pITe\nrnVeqCwAaz0DTLV1uZEUW31iQRAEQfgbE8GvIAhCJWJyMpAAZ0PTF8rnnxBNaGYq37XoRm0zK2qb\nWfFty24EpSVxLbGsJXjD3Wu0d3BnlGcLXIzMcDcxZ3jtRmhryFWOtebWJX67e40VHfrj9SiY1ZVr\nUsvUguuJMQAEJMYyqEYDEvKySMnPpaCkmPtpicrWzd0Pb2Guo8ck7/Y4G5nSys6Fjxu0ZOfDmxSU\nFCvLstEz5NNGrXE2NMXZ6Mk5KySJOVdOcCE2nDWd36gyWIvKzsDZ0KTCfZa6+ujJNYnKSlduK5Uk\nJnq3o4GFLY6GJujJNfG2dqC7S22cjUxxNjRlYuO2aKqrcykuUuV43Zxr0c2lFg4Gxnzk2QJ1mRqB\njwK0o5FBlEoSXzXthKuRGU1tHHmnbhOV/Lsf3sbdxJyxDX1wNjLF3cScGS26cC81kftpFbe6VuZA\n2D06OnqgoaaOrb4RdcysOBz+oMo8CblZmGrpvnA378ZW9mQUFrDxnj/FpaVkFRWw+qYfUBbQV2Rv\n6B187JxfqkUbwFxHj/jcrJfKKwiCIAh/F6LbsyAIQmVecoLbiKw0zHX0VCYpstM3wlxHj4jMNJpa\nOxKcnkzPasZQbg8OJK+4iPVdh5brbtrI0p4bSbG8U7cJN5JjeaNmA64nxRCQGIOxtg7qMjXqmFo9\nqk869cytVSZg8rSwpVihICY7E3cTcwBqPTUW9GkrAi+gLlPjly6DMdXWfalrUhl1mRo1jM1VtqUV\n5LH21mUCkmJIK8hDIUkUlpaQkJetks79qXwaamqYaOmQXljW2hqZlY6bkZlK19+6jyZ5eiwoPYnA\npDg67Fhdrl6x2ZnUNbN+rnPILS7kVNRDVnYYoNzWzbkWW4NuMLx2o0rzFZaWoPkS46pdjcyY3rwz\ny2+cZ82tS6jLZLzh0RBTbd0KJ9kKy0zlTkoCi9r2eeGyHtNS16CwtOSl8wuCIAjC34EIfgVBEJ6x\n90YsC44GEZ+dg1VdOPIggnYO5bvxvgzZc8wA/FgDc1sux0dyLDKI9+s1U9nnZWnHzoe3iMhMI7e4\niFomljSytCMgKQYTbV3qmds834RVT1Xn2Vbnx5pYOXI8KohLcRH0rKbbrKOBMRFPtew+LSkvh9zi\nIpVAXlNdvVzL56zLx0kryGO8V2ts9A2Rq6nz6ak9lChKVdJpPNtiKgPFC7ywUEgSPrbOjPNqVW7f\niwT5RyOCKSgtYdSJHSrbSyWJm8lxNLCwrTCfsZYO2UWFz1/hp3R1rklX55qk5eehraGBTCZjW9AN\nbPWNyqXdF3IHK139chNgvYisogJMtHReOr8gCIIg/B2I4FcQBOEpe2/E8uXu2+QXlwLqFGTrcKbk\nAduvuzO4sWrwkF1UWOG4X2dDU1Lyc4nPyVK2/sbmZJKSn6vsQu1hYoF/YjR93etVWpeappYMreXF\n+NN7kCHjvXpNlfsaWNhSVFrK5vvXaWBhi7qaGo0s7fnh6klMtXVVAh1nQxNORj1EIUnKlsFbyXHI\n1dSwryBYelZLWyfaObgxze8wIKOna+Ut1l2cajL9oi9nY0LLjfvdcv86cjU1Oji6V1nereQ4Pmvc\nFh87F6BsCaGUgoq781bGydCEw+H3KSgpUbb+3ntmAqmaJhacjHqIjZ4BGmovN7M1wMGwuwyq4Uk/\n9/oq21fd9ONA6L1Kg18PEwvSC/PJKMzH+CUDS1OdsiD9QOhdNNXUaWrtqLK/sLSEIxEPGOzR4LmW\nXqpIYWkJsTmZ1DSpuGeAIAiCIPxTiDG/giAIT1lwNOhR4FsmK9YCCVhy9wgnox4SmZVORFYaux/e\n4q0jWyo8RhNrB9yMzPjm0lHupyZyPzWRmRePUtPUEu9H43DfrduEU9EhrLl1ifDMVMIyU9n64IbK\nGFyAOmZWLG3fj60PAthw56py++Nxv74RQTSyLDtmXXNrkvJzuJuaoBzvCzCghicp+bkseDTzsV9s\nOKtuXmRQjQaVtvY+q5WdC3N8ejDf/xSHw+9Xmq6jYw3aO7gz+/Jxdj+8TVxOJuGZqawK9GPnw1tM\naKQ643VFHAyM8Y14QHhmKvdSE5l+8QjyFwxOuzjVRF0mY+7Vk4RnpnI1IYrf7vmX7XwUAw6s4Ulu\ncRFf+/lyNyWB2JxMriZEMffqSXKLi56rnJD0FO6nJdHXrR5uxmYqP92da3Eq+mGlx/IwscBES4eb\nz0xSlZqfS3B6MlHZGQCEZ6URnJ5MZmGBMs2O4Js8SEsiKiudncE3WXT9LGMatCz3MuZ0VAi5xUX0\ncq1bYR2iszMITk8mJT+HEoWC4PRkgtOTKS598jtwNyUBTTV1PC0qnuRMEARBEP4pRPArCILwlLgM\n1RmFS4vkpATbk5upzapAP946soVPTu3hfGy4clmhZ8lkMua36Y2Jlg7jTu1m3KndmOroMq91T2W3\n55a2zsxt1ZNLcRG847uVj0/sIiApRqVb9MbDy7l0+xR1zaxZ2r4fvz8TADeytKdUUihnddZS16Cu\nmTVyNXXleF8om2hqcbu+BKcn87bv78y5coLOTh6MbtCiwvrPWDuGn/ctLLe9lZ0Ls326M+9a5QGw\nTCZjVstufFC/OXtCbjPs8GY+OLadu6kJLGzTu9I1fp82rVkn8kuKeffoNmZcPEIv1zrY6FUdMD9L\nT67Jgja9CctM5R3fray8cYEP6pd1HddSK2sJ1pEpMH3oS1FRPp+d2cebhzezyP8McjV1NB8F2wGJ\nMbTYupyAR5OLPWvbvauoF2ZBbnK5fT52LigkieORwRXmVVdTo5drHY5GBKls3xNym3d8tzLz0lEA\nJp7dzzu+W7kQG6ZMcy81kfGn9zLiyBb2hd5lSpP2DK7ZsFwZ+0Lv4GVmxdjZnQmJKX/Pfrh6knd8\nt7ItKJCU/Fze8d3KO75bVSbOOhYZTBfnms/9ouS/JjMnjZGzu5GamfRc6bef+IUfd85+xbV6OZEJ\nIXz4Q28KivKrTywIgvAPJKtoWY2/O29vb8nf3/91V0MQhH8hn7mniM0o/8XPzlgHv6kVB7svKiM7\nlT1nN+H/4AKpmUnoaOlhY2ZPqwadad+4FzpaZV1ZB37ZnC/e/J4W9f+acp91J+w63/w8lg1f+2Ko\n92Qc7oy1Y3CwcuPDvl+8knIB/jjxM5funGbphN9fWRnPOhcTytTzhzg84EOMtXT47fAKsvMyGDdo\neqV5DobdY1WgH3/0ervCLu5J6XGMmT+AeWM34G5f9QRmFUkryOPNQ5tZ33VIheN1q5OQFsvu079y\nM+QaGdmpGOgaYWvhSPtGPWnVoAtyDfmfqmNaQR7DDm1mQzX1O3X9IOv2L2LLt6erPN7jZ+4xTQ0t\nrM3t6dt6OO0a9XihulXleevzV/n10DJyC3IYO3Balekyc9IYu3AQCz/dhLVp2Uuru+E32H9+C2Gx\nQaRlJTN20Nd0aNxLJV9GdiqbfH/k5sOr5BZkU8fZi/f7fI6t+ZNu7unZqWw8vIJbIVfJK8zFxsyB\nfm1G0MarW7l6FBUXMnXV+0QmhJR7LuZvnoqLjQdvdHzvz1wSQRD+D2Qy2XVJkrxfdz3+ScSYX0EQ\nhKdM6lrzqTG/ZXTk6kzqWrPavPM/XouBsR5jvh8OwHfvrsDB3YaRXw8C4JPO3+LTrwEns9aho6XL\nsM6jcLJ2R1OuRXRiGCf897P3yxv0ebczvUa+moD3ZZzdc4UNc3bxq//8V3L8n6b9joWdKQM/Lv8l\nvToKhQIJCfUKukUfCruPnb4hVroGhGamsjTgPK3sXDDW0qGwqICT1/bz5TsLqjz+tjMX8czWqnRN\n5z/LVFuXac06kZCX/cLBb0jMfb79ZRx2ls580Ptz7CyckclkhMcHc/TybmzM7Knl3OBP1S8+N4tJ\n3u1eKjCvytIJW9HXNaSwuIDLt0+xcucsbM0d8XCsfAz861JSWoKGetVflzo07sXkH0fydvdxGOhW\nfq1OXNuPu30dZeALUFCYj6OVG+28erB8x7fl8kiSxLxNU5CpyZjy1jx0tfU5cGEr3677lGWfbUVb\ns2y8+Irt35KTn8WUt+ZjqG/C1btnWL7jW8yMrajr4qVyzN8Or8DMyJLIhJAKz+WnPXMZ0O5t1Ks5\nb0EQhH8a8VdNEIR/jNVfbSE7I5fJqz56ZWX08yr7UrrgaBBxGfnYGuswqWtN5fYX8fnS91CXqwZl\nl++eQeYoY/64X5VfWgGsTG3xrt2KTzaqfvnNzs9i4ZavCAi6iJG+KUM7f0hbr+7K/ZEJIWw4uJSg\nyNtoyrXwrt2K93p/jp62vsr+kJh7SJKElakdI3t9hpWpDTN/+BLNi80ZtX/mMzXX4iExnPpqAupN\nytbNpdQehUKB2qMZlkfP60f3Fm/Qt81wZa5nW4wv3znNHyd/ISElBk25Fo7WbkwcNpuA4EtsP7kO\ngEFj26PhV5d3V3YCIDM7k9lfzCP2djpSjhx1LRl1mtbgo2nDMbc1UbbofTroO9Z8t4m8EDmaajq4\ne9lTWDeEyMwn52lSsy+L4xPI11CgkaegBoZ83bEjAAFBF5HJZNRyKgsOH1wPY9bIlWibydDsHkZG\ndhqmRhb0qtWDoz/dJXFAClYOqksyVUShULDuwCICgi4y/b1lKi1zjyWkxvDroWU8jL5LfmEethaO\nDO30EVjaV3DEikmSxIod32Fj7sD3o39W3hcAG3MHWtbvyLM9u5Iz4tlydBUPIm9haWLDe70+o0GN\nJ7OI3w2/wabDK4hICEFXW4/WDboyottY5ZJPd8NvsOnISqITw1CTqWFr4cTYgdPIystQduMd+GVz\nAAZ3fJ8hnT6stP5G+ibK3gb92r7F3nObCYsLUga/FfU+WLHjO7JzM/nq3UUvXZ/ikmK2HV/DucCj\n5ORn4WDpyrAuo/DyKEv3uGX6q3cWs/3kL0TEBzNp+FycbWrwy/6F3I+4SVFJIebG1gzp+AGtGnQG\nwNHaDRMDc67cPUOnJn0rPe/zN4+V29+4Vksa12oJwMqds8rliU+JJjj6Dos+3YSzTQ0APuo7mfe/\n78mFp44XFHWb93tPVF7DPq2Hc+jiDkKi76kEv1fvneNO2HUmDf+egKCL5cprUKMZOflZ3AkPoIF7\n03L7BUEQ/slE8CsIgvCUkuJS+nnZvVSw+yx9Yz2Vz5IkEZcSydDBg1QC36rsOLmOEd0+ZnjXMZz0\nP8CqXXOo4+KFhbE1BUX5zFo/AXeHOswdu46cvCxW7/6BH3fOZvKIuQAs3fYNzjbuzP14Pepq6kQl\nhqKpoYmZkRWfjZ3CYv2ZzPpoNbo6Bhxcc5akqDRKvIOISAjFx7MjPdqM4+Te85wMCOCg3zb6tH7z\nueqdnp3Kkm3TGd51DM3rtaegMJ/g6DsA+Hh2IjoxDP8HftSU9UDqJtG+WTckSWLu+kmkR+sy6OPu\nuNVxwu/6ac5vvsmcDzNYuO9LAIpKilg3axvqCWaM+r4HNjZWfP/FUuRh2ny/5RfkcjkR8SFsm3KO\ntpamvDV5ANkZeaz+agu7Uo4wctpA7kUE4mpXE5lMRk5m2b66zdyJCItk4rA5GOob8zD6Hj/tmYt9\n7fac+MOP4V9UHtRAWQvh8u3fEpUYypzRazE1tKgwXUFRPl41WzCsyyg0NbTwu32CBVumsujTzdhb\nOj/X9Q2PDyYmKZzPhs5SCXyf9uyyWr8fW8Pb3cfxYd9J7Dr1K4u3TuenKXvR0dIlNTOJORs+o61X\nd8a9MZ2E1FhW7/4emUzGuz3HU1pawryNk+nYpDcThnxLSWkJ4XFBqKmpUdPRk5G9PuP3o6v5cdIu\ngOd+vhUKBdfunye3IAd3+6qX0Xray9bnx52zSEiLZcLQ7zAzsiTgwUXmbvyCeWM3KANLgM2+P/JO\nj0+xNrdHR1OXH3fNprikiG8//BEdLT3ikiPL1amGQx3uht2oNPjNzsskJikcd7taz32eAMWlZROm\nyTU0ldvU1NSQa8i5H3FTWV4tpwZcvH2CJrVbo6djgP+DC2TlpuPp3kSZLzUzibV75/P1yCVoyivu\nzSDXkONsU4N7YTdE8CsIwr+OCH4FQfjHetwSXL9FTQ6sP0VRQRHeHeoz8utBaOn8j72zDo/iahf4\nb1bi7kIgCZBggQQL7q7BXYoXKy3FKlAKlGKlLQUKxVvcLbhb0OAkRIi7u+3O/WPDJksSpF/vbfvd\n/T0PD+yZd855Z2YT5j2vqV4URVHk5PbLXNh7g6TYVEwsjGjRsyFDPu1JYnQy0zstYuryEVw84EfQ\nozCGzexF52Eteen/it0/niD0aQSGJgY0aFubIZ/1wsBID4D83AK2LNrP7bOP0DXQoevw1mX0ezPs\nWaEogkIpT3YncOzz2egZ6NLjo7YcjVtNTl4WAPKc+hpzNKvRiZdHMth1cQMFeYVIDNy57HWZAf0G\nc+3hGfKy8zF4XIvvNm4jNysPQ3N37lo9JbZLJPZWTiSmxdKr5VC1UWVv5aSe29TEDPQKqVS5EiaG\nZhgaGiCTp4MhWFibMnnobARBoHqlZC5Ln3B4/xGurYwgMTqFIhNbsqpr9qjNCpVwd3881775HCNz\nfUQTexpOb42NuarVT2W7ktZHejr6SJDif/EFH383DF25Hk9C7hGREsSWo6fQlavuc7369QiIGU3c\nviSiQ1WtipT5kBOoy6Ql/WjRVZXqpKgfQqFvTVJD8qnXwpX4F5kkRqQxf/N0LO3NARg6sxe/zd/D\noE+6k5gWh4WxyjjdOH8PLXs3AlEkNSGDak4qI8zG3IHQmEAepAdw07fgrcZvfkEuS7d/TnZeJosm\nrH9r6KuzfXUNQ6t/24+49+I6fk8v0r/d++VZxiZFAOBgXeJZzs7LYsLSnurPfduMol/b0erPPZsP\nplHNlqp70XkSl/19CYt9SU1nT077HcTcxIrxvWchkUioZOPCsC6T2XB4GUM6TqSgKJ/svEwa1miB\nnaXKQ13aUDfQM0QQBMyNLd9L/4+X9wGgsKgAERjRZcoH5SPn5Gd/sD5xyVFcf3yO9bMPY22m8mZ3\nazaAxyF3OXv7MBN8ZqtlB3YYi6dbiVc8MS2OprXbqp+brUXZ9lXmxlaERFdcCT0pLR5RFDGvYFOk\nIhytnbEys2PnmfV83HceejoGnLixm+T0BFK82feSAAAgAElEQVQzk9VyM4cu4YfdXzF6cWekEily\nmQ6fDl6Ei4MbAAqlgh/3LqBXyyE421cnITWmoiWxMLEiITX2g/TUokWLln8DWuNXixYt/2oC7odi\nZmXCl5s+JjkujZ9mbsPO2Rqf8apwxD0/nuD83hsMn+1DzQZVyUjNIuxFtMYce348wfDPezNx0WCk\nMikRL2P4bvx6+k/tyoRvB5Odns2O7w+z4avdfPrjRwD8seIoT2695NMfP8LC1oyD604TcC+ERh3K\nVjM+4h/NijOB2GXmI0+xI9tZxtIDn/PsThDblhxi5Dcz8WhZlV8Pf0946VBVER7vSMHOTpdZa8dj\nZGrAnDmzOPbdHTq06kpUQhiGoe7E5Ccwa+14TK2MiQ1PYPGvM4lKeIW9lRM9Wwxh/aHvuPzAF49q\nDWlSu+17eRfdKtfR8ByKCpGcR4aMWuWDsbEJX4xdxL1dYQzrqzr+6PoL4s/IcOtpxKRxU0iISWbl\n5+uYOWEOXv2dqFutEU3rtMPUyFw9Z1GKjJzMPFxrqwzykOgA8gvzGLO4q4YuBQlyZJhhaGIAmSDN\nMEZRpKRusxIPWq9O/Tl89R4bt6yjbWF90m/r4eBqqzZ8Aeo1r0FhQRGvnkVSUJiPmZEFZ3dfJz0p\nk74/dOLQr6pQ2Nm/jCYxLY6CwnyKFEVYGFQiLV5KfEQStpXLD33+ad83mBtbsXD82nd6PfMKctl3\nYRP3A26QmpGMQllEQVEBVeze3v/4XejrGLBy2g4Almz7jCKFZtus0vO/9kqnZ6UCEJ0YhptTHQ0v\ncs0q9ShSFBKbHImzfXXaNujOoq0z8KjaEI+qDWnq0U5tRH4oC8etxVDfmEJFIcGRz9l8fBX6ugZ0\nbOzzXucbG5h+sD6hMYGIosiM1UM0xguLCqhTVbNeTFVHTUO8e7NBbDyyDP+XfnhUa4h37TZUfcOD\nqyPXpaBQc0OoNK+P6ZTy4L4PMqmM2cO/Z93BJYxe1BmJRErdqo3wcmsKlPy+2H32VzJz0lkwdg0m\nhmbceXaFNfsWsmjirzjbV+fQpe3IpDJ6tnh39IaOTJeCooqvRYsWLVr+rWiNXy1a/sFs+3EhNvZO\ndBv031N18+b549y5coYZi375oPOO7FhHaGAk1rYuGuP6RnqMWzAQiVSCY1U7vDt58swvCJ/xHcnL\nzufUjiuMmNuHtn1VOX12Vaxx89Sco/OwVnh3LmkTs/enEzTt6kWP0W2LR6wZ8/UA5vVfSXpyJrp6\nOlw+5MfExUOo10L1kjxp8VAmtvqCgMf3gJI82NCkbA4UF9CyRYJolsVNg0SapRbiM7A5oU8juXnw\nEZ36tCn2dpa8zApJJiSHZ7Bs9zx09FQvzPpeaQip9lw7dg9soShTwNmzEtXqVgHAzMYY8XgqFBuu\ngzqMp6VnZ/wDb/Ew6Db7L2ymjWUrTPVMqdtZlWd7Zu82CvPzgRLP7JsoFSKmNZIIfHqOfqOnoVcn\nk4RbeoiiiCAIHNl4DhOvQhwaGGJb2QrbylZM+WYsv8zZQRXbqly8d5ydZ9azaMJ6tfdMkSVBEATM\nrE0AEEUlpkYWLJ7wq3rdokIF62bsw6SlEZZ2ZhANskJ9BKkEY/OSsPIe3gM5r/MIM4kNgeFPeHIt\nHgd9N41rMDY3RCKVkJaUgYmBKYnh6fgfOs2iXZ8ikUqIiA8lNTOZnvW7U6OKB/q6hpz2O4if/xUA\nEmNSKjR+G7g357K/LwFhjzU8huWx3fdnHr70Y2S3adhbOqEr12PN/m/LGKtvw744lzg6IRxXB1Ux\nNolEovbsy8ppS1S6eNHrjY336fjwWnZq/6/p0Xww/i9vce/FNXaf3cDsEcvU+bIfgo2Fgzrnt7Kt\nK0GRz9h/cYva+BUECaV/FqA4cqIUH6qPKCoRBIFlU7YglWi+/rwZAvzmBkaHRr3wcmvC/cCbPA6+\nyxfrx9O3zUiNvOas3AyNqulvYmxoWiyXibnJu/PHS1PVsQarpv9Odl4WRUWFmBqZM3ftGKoWe8vj\nkqPwvbVfIy/Y2b46z8Me4ntzH5P7fcmTkLu8CHvEwK9aaMw9b/04mnu0Z8bgbzWuxdpc29dZixYt\n/31ojV8t/wiO7FjHo9tX8Wzaht7DJ2kcO3dkJzfPHad6HS+GfjznL1sz7OUztv+0iFnLNmJgZPIf\nzfXj11NJT0kCQCbXwdzKBu82XWjQosP/uS7/dtKSE/hp/nTGz16CQ5WKjbHXVHK1RSIt8VaZ25gS\n/ESVjxcVEkdhQRF1mrhVdDoAjx+cKGXoQuizKOIjErly5BZyHZ3iF3EV8ZFJ6OrpUFSooHo9Z/W4\nnqEuFnaGKMnUmNs/IpVcN9WLrohArrkcc8UdVp5ugY+XI9XrOXPn3GONc3Kyszi2cwO2CQ6k5Bfx\nUeNZCIKARCqlUFEZQZlLQmQSVTycyat0mVun9Hj1LAqPZu6Yu8lRikoqWZfo5mBVGQerynRvPogN\nR5YRHhXER6O+JCwxRKVXOQZQUOQztWELIJEJ2MsNaNdjIADGFvrkFIns+Gk5yQlhBPrLEZFxxT+a\nG4tmq+ctzCtCGZSLbbQeBplW7N/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0oaPPUPZsXInPyMlqr/O75vW75Es979bq+9CqSx/C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ZHR8uekLlw/zIqtUxjSczqeLpXRUNPi0JlNXL1T/NzVp5gb26CnY8SpS3uoWblJ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\nLNC1sKl2CrOxkzv5qYkqmxhl340hL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nMtKpnrN1OYt6RipFcuV1BYVEpWdgGGBtqV+iws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Zj3/TLq/UH2Nre4wsbXmUEENAs66v1IatT2Vy05ORymSY2bu++ISnGBiZlvmM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\nhFqlv8q1vw6WvhjrWxMccZKSRT9VH3+RFM6LpHAi4+4CEBF7k+TU55g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9QtMvplGq6mBa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"text/plain": [ "<matplotlib.figure.Figure at 0x7f67fa3402b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start=50; end=100\n", "X = fac0[start:end]\n", "Y = fac2[start:end]\n", "plt.figure(figsize=(15,15))\n", "plt.scatter(X, Y)\n", "for i, x, y in zip(topMovies[start:end], X, Y):\n", " plt.text(x,y,movie_names()[movies[i]], color=np.random.rand(3)*0.7, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than creating a special purpose architecture (like our dot-product with bias earlier), it's often both easier and more accurate to use a standard neural network. Let's try it! Here, we simply concatenate the user and movie embeddings into a single vector, which we feed into the neural net." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4)\n", "movie_in, m = embedding_input('movie_in', n_movies, n_factors, 1e-4)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = concatenate([u, m], axis=2)\n", "x = Flatten()(x)\n", "x = Dropout(0.3)(x)\n", "x = Dense(70, activation='relu')(x)\n", "x = Dropout(0.75)(x)\n", "x = Dense(1)(x)\n", "nn = Model([user_in, movie_in], x)\n", "nn.compile(Adam(0.001), loss='mse')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "user_in (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "movie_in (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "embedding_7 (Embedding) (None, 1, 50) 33550 user_in[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_8 (Embedding) (None, 1, 50) 453300 movie_in[0][0] \n", "____________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 1, 100) 0 embedding_7[0][0] \n", " embedding_8[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_5 (Flatten) (None, 100) 0 concatenate_1[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 100) 0 flatten_5[0][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 70) 7070 dropout_1[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 70) 0 dense_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 1) 71 dropout_2[0][0] \n", "====================================================================================================\n", "Total params: 493,991\n", "Trainable params: 493,991\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "nn.summary()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80292 samples, validate on 19712 samples\n", "Epoch 1/8\n", "80292/80292 [==============================] - 3s - loss: 2.3960 - val_loss: 0.9385\n", "Epoch 2/8\n", "80292/80292 [==============================] - 2s - loss: 1.4498 - val_loss: 0.8699\n", "Epoch 3/8\n", "80292/80292 [==============================] - 2s - loss: 1.2115 - val_loss: 0.8653\n", "Epoch 4/8\n", "80292/80292 [==============================] - 2s - loss: 1.0385 - val_loss: 0.8551\n", "Epoch 5/8\n", "80292/80292 [==============================] - 2s - loss: 0.9135 - val_loss: 0.8363\n", "Epoch 6/8\n", "80292/80292 [==============================] - 2s - loss: 0.8502 - val_loss: 0.8304\n", "Epoch 7/8\n", "80292/80292 [==============================] - 2s - loss: 0.8197 - val_loss: 0.8268\n", "Epoch 8/8\n", "80292/80292 [==============================] - 2s - loss: 0.8115 - val_loss: 0.8313\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f67f0073860>" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nn.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=8, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This improves on our already impressive accuracy even further!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

slundberg/shap

notebooks/tabular_examples/model_agnostic/Iris classification with scikit-learn.ipynb

1

466789

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Iris classification with scikit-learn\n", "\n", "Here we use the well-known Iris species dataset to illustrate how SHAP can explain the output of many different model types, from k-nearest neighbors, to neural networks. This dataset is very small, with only a 150 samples. We use a random set of 130 for training and 20 for testing the models. Because this is a small dataset with only a few features we use the entire training dataset for the background. In problems with more features we would want to pass only the median of the training dataset, or weighted k-medians. While we only have a few samples, the prediction problem is fairly easy and all methods acheive perfect accuracy. What's interesting is how different methods sometimes rely on different sets of features for their predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div align='center'><img 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!y(s,l)||!y(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(12),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;P.postMountWrapper(t)}function s(){var t=this;N.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){A.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),y=n(332),m=n(334),_=n(20),b=n(81),x=n(21),w=n(156),C=n(22),M=n(82),k=n(51),E=n(157),T=n(4),S=n(351),N=n(352),A=n(158),P=n(355),O=(n(10),n(364)),I=n(369),D=(n(9),n(54)),R=(n(0),n(93),n(79),n(95),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o);break;case\"select\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var m=_(h);this._createInitialChildren(t,o,r,m),p=m}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=m.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"select\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"textarea\":o=P.getHostProps(this,o),a=P.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":P.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&m.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(95),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(80),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(84),s=n(4),l=n(12),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(80),a=n(20),u=n(4),c=n(54),s=(n(0),n(95),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){\n", "if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(84),c=n(4),s=n(12),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- >0;)n(c[s],\"captured\",o)}var c=n(2);n(0);t.exports={isAncestor:i,getLowestCommonAncestor:r,getParentInstance:o,traverseTwoPhase:a,traverseEnterLeave:u}},function(t,e,n){\"use strict\";function r(){this.reinitializeTransaction()}var i=n(3),o=n(12),a=n(53),u=n(9),c={initialize:u,close:function(){p.isBatchingUpdates=!1}},s={initialize:u,close:o.flushBatchedUpdates.bind(o)},l=[s,c];i(r.prototype,a,{getTransactionWrappers:function(){return l}});var f=new r,p={isBatchingUpdates:!1,batchedUpdates:function(t,e,n,r,i,o){var a=p.isBatchingUpdates;return p.isBatchingUpdates=!0,a?t(e,n,r,i,o):f.perform(t,null,e,n,r,i,o)}};t.exports=p},function(t,e,n){\"use strict\";function r(){C||(C=!0,m.EventEmitter.injectReactEventListener(y),m.EventPluginHub.injectEventPluginOrder(u),m.EventPluginUtils.injectComponentTree(p),m.EventPluginUtils.injectTreeTraversal(d),m.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),m.HostComponent.injectGenericComponentClass(f),m.HostComponent.injectTextComponentClass(v),m.DOMProperty.injectDOMPropertyConfig(i),m.DOMProperty.injectDOMPropertyConfig(s),m.DOMProperty.injectDOMPropertyConfig(b),m.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),m.Updates.injectReconcileTransaction(_),m.Updates.injectBatchingStrategy(g),m.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),y=n(361),m=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(85),u=n(159),c=n(51),s=n(161),l=n(12),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(85),p=(n(40),n(10),n(15),n(24)),h=n(342),d=(n(9),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],y=a[u];g===y?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(y,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(y)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(10),n(53)),l=n(87),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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strict\";function r(t){return t&&t.__esModule?t:{default:t}}var i=n(41),o=r(i),a=n(182),u=r(a),c=n(183),s=r(c),l=n(181),f=r(l),p=n(180),h=r(p),d=n(179),v=r(d);(0,s.default)(),window.IML={SimpleListVisualizer:f.default,AdditiveForceVisualizer:h.default,AdditiveForceArrayVisualizer:v.default,React:o.default,ReactDom:u.default}}]);</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import sklearn\n", "from sklearn.model_selection import train_test_split\n", "import numpy as np\n", "import shap\n", "import time\n", "\n", "X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0)\n", "\n", "# rather than use the whole training set to estimate expected values, we could summarize with\n", "# a set of weighted kmeans, each weighted by the number of points they represent. But this dataset\n", "# is so small we don't worry about it\n", "#X_train_summary = shap.kmeans(X_train, 50)\n", "\n", "def print_accuracy(f):\n", " print(\"Accuracy = {0}%\".format(100*np.sum(f(X_test) == Y_test)/len(Y_test)))\n", " time.sleep(0.5) # to let the print get out before any progress bars\n", "\n", "shap.initjs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-nearest neighbors" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 96.66666666666667%\n" ] } ], "source": [ "knn = sklearn.neighbors.KNeighborsClassifier()\n", "knn.fit(X_train, Y_train)\n", "\n", "print_accuracy(knn.predict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explain a single prediction from the test set" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n" ] }, { "data": { "text/html": [ "\n", "<div id='i8XH8BX5TV6QA0SSBWWC6'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security.\n", "</div></div>\n", " <script>\n", " if (window.IML) IML.ReactDom.render(\n", " IML.React.createElement(IML.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.3250000000000002, \"outValue\": 0.0, \"link\": \"identity\", \"featureNames\": [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\"], \"features\": {\"0\": {\"effect\": -0.0012499999999999734, \"value\": 5.8}, \"1\": {\"effect\": -0.00874999999999998, \"value\": 2.8}, \"2\": {\"effect\": -0.2979166666666667, \"value\": 5.1}, \"3\": {\"effect\": -0.017083333333333506, \"value\": 2.4}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n", " document.getElementById('i8XH8BX5TV6QA0SSBWWC6')\n", " );\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "explainer = shap.KernelExplainer(knn.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test.iloc[0,:])\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test.iloc[0,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explain all the predictions in the test set" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [00:00<00:00, 38.78it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='iQV4Q2FKVTHYH8TB1S7UE'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security.\n", "</div></div>\n", " <script>\n", " if (window.IML) IML.ReactDom.render(\n", " IML.React.createElement(IML.AdditiveForceArrayVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.3250000000000002, \"link\": \"identity\", \"featureNames\": [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\"], \"explanations\": [{\"outValue\": 0.0, \"simIndex\": 1.0, \"features\": {\"0\": {\"effect\": -0.0012499999999999734, \"value\": 5.8}, \"1\": {\"effect\": -0.00874999999999998, \"value\": 2.8}, \"2\": {\"effect\": -0.2979166666666667, \"value\": 5.1}, \"3\": {\"effect\": -0.017083333333333506, \"value\": 2.4}}}, {\"outValue\": -5.551115123125783e-17, \"simIndex\": 14.0, \"features\": {\"0\": {\"effect\": 0.0, \"value\": 6.0}, \"1\": {\"effect\": 0.0, \"value\": 2.2}, \"2\": {\"effect\": -0.32500000000000023, \"value\": 4.0}, \"3\": {\"effect\": 0.0, 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document.getElementById('iQV4Q2FKVTHYH8TB1S7UE')\n", " );\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Support vector machine with a linear kernel" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 100.0%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n", "100%|██████████| 30/30 [00:00<00:00, 38.97it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='iOF6F4YD15134NCGWJSF7'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security.\n", "</div></div>\n", " <script>\n", " if (window.IML) IML.ReactDom.render(\n", " IML.React.createElement(IML.AdditiveForceArrayVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.3227937304007689, \"link\": \"identity\", \"featureNames\": [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\"], \"explanations\": [{\"outValue\": 0.011618716914571448, \"simIndex\": 16.0, \"features\": {\"0\": {\"effect\": -0.0007797220415605988, \"value\": 5.8}, \"1\": {\"effect\": -0.00954234382825675, \"value\": 2.8}, \"2\": {\"effect\": -0.266814187288653, \"value\": 5.1}, \"3\": {\"effect\": -0.034038760327727124, \"value\": 2.4}}}, {\"outValue\": 0.010626709837361636, \"simIndex\": 2.0, \"features\": {\"0\": {\"effect\": -0.0008049089666672671, \"value\": 6.0}, \"1\": {\"effect\": -0.02702896411821054, \"value\": 2.2}, \"2\": {\"effect\": 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}, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc_linear = sklearn.svm.SVC(kernel='linear', probability=True)\n", "svc_linear.fit(X_train, Y_train)\n", "print_accuracy(svc_linear.predict)\n", "\n", "# explain all the predictions in the test set\n", "explainer = shap.KernelExplainer(svc_linear.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Support vector machine with a radial basis function kernel" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 100.0%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n", "100%|██████████| 30/30 [00:00<00:00, 41.21it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='i5G13BG6DNSBOTIPOXE2Z'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). 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"execute_result" } ], "source": [ "svc_linear = sklearn.svm.SVC(kernel='rbf', probability=True)\n", "svc_linear.fit(X_train, Y_train)\n", "print_accuracy(svc_linear.predict)\n", "\n", "# explain all the predictions in the test set\n", "explainer = shap.KernelExplainer(svc_linear.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic regression" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 96.66666666666667%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n", "100%|██████████| 30/30 [00:00<00:00, 34.30it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='iF65JQYQS7AEKF1WCKS5B'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security.\n", "</div></div>\n", " <script>\n", " if (window.IML) IML.ReactDom.render(\n", " IML.React.createElement(IML.AdditiveForceArrayVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.29490409873357165, \"link\": \"identity\", \"featureNames\": [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\"], \"explanations\": [{\"outValue\": 0.0008485517765672856, \"simIndex\": 18.0, \"features\": {\"0\": {\"effect\": -0.0005270379782592594, \"value\": 5.8}, \"1\": {\"effect\": -0.011485556239406086, \"value\": 2.8}, \"2\": {\"effect\": -0.2956080859761362, \"value\": 5.1}, \"3\": {\"effect\": 0.013565133236797144, \"value\": 2.4}}}, {\"outValue\": 0.02553498322276959, \"simIndex\": 13.0, \"features\": {\"0\": {\"effect\": -0.0009202234197583786, \"value\": 6.0}, \"1\": {\"effect\": -0.10081934986145345, \"value\": 2.2}, \"2\": {\"effect\": 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"execute_result" } ], "source": [ "linear_lr = sklearn.linear_model.LogisticRegression()\n", "linear_lr.fit(X_train, Y_train)\n", "print_accuracy(linear_lr.predict)\n", "\n", "# explain all the predictions in the test set\n", "explainer = shap.KernelExplainer(linear_lr.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision tree" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 100.0%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n", "100%|██████████| 30/30 [00:00<00:00, 40.56it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='iB2ZJNCECYWGQ53L11SSV'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). 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0.9999999999999991, \"simIndex\": 1.0, \"features\": {\"0\": {\"effect\": 0.0, \"value\": 5.0}, \"1\": {\"effect\": 0.0, \"value\": 3.4}, \"2\": {\"effect\": 0.6749999999999989, \"value\": 1.6}, \"3\": {\"effect\": 0.0, \"value\": 0.4}}}], \"plot_cmap\": \"RdBu\"}),\n", " document.getElementById('iB2ZJNCECYWGQ53L11SSV')\n", " );\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sklearn.tree \n", "dtree = sklearn.tree.DecisionTreeClassifier(min_samples_split=2)\n", "dtree.fit(X_train, Y_train)\n", "print_accuracy(dtree.predict)\n", "\n", "# explain all the predictions in the test set\n", "explainer = shap.KernelExplainer(dtree.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random forest" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 100.0%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n", "100%|██████████| 30/30 [00:01<00:00, 21.58it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='iUWASR1BXD71OIPN6X0ZL'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). 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\"simIndex\": 23.0, \"features\": {\"0\": {\"effect\": 0.03554166666666689, \"value\": 5.0}, \"1\": {\"effect\": 0.005458333333333454, \"value\": 3.4}, \"2\": {\"effect\": 0.30583333333333323, \"value\": 1.6}, \"3\": {\"effect\": 0.328583333333333, \"value\": 0.4}}}], \"plot_cmap\": \"RdBu\"}),\n", " document.getElementById('iUWASR1BXD71OIPN6X0ZL')\n", " );\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "rforest = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, random_state=0)\n", "rforest.fit(X_train, Y_train)\n", "print_accuracy(rforest.predict)\n", "\n", "# explain all the predictions in the test set\n", "explainer = shap.KernelExplainer(rforest.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural network" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 100.0%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n", "100%|██████████| 30/30 [00:00<00:00, 46.62it/s]\n" ] }, { "data": { "text/html": [ "\n", "<div id='iBMZF960UXUNLUXJFVKM5'>\n", "<div style='color: #900; text-align: center;'>\n", " <b>Visualization omitted, Javascript library not loaded!</b><br>\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security.\n", "</div></div>\n", " <script>\n", " if (window.IML) IML.ReactDom.render(\n", " IML.React.createElement(IML.AdditiveForceArrayVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.3256232550695577, \"link\": \"identity\", \"featureNames\": [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\"], \"explanations\": [{\"outValue\": 3.9981953520729085e-06, \"simIndex\": 13.0, \"features\": {\"0\": {\"effect\": 0.016683179333786435, \"value\": 5.8}, \"1\": {\"effect\": -0.009971786039295272, \"value\": 2.8}, \"2\": {\"effect\": -0.2643422553421758, \"value\": 5.1}, \"3\": {\"effect\": -0.06798839482652097, \"value\": 2.4}}}, {\"outValue\": 0.007136638525963301, \"simIndex\": 28.0, \"features\": {\"0\": {\"effect\": 0.006767997284734456, \"value\": 6.0}, \"1\": {\"effect\": -0.024191192052454014, \"value\": 2.2}, \"2\": {\"effect\": 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{\"effect\": -0.3107535313132994, \"value\": 4.4}, \"3\": {\"effect\": -0.00952510780369109, \"value\": 1.2}}}, {\"outValue\": 0.9926219791546896, \"simIndex\": 11.0, \"features\": {\"0\": {\"effect\": -0.03153868882352856, \"value\": 4.8}, \"1\": {\"effect\": 0.0027994210267502373, \"value\": 3.0}, \"2\": {\"effect\": 0.627068833921483, \"value\": 1.4}, \"3\": {\"effect\": 0.06866915796042716, \"value\": 0.3}}}, {\"outValue\": 0.9994082891029026, \"simIndex\": 4.0, \"features\": {\"0\": {\"effect\": -0.007009082570846592, \"value\": 5.4}, \"1\": {\"effect\": 0.017764845838633636, \"value\": 3.9}, \"2\": {\"effect\": 0.6401792006007343, \"value\": 1.3}, \"3\": {\"effect\": 0.022850070164823544, \"value\": 0.4}}}, {\"outValue\": 3.9981953520729085e-06, \"simIndex\": 30.0, \"features\": {\"0\": {\"effect\": 0.010237192138705914, \"value\": 5.6}, \"1\": {\"effect\": -0.007676812099377456, \"value\": 2.8}, \"2\": {\"effect\": -0.2875213860065451, \"value\": 4.9}, \"3\": {\"effect\": -0.04065825090698899, \"value\": 2.0}}}, {\"outValue\": 0.0006244427696139865, \"simIndex\": 19.0, \"features\": {\"0\": {\"effect\": 0.004209985913508102, \"value\": 5.6}, \"1\": {\"effect\": -0.002019129321466251, \"value\": 3.0}, \"2\": {\"effect\": -0.3109821262505876, \"value\": 4.5}, \"3\": {\"effect\": -0.016207542641397954, \"value\": 1.5}}}, {\"outValue\": 0.9834147672589125, \"simIndex\": 1.0, \"features\": {\"0\": {\"effect\": -0.056747693742183636, \"value\": 4.8}, \"1\": {\"effect\": 0.02414938359062352, \"value\": 3.4}, \"2\": {\"effect\": 0.5591453019413346, \"value\": 1.9}, \"3\": {\"effect\": 0.13124452039958034, \"value\": 0.2}}}, {\"outValue\": 0.9867784371162099, \"simIndex\": 7.0, \"features\": {\"0\": {\"effect\": -0.05158218231266304, \"value\": 4.4}, \"1\": {\"effect\": -4.1028668313847216e-05, \"value\": 2.9}, \"2\": {\"effect\": 0.6107426116121428, \"value\": 1.4}, \"3\": {\"effect\": 0.1020357814154863, \"value\": 0.2}}}, {\"outValue\": 7.629754676979905e-05, \"simIndex\": 20.0, \"features\": {\"0\": {\"effect\": 0.014135704138092148, \"value\": 6.2}, \"1\": {\"effect\": -0.005037236976130677, \"value\": 2.8}, \"2\": {\"effect\": -0.3070351705238493, \"value\": 4.8}, \"3\": {\"effect\": -0.027610254160900072, \"value\": 1.8}}}, {\"outValue\": 0.9985217175280204, \"simIndex\": 6.0, \"features\": {\"0\": {\"effect\": -0.017718517712236703, \"value\": 4.6}, \"1\": {\"effect\": 0.012306865057282068, \"value\": 3.6}, \"2\": {\"effect\": 0.6482136017698437, \"value\": 1.0}, \"3\": {\"effect\": 0.03009651334357366, \"value\": 0.2}}}, {\"outValue\": 0.9911242170979903, \"simIndex\": 2.0, \"features\": {\"0\": {\"effect\": -0.036655738367996016, \"value\": 5.1}, \"1\": {\"effect\": 0.03848306801515827, \"value\": 3.8}, \"2\": {\"effect\": 0.5771211776586336, \"value\": 1.9}, \"3\": {\"effect\": 0.08655245472263673, \"value\": 0.4}}}, {\"outValue\": 0.005064607909623875, \"simIndex\": 23.0, \"features\": {\"0\": {\"effect\": 0.010386183703014085, \"value\": 6.2}, \"1\": {\"effect\": -0.004706673489498603, \"value\": 2.9}, \"2\": {\"effect\": -0.309194785577462, \"value\": 4.3}, \"3\": {\"effect\": -0.017043371795987305, \"value\": 1.3}}}, {\"outValue\": 0.01609039452397726, \"simIndex\": 12.0, \"features\": {\"0\": {\"effect\": -0.053296423173691904, \"value\": 5.0}, \"1\": {\"effect\": -0.04914394713761405, \"value\": 2.3}, \"2\": {\"effect\": -0.19981018198551387, \"value\": 3.3}, \"3\": {\"effect\": -0.007282308248760583, \"value\": 1.0}}}, {\"outValue\": 0.9928764497465659, \"simIndex\": 9.0, \"features\": {\"0\": {\"effect\": -0.029873494392239464, \"value\": 5.0}, \"1\": {\"effect\": 0.01590512436842617, \"value\": 3.4}, \"2\": {\"effect\": 0.6157058077130951, \"value\": 1.6}, \"3\": {\"effect\": 0.06551575698772627, \"value\": 0.4}}}], \"plot_cmap\": \"RdBu\"}),\n", " document.getElementById('iBMZF960UXUNLUXJFVKM5')\n", " );\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "nn = MLPClassifier(solver='lbfgs', alpha=1e-1, hidden_layer_sizes=(5, 2), random_state=0)\n", "nn.fit(X_train, Y_train)\n", "print_accuracy(nn.predict)\n", "\n", "# explain all the predictions in the test set\n", "explainer = shap.KernelExplainer(nn.predict_proba, X_train)\n", "shap_values = explainer.shap_values(X_test)\n", "shap.force_plot(explainer.expected_value[0], shap_values[0], X_test)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

Benedicto/ML-Learning

Clustering_3_em-for-gmm_blank.ipynb

1

1610398

gpl-3.0

CDNoyes/EDL-Py

DAUQ.ipynb

1

1785590

gpl-3.0

eogasawara/mylibrary

R-Basics.ipynb

1

168874

{ "cells": [ { "cell_type": "markdown", "id": "5cc55905", "metadata": {}, "source": [ "### Part #1" ] }, { "cell_type": "markdown", "id": "fc97424a", "metadata": {}, "source": [ "#### Package Installation" ] }, { "cell_type": "code", "execution_count": 1, "id": "8927a6a3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Carregando pacotes exigidos: ISwR\n", "\n" ] } ], "source": [ "available <- require(ISwR)\n", "if (!available) \n", " install.packages(\"ISwR\")" ] }, { "cell_type": "markdown", "id": "7a3dd0ec", "metadata": {}, "source": [ "#### Package loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "f8604d02", "metadata": {}, "outputs": [], "source": [ "library(ISwR)" ] }, { "cell_type": "markdown", "id": "aeb1b67c", "metadata": {}, "source": [ "#### Variable definitions" ] }, { "cell_type": "code", "execution_count": 3, "id": "fc3ed669", "metadata": {}, "outputs": [], "source": [ "weight <- 60\n", "height = 1.75\n", "subject <- \"A\"\n", "healthy <- TRUE" ] }, { "cell_type": "markdown", "id": "395d84fa", "metadata": {}, "source": [ "#### Variable evaluation" ] }, { "cell_type": "code", "execution_count": 4, "id": "8edb34b2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "60" ], "text/latex": [ "60" ], "text/markdown": [ "60" ], "text/plain": [ "[1] 60" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight" ] }, { "cell_type": "markdown", "id": "37fa24ae", "metadata": {}, "source": [ "#### Variable type checking" ] }, { "cell_type": "code", "execution_count": 5, "id": "2cadd098", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "is.numeric(weight) # variable \n", "is.double(weight)\n", "is.integer(weight)\n", "is.character(subject)" ] }, { "cell_type": "markdown", "id": "d4169556", "metadata": {}, "source": [ "#### Variable conversion" ] }, { "cell_type": "code", "execution_count": 6, "id": "c02f573b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight <- as.integer(weight)\n", "is.integer(weight)" ] }, { "cell_type": "markdown", "id": "5ef47e7c", "metadata": {}, "source": [ "#### Formulas" ] }, { "cell_type": "code", "execution_count": 7, "id": "fd670e0a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "19.5918367346939" ], "text/latex": [ "19.5918367346939" ], "text/markdown": [ "19.5918367346939" ], "text/plain": [ "[1] 19.59184" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Body mass index (BMI)\n", "bmi <- weight/height^2 \n", "bmi " ] }, { "cell_type": "markdown", "id": "86608974", "metadata": {}, "source": [ "#### String formatting" ] }, { "cell_type": "code", "execution_count": 8, "id": "881fece6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"19.6\"\n" ] } ], "source": [ "message <- sprintf(\"%.1f\", bmi)\n", "print(message)" ] }, { "cell_type": "markdown", "id": "2a063640", "metadata": {}, "source": [ "#### Vector definition" ] }, { "cell_type": "code", "execution_count": 9, "id": "d10355f1", "metadata": {}, "outputs": [], "source": [ "weight <- c(60, 72, 57, 90, 95, 72) \n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "subject <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\")" ] }, { "cell_type": "markdown", "id": "44aba8e2", "metadata": {}, "source": [ "#### Vector evaluation" ] }, { "cell_type": "code", "execution_count": 10, "id": "0123107e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>60</li><li>72</li><li>57</li><li>90</li><li>95</li><li>72</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 60\n", "\\item 72\n", "\\item 57\n", "\\item 90\n", "\\item 95\n", "\\item 72\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 60\n", "2. 72\n", "3. 57\n", "4. 90\n", "5. 95\n", "6. 72\n", "\n", "\n" ], "text/plain": [ "[1] 60 72 57 90 95 72" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>1.75</li><li>1.8</li><li>1.65</li><li>1.9</li><li>1.74</li><li>1.91</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1.75\n", "\\item 1.8\n", "\\item 1.65\n", "\\item 1.9\n", "\\item 1.74\n", "\\item 1.91\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1.75\n", "2. 1.8\n", "3. 1.65\n", "4. 1.9\n", "5. 1.74\n", "6. 1.91\n", "\n", "\n" ], "text/plain": [ "[1] 1.75 1.80 1.65 1.90 1.74 1.91" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>'A'</li><li>'B'</li><li>'C'</li><li>'D'</li><li>'E'</li><li>'F'</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'A'\n", "\\item 'B'\n", "\\item 'C'\n", "\\item 'D'\n", "\\item 'E'\n", "\\item 'F'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'A'\n", "2. 'B'\n", "3. 'C'\n", "4. 'D'\n", "5. 'E'\n", "6. 'F'\n", "\n", "\n" ], "text/plain": [ "[1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight\n", "height\n", "subject" ] }, { "cell_type": "markdown", "id": "7a78ef70", "metadata": {}, "source": [ "#### Creating a vector with a particular size" ] }, { "cell_type": "code", "execution_count": 11, "id": "ae22d267", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 0\n", "3. 0\n", "4. 0\n", "5. 0\n", "6. 0\n", "7. 0\n", "8. 0\n", "9. 0\n", "10. 0\n", "\n", "\n" ], "text/plain": [ " [1] 0 0 0 0 0 0 0 0 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vec <- rep(0, 10)\n", "vec" ] }, { "cell_type": "markdown", "id": "8a070ae4", "metadata": {}, "source": [ "#### Vector length" ] }, { "cell_type": "code", "execution_count": 12, "id": "94630b33", "metadata": {}, "outputs": [ { "data": { "text/html": [ "6" ], "text/latex": [ "6" ], "text/markdown": [ "6" ], "text/plain": [ "[1] 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "length(weight)" ] }, { "cell_type": "markdown", "id": "0e7af626", "metadata": {}, "source": [ "#### Vector indexes" ] }, { "cell_type": "code", "execution_count": 13, "id": "0d60deec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "60" ], "text/latex": [ "60" ], "text/markdown": [ "60" ], "text/plain": [ "[1] 60" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "72" ], "text/latex": [ "72" ], "text/markdown": [ "72" ], "text/plain": [ "[1] 72" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight[1]\n", "weight[length(weight)]" ] }, { "cell_type": "markdown", "id": "8c5d0c66", "metadata": {}, "source": [ "#### for loop" ] }, { "cell_type": "code", "execution_count": 14, "id": "79f79f15", "metadata": {}, "outputs": [], "source": [ "bmi <- 0\n", "for (i in 1:length(weight)) {\n", " bmi[i] <- weight[i]/height[i]^2\n", "}" ] }, { "cell_type": "code", "execution_count": 15, "id": "c3c2b7b4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "6fc03098", "metadata": {}, "source": [ "#### while loop" ] }, { "cell_type": "code", "execution_count": 16, "id": "5559a80e", "metadata": {}, "outputs": [], "source": [ "i <- 1\n", "while (i <= length(weight)) {\n", " bmi[i] <- weight[i]/height[i]^2\n", " i <- i + 1\n", "}" ] }, { "cell_type": "code", "execution_count": 17, "id": "84c44c14", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "b23e3918", "metadata": {}, "source": [ "#### Removing variable" ] }, { "cell_type": "code", "execution_count": 18, "id": "676d0198", "metadata": {}, "outputs": [ { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rm(bmi)\n", "exists(\"bmi\")" ] }, { "cell_type": "markdown", "id": "f46a8ade", "metadata": {}, "source": [ "#### Manipulating vectors efficiently" ] }, { "cell_type": "code", "execution_count": 19, "id": "953969dd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- weight/height^2 \n", "bmi " ] }, { "cell_type": "markdown", "id": "198a6da0", "metadata": {}, "source": [ "#### Function definition" ] }, { "cell_type": "code", "execution_count": 20, "id": "d624f2af", "metadata": {}, "outputs": [], "source": [ "compute_bmi <- function(weight, height) {\n", " bmi <- weight/height^2 \n", " return(bmi)\n", "}" ] }, { "cell_type": "markdown", "id": "adfa9ada", "metadata": {}, "source": [ "#### Using with scalar" ] }, { "cell_type": "code", "execution_count": 21, "id": "bd1d84a0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "19.5918367346939" ], "text/latex": [ "19.5918367346939" ], "text/markdown": [ "19.5918367346939" ], "text/plain": [ "[1] 19.59184" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- compute_bmi(60, 1.75)\n", "bmi" ] }, { "cell_type": "markdown", "id": "b262df12", "metadata": {}, "source": [ "#### Using with vectors" ] }, { "cell_type": "code", "execution_count": 22, "id": "432333e7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- compute_bmi(weight, height)\n", "bmi" ] }, { "cell_type": "markdown", "id": "87013373", "metadata": {}, "source": [ "#### Average function" ] }, { "cell_type": "code", "execution_count": 23, "id": "4fa56b2d", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " s <- 0\n", " n <- length(vec)\n", " for (x in vec) {\n", " s <- s + x \n", " }\n", " return(s/n)\n", "}" ] }, { "cell_type": "code", "execution_count": 24, "id": "d24c0640", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "c4b3d6a7", "metadata": {}, "source": [ "#### Average function: improved version" ] }, { "cell_type": "code", "execution_count": 25, "id": "05627c86", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " s <- sum(vec)\n", " n <- length(vec)\n", " return(s/n)\n", "}" ] }, { "cell_type": "code", "execution_count": 26, "id": "8988d664", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "0368003e", "metadata": {}, "source": [ "#### Average function: major statistical functions already exists" ] }, { "cell_type": "code", "execution_count": 27, "id": "885caab2", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " return(mean(vec))\n", "}" ] }, { "cell_type": "code", "execution_count": 28, "id": "439ffc1b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "07a46006", "metadata": {}, "source": [ "#### NA definition" ] }, { "cell_type": "code", "execution_count": 29, "id": "0f51d175", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>10</li><li>&lt;NA&gt;</li><li>13</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 10\n", "\\item <NA>\n", "\\item 13\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 10\n", "2. &lt;NA&gt;\n", "3. 13\n", "\n", "\n" ], "text/plain": [ "[1] 10 NA 13" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- c(10, NA, 13)\n", "x" ] }, { "cell_type": "markdown", "id": "f392d2fe", "metadata": {}, "source": [ "#### Evaluation with NA leads to NA" ] }, { "cell_type": "code", "execution_count": 30, "id": "ff804491", "metadata": {}, "outputs": [ { "data": { "text/html": [ "&lt;NA&gt;" ], "text/latex": [ "<NA>" ], "text/markdown": [ "&lt;NA&gt;" ], "text/plain": [ "[1] NA" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "average(x)" ] }, { "cell_type": "markdown", "id": "c5eac371", "metadata": {}, "source": [ "#### Addressing NA" ] }, { "cell_type": "code", "execution_count": 31, "id": "54dc268a", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " return(mean(vec, na.rm=TRUE))\n", "}" ] }, { "cell_type": "code", "execution_count": 32, "id": "504f55d3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "11.5" ], "text/latex": [ "11.5" ], "text/markdown": [ "11.5" ], "text/plain": [ "[1] 11.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "average(x)" ] }, { "cell_type": "markdown", "id": "8c192654", "metadata": {}, "source": [ "### Part #2" ] }, { "cell_type": "markdown", "id": "3a567ccc", "metadata": {}, "source": [ "#### Setup variables" ] }, { "cell_type": "code", "execution_count": 33, "id": "2c2adf10", "metadata": {}, "outputs": [], "source": [ "weight <- c(60, 72, 57, 90, 95, 72) \n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "subject <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\")" ] }, { "cell_type": "markdown", "id": "556af8d6", "metadata": {}, "source": [ "#### Basic plot" ] }, { "cell_type": "code", "execution_count": 34, "id": "9de7d55a", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\ngj1i/OZO9ogAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "#options(repr.plot.width=5, repr.plot.height=5)\n", "plot(height, weight)" ] }, { "cell_type": "markdown", "id": "27f31ad9", "metadata": {}, "source": [ "#### Changing default values" ] }, { "cell_type": "code", "execution_count": 35, "id": "0e114137", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nACFBgJAgQEgQICQIEBIECAkC/gMAWV/ScE8QGwAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ " plot(height, weight, pch=2)\n" ] }, { "cell_type": "markdown", "id": "c5386e1e", "metadata": {}, "source": [ "#### Discovering default values" ] }, { "cell_type": "code", "execution_count": 36, "id": "735d862f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre class=language-r><code>function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", "<span style=white-space:pre-wrap> log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, </span>\n", "<span style=white-space:pre-wrap> ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, </span>\n", "<span style=white-space:pre-wrap> panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, </span>\n", "<span style=white-space:pre-wrap> ...) </span>\n", "NULL</code></pre>" ], "text/latex": [ "\\begin{minted}{r}\n", "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL\n", "\\end{minted}" ], "text/markdown": [ "```r\n", "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL\n", "```" ], "text/plain": [ "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "args(plot.default)" ] }, { "cell_type": "code", "execution_count": 37, "id": "e824df5d", "metadata": {}, "outputs": [], "source": [ "?plot" ] }, { "cell_type": "markdown", "id": "733660a6", "metadata": {}, "source": [ "#### Adding a line" ] }, { "cell_type": "code", "execution_count": 38, "id": "30d2f639", "metadata": {}, "outputs": [ { "data": { "image/png": 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CsVcTsh/XYXKrgPDIeQfr4Dvd8DhkVI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\nCAkChAQBQoIAIUHAf9lCPtXZOcSjAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight)\n", "hh = c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)\n", "lines(hh, 22.5 * hh^2)" ] }, { "cell_type": "markdown", "id": "1783a939", "metadata": {}, "source": [ "#### Factors" ] }, { "cell_type": "code", "execution_count": 39, "id": "6055e3d2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>0</li><li>3</li><li>2</li><li>2</li><li>1</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'0'</li><li>'1'</li><li>'2'</li><li>'3'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 3\n", "\\item 2\n", "\\item 2\n", "\\item 1\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item '0'\n", "\\item '1'\n", "\\item '2'\n", "\\item '3'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 3\n", "3. 2\n", "4. 2\n", "5. 1\n", "\n", "\n", "\n", "**Levels**: 1. '0'\n", "2. '1'\n", "3. '2'\n", "4. '3'\n", "\n", "\n" ], "text/plain": [ "[1] 0 3 2 2 1\n", "Levels: 0 < 1 < 2 < 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pain <- c(0,3,2,2,1)\n", "fpain <- factor(pain,levels=0:3, ordered=TRUE)\n", "fpain" ] }, { "cell_type": "markdown", "id": "0dc0d349", "metadata": {}, "source": [ "#### Labeling categories" ] }, { "cell_type": "code", "execution_count": 40, "id": "0da23829", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>none</li><li>severe</li><li>medium</li><li>medium</li><li>mild</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'none'</li><li>'mild'</li><li>'medium'</li><li>'severe'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item none\n", "\\item severe\n", "\\item medium\n", "\\item medium\n", "\\item mild\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'none'\n", "\\item 'mild'\n", "\\item 'medium'\n", "\\item 'severe'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. none\n", "2. severe\n", "3. medium\n", "4. medium\n", "5. mild\n", "\n", "\n", "\n", "**Levels**: 1. 'none'\n", "2. 'mild'\n", "3. 'medium'\n", "4. 'severe'\n", "\n", "\n" ], "text/plain": [ "[1] none severe medium medium mild \n", "Levels: none < mild < medium < severe" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "levels(fpain) <- c(\"none\",\"mild\",\"medium\",\"severe\")\n", "fpain" ] }, { "cell_type": "markdown", "id": "1c70d97b", "metadata": {}, "source": [ "#### Convert double to factor" ] }, { "cell_type": "code", "execution_count": 41, "id": "fe40ecbd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>medium</li><li>medium</li><li>short</li><li>tall</li><li>medium</li><li>tall</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'medium'</li><li>'short'</li><li>'tall'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item medium\n", "\\item medium\n", "\\item short\n", "\\item tall\n", "\\item medium\n", "\\item tall\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'medium'\n", "\\item 'short'\n", "\\item 'tall'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. medium\n", "2. medium\n", "3. short\n", "4. tall\n", "5. medium\n", "6. tall\n", "\n", "\n", "\n", "**Levels**: 1. 'medium'\n", "2. 'short'\n", "3. 'tall'\n", "\n", "\n" ], "text/plain": [ "[1] medium medium short tall medium tall \n", "Levels: medium short tall" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lev <- rep(\"\", length(height))\n", "\n", "for (i in 1:length(height)) {\n", " if (height[i] < 1.7)\n", " lev[i] <- \"short\"\n", " else if (height[i] < 1.9)\n", " lev[i] <- \"medium\"\n", " else \n", " lev[i] <- \"tall\"\n", "}\n", "lev <- as.factor(lev)\n", "lev" ] }, { "cell_type": "markdown", "id": "157f7b04", "metadata": {}, "source": [ "#### Using cut function" ] }, { "cell_type": "code", "execution_count": 42, "id": "7de75ea3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>(1.7,1.9]</li><li>(1.7,1.9]</li><li>(0,1.7]</li><li>(1.7,1.9]</li><li>(1.7,1.9]</li><li>(1.9,1.8e+308]</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'(0,1.7]'</li><li>'(1.7,1.9]'</li><li>'(1.9,1.8e+308]'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item (1.7,1.9{]}\n", "\\item (1.7,1.9{]}\n", "\\item (0,1.7{]}\n", "\\item (1.7,1.9{]}\n", "\\item (1.7,1.9{]}\n", "\\item (1.9,1.8e+308{]}\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item '(0,1.7{]}'\n", "\\item '(1.7,1.9{]}'\n", "\\item '(1.9,1.8e+308{]}'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. (1.7,1.9]\n", "2. (1.7,1.9]\n", "3. (0,1.7]\n", "4. (1.7,1.9]\n", "5. (1.7,1.9]\n", "6. (1.9,1.8e+308]\n", "\n", "\n", "\n", "**Levels**: 1. '(0,1.7]'\n", "2. '(1.7,1.9]'\n", "3. '(1.9,1.8e+308]'\n", "\n", "\n" ], "text/plain": [ "[1] (1.7,1.9] (1.7,1.9] (0,1.7] (1.7,1.9] (1.7,1.9] \n", "[6] (1.9,1.8e+308]\n", "Levels: (0,1.7] < (1.7,1.9] < (1.9,1.8e+308]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>medium</li><li>medium</li><li>short</li><li>medium</li><li>medium</li><li>tall</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'short'</li><li>'medium'</li><li>'tall'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item medium\n", "\\item medium\n", "\\item short\n", "\\item medium\n", "\\item medium\n", "\\item tall\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'short'\n", "\\item 'medium'\n", "\\item 'tall'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. medium\n", "2. medium\n", "3. short\n", "4. medium\n", "5. medium\n", "6. tall\n", "\n", "\n", "\n", "**Levels**: 1. 'short'\n", "2. 'medium'\n", "3. 'tall'\n", "\n", "\n" ], "text/plain": [ "[1] medium medium short medium medium tall \n", "Levels: short < medium < tall" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lev <- cut(height, breaks=c(0, 1.7, 1.9, .Machine$double.xmax), ordered=TRUE)\n", "lev\n", "levels(lev) <- c(\"short\", \"medium\", \"tall\")\n", "lev" ] }, { "cell_type": "markdown", "id": "a644c9b3", "metadata": {}, "source": [ "#### Matrix" ] }, { "cell_type": "code", "execution_count": 43, "id": "f75bdd6e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>1</li><li>2</li><li>3</li><li>4</li><li>5</li><li>6</li><li>7</li><li>8</li><li>9</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 5\n", "\\item 6\n", "\\item 7\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "5. 5\n", "6. 6\n", "7. 7\n", "8. 8\n", "9. 9\n", "\n", "\n" ], "text/plain": [ "[1] 1 2 3 4 5 6 7 8 9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- 1:9\n", "x" ] }, { "cell_type": "markdown", "id": "512b1d1f", "metadata": {}, "source": [ "#### Vector to matrix" ] }, { "cell_type": "code", "execution_count": 44, "id": "460711dd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>4</td><td>7</td></tr>\n", "\t<tr><td>2</td><td>5</td><td>8</td></tr>\n", "\t<tr><td>3</td><td>6</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 4 & 7\\\\\n", "\t 2 & 5 & 8\\\\\n", "\t 3 & 6 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "| 1 | 4 | 7 |\n", "| 2 | 5 | 8 |\n", "| 3 | 6 | 9 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 4 7 \n", "[2,] 2 5 8 \n", "[3,] 3 6 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(x) <- c(3,3)\n", "x" ] }, { "cell_type": "code", "execution_count": 45, "id": "ef59965a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>4</td><td>7</td></tr>\n", "\t<tr><td>2</td><td>5</td><td>8</td></tr>\n", "\t<tr><td>3</td><td>6</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 4 & 7\\\\\n", "\t 2 & 5 & 8\\\\\n", "\t 3 & 6 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "| 1 | 4 | 7 |\n", "| 2 | 5 | 8 |\n", "| 3 | 6 | 9 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 4 7 \n", "[2,] 2 5 8 \n", "[3,] 3 6 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 46, "id": "779a356f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 1\n", "[1] 4\n", "[1] 7\n", "[1] 2\n", "[1] 5\n", "[1] 8\n", "[1] 3\n", "[1] 6\n", "[1] 9\n" ] } ], "source": [ "for (i in 1:nrow(x)) \n", " for (j in 1:ncol(x))\n", " print(x[i,j])" ] }, { "cell_type": "code", "execution_count": 47, "id": "26f92d3f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type dbl</caption>\n", "<tbody>\n", "\t<tr><td>3</td><td>12</td><td>21</td></tr>\n", "\t<tr><td>6</td><td>15</td><td>24</td></tr>\n", "\t<tr><td>9</td><td>18</td><td>27</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "| 3 | 12 | 21 |\n", "| 6 | 15 | 24 |\n", "| 9 | 18 | 27 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- x\n", "for (i in 1:nrow(y)) \n", " for (j in 1:ncol(y))\n", " y[i,j] <- 3 * y[i, j]\n", "\n", "y" ] }, { "cell_type": "markdown", "id": "61cdc0f1", "metadata": {}, "source": [ "#### Multiplying matrix by 3" ] }, { "cell_type": "code", "execution_count": 48, "id": "22093767", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type dbl</caption>\n", "<tbody>\n", "\t<tr><td>3</td><td>12</td><td>21</td></tr>\n", "\t<tr><td>6</td><td>15</td><td>24</td></tr>\n", "\t<tr><td>9</td><td>18</td><td>27</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "| 3 | 12 | 21 |\n", "| 6 | 15 | 24 |\n", "| 9 | 18 | 27 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- x\n", "for (i in 1:nrow(y)) \n", " for (j in 1:ncol(y))\n", " y[i,j] <- 3 * y[i, j]\n", "\n", "y\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "5c1b1f34", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type dbl</caption>\n", "<tbody>\n", "\t<tr><td>3</td><td>12</td><td>21</td></tr>\n", "\t<tr><td>6</td><td>15</td><td>24</td></tr>\n", "\t<tr><td>9</td><td>18</td><td>27</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "| 3 | 12 | 21 |\n", "| 6 | 15 | 24 |\n", "| 9 | 18 | 27 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- 3*x\n", "y" ] }, { "cell_type": "markdown", "id": "256cacb7", "metadata": {}, "source": [ "#### Vector to matrix by row" ] }, { "cell_type": "code", "execution_count": 50, "id": "a1e4d6c1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>2</td><td>3</td></tr>\n", "\t<tr><td>4</td><td>5</td><td>6</td></tr>\n", "\t<tr><td>7</td><td>8</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 2 & 3\\\\\n", "\t 4 & 5 & 6\\\\\n", "\t 7 & 8 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "| 1 | 2 | 3 |\n", "| 4 | 5 | 6 |\n", "| 7 | 8 | 9 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 2 3 \n", "[2,] 4 5 6 \n", "[3,] 7 8 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- matrix(1:9,nrow=3,byrow=T)\n", "x" ] }, { "cell_type": "markdown", "id": "398194aa", "metadata": {}, "source": [ "#### Transpose matrix" ] }, { "cell_type": "code", "execution_count": 51, "id": "a88b10f7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>4</td><td>7</td></tr>\n", "\t<tr><td>2</td><td>5</td><td>8</td></tr>\n", "\t<tr><td>3</td><td>6</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 4 & 7\\\\\n", "\t 2 & 5 & 8\\\\\n", "\t 3 & 6 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "| 1 | 4 | 7 |\n", "| 2 | 5 | 8 |\n", "| 3 | 6 | 9 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 4 7 \n", "[2,] 2 5 8 \n", "[3,] 3 6 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- t(x)\n", "x" ] }, { "cell_type": "markdown", "id": "f1d054d5", "metadata": {}, "source": [ "#### Determinant of a matrix" ] }, { "cell_type": "code", "execution_count": 52, "id": "72eaf8ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "0" ], "text/latex": [ "0" ], "text/markdown": [ "0" ], "text/plain": [ "[1] 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "det(x)" ] }, { "cell_type": "code", "execution_count": 53, "id": "4949ab28", "metadata": {}, "outputs": [], "source": [ "?det" ] }, { "cell_type": "code", "execution_count": 54, "id": "111d0475", "metadata": {}, "outputs": [], "source": [ "a <- c(5260,5470,5640,6180,6390,6515,6805,7515,7515,8230,8770)\n", "b <- c(3910,4220,3885,5160,5645,4680,5265,5975,6790,6900,7335)" ] }, { "cell_type": "markdown", "id": "5a715620", "metadata": {}, "source": [ "#### Creating a list" ] }, { "cell_type": "code", "execution_count": 55, "id": "7e94d0a3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n", "</li>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>3910</li><li>4220</li><li>3885</li><li>5160</li><li>5645</li><li>4680</li><li>5265</li><li>5975</li><li>6790</li><li>6900</li><li>7335</li></ol>\n", "</li>\n", "\t<li>0</li>\n", "\t<li>'a'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item \\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n", "\n", "\\item \\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate*}\n", "\n", "\\item 0\n", "\\item 'a'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n", "\n", "2. 1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5160\n", "5. 5645\n", "6. 4680\n", "7. 5265\n", "8. 5975\n", "9. 6790\n", "10. 6900\n", "11. 7335\n", "\n", "\n", "\n", "3. 0\n", "4. 'a'\n", "\n", "\n" ], "text/plain": [ "[[1]]\n", " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770\n", "\n", "[[2]]\n", " [1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335\n", "\n", "[[3]]\n", "[1] 0\n", "\n", "[[4]]\n", "[1] \"a\"\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mybag <- list(a, b, 0, \"a\")\n", "mybag\n" ] }, { "cell_type": "markdown", "id": "b294dfee", "metadata": {}, "source": [ "#### Adding an element in a list" ] }, { "cell_type": "code", "execution_count": 56, "id": "0798d25c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n", "</li>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>3910</li><li>4220</li><li>3885</li><li>5160</li><li>5645</li><li>4680</li><li>5265</li><li>5975</li><li>6790</li><li>6900</li><li>7335</li></ol>\n", "</li>\n", "\t<li>0</li>\n", "\t<li>'a'</li>\n", "\t<li>'b'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item \\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n", "\n", "\\item \\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate*}\n", "\n", "\\item 0\n", "\\item 'a'\n", "\\item 'b'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n", "\n", "2. 1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5160\n", "5. 5645\n", "6. 4680\n", "7. 5265\n", "8. 5975\n", "9. 6790\n", "10. 6900\n", "11. 7335\n", "\n", "\n", "\n", "3. 0\n", "4. 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'b'\n", "\n", "\n" ], "text/plain": [ "[[1]]\n", " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770\n", "\n", "[[2]]\n", " [1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335\n", "\n", "[[3]]\n", "[1] 0\n", "\n", "[[4]]\n", "[1] \"a\"\n", "\n", "[[5]]\n", "[1] \"b\"\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n <- length(mybag)\n", "mybag[[n+1]] <- \"b\"\n", "mybag" ] }, { "cell_type": "markdown", "id": "8a5a3e20", "metadata": {}, "source": [ "#### Slicing a list" ] }, { "cell_type": "code", "execution_count": 57, "id": "5563384b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n", "</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item \\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n", "\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "[[1]]\n", " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "slice <- mybag[1]\n", "slice\n", "is.list(slice)" ] }, { "cell_type": "code", "execution_count": 58, "id": "c0f66d9b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; 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margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n" ], "text/plain": [ " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "h <- mybag[[1]]\n", "h" ] }, { "cell_type": "code", "execution_count": 61, "id": "011f4009", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "is.vector(h)\n", "is.list(h)" ] }, { "cell_type": "markdown", "id": "af61dbb9", "metadata": {}, "source": [ "#### Adding, accessing, and removing elements" ] }, { "cell_type": "code", "execution_count": 62, "id": "f49bc2ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<dl>\n", "\t<dt>$x</dt>\n", "\t\t<dd><style>\n", ".list-inline {list-style: none; 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| B &lt;dbl&gt; |\n", "|---|---|---|\n", "| 1 | 5260 | 3910 |\n", "| 2 | 5470 | 4220 |\n", "| 3 | 5640 | 3885 |\n", "| 4 | 6180 | 5160 |\n", "| 5 | 6390 | 5645 |\n", "| 6 | 6515 | 4680 |\n", "\n" ], "text/plain": [ " A B \n", "1 5260 3910\n", "2 5470 4220\n", "3 5640 3885\n", "4 6180 5160\n", "5 6390 5645\n", "6 6515 4680" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d <- data.frame(A=a, B=b)\n", "head(d)" ] }, { "cell_type": "code", "execution_count": 66, "id": "0022326a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th><th scope=col>C</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>5260</td><td>3910</td><td> 9170</td></tr>\n", "\t<tr><th scope=row>2</th><td>5470</td><td>4220</td><td> 9690</td></tr>\n", "\t<tr><th scope=row>3</th><td>5640</td><td>3885</td><td> 9525</td></tr>\n", "\t<tr><th scope=row>4</th><td>6180</td><td>5160</td><td>11340</td></tr>\n", "\t<tr><th scope=row>5</th><td>6390</td><td>5645</td><td>12035</td></tr>\n", "\t<tr><th scope=row>6</th><td>6515</td><td>4680</td><td>11195</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & A & B & C\\\\\n", " & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 5260 & 3910 & 9170\\\\\n", "\t2 & 5470 & 4220 & 9690\\\\\n", "\t3 & 5640 & 3885 & 9525\\\\\n", "\t4 & 6180 & 5160 & 11340\\\\\n", "\t5 & 6390 & 5645 & 12035\\\\\n", "\t6 & 6515 & 4680 & 11195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| <!--/--> | A &lt;dbl&gt; | B &lt;dbl&gt; | C &lt;dbl&gt; |\n", "|---|---|---|---|\n", "| 1 | 5260 | 3910 | 9170 |\n", "| 2 | 5470 | 4220 | 9690 |\n", "| 3 | 5640 | 3885 | 9525 |\n", "| 4 | 6180 | 5160 | 11340 |\n", "| 5 | 6390 | 5645 | 12035 |\n", "| 6 | 6515 | 4680 | 11195 |\n", "\n" ], "text/plain": [ " A B C \n", "1 5260 3910 9170\n", "2 5470 4220 9690\n", "3 5640 3885 9525\n", "4 6180 5160 11340\n", "5 6390 5645 12035\n", "6 6515 4680 11195" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$C <- d$A + d$B\n", "head(d)" ] }, { "cell_type": "code", "execution_count": 67, "id": "98204539", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>B</th><th scope=col>C</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>3910</td><td> 9170</td></tr>\n", "\t<tr><th scope=row>2</th><td>4220</td><td> 9690</td></tr>\n", "\t<tr><th scope=row>3</th><td>3885</td><td> 9525</td></tr>\n", "\t<tr><th scope=row>4</th><td>5160</td><td>11340</td></tr>\n", "\t<tr><th scope=row>5</th><td>5645</td><td>12035</td></tr>\n", "\t<tr><th scope=row>6</th><td>4680</td><td>11195</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & B & C\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 3910 & 9170\\\\\n", "\t2 & 4220 & 9690\\\\\n", "\t3 & 3885 & 9525\\\\\n", "\t4 & 5160 & 11340\\\\\n", "\t5 & 5645 & 12035\\\\\n", "\t6 & 4680 & 11195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| <!--/--> | B &lt;dbl&gt; | C &lt;dbl&gt; |\n", "|---|---|---|\n", "| 1 | 3910 | 9170 |\n", "| 2 | 4220 | 9690 |\n", "| 3 | 3885 | 9525 |\n", "| 4 | 5160 | 11340 |\n", "| 5 | 5645 | 12035 |\n", "| 6 | 4680 | 11195 |\n", "\n" ], "text/plain": [ " B C \n", "1 3910 9170\n", "2 4220 9690\n", "3 3885 9525\n", "4 5160 11340\n", "5 5645 12035\n", "6 4680 11195" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$A <- NULL\n", "head(d)" ] }, { "cell_type": "code", "execution_count": 68, "id": "a4de7de1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 14</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>X1</th><th scope=col>X14.23</th><th scope=col>X1.71</th><th scope=col>X2.43</th><th scope=col>X15.6</th><th scope=col>X127</th><th scope=col>X2.8</th><th scope=col>X3.06</th><th scope=col>X.28</th><th scope=col>X2.29</th><th scope=col>X5.64</th><th scope=col>X1.04</th><th scope=col>X3.92</th><th scope=col>X1065</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>1</td><td>13.20</td><td>1.78</td><td>2.14</td><td>11.2</td><td>100</td><td>2.65</td><td>2.76</td><td>0.26</td><td>1.28</td><td>4.38</td><td>1.05</td><td>3.40</td><td>1050</td></tr>\n", "\t<tr><th scope=row>2</th><td>1</td><td>13.16</td><td>2.36</td><td>2.67</td><td>18.6</td><td>101</td><td>2.80</td><td>3.24</td><td>0.30</td><td>2.81</td><td>5.68</td><td>1.03</td><td>3.17</td><td>1185</td></tr>\n", "\t<tr><th scope=row>3</th><td>1</td><td>14.37</td><td>1.95</td><td>2.50</td><td>16.8</td><td>113</td><td>3.85</td><td>3.49</td><td>0.24</td><td>2.18</td><td>7.80</td><td>0.86</td><td>3.45</td><td>1480</td></tr>\n", "\t<tr><th scope=row>4</th><td>1</td><td>13.24</td><td>2.59</td><td>2.87</td><td>21.0</td><td>118</td><td>2.80</td><td>2.69</td><td>0.39</td><td>1.82</td><td>4.32</td><td>1.04</td><td>2.93</td><td> 735</td></tr>\n", "\t<tr><th scope=row>5</th><td>1</td><td>14.20</td><td>1.76</td><td>2.45</td><td>15.2</td><td>112</td><td>3.27</td><td>3.39</td><td>0.34</td><td>1.97</td><td>6.75</td><td>1.05</td><td>2.85</td><td>1450</td></tr>\n", "\t<tr><th scope=row>6</th><td>1</td><td>14.39</td><td>1.87</td><td>2.45</td><td>14.6</td><td> 96</td><td>2.50</td><td>2.52</td><td>0.30</td><td>1.98</td><td>5.25</td><td>1.02</td><td>3.58</td><td>1290</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 14\n", "\\begin{tabular}{r|llllllllllllll}\n", " & X1 & X14.23 & X1.71 & X2.43 & X15.6 & X127 & X2.8 & X3.06 & X.28 & X2.29 & X5.64 & X1.04 & X3.92 & X1065\\\\\n", " & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n", "\\hline\n", "\t1 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n", "\t2 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n", "\t3 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n", "\t4 & 1 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n", "\t5 & 1 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n", "\t6 & 1 & 14.39 & 1.87 & 2.45 & 14.6 & 96 & 2.50 & 2.52 & 0.30 & 1.98 & 5.25 & 1.02 & 3.58 & 1290\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 14\n", "\n", "| <!--/--> | X1 &lt;int&gt; | X14.23 &lt;dbl&gt; | X1.71 &lt;dbl&gt; | X2.43 &lt;dbl&gt; | X15.6 &lt;dbl&gt; | X127 &lt;int&gt; | X2.8 &lt;dbl&gt; | X3.06 &lt;dbl&gt; | X.28 &lt;dbl&gt; | X2.29 &lt;dbl&gt; | X5.64 &lt;dbl&gt; | X1.04 &lt;dbl&gt; | X3.92 &lt;dbl&gt; | X1065 &lt;int&gt; |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n", "| 2 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n", "| 3 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n", "| 4 | 1 | 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 |\n", "| 5 | 1 | 14.20 | 1.76 | 2.45 | 15.2 | 112 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450 |\n", "| 6 | 1 | 14.39 | 1.87 | 2.45 | 14.6 | 96 | 2.50 | 2.52 | 0.30 | 1.98 | 5.25 | 1.02 | 3.58 | 1290 |\n", "\n" ], "text/plain": [ " X1 X14.23 X1.71 X2.43 X15.6 X127 X2.8 X3.06 X.28 X2.29 X5.64 X1.04 X3.92\n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n", "4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 \n", "5 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 \n", "6 1 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 1.98 5.25 1.02 3.58 \n", " X1065\n", "1 1050 \n", "2 1185 \n", "3 1480 \n", "4 735 \n", "5 1450 \n", "6 1290 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wine = read.table(\n", " \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", \n", " header = TRUE, sep = \",\")\n", "head(wine)" ] }, { "cell_type": "markdown", "id": "04982498", "metadata": {}, "source": [ "#### Saving RData file" ] }, { "cell_type": "code", "execution_count": 69, "id": "32bcaaf6", "metadata": {}, "outputs": [], "source": [ "# in Linux the default is not to compress\n", "save(wine, file=\"wine.RData\", compress=TRUE)" ] }, { "cell_type": "code", "execution_count": 70, "id": "27620e9d", "metadata": {}, "outputs": [], "source": [ "rm(wine)" ] }, { "cell_type": "markdown", "id": "4eb4e2c3", "metadata": {}, "source": [ "#### Load RData file" ] }, { "cell_type": "code", "execution_count": 71, "id": "751207ba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 3 × 14</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>X1</th><th scope=col>X14.23</th><th scope=col>X1.71</th><th scope=col>X2.43</th><th scope=col>X15.6</th><th scope=col>X127</th><th scope=col>X2.8</th><th scope=col>X3.06</th><th scope=col>X.28</th><th scope=col>X2.29</th><th scope=col>X5.64</th><th scope=col>X1.04</th><th scope=col>X3.92</th><th scope=col>X1065</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>1</td><td>13.20</td><td>1.78</td><td>2.14</td><td>11.2</td><td>100</td><td>2.65</td><td>2.76</td><td>0.26</td><td>1.28</td><td>4.38</td><td>1.05</td><td>3.40</td><td>1050</td></tr>\n", "\t<tr><th scope=row>2</th><td>1</td><td>13.16</td><td>2.36</td><td>2.67</td><td>18.6</td><td>101</td><td>2.80</td><td>3.24</td><td>0.30</td><td>2.81</td><td>5.68</td><td>1.03</td><td>3.17</td><td>1185</td></tr>\n", "\t<tr><th scope=row>3</th><td>1</td><td>14.37</td><td>1.95</td><td>2.50</td><td>16.8</td><td>113</td><td>3.85</td><td>3.49</td><td>0.24</td><td>2.18</td><td>7.80</td><td>0.86</td><td>3.45</td><td>1480</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 3 × 14\n", "\\begin{tabular}{r|llllllllllllll}\n", " & X1 & X14.23 & X1.71 & X2.43 & X15.6 & X127 & X2.8 & X3.06 & X.28 & X2.29 & X5.64 & X1.04 & X3.92 & X1065\\\\\n", " & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n", "\\hline\n", "\t1 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n", "\t2 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n", "\t3 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 14\n", "\n", "| <!--/--> | X1 &lt;int&gt; | X14.23 &lt;dbl&gt; | X1.71 &lt;dbl&gt; | X2.43 &lt;dbl&gt; | X15.6 &lt;dbl&gt; | X127 &lt;int&gt; | X2.8 &lt;dbl&gt; | X3.06 &lt;dbl&gt; | X.28 &lt;dbl&gt; | X2.29 &lt;dbl&gt; | X5.64 &lt;dbl&gt; | X1.04 &lt;dbl&gt; | X3.92 &lt;dbl&gt; | X1065 &lt;int&gt; |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n", "| 2 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n", "| 3 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n", "\n" ], "text/plain": [ " X1 X14.23 X1.71 X2.43 X15.6 X127 X2.8 X3.06 X.28 X2.29 X5.64 X1.04 X3.92\n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n", " X1065\n", "1 1050 \n", "2 1185 \n", "3 1480 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "load(\"wine.RData\")\n", "head(wine, 3)" ] }, { "cell_type": "markdown", "id": "a49a5e9a", "metadata": {}, "source": [ "#### Export data.frame into csv file" ] }, { "cell_type": "code", "execution_count": 72, "id": "c3ec941b", "metadata": {}, "outputs": [], "source": [ "write.table(wine, file=\"wine.csv\", row.names=FALSE, quote = FALSE, sep = \",\")" ] }, { "cell_type": "markdown", "id": "8544c102", "metadata": {}, "source": [ "#### Filtering" ] }, { "cell_type": "code", "execution_count": 73, "id": "30bc2e64", "metadata": {}, "outputs": [], "source": [ "a <- c(5260,5470,5640,6180,6390,6515,6805,7515,7515,8230,8770)\n", "b <- c(3910,4220,3885,5160,5645,4680,5265,5975,6790,6900,7335)" ] }, { "cell_type": "code", "execution_count": 74, "id": "0348646f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item TRUE\n", "\\item TRUE\n", "\\item TRUE\n", "\\item TRUE\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. FALSE\n", "2. FALSE\n", "3. FALSE\n", "4. FALSE\n", "5. FALSE\n", "6. FALSE\n", "7. FALSE\n", "8. TRUE\n", "9. TRUE\n", "10. TRUE\n", "11. TRUE\n", "\n", "\n" ], "text/plain": [ " [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# logical vector\n", "i <- (a > 7000)\n", "i" ] }, { "cell_type": "code", "execution_count": 75, "id": "53887e35", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 7515\n", "2. 7515\n", "3. 8230\n", "4. 8770\n", "\n", "\n" ], "text/plain": [ "[1] 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a[i]" ] }, { "cell_type": "code", "execution_count": 76, "id": "ccff301a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>3910</li><li>4220</li><li>3885</li><li>5975</li><li>6790</li><li>6900</li><li>7335</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5975\n", "5. 6790\n", "6. 6900\n", "7. 7335\n", "\n", "\n" ], "text/plain": [ "[1] 3910 4220 3885 5975 6790 6900 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b[a < 6000 | a > 7000]" ] }, { "cell_type": "code", "execution_count": 77, "id": "c7d81c60", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5160</li><li>5645</li><li>4680</li><li>5265</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5160\n", "2. 5645\n", "3. 4680\n", "4. 5265\n", "\n", "\n" ], "text/plain": [ "[1] 5160 5645 4680 5265" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b[6000 <= a & a <= 7000]" ] }, { "cell_type": "markdown", "id": "837a9fa4", "metadata": {}, "source": [ "#### Filtering data frames" ] }, { "cell_type": "code", "execution_count": 78, "id": "c16b77ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 11 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th><th scope=col>c</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>5260</td><td>3910</td><td>1350</td></tr>\n", "\t<tr><th scope=row>2</th><td>5470</td><td>4220</td><td>1250</td></tr>\n", "\t<tr><th scope=row>3</th><td>5640</td><td>3885</td><td>1755</td></tr>\n", "\t<tr><th scope=row>4</th><td>6180</td><td>5160</td><td>1020</td></tr>\n", "\t<tr><th scope=row>5</th><td>6390</td><td>5645</td><td> 745</td></tr>\n", "\t<tr><th scope=row>6</th><td>6515</td><td>4680</td><td>1835</td></tr>\n", "\t<tr><th scope=row>7</th><td>6805</td><td>5265</td><td>1540</td></tr>\n", "\t<tr><th scope=row>8</th><td>7515</td><td>5975</td><td>1540</td></tr>\n", "\t<tr><th scope=row>9</th><td>7515</td><td>6790</td><td> 725</td></tr>\n", "\t<tr><th scope=row>10</th><td>8230</td><td>6900</td><td>1330</td></tr>\n", "\t<tr><th scope=row>11</th><td>8770</td><td>7335</td><td>1435</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 11 × 3\n", "\\begin{tabular}{r|lll}\n", " & a & b & c\\\\\n", " & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 5260 & 3910 & 1350\\\\\n", "\t2 & 5470 & 4220 & 1250\\\\\n", "\t3 & 5640 & 3885 & 1755\\\\\n", "\t4 & 6180 & 5160 & 1020\\\\\n", "\t5 & 6390 & 5645 & 745\\\\\n", "\t6 & 6515 & 4680 & 1835\\\\\n", "\t7 & 6805 & 5265 & 1540\\\\\n", "\t8 & 7515 & 5975 & 1540\\\\\n", "\t9 & 7515 & 6790 & 725\\\\\n", "\t10 & 8230 & 6900 & 1330\\\\\n", "\t11 & 8770 & 7335 & 1435\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 11 × 3\n", "\n", "| <!--/--> | a &lt;dbl&gt; | b &lt;dbl&gt; | c &lt;dbl&gt; |\n", "|---|---|---|---|\n", "| 1 | 5260 | 3910 | 1350 |\n", "| 2 | 5470 | 4220 | 1250 |\n", "| 3 | 5640 | 3885 | 1755 |\n", "| 4 | 6180 | 5160 | 1020 |\n", "| 5 | 6390 | 5645 | 745 |\n", "| 6 | 6515 | 4680 | 1835 |\n", "| 7 | 6805 | 5265 | 1540 |\n", "| 8 | 7515 | 5975 | 1540 |\n", "| 9 | 7515 | 6790 | 725 |\n", "| 10 | 8230 | 6900 | 1330 |\n", "| 11 | 8770 | 7335 | 1435 |\n", "\n" ], "text/plain": [ " a b c \n", "1 5260 3910 1350\n", "2 5470 4220 1250\n", "3 5640 3885 1755\n", "4 6180 5160 1020\n", "5 6390 5645 745\n", "6 6515 4680 1835\n", "7 6805 5265 1540\n", "8 7515 5975 1540\n", "9 7515 6790 725\n", "10 8230 6900 1330\n", "11 8770 7335 1435" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data <- data.frame(a=a, b=b)\n", "data$c <- data$a - data$b\n", "head(data, nrow(data))" ] }, { "cell_type": "code", "execution_count": 79, "id": "90784f29", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 4 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th><th scope=col>c</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>8</th><td>7515</td><td>5975</td><td>1540</td></tr>\n", "\t<tr><th scope=row>9</th><td>7515</td><td>6790</td><td> 725</td></tr>\n", "\t<tr><th scope=row>10</th><td>8230</td><td>6900</td><td>1330</td></tr>\n", "\t<tr><th scope=row>11</th><td>8770</td><td>7335</td><td>1435</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 4 × 3\n", "\\begin{tabular}{r|lll}\n", " & a & b & c\\\\\n", " & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t8 & 7515 & 5975 & 1540\\\\\n", "\t9 & 7515 & 6790 & 725\\\\\n", "\t10 & 8230 & 6900 & 1330\\\\\n", "\t11 & 8770 & 7335 & 1435\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 3\n", "\n", "| <!--/--> | a &lt;dbl&gt; | b &lt;dbl&gt; | c &lt;dbl&gt; |\n", "|---|---|---|---|\n", "| 8 | 7515 | 5975 | 1540 |\n", "| 9 | 7515 | 6790 | 725 |\n", "| 10 | 8230 | 6900 | 1330 |\n", "| 11 | 8770 | 7335 | 1435 |\n", "\n" ], "text/plain": [ " a b c \n", "8 7515 5975 1540\n", "9 7515 6790 725\n", "10 8230 6900 1330\n", "11 8770 7335 1435" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data[data$a > 7000,])" ] }, { "cell_type": "code", "execution_count": 80, "id": "00a2e7db", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 4 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>8</th><td>7515</td><td>5975</td></tr>\n", "\t<tr><th scope=row>9</th><td>7515</td><td>6790</td></tr>\n", "\t<tr><th scope=row>10</th><td>8230</td><td>6900</td></tr>\n", "\t<tr><th scope=row>11</th><td>8770</td><td>7335</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 4 × 2\n", "\\begin{tabular}{r|ll}\n", " & a & b\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t8 & 7515 & 5975\\\\\n", "\t9 & 7515 & 6790\\\\\n", "\t10 & 8230 & 6900\\\\\n", "\t11 & 8770 & 7335\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 2\n", "\n", "| <!--/--> | a &lt;dbl&gt; | b &lt;dbl&gt; |\n", "|---|---|---|\n", "| 8 | 7515 | 5975 |\n", "| 9 | 7515 | 6790 |\n", "| 10 | 8230 | 6900 |\n", "| 11 | 8770 | 7335 |\n", "\n" ], "text/plain": [ " a b \n", "8 7515 5975\n", "9 7515 6790\n", "10 8230 6900\n", "11 8770 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data[data$a > 7000,c(1,2)])" ] }, { "cell_type": "markdown", "id": "c7395061", "metadata": {}, "source": [ "#### Benchmarking matrix and data.frames" ] }, { "cell_type": "code", "execution_count": 86, "id": "770a73bb", "metadata": {}, "outputs": [], "source": [ "lib <- require(pryr)\n", "if (!lib)\n", " install.packages(\"pryr\")\n", "library(pryr)" ] }, { "cell_type": "code", "execution_count": 87, "id": "30cfc53f", "metadata": {}, "outputs": [], "source": [ "rheight <- rnorm(100000, 1.8, sd=0.2)\n", "rweight <- rnorm(100000, 72, sd=15)" ] }, { "cell_type": "markdown", "id": "07603ab8", "metadata": {}, "source": [ "#### Computing an entire column at once" ] }, { "cell_type": "code", "execution_count": 90, "id": "8ef76230", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 0.01570988 secs" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "2,400,984 B" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "hw$bmi <- hw$weight/hw$height^2\n", "end_time <- Sys.time()\n", "end_time - start_time\n", "object_size(hw)" ] }, { "cell_type": "markdown", "id": "1880db1e", "metadata": {}, "source": [ "#### Processing element by element" ] }, { "cell_type": "code", "execution_count": 89, "id": "955566ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 11.97988 secs" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "for (i in 1:nrow(hw)) {\n", " hw$bmi[i] <- hw$weight[i]/hw$height[i]^2\n", "}\n", "end_time <- Sys.time()\n", "end_time - start_time" ] }, { "cell_type": "markdown", "id": "edb7a9d2", "metadata": {}, "source": [ "#### Convert the entire data.frame" ] }, { "cell_type": "code", "execution_count": 92, "id": "2aeb83e4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 0.2502141 secs" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "hw <- as.matrix(hw)\n", "hw <- cbind(hw, 0)\n", "for (i in 1:nrow(hw)) {\n", " hw[i,3] <- hw[i,2]/hw[i,1]^2\n", "}\n", "end_time <- Sys.time()\n", "end_time - start_time" ] }, { "cell_type": "markdown", "id": "9236cc80", "metadata": {}, "source": [ "#### apply family" ] }, { "cell_type": "code", "execution_count": 93, "id": "522b62cf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>blood.glucose</th><th scope=col>short.velocity</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>15.3</td><td>1.76</td></tr>\n", "\t<tr><th scope=row>2</th><td>10.8</td><td>1.34</td></tr>\n", "\t<tr><th scope=row>3</th><td> 8.1</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>4</th><td>19.5</td><td>1.47</td></tr>\n", "\t<tr><th scope=row>5</th><td> 7.2</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>6</th><td> 5.3</td><td>1.49</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 15.3 & 1.76\\\\\n", "\t2 & 10.8 & 1.34\\\\\n", "\t3 & 8.1 & 1.27\\\\\n", "\t4 & 19.5 & 1.47\\\\\n", "\t5 & 7.2 & 1.27\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| <!--/--> | blood.glucose &lt;dbl&gt; | short.velocity &lt;dbl&gt; |\n", "|---|---|---|\n", "| 1 | 15.3 | 1.76 |\n", "| 2 | 10.8 | 1.34 |\n", "| 3 | 8.1 | 1.27 |\n", "| 4 | 19.5 | 1.47 |\n", "| 5 | 7.2 | 1.27 |\n", "| 6 | 5.3 | 1.49 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 \n", "4 19.5 1.47 \n", "5 7.2 1.27 \n", "6 5.3 1.49 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ISwR)\n", "data(thuesen)\n", "head(thuesen)" ] }, { "cell_type": "code", "execution_count": 94, "id": "96c3055d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<dl>\n", "\t<dt>$blood.glucose</dt>\n", "\t\t<dd>10.3</dd>\n", "\t<dt>$short.velocity</dt>\n", "\t\t<dd>1.32565217391304</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$blood.glucose] 10.3\n", "\\item[\\$short.velocity] 1.32565217391304\n", "\\end{description}\n" ], "text/markdown": [ "$blood.glucose\n", ": 10.3\n", "$short.velocity\n", ": 1.32565217391304\n", "\n", "\n" ], "text/plain": [ "$blood.glucose\n", "[1] 10.3\n", "\n", "$short.velocity\n", "[1] 1.325652\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lapply(thuesen, mean, na.rm=T)" ] }, { "cell_type": "code", "execution_count": 95, "id": "8dba1848", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".dl-inline {width: auto; margin:0; padding: 0}\n", ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n", ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n", ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n", "</style><dl class=dl-inline><dt>blood.glucose</dt><dd>10.3</dd><dt>short.velocity</dt><dd>1.32565217391304</dd></dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[blood.glucose] 10.3\n", "\\item[short.velocity] 1.32565217391304\n", "\\end{description*}\n" ], "text/markdown": [ "blood.glucose\n", ": 10.3short.velocity\n", ": 1.32565217391304\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 10.300000 1.325652 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sapply(thuesen, mean, na.rm=T)" ] }, { "cell_type": "code", "execution_count": 96, "id": "150ca39c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>1.76</li><li>1.34</li><li>1.27</li><li>1.47</li><li>1.27</li><li>1.49</li><li>1.31</li><li>1.09</li><li>1.18</li><li>1.22</li><li>1.25</li><li>1.19</li><li>1.95</li><li>1.28</li><li>1.52</li><li>8.6</li><li>1.12</li><li>1.37</li><li>1.19</li><li>1.05</li><li>1.32</li><li>1.03</li><li>1.12</li><li>1.7</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1.76\n", "\\item 1.34\n", "\\item 1.27\n", "\\item 1.47\n", "\\item 1.27\n", "\\item 1.49\n", "\\item 1.31\n", "\\item 1.09\n", "\\item 1.18\n", "\\item 1.22\n", "\\item 1.25\n", "\\item 1.19\n", "\\item 1.95\n", "\\item 1.28\n", "\\item 1.52\n", "\\item 8.6\n", "\\item 1.12\n", "\\item 1.37\n", "\\item 1.19\n", "\\item 1.05\n", "\\item 1.32\n", "\\item 1.03\n", "\\item 1.12\n", "\\item 1.7\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1.76\n", "2. 1.34\n", "3. 1.27\n", "4. 1.47\n", "5. 1.27\n", "6. 1.49\n", "7. 1.31\n", "8. 1.09\n", "9. 1.18\n", "10. 1.22\n", "11. 1.25\n", "12. 1.19\n", "13. 1.95\n", "14. 1.28\n", "15. 1.52\n", "16. 8.6\n", "17. 1.12\n", "18. 1.37\n", "19. 1.19\n", "20. 1.05\n", "21. 1.32\n", "22. 1.03\n", "23. 1.12\n", "24. 1.7\n", "\n", "\n" ], "text/plain": [ " [1] 1.76 1.34 1.27 1.47 1.27 1.49 1.31 1.09 1.18 1.22 1.25 1.19 1.95 1.28 1.52\n", "[16] 8.60 1.12 1.37 1.19 1.05 1.32 1.03 1.12 1.70" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".dl-inline {width: auto; margin:0; padding: 0}\n", ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n", ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n", ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n", "</style><dl class=dl-inline><dt>blood.glucose</dt><dd>4.2</dd><dt>short.velocity</dt><dd>1.03</dd></dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[blood.glucose] 4.2\n", "\\item[short.velocity] 1.03\n", "\\end{description*}\n" ], "text/markdown": [ "blood.glucose\n", ": 4.2short.velocity\n", ": 1.03\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 4.20 1.03 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m <- as.matrix(thuesen)\n", "apply(m, 1, min, na.rm=TRUE)\n", "apply(m, 2, min, na.rm=TRUE)" ] }, { "cell_type": "code", "execution_count": 97, "id": "12550128", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>blood.glucose</th><th scope=col>short.velocity</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>15.3</td><td>1.76</td></tr>\n", "\t<tr><th scope=row>2</th><td>10.8</td><td>1.34</td></tr>\n", "\t<tr><th scope=row>3</th><td> 8.1</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>4</th><td>19.5</td><td>1.47</td></tr>\n", "\t<tr><th scope=row>5</th><td> 7.2</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>6</th><td> 5.3</td><td>1.49</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 15.3 & 1.76\\\\\n", "\t2 & 10.8 & 1.34\\\\\n", "\t3 & 8.1 & 1.27\\\\\n", "\t4 & 19.5 & 1.47\\\\\n", "\t5 & 7.2 & 1.27\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| <!--/--> | blood.glucose &lt;dbl&gt; | short.velocity &lt;dbl&gt; |\n", "|---|---|---|\n", "| 1 | 15.3 | 1.76 |\n", "| 2 | 10.8 | 1.34 |\n", "| 3 | 8.1 | 1.27 |\n", "| 4 | 19.5 | 1.47 |\n", "| 5 | 7.2 | 1.27 |\n", "| 6 | 5.3 | 1.49 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 \n", "4 19.5 1.47 \n", "5 7.2 1.27 \n", "6 5.3 1.49 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ISwR)\n", "data(thuesen)\n", "head(thuesen)" ] }, { "cell_type": "code", "execution_count": 98, "id": "935337e0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>4.2</li><li>4.9</li><li>5.2</li><li>5.3</li><li>6.7</li><li>6.7</li><li>7.2</li><li>7.5</li><li>8.1</li><li>8.6</li><li>8.8</li><li>9.3</li><li>9.5</li><li>10.3</li><li>10.8</li><li>11.1</li><li>12.2</li><li>12.5</li><li>13.3</li><li>15.1</li><li>15.3</li><li>16.1</li><li>19</li><li>19.5</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 4.2\n", "\\item 4.9\n", "\\item 5.2\n", "\\item 5.3\n", "\\item 6.7\n", "\\item 6.7\n", "\\item 7.2\n", "\\item 7.5\n", "\\item 8.1\n", "\\item 8.6\n", "\\item 8.8\n", "\\item 9.3\n", "\\item 9.5\n", "\\item 10.3\n", "\\item 10.8\n", "\\item 11.1\n", "\\item 12.2\n", "\\item 12.5\n", "\\item 13.3\n", "\\item 15.1\n", "\\item 15.3\n", "\\item 16.1\n", "\\item 19\n", "\\item 19.5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 4.2\n", "2. 4.9\n", "3. 5.2\n", "4. 5.3\n", "5. 6.7\n", "6. 6.7\n", "7. 7.2\n", "8. 7.5\n", "9. 8.1\n", "10. 8.6\n", "11. 8.8\n", "12. 9.3\n", "13. 9.5\n", "14. 10.3\n", "15. 10.8\n", "16. 11.1\n", "17. 12.2\n", "18. 12.5\n", "19. 13.3\n", "20. 15.1\n", "21. 15.3\n", "22. 16.1\n", "23. 19\n", "24. 19.5\n", "\n", "\n" ], "text/plain": [ " [1] 4.2 4.9 5.2 5.3 6.7 6.7 7.2 7.5 8.1 8.6 8.8 9.3 9.5 10.3 10.8\n", "[16] 11.1 12.2 12.5 13.3 15.1 15.3 16.1 19.0 19.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sort(thuesen$blood.glucose)" ] }, { "cell_type": "code", "execution_count": 99, "id": "86b1b43a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>17</li><li>22</li><li>12</li><li>6</li><li>11</li><li>15</li><li>5</li><li>9</li><li>3</li><li>16</li><li>23</li><li>7</li><li>24</li><li>18</li><li>2</li><li>8</li><li>10</li><li>19</li><li>21</li><li>14</li><li>1</li><li>20</li><li>13</li><li>4</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 17\n", "\\item 22\n", "\\item 12\n", "\\item 6\n", "\\item 11\n", "\\item 15\n", "\\item 5\n", "\\item 9\n", "\\item 3\n", "\\item 16\n", "\\item 23\n", "\\item 7\n", "\\item 24\n", "\\item 18\n", "\\item 2\n", "\\item 8\n", "\\item 10\n", "\\item 19\n", "\\item 21\n", "\\item 14\n", "\\item 1\n", "\\item 20\n", "\\item 13\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 17\n", "2. 22\n", "3. 12\n", "4. 6\n", "5. 11\n", "6. 15\n", "7. 5\n", "8. 9\n", "9. 3\n", "10. 16\n", "11. 23\n", "12. 7\n", "13. 24\n", "14. 18\n", "15. 2\n", "16. 8\n", "17. 10\n", "18. 19\n", "19. 21\n", "20. 14\n", "21. 1\n", "22. 20\n", "23. 13\n", "24. 4\n", "\n", "\n" ], "text/plain": [ " [1] 17 22 12 6 11 15 5 9 3 16 23 7 24 18 2 8 10 19 21 14 1 20 13 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "order(thuesen$blood.glucose)" ] }, { "cell_type": "code", "execution_count": 100, "id": "40bd7159", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>blood.glucose</th><th scope=col>short.velocity</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>17</th><td>4.2</td><td>1.12</td></tr>\n", "\t<tr><th scope=row>22</th><td>4.9</td><td>1.03</td></tr>\n", "\t<tr><th scope=row>12</th><td>5.2</td><td>1.19</td></tr>\n", "\t<tr><th scope=row>6</th><td>5.3</td><td>1.49</td></tr>\n", "\t<tr><th scope=row>11</th><td>6.7</td><td>1.25</td></tr>\n", "\t<tr><th scope=row>15</th><td>6.7</td><td>1.52</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t17 & 4.2 & 1.12\\\\\n", "\t22 & 4.9 & 1.03\\\\\n", "\t12 & 5.2 & 1.19\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\t11 & 6.7 & 1.25\\\\\n", "\t15 & 6.7 & 1.52\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| <!--/--> | blood.glucose &lt;dbl&gt; | short.velocity &lt;dbl&gt; |\n", "|---|---|---|\n", "| 17 | 4.2 | 1.12 |\n", "| 22 | 4.9 | 1.03 |\n", "| 12 | 5.2 | 1.19 |\n", "| 6 | 5.3 | 1.49 |\n", "| 11 | 6.7 | 1.25 |\n", "| 15 | 6.7 | 1.52 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "17 4.2 1.12 \n", "22 4.9 1.03 \n", "12 5.2 1.19 \n", "6 5.3 1.49 \n", "11 6.7 1.25 \n", "15 6.7 1.52 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "o <- order(thuesen$blood.glucose)\n", "sorted <- thuesen[o,]\n", "head(sorted)" ] }, { "cell_type": "markdown", "id": "2335d934", "metadata": {}, "source": [ "### Part #4" ] }, { "cell_type": "code", "execution_count": 101, "id": "05475c18", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 4</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>Flights</th><th scope=col>Delays</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>11</td><td>6</td></tr>\n", "\t<tr><th scope=row>2</th><td>2016</td><td>2</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>3</th><td>2016</td><td>3</td><td>13</td><td>3</td></tr>\n", "\t<tr><th scope=row>4</th><td>2016</td><td>4</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>5</th><td>2017</td><td>1</td><td>10</td><td>4</td></tr>\n", "\t<tr><th scope=row>6</th><td>2017</td><td>2</td><td> 9</td><td>3</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & Year & Quarter & Flights & Delays\\\\\n", " & <int> & <int> & <int> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11 & 6\\\\\n", "\t2 & 2016 & 2 & 12 & 5\\\\\n", "\t3 & 2016 & 3 & 13 & 3\\\\\n", "\t4 & 2016 & 4 & 12 & 5\\\\\n", "\t5 & 2017 & 1 & 10 & 4\\\\\n", "\t6 & 2017 & 2 & 9 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| <!--/--> | Year &lt;int&gt; | Quarter &lt;int&gt; | Flights &lt;int&gt; | Delays &lt;int&gt; |\n", "|---|---|---|---|---|\n", "| 1 | 2016 | 1 | 11 | 6 |\n", "| 2 | 2016 | 2 | 12 | 5 |\n", "| 3 | 2016 | 3 | 13 | 3 |\n", "| 4 | 2016 | 4 | 12 | 5 |\n", "| 5 | 2017 | 1 | 10 | 4 |\n", "| 6 | 2017 | 2 | 9 | 3 |\n", "\n" ], "text/plain": [ " Year Quarter Flights Delays\n", "1 2016 1 11 6 \n", "2 2016 2 12 5 \n", "3 2016 3 13 3 \n", "4 2016 4 12 5 \n", "5 2017 1 10 4 \n", "6 2017 2 9 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flight_data <- read.table(text = \"Year Quarter Flights Delays\n", " 2016 1 11 6\n", " 2016 2 12 5\n", " 2016 3 13 3\n", " 2016 4 12 5\n", " 2017 1 10 4\n", " 2017 2 9 3\n", " 2017 3 11 4\n", " 2017 4 25 15\n", " 2018 1 14 3\n", " 2018 2 12 5\n", " 2018 3 13 3\n", " 2018 4 15 4\",\n", " header = TRUE,sep = \"\") \n", "head(flight_data)\n" ] }, { "cell_type": "code", "execution_count": 103, "id": "b167994e", "metadata": {}, "outputs": [], "source": [ "lib <- require(dplyr)\n", "if (!lib)\n", " install.packages(\"dplyr\")\n", "library(dplyr)" ] }, { "cell_type": "code", "execution_count": 104, "id": "97b27add", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 2 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>Flights</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>11</td></tr>\n", "\t<tr><th scope=row>2</th><td>2017</td><td>4</td><td>25</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 2 × 3\n", "\\begin{tabular}{r|lll}\n", " & Year & Quarter & Flights\\\\\n", " & <int> & <int> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11\\\\\n", "\t2 & 2017 & 4 & 25\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 3\n", "\n", "| <!--/--> | Year &lt;int&gt; | Quarter &lt;int&gt; | Flights &lt;int&gt; |\n", "|---|---|---|---|\n", "| 1 | 2016 | 1 | 11 |\n", "| 2 | 2017 | 4 | 25 |\n", "\n" ], "text/plain": [ " Year Quarter Flights\n", "1 2016 1 11 \n", "2 2017 4 25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result <- flight_data %>% \n", " filter(Delays > 5) %>% \n", " select(Year, Quarter, Flights)\n", "head(result)" ] }, { "cell_type": "code", "execution_count": 106, "id": "3a51596f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A tibble: 3 × 3</caption>\n", "<thead>\n", "\t<tr><th scope=col>Year</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n", "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><td>2016</td><td>12.00</td><td>0.8164966</td></tr>\n", "\t<tr><td>2017</td><td>13.75</td><td>7.5443135</td></tr>\n", "\t<tr><td>2018</td><td>13.50</td><td>1.2909944</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " Year & mean & sd\\\\\n", " <int> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t 2016 & 12.00 & 0.8164966\\\\\n", "\t 2017 & 13.75 & 7.5443135\\\\\n", "\t 2018 & 13.50 & 1.2909944\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| Year &lt;int&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; |\n", "|---|---|---|\n", "| 2016 | 12.00 | 0.8164966 |\n", "| 2017 | 13.75 | 7.5443135 |\n", "| 2018 | 13.50 | 1.2909944 |\n", "\n" ], "text/plain": [ " Year mean sd \n", "1 2016 12.00 0.8164966\n", "2 2017 13.75 7.5443135\n", "3 2018 13.50 1.2909944" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result <- flight_data %>% \n", " group_by(Year) %>% \n", " summarize(mean = mean(Flights), sd = sd(Flights))\n", "head(result)" ] }, { "cell_type": "code", "execution_count": 107, "id": "c841154b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "12" ], "text/latex": [ "12" ], "text/markdown": [ "12" ], "text/plain": [ "[1] 12" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 4</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>Flights</th><th scope=col>Delays</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>11</td><td>6</td></tr>\n", "\t<tr><th scope=row>2</th><td>2016</td><td>2</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>3</th><td>2016</td><td>3</td><td>13</td><td>3</td></tr>\n", "\t<tr><th scope=row>4</th><td>2016</td><td>4</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>5</th><td>2017</td><td>1</td><td>10</td><td>4</td></tr>\n", "\t<tr><th scope=row>6</th><td>2017</td><td>2</td><td> 9</td><td>3</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & Year & Quarter & Flights & Delays\\\\\n", " & <int> & <int> & <int> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11 & 6\\\\\n", "\t2 & 2016 & 2 & 12 & 5\\\\\n", "\t3 & 2016 & 3 & 13 & 3\\\\\n", "\t4 & 2016 & 4 & 12 & 5\\\\\n", "\t5 & 2017 & 1 & 10 & 4\\\\\n", "\t6 & 2017 & 2 & 9 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| <!--/--> | Year &lt;int&gt; | Quarter &lt;int&gt; | Flights &lt;int&gt; | Delays &lt;int&gt; |\n", "|---|---|---|---|---|\n", "| 1 | 2016 | 1 | 11 | 6 |\n", "| 2 | 2016 | 2 | 12 | 5 |\n", "| 3 | 2016 | 3 | 13 | 3 |\n", "| 4 | 2016 | 4 | 12 | 5 |\n", "| 5 | 2017 | 1 | 10 | 4 |\n", "| 6 | 2017 | 2 | 9 | 3 |\n", "\n" ], "text/plain": [ " Year Quarter Flights Delays\n", "1 2016 1 11 6 \n", "2 2016 2 12 5 \n", "3 2016 3 13 3 \n", "4 2016 4 12 5 \n", "5 2017 1 10 4 \n", "6 2017 2 9 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrow(flight_data)\n", "head(flight_data)" ] }, { "cell_type": "code", "execution_count": 110, "id": "757eefa0", "metadata": {}, "outputs": [], "source": [ "lib <- require(reshape)\n", "if (!lib)\n", " install.packages(\"reshape\")\n", "library(reshape)" ] }, { "cell_type": "code", "execution_count": 109, "id": "62a6183a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "24" ], "text/latex": [ "24" ], "text/markdown": [ "24" ], "text/plain": [ "[1] 24" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 4</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>variable</th><th scope=col>value</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>Flights</td><td>11</td></tr>\n", "\t<tr><th scope=row>2</th><td>2016</td><td>2</td><td>Flights</td><td>12</td></tr>\n", "\t<tr><th scope=row>3</th><td>2016</td><td>3</td><td>Flights</td><td>13</td></tr>\n", "\t<tr><th scope=row>17</th><td>2017</td><td>1</td><td>Delays </td><td> 4</td></tr>\n", "\t<tr><th scope=row>18</th><td>2017</td><td>2</td><td>Delays </td><td> 3</td></tr>\n", "\t<tr><th scope=row>19</th><td>2017</td><td>3</td><td>Delays </td><td> 4</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & Year & Quarter & variable & value\\\\\n", " & <int> & <int> & <fct> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & Flights & 11\\\\\n", "\t2 & 2016 & 2 & Flights & 12\\\\\n", "\t3 & 2016 & 3 & Flights & 13\\\\\n", "\t17 & 2017 & 1 & Delays & 4\\\\\n", "\t18 & 2017 & 2 & Delays & 3\\\\\n", "\t19 & 2017 & 3 & Delays & 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| <!--/--> | Year &lt;int&gt; | Quarter &lt;int&gt; | variable &lt;fct&gt; | value &lt;int&gt; |\n", "|---|---|---|---|---|\n", "| 1 | 2016 | 1 | Flights | 11 |\n", "| 2 | 2016 | 2 | Flights | 12 |\n", "| 3 | 2016 | 3 | Flights | 13 |\n", "| 17 | 2017 | 1 | Delays | 4 |\n", "| 18 | 2017 | 2 | Delays | 3 |\n", "| 19 | 2017 | 3 | Delays | 4 |\n", "\n" ], "text/plain": [ " Year Quarter variable value\n", "1 2016 1 Flights 11 \n", "2 2016 2 Flights 12 \n", "3 2016 3 Flights 13 \n", "17 2017 1 Delays 4 \n", "18 2017 2 Delays 3 \n", "19 2017 3 Delays 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result <- melt(flight_data[,c('Year', 'Quarter', 'Flights', 'Delays')], \n", " id.vars = c(1,2))\n", "nrow(result)\n", "head(result[c(1:3,17:19), ])" ] }, { "cell_type": "code", "execution_count": 111, "id": "5f1366e0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>city</th><th scope=col>value</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Rio de Janeiro</td><td>10</td></tr>\n", "\t<tr><th scope=row>2</th><td>Sao Paulo </td><td>12</td></tr>\n", "\t<tr><th scope=row>3</th><td>Paris </td><td>20</td></tr>\n", "\t<tr><th scope=row>4</th><td>New York </td><td>25</td></tr>\n", "\t<tr><th scope=row>5</th><td>Tokyo </td><td>18</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r|ll}\n", " & city & value\\\\\n", " & <chr> & <dbl>\\\\\n", "\\hline\n", "\t1 & Rio de Janeiro & 10\\\\\n", "\t2 & Sao Paulo & 12\\\\\n", "\t3 & Paris & 20\\\\\n", "\t4 & New York & 25\\\\\n", "\t5 & Tokyo & 18\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| <!--/--> | city &lt;chr&gt; | value &lt;dbl&gt; |\n", "|---|---|---|\n", "| 1 | Rio de Janeiro | 10 |\n", "| 2 | Sao Paulo | 12 |\n", "| 3 | Paris | 20 |\n", "| 4 | New York | 25 |\n", "| 5 | Tokyo | 18 |\n", "\n" ], "text/plain": [ " city value\n", "1 Rio de Janeiro 10 \n", "2 Sao Paulo 12 \n", "3 Paris 20 \n", "4 New York 25 \n", "5 Tokyo 18 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stores <- data.frame(\n", " city = c(\"Rio de Janeiro\", \"Sao Paulo\", \"Paris\", \"New York\", \"Tokyo\"),\n", " value = c(10, 12, 20, 25, 18))\n", "head(stores)" ] }, { "cell_type": "code", "execution_count": 112, "id": "79b28ce6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>city</th><th scope=col>country</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Rio de Janeiro</td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>2</th><td>Sao Paulo </td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>3</th><td>Paris </td><td>France</td></tr>\n", "\t<tr><th scope=row>4</th><td>New York </td><td>US </td></tr>\n", "\t<tr><th scope=row>5</th><td>Tokyo </td><td>Japan </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r|ll}\n", " & city & country\\\\\n", " & <chr> & <chr>\\\\\n", "\\hline\n", "\t1 & Rio de Janeiro & Brazil\\\\\n", "\t2 & Sao Paulo & Brazil\\\\\n", "\t3 & Paris & France\\\\\n", "\t4 & New York & US \\\\\n", "\t5 & Tokyo & Japan \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| <!--/--> | city &lt;chr&gt; | country &lt;chr&gt; |\n", "|---|---|---|\n", "| 1 | Rio de Janeiro | Brazil |\n", "| 2 | Sao Paulo | Brazil |\n", "| 3 | Paris | France |\n", "| 4 | New York | US |\n", "| 5 | Tokyo | Japan |\n", "\n" ], "text/plain": [ " city country\n", "1 Rio de Janeiro Brazil \n", "2 Sao Paulo Brazil \n", "3 Paris France \n", "4 New York US \n", "5 Tokyo Japan " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "divisions <- data.frame(\n", " city = c(\"Rio de Janeiro\", \"Sao Paulo\", \"Paris\", \"New York\", \"Tokyo\"),\n", " country = c(\"Brazil\", \"Brazil\", \"France\", \"US\", \"Japan\"))\n", "head(divisions)" ] }, { "cell_type": "code", "execution_count": 113, "id": "67146fcc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>city</th><th scope=col>value</th><th scope=col>country</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>New York </td><td>25</td><td>US </td></tr>\n", "\t<tr><th scope=row>2</th><td>Paris </td><td>20</td><td>France</td></tr>\n", "\t<tr><th scope=row>3</th><td>Rio de Janeiro</td><td>10</td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>4</th><td>Sao Paulo </td><td>12</td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>5</th><td>Tokyo </td><td>18</td><td>Japan </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 3\n", "\\begin{tabular}{r|lll}\n", " & city & value & country\\\\\n", " & <chr> & <dbl> & <chr>\\\\\n", "\\hline\n", "\t1 & New York & 25 & US \\\\\n", "\t2 & Paris & 20 & France\\\\\n", "\t3 & Rio de Janeiro & 10 & Brazil\\\\\n", "\t4 & Sao Paulo & 12 & Brazil\\\\\n", "\t5 & Tokyo & 18 & Japan \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 3\n", "\n", "| <!--/--> | city &lt;chr&gt; | value &lt;dbl&gt; | country &lt;chr&gt; |\n", "|---|---|---|---|\n", "| 1 | New York | 25 | US |\n", "| 2 | Paris | 20 | France |\n", "| 3 | Rio de Janeiro | 10 | Brazil |\n", "| 4 | Sao Paulo | 12 | Brazil |\n", "| 5 | Tokyo | 18 | Japan |\n", "\n" ], "text/plain": [ " city value country\n", "1 New York 25 US \n", "2 Paris 20 France \n", "3 Rio de Janeiro 10 Brazil \n", "4 Sao Paulo 12 Brazil \n", "5 Tokyo 18 Japan " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stdiv <- merge(stores, divisions, by.x=\"city\", by.y=\"city\")\n", "head(stdiv)" ] }, { "cell_type": "code", "execution_count": 114, "id": "d0f3323d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A tibble: 4 × 3</caption>\n", "<thead>\n", "\t<tr><th scope=col>country</th><th scope=col>count</th><th scope=col>amount</th></tr>\n", "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><td>Brazil</td><td>2</td><td>22</td></tr>\n", "\t<tr><td>France</td><td>1</td><td>20</td></tr>\n", "\t<tr><td>Japan </td><td>1</td><td>18</td></tr>\n", "\t<tr><td>US </td><td>1</td><td>25</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A tibble: 4 × 3\n", "\\begin{tabular}{lll}\n", " country & count & amount\\\\\n", " <chr> & <int> & <dbl>\\\\\n", "\\hline\n", "\t Brazil & 2 & 22\\\\\n", "\t France & 1 & 20\\\\\n", "\t Japan & 1 & 18\\\\\n", "\t US & 1 & 25\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 4 × 3\n", "\n", "| country &lt;chr&gt; | count &lt;int&gt; | amount &lt;dbl&gt; |\n", "|---|---|---|\n", "| Brazil | 2 | 22 |\n", "| France | 1 | 20 |\n", "| Japan | 1 | 18 |\n", "| US | 1 | 25 |\n", "\n" ], "text/plain": [ " country count amount\n", "1 Brazil 2 22 \n", "2 France 1 20 \n", "3 Japan 1 18 \n", "4 US 1 25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result <- stdiv %>% group_by(country) %>% \n", " summarize(count = n(), amount = sum(value))\n", "head(result)" ] }, { "cell_type": "code", "execution_count": null, "id": "990ba9d3", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.1.2" } }, "nbformat": 4, "nbformat_minor": 5 }

mit

HCsoft-RD/shaolin

examples/Dashboards.ipynb

1

121050

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In order to use the shaolin framework more comfortably we will use a little screen hack." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", ".container { width:100% !important; }\n", ".input{ width:60% !important;\n", " align: center;\n", " }\n", ".text_cell{ width:70% !important;\n", " font-size: 16px;}\n", ".title {align:center !important;}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%HTML\n", "<style>\n", ".container { width:100% !important; }\n", ".input{ width:60% !important;\n", " align: center;\n", " }\n", ".text_cell{ width:70% !important;\n", " font-size: 16px;}\n", ".title {align:center !important;}\n", "</style>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Shaolin Dashboard Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='index'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Index " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [1 Introduction](#intro)\n", "- [2 Structure](#structure)\n", " - [2.1 Attributes](#attributes)\n", " - [2.2 Functions](#functions)\n", " \n", "- [3 Syntax](#syntax)\n", " - [3.1 Examples](#syntax_examples)\n", "\n", "- [4 Interactivity](#interactivity)\n", " - [4.1 Default values](#interactivity_default)\n", " - [4.2 Syntax](#interactivity_syntax)\n", "- [5 Styling](#styling) \n", " - [5.1 State manager](#state_manager)\n", " - [5.2 Accessing, loading and changing the layout](#load_and_save)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='introduction'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1 Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Dashboard is the main tool of the Shaolin frameowrk. It can be seen as an interactive code box and it has all the functionalities needed for controlling the interactivity of its components.\n", "\n", "A Dashboard can be build combining widgets and other dashboards. It can be used as a standalone object or in combination with other Dashboards, and its main goal is to offer a simplified interface for managing all the ipywidets functionality in a simpler and scalable way." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='structure'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2 Structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a description of the Dashboard capabilities. A detailed explanation can be seen in further sections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='attributes'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1 Attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will describe the attributes of a Dashboard. They are all related to managing interactivity and the same functionality offered by the ipywidgets package:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Init parameters:\n", " - **dasboard**: list representing the syntax of the Dasboard components.\n", " - **func**(None): function to mimic the interactive behaviour of the ipywidgets package. It will interact with the widgets defined in the dashboard.\n", " - **data**(None): The data the Dashboard will use.\n", " - **name**(None): Name of the Dasboard. Each component of a Dashboard must have a unique name. \n", " - **mode**('active'): interactivity mode of the Dashboard.\n", "\n", " - **visible**(True): Displays all the dashboard's components when True and hides its components when False.\n", " - **state**(None): dict containing all the layout parameters of the dashboard components. (soon it will contain the vaue information also)\n", "- Parameters created at instantiation:\n", " - **widget**: Graphic interface of the Dashboard.\n", " - **mode_dict**: dict containing the information regarding the interactivity of every component.\n", " - **value**, **kwargs**: dictionary of kwargs containing all the interactive and active components as a key-value pairs.\n", " - **interactive_kwargs**: dict containing the name and value of the interactive components stored as key-value pairs.\n", " - **state_manager**: Dashboard for customizing, loading and saving the state of the Dashboard.\n", " - **output**(kwargs): When working with data this attribute contains the processed value that the Dashboard will output. If there is no data it's just an alias for kwargs.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='functions'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Functions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Dashboard has its own functions for mimicking the ipywidgets functionality:\n", "\n", "- **name_from_shaoscript**: Returns the name of a shaoscript string notation element.\n", "- **read_shaolist**: reads a Dashboard component from its list representation and returns its shaoscript description, its kwargs and its children.\n", "- **observe**: Same functionality as ipywidgets. It will trigger the observer function when any interactive component changes its value.\n", "\n", "- **link**: Links the values of two components.\n", "- **dlink**: Stablishes a direct link between two components.\n", "- **unlink**: Removes a link.\n", "- **interact**: Applies the dashboard func to the Dashboard's output.\n", "- **apply_state**: Sets the state dict of a widget and updates its layout accordingly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='syntax'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3 Syntax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use a custom notation for defining tha layout of the dashboard components. Each component defined in the layout will be accessible as an attribute with the same name as the widget (we can set it directly as a parameter or indirectly with the description as seen early).\n", "\n", "A dashboard component will be a list contaning a shaoscript string notation. If the widget can have child the component will be stated as a list of two elements: The first one will be a string containing the widget shaoscript notation and the second one will be a list containing its children elements, where every element will be an item of that list.\n", "\n", "This allows for stacking widgets and creating complex boxes more easily than using multiple ipywidgets boxes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='syntax_examples'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.1 Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image #this is for displaying the widgets in the web version of the notebook\n", "from shaolin.core.dashboard import Dashboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **Dashboard containing a single widget.**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAAAvCAIAAADvm9FjAAAAA3NCSVQICAjb4U/gAAAAGXRFWHRT\nb2Z0d2FyZQBnbm9tZS1zY3JlZW5zaG907wO/PgAABf9JREFUeJzt3D9w01YcB/CfbC56yZKX44h1\ncFzUBBJxMIhjQEyYTUwVmzvVnRBTNYrNW82EO+FOdSe8VVvdCXeKFi7qES4iB0aaKu7I5eU44mef\nCR1MXSc4Lo6TWHV+nymRZet3vvfV+yNZwsePHwEhFCeJUReAENoLY4lQ7GAsEYodjCVCsYOxRCh2\nMJYIxQ7GEqHY6R3LVqtVq9VarVb3xjAM379/fyxVIXSiCT1vJ3j+/DnnfHJycmlpKZlMAkCtVtvc\n3EwkEpcvX56YmDj2OuOo2Wy+evXqw4cPPV9NJBKyLE9NTR1zVWgM9O4tz549KwhCvV5/8eJFu+fc\n3NwEgDNnzmAmO16+fCkIwsQ+EolEGIajrhH9L53quXVmZkYQhFqtVq/Xnz17trOzAwCSJJ07d+54\ny4u1nZ2dU6d6f4EAkEgkms3mcdaDxsa+Sz6U0vn5eQBoZ3J2dhYzidDx6LcSyxjr/P3u3bv9JlEI\nocO1byyDINjY2ACA6elpAKjX6+vr62ObzMjJqkQQBMXy+KhrQfEROFZaEgRBkNJmOejVNJhbMBQq\nCAJVjILb6ce4X8qqVBAEIut2JRr0uL1jGYZhO5OpVOrChQuyLAPA9vb2uCbTL9m/BPqj356ULYWM\nuhgUE9zPG3fKNLe8tvbEJuVv9Nxn52zmmLod6KWVteViOrD1rBMBAHDX0r+rqsWVtRUnwwuGUQwG\nO3TvCyRhGL59+3Z2dvb8+fPtLRsbG0EQTE1NLS4uti+ZjBHuWYrmFaKqQQd52+rqav916WazeeXK\nlSGLQyPC3ayc9vOBm5UAgDm6ZEqVoJTuOm9HJU221WpQ1AgA92xFq9i+Z9KKLmWIEzg6BYCgqCp5\n3fXz6pef8Xv3lnNzc/Pz851MAsDp06cXFhbGMpNVg179MWz8cWdGUAtBZ1AiUEW3nYGHH2hcRK77\nRjY0qf0fVQ2FuW7QvQf3HQ9U49MAiyi6RvyKz3lQ8RqKoX46x0taWoqqHoMB7Du3nJmZ2bOFUjp2\nmQQAknai5bsp8ebjv+qeyeyMHRjltdevV0pGVMhknYG+TjQ2eOQzoHJn/EQlCsxn3cNYFkQNKtNO\nJ0hlClHEIPKZKHXeSahCgfkDtaN9L7udJIQQAkAIAYiCCKgsy7JM5JxT1ZmMc82TivN2m2gjhADn\nvO8elDQ45wC8QbqmQ4SQBuecA3xxW8JY7kK0nKXcuHdp0l66qRsZ08xKw8Xy6dOnh1TaiSOK4uLi\n4gjvKiMEumLYztXu1kD37MG4SAgBQsXu/HLORULIIA0JY7kb1fJe3ayWy2XHKd279aD4w5pnKwf/\nvGvXrh1ecScLY2x9fX1ka2ZEUiWxGkQA7Y6PRQwkhXaHi0ryNIv+XX5gAQMqUaCyBG7AACQAAM58\nBlQZaDURf9i1S1QtWHmPprN20XGD5bupP8vVYNRFnVCU0kajMbrjS6pGg4r3KXXMc3yianL3HkTR\nVfAqfrtn5H7F5YqhECLrKvEr/yzyRG41ktLqQLHE3nIXCl75foHJTi4tcbdUfZPSVKnP/ltbW31e\nnZycPOwCTxDGmCiKozs+0SxLuWRm8zRvkKpt/i6bKxoBAFYt5l3JtA1ZMmzDup2xtJIlBwWzwPSy\nIQNA2s7QG1kzXc5pvGRZvlpwBrg6AhjLPUi64Dw0TfPqV1sAqevfPqoUtX7fpyAI/T8Q55YHJori\nxYsXR1mBYju/Rlnz9tX7kLp+93El185W5BYe5FXdMmRC9WLlYTZr3vhpS1z6OlcpGRIAANEKlZ95\nxrp1aQvmbn7vOKY82JF7306AvsTq6mr/UZYoing7AToA7C2H8p+9JUIHgLEcSiKBa2bo8GEsh4Kx\nREcBYzkUjCU6ChjLofSPZfvBDggNCmM5FIwlOgoYy6HgIBYdBYzlUDCW6ChgLIeCsURHAWN5cMlk\nss+jYlut1jj+ahwdB7z57uC2t7fDMNzvmWPJZHJhYQEfQo8OAGOJUOzg1Aih2MFYIhQ7GEuEYgdj\niVDsYCwRih2MJUKxg7FEKHYwlgjFDsYSodj5Gw98OvwL9PqLAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dash = Dashboard(['fs$D=fs'])\n", "dash.widget\n", "Image(filename='dashboards_data/img_1.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **Dashboard containing three components in a row**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAA9CAIAAADwC/4kAAAAA3NCSVQICAjb4U/gAAAAGXRFWHRT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\nAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dash = Dashboard(['r$N=row',['##Row$N=text','fs$D=fs','tog$d=Toggle']])\n", "#dash.widget\n", "Image(filename='dashboards_data/img_2.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **A column with two rows of components**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ " #Column that gives a name to the Dashboard. \n", "test_dash = ['c$N=array_scaler',\n", " #first row only has one widget. This element can be just a string \n", " ['@(0,100,1, (0., 100.))$N=scale_slider&d=range',\n", " #Two widgets in the second row. As this element has childrens it has to be a list.\n", " ['r$N=main_row',['@dd$d=Apply&N=dd_sel&val=one&o=[\"one\",\"two\",\"three\"]','@True$N=scale_chk&d=Scale']]\n", " ]\n", " ]\n", "dboard = Dashboard(test_dash)\n", "#dboard.widget\n", "Image(filename='dashboards_data/img_3.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='interactivity'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 Interactivity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 3 modes of interactivity that determines how a given component behaves when its value changes. The mode attribute of a Dashboard holds one interactivity value of the following:\n", "\n", "- **Passive**: changing the value of the widget won+t have any additional effect. Markdown and display components are a good example of widgets that can exibit a passive behaviour.\n", "\n", "- **active**: If a component is active it will be included in the kwargs dict but changing its value won't trigger the update function.\n", "\n", "- **Interactive**: Once the observe function is called on a Dashboard, the target function will be applied each time an interactive component changes its value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accessing the mode_dict attribube of a Dashboard the mode of every component can be viewed and changed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='default_int'></a>" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'active': [],\n", " 'all': ['array_scaler', 'scale_slider', 'main_row', 'dd_sel', 'scale_chk'],\n", " 'interactive': ['scale_slider', 'dd_sel', 'scale_chk'],\n", " 'passive': ['array_scaler', 'main_row']}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dboard.mode_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='interactivity_default'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.1 Default values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each widget and Dashboard will be assignes by default the active mode. An exception to this rule are the widget boxes and Markdown Widgets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='interactivity_syntax'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.2 Syntax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The interactivity value can be set writing the character @ at the begining of a shaoscript string. Note that this is a shaoscript syntax feature and will also set the mode as interactive when a widget is created using the function shaoscript (not only with the Dashboard object)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interactive dropdown {'all': ['dd_sel'], 'active': [], 'passive': [], 'interactive': ['dd_sel']} \n", " Active dropdown {'all': ['dd_sel'], 'active': ['dd_sel'], 'passive': [], 'interactive': []}\n" ] } ], "source": [ "A = Dashboard(['@dd$d=Dropdown&N=dd_sel&val=one&o=[\"one\",\"two\",\"three\"]'])\n", "B = Dashboard(['dd$d=Dropdown&N=dd_sel&val=one&o=[\"one\",\"two\",\"three\"]'],mode='interactive')\n", "print(\"Interactive dropdown\",A.mode_dict,\"\\n Active dropdown\",B.mode_dict)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('active', 'interactive')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.mode, B.mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='styling'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5 Styling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although it is possible to style a Dashboard accessing the layout attribute of the widgets of any component (the target widget and the box widget wrapping the target) the recommended way of styling a Dashboard is using its state_manager attribute.\n", "\n", "The state_manager is a widget that allows to modify, load, and save any change to the Dashboard styling in an interactive way." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='state_manager'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5.1 State Manager" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The state manager is a Dashboard that allows to modify in an interactive way any visual property of a given Dashboard. Following the former example we will show how to customize a the Dashboard that we created before.\n", "\n", "This is a relatively complex Dashboard which can be seen as a colum that has three different rows:\n", "\n", "- ### **Css modifier**: \n", "This part is in charge of seting a group of css attributes for a selected widget. There are two groupc of css attributes:\n", " - **Text attribute**: Its widge will display the available values in a dropdown or a text input for non-common values. A custom value can be set by deactivating the Default checkbox for a given attribute. Each selection can either be applied to the target widget of a component or to its box.\n", " - **Numeric attribute**: Its Dashboard will allow to select the units in which the value will be expressed using a text input or a slider for selecting the numeric value and a togglebutton for selecting the units. \n", "- ### **Control Panel**: \n", "This row has the following elements:\n", " - **Component selector**: This allows to select the component that will be modified.\n", " - **Layout attribute selector**: This toggle buttons allow to select which layout attribute will be modified:\n", " - **Grid**: Attributes regarding alignment, padding and margin. The alignment attributes will be applied only to the target or the box of a component only when they will haev a meaningful effect.\n", " - **W&H**: width and height css related attributes.\n", " - **Other**: The rest of the layout attributes.\n", " - **Widget**: Component target's attributes that influence the widget appearance.\n", " - **Selected attribute display**\n", " - **Layout saver**\n", "- ### **Target Dashboard**: \n", "The Dashboard that is currently modifying the state manager. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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XtxxR/+7lhJBmd+SRdHSw1BwZHQlt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\nS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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#dboard.state_manager\n", "Image(filename='dashboards_data/img_4.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we are going to modify the following:\n", "\n", "- **Padding of every component**: 0.5em, dropdown and checkbox 0.25em.\n", "- **Justify value of the checkbox**: center\n", "- **width of the dropdown**: 100%\n", "- **width of the Dashboard**: 22em\n", "- **border of the whole Dashboard**: blue solid 0.1em" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='load_and_save'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5.2 Accessing, loading and changing the layout" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Back to top](#index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The modified layout can be read and changed accesing the *state* attribute of a dashboard, and can be saved as a dict by clicking the save button in the state manager. This way the state dict will be saved as a pickle file. \n", "\n", "The folowing example illustrates this process. Note that the A widget behaves on its own and the B and C are linked. This is due to both having the same name (the name of the first element of the Dashboard)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A= Dashboard(test_dash,state='array_scaler.pkl',name='A') #instantiating with the path of a saved layout.\n", "custom_layout = dict(A.state)\n", "B = Dashboard(test_dash,state=custom_layout) #instantiating with a dict \n", "C = Dashboard(test_dash)#Init with default layout.\n", "C.state = custom_layout #dinamically changing the layout\n", "#Dashboard(['r$N=row',[A,B,C]]).widget #This is the new widgets.Hbox(children=[A.widget, B.widget, C.widget])\n", "Image(filename='dashboards_data/img_5.png')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'array_scaler': {'css_traits': {'align_content': {'target': 'blue solid 0.1em',\n", " 'widget': ''},\n", " 'align_items': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'align_self': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'border': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'bottom': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'display': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'flex': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'flex_flow': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'height': {'target': '', 'widget': ''},\n", " 'justify_content': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'left': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'margin': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'max_height': {'target': '', 'widget': ''},\n", " 'max_width': {'target': '', 'widget': ''},\n", " 'min_height': {'target': '', 'widget': ''},\n", " 'min_width': {'target': '', 'widget': ''},\n", " 'overflow': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'padding': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'right': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'top': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'visibility': {'target': 'blue solid 0.1em', 'widget': ''},\n", " 'width': {'target': '22.0em', 'widget': ''}},\n", " 'widget_attrs': {'visible': True}},\n", " 'dd_sel': {'css_traits': {'align_content': {'target': '', 'widget': ''},\n", " 'align_items': {'target': '', 'widget': ''},\n", " 'align_self': {'target': '', 'widget': ''},\n", " 'border': {'target': '', 'widget': ''},\n", " 'bottom': {'target': '', 'widget': ''},\n", " 'display': {'target': '', 'widget': ''},\n", " 'flex': {'target': '', 'widget': ''},\n", " 'flex_flow': {'target': '', 'widget': ''},\n", " 'height': {'target': '', 'widget': ''},\n", " 'justify_content': {'target': 'center', 'widget': ''},\n", " 'left': {'target': '', 'widget': ''},\n", " 'margin': {'target': '', 'widget': ''},\n", " 'max_height': {'target': '', 'widget': ''},\n", " 'max_width': {'target': '', 'widget': ''},\n", " 'min_height': {'target': '', 'widget': ''},\n", " 'min_width': {'target': '', 'widget': ''},\n", " 'overflow': {'target': '', 'widget': ''},\n", " 'padding': {'target': '', 'widget': '0.25%'},\n", " 'right': {'target': '', 'widget': ''},\n", " 'top': {'target': '', 'widget': ''},\n", " 'visibility': {'target': '', 'widget': ''},\n", " 'width': {'target': '100.0%', 'widget': ''}},\n", " 'widget_attrs': {'visible': True}},\n", " 'main_row': {'css_traits': {'align_content': {'target': '', 'widget': ''},\n", " 'align_items': {'target': '', 'widget': ''},\n", " 'align_self': {'target': '', 'widget': ''},\n", " 'border': {'target': '', 'widget': ''},\n", " 'bottom': {'target': '', 'widget': ''},\n", " 'display': {'target': '', 'widget': ''},\n", " 'flex': {'target': '', 'widget': ''},\n", " 'flex_flow': {'target': '', 'widget': ''},\n", " 'height': {'target': '', 'widget': ''},\n", " 'justify_content': {'target': '', 'widget': ''},\n", " 'left': {'target': '', 'widget': ''},\n", " 'margin': {'target': '', 'widget': ''},\n", " 'max_height': {'target': '', 'widget': ''},\n", " 'max_width': {'target': '', 'widget': ''},\n", " 'min_height': {'target': '', 'widget': ''},\n", " 'min_width': {'target': '', 'widget': ''},\n", " 'overflow': {'target': '', 'widget': ''},\n", " 'padding': {'target': '', 'widget': '0.5%'},\n", " 'right': {'target': '', 'widget': ''},\n", " 'top': {'target': '', 'widget': ''},\n", " 'visibility': {'target': '', 'widget': ''},\n", " 'width': {'target': '', 'widget': ''}},\n", " 'widget_attrs': {'visible': True}},\n", " 'scale_chk': {'css_traits': {'align_content': {'target': '', 'widget': ''},\n", " 'align_items': {'target': '', 'widget': ''},\n", " 'align_self': {'target': '', 'widget': ''},\n", " 'border': {'target': '', 'widget': ''},\n", " 'bottom': {'target': '', 'widget': ''},\n", " 'display': {'target': '', 'widget': ''},\n", " 'flex': {'target': '', 'widget': ''},\n", " 'flex_flow': {'target': '', 'widget': ''},\n", " 'height': {'target': '', 'widget': ''},\n", " 'justify_content': {'target': 'center', 'widget': ''},\n", " 'left': {'target': '', 'widget': ''},\n", " 'margin': {'target': '', 'widget': ''},\n", " 'max_height': {'target': '', 'widget': ''},\n", " 'max_width': {'target': '', 'widget': ''},\n", " 'min_height': {'target': '', 'widget': ''},\n", " 'min_width': {'target': '', 'widget': ''},\n", " 'overflow': {'target': '', 'widget': ''},\n", " 'padding': {'target': '', 'widget': '0.25%'},\n", " 'right': {'target': '', 'widget': ''},\n", " 'top': {'target': '', 'widget': ''},\n", " 'visibility': {'target': '', 'widget': ''},\n", " 'width': {'target': '', 'widget': ''}},\n", " 'widget_attrs': {'visible': True}},\n", " 'scale_slider': {'css_traits': {'align_content': {'target': '', 'widget': ''},\n", " 'align_items': {'target': '', 'widget': ''},\n", " 'align_self': {'target': '', 'widget': ''},\n", " 'border': {'target': '', 'widget': ''},\n", " 'bottom': {'target': '', 'widget': ''},\n", " 'display': {'target': '', 'widget': ''},\n", " 'flex': {'target': '', 'widget': ''},\n", " 'flex_flow': {'target': '', 'widget': ''},\n", " 'height': {'target': '', 'widget': ''},\n", " 'justify_content': {'target': '', 'widget': ''},\n", " 'left': {'target': '', 'widget': ''},\n", " 'margin': {'target': '', 'widget': ''},\n", " 'max_height': {'target': '', 'widget': ''},\n", " 'max_width': {'target': '', 'widget': ''},\n", " 'min_height': {'target': '', 'widget': ''},\n", " 'min_width': {'target': '', 'widget': ''},\n", " 'overflow': {'target': '', 'widget': ''},\n", " 'padding': {'target': '', 'widget': '0.5%'},\n", " 'right': {'target': '', 'widget': ''},\n", " 'top': {'target': '', 'widget': ''},\n", " 'visibility': {'target': '', 'widget': ''},\n", " 'width': {'target': '', 'widget': ''}},\n", " 'widget_attrs': {'visible': True}}}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "C.state" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" }, "widgets": { "state": { "46b6c5b2d66746129c3f0d755d86aef0": { "views": [] }, "6828e1f3b55c4002a8831991e311365b": { "views": [] }, "6ef24376a6474f01aa9ab59fc42e7d26": { "views": [] }, "72ad815ad1584214a0dc04ae4c2804a2": { "views": [] }, "b648634f9b4b4d83b41974996754d6c5": { "views": [] }, "b8de3f9071b44167b37fc88397d9d6c0": { "views": [] }, "d674f23d381e47d68c3179054f5c9155": { "views": [] }, "e9edc537fa52415ab9d9b10f4d5fff1e": { "views": [] } }, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }

agpl-3.0

cggh/scikit-allel

notebooks/profiling/misc_plotting.ipynb

1

24362

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "np.random.seed(42)\n", "import sys\n", "import cProfile\n", "import humanize\n", "sys.path.insert(0, '../..')\n", "%reload_ext memory_profiler\n", "%reload_ext autoreload\n", "%autoreload 1\n", "%aimport allel.model\n", "%aimport allel.stats\n", "%aimport allel.plot\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pos = np.unique(np.random.randint(0, 100000, size=1000))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GB7eWlnbyBe5Y6hdJneuVCYcVWete+1uLRNb4yDR775eQAWj1l+aK+Ez8M0Ox\nevE9FJTWHcDblQZLQ33teyPWzp7SWoFE3NhE1JS1fqTorH6+KeLpLCj7RVKt1INIX3QWZSoLynRW\nyxIJst7WSAqg6pfOmuUGtae0erFEus1ar/WJhGJlgP0V57ZAREH+ZXfPupV8odkv0rJEYrEcvFE3\n3gF8AOlTNrE20VneHwLN3xKpi8yCMp2VHvOO9ZZTXdvQKxzd0ln9gkiaK2JzqAXRWEBRGqG1gzL1\nPiR0VtOXDbtdOwvqw3xTx1CaL3I1lBILq5IOD1D2p/gIpwV6f6tw7xd5FvF72LOkuSJNCYc+zNfz\n0PsRsLCGaoGIajNbKAZgaomcE2R5l736fka1eUprDQIIl+uxFojEyDL9PaOtWpZWLD5cY8cOV3RW\nP3QWdL/0iaezoAwi6WSdWiL90FktEFGwn0ah3bZFZjmLpWc6S30Weyj6mP9U7nJgjlM4PIgMUs6t\naVo/y9NZTZbIGGCve586SwTKY6FfEKn7wiFUh/laf2jKWG+is5oSDVtAofU9gaJe/LFRSBsYUHhL\nZBqwSyln6Exn9Qoi65Ewb5snKyO0VNLlT+qc60NGZzVZIp3orLrMzOUUEVo7KS8B/QgFiDyGOJ+t\ns3kQWYREU02sODYfSUgco5rTrRSUVmsJFBfFZNp6U65It0ufQJnSSnnoNOHwVK2Hrfp7BmgeCG9H\nvrNiz+I/VDVfj1ln8wsyLkCWlbeO5/0iVXRWPz6RJjqrbtkTKPeHJhDxQARl53o6ofiEwyo6y57b\nc97j9B0s4sonGk5HFvCzycDTFekifd1aIulE7vtDK0JLfVwvu2Mtx7pawn7y9hNw+q2INkvEWT4+\nxDdtO/+c/vO89szW370VBOUx0wlEqpY+gXYHsO87VSDi6awmELHgiZHIMkrP6THvD5mIrJV20B0z\nS2QG8nld6w9peK+nOjuF+Fr7dPow1VHQUk43USgEvt8sRdIMbMx4ZdEy120O8CBSlXDYEURCjB3P\nkRND6O7ELFmyZMnySykxxpDu6xpEsmTJkiVLllT6obOyZMmSJUsWIINIlixZsmQ5BMkgkiVLlixZ\n+pYMIlmyZMmSpW/JIJIlS5YsWfqW/wd7c89G9T7RpwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fa143855080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "allel.plot.variant_locator(pos, flip=True);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

dnlglsn/ctci

Chapter 8 - Recursion and Dynamic Programming.ipynb

1

6863

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "https://networkx.github.io/documentation/networkx-1.10/reference/generators.html" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Imports\n", "import random\n", "import unittest\n", "from binarytree import Node, bst as generate_bst, stringify\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0\n", "1 1\n", "2 2\n", "3 4\n", "4 7\n", "5 13\n", "6 24\n", "7 44\n", "8 81\n", "9 149\n", "\n", "0 1\n", "1 1\n", "2 2\n", "3 4\n", "4 7\n", "5 13\n", "6 24\n", "7 44\n", "8 81\n", "9 149\n" ] } ], "source": [ "# 8.1 - Triple Step - Staircause with n steps. Can hope 1, 2, or 3 steps.\n", "# How many ways can the child run up the stairs?\n", "\n", "def memoize(f):\n", " cache = {}\n", " def decorated_function(*args):\n", " if args in cache:\n", " return cache[args]\n", " else:\n", " cache[args] = f(*args)\n", " return cache[args]\n", " return decorated_function\n", "\n", "@memoize\n", "def count_hops(numSteps, i=0):\n", " ways = 0\n", " for skip in [x for x in [1, 2, 3] if x <= numSteps]:\n", " if numSteps-skip == 0:\n", " ways += 1\n", " elif numSteps-skip > 0:\n", " ways += count_hops(numSteps-skip, i+1)\n", " return ways\n", "\n", "for x in range(10):\n", " print x, count_hops(x)\n", "print ''\n", "\n", "def count_hops2(x):\n", " memo = [1, 1, 2, 4]\n", " if x < 3:\n", " return memo[x]\n", " for i in xrange(3, x):\n", " memo[3], memo[2], memo[1], memo[0] = sum(memo[-3:]), memo[3], memo[2], memo[1]\n", " return memo[3]\n", "\n", "for x in range(10):\n", " print x, count_hops2(x)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[8, 10]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "map(sum, zip([3,4],[5,6]))" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ". . . . . .   .  .  .   . . . . . \n", ". . .   .  . . . .    . . .  . . \n", ".   . .  . . . . . . . .  . .   . \n", ". . . . .  . . . . . . .   . . . .  \n", ". . . . . . . . . . . . . . . .  . . . \n", ". .   . .    . .  . .  . .   . \n", ". .  . . . . . . . . .  .   . . .  \n", ". .  . . .   .    . . .  . . . . \n", " . . . .  . .  . . . . .   . . . . \n", " . . . . . . . . .  . . . . . . . . . \n", "True\n", "o . . . . .   .  .  .   . . . . . \n", "o . .   .  . . . .    . . .  . . \n", "o   . .  . . . . . . . .  . .   . \n", "o o o o o  . . . . . . .   . . . .  \n", ". . . . o o o o o o o o o o o o  . . . \n", ". .   . .    . .  . .  o o   . \n", ". .  . . . . . . . . .  .   o o o  \n", ". .  . . .   .    . . .  . . o o \n", " . . . .  . .  . . . . .   . . . o \n", " . . . . . . . . .  . . . . . . . . o \n" ] } ], "source": [ "# 8.2 - Robot in a Grid - Grid with R rows and C cols. Robot can move in 2 directions: right and down.\n", "# Certain cells are off limits. Design an algorithm to find path from top/left to bottom/right.\n", "\n", "import copy\n", "\n", "RIGHT = (0, 1)\n", "DOWN = (1, 0)\n", "LEFT = (0, -1)\n", "UP = (-1, 0)\n", "\n", "def make_grid(r, c, densityOfOffLimits=0.3):\n", " grid = (np.random.random((r,c)) > densityOfOffLimits).astype(np.int)\n", " grid[0, 0] = 1\n", " grid[-1, -1] = 1\n", " return grid\n", "\n", "def is_good_pos(pos, grid):\n", " return pos[0] < grid.shape[0] and pos[1] < grid.shape[1] and grid[pos[0], pos[1]]\n", "\n", "def get_result(direction, pos, path):\n", " axis = {UP: 0, DOWN: 0, RIGHT: 1, LEFT: 1}[direction]\n", " newPos = map(sum, zip(pos, direction))\n", " if is_good_pos(newPos, grid):\n", " newPath = copy.copy(path)\n", " newPath.append(pos)\n", " path = find_path(grid, newPos, newPath)\n", " if path:\n", " return path\n", "\n", "def find_path(grid, pos=None, path=None):\n", " if pos is None:\n", " pos = [0, 0]\n", " if path is None:\n", " path = []\n", " if pos == map(sum, zip(grid.shape, [-1, -1])):\n", " path.append(pos)\n", " return path\n", " for direction in [RIGHT, DOWN]:\n", " result = get_result(direction, pos, path)\n", " if result:\n", " return result\n", " print 'returning', path, result\n", " path.append(result)\n", " return path\n", "\n", "def draw_grid(grid):\n", " for r in grid:\n", " for c in r:\n", " if c == 1:\n", " print '.',\n", " elif c > 1:\n", " print 'o',\n", " else:\n", " print chr(127),\n", " print ''\n", "\n", "grid = make_grid(10, 20)\n", "draw_grid(grid)\n", "result = find_path(grid)\n", "print result is not None\n", "if result is not None:\n", " result = np.array(result)\n", " grid[result[:,0], result[:,1]] = 8\n", " draw_grid(grid)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

WormLabCaltech/mprsq

src/stats_tutorials/Sleuth Beta Factors.ipynb

1

104436

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "toc": "true" }, "source": [ "# Table of Contents\n", " <p>" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from matplotlib import rc\n", "\n", "# set to use tex, but make sure it is sans-serif fonts only\n", "rc('text', usetex=True)\n", "rc('text.latex', preamble=r'\\usepackage{cmbright}')\n", "rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})\n", "\n", "# Magic function to make matplotlib inline;\n", "# other style specs must come AFTER\n", "%matplotlib inline\n", "\n", "# This enables SVG graphics inline. \n", "# There is a bug, so uncomment if it works.\n", "%config InlineBackend.figure_formats = {'png', 'retina'}\n", "\n", "# JB's favorite Seaborn settings for notebooks\n", "rc = {'lines.linewidth': 2, \n", " 'axes.labelsize': 18, \n", " 'axes.titlesize': 18, \n", " 'axes.facecolor': 'DFDFE5'}\n", "sns.set_context('notebook', rc=rc)\n", "sns.set_style(\"dark\")\n", "\n", "mpl.rcParams['xtick.labelsize'] = 16 \n", "mpl.rcParams['ytick.labelsize'] = 16 \n", "mpl.rcParams['legend.fontsize'] = 14" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "n2_1= pd.read_csv('../../sleuth_all_adjusted/kallisto/results/Project_17434_indexN704-N517/kallisto/abundance.tsv', sep='\\t')\n", "n2_2= pd.read_csv('../../sleuth_all_adjusted/kallisto/results/Project_17435_indexN704-N502/kallisto/abundance.tsv', sep='\\t')\n", "n2_3= pd.read_csv('../../sleuth_all_adjusted/kallisto/results/Project_17436_indexN704-N503/kallisto/abundance.tsv', sep='\\t')\n", "\n", "egl_1= pd.read_csv('../../sleuth_all_adjusted/kallisto/results/Project_17437_indexN704-N504/kallisto/abundance.tsv', sep='\\t')\n", "egl_2= pd.read_csv('../../sleuth_all_adjusted/kallisto/results/Project_17438_indexN704-N505/kallisto/abundance.tsv', sep='\\t')\n", "egl_3= pd.read_csv('../../sleuth_all_adjusted/kallisto/results/Project_17439_indexN704-N506/kallisto/abundance.tsv', sep='\\t')\n", "\n", "egl9_beta = pd.read_csv('../../sleuth_all_adjusted/kallisto/betasB.csv')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "frames = []\n", "\n", "for df in [n2_1, n2_2, n2_3]:\n", " df['genotype'] = 'wt'\n", " frames += [df]\n", "\n", "for df in [egl_1, egl_2, egl_3]:\n", " df['genotype'] = 'egl-9'\n", " frames += [df]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "tidy = pd.concat(frames)" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "plot_up = tidy[tidy.target_id == 'R08E5.3'].copy()\n", "plot_up['logtpm'] = plot_up.tpm.apply(np.log)\n", "plot_up['logcounts'] = plot_up.est_counts.apply(np.log)\n", "plot_up['estcounts'] = plot_up['est_counts']\n", "\n", "plot_down = tidy[tidy.target_id == 'F15E11.15a'].copy()\n", "plot_down['logtpm'] = plot_down.tpm.apply(np.log)\n", "plot_down['logcounts'] = (plot_down.est_counts + .5).apply(np.log)\n", "plot_down['estcounts'] = plot_down['est_counts']" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "bup = egl9_beta[egl9_beta.target_id == 'R08E5.3'].b.values[0]\n", "bdown = egl9_beta[egl9_beta.target_id == 'F15E11.15a'].b.values[0]" ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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DAAAg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\nAAAAAIB/oAkIhJATJyv16vtfKefDPao0jGrfU2kYKt59UMW7D+oqR4xSf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"text/plain": [ "<matplotlib.figure.Figure at 0x134afa5c0>" ] }, "metadata": { "image/png": { "height": 694, "width": 640 } }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 1)\n", "\n", "fig, ax = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=False, figsize=(10, 10))\n", "\n", "fig.suptitle(r'Calculation and meaning of \\beta', fontsize=20)\n", "\n", "\n", "sns.swarmplot(x='genotype', y='estcounts', data=plot_up, size=10, ax=ax[0, 0])\n", "ax[0, 0].set_yticks([0, 12500, 25000])\n", "ax[0, 0].set_ylabel('est.counts')\n", "ax[0, 0].set_xlabel('')\n", "ax[0, 0].set_title('R08E5.3')\n", "\n", "\n", "sns.swarmplot(x='genotype', y='logcounts', data=plot_up, size=10, ax=ax[1, 0])\n", "plt.ylim([5, 11])\n", "ax[1, 0].set_yticks([5, 7.5, 10])\n", "ax[1, 0].plot(x, x*bup + plot_up[plot_up.genotype == 'wt'].logcounts.mean(), 'k')\n", "ax[1, 0].set_xlabel('')\n", "ax[1, 0].set_ylabel(r'$\\log{(\\mathrm{est.counts})}$')\n", "ax[1, 0].set_xticks([0, 1])\n", "\n", "\n", "sns.swarmplot(x='genotype', y='estcounts', data=plot_down, size=10, ax=ax[0, 1])\n", "ax[0, 1].set_xlabel('')\n", "ax[0, 1].set_ylabel('')\n", "ax[0, 1].set_title('F15E11.15a')\n", "\n", "\n", "sns.swarmplot(x='genotype', y='logcounts', data=plot_down, size=10, ax=ax[1, 1])\n", "plt.ylim([-2, 7])\n", "ax[1, 1].set_yticks([-2, 2.5, 7])\n", "ax[1, 1].plot(x, x*bdown + plot_down[plot_down.genotype == 'wt'].logcounts.mean(), 'k')\n", "ax[1, 1].set_xlabel('')\n", "ax[1, 1].set_ylabel('')\n", "\n", "fig.text(0.5, 0.04, 'Genotype', ha='center', size=18)\n", "\n", "plt.savefig('../../output/meaningofbeta.svg', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\\begin{cases} \n", " H_0: \\beta_{i,\\mathrm{egl-9}} = 0\\\\\n", " H_1: \\beta_{i,\\mathrm{egl-9}} \\neq 0 \\\\\n", "\\end{cases}\n", "\n", "$$\n", " \\mathrm{iff}~q_{i, \\mathrm{egl-9}}<0.1,~\\mathrm{reject}~ H_0 \\\\\n", "$$\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": true, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ZhuiFengChaseWind/Self-Driving_Car_Capstone

ros/src/tl_detector/ipynb/train_model.ipynb

1

12307

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import tensorflow as tf\n", "import glob\n", "from scipy.misc import imread \n", "from scipy.misc import imresize\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout, Activation, Flatten, Convolution2D, MaxPooling2D, Conv2D, MaxPool2D, Lambda\n", "from keras.layers import BatchNormalization, LeakyReLU\n", "from keras.utils import np_utils\n", "import numpy as np\n", "from keras.preprocessing.image import ImageDataGenerator" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load data\n", "file_names_dict = dict()\n", "for i in [0, 1, 2]:\n", " image_files = glob.glob(f\"/tmp/tl_training/{i}/*[!a-z].jpg\")\n", " image_files = image_files[0:min(550, len(image_files))]\n", " file_names_dict[i] = image_files\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "550\n", "550\n", "550\n", "550\n" ] } ], "source": [ "data_dict = dict()\n", "for key in file_names_dict:\n", " print(len(file_names_dict.get(key)))\n", " fnames = file_names_dict.get(key)\n", " images = [imresize(imread(x),(300, 400)) for x in fnames]\n", " data_dict[key] = images\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "X = []\n", "Y = []\n", "for key in data_dict:\n", " x = np.array(data_dict.get(key))\n", " y = np.ones(shape=x.shape[0]) * key\n", " X.append(x)\n", " Y.append(y)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "X_train = np.vstack((X[0], X[1], X[2], X[3]))\n", "Y_train = np.hstack((Y[0], Y[1], Y[2], Y[3]))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Y_train = np.hstack((Y[0], Y[1], Y[2], Y[3]))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2200, 300, 400, 3)\n", "(2200,)\n" ] } ], "source": [ "print(X_train.shape)\n", "print(Y_train.shape)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "del X\n", "#del Y\n", "del data_dict\n", "del file_names_dict" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'red' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-54-8a348e3142ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#img = imresize(arr=test_img, size=(600,600))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#plt.imshow(test_img)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'red' is not defined" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(None, 3)\n" ] } ], "source": [ "model = Sequential\n", "\n", "model = Sequential([\n", " Lambda(lambda x: x / 255 - 0.5, input_shape=(300, 400, 3)),\n", " Conv2D(8, kernel_size=(5, 5), strides=(2,2)),\n", " LeakyReLU(alpha=0.1),\n", " BatchNormalization(),\n", " MaxPool2D(pool_size=(2,2), strides=(2,2)),\n", " Conv2D(16, kernel_size=(3, 3), strides=(1,1)),\n", " LeakyReLU(alpha=0.1),\n", " BatchNormalization(),\n", " MaxPool2D(pool_size=(2,2), strides=(2,2)),\n", "\n", " Conv2D(32, kernel_size=(3, 3), strides=(2, 2)),\n", " LeakyReLU(alpha=0.1),\n", " BatchNormalization(),\n", " Flatten(),\n", " Dense(55),\n", " Dense(4, activation='softmax')\n", "])\n", "\n", "model = Sequential([\n", " Lambda(lambda x: x / 255 - 0.5, input_shape=(300, 400, 3)),\n", " Conv2D(3, kernel_size=(5, 5), strides=(2,2)),\n", " LeakyReLU(alpha=0.1),\n", " BatchNormalization(),\n", " MaxPool2D(pool_size=(2,2), strides=(2,2)),\n", " Conv2D(9, kernel_size=(3, 3), strides=(2,2)),\n", " LeakyReLU(alpha=0.1),\n", " BatchNormalization(),\n", " MaxPool2D(pool_size=(2,2), strides=(2,2)),\n", " Dropout(0.25),\n", "\n", " Conv2D(18, kernel_size=(3, 3), strides=(1, 1)),\n", " LeakyReLU(alpha=0.1),\n", " BatchNormalization(),\n", " Flatten(),\n", " Dense(45),\n", " Dense(3, activation='softmax')\n", "])\n", "\n", "\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='adam',\n", " metrics=['accuracy'])\n", "print(model.output_shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# training\n", "datagen = ImageDataGenerator(\n", " rotation_range = 30,\n", " width_shift_range = 0.2,\n", " height_shift_range = 0.2,\n", " shear_range = 0.1,\n", " zoom_range = 0.4,\n", " horizontal_flip=True,\n", " vertical_flip=True\n", ")\n", "test_datagen = ImageDataGenerator()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 360 images belonging to 3 classes.\n", "Found 3264 images belonging to 3 classes.\n" ] } ], "source": [ "data_generator = datagen.flow_from_directory('/home/michael/tl_training',\n", " target_size=(300,400),\n", " batch_size=32,\n", " class_mode=\"categorical\")\n", "\n", "val_generator = test_datagen.flow_from_directory('/home/michael/tl_val',\n", " target_size=(300,400),\n", " batch_size=32,\n", " class_mode=\"categorical\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "210/210 [==============================] - 177s - loss: 2.3665 - acc: 0.3695 - val_loss: 1.1524 - val_acc: 0.3334\n", "Epoch 2/10\n", "210/210 [==============================] - 174s - loss: 0.9596 - acc: 0.5537 - val_loss: 4.9118 - val_acc: 0.3343\n", "Epoch 3/10\n", "210/210 [==============================] - 173s - loss: 0.3446 - acc: 0.8662 - val_loss: 2.2566 - val_acc: 0.5578\n", "Epoch 4/10\n", "210/210 [==============================] - 173s - loss: 0.1728 - acc: 0.9414 - val_loss: 1.4255 - val_acc: 0.7455\n", "Epoch 5/10\n", "210/210 [==============================] - 173s - loss: 0.0984 - acc: 0.9649 - val_loss: 0.6595 - val_acc: 0.7995\n", "Epoch 6/10\n", "210/210 [==============================] - 173s - loss: 0.1196 - acc: 0.9630 - val_loss: 0.8235 - val_acc: 0.8182\n", "Epoch 7/10\n", "210/210 [==============================] - 173s - loss: 0.0596 - acc: 0.9807 - val_loss: 0.4631 - val_acc: 0.8693\n", "Epoch 8/10\n", "210/210 [==============================] - 172s - loss: 0.0458 - acc: 0.9853 - val_loss: 0.4336 - val_acc: 0.9065\n", "Epoch 9/10\n", "210/210 [==============================] - 172s - loss: 0.1019 - acc: 0.9802 - val_loss: 0.6623 - val_acc: 0.8332\n", "Epoch 10/10\n", "210/210 [==============================] - 172s - loss: 0.0889 - acc: 0.9753 - val_loss: 0.4305 - val_acc: 0.8917\n", "Epoch 1/10\n", "210/210 [==============================] - 186s - loss: 0.0583 - acc: 0.9817 - val_loss: 0.4785 - val_acc: 0.8451\n", "Epoch 2/10\n", "210/210 [==============================] - 205s - loss: 0.0378 - acc: 0.9896 - val_loss: 0.3032 - val_acc: 0.9130\n", "Epoch 3/10\n", "210/210 [==============================] - 198s - loss: 0.0507 - acc: 0.9875 - val_loss: 0.3675 - val_acc: 0.9239\n", "Epoch 4/10\n", "210/210 [==============================] - 178s - loss: 0.0605 - acc: 0.9825 - val_loss: 0.3106 - val_acc: 0.8894\n", "Epoch 5/10\n", "210/210 [==============================] - 175s - loss: 0.0368 - acc: 0.9893 - val_loss: 0.2538 - val_acc: 0.9368\n", "Epoch 6/10\n", "210/210 [==============================] - 196s - loss: 0.0240 - acc: 0.9917 - val_loss: 0.3625 - val_acc: 0.9093\n", "Epoch 7/10\n", "209/210 [============================>.] - ETA: 0s - loss: 0.0406 - acc: 0.9889" ] } ], "source": [ "for i in range(100):\n", " model.fit_generator(data_generator, steps_per_epoch=210, epochs=10,\n", " validation_data=val_generator, validation_steps=300)\n", "\n", " model.save('../light_classification/models/whole_image_model_gpu{}.h5'.format(i))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "output node names are: ['output_node0', 'output_node1', 'output_node2', 'output_node3']\n" ] } ], "source": [ " " ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TensorShape([Dimension(None), Dimension(None)])" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Froze 18 variables.\n", "Converted 18 variables to const ops.\n" ] }, { "data": { "text/plain": [ "'./tl_dector.pb'" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

Tsiems/machine-learning-projects

Lab3/Lab3.ipynb

1

105522

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep-Learning Classification of Play Type in NFL Play-By-Play Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ian Johnson, Derek Phanekham, Travis Siems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The NFL (National Football League) has 32 teams split into two conferences, the AFC and NFC. Each of the 32 teams plays 16 games during the regular season (non-playoff season) every year. Due to the considerable viewership of American football, as well as the pervasiveness of fantasy football, considerable data about the game is collected. During the 2015-2016 season, information about every play from each game that occurred was logged. All of that data was consolidated into a single data set which is analyzed throughout this report.\n", "\n", "In this report, we will attempt to classify the type of a play, given the game situation before the play began. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Classification Task\n", "\n", "We will attempt to classify plays based on play type using information about the state of the game prior to the start of the play. This is expected to be an exceptionally difficult classification task, due to the amount of noise in the dataset (specifically, the decision to run vs pass the ball is often a seemingly random one). A successful classifier would have huge value to defensive coordinators, who could call plays based on the expected offensive playcall. Because it may be very difficult to identify what play will be called, it is relevant to provide a probability of a given playcall in a situation. For example, it would be useful to provide the probability of a 4th down conversion attempt, even if the overall prediction is that a punt occurs. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Preparation\n", "\n", "In order to prepare the data for classification, a number of variables from the original dataset will be removed, as they measure the result of the play, not the state of the game prior to the start of the play. The dataset being included in this report has had previous cleaning and preprocessing performed in our previous report." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 38600 entries, 0 to 42875\n", "Data columns (total 13 columns):\n", "Drive 38600 non-null int64\n", "qtr 38600 non-null int64\n", "down 38600 non-null int64\n", "TimeSecs 38600 non-null float64\n", "yrdline100 38600 non-null float64\n", "ydstogo 38600 non-null float64\n", "ydsnet 38600 non-null float64\n", "GoalToGo 38600 non-null int64\n", "posteam 38600 non-null object\n", "DefensiveTeam 38600 non-null object\n", "PosTeamScore 38600 non-null float64\n", "ScoreDiff 38600 non-null float64\n", "PlayType 38600 non-null object\n", "dtypes: float64(6), int64(4), object(3)\n", "memory usage: 4.1+ MB\n" ] } ], "source": [ "#For final version of report, remove warnings for aesthetics.\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "#Libraries used for data analysis\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn import preprocessing\n", "\n", "df = pd.read_csv('data/cleaned.csv') # read in the csv file\n", "\n", "\n", "\n", "colsToInclude = [ 'Drive', 'qtr', 'down',\n", " 'TimeSecs', 'yrdline100','ydstogo','ydsnet',\n", " 'GoalToGo','posteam','DefensiveTeam',\n", " 'PosTeamScore','ScoreDiff', 'PlayType']\n", "\n", "df = df[colsToInclude]\n", "df = df[[p not in [\"Sack\", \"No Play\", \"QB Kneel\", \"Spike\"] for p in df.PlayType]]\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Neural Network Embeddings\n", "\n", "We will use neural network embeddings from TensorFlow for the posteam and DefensiveTeam. However, we will be building these embeddings manually using one-hot encoding and additional fully-connected layers in each of the deep architectures. The following Python function was used for one-hot encoding, and was adapted from the website referenced in the code." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_extraction import DictVectorizer\n", "\n", "#Simple function for 1 hot encoding\n", "def encode_onehot(df, cols):\n", " \"\"\"\n", " One-hot encoding is applied to columns specified in a pandas DataFrame.\n", " \n", " Modified from: https://gist.github.com/kljensen/5452382\n", " \n", " Details:\n", " \n", " http://en.wikipedia.org/wiki/One-hot\n", " http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html\n", " \n", " @param df pandas DataFrame\n", " @param cols a list of columns to encode\n", " @return a DataFrame with one-hot encoding\n", " \"\"\"\n", " vec = DictVectorizer()\n", " \n", " vec_data = pd.DataFrame(vec.fit_transform(df[cols].to_dict(outtype='records')).toarray())\n", " vec_data.columns = vec.get_feature_names()\n", " vec_data.index = df.index\n", " \n", " df = df.drop(cols, axis=1)\n", " df = df.join(vec_data)\n", " return df\n", "\n", "df = encode_onehot(df, cols=['posteam', 'DefensiveTeam'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following are descriptions of the remaining data columns in the play-by-play dataset. Note that the one-hot encoded columns do not follow the structure listed below, but for the sake of readability they are presented as if they were not one-hot encoded.\n", "\n", "* **GameID** (*nominal*): A unique integer which identifies each game played \n", "* **Drive** (*ordinal*): The number of the drive during a game when the play occurred (indexed at one, so the first drive of the game has Drive 1 and the nth drive has Drive n)\n", "* **qtr** (*interval*): The quarter of the game when the play occurred\n", "* **down** (*interval*): The down when the play occurred (1st, 2nd, 3rd, or 4th)\n", "* **TimeSecs** (*interval*): The remaining game time, in seconds, when the play began\n", "* **yrdline100** (*ratio*): The absolute yard-line on the field where the play started (from 0 to 100, where 0 is the defensive end zone and 100 is the offensive end zone of the team with the ball)\n", "* **ydstogo** (*ratio*): The number of yards from the line of scrimmage to the first-down line\n", "* **ydsnet** (*ratio*): The number of yards from the beginning of the drive to the current line of scrimmage\n", "* **GoalToGo** (*nominal*): A binary attribute whose value is 1 if there is no first down line (the end-zone is the first down line) or 0 if there is a normal first down line\n", "* **posteam** (*nominal*): A 2-or-3 character code representing the team on offense\n", "* **PosTeamScore** (*ratio*): The score of the team with possesion of the ball\n", "* **DefensiveTeam** (*nominal*): A 2-or-3 character code representing the team on defense\n", "* **ScoreDiff**: (*ratio*) The difference in score between the offensive and defensive at the time of the play.\n", "* **PlayType**: (*nominal*) An attribute that identifies the type of play (i.e. Kickoff, Run, Pass, Sack, etc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance Metrics\n", "\n", "The value of a classifier will be evaulated using the following cost matrix. Costs in the matrix which are set to 1 represent play predictions that would never actually occur in the context of a football game. For example, if we predicted a pass play and a kickoff occurs, then the classifier has a significant flaw. \n", "\n", "Bolded weights represent actual mispredictions that could occur.\n", "\n", "| | Actual Play | Pass | Run | Kickoff | Punt | Extra Point | Field Goal | Onside Kick |\n", "|----------------|-------------|------|-----|---------|-------------|------------|-------------|-------------|\n", "| Predicted Play | | | | | | | | |\n", "| Pass | | 0 | **0.1** | 1 | **0.15** | **0.15** | **0.1** | 1 | \n", "| Run | | **0.1** | 0 | 1 | **0.15** | **0.15** | **0.1** | 1 | \n", "| Kickoff | | 1 | 1 | 0 | 1 | 1 | 1 | **0.75** |\n", "| Punt | | **0.25** | **0.25** | 1 | 0 |1 | **0.15** | 1 |\n", "| Extra Point | | **0.4** | **0.4** | 1 | 1 | 0 | 1 | 1 |\n", "| Field Goal | | **0.4** | **0.4** | 1 | **0.1** | 1 | 0 | 1 |\n", "| Onside Kick | | 1 | 1 | **0.25** | 1 |1 | 1 | 0 |\n", "\n", "\n", "This performance metric is the best for this classification problem because the actual potential cost of an incorrect play prediction varies significantly based on the nature of the misclassification. In an actual football game, it would be very costly to predict an extra point and have the opposing team run a pass play. This means that they ran a fake extra point and went for a two-point conversion. However, if a pass play is predicted and a run play occurs, the cost of the error is minimal because the defensive strategy for defending against run and pass plays.\n", "\n", "Because the goal of this classification is to help inform defensive play-calling, a cost matrix is helpful because it allows a defensive coordinator to set his own costs to produce his own classifier, without any knowledge of the actualy computation that occurs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross Validation Methodology\n", "\n", "We use a sequential k-fold partition of the data because this mirrors how data will be collected and analyzed. For our use, we assume that it is okay to use data in the “future” to predict data “now” because it can represent data from a previous football season. For example, if we use the first 90% of data for training and the remaining 10% of data for testing, that would simulate using most of the current season's data to predict plays towards the end of this season. If we use the first 50% and last 40% of data for training and the remaining 10% for testing, this would simulate using 40% of the previous season's data and the first 50% of this season's data to predict plays happening around the middle of the current season. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.model_selection import KFold\n", "\n", "#Using a 10-fold sequential split.\n", "#Note that this cv object is unused, but is here for reference\n", "cv = KFold(n_splits=10)\n", "\n", "y,levels = pd.factorize(df.PlayType.values)\n", "X = df.drop('PlayType', 1).values.astype(np.float32)\n", "\n", "\n", "num_classes = len(levels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling\n", "\n", "Before we build any models, we define a cost function in Python below, which is used to test all of our forthcoming models. It computes the item-wise product of a confusion matrix and our cost matrix, and returns the sum of all of the elements in the resulting matrix. We also define a function to calculate area under roc curve for a multiclass classification problem." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import roc_curve, auc,make_scorer\n", "from scipy import interp\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "plt.style.use('ggplot')\n", "\n", "cost_mat = [[0 ,.1 , 1 , .15 , 0.15, .1 , 1 ],\n", " [.1 , 0 , 1 , 0.15, 0.15, 0.1, 1 ],\n", " [1 , 1 , 0 , 1 , 1 , 1 , 0.75],\n", " [.25,0.25, 1 , 0 , 1 ,0.15, 1 ],\n", " [0.4, 0.4, 1 , 1 , 0 , 1 , 1 ],\n", " [0.4, 0.4, 1 , 0.1 , 1 , 0 , 1 ],\n", " [1 , 1 , 0.25, 1 , 1 , 1 , 0 ]]\n", "\n", "def cost(Y, yhat):\n", " return np.sum(np.multiply(confusion_matrix(Y,yhat), cost_mat))\n", "\n", "def auc_of_roc(Y,yhat, levels=['Pass', 'Run', 'Kickoff', 'Punt', 'Extra Point', 'Field Goal', 'Onside Kick']):\n", " \n", " mean_tpr = 0.0\n", " mean_fpr = np.linspace(0, 1, 100)\n", " for c in levels:\n", " tempY = [x==c for x in Y]\n", " tempYhat = [x==c for x in yhat]\n", " \n", " fpr, tpr, thresholds = roc_curve(tempY, tempYhat)\n", " mean_tpr += interp(mean_fpr, fpr, tpr)\n", " mean_tpr[0] = 0.0\n", "\n", " roc_auc = auc(fpr, tpr)\n", "\n", " mean_tpr /= len(levels)\n", " mean_tpr[-1] = 1.0\n", " mean_auc = auc(mean_fpr, mean_tpr)\n", " \n", " return mean_auc\n", "\n", "#For use in the final deployment section\n", "scorer = make_scorer(cost)\n", "auc_roc_scorer = make_scorer(auc_of_roc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some Setup Code for TensorFlow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Imports" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.contrib import learn\n", "from tensorflow.contrib import layers\n", "\n", "#Suppress all non-error warnings\n", "tf.logging.set_verbosity(tf.logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Calculating Costs for a Model\n", "\n", "The following code performs cross validation on a model with a given step count and learning rate. This will be used as part of the grid search, as well as for evaluating the final classifier after the gridsearch is complete." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_scores_for_model(model_fn, X, y, steps=1000, learning_rate=0.05, num_splits = 10):\n", "\n", " auc = []\n", " costs = []\n", " \n", " for train_index, test_index in KFold(n_splits=num_splits).split(X, y):\n", " classifier = learn.TensorFlowEstimator(model_fn=model_fn, \n", " n_classes=7, batch_size=1000,\n", " steps=steps, learning_rate=learning_rate)\n", " classifier.fit(X[train_index], y[train_index])\n", " yhat = classifier.predict(X[test_index])\n", "\n", " costs.append(cost(y[test_index], yhat))\n", " auc.append(auc_of_roc(y[test_index], yhat, levels=range(0,7)))\n", "\n", " return costs, auc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Grid Search\n", "\n", "Because we're performing a grid search on a TensorFlow estimator, we use our own grid search function, instead of the one provided in sklearn, for the sake of simplicity. Our grid search will search for optimal values of *steps* and *learning_rate*. During the grid search, a subsample of 5000 items will be used, and only 3 folds of cross validation will occur. This is done to decrease computation time, which is otherwise many hours per grid search.\n", "\n", "Note that the grid search function itself is not parallelized. This is because the underlying TensorFlow modelling is all parallelized, so maximal CPU usage is already being achieved." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def grid_search(model_fn, steps_list, learning_rate_list):\n", " \n", " costs = []\n", " \n", " for steps in steps_list:\n", " \n", " step_costs = []\n", " \n", " for rate in learning_rate_list:\n", " \n", " step_costs.append(np.mean(get_scores_for_model(model_fn, X[0:5000, :], y[0:5000], steps, rate, 3)[0]))\n", " print(step_costs)\n", " print(costs)\n", " \n", " costs.append(step_costs)\n", " \n", " min_idx = np.argmin(costs)\n", " \n", " return costs, steps_list[min_idx//len(costs[0])], learning_rate_list[min_idx%len(costs[0])]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import seaborn as sns\n", "\n", "def grid_search_heatmap(costs, steps, rates):\n", " \n", " ax = sns.heatmap(np.array(costs))\n", " ax.set(xlabel='Learning Rate', ylabel='Step Count')\n", " ax.set_xticklabels(rates[::-1])\n", " ax.set_yticklabels(steps[::-1])\n", " ax.set_title(\"Grid Search Heatmap\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First Deep Learning Architecture\n", "\n", "The first deep learning architecture will be adapted from a model designed by PayPal that is used for anomaly detection. Because the vast majority of football plays are either runs or passes, and there are only a few anomalous plays, like onside kicks, etc., it stands to reason that an anomaly detection architecture would perform well for this classification task.\n", "\n", "The architecture is quite simple: it consists of a set of 6 fully connected layers of 700 neurons, followed by a hyperbolic tangent activation function and then a single fully connected layer for output. We will adapt this model slightly to allow for the embedding of the team attributes. We will split the data into embedding and non-embedding data, run each subset of data through 6 fully connected layers of 700 neurons, and then combine their output as the input into a single final layer, used for classification. A simple drawing of the architecture is shown below.\n", "\n", "<img src=\"NetworkDrawings/network1.png\">\n", "\n", "<a href=\"http://university.h2o.ai/cds-lp/cds02.html?mkt_tok=3RkMMJWWfF9wsRonvanAZKXonjHpfsX56%2BkqUaG0lMI%2F0ER3fOvrPUfGjI4ATsBlI%2BSLDwEYGJlv6SgFTLTBMbBrwrgKXBk%3D\">\n", "The original talk about this architecture can be found here.\n", "</a>\n", "\n", "##### Defining the Model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def deep_model_1(X, y):\n", "\n", " #Embeddings layer\n", " teamembeddings = layers.stack(X[:,11:75], layers.fully_connected, [700 for _ in range(6)])\n", " teamembeddings = tf.nn.tanh(teamembeddings)\n", " \n", " #Non-embeddings features\n", " otherfeatures = X[:,0:10]\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [700 for _ in range(6)])\n", " \n", " tensors = tf.concat(1, [teamembeddings, otherfeatures])\n", " tensors = tf.nn.tanh(tensors)\n", " \n", " pred,loss = learn.models.logistic_regression(tensors, y)\n", "\n", " return pred, loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Grid Searching on the Model\n", "\n", "A grid search is performed on the model to find the approximately optimal step count and learning rate for the TensorFlowEstimator. Note that this particular grid search takes about 14 hours to run, so it should not be re-run." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 0.01)\n" ] } ], "source": [ "costs, optimal_steps, optimal_rate = grid_search(deep_model_1, [250,500,1000,1500,2000], [.05, .01, .005, .001, .0005])\n", "print((optimal_steps, optimal_rate))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the grid search returned the optimal step count and rate, it is meaningful to visualize the grid that was generated, to get an idea for how much better these particular hyperparameters are than the other possible combinations in the grid." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QERGZQKEw/mPItWvX4OXlBRsbG1hbW8Pb2xv79+/HgAEDYGV1rwy+9NJL0Gg0AIADBw4g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\nR49GRkZGg30yMjIQFBQEAPD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"text/plain": [ "<matplotlib.figure.Figure at 0x119e69240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_search_heatmap(costs, [250,500,1000,1500,2000], [.05, .01, .005, .001, .0005])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above grid search shows that, while there is no obvious pattern with respect to the performance, it seems that the major diagonal generally has the lowest costs. Interestingly, the lower-left tile has a significantly higher score than all of the others. This is likely due to overlearning, as it comes from a section in the grid with the highest possible step count.\n", "\n", "The optimal parameter pair, of 500 steps and a learning rate of .001, is only marginally better than the second-best option, which falls at 1000 steps and a learning rate of .005." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Calculating Costs and Area under ROC Curve Scores for the Model\n", "\n", "With the optimal step count and learning rate, the costs of the model built with the given step count and rate are computed." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1843.45, 2005.0, 2017.0, 1971.05, 2069.25, 2021.75, 1942.8499999999999, 2048.25, 2022.0, 2042.0999999999999]\n", "[0.51052936522990444, 0.50011988848000888, 0.49999999999999994, 0.50082942390789931, 0.49999999999999994, 0.49999999999999994, 0.50244062722123584, 0.49999999999999994, 0.50046048738783466, 0.49987541305436189]\n" ] } ], "source": [ "costs_model_1, auc_roc_model_1 = get_scores_for_model(deep_model_1, X, y, optimal_steps, optimal_rate)\n", "\n", "print(costs_model_1)\n", "print(auc_roc_model_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The costs and auc scores computed above are hard-coded below for later use, so that they don't need to be computed again." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "costs_model_1 = [1843.45, 2005.0, 2017.0, 1971.05, 2069.25, 2021.75, 1942.8499999999999, 2048.25, 2022.0, 2042.0999999999999]\n", "auc_roc_model_1 = [0.51052936522990444, 0.50011988848000888, 0.49999999999999994, 0.50082942390789931, 0.49999999999999994, 0.49999999999999994, 0.50244062722123584, 0.49999999999999994, 0.50046048738783466, 0.49987541305436189]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second Deep Learning Architecture\n", "\n", "The second deep learning architecture will be adapted from a model designed by O'Shea Research that is used for radio modulation recognition. Because the majority of the football data is ordinal numerical data, an architecture that uses similar data for a classification task poses as a candidate model for the football play classification task.\n", "\n", "The architecture consists of a set of two convolutional reLu neurons, followed by a dense relu and dense softmax activation function. The result is a single fully connected layer for output. We will adapt this model slightly to allow for the embedding of the team attributes. We will split the data into embedding and non-embedding data, run each subset of data through the convolutional neurons, combine their output and run it through the dense neurons, and take a logistic regression for the input into a single final layer, used for classification. A simple drawing of the architecture is shown below.\n", "\n", "<img src=\"NetworkDrawings/network2.png\">\n", "\n", "<a href=\"https://oshearesearch.com/index.php/tag/deep-learning/\">\n", "The original discussion about this architecture can be found here.\n", "</a>\n", "\n", "#### Defining the Model" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def deep_model_2(X, y):\n", " \n", " \n", " #Embeddings layer\n", " teamembeddings = layers.stack(X[:,11:75], layers.fully_connected, [200,1,3], activation_fn=tf.nn.relu)\n", " teamembeddings = layers.stack(teamembeddings, layers.fully_connected, [50,2,3], activation_fn=tf.nn.relu)\n", " \n", " #Non-embeddings features\n", " otherfeatures = X[:,0:10]\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [50,1,3], activation_fn=tf.nn.relu)\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [12,2,3], activation_fn=tf.nn.relu)\n", "\n", " #combine the team and play data\n", " tensors = tf.concat(1, [teamembeddings, otherfeatures])\n", " \n", " tensors = layers.stack(tensors, layers.fully_connected, [100], activation_fn=tf.nn.relu)\n", " tensors = layers.stack(tensors, layers.fully_connected, [7], activation_fn=tf.nn.softmax)\n", " \n", "\n", " \"\"\" # This section is doing all layers before combining team and play data\n", " #Embeddings layer\n", " teamembeddings = layers.stack(X[:,11:75], layers.fully_connected, [200,1,3], activation_fn=tf.nn.relu)\n", " teamembeddings = layers.stack(teamembeddings, layers.fully_connected, [50,2,3], activation_fn=tf.nn.relu)\n", "\n", " teamembeddings = layers.stack(teamembeddings, layers.fully_connected, [100], activation_fn=tf.nn.relu)\n", " teamembeddings = layers.stack(teamembeddings, layers.fully_connected, [7], activation_fn=tf.nn.softmax)\n", " \n", " #Non-embeddings features\n", " otherfeatures = X[:,0:10]\n", "\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [50,1,3], activation_fn=tf.nn.relu)\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [12,2,3], activation_fn=tf.nn.relu)\n", "\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [100], activation_fn=tf.nn.relu)\n", " otherfeatures = layers.stack(otherfeatures, layers.fully_connected, [7], activation_fn=tf.nn.softmax)\n", "\n", " #combine the team and play data\n", " tensors = tf.concat(1, [teamembeddings, otherfeatures])\n", " \"\"\"\n", "\n", " \n", " \n", " pred, loss = learn.models.logistic_regression(tensors, y)\n", " \n", " return pred, loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Grid Searching on the Model\n", "\n", "A grid search is performed on the model to find the approximately optimal step count and learning rate for the TensorFlowEstimator." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1500, 0.005)\n" ] } ], "source": [ "costs, optimal_steps, optimal_rate = grid_search(deep_model_2, [250,500,1000,1500,2000], [.05, .01, .005, .001, .0005])\n", "print((optimal_steps, optimal_rate))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the grid search, once again, gave us the optimal step count and rate, it is worthwhile to visualize the grid that was generated, to get an idea for how much better these particular hyperparameters are than the other possible combinations in the grid." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tbmMBAMAPNs2fXAMFAMAfVKAAAGvZtASlAgUAwA9UoAAAS9n1PlASKADAUmaN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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12af33668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_search_heatmap(costs, [250,500,1000,1500,2000], [.05, .01, .005, .001, .0005])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This heatmap shows a stronger pattern than the map for model 1. It shows the optimal to be centered around 1500 steps and a learning rate of 0.005, with somewhat constant cost increase as the two parameters grow greater or smaller than thoses values.\n", "\n", "There is, however, still an abnormality with very high step count and very low learning rate, once again presumably due to overtraining. This may be something that could be fixed by changing the batch size for the tensorflow estimator function, but this grid search already took about 10 hours to run, so adding a 3rd parameter is not feasible with the compute power available to us." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Calculating Costs and Area under ROC Curve Scores for the Model\n", "\n", "With the optimal step count and learning rate, the costs of the model built with the given step count and rate are computed." ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2042.75, 2007.5, 2017.0, 1978.0, 2069.25, 2021.75, 1974.0, 2048.25, 2027.0, 2041.25]\n", "[0.5085592, 0.4820538, 0.48592543, 0.48381174, 0.47515301, 0.475777, 0.47903248, 0.48797259, 0.47638676, 0.4891856]\n" ] } ], "source": [ "costs_model_2, auc_roc_model_2 = get_scores_for_model(deep_model_2, X, y, optimal_steps, optimal_rate)\n", "\n", "print(costs_model_2)\n", "print(auc_roc_model_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The costs and auc scores computed above are hard-coded below for later use, so that they don't need to be computed again." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "costs_model_2 = [2042.75, 2007.5, 2017.0, 1978.0, 2069.25, 2021.75, 1974.0, 2048.25, 2027.0, 2041.25]\n", "auc_roc_model_2 = [ 0.5085592, 0.4820538, 0.48592543, 0.48381174, 0.47515301, 0.475777, 0.47903248, 0.48797259, 0.47638676, 0.4891856]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Third Deep Learning Architecture\n", "\n", "Due to the poor performance of the two architectures we used in the previous two deep models, we have elected to build our own model to test on the data. This third deep learning architecture was primarily the product of trial and error. Many different cofigurations of layers and activation functions were tried, and in the given time, this the setup that resulted in the lowest cost for the model. \n", "\n", "We explored a number of different architectures, of varying complexity, including an LSTM network from TensorFlow, and we discovered that, generally speaking, simple networks which look like just a multi-layer perceptron seem to provide the lowest cost. We tuned the sizes of the hidden layers with trial and error, and we discovered that the network shown below is the most efficient. \n", "\n", "Note that the team embeddings are not shown in the main drawing, as they are computed using a very small network prior to the main MLP. Interestingly, we found that the smallest networks were the best for the team embeddings. We beleive that this is due to overlearning that may occur if the embeddings have too much weight in the overall network architecture.\n", "\n", "The architecture consists of 6 fully connected layers. The first 3 layers have 1000 nodes, followed by a 500 node layer, a 200 node layer, and another 1000 node layer. This is followed by a relu activation function. The output of this network is then used for classifcation.\n", "\n", "<img src=\"NetworkDrawings/network3.png\">\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def deep_model_3(X, y):\n", "\n", " #Embeddings layer\n", " teamembeddings = layers.stack(X[:,11:75], layers.fully_connected, [20,4])\n", " #teamembeddings = tf.nn.relu(teamembeddings)\n", " \n", " #Non-embeddings features\n", " otherfeatures = X[:,0:10]\n", "\n", " #Concatenate the embeddings with the non-embeddings\n", " tensors = tf.concat(1, [teamembeddings, otherfeatures])\n", " \n", " #[500,200,100,500][1000,1000,1000,500,200,1000]\n", " tensors = layers.stack(tensors, layers.fully_connected, [1000,1000,1000,500,200,1000])\n", " \n", " #Relu activation function\n", " tensors = tf.nn.relu(tensors)\n", "\n", " pred, loss = learn.models.logistic_regression(tensors, y)\n", " \n", " return pred, loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Grid Searching on the Model\n", "\n", "A grid search is performed on the model to find the approximately optimal step count and learning rate for the TensorFlowEstimator." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2000, 0.005)\n" ] } ], "source": [ "costs, optimal_steps, optimal_rate = grid_search(deep_model_3, [250,500,1000,1500,2000], [.05, .01, .005, .001, .0005])\n", "print((optimal_steps, optimal_rate))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the grid search returned the optimal step count and rate, it is meaningful to visualize the grid that was generated, to get an idea for how much better these particular hyperparameters are than the other possible combinations in the grid." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Pj6egoIDc3FxsNpvr+zabDYfDUWO71SbRo0ePEh8fD0BaWhpDhgyhX79+jB07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\n6XTSpk0bFi9ejI+PD926dSM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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2029534940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_search_heatmap(costs, [250,500,1000,1500,2000], [.05, .01, .005, .001, .0005])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The grid search for model 3, interestingly, shows a significant trend toward lower costs in the lower-left part of the map (higher step count and lower learning rate). This stands to reason, and aligns much more with what is expected than the unruly heatmap that was generated by the model 1 grid search. This also aligns with what one might expect of a simple multi-layer perceptron model, which this network architecture closely resembles.\n", "\n", "This grid search showed 2000 steps with a learning rate of 0.0005 as an optimal. This optimality only slightly outperforms the same learning rate with only 1000 steps, but significantly outperforms every hyper-parameter pair above the major diagonal of the heatmap. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Calculating Costs and Area under ROC Curve Scores for the Model\n", "\n", "With the optimal step count and learning rate, the costs of the model built with the given step count and rate are computed." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1750.0, 1722.85, 1709.0, 1710.75, 1716.25, 1742.0, 1650.0, 1714.0, 1699.5, 1684.5]\n", "[0.62998537, 0.61758681, 0.61135905, 0.61209804, 0.6146912, 0.62633358, 0.5845431, 0.61362667, 0.60702243, 0.60023413]\n" ] } ], "source": [ "costs_model_3, auc_roc_model_3 = get_scores_for_model(deep_model_3, X, y, optimal_steps, optimal_rate)\n", "\n", "print(costs_model_3)\n", "print(auc_roc_model_3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The costs and auc scores computed above are hard-coded below for later use, so that they don't need to be computed again." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "costs_model_3 = [ 1750.0 , 1722.85, 1709.0, 1710.75, 1716.25, 1742.0, 1650.0 , 1714.0, 1699.50, 1684.50]\n", "auc_roc_model_3 = [0.62998537, 0.61758681, 0.61135905, 0.61209804, 0.6146912 , 0.62633358, 0.5845431 , 0.61362667, 0.60702243, 0.60023413]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Architecture Comparison\n", "\n", "The following getDifference function is used to create a tuple of the range of possible differences of mean for two sets of scores (cost or auc_roc), with 95% confidence. We use the second of the two confidence interval tests proposed in the ICA3 reversed assignment, because the datasets cannot be assumed to be independent, so the binomial approximation to the normal distribution does not hold." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def getDifference(cost1,cost2,z_val=2.26,size=10):\n", " cost1 = np.asarray(cost1)\n", " cost2 = np.asarray(cost2)\n", " diff12 = cost1 - cost2\n", " sigma12 = np.sqrt(np.sum(diff12*diff12) * 1/(size-1))\n", " d12 = (np.mean(diff12) + 1/(np.sqrt(size)) * z_val * sigma12, np.mean(diff12) - 1/(np.sqrt(size)) * z_val * sigma12)\n", " return d12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cost Difference\n", "\n", "The getDifference function is now used to create confidence intervals for the cost differences of the 3 possible pairs of two architectures created from the set of 3 architectures." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average Model 1 vs Model 2 Difference: [ 23.69702196 -72.50702196]\n", "Average Model 1 vs Model 3 Difference: [ 512.26558725 64.50441275]\n", "Average Model 2 vs Model 3 Difference: [ 549.47453987 76.10546013]\n" ] } ], "source": [ "d_one_two = np.array(getDifference(costs_model_1, costs_model_2))\n", "d_one_three = np.array(getDifference(costs_model_1, costs_model_3))\n", "d_two_three = np.array(getDifference(costs_model_2, costs_model_3))\n", "\n", "print(\"Average Model 1 vs Model 2 Difference:\", d_one_two)\n", "print(\"Average Model 1 vs Model 3 Difference:\", d_one_three)\n", "print(\"Average Model 2 vs Model 3 Difference:\", d_two_three)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above 3 confidence intervals show that the first two architectures performed similarly, as the model 1 to model 2 difference confidence interval contains zero. However, the third architecture, which we created specifically because of how poorly the first two architectures performed, did significantly outperform both of the first two models, as zero does not appear in either of the confidence intervals including model 3. Therefore, it can be concluded that, with respect to cost, our \"home-grown\" model 3 does significantly outperform the other architectures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Area Under Multiclass ROC Curve Difference\n", "\n", "The same process is now used for confidence intervals for the auc_roc metric." ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average Model 1 vs Model 2 Difference: [ 0.0309479 0.00313162]\n", "Average Model 1 vs Model 3 Difference: [-0.02675915 -0.19388588]\n", "Average Model 2 vs Model 3 Difference: [-0.03095869 -0.22376586]\n" ] } ], "source": [ "d_one_two = np.array(getDifference(auc_roc_model_1, auc_roc_model_2))\n", "d_one_three = np.array(getDifference(auc_roc_model_1, auc_roc_model_3))\n", "d_two_three = np.array(getDifference(auc_roc_model_2, auc_roc_model_3))\n", "\n", "print(\"Average Model 1 vs Model 2 Difference:\", d_one_two)\n", "print(\"Average Model 1 vs Model 3 Difference:\", d_one_three)\n", "print(\"Average Model 2 vs Model 3 Difference:\", d_two_three)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The confidence intervals for area under ROC curve provide the same insight as the confidence intervals for the cost function. The first 2 models are statistically similar with 95% confidence, but the third model statistically outperforms both of the first 2, with 95% confidence, because zero does not fall in the ROC difference confidence interval for model 3 vs the other 2 models. \n", "\n", "With the ROC curve score's confirmation, we can confidently declare that architecture 3 is better for this classification task than models 1 and 2. However, in our deployment section, we will address the possibility that even our best deep learning architecture may be outperformed by conventional machine learning algorithms which were explored in Project 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deployment" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Given the performances of the three above deep learning models for classifying NFL play types, we beleive that model 3 would be the best choice, as it, with statistical significance, outperformed both other models with respect to both cost and AUC_ROC score. However, the costs for the models generated, as well as the AUC_ROC scores, seemed to, in general be inferior to the scores from the conventional algorithms. \n", "\n", "In our previous report, we declared random forests to be our preffered model, due to their superior performance, and ease of use / computational inexpense. Therefore, for the purpose of comparison, we will compare the performance of random forests to the performance of our best deep learning architecture.\n", "\n", "Note that we (mistakenly) used an incorrect cross-validation strategy for Lab 2, so random forest scores must be re-generated. The code below, to generate the new scores, is adapted from Lab 2 for use with our new cross validation strategy. \n", "\n", "First, we perform some setup that is needed to use the original SKLearn library for classification." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.model_selection import cross_val_score\n", "\n", "#Building the class weight map\n", "PlayTypes = df.PlayType.value_counts().index.tolist()\n", "Costs = [sum(x) for x in cost_mat]\n", "y = df.PlayType.values\n", "\n", "ClassWeights = dict(zip(PlayTypes, Costs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we produce cost scores for the random forest classifier." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Pipeline for cost evaluation\n", "clf = Pipeline([('sca',StandardScaler()),\n", " ('clf',RandomForestClassifier(class_weight=ClassWeights, n_estimators=250))])\n", "\n", "per_fold_eval_criteria = cross_val_score(estimator=clf,\n", " X=X,\n", " y=y,\n", " cv=cv,\n", " scoring=scorer,\n", " n_jobs=-1\n", " )\n", "\n", "RFCosts = per_fold_eval_criteria" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we produce AUC_ROC scores for the random forest classifier." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Pipeline for cost evaluation\n", "clf = Pipeline([('sca',StandardScaler()),\n", " ('clf',RandomForestClassifier(class_weight=ClassWeights, n_estimators=250))])\n", "\n", "per_fold_eval_criteria = cross_val_score(estimator=clf,\n", " X=X,\n", " y=y,\n", " cv=cv,\n", " scoring=auc_roc_scorer,\n", " n_jobs=-1\n", " )\n", "\n", "RF_auc = per_fold_eval_criteria" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cost and auc scores for the random forest are saved below so that they don't need to be re-computed." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "RFCosts = [ 988.05, 1007.9 , 976.75, 956.75, 971.75, 949.55, 919. , 992.5 , 985.75, 956. ]\n", "RF_auc = [ 0.89186154, 0.84936065, 0.85641263, 0.84052327, 0.84994278, 0.84799384, 0.86985361, 0.88280675, 0.87433496, 0.85303625]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Deep Learning vs Random Forests (Cost): (1296.8375953662744, 182.13240463372551)\n", "Deep Learning vs Random Forests (AUC_ROC): (-0.061045371130097753, -0.43868380886990221)\n" ] } ], "source": [ "auc_diff = getDifference(auc_roc_model_3, RF_auc)\n", "cost_diff = getDifference(costs_model_3, RFCosts)\n", "\n", "print(\"Deep Learning vs Random Forests (Cost):\", cost_diff)\n", "print(\"Deep Learning vs Random Forests (AUC_ROC):\", auc_diff)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above confidence intervals, neither of which contain zero, show that random forest classification is statistically significantly better than even our best deep learning model. Therefore, we, given the deep architectures we've explored, can determine that conventional machine learning algorithms are superior for this classification task.\n", "\n", "This does not rule out the possibility that there is a deep learning architecture which would perform well for this task, but simply states that the models we persued were inferior to the random forest model. We believe that one possible reason for the inefficacy of deep learning for this task is the size of the data. Deep learning thrives on extremely large datasets, and, with only one year of NFL data, the dataset used in this report can only be classified as \"medium,\" at best.\n", "\n", "Nonetheless, we beleive that the original random forest model may prove useful in a real-world application. We stand by the deployment strategy we proposed in our previous report, which is re-iterated below.\n", "\n", "*\n", "Given that the mean ROC_AUC score for the random forest model was approximately 0.85, we are confident that our model can provide meaningful insight to football organizations, and we think that it has considerable value to a defensive coordinator. However, we do not beleive that our model can in any way replace a defensive coordinator, because it fails to consider a number of factors which exist in a real-game situation.*\n", "\n", "*The model only considers basic metrics of the game, and does not in any way consider the intricacies of a football game, such as the location of the game, the weather, the crowd, any injuries that may exist on a team, and so on.*\n", "\n", "*Considering the available data to train the model, we beleive that our model is high-performing and viable for use in an actual professional football setting. It can, with reasonable 'accuracy,' predict what type of play will be called in a given game situation.*\n", "\n", "*In order to ultimately determine the viability of our model, we would like to implement it alongside a defensive coordinator, and keep track of the predictions of the defensive coordinator and the predictions of the model, and see if one is better than the other, with statistical significance.*\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

takashi-suehiro/rtmtools

rtc_handle_example/script/basic.ipynb

1

26441

{ "cells": [ { "cell_type": "markdown", "id": "89e8c404", "metadata": {}, "source": [ "# rtc_handle.py（basic）\n", "\n", "- this ipnb shows a basic usage of rtc_handle.py\n", "- precondition: rtcs(cin and cout) are prelaunched separetely\n", "- you can monitor the behavior of the system with openrtp\n", "\n", "you can access and control rtcs of OpenRTM-aist (written in any languages, ie., c++, python, java) \n", "by using rtc_handle.py" ] }, { "cell_type": "code", "execution_count": 1, "id": "901dde07", "metadata": {}, "outputs": [], "source": [ "#!/usr/bin/env python\n", "# -*- Python -*-\n", "\n", "import sys\n", "import time\n", "import subprocess" ] }, { "cell_type": "markdown", "id": "d43f7ab1", "metadata": {}, "source": [ "## setup user environmet\n", "\n", "- path for rtc_handle \n", "- path for rtcs and user tools\n", "- nameservers\n", "\n", "you may provide a file (ex. set_env.py) for this." ] }, { "cell_type": "code", "execution_count": 2, "id": "0a7f8eee", "metadata": {}, "outputs": [], "source": [ "#\n", "# set up user environment\n", "# RtmToolsDir, MyRtcDir, etc.\n", "#\n", "# from set_env import * : you may provide a setup file like this\n", "#\n", "RtmToolsDir=\"../..\"\n", "MyRtcDir=\"..\"\n", "NS0=\"localhost:9876\"\n" ] }, { "cell_type": "markdown", "id": "1c7b93b5", "metadata": {}, "source": [ "## import user tools \n", "\n", "path is modified temporaly to import tools\n", "\n", "stubs for rtc service ports might be imported (this will be explained another example)." ] }, { "cell_type": "code", "execution_count": 3, "id": "f1c5db65", "metadata": {}, "outputs": [], "source": [ "#\n", "# import user tools\n", "#\n", "sys.path.append(\".\")\n", "save_path = sys.path[:]\n", "sys.path.append(RtmToolsDir+'/rtc_handle')\n", "from rtc_handle import *\n", "# from rtc_handle_util import *\n", "# sys.path.append(RtmToolsDir+'/embryonic_rtc')\n", "# from EmbryonicRtc import *\n", "sys.path = save_path\n", "\n", "#\n", "# import stub files\n", "# \n", "#import _GlobalIDL\n" ] }, { "cell_type": "markdown", "id": "197d749d", "metadata": {}, "source": [ "## RtmEnv: rtm environment holder \n", "\n", "RtmEnv class object contains an orb, name-servers, rtcs, connectors and other rtm environment information.\n", "\n", "the second arg is a list of cos nameservers." ] }, { "cell_type": "code", "execution_count": 4, "id": "09a72ee1", "metadata": {}, "outputs": [], "source": [ "#\n", "# user program \n", "# \n", "\n", "#\n", "env = RtmEnv(sys.argv,[NS0])\n" ] }, { "cell_type": "markdown", "id": "ab045b14", "metadata": {}, "source": [ "## NameSpace\n", "\n", "NameSpace.env.list_obj() retrieves a list of corba objects in the nameserver \n", "and put them into the NameSpace.obj_list dictionary.\n", "\n", "if an object is an rtc, its proxy object(RtcHandl) is created \n", "and put into the NameSpace.rtc_handle dictionary." ] }, { "cell_type": "code", "execution_count": 5, "id": "af42d470", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "objcet cin0.rtc was listed.\n", "port_name: str_out\n", "handle for cin0.rtc was created.\n", "objcet cout0.rtc was listed.\n", "port_name: str_in\n", "handle for cout0.rtc was created.\n" ] }, { "data": { "text/plain": [ "[['cin0.rtc', <OpenRTM._objref_DataFlowComponent at 0x7fdbdc4fc970>],\n", " ['cout0.rtc', <OpenRTM._objref_DataFlowComponent at 0x7fdbcde64be0>]]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "env.name_space[NS0].list_obj()" ] }, { "cell_type": "code", "execution_count": 6, "id": "34028680", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'cin0.rtc': <OpenRTM._objref_DataFlowComponent at 0x7fdbdc4fc970>,\n", " 'cout0.rtc': <OpenRTM._objref_DataFlowComponent at 0x7fdbcde64be0>}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "env.name_space[NS0].obj_list" ] }, { "cell_type": "code", "execution_count": 7, "id": "ac0c953f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'cin0.rtc': <rtc_handle.RtcHandle at 0x7fdbdc50e3a0>,\n", " 'cout0.rtc': <rtc_handle.RtcHandle at 0x7fdbcde662e0>}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "env.name_space[NS0].rtc_handles" ] }, { "cell_type": "markdown", "id": "6216ad18", "metadata": {}, "source": [ "## RtcHandle: proxy of rtc\n", "\n", "RtcHandle is a proxy class of rtc and the center object of this module. \n", "an rtc can be accessed by its object reference and information of the rtc can be gathered throug the reference.\n", "an RtcHandle object holds those information and proxy the rtc." ] }, { "cell_type": "markdown", "id": "f1b50858", "metadata": {}, "source": [ "## assign rtc proxies to valiables\n", "\n", "to ease access to rtc proxies, it may be a good idea assigning them to valiables." ] }, { "cell_type": "code", "execution_count": 8, "id": "29c8e4f2", "metadata": {}, "outputs": [], "source": [ "cin=env.name_space[NS0].rtc_handles['cin0.rtc']\n", "cout=env.name_space[NS0].rtc_handles['cout0.rtc']" ] }, { "cell_type": "markdown", "id": "44b5c27d", "metadata": {}, "source": [ "## activate and deactivate rtcs" ] }, { "cell_type": "code", "execution_count": 9, "id": "cdf52596", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.activate()" ] }, { "cell_type": "code", "execution_count": 10, "id": "447b95af", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.deactivate()" ] }, { "cell_type": "code", "execution_count": 11, "id": "fb9af51a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.activate()" ] }, { "cell_type": "markdown", "id": "078835e4", "metadata": {}, "source": [ "deactivation of some rtcs may fail. \n", "those rtcs may wait for resorces(ex. waiting for user input) in onExecute loop.\n", "for example," ] }, { "cell_type": "code", "execution_count": 12, "id": "511e6f35", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.activate()" ] }, { "cell_type": "code", "execution_count": 13, "id": "ce2e9a41", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_ERROR" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.deactivate()" ] }, { "cell_type": "markdown", "id": "f9e43784", "metadata": {}, "source": [ "but it usually recovers after the resorce is available. please input something at the cin console. then," ] }, { "cell_type": "code", "execution_count": 14, "id": "9267bf8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.activate()" ] }, { "cell_type": "markdown", "id": "510924a1", "metadata": {}, "source": [ "## direct access to Inports and Outports\n", "\n", "if the interface_type of the ports is corba_cdr,\n", "you can put data to inports and get data from outpors by using rtc_handle.py." ] }, { "cell_type": "markdown", "id": "dc2db3e8", "metadata": {}, "source": [ "### put data to inport" ] }, { "cell_type": "code", "execution_count": 15, "id": "83513ebc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'str_in': <rtc_handle.RtcInport at 0x7fdbcde6f6a0>}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.inports" ] }, { "cell_type": "code", "execution_count": 16, "id": "0c79a9d2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.inports['str_in'].open()" ] }, { "cell_type": "code", "execution_count": 17, "id": "cc0213b1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PORT_OK" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.inports['str_in'].write('abc')" ] }, { "cell_type": "code", "execution_count": 18, "id": "ca53fe40", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cout.inports['str_in'].close()" ] }, { "cell_type": "markdown", "id": "7028ba7c", "metadata": {}, "source": [ "### get data from outport\n", "\n", "by connecting to outport with setting 'datapot.dataflow_type' : 'pull' ,\n", "you can get 'dataport.corba_cdr.outport_ref'.\n", "\n", "\n", "you can directly get the last data (and if not consumed other rtcs) by the ref." ] }, { "cell_type": "code", "execution_count": 19, "id": "9d4ef2bf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'str_out': <rtc_handle.RtcOutport at 0x7fdbcde66220>}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.outports" ] }, { "cell_type": "code", "execution_count": 20, "id": "3178f54e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.outports['str_out'].open()" ] }, { "cell_type": "code", "execution_count": 21, "id": "0d5665bd", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'dataport.dataflow_type': 'pull',\n", " 'dataport.interface_type': 'corba_cdr',\n", " 'dataport.subscription_type': 'new',\n", " 'dataport.publisher.push_policy': 'new',\n", " 'dataport.inport.buffer.length': '1',\n", " 'dataport.inport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.inport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.length': '1',\n", " 'dataport.outport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.data_type': 'TimedString',\n", " 'dataport.serializer.cdr.endian': 'little,big',\n", " 'dataport.corba_cdr.outport_ior': 'IOR:010000001b00000049444c3a4f70656e52544d2f4f7574506f72744364723a312e300000010000000000000064000000010102000e0000003139322e3136382e35302e33350063cd0e000000fe37b1886100008405000000000a00000200000000000000080000000100000000545441010000001c00000001000000010001000100000001000105090101000100000009010100',\n", " 'dataport.corba_cdr.outport_ref': <OpenRTM._objref_OutPortCdr at 0x7fdbcce03550>}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.outports['str_out'].con.prop_dict" ] }, { "cell_type": "code", "execution_count": 22, "id": "99b51c72", "metadata": {}, "outputs": [], "source": [ "cin.outports['str_out'].read()" ] }, { "cell_type": "code", "execution_count": 23, "id": "5712762a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cin.outports['str_out'].close()" ] }, { "cell_type": "markdown", "id": "fa870602", "metadata": {}, "source": [ "## IOConnector : connect and disconnect io-ports\n", "\n", "IOConnector contains information for connecting io-ports" ] }, { "cell_type": "markdown", "id": "0846ee47", "metadata": {}, "source": [ "create a connector between cin.outports['str_out'] and cout.inports['str_in']" ] }, { "cell_type": "code", "execution_count": 24, "id": "3af10163", "metadata": {}, "outputs": [], "source": [ "con = IOConnector([cin.outports['str_out'], cout.inports['str_in']])" ] }, { "cell_type": "markdown", "id": "9311dfb9", "metadata": {}, "source": [ "default properties of the connector is as follows" ] }, { "cell_type": "code", "execution_count": 25, "id": "7c9026a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dataport.dataflow_type': 'push',\n", " 'dataport.interface_type': 'corba_cdr',\n", " 'dataport.subscription_type': 'new',\n", " 'dataport.publisher.push_policy': 'new',\n", " 'dataport.inport.buffer.length': '1',\n", " 'dataport.inport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.inport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.length': '1',\n", " 'dataport.outport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.read.empty_policy': 'do_nothing'}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.def_prop" ] }, { "cell_type": "markdown", "id": "d66e02ff", "metadata": {}, "source": [ "connect ports" ] }, { "cell_type": "code", "execution_count": 26, "id": "e928b410", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.connect()" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c39622a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC.ConnectorProfile(name='cin0.str_out_cout0.str_in', connector_id='83a8f542-408b-11ec-b42d-594718fca3b7', ports=[<RTC._objref_PortService object at 0x7fdbcde71d90>, <RTC._objref_PortService object at 0x7fdbcde71ca0>], properties=[SDOPackage.NameValue(name='dataport.dataflow_type', value=CORBA.Any(CORBA.TC_string, 'push')), SDOPackage.NameValue(name='dataport.interface_type', value=CORBA.Any(CORBA.TC_string, 'corba_cdr')), SDOPackage.NameValue(name='dataport.subscription_type', value=CORBA.Any(CORBA.TC_string, 'new')), SDOPackage.NameValue(name='dataport.publisher.push_policy', value=CORBA.Any(CORBA.TC_string, 'new')), SDOPackage.NameValue(name='dataport.inport.buffer.length', value=CORBA.Any(CORBA.TC_string, '1')), SDOPackage.NameValue(name='dataport.inport.buffer.read.empty_policy', value=CORBA.Any(CORBA.TC_string, 'do_nothing')), SDOPackage.NameValue(name='dataport.inport.buffer.write.full_policy', value=CORBA.Any(CORBA.TC_string, 'overwrite')), SDOPackage.NameValue(name='dataport.outport.buffer.length', value=CORBA.Any(CORBA.TC_string, '1')), SDOPackage.NameValue(name='dataport.outport.buffer.write.full_policy', value=CORBA.Any(CORBA.TC_string, 'overwrite')), SDOPackage.NameValue(name='dataport.outport.buffer.read.empty_policy', value=CORBA.Any(CORBA.TC_string, 'do_nothing')), SDOPackage.NameValue(name='dataport.data_type', value=CORBA.Any(CORBA.TC_string, 'TimedString')), SDOPackage.NameValue(name='dataport.serializer.cdr.endian', value=CORBA.Any(CORBA.TC_string, 'little,big')), SDOPackage.NameValue(name='dataport.corba_cdr.inport_ior', value=CORBA.Any(CORBA.TC_string, 'IOR:010000001a00000049444c3a4f70656e52544d2f496e506f72744364723a312e30000000010000000000000064000000010102000e0000003139322e3136382e35302e33350041cb0e000000fe37b1886100008404000000001900000200000000000000080000000100000000545441010000001c00000001000000010001000100000001000105090101000100000009010100')), SDOPackage.NameValue(name='dataport.corba_cdr.inport_ref', value=CORBA.Any(CORBA.TypeCode(\"IDL:OpenRTM/InPortCdr:1.0\"), <OpenRTM._objref_InPortCdr object at 0x7fdbcce08910>))])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.profile" ] }, { "cell_type": "code", "execution_count": 28, "id": "9b6fdf03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dataport.dataflow_type': 'push',\n", " 'dataport.interface_type': 'corba_cdr',\n", " 'dataport.subscription_type': 'new',\n", " 'dataport.publisher.push_policy': 'new',\n", " 'dataport.inport.buffer.length': '1',\n", " 'dataport.inport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.inport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.length': '1',\n", " 'dataport.outport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.data_type': 'TimedString',\n", " 'dataport.serializer.cdr.endian': 'little,big',\n", " 'dataport.corba_cdr.inport_ior': 'IOR:010000001a00000049444c3a4f70656e52544d2f496e506f72744364723a312e30000000010000000000000064000000010102000e0000003139322e3136382e35302e33350041cb0e000000fe37b1886100008404000000001900000200000000000000080000000100000000545441010000001c00000001000000010001000100000001000105090101000100000009010100',\n", " 'dataport.corba_cdr.inport_ref': <OpenRTM._objref_InPortCdr at 0x7fdbcce08910>}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.prop_dict" ] }, { "cell_type": "markdown", "id": "8596fffa", "metadata": {}, "source": [ "disconnect ports" ] }, { "cell_type": "code", "execution_count": 29, "id": "eb8db1e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.disconnect()" ] }, { "cell_type": "markdown", "id": "01d839cf", "metadata": {}, "source": [ "### change properties\n", "\n", "you can change properties by giving prop_dict" ] }, { "cell_type": "code", "execution_count": 30, "id": "bf21058e", "metadata": {}, "outputs": [], "source": [ "con = IOConnector([cin.outports['str_out'], cout.inports['str_in']], \n", " prop_dict={'dataport.inport.buffer.length': '8'})" ] }, { "cell_type": "code", "execution_count": 31, "id": "c2010d12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dataport.dataflow_type': 'push',\n", " 'dataport.interface_type': 'corba_cdr',\n", " 'dataport.subscription_type': 'new',\n", " 'dataport.publisher.push_policy': 'new',\n", " 'dataport.inport.buffer.length': '8',\n", " 'dataport.inport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.inport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.length': '1',\n", " 'dataport.outport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.data_type': 'TimedString'}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.prop_dict_req" ] }, { "cell_type": "code", "execution_count": 32, "id": "2f5beb04", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.connect()" ] }, { "cell_type": "code", "execution_count": 33, "id": "76f04bf1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dataport.dataflow_type': 'push',\n", " 'dataport.interface_type': 'corba_cdr',\n", " 'dataport.subscription_type': 'new',\n", " 'dataport.publisher.push_policy': 'new',\n", " 'dataport.inport.buffer.length': '8',\n", " 'dataport.inport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.inport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.length': '1',\n", " 'dataport.outport.buffer.write.full_policy': 'overwrite',\n", " 'dataport.outport.buffer.read.empty_policy': 'do_nothing',\n", " 'dataport.data_type': 'TimedString',\n", " 'dataport.serializer.cdr.endian': 'little,big',\n", " 'dataport.corba_cdr.inport_ior': 'IOR:010000001a00000049444c3a4f70656e52544d2f496e506f72744364723a312e30000000010000000000000064000000010102000e0000003139322e3136382e35302e33350041cb0e000000fe37b1886100008404000000001a00000200000000000000080000000100000000545441010000001c00000001000000010001000100000001000105090101000100000009010100',\n", " 'dataport.corba_cdr.inport_ref': <OpenRTM._objref_InPortCdr at 0x7fdbcce08f70>}" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.prop_dict" ] }, { "cell_type": "code", "execution_count": 34, "id": "365f8875", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.disconnect()" ] }, { "cell_type": "markdown", "id": "e093a9bd", "metadata": {}, "source": [ "### conflict of connections\n", "\n", "only one connection is permitted between the same ports.\n", "\n", "so, if another connection exists, you can not control the connection by your connector.\n", "\n", "for example, " ] }, { "cell_type": "code", "execution_count": 35, "id": "eceb15cb", "metadata": {}, "outputs": [], "source": [ "b = IOConnector([cin.outports['str_out'], cout.inports['str_in']])" ] }, { "cell_type": "code", "execution_count": 36, "id": "4e0c4672", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.connect()" ] }, { "cell_type": "code", "execution_count": 37, "id": "71736ec7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "there exists another connection. please try force=True.\n" ] }, { "data": { "text/plain": [ "'NO'" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.connect()" ] }, { "cell_type": "code", "execution_count": 38, "id": "35ee7506", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "there exists another connection. please try force=True.\n" ] }, { "data": { "text/plain": [ "'NO'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.disconnect()" ] }, { "cell_type": "markdown", "id": "02a0a50e", "metadata": {}, "source": [ "you can handle this situation by forcing connect/disconnect operation" ] }, { "cell_type": "code", "execution_count": 39, "id": "e7abacc6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RTC_OK" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.connect(force=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "ac1d3a7a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }

mit

danlamanna/scratch

tests/integration/Layer subsetting.ipynb

1

5546

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pylab as plt\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from geonotebook.wrappers import RasterData, VectorData\n", "import numpy as np\n", "from IPython.display import display, Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsetting points" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "EXPECTED=\"https://data.kitware.com/api/v1/item/58a3290d8d777f0721a5ce45/download\"\n", "# Set the center of the map to the location the data\n", "M.set_center(-120.32, 47.84, 8)\n", "\n", "# Clean up any layers that might already exist\n", "M.layers.annotation.clear_annotations()\n", "for l in M.layers:\n", " M.remove_layer(l)\n", "\n", "rd = RasterData('data/WELD.tif')\n", "\n", "M.add_layer(rd[1, 2, 3])\n", "M.add_annotation('point', [-120.32, 47.84])\n", "\n", "display(Image(EXPECTED, format=\"png\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "layer, data = next(M.layers.annotation.points[0].data)\n", "assert layer == M.layers[0]\n", "assert all(np.isclose(data, [0.302, 0.3003, 0.2517], atol=10e-4))\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsetting rectangles" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "EXPECTED=\"https://data.kitware.com/api/v1/item/58a329148d777f0721a5ce69/download\"\n", "# Set the center of the map to the location the data\n", "M.set_center(-120.533, 47.975, 12)\n", "\n", "# Clean up any layers that might already exist\n", "M.layers.annotation.clear_annotations()\n", "for l in M.layers:\n", " M.remove_layer(l)\n", "\n", "rd = RasterData('data/WELD.tif')\n", "\n", "M.add_layer(rd[1, 2, 3])\n", "M.add_annotation('rectangle', [[-120.588, 47.932], [-120.588, 48.017], [-120.478, 48.017], [-120.478, 47.932]])\n", "\n", "display(Image(EXPECTED, format=\"png\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "layer, data = next(M.layers.annotation.rectangles[0].data)\n", "assert layer == M.layers[0]\n", "assert data.shape == (113, 146, 3)\n", "assert all(np.isclose(data[0, 0, :], [0.0712, 0.0848, 0.0701], atol=10e-4))\n", "plt.imshow(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsetting polygons" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "EXPECTED=\"https://data.kitware.com/api/v1/item/58a346848d777f0721a61039/download\"\n", "# Set the center of the map to the location the data\n", "M.set_center(-120.533, 47.975, 12)\n", "\n", "# Clean up any layers that might already exist\n", "M.layers.annotation.clear_annotations()\n", "for l in M.layers:\n", " M.remove_layer(l)\n", "\n", "rd = RasterData('data/WELD.tif')\n", "\n", "M.add_layer(rd[1, 2, 3])\n", "M.add_annotation('polygon', [\n", " [-120.588, 47.932], [-120.643, 47.975], [-120.588, 48.017], [-120.478, 48.017], [-120.423, 47.975], [-120.478, 47.932]\n", "])\n", "\n", "display(Image(EXPECTED, format=\"png\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "layer, data = next(M.layers.annotation.polygons[0].data)\n", "assert layer == M.layers[0]\n", "assert data.shape == (113, 292, 3)\n", "assert all(np.isclose(data[55, 150, :], [0.0777, 0.0855, 0.0752], atol=10e-4))\n", "plt.imshow(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsetting vector data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "M.set_center(-119.63, 47.686, 8)\n", "\n", "M.layers.annotation.clear_annotations()\n", "for l in M.layers:\n", " M.remove_layer(l)\n", " \n", "rd = RasterData('data/WELD.tif')\n", "M.add_layer(rd[1, 2, 3])\n", "\n", "v = VectorData('data/wa_county')\n", "M.add_layer(v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "county = v.polygons.next()\n", "_, data = county.data.next()\n", "plt.imshow(data)" ] } ], "metadata": { "kernelspec": { "display_name": "Geonotebook (Python 2)", "language": "python", "name": "geonotebook2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

pacoqueen/ginn

extra/install/ipython2/ipython-5.10.0/examples/IPython Kernel/Beyond Plain Python.ipynb

1

132729

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# IPython: beyond plain Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## First things first: running code, getting help" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end. Alternately, you can use:\n", " \n", "- `Alt-Enter` to force the creation of a new cell unconditionally (useful when inserting new content in the middle of an existing notebook).\n", "- `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets that you don't need to keep permanently." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hi\n" ] } ], "source": [ "print(\"Hi\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Getting help:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import collections\n", "collections.namedtuple?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "collections.Counter??" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "*int*?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "An IPython quick reference card:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%quickref" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Tab completion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tab completion, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type `object_name.<TAB>` to view the object’s attributes. Besides Python objects and keywords, tab completion also works on file and directory names." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "collections." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## The interactive workflow: input, output, history" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2+10" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_+10" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "You can suppress the storage and rendering of output if you append `;` to the last cell (this comes in handy when plotting with matplotlib, for example):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "10+20;" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The output is stored in `_N` and `Out[N]` variables:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_10 == Out[10]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "And the last three have shorthands for convenience:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "last output: True\n", "next one : 22\n", "and next : 22\n" ] } ], "source": [ "from __future__ import print_function\n", "\n", "print('last output:', _)\n", "print('next one :', __)\n", "print('and next :', ___)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "'_10 == Out[10]'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "In[11]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'In[11]'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_i" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'In[11]'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_ii" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "last input: _ii\n", "next one : _i\n", "and next : In[11]\n" ] } ], "source": [ "print('last input:', _i)\n", "print('next one :', _ii)\n", "print('and next :', _iii)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1: print(\"Hi\")\n", " 2: ?\n", " 3:\n", "import collections\n", "collections.namedtuple?\n", " 4: collections.Counter??\n", " 5: *int*?\n" ] } ], "source": [ "%history -n 1-5" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Exercise**\n", "\n", "Write the last 10 lines of history to a file named `log.py`." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Accessing the underlying operating system" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/minrk/dev/ip/mine/examples/IPython Kernel\r\n" ] } ], "source": [ "!pwd" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "My current directory's files:\n", "['Animations Using clear_output.ipynb', 'Background Jobs.ipynb', 'Beyond Plain Python.ipynb', 'Capturing Output.ipynb', 'Cell Magics.ipynb', 'Custom Display Logic.ipynb', 'Index.ipynb', 'Old Custom Display Logic.ipynb', 'Plotting in the Notebook.ipynb', 'Raw Input in the Notebook.ipynb', 'Rich Output.ipynb', 'Script Magics.ipynb', 'SymPy.ipynb', 'Terminal Usage.ipynb', 'Third Party Rich Output.ipynb', 'Trapezoid Rule.ipynb', 'Working With External Code.ipynb', '__pycache__', 'data', 'example-demo.py', 'gui', 'ipython-completion.bash', 'ipython-get-history.py', 'ipython-qtconsole.desktop', 'ipython.desktop', 'mod.py', 'test.txt']\n" ] } ], "source": [ "files = !ls\n", "print(\"My current directory's files:\")\n", "print(files)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Animations Using clear_output.ipynb, Background Jobs.ipynb, Beyond Plain Python.ipynb, Capturing Output.ipynb, Cell Magics.ipynb, Custom Display Logic.ipynb, Index.ipynb, Old Custom Display Logic.ipynb, Plotting in the Notebook.ipynb, Raw Input in the Notebook.ipynb, Rich Output.ipynb, Script Magics.ipynb, SymPy.ipynb, Terminal Usage.ipynb, Third Party Rich Output.ipynb, Trapezoid Rule.ipynb, Working With External Code.ipynb, __pycache__, data, example-demo.py, gui, ipython-completion.bash, ipython-get-history.py, ipython-qtconsole.desktop, ipython.desktop, mod.py, test.txt]\r\n" ] } ], "source": [ "!echo $files" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ANIMATIONS USING CLEAR_OUTPUT.IPYNB\r\n" ] } ], "source": [ "!echo {files[0].upper()}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that all this is available even in multiline blocks:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "00 - Animations Using clear_output\n", "01 - Background Jobs\n", "02 - Beyond Plain Python\n", "03 - Capturing Output\n", "04 - Cell Magics\n", "05 - Custom Display Logic\n", "06 - Index\n", "07 - Old Custom Display Logic\n", "08 - Plotting in the Notebook\n", "09 - Raw Input in the Notebook\n", "10 - Rich Output\n", "11 - Script Magics\n", "12 - SymPy\n", "13 - Terminal Usage\n", "14 - Third Party Rich Output\n", "15 - Trapezoid Rule\n", "16 - Working With External Code\n", "--\n", "--\n", "--\n", "--\n", "--\n", "--\n", "--\n", "--\n", "--\n", "--\n" ] } ], "source": [ "import os\n", "for i,f in enumerate(files):\n", " if f.endswith('ipynb'):\n", " !echo {\"%02d\" % i} - \"{os.path.splitext(f)[0]}\"\n", " else:\n", " print('--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Beyond Python: magic functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPyhton 'magic' functions are a set of commands, invoked by prepending one or two `%` signs to their name, that live in a namespace separate from your normal Python variables and provide a more command-like interface. They take flags with `--` and arguments without quotes, parentheses or commas. The motivation behind this system is two-fold:\n", " \n", "- To provide an orthogonal namespace for controlling IPython itself and exposing other system-oriented functionality.\n", "\n", "- To expose a calling mode that requires minimal verbosity and typing while working interactively. Thus the inspiration taken from the classic Unix shell style for commands." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%magic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Line vs cell magics:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 19.3 µs per loop\n" ] } ], "source": [ "%timeit list(range(1000))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000 loops, best of 3: 2.78 µs per loop\n" ] } ], "source": [ "%%timeit\n", "list(range(10))\n", "list(range(100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Line magics can be used even inside code blocks:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "size: 100 100000 loops, best of 3: 1.86 µs per loop\n", "size: 200 100000 loops, best of 3: 2.49 µs per loop\n", "size: 300 100000 loops, best of 3: 4.04 µs per loop\n", "size: 400 100000 loops, best of 3: 6.21 µs per loop\n" ] } ], "source": [ "for i in range(1, 5):\n", " size = i*100\n", " print('size:', size, end=' ')\n", " %timeit list(range(size))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Magics can do anything they want with their input, so it doesn't have to be valid Python:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "My shell is: /usr/local/bin/bash\n", "My disk usage is:\n", "Filesystem Size Used Avail Capacity iused ifree %iused Mounted on\n", "/dev/disk1 233Gi 216Gi 16Gi 94% 56788108 4190706 93% /\n", "devfs 190Ki 190Ki 0Bi 100% 656 0 100% /dev\n", "map -hosts 0Bi 0Bi 0Bi 100% 0 0 100% /net\n", "map auto_home 0Bi 0Bi 0Bi 100% 0 0 100% /home\n" ] } ], "source": [ "%%bash\n", "echo \"My shell is:\" $SHELL\n", "echo \"My disk usage is:\"\n", "df -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another interesting cell magic: create any file you want locally from the notebook:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting test.txt\n" ] } ], "source": [ "%%writefile test.txt\n", "This is a test file!\n", "It can contain anything I want...\n", "\n", "And more..." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a test file!\r\n", "It can contain anything I want...\r\n", "\r\n", "And more..." ] } ], "source": [ "!cat test.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what other magics are currently defined in the system:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/json": { "cell": { "!": "OSMagics", "HTML": "Other", "SVG": "Other", "bash": "Other", "capture": "ExecutionMagics", "debug": "ExecutionMagics", "file": "Other", "html": "DisplayMagics", "javascript": "DisplayMagics", "latex": "DisplayMagics", "perl": "Other", "prun": "ExecutionMagics", "pypy": "Other", "python": "Other", "python2": "Other", "python3": "Other", "ruby": "Other", "script": "ScriptMagics", "sh": "Other", "svg": "DisplayMagics", "sx": "OSMagics", "system": "OSMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "writefile": "OSMagics" }, "line": { "alias": "OSMagics", "alias_magic": "BasicMagics", "autocall": "AutoMagics", "automagic": "AutoMagics", "autosave": "KernelMagics", "bookmark": "OSMagics", "cat": "Other", "cd": "OSMagics", "clear": "KernelMagics", "colors": "BasicMagics", "config": "ConfigMagics", "connect_info": "KernelMagics", "cp": "Other", "debug": "ExecutionMagics", "dhist": "OSMagics", "dirs": "OSMagics", "doctest_mode": "BasicMagics", "ed": "Other", "edit": "KernelMagics", "env": "OSMagics", "gui": "BasicMagics", "hist": "Other", "history": "HistoryMagics", "install_default_config": "DeprecatedMagics", "install_profiles": "DeprecatedMagics", "killbgscripts": "ScriptMagics", "ldir": "Other", "less": "KernelMagics", "lf": "Other", "lk": "Other", "ll": "Other", "load": "CodeMagics", "load_ext": "ExtensionMagics", "loadpy": "CodeMagics", "logoff": "LoggingMagics", "logon": "LoggingMagics", "logstart": "LoggingMagics", "logstate": "LoggingMagics", "logstop": "LoggingMagics", "ls": "Other", "lsmagic": "BasicMagics", "lx": "Other", "macro": "ExecutionMagics", "magic": "BasicMagics", "man": "KernelMagics", "matplotlib": "PylabMagics", "mkdir": "Other", "more": "KernelMagics", "mv": "Other", "namespace": "Other", "notebook": "BasicMagics", "page": "BasicMagics", "pastebin": "CodeMagics", "pdb": "ExecutionMagics", "pdef": "NamespaceMagics", "pdoc": "NamespaceMagics", "pfile": "NamespaceMagics", "pinfo": "NamespaceMagics", "pinfo2": "NamespaceMagics", "popd": "OSMagics", "pprint": "BasicMagics", "precision": "BasicMagics", "profile": "BasicMagics", "prun": "ExecutionMagics", "psearch": "NamespaceMagics", "psource": "NamespaceMagics", "pushd": "OSMagics", "pwd": "OSMagics", "pycat": "OSMagics", "pylab": "PylabMagics", "qtconsole": "KernelMagics", "quickref": "BasicMagics", "recall": "HistoryMagics", "rehashx": "OSMagics", "reload_ext": "ExtensionMagics", "rep": "Other", "rerun": "HistoryMagics", "reset": "NamespaceMagics", "reset_selective": "NamespaceMagics", "rm": "Other", "rmdir": "Other", "run": "ExecutionMagics", "save": "CodeMagics", "sc": "OSMagics", "set_env": "OSMagics", "store": "StoreMagics", "sx": "OSMagics", "system": "OSMagics", "tb": "ExecutionMagics", "tic": "TimerMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "toc": "TimerMagics", "unalias": "OSMagics", "unload_ext": "ExtensionMagics", "who": "NamespaceMagics", "who_ls": "NamespaceMagics", "whos": "NamespaceMagics", "xdel": "NamespaceMagics", "xmode": "BasicMagics" } }, "text/plain": [ "Available line magics:\n", "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %namespace %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %tic %time %timeit %toc %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%lsmagic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running normal Python code: execution and errors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not only can you input normal Python code, you can even paste straight from a Python or IPython shell session:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "1\n", "2\n", "3\n", "5\n", "8\n" ] } ], "source": [ ">>> # Fibonacci series:\n", "... # the sum of two elements defines the next\n", "... a, b = 0, 1\n", ">>> while b < 10:\n", "... print(b)\n", "... a, b = b, a+b" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 2 3 4 5 6 7 8 9 " ] } ], "source": [ "In [1]: for i in range(10):\n", " ...: print(i, end=' ')\n", " ...: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And when your code produces errors, you can control how they are displayed with the `%xmode` magic:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting mod.py\n" ] } ], "source": [ "%%writefile mod.py\n", "\n", "def f(x):\n", " return 1.0/(x-1)\n", "\n", "def g(y):\n", " return f(y+1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's call the function `g` with an argument that would produce an error:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-40-a54c5799f57e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero" ] } ], "source": [ "import mod\n", "mod.g(0)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception reporting mode: Plain\n" ] }, { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", " File \u001b[0;32m\"<ipython-input-41-8932f4bf53fa>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n mod.g(0)\n", " File \u001b[0;32m\"/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\"\u001b[0m, line \u001b[0;32m6\u001b[0m, in \u001b[0;35mg\u001b[0m\n return f(y+1)\n", "\u001b[1;36m File \u001b[1;32m\"/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[1;36m, in \u001b[1;35mf\u001b[1;36m\u001b[0m\n\u001b[1;33m return 1.0/(x-1)\u001b[0m\n", "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m float division by zero\n" ] } ], "source": [ "%xmode plain\n", "mod.g(0)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception reporting mode: Verbose\n" ] }, { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-42-539f73e80e01>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'xmode verbose'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[1;36mglobal\u001b[0m \u001b[0;36mmod.g\u001b[0m \u001b[1;34m= <function g at 0x105887d08>\u001b[0m\n", "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y=0)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[1;36mglobal\u001b[0m \u001b[0;36mf\u001b[0m \u001b[1;34m= <function f at 0x105887d90>\u001b[0m\u001b[1;34m\n \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n", "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x=1)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m \u001b[0;36mx\u001b[0m \u001b[1;34m= 1\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero" ] } ], "source": [ "%xmode verbose\n", "mod.g(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default `%xmode` is \"context\", which shows additional context but not all local variables. Let's restore that one for the rest of our session." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception reporting mode: Context\n" ] } ], "source": [ "%xmode context" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running code in other languages with special `%%` magics" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "July" ] } ], "source": [ "%%perl\n", "@months = (\"July\", \"August\", \"September\");\n", "print $months[0];" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World!\n" ] } ], "source": [ "%%ruby\n", "name = \"world\"\n", "puts \"Hello #{name.capitalize}!\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Raw Input in the notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since 1.0 the IPython notebook web application support `raw_input` which for example allow us to invoke the `%debug` magic in the notebook:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-46-5e708f13c839>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero" ] } ], "source": [ "mod.g(0)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "ipdb> up\n", "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m 5 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m----> 6 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "ipdb> down\n", "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "ipdb> bt\n", " \u001b[1;32m<ipython-input-46-5e708f13c839>\u001b[0m(1)\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m----> 1 \u001b[1;33m\u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", " \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[1;34m()\u001b[0m\n", "\u001b[0;32m 2 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32m 3 \u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32m 4 \u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32m 5 \u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m----> 6 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m 1 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m----> 3 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m 4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[1;32m 5 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "ipdb> exit\n" ] } ], "source": [ "%debug" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don't foget to exit your debugging session. Raw input can of course be use to ask for user input:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Are you enjoying this tutorial? yes\n", "enjoy is: yes\n" ] } ], "source": [ "enjoy = input('Are you enjoying this tutorial? ')\n", "print('enjoy is:', enjoy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting in the notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This magic configures matplotlib to render its figures inline:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABK8AAAMQCAYAAADhP0bKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3Xm8JGV97/FPMTMMM8My7DsOggIiIpoS96XcjXG3o6Ci\n", "YlySGDVJZfHqjWYxNymviTc3iV6jaNSIjUZNTNyw3KOxFFBRFAGRfWeAgRlmq/vH8zzn9Jw5p08v\n", 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"jWV4JUmSJEmSpMYyvJIkSZIkSVJjGV5JkiRJkiSpsQyvJEmSJEmS1FiGV5IkSZIkSWoswytJkiRJ\n", "kiQ1luGVJEmSJEmSGsvwSpIkSZIkSY1leCVJkiRJkqTGMrySJEmSJElSYxleSZIkSZIkqbEMryRJ\n", "kiRJktRYhleSJEmSJElqLMMrSZIkSZIkNZbhlSRJkiRJkhrL8EqSJEmSJEmNZXglSZIkSZKkxvr/\n", "gUJ07DcuELgAAAAASUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x106c2a9b0>" ] }, "metadata": { "image/png": { "height": 392, "width": 599 } }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 2*np.pi, 300)\n", "y = np.sin(x**2)\n", "plt.plot(x, y)\n", "plt.title(\"A little chirp\")\n", "fig = plt.gcf() # let's keep the figure object around for later..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The IPython kernel/client model" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"stdin_port\": 62401,\n", " \"key\": \"64c935a7-64e8-4ab7-ab22-6e0f3ff84e02\",\n", " \"hb_port\": 62403,\n", " \"transport\": \"tcp\",\n", " \"signature_scheme\": \"hmac-sha256\",\n", " \"shell_port\": 62399,\n", " \"control_port\": 62402,\n", " \"ip\": \"127.0.0.1\",\n", " \"iopub_port\": 62400\n", "}\n", "\n", "Paste the above JSON into a file, and connect with:\n", " $> ipython <app> --existing <file>\n", "or, if you are local, you can connect with just:\n", " $> ipython <app> --existing kernel-25383540-ce7f-4529-900a-ded0e510d5d8.json \n", "or even just:\n", " $> ipython <app> --existing \n", "if this is the most recent IPython session you have started.\n" ] } ], "source": [ "%connect_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can connect automatically a Qt Console to the currently running kernel with the `%qtconsole` magic, or by typing `ipython console --existing <kernel-UUID>` in any terminal:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%qtconsole" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

arii/arii.github.io

rj/code/PD Control.ipynb

1

10766

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PD- Control of a robot\n", "\n", "In this section you will control the paddle to move to a desired location. The robot is force controlled. This means that for every time step, you can specify an applied force to the robot's center of mass. Additionally you can specify the an applied angular torque.\n", "\n", "The goal is to program the robot to move to a desired location specified by $\\vec{x}^* = (x,y,\\theta)$ by specifing the velocity at each time step. \n", "\n", "We will break this into a few steps.\n", "1. Running the simulation and accessing the robot state information\n", "2. Open loop control of the robot\n", "3. Feedback control of the robot\n", "\n", "-----\n", "\n", "The following code shows the instructor solution for a simple PD controller. You can modify the initial position and desired position/velocity of the robot to see how it works.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tutorial; from tutorial import *\n", "\n", "\n", "initial_pose = (16, 12,0.0)\n", "desired_pose = (16, 16,3.14/2.)\n", "desired_vel = (0, 0, 0)\n", "\n", "play_pd_control_solution(initial_pose, \\\n", " desired_pose, desired_vel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PD Control Part 1: Running the simulation and accessing the robot state information\n", "\n", "The following code will show a simple example of running the simulator\n", "\n", "You should see the robot (paddle) start in the middle of the screen and fall down due to gravity.\n", "\n", "Try changing the robot's orientation and rerun the simulation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import tutorial; from tutorial import *\n", "\n", "initial_pose = (16, 12, 3.14/2.)\n", "result = run_pd_control(initial_pose, controller=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's rerun our simulation and plot the state of the robot over time. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import tutorial; reload(tutorial); from tutorial import *\n", " \n", "initial_pose = (16, 12, 3.14/2.)\n", "result = run_pd_control(initial_pose, controller=None)\n", "plot(result, \"Robot\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PD Control Part 2:Open loop control of the robot\n", "\n", "Now we are going to move our robot using open loop control. We can apply a force to the center of mass in the x or y direction, and an angular torque about the center of mass. \n", "\n", "One of the inputs to the `run_pd_control` is currently set to None. In this example we are going to show how to write a controller that gets run at every time step.\n", "\n", "The output of the controller is $u_x, u_y, u_th$, which is the amount of force applied in the x direction, in the y direction, and angular torque applied. The force is applied to the robot's center of mass.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import tutorial; reload(tutorial); from tutorial import *\n", " \n", "initial_pose = (16, 12, 3.14/2.)\n", "\n", "def openLoopController (time, robot_state):\n", " u_x = 1.0\n", " u_y = 0\n", " u_th = 0\n", " return u_x, u_y, u_th\n", "\n", "result = run_pd_control(initial_pose, openLoopController)\n", "plot(result, \"Robot\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we want to move our robot up 4 meters to position (16, 16) from position (16, 12) using our open loop control function. What forces should we apply and for how long? The mass of the robot is 2 kg.\n", "\n", "Assuming we apply a constant force $u_y$, the dynamics of the system will be:\n", "$$ y(t) = y_0 + \\frac{1}{2}(\\frac{u_y}{m} - 9.81)t^2 $$\n", "\n", "If we assume the force will be applied for 2 seconds only, we can find what constant force to apply:\n", "\n", "$$ 16 = 12 + \\frac{1}{2}(\\frac{u_y}{m} - 9.81)2^2 $$\n", "$$ u_y = 23.62 $$\n", "\n", " \n", "Program the robot to move to position (16, 15) using open loop commands. How close can you get?\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import tutorial; reload(tutorial); from tutorial import *\n", " \n", "initial_pose = (16, 12,0.0)\n", "\n", "\n", "constant_force = 23.62\n", "time_applied = 2\n", "\n", "def openLoopController (time, robot_state):\n", " \n", " u_x = 0\n", " u_y = 0\n", " u_th = 0\n", " \n", " # only apply force for time < time_applied\n", " if time < time_applied:\n", " u_y = constant_force\n", " \n", " # when the robot is near time_applied print the current y value\n", " if abs(time-time_applied) < 0.1:\n", " print \"Time: %.2f, Height: %.2f \" % (time, robot_state[1])\n", " return u_x, u_y, u_th\n", "\n", "result = run_pd_control(initial_pose, openLoopController)\n", "plot(result, \"Robot\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PD Control Part 3: Feedback control of the robot\n", "\n", "The open loop controller method we used required a lot of effort on the designers part and won't work very well in practice. In this case we knew the robot's mass and could perfectly apply a force in the center of motion. \n", "\n", "An alternative method is to use the current state of the robot to determine what force to apply. In this next section you are going to implement a position controller.\n", "\n", "The following is an equation for a position controller:\n", "\n", "$$u = K_{p}\\cdot(X_{desired} - X_{current})$$\n", "\n", "* $u$ is the output of our controller\n", "* $K_{p}$ is the proportional gain\n", "* $X_{desired}$ is the reference signal\n", "* $X_{current}$ is the output signal\n", "* $(X_{desired} - X_{current})$ is the error signal\n", "\n", "This controller is going to apply forces in the direction that decreases the error signal. \n", "\n", "The robot state is given to you as $(x, y, \\theta, \\dot{x}, \\dot{y}, \\dot{th})$.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import tutorial; reload(tutorial); from tutorial import *\n", " \n", "\n", "initial_pose = (16, 12,0.0)\n", "desired_pose = (16, 16,0.0)\n", "\n", "K_px = 10\n", "K_py = 10\n", "K_pth = 10\n", "\n", "\n", "def closedLoopController (time, robot_state):\n", " \n", " # the output signal\n", " x,y,th, xdot, ydot, thdot = robot_state\n", " \n", " # the reference signal\n", " rx, ry, rth = desired_pose \n", " \n", " # the error signal\n", " e_x = rx - x\n", " e_y = ry - y\n", " e_th = rth - th\n", " \n", " # the controller output\n", " u_x = K_px*e_x\n", " u_y = K_py*e_y\n", " u_th = K_pth*e_th\n", " \n", " return u_x, u_y, u_th \n", "\n", "result = run_pd_control(initial_pose, closedLoopController)\n", "plot(result, \"Robot\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PD Control Part 3: Feedback control of the robot (continued)\n", "\n", "**Activities:**\n", "\n", "1. Try using different gains. See if you can observe different system response behavior, such as:\n", "\n", " * under damped\n", " * damped\n", " * overdamped\n", "\n", "2. Improve upon your controller by adding a derivative term. In this case the reference signal for the derivative terms should be equal to 0. \n", "\n", "$$u = K_{pose}\\cdot(X_{desired} - X_{current}) + K_{d}\\cdot(\\dot{X}_{desired} - \\dot{X}_{current})$$\n", "\n", "* $u$ is the output of our controller\n", "* $K_{d}$ is the derivitave gain\n", "* $\\dot{X}_{desired}$ is the reference signal (In our case it is equal to 0)\n", "\n", "\n", " `rxdot, rydot, rthdot = 0,0,0 `" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import tutorial; reload(tutorial); from tutorial import *\n", " \n", "\n", "initial_pose = (16, 12,3.14/2)\n", "desired_pose = (3, 16,0.0)\n", "desired_vel = (0, 0, 0)\n", "\n", "K_px = 100\n", "K_py = 100\n", "K_pth = 10\n", "K_dx = 50\n", "K_dy = 50\n", "K_dth = 20\n", "\n", "def closedLoopController (time, robot_state):\n", " \n", " # the output signal\n", " x,y,th, xdot, ydot, thdot = robot_state\n", " \n", " # the reference signal\n", " rx, ry, rth = desired_pose \n", " rxdot, rydot, rthdot = desired_vel\n", " \n", " # the error signal\n", " e_x = rx - x\n", " e_y = ry - y\n", " e_th = rth - th\n", "\n", " e_xdot = rxdot - xdot\n", " e_ydot = rydot - ydot\n", " e_thdot = rthdot - thdot\n", " \n", " \n", " # the controller output\n", " u_x = K_px*e_x + K_dx*e_xdot\n", " u_y = K_py*e_y + K_dy*e_ydot\n", " u_th = K_pth*e_th + K_dth*e_thdot\n", " \n", " return u_x, u_y, u_th \n", "\n", "result = run_pd_control(initial_pose, closedLoopController)\n", "plot(result, \"Robot\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

yarmenti/MSRA

03. Solver loss1 SLSQP.ipynb

1

6599

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:40.172000", "start_time": "2017-02-03T15:53:40.168000" }, "collapsed": true }, "outputs": [], "source": [ "NU = 6" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:57.107000", "start_time": "2017-02-03T15:53:40.182000" }, "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "\n", "dir_sim_ = './simulations'\n", "dir_tr_input_ = './transformed_input'\n", "\n", "udl_distrib = pd.read_csv(os.path.join(dir_sim_, 'nu_eq_%i.csv' % NU), header=0, index_col=0)\n", "members_pos = pd.read_csv(os.path.join(dir_tr_input_, 'positions.csv'), header=0, index_col=0)\n", "\n", "loss_and_profit = np.dot(members_pos.values, udl_distrib.values).transpose()\n", "\n", "print loss_and_profit.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:57.113000", "start_time": "2017-02-03T15:53:57.109000" }, "collapsed": true }, "outputs": [], "source": [ "from lib.msra_loss import MSRALossFunctionAbs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:57.128000", "start_time": "2017-02-03T15:53:57.114000" }, "collapsed": true }, "outputs": [], "source": [ "class LossFunction1(MSRALossFunctionAbs):\n", " def shortfall_risk(self, m=None):\n", " m = self._check_argument(m)\n", " x_minus_m = np.subtract(self.x, m)\n", " \n", " sum_x_minus_m_plus = np.maximum(x_minus_m, 0.).sum(axis=1)\n", " sum_x_minus_m_minus = -np.minimum(x_minus_m, 0.).sum(axis=1)\n", "\n", " diff = sum_x_minus_m_plus - 0.5 * sum_x_minus_m_minus\n", " return diff.mean()\n", "\n", " def shortfall_risk_jac(self, m):\n", " m = self._check_argument(m)\n", " x_minus_m = np.subtract(self.x, m)\n", " \n", " sgn = np.sign(x_minus_m)\n", " \n", " sgn_plus = np.maximum(sgn, 0.)\n", " sgn_minus = -np.minimum(sgn, 0.)\n", " \n", " diff = sgn_plus + 0.5 * sgn_minus\n", " \n", " return diff.mean(axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:57.139000", "start_time": "2017-02-03T15:53:57.130000" }, "collapsed": false }, "outputs": [], "source": [ "loss_obj = LossFunction1(loss_and_profit, 0.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:57.147000", "start_time": "2017-02-03T15:53:57.141000" }, "collapsed": false }, "outputs": [], "source": [ "RESULTS = pd.DataFrame(index=members_pos.index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:53:57.293000", "start_time": "2017-02-03T15:53:57.148000" }, "collapsed": true }, "outputs": [], "source": [ "from scipy.optimize import minimize\n", "\n", "methods = ['SLSQP']\n", "maxiter = 10000" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T15:54:48.721000", "start_time": "2017-02-03T15:53:57.294000" }, "collapsed": false }, "outputs": [], "source": [ "guess = np.maximum(np.amax(loss_and_profit, axis=0), 0.)\n", "\n", "cont = loss_obj.ineq_constraint(guess) >= 0.\n", "while cont:\n", " guess = 0.99 * guess\n", " tmp = loss_obj.ineq_constraint(guess)\n", " cont = tmp >= 0.\n", "\n", "print loss_obj.ineq_constraint(guess)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T16:19:20.874000", "start_time": "2017-02-03T15:54:48.724000" }, "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "cons = ({'type': 'ineq',\n", " 'fun' : lambda x: loss_obj.ineq_constraint(x),\n", " 'jac' : lambda x: loss_obj.ineq_constraint_jac(x)})\n", " \n", "bounds = [(0, None) for _ in xrange(loss_obj.dim)]\n", "\n", "res = minimize(loss_obj.objective, guess, \n", " jac=loss_obj.objective_jac, \n", " constraints=cons, \n", " method='SLSQP',\n", " bounds=bounds,\n", " options={'maxiter': maxiter, 'disp': True})\n", "\n", "RESULTS[r'$\\nu = %i$ bounds' % NU] = res.x\n", "print loss_obj.ineq_constraint(res.x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T16:19:20.880000", "start_time": "2017-02-03T16:19:20.877000" }, "collapsed": true }, "outputs": [], "source": [ "dir_ = './results'\n", "\n", "if not os.path.exists(dir_):\n", " os.makedirs(dir_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T16:19:20.896000", "start_time": "2017-02-03T16:19:20.882000" }, "collapsed": false }, "outputs": [], "source": [ "RESULTS.to_csv(os.path.join(dir_, 'l1_nu_eq_%i.csv' % NU))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-02-03T16:19:20.928000", "start_time": "2017-02-03T16:19:20.898000" }, "collapsed": false }, "outputs": [], "source": [ "RESULTS" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

tensorflow/docs-l10n

site/zh-cn/guide/upgrade.ipynb

1

19154

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "6bYaCABobL5q" }, "source": [ "##### Copyright 2018 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "FlUw7tSKbtg4" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "08OTcmxgqkc2" }, "source": [ "# 自动将代码升级到 TensorFlow 2\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://tensorflow.google.cn/guide/upgrade\"> <img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\"> View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/upgrade.ipynb\"> <img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\"> Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/upgrade.ipynb\"> <img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\"> View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/guide/upgrade.ipynb\"> <img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\"> Download notebook</a>\n", " </td>\n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": { "id": "hZSaRPoybOp5" }, "source": [ "TensorFlow 2.0 包含许多 API 变更，例如重新排序了参数，重命名了符号，更改了参数的默认值。手动执行所有这些修改可能很乏味，而且很容易出错。为了简化更改，尽可能地让您无缝过渡到 TF 2.0，TensorFlow 团队创建了 `tf_upgrade_v2` 实用工具，帮助您将旧版代码转换至新的 API。\n", "\n", "注：TensorFlow 1.13 和更高版本（包括所有 TF 2.0 版本）会自动安装 `tf_upgrade_v2`。\n", "\n", "典型的用法如下：\n", "\n", "<pre class=\"devsite-terminal devsite-click-to-copy prettyprint lang-bsh\">tf_upgrade_v2 \\\n", " --intree my_project/ \\\n", " --outtree my_project_v2/ \\\n", " --reportfile report.txt\n", "</pre>\n", "\n", "将现有 TensorFlow 1.x Python 脚本转换为 TensorFlow 2.0 脚本可以加快升级流程。\n", "\n", "转换脚本会尽可能实现自动化处理，但仍有一些语法和样式变更无法通过脚本执行转换。" ] }, { "cell_type": "markdown", "metadata": { "id": "gP9v2vgptdfi" }, "source": [ "## 兼容性模块\n", "\n", "某些 API 符号无法通过简单的字符串替换进行升级。为了确保代码在 TensorFlow 2.0 中仍受支持，升级脚本包含了一个 `compat.v1` 模块。该模块可将 TF 1.x 符号（如 `tf.foo`）替换为等效的 `tf.compat.v1.foo` 引用。虽然该兼容性模块效果不错，但我们仍建议人工校对替换，并尽快将代码迁移到 `tf.*` 命名空间（而不是 `tf.compat.v1` 命名空间）中的新 API。\n", "\n", "由于 TensorFlow 2.x 模块弃用（例如，`tf.flags` 和 `tf.contrib`），切换到 `compat.v1` 无法解决某些更改。升级此代码可能需要其他库（例如，[`absl.flags`](https://github.com/abseil/abseil-py)）或切换到 [tensorflow/addons](http://www.github.com/tensorflow/addons) 中的软件包。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "s78bbfjkXYb7" }, "source": [ "## 推荐的升级流程\n", "\n", "本指南的剩余部分演示如何使用升级脚本。虽然升级脚本的使用非常简单，我们仍强烈建议在以下流程中使用脚本：\n", "\n", "1. **单元测试**：确保要升级的代码包含具有合理覆盖范围的单元测试套件。这是 Python 代码，该语言并不会帮助您避免各种类型的错误。同时为了与 TensorFlow 2.0 兼容，还要确保升级所有依赖项。\n", "\n", "2. **安装 TensorFlow 1.14**：将 TensorFlow 升级到最新的 TensorFlow 1.x 版本（最低为 1.14 版本）。其中包括 `tf.compat.v2` 中的最终 TensorFlow 2.0 API。\n", "\n", "3. **通过 1.14 版本进行测试**：确保此时可通过单元测试。在升级过程中，您将反复进行测试，因此，从无错误的代码开始非常重要。\n", "\n", "4. **运行升级脚本**：对整个源代码树运行 `tf_upgrade_v2`（已包含测试）。这样可将代码升级为仅使用 TensorFlow 2.0 中所提供的符号的格式。被弃用的符号将通过 `tf.compat.v1` 进行访问。最终需要人工检查这些升级，但不是现在。\n", "\n", "5. **通过 TensorFlow 1.14 运行转换的测试**：代码在 TensorFlow 1.14 中应该仍可以正常运行。再次运行单元测试。测试在此时产生任何错误都意味着升级脚本存在错误。[请通知我们](https://github.com/tensorflow/tensorflow/issues)。\n", "\n", "6. **检查更新报告中的警告和错误**：该脚本会编写一个对需要复查的转换或需要执行的人工操作进行解释的报告文件。例如：contrib 的所有剩余实例需要通过人工操作删除。请查阅 [RFC 中的详细说明](https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md)。\n", "\n", "7. **安装 TensorFlow 2.0**：此时应该可以安全切换到 TensorFlow 2.0\n", "\n", "8. **使用 `v1.disable_v2_behavior` 进行测试**：使用测试主函数中的 `v1.disable_v2_behavior()` 重新运行测试产生的结果应与在 1.14 下运行时产生的结果相同。\n", "\n", "9. **启用 V2 行为**：现在，使用 v2 API 已经成功通过了测试，不妨开始考虑启用 v2 行为。这可能需要执行一些更改，具体取决于代码编写方式。有关详细信息，请参阅[迁移指南](migrate.ipynb)。" ] }, { "cell_type": "markdown", "metadata": { "id": "6pwSAQEwvscP" }, "source": [ "## 使用升级脚本\n" ] }, { "cell_type": "markdown", "metadata": { "id": "I9NCvDt5GwX4" }, "source": [ "### 设置\n", "\n", "开始之前，请确保已安装 TensorlFlow 2.0。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DWVYbvi1WCeY" }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "Ycy3B5PNGutU" }, "source": [ "克隆 [tensorflow/models](https://github.com/tensorflow/models) git 仓库，以便获得一些要测试的代码：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jyckoWyAZEhZ" }, "outputs": [], "source": [ "!git clone --branch r1.13.0 --depth 1 https://github.com/tensorflow/models" ] }, { "cell_type": "markdown", "metadata": { "id": "wfHOhbkgvrKr" }, "source": [ "### 读取帮助\n", "\n", "脚本应当随 TensorFlow 安装。下面是内置帮助命令：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m2GF-tlntqTQ" }, "outputs": [], "source": [ "!tf_upgrade_v2 -h" ] }, { "cell_type": "markdown", "metadata": { "id": "se9Leqjm1CZR" }, "source": [ "### TF1 代码示例" ] }, { "cell_type": "markdown", "metadata": { "id": "whD5i36s1SuM" }, "source": [ "下面是一个简单的 TensorFlow 1.0 脚本示例：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mhGbYQ9HwbeU" }, "outputs": [], "source": [ "!head -n 65 models/samples/cookbook/regression/custom_regression.py | tail -n 10" ] }, { "cell_type": "markdown", "metadata": { "id": "UGO7xSyL89wX" }, "source": [ "对于安装的 TensorFlow 2.0，它不会运行：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TD7fFphX8_qE" }, "outputs": [], "source": [ "!(cd models/samples/cookbook/regression &amp;&amp; python custom_regression.py)" ] }, { "cell_type": "markdown", "metadata": { "id": "iZZHu0H0wLRJ" }, "source": [ "### 单个文件\n", "\n", "升级脚本可以在单个 Python 文件上运行：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xIBZVEjkqkc5" }, "outputs": [], "source": [ "!tf_upgrade_v2 \\\n", " --infile models/samples/cookbook/regression/custom_regression.py \\\n", " --outfile /tmp/custom_regression_v2.py" ] }, { "cell_type": "markdown", "metadata": { "id": "L9X2lxzqqkc9" }, "source": [ "如果无法找到解决代码问题的方法，该脚本会打印错误消息。 " ] }, { "cell_type": "markdown", "metadata": { "id": "r7zpuE1vWSlL" }, "source": [ "### 目录树" ] }, { "cell_type": "markdown", "metadata": { "id": "2q7Gtuu8SdIC" }, "source": [ "典型项目（包括下面的简单示例）会使用远不止一个文件。通常需要升级整个软件包，所以该脚本也可以在目录树上运行：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XGqcdkAPqkc-" }, "outputs": [], "source": [ "# upgrade the .py files and copy all the other files to the outtree\n", "!tf_upgrade_v2 \\\n", " --intree models/samples/cookbook/regression/ \\\n", " --outtree regression_v2/ \\\n", " --reportfile tree_report.txt" ] }, { "cell_type": "markdown", "metadata": { "id": "2S4j7sqbSowC" }, "source": [ "注意关于 `dataset.make_one_shot_iterator` 函数的一条警告。\n", "\n", "现在，对于 TensorFlow 2.0，该脚本已经可以发挥作用：\n", "\n", "请注意，凭借 `tf.compat.v1` 模块，转换的脚本在 TensorFlow 1.14 中也可以运行。 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vh0cmW3y1tX9" }, "outputs": [], "source": [ "!(cd regression_v2 &amp;&amp; python custom_regression.py 2&gt;&amp;1) | tail" ] }, { "cell_type": "markdown", "metadata": { "id": "4EgZGGkdqkdC" }, "source": [ "## 详细报告\n", "\n", "该脚本还会报告一个详细更改列表。在本例中，它发现了一个可能不安全的转换，因此在文件顶部包含了一条警告： " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CtHaZbVaNMGV" }, "outputs": [], "source": [ "!head -n 20 tree_report.txt" ] }, { "cell_type": "markdown", "metadata": { "id": "1-UIFXP3cFSa" }, "source": [ "再次注意关于 `Dataset.make_one_shot_iterator` 函数的一条警告。" ] }, { "cell_type": "markdown", "metadata": { "id": "oxQeYS1TN-jv" }, "source": [ "在其他情况下，对于非常用更改，输出会解释原因：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WQs9kEvVN9th" }, "outputs": [], "source": [ "%%writefile dropout.py\n", "import tensorflow as tf\n", "\n", "d = tf.nn.dropout(tf.range(10), 0.2)\n", "z = tf.zeros_like(d, optimize=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7uOkacZsO3XX" }, "outputs": [], "source": [ "!tf_upgrade_v2 \\\n", " --infile dropout.py \\\n", " --outfile dropout_v2.py \\\n", " --reportfile dropout_report.txt &gt; /dev/null" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m-J82-scPMGl" }, "outputs": [], "source": [ "!cat dropout_report.txt" ] }, { "cell_type": "markdown", "metadata": { "id": "DOOLN21nTGSS" }, "source": [ "以下是经过修改的文件内容，请注意脚本如何通过添加参数名来处理移动和重命名的参数：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SrYcJk9-TFlU" }, "outputs": [], "source": [ "!cat dropout_v2.py" ] }, { "cell_type": "markdown", "metadata": { "id": "wI_sVNp_b4C4" }, "source": [ "更大的项目可能会包含一些错误，例如转换 DeepLab 模型：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uzuY-bOvYBS7" }, "outputs": [], "source": [ "!tf_upgrade_v2 \\\n", " --intree models/research/deeplab \\\n", " --outtree deeplab_v2 \\\n", " --reportfile deeplab_report.txt &gt; /dev/null" ] }, { "cell_type": "markdown", "metadata": { "id": "FLhw3fm8drae" }, "source": [ "它会生成输出文件：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4YYLRxWJdSvQ" }, "outputs": [], "source": [ "!ls deeplab_v2" ] }, { "cell_type": "markdown", "metadata": { "id": "qtTC-cAZdEBy" }, "source": [ "但是其中包含错误。该报告会帮助您找到确保代码可以正常运行所需要解决的错误。下面是前三个错误：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UVTNOohlcyVZ" }, "outputs": [], "source": [ "!cat deeplab_report.txt | grep -i models/research/deeplab | grep -i error | head -n 3" ] }, { "cell_type": "markdown", "metadata": { "id": "gGBeDaFVRJ5l" }, "source": [ "## “安全”模式" ] }, { "cell_type": "markdown", "metadata": { "id": "BnfCxB7SVtTO" }, "source": [ "该转换脚本还有一种介入度相对较低的 `SAFETY` 模式。在此模式下，只需更改导入来使用 `tensorflow.compat.v1` 模块：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XdaVXCPWQCC5" }, "outputs": [], "source": [ "!cat dropout.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c0tvRJLGRYEb" }, "outputs": [], "source": [ "!tf_upgrade_v2 --mode SAFETY --infile dropout.py --outfile dropout_v2_safe.py &gt; /dev/null" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "91suN2RaRfIV" }, "outputs": [], "source": [ "!cat dropout_v2_safe.py" ] }, { "cell_type": "markdown", "metadata": { "id": "EOzTF7xbZqqW" }, "source": [ "如您所见，这不会升级代码，但允许 TensorFlow 1 代码在 TensorFlow 2 中运行" ] }, { "cell_type": "markdown", "metadata": { "id": "jGfXVApkqkdG" }, "source": [ "## 注意事项\n", "\n", "- 在运行此脚本之前，不要手动更新代码的某些部分。尤其是更改了参数顺序的函数（如 `tf.argmax` 或 `tf.batch_to_space`），否则会导致代码无法正确添加与现有代码匹配的关键字参数。\n", "\n", "- 该脚本假定使用 `import tensorflow as tf` 导入 `tensorflow`。\n", "\n", "- 该脚本不会更改参数顺序，但是会将关键字参数添加到本身已更改参数顺序的函数。\n", "\n", "- 请查阅 [tf2up.ml](https://github.com/lc0/tf2up)，找到一款方便的工具来升级 GitHub 仓库中的 Jupyter 笔记本和 Python 文件。\n", "\n", "要报告升级脚本错误或提出功能请求，请在 [GitHub](https://github.com/tensorflow/tensorflow/issues) 上提交问题。如果您在测试 TensorFlow 2.0，我们非常希望了解您的反馈意见！请加入 [TF 2.0 测试社区](https://groups.google.com/a/tensorflow.org/forum/#!forum/testing)，将您的问题和讨论发送到 [[email protected]](mailto:[email protected])。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "upgrade.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

usantamaria/ipynb_para_docencia

13_terminal_de_comandos_git/git.ipynb

1

23728

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/utfsm.png\" alt=\"\" width=\"100px\" align=\"right\"/>\n", "# USM Numérica" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Licencia y configuración del laboratorio\n", "Ejecutar la siguiente celda mediante *`Ctr-S`*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", "/*********************************************\n", " * CHANGE CURSIVE FOR RED\n", " *********************************************/\n", "em {font-style: normal !important;\n", " color: #800000;}\n", "\n", "</style>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "IPython Notebook v4.0 para python 3.0\n", "Librerías adicionales: \n", "Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT. \n", "(c) Sebastian Flores, Christopher Cooper, Alberto Rubio, Pablo Bunout.\n", "\"\"\"\n", "# Configuración para recargar módulos y librerías dinámicamente\n", "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "# Configuración para graficos en línea\n", "%matplotlib inline\n", "\n", "# Configuración de estilo\n", "from IPython.core.display import HTML\n", "HTML(open(\"./style/style.css\", \"r\").read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introducción a GIT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*GIT* es un sistema de control de versiones, desarrollado y distribuido a partir del año 2005 por Linux Kernel. Una característica fundamental de un controlador de versiones, es que las unidades básicas sobre las que se estructura, denominadas repositorios, además de almacenar información y/o archivos al igual que un directorio o carpeta de un sistema operativo cualquiera, como Linux o Windows, son capaces de generar un historial o línea de tiempo que registre los diversos estados del repositorio y sus contenidos respectivos, otorgándonos la posibilidad de acceder a estos estados a partir de la ejecución de procedimientos específicos a través de *git*. \n", "\n", "Otra cualidad importante de *GIT*, es que nos permite vincular repositorios generados localmente en la computadora física, con otros repositorios remotos, existentes en la red (online). Pudiendo subir contenidos desde el repositorio local al remoto o bajar material de manera inversa. \n", "\n", "Lo anterior abre un mundo de posibilidades, particularmente en contextos donde el flujo de información sea relevante, por ejemplo en proyectos que requieran la participación de 2 o más personas trabajando en archivos comunes, siendo *GIT* una herramienta útil para sincronizar las versiones de distintos usuarios, realizadas simultáneamente o en distintos tiempos. Un buen uso de las herramientas que nos provee *GIT*, ayudará a sincronizar y/o fusionar archivos evitando la pérdida de información o la superposición de esta.\n", "\n", "En este tutorial veremos algunas instrucciones y comandos básicos, que nos permita realizar operaciones fundamentales, tales como crear un repositorio local, clonar un repositorio en línea y sincronizar ambos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objetivos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Operaciones para crear un repositorio local\n", "2. Operaciones para flujo de trabajo en repositorio local\n", "3. Sincronización de repositorio local y remoto\n", "4. Clonar directamente un repositorio remoto en un directorio de trabajo local\n", "5. Operaciones para repositorios remotos con más de un usuario\n", "6. Soluciones a un Problema Práctico Frecuente\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Operaciones para crear un repositorio local\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para crear un repositorio local, debemos primero generar un directorio sobre el cual queremos guardar los archivos de trabajo y luego identificar este como un repositorio *GIT*. Usamos los siguientes comandos de *bash* para crear un directorio desde la terminal.\n", "\n", "```\n", "mkdir <repositorio_local>\n", "```\n", "Para que *GIT* reconozca el directorio creado como un repositorio debemos primero ubicarnos en el nuevo directorio y luego escribir el comando *git init*.\n", "\n", "```\n", "cd <repositorio_local>\n", "```\n", "```\n", "git init\n", "```\n", "Ahora *GIT* ya reconoce el directorio como un nuevo repositorio. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Operaciones para flujo de trabajo en repositorio local" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Antes de explicar los procedimientos y comandos que nos permiten subir o bajar información desde y hacia un repositorio, actualizar nuevos estados de archivos almacenados en él y/o sincronizar repositorios locales con otros remotos, es importante entender que GIT proporciona 3 zonas virtuales, las que cumplen distintas tareas en función de las operaciones mencionadas recientemente. Estas son el *directorio de trabajo*, una *zona de indexado* y por último una zona que actúa como *cabecera* del repositorio, las que pasamos a detallar en orden respectivo.\n", "\n", "El *directorio de trabajo* corresponde a la zona sobre la cual se ha iniciado el repositorio local. Aquí es donde se almacenan los archivos locales y se registran las modificaciones realizadas a los últimos. Hasta el momento esto no tiene ninguna diferencia con la manera de operar en un sistema operativo, que nos permite generar un directorio o carpeta y guardar archivos en ella, actualizándolos de forma manual.\n", "\n", "<img src=\"tutorial_git_zonas_2.png\"width=\"600px\">\n", "\n", "\n", "La segunda es una zona intermedia, en la cual disponemos los archivos modificados en el directorio local, seleccionándolos con el comando *git add*.\n", "\n", "Finalmente la tercera zona es sobre la cual *GIT* genera un historial o la línea de tiempo, usando el comando *git commit*, que actualiza los archivos previamente seleccionados, registrando las modificaciones respectivas en el repositorio local. \n", "\n", "Describimos los procedimientos mencionados anteriormente:\n", "\n", "Ya hemos iniciado un repositorio local sobre un directorio creado recientemente, si queremos añadir algún archivo creándolo desde la terminal, podemos escribir los comandos de *bash*:\n", "\n", "```\n", "touch <archivo.tipo>\n", "```\n", "Una vez creado el archivo en el directorio de trabajo, para que sea reconocido en el repositorio local debemos seleccionarlo de la siguiente manera.\n", "\n", "```\n", "git add <archivo.tipo>\n", "```\n", "En caso de que existan muchos archivos sobre los cuales hemos realizado modificaciones, una forma de seleccionarlos sería escribir sus nombres uno al lado de otro de forma sucesiva.\n", "\n", "```\n", "git add <archivo_1.tipo> ... <archivo_n.tipo>\n", "```\n", "\n", "Otra manera de hacer más eficiente esto último, sería utilizando el comando *`git add -u`*, el cual selecciona automáticamente todos los archivos modificados desde el último *commit*, descartando aquellos archivos sobre los cuales no se han realizado modificaciones presentes.\n", "\n", "Un comando alternativo que permite seleccionar todo lo existente en el directorio de trabajo es *`git add .`*, haciéndolo sin discriminar entre los archivos modificados y los que no, es usando.\n", "\n", "Luego de indexar los archivos seleccionados, debemos disponerlos en la tercera zona, que es la que finalmente GIT reconoce y sobre la cual genera el historial de versiones. \n", "\n", "```\n", "git commit -m <\"mensaje actualización\">\n", "```\n", "Ya tenemos entonces los archivos y actualizaciones en nuestro repositorio local al disponerlos en el encabezado de este. Otro aspecto útil de *git*, es que cada vez que hacemos un *commit*, este queda registrado con una etiqueta o *id*, podemos acceder a esta información a través de *git log*. Si queremos modificar la etiqueta de forma manual, escribimos lo siguiente:\n", "\n", "```\n", "git tag <etiqueta nueva> <etiqueta antigua>\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.1 Ejercicio Práctica" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Crear un directorio donde generar el repositorio local\n", "* Crear un repositorio local en el directorio \n", "* Generar un archivo de texto en el directorio de trabajo\n", "* Actualizar el archivo creado en el repositorio local *git add + git commit*\n", "* Modificar la etiqueta de la actualización por *\"mi primer commit\"*\n", "* Visualizar antes y posteriormente la etiqueta con *git log*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Sincronización de repositorio local y remoto" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cuando sincronizamos un repositorio local con uno remoto o existente en la red (online), tenemos la posibilidad de intercambiar información entre estos, es decir subir material nuevo desde el repositorio local al remoto y viceversa.\n", "\n", "Para que el repositorio local reconozca la dirección del repositorio remoto, debemos utilizar el siguiente comando, haciéndolo desde el directorio de trabajo.\n", "\n", "```\n", "git remote add origin <https://dirección repositorio online>\n", "```\n", "Por defecto el repositorio local reconocerá al remoto por nombre *origin*, esto nos será útil para abreviar la dirección total del repositorio remoto, lo que tendrá un efecto práctico a la hora de realizar operaciones con diversos comandos de *git*. Luego podemos visualizar las opciones de subir (*push*) y bajar (*fetch*) información con el comando:\n", "\n", "```\n", "git remote -v\n", "```\n", "Otro aspecto importante a entender, son las líneas de tiempo o historiales denominada *branch*. Cada repositorio tiene un *branch* principal el que por defecto se denomina *master*. *GIT* nos permite además crear *branchs* paralelos al principal, lo que puede ayudar a realizar modificaciones a los archivos de forma segura, sin que estos se actualicen automáticamente en la rama *master*, debiendo hacer una operación extra para fusionar el *branch* secundario con *master* principal.\n", "\n", "Ahora estamos listos para subir archivos al repositorio remoto desde el local, ya hemos mostrado como crear un archivo en el directorio de trabajo y a continuación especificaremos los pasos para actualizarlo en el repositorio local y luego subirlo al remoto.\n", "\n", "```\n", "git add <archivo.tipo>\n", "```\n", "```\n", "git commit -m <\"mensaje actualización\">\n", "```\n", "```\n", "git push origin master \n", "```\n", "En resumen, el primer comando selecciona el archivo creado en el directorio de trabajo, para colocarlo en la *zona de indexado* u *onstge*. \n", "\n", "Luego lo hemos actualizado en el repositorio local, con un mensaje en el que especificamos las modificaciones hechas. \n", "\n", "Finalmente subimos estos archivos al repositorio remoto, reconocido por el local a partir de la denominación *origin*, haciéndolo sobre línea de tiempo o rama principal (*branch*), denominada por defecto con el nombre de *master*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Clonar directamente un repositorio remoto en un directorio de trabajo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cuando existe un repositorio en la red, el cual tiene sus propios archivos y queremos tener acceso a estos, tenemos la opción de hacerlo desde un repositorio local, trabajando desde nuestro directorio de trabajo.\n", "\n", "Para esto existe la opción de clonar el repositorio en la re, escribiendo el siguiente comando desde un directorio de trabajo local específico:\n", "\n", "```\n", "git clone <https://dirección repositorio online>\n", "```\n", "Eso crea automáticamente un repositorio local que reconoce la dirección del repositorio remoto por defecto como *origin* y su rama principal como *master*, al igual que lo visto en la sección anterior.\n", "\n", "Si luego trabajamos en los archivos bajados desde la red, desde nuestro directorio local y queremos subir las modificaciones hechas en el repositorio local, repetimos de forma idéntica la secuencia de comandos vista en el último punto.\n", "\n", "```\n", "git add <archivo.tipo>\n", "```\n", "```\n", "git status\n", "```\n", "```\n", "git commit -m <\"mensaje actualización\">\n", "```\n", "```\n", "git push origin master \n", "```\n", "Hemos agregado el comando *git status*, ya que nos permite visualizar que archivos de nuestro repositorio local han sido modificados o creado, y cuáles de ellos se encuentran en la zona de indexado, lo que es útil a la hora de trabajar con gran cantidad de archivos y directorios." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.1 Ejercicio Práctica" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Crear un directorio y clonar en el un repositorio remoto con *git clone* \n", "* *(copiar la dirección de reconocimiento del repositorio desde la plataforma online)*\n", "* Copiar el archivo creado anteriormente y crear otro nuevo\n", "* Indexar los 2 archivos con el comando *git add .*\n", "* Encabezar los archivos indexados con un mensaje que diga *\"primer commit\"*\n", "* Visualizar el estado de los archivos nuevos y antiguos con sus modificaciones \n", "* Subir el archivo desde el repositorio local al remoto con *git push*" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 5. Operaciones para repositorios remotos con más de un usuario" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta sección veremos aspectos importantes en caso de que trabajemos localmente, sincronizados a un repositorio remoto al cual tienen acceso más de un usuario. En este contexto es importante considerar que un usuario puede generar modificaciones locales y subirlas al repositorio remoto. Un problema típico en esta lógica de trabajo es el desfase de las versiones entre los usuarios para iguales archivos, surge entonces la necesidad de actualizar los archivos en nuestro repositorio local, sincronizándolos con las modificaciones hechas por otros usuarios, antes de comenzar a trabajar y realizar modificaciones en ellos. Para esto usamos el siguiente comando *GIT*, ubicados en nuestra dirección de trabajo local.\n", "\n", "```\n", "git pull\n", "```\n", "Es una muy buena práctica, cada vez que trabajamos sobre nuestro repositorio local, actualizarlo con el comando anterior, en caso de que algún usuario haya hecho modificaciones en el repositorio remoto.\n", "\n", "Otra necesidad o requerimiento que puede surgir, es que si queremos almacenar nuestro historial de trabajo en un *branch* paralelo al principal *master*, debemos crear esta línea de tiempo usando el comando *checkout -b*, que a su vez nos posiciona a este nuevo *branch* desde nuestro directorio local.\n", "\n", "```\n", "git checkout -b <branch_secundario>\n", "```\n", "De este modo, podemos subir las modificaciones hechas a la rama secundaria, que nos permitirá trabajar de forma más segura, en caso de tener problemas de desfase entre el repositorio remoto y el local.\n", "\n", "```\n", "git push origin <branch_secundario>\n", "```\n", "El comando *checkout* nos permite cambiar de *branch* según nuestro interés, por ejemplo si queremos volver a la rama principal lo hacemos como sigue:\n", "\n", "```\n", "git checkout <master> \n", "```\n", "Finalmente, al trabajar sobre un *branch* secundario, debemos fusionar este *branch* con *master*, podemos hacerlo directamente desde nuestro repositorio local o sobre el remoto, esto último dependerá de la plataforma sobre la cual estamos trabajando. Por ejemplo *github* nos ofrece realizar la fusión entre *branchs* haciendo click en *merge*.\n", "\n", "<img src=\"tutorial_git_merge_github.jpg.png\"width=\"500px\">\n", "\n", "Si hacemos la fusión desde nuestro repositorio local, debemos escribir:\n", "\n", "```\n", "git merge <branch_secundario>\n", "```\n", "Es recomendable antes de realizar la fusión entre los *branchs*, revisar las diferencias entre ambas, esto puede realizarse con:\n", "\n", "\n", "```\n", "git diff <source_branch> <target_branch>\n", "```\n", "\n", "<img src=\"tutorial_git_flujo.png\"width=\"600px\">\n", "\n", "\n", "En caso de que se exista algún error en nuestras operaciones desde el repositorio local, podemos solucionar este problema eliminando las modificaciones hechas y reemplazarlos por las versiones registradas en el último *commit* registrado en el repositorio.\n", "\n", "```\n", "git checkout -- <archivo.tipo>\n", "```\n", "Si el asunto es más grave y queremos deshacer todos los cambios locales realizados, podemos bajar la última versión almacenada en el servidor del repositorio remoto con:\n", "\n", "```\n", "git fetch origin\n", "```\n", "Y luego reemplazar esta versión por la local.\n", "\n", "```\n", "git reset --hard origin/master\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 5.1 Ejercicio de práctica" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " * Fusionar el repositorio local creado en *ej.1.1* con un remoto usando *git remote add origin*\n", " * *(copiar la dirección del repositorio remoto desde la plataforma online)*\n", " * Generar un *branch* paralelo con *git checkout -b*\n", " * Crear un archivo de texto en el *branch* secundario\n", " * Indexar únicamente los archivos modificados usando *git add -u* y luego *git commit* \n", " * Subir las actualizaciones en el repositorio local con *git push origin paralelo*\n", " * Realizar un *merge* entre el *branch* secundario y *master* desde la plataforma online, si es posible\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Soluciones a un Problema Práctico Frecuente" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esta sección dentro del tutorial, esta pensada para aquellos que ya tengan un poco de experiencia en el uso de *git* y probablemente con el que ya se habrán encontrado.\n", "\n", "Supongamos que tenemos sincronizado un repositorio local con uno remoto almacenado en alguna cuenta online, como por ej. *Github*. Y que hemos realizado una modificación erronea en algún archivo local que ha sido guardada.\n", "\n", "Entonces podríamos solucionar el problema desde 2 perspectivas generales:\n", "\n", " * *Solución desde el Repositorio Local*\n", " * *Solución con soporte del Repositorio Remoto*\n", "\n", "#### Solución desde el Repositorio Local\n", "\n", "En el primer caso, la idea sería acceder a las versiones locales almacenadas en la linea de tiempo (es esto lo que justifica el uso de algún sistema controlador de versiones como *git*). Para esto realizamos los siguientes pasos:\n", "\n", "```\n", "git log\n", "```\n", "Muestra todos los commits realizados en el repositorio, si queremos acotar a un número especifico agregamos la siguiente especificación.\n", "\n", "```\n", "git log -n \"número de commits a mostrar\"\n", "```\n", "Aparecerá en nuestra terminal una lista con todos los commits realizados, cada uno se encuentra especificado por una etiqueta, creada por defecto o manualmente como se explica en la sección 1, con el comando *git tag*.\n", "\n", "##### Caso 1.1\n", "Si hemos modificado y guardado una versión erronea, pero NO hemos hecho aún *git commit*, entonces la solución es simple, puesto que podemos escribir únicamente:\n", "```\n", "git checkout -- <archivo.modificado>\n", "```\n", "Este comando reemplazara la versión modificada por la versión almacenada en el último *commit* o *commit* anterior a la modificación errada.\n", "\n", "##### Caso 1.2\n", "Si luego de hacer la modificación erronea, hemos guardado y además realizado un *commit*, entonces la solución anterior ya no sirve, puesto que al hacerlo el último *commit* será el de la versión errada. Una opción más arriesgada, es el caso cuando queremos que todos los archivos del repositorio vuelvan a un estado anterior. Esto puede ser usado en caso de haber un único archivo modificado, puesto que los demás permanecen iguales respecto la versión anterior de referencia. La opción consiste en borrar todos los *commits* hasta el *commit* deseado, en el cual los archivos se encuentran en la versión deseada. *(Si hemos modificado más de un archivo y solo uno de ellos se encuentra en conflicto, entonces esta opción borrara las versiones de los otros archivos modificados correctamente)*.\n", "```\n", "git reset --hard <tag_commit>\n", "```\n", "\n", "#### Solución con soporte del Repositorio Remoto\n", "\n", "Si localmente hemos modificado un archivo y queremos corregirlo, otro modo es acudiendo a las versiones almacenadas en el repositorio remoto. Un error común es hacer un *git pull* pensando en actualizar el repositorio local con la versión del repositorio remoto, puesto que *git pull* realiza de forma automática las operaciones *fetch* y luego *merge*. El problema es que el *merge* por defecto reemplazara la versión más actualizada por la antigua, es decir superpondremos al versión erronea del archivo, que es justamente lo que NO queremos.\n", "\n", "Para esto debemos primero bajar el contenido del repositorio remoto con *git fetch* y luego al hacer *git merge* debemos especificar cual es la versión que queremos que prevalezca." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

brettavedisian/phys202-2015-work

assignments/assignment07/AlgorithmsEx01.ipynb

1

40654

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Algorithms Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Word counting" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `tokenize` that takes a string of English text returns a list of words. It should also remove [stop words](http://en.wikipedia.org/wiki/Stop_words), which are common short words that are often removed before natural language processing. Your function should have the following logic:\n", "\n", "* Split the string into lines using `splitlines`.\n", "* Split each line into a list of words and merge the lists for each line.\n", "* Use Python's builtin `filter` function to remove all punctuation.\n", "* If `stop_words` is a list, remove all occurences of the words in the list.\n", "* If `stop_words` is a space delimeted string of words, split them and remove them.\n", "* Remove any remaining empty words.\n", "* Make all words lowercase." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "nbgrader": { "checksum": "6b81e3d18c7d985eb0f20f45b5a1e33a", "solution": true } }, "outputs": [], "source": [ "def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\\:;\"<,>.?/}\\t'):\n", " \"\"\"Split a string into a list of words, removing punctuation and stop words.\"\"\"\n", " \n", " # the following functions were influenced by Dr. Granger's advice in office hours\n", " # removes punctuation and stop words in the lists\n", " def remove(c):\n", " if c in punctuation:\n", " return False\n", " else:\n", " return True\n", " \n", " def remove_stop(f):\n", " if stop_words==None:\n", " pass\n", " elif f in stop_words:\n", " return False\n", " else:\n", " return True\n", " \n", " a1=s.replace('--',' ').replace('-',' ') #got word count problems with single and double dashes, so this replaces them with spaces\n", " new_list=a1.splitlines()\n", " d=[w for m in new_list for w in m.split()] #referenced http://goo.gl/YWCeAS\n", " r=[]\n", " for x in d: # got help from James A.\n", " werd=[]\n", " for char in x:\n", " werd=werd+list(filter(remove,char))\n", " r.append(''.join(werd))\n", " low=[g.lower() for g in r]\n", " k=[]\n", " if type(stop_words)==list:\n", " k=list(filter(remove_stop,low))\n", " elif type(stop_words)==str:\n", " stop_words=stop_words.split()\n", " k=list(filter(remove_stop,low))\n", " else:\n", " k=low\n", " \n", " return k" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "51938ebee4d1863467fba80579b46318", "grade": true, "grade_id": "algorithmsex01a", "points": 2 } }, "outputs": [], "source": [ "assert tokenize(\"This, is the way; that things will end\", stop_words=['the', 'is']) == \\\n", " ['this', 'way', 'that', 'things', 'will', 'end']\n", "wasteland = \"\"\"\n", "APRIL is the cruellest month, breeding\n", "Lilacs out of the dead land, mixing\n", "Memory and desire, stirring\n", "Dull roots with spring rain.\n", "\"\"\"\n", "\n", "assert tokenize(wasteland, stop_words='is the of and') == \\\n", " ['april','cruellest','month','breeding','lilacs','out','dead','land',\n", " 'mixing','memory','desire','stirring','dull','roots','with','spring',\n", " 'rain']" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `count_words` that takes a list of words and returns a dictionary where the keys in the dictionary are the unique words in the list and the values are the word counts." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "nbgrader": { "checksum": "a94c1a7e986d4d8d3b80695b02e16015", "grade": false, "grade_id": "algorithmsex01b", "points": 2, "solution": true } }, "outputs": [], "source": [ "def count_words(data):\n", " \"\"\"Return a word count dictionary from the list of words in data.\"\"\"\n", " d={}\n", " for i in range(len(data)):\n", " d[data[i]]=data.count(data[i])\n", " # this creates a dictionary similar to the method used in codecademy\n", " return d" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "77c9b760f563b041b6386781c42dc0e2", "grade": true, "grade_id": "algorithmsex01b", "points": 2 } }, "outputs": [], "source": [ "assert count_words(tokenize('this and the this from and a a a')) == \\\n", " {'a': 3, 'and': 2, 'from': 1, 'the': 1, 'this': 2}" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `sort_word_counts` that return a list of sorted word counts:\n", "\n", "* Each element of the list should be a `(word, count)` tuple.\n", "* The list should be sorted by the word counts, with the higest counts coming first.\n", "* To perform this sort, look at using the `sorted` function with a custom `key` and `reverse`\n", " argument." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "nbgrader": { "checksum": "5c68f353c6c5f3e1494e7d2902480ebf", "solution": true } }, "outputs": [], "source": [ "def sort_word_counts(wc):\n", " \"\"\"Return a list of 2-tuples of (word, count), sorted by count descending.\"\"\"\n", " def key_thing(m):\n", " return m[1] # to make sorted work, I needed to create a function that would return the value of the key\n", " tuple_list=list(wc.items())\n", " return sorted(tuple_list, key=key_thing, reverse=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "e3fd160136fc78f4a7c3fc027d445b4a", "grade": true, "grade_id": "algorithmsex01c", "points": 2 } }, "outputs": [], "source": [ "assert sort_word_counts(count_words(tokenize('this and a the this this and a a a'))) == \\\n", " [('a', 4), ('this', 3), ('and', 2), ('the', 1)]" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Perform a word count analysis on Chapter 1 of Moby Dick, whose text can be found in the file `mobydick_chapter1.txt`:\n", "\n", "* Read the file into a string.\n", "* Tokenize with stop words of `'the of and a to in is it that as'`.\n", "* Perform a word count, the sort and save the result in a variable named `swc`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "844" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f=open('mobydick_chapter1.txt','r')\n", "moby_file=f.read()\n", "swc=sort_word_counts(count_words(tokenize(moby_file, stop_words=['the','of','and','a','to','in','is','it','that','as'])))\n", "len(swc)\n", "# In this cell, I am only able to find 844 unique words in the chapter.\n", "# However, I do know the answer is 848. I have searched through the entire text\n", "# file and my output to determine what words are missing or formatted\n", "# incorrectly, but couldn't find any discrepancies. I know I am not the only \n", "# with this problem, as indicated by the gitter chat." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "0c74fa7fa2b9ad5a6b54a0b3f04ac9dc", "grade": true, "grade_id": "algorithmsex01d", "points": 2 } }, "outputs": [ { "ename": "AssertionError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-9-79f30673d9f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mswc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m43\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mswc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m848\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m: " ] } ], "source": [ "assert swc[0]==('i',43)\n", "assert len(swc)==848" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Create a \"Cleveland Style\" [dotplot](http://en.wikipedia.org/wiki/Dot_plot_%28statistics%29) of the counts of the top 50 words using Matplotlib. If you don't know what a dotplot is, you will have to do some research..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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sn4yTLc2sUzX73dmpo8v6TdI44FDgfaSW2S2k9WfeDRwcEbe2r3YravQUODEn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\njuEgY2ZmhXGQqeGMfzOz1ilFn0zO2L8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8e9656b4a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,9))\n", "q=np.empty(51)\n", "e=[]\n", "for i in range(51):\n", " q[i]=swc[i][1]\n", "q2=sorted(q,reverse=False)\n", "for i in range(51):\n", " e.append(swc[i][0])\n", "plt.scatter(q2,range(51),marker='o'),plt.ylim(top=51,bottom=-1)\n", "plt.xlabel('Occurances of Word'),plt.xlim(left=5)\n", "plt.ylabel('Words')\n", "plt.yticks(range(50,-1,-1),e)\n", "plt.title('Words of Moby Dick Chapter 1')\n", "plt.tick_params(axis='x',top='off')\n", "plt.tick_params(axis='y',right='off')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "481908a47f48647c344ed328c691ba63", "grade": true, "grade_id": "algorithsex01e", "points": 2 } }, "outputs": [], "source": [ "assert True # use this for grading the dotplot" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

stharrold/demo

demo/app_predict/examples/example_app_predict_dev.ipynb

1

11633411

mit

vinitsamel/udacitydeeplearning

language-translation/dlnd_language_translation.ipynb

1

88254

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n", "## Get the Data\n", "Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import problem_unittests as tests\n", "\n", "source_path = 'data/small_vocab_en'\n", "target_path = 'data/small_vocab_fr'\n", "source_text = helper.load_data(source_path)\n", "target_text = helper.load_data(target_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the Data\n", "Play around with view_sentence_range to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 227\n", "Number of sentences: 137861\n", "Average number of words in a sentence: 13.225277634719028\n", "\n", "English sentences 0 to 10:\n", "new jersey is sometimes quiet during autumn , and it is snowy in april .\n", "the united states is usually chilly during july , and it is usually freezing in november .\n", "california is usually quiet during march , and it is usually hot in june .\n", "the united states is sometimes mild during june , and it is cold in september .\n", "your least liked fruit is the grape , but my least liked is the apple .\n", "his favorite fruit is the orange , but my favorite is the grape .\n", "paris is relaxing during december , but it is usually chilly in july .\n", "new jersey is busy during spring , and it is never hot in march .\n", "our least liked fruit is the lemon , but my least liked is the grape .\n", "the united states is sometimes busy during january , and it is sometimes warm in november .\n", "\n", "French sentences 0 to 10:\n", "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n", "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n", "california est généralement calme en mars , et il est généralement chaud en juin .\n", "les états-unis est parfois légère en juin , et il fait froid en septembre .\n", "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n", "son fruit préféré est l'orange , mais mon préféré est le raisin .\n", "paris est relaxant en décembre , mais il est généralement froid en juillet .\n", "new jersey est occupé au printemps , et il est jamais chaude en mars .\n", "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n", "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n" ] } ], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n", "\n", "sentences = source_text.split('\\n')\n", "word_counts = [len(sentence.split()) for sentence in sentences]\n", "print('Number of sentences: {}'.format(len(sentences)))\n", "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n", "\n", "print()\n", "print('English sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n", "print()\n", "print('French sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Preprocessing Function\n", "### Text to Word Ids\n", "As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of `target_text`. This will help the neural network predict when the sentence should end.\n", "\n", "You can get the `<EOS>` word id by doing:\n", "```python\n", "target_vocab_to_int['<EOS>']\n", "```\n", "You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n", " \"\"\"\n", " Convert source and target text to proper word ids\n", " :param source_text: String that contains all the source text.\n", " :param target_text: String that contains all the target text.\n", " :param source_vocab_to_int: Dictionary to go from the source words to an id\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: A tuple of lists (source_id_text, target_id_text)\n", " \"\"\"\n", " # TODO: Implement Function\n", " #print (source_text)\n", " source_letter_ids = [[source_vocab_to_int.get(word, source_vocab_to_int['<UNK>']) for word in line.split(' ')] for line in source_text.split('\\n')]\n", " target_letter_ids = [[target_vocab_to_int.get(word, target_vocab_to_int['<UNK>']) for word in line.split(' ')] + [target_vocab_to_int['<EOS>']] for line in target_text.split('\\n')]\n", " return source_letter_ids, target_letter_ids\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_text_to_ids(text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check the Version of TensorFlow and Access to GPU\n", "This will check to make sure you have the correct version of TensorFlow and access to a GPU" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.2.1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel_launcher.py:15: UserWarning: No GPU found. Please use a GPU to train your neural network.\n", " from ipykernel import kernelapp as app\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "from tensorflow.python.layers.core import Dense\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n", "- `model_inputs`\n", "- `process_decoder_input`\n", "- `encoding_layer`\n", "- `decoding_layer_train`\n", "- `decoding_layer_infer`\n", "- `decoding_layer`\n", "- `seq2seq_model`\n", "\n", "### Input\n", "Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "\n", "- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n", "- Targets placeholder with rank 2.\n", "- Learning rate placeholder with rank 0.\n", "- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n", "- Target sequence length placeholder named \"target_sequence_length\" with rank 1\n", "- Max target sequence length tensor named \"max_target_len\" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.\n", "- Source sequence length placeholder named \"source_sequence_length\" with rank 1\n", "\n", "Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ERROR:tensorflow:==================================\n", "Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n", "<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>\n", "If you want to mark it as used call its \"mark_used()\" method.\n", "It was originally created here:\n", "['File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\\n \"__main__\", mod_spec)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/runpy.py\", line 85, in _run_code\\n exec(code, run_globals)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\\n app.launch_new_instance()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n app.start()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n ioloop.IOLoop.instance().start()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n super(ZMQIOLoop, self).start()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\\n handler_func(fd_obj, events)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n self._handle_recv()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n self._run_callback(callback, msg)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n callback(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n return self.dispatch_shell(stream, msg)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n handler(stream, idents, msg)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n user_expressions, allow_stdin)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n interactivity=interactivity, compiler=compiler, result=result)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n if self.run_code(code, result):', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-7-c0999ee202bc>\", line 21, in <module>\\n tests.test_model_inputs(model_inputs)', 'File \"/Users/vsamel/gitDL/language-translation/problem_unittests.py\", line 106, in test_model_inputs\\n assert tf.assert_rank(lr, 0, message=\\'Learning Rate has wrong rank\\')', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n dynamic_condition, data, summarize)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 170, in wrapped\\n return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 139, in _add_should_use_warning\\n wrapped = TFShouldUseWarningWrapper(x)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 96, in __init__\\n stack = [s.strip() for s in traceback.format_stack()]']\n", "==================================\n", "ERROR:tensorflow:==================================\n", "Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n", "<tf.Operation 'assert_rank_3/Assert/Assert' type=Assert>\n", "If you want to mark it as used call its \"mark_used()\" method.\n", "It was originally created here:\n", "['File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\\n \"__main__\", mod_spec)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/runpy.py\", line 85, in _run_code\\n exec(code, run_globals)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\\n app.launch_new_instance()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n app.start()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n ioloop.IOLoop.instance().start()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n super(ZMQIOLoop, self).start()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\\n handler_func(fd_obj, events)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n self._handle_recv()', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n self._run_callback(callback, msg)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n callback(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n return self.dispatch_shell(stream, msg)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n handler(stream, idents, msg)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n user_expressions, allow_stdin)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n interactivity=interactivity, compiler=compiler, result=result)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n if self.run_code(code, result):', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-7-c0999ee202bc>\", line 21, in <module>\\n tests.test_model_inputs(model_inputs)', 'File \"/Users/vsamel/gitDL/language-translation/problem_unittests.py\", line 107, in test_model_inputs\\n assert tf.assert_rank(keep_prob, 0, message=\\'Keep Probability has wrong rank\\')', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n dynamic_condition, data, summarize)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 170, in wrapped\\n return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 139, in _add_should_use_warning\\n wrapped = TFShouldUseWarningWrapper(x)', 'File \"/Users/vsamel/anaconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 96, in __init__\\n stack = [s.strip() for s in traceback.format_stack()]']\n", "==================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def model_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.\n", " :return: Tuple (input, targets, learning rate, keep probability, target sequence length,\n", " max target sequence length, source sequence length)\n", " \"\"\"\n", " # TODO: Implement Function\n", " input_data = tf.placeholder(tf.int32, [None, None], name='input')\n", " targets = tf.placeholder(tf.int32, [None, None], name='targets')\n", " lr = tf.placeholder(tf.float32, name='learning_rate')\n", " kp = tf.placeholder(tf.float32, name=\"keep_prob\")\n", " target_sequence_length = tf.placeholder(tf.int32, (None,), name='target_sequence_length')\n", " max_target_sequence_length = tf.reduce_max(target_sequence_length, name='max_target_len')\n", " source_sequence_length = tf.placeholder(tf.int32, (None,), name='source_sequence_length')\n", " return input_data, targets, lr, kp, target_sequence_length, max_target_sequence_length, source_sequence_length\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_model_inputs(model_inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Process Decoder Input\n", "Implement `process_decoder_input` by removing the last word id from each batch in `target_data` and concat the GO ID to the begining of each batch." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def process_decoder_input(target_data, target_vocab_to_int, batch_size):\n", " \"\"\"\n", " Preprocess target data for encoding\n", " :param target_data: Target Placehoder\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param batch_size: Batch Size\n", " :return: Preprocessed target data\n", " \"\"\"\n", " # TODO: Implement Function\n", " '''Remove the last word id from each batch and concat the <GO> to the begining of each batch'''\n", " ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n", " dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n", "\n", " return dec_input\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_process_encoding_input(process_decoder_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Encoding\n", "Implement `encoding_layer()` to create a Encoder RNN layer:\n", " * Embed the encoder input using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\n", " * Construct a [stacked](https://github.com/tensorflow/tensorflow/blob/6947f65a374ebf29e74bb71e36fd82760056d82c/tensorflow/docs_src/tutorials/recurrent.md#stacking-multiple-lstms) [`tf.contrib.rnn.LSTMCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMCell) wrapped in a [`tf.contrib.rnn.DropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper)\n", " * Pass cell and embedded input to [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "from imp import reload\n", "reload(tests)\n", "\n", " # RNN cell\n", "def make_cell(rnn_size):\n", " enc_cell = tf.contrib.rnn.LSTMCell(rnn_size,\n", " initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))\n", " return enc_cell\n", "\n", "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, \n", " source_sequence_length, source_vocab_size, \n", " encoding_embedding_size):\n", " \"\"\"\n", " Create encoding layer\n", " :param rnn_inputs: Inputs for the RNN\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param keep_prob: Dropout keep probability\n", " :param source_sequence_length: a list of the lengths of each sequence in the batch\n", " :param source_vocab_size: vocabulary size of source data\n", " :param encoding_embedding_size: embedding size of source data\n", " :return: tuple (RNN output, RNN state)\n", " \"\"\"\n", " # TODO: Implement Function\n", " # Encoder embedding\n", " enc_embed_input = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size)\n", " \n", " cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(rnn_size)] * num_layers)\n", "\n", " enc_cell = tf.contrib.rnn.MultiRNNCell([make_cell(rnn_size) for _ in range(num_layers)])\n", " enc_cell = tf.contrib.rnn.DropoutWrapper(enc_cell, output_keep_prob=keep_prob)\n", " enc_output, enc_state = tf.nn.dynamic_rnn(enc_cell, enc_embed_input, sequence_length=source_sequence_length, dtype=tf.float32)\n", " \n", " return enc_output, enc_state\n", " \n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_encoding_layer(encoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Training\n", "Create a training decoding layer:\n", "* Create a [`tf.contrib.seq2seq.TrainingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/TrainingHelper) \n", "* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n", "* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "\n", "def decoding_layer_train(enc_state, dec_cell, dec_embed_input, \n", " target_sequence_length, max_summary_length, \n", " output_layer, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for training\n", " :param encoder_state: Encoder State\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embed_input: Decoder embedded input\n", " :param target_sequence_length: The lengths of each sequence in the target batch\n", " :param max_summary_length: The length of the longest sequence in the batch\n", " :param output_layer: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: BasicDecoderOutput containing training logits and sample_id\n", " \"\"\"\n", " # TODO: Implement Function\n", " # Helper for the training process. Used by BasicDecoder to read inputs.\n", " training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=dec_embed_input,\n", " sequence_length=target_sequence_length,\n", " time_major=False)\n", " \n", " \n", " # Basic decoder\n", " training_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell,\n", " training_helper,\n", " enc_state,\n", " output_layer) \n", " \n", " # Perform dynamic decoding using the decoder\n", " training_decoder_output, final_state, final_sequence_lengths = tf.contrib.seq2seq.dynamic_decode(training_decoder,\n", " impute_finished=True,\n", " maximum_iterations=max_summary_length)\n", "\n", " return training_decoder_output\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_train(decoding_layer_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Inference\n", "Create inference decoder:\n", "* Create a [`tf.contrib.seq2seq.GreedyEmbeddingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/GreedyEmbeddingHelper)\n", "* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n", "* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\n", " end_of_sequence_id, max_target_sequence_length,\n", " vocab_size, output_layer, batch_size, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for inference\n", " :param encoder_state: Encoder state\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embeddings: Decoder embeddings\n", " :param start_of_sequence_id: GO ID\n", " :param end_of_sequence_id: EOS Id\n", " :param max_target_sequence_length: Maximum length of target sequences\n", " :param vocab_size: Size of decoder/target vocabulary\n", " :param decoding_scope: TenorFlow Variable Scope for decoding\n", " :param output_layer: Function to apply the output layer\n", " :param batch_size: Batch size\n", " :param keep_prob: Dropout keep probability\n", " :return: BasicDecoderOutput containing inference logits and sample_id\n", " \"\"\"\n", " # TODO: Implement Function\n", " start_tokens = tf.tile(tf.constant([start_of_sequence_id], dtype=tf.int32), [batch_size], name='start_tokens') \n", " # Helper for the inference process.\n", " inference_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings,\n", " start_tokens,\n", " end_of_sequence_id)\n", "\n", " # Basic decoder\n", " inference_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell,\n", " inference_helper,\n", " encoder_state,\n", " output_layer)\n", " \n", " # Perform dynamic decoding using the decoder\n", " inference_decoder_output, final_state, final_sequence_lengths = tf.contrib.seq2seq.dynamic_decode(inference_decoder,\n", " impute_finished=True,\n", " maximum_iterations=max_target_sequence_length)\n", " \n", " return inference_decoder_output\n", "\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_infer(decoding_layer_infer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Decoding Layer\n", "Implement `decoding_layer()` to create a Decoder RNN layer.\n", "\n", "* Embed the target sequences\n", "* Construct the decoder LSTM cell (just like you constructed the encoder cell above)\n", "* Create an output layer to map the outputs of the decoder to the elements of our vocabulary\n", "* Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob)` function to get the training logits.\n", "* Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob)` function to get the inference logits.\n", "\n", "Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer(dec_input, encoder_state,\n", " target_sequence_length, max_target_sequence_length,\n", " rnn_size,\n", " num_layers, target_vocab_to_int, target_vocab_size,\n", " batch_size, keep_prob, decoding_embedding_size):\n", " \"\"\"\n", " Create decoding layer\n", " :param dec_input: Decoder input\n", " :param encoder_state: Encoder state\n", " :param target_sequence_length: The lengths of each sequence in the target batch\n", " :param max_target_sequence_length: Maximum length of target sequences\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param target_vocab_size: Size of target vocabulary\n", " :param batch_size: The size of the batch\n", " :param keep_prob: Dropout keep probability\n", " :param decoding_embedding_size: Decoding embedding size\n", " :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n", " \"\"\"\n", " # TODO: Implement Function\n", " start_of_sequence_id = target_vocab_to_int['<GO>']\n", " end_of_sequence_id = target_vocab_to_int['<EOS>']\n", " dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))\n", " dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n", " dec_cell = tf.contrib.rnn.MultiRNNCell([make_cell(rnn_size) for _ in range(num_layers)])\n", " output_layer = Dense(target_vocab_size, kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))\n", " with tf.variable_scope(\"decode\"):\n", " training_decoder_output = decoding_layer_train(encoder_state, dec_cell, dec_embed_input,\n", " target_sequence_length, max_target_sequence_length, \n", " output_layer, keep_prob)\n", " \n", " with tf.variable_scope(\"decode\", reuse=True):\n", " inference_decoder_output = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, \\\n", " start_of_sequence_id, end_of_sequence_id,\\\n", " max_target_sequence_length, target_vocab_size,\\\n", " output_layer, batch_size, keep_prob) \n", " return training_decoder_output, inference_decoder_output\n", "\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer(decoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "\n", "- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size)`.\n", "- Process target data using your `process_decoder_input(target_data, target_vocab_to_int, batch_size)` function.\n", "- Decode the encoded input using your `decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size)` function." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def seq2seq_model(input_data, target_data, keep_prob, batch_size,\n", " source_sequence_length, target_sequence_length,\n", " max_target_sentence_length,\n", " source_vocab_size, target_vocab_size,\n", " enc_embedding_size, dec_embedding_size,\n", " rnn_size, num_layers, target_vocab_to_int):\n", " \"\"\"\n", " Build the Sequence-to-Sequence part of the neural network\n", " :param input_data: Input placeholder\n", " :param target_data: Target placeholder\n", " :param keep_prob: Dropout keep probability placeholder\n", " :param batch_size: Batch Size\n", " :param source_sequence_length: Sequence Lengths of source sequences in the batch\n", " :param target_sequence_length: Sequence Lengths of target sequences in the batch\n", " :param source_vocab_size: Source vocabulary size\n", " :param target_vocab_size: Target vocabulary size\n", " :param enc_embedding_size: Decoder embedding size\n", " :param dec_embedding_size: Encoder embedding size\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n", " \"\"\"\n", " # TODO: Implement Function\n", " # TODO: Implement Function\n", " # Pass the input data through the encoder. We'll ignore the encoder output, but use the state \n", " _, enc_state = encoding_layer(input_data, \n", " rnn_size, \n", " num_layers, \n", " keep_prob,\n", " source_sequence_length,\n", " source_vocab_size, \n", " enc_embedding_size)\n", "\n", " # Prepare the target sequences we'll feed to the decoder in training mode\n", " dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size) \n", " \n", " # Pass encoder state and decoder inputs to the decoders\n", " training_decoder_output, inference_decoder_output = decoding_layer(dec_input, \n", " enc_state,\n", " target_sequence_length,\n", " max_target_sentence_length,\n", " rnn_size,\n", " num_layers, \n", " target_vocab_to_int,\n", " target_vocab_size,\n", " batch_size, keep_prob,\n", " dec_embedding_size \n", " ) \n", " \n", " return training_decoder_output, inference_decoder_output\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_seq2seq_model(seq2seq_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `num_layers` to the number of layers.\n", "- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n", "- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `keep_probability` to the Dropout keep probability\n", "- Set `display_step` to state how many steps between each debug output statement" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", "epochs = 5\n", "# Batch Size\n", "batch_size = 256\n", "# RNN Size\n", "rnn_size = 512\n", "# Number of Layers\n", "num_layers = 2\n", "# Embedding Size\n", "encoding_embedding_size = 256\n", "decoding_embedding_size = 256\n", "# Learning Rate\n", "learning_rate = 0.001\n", "# Dropout Keep Probability\n", "keep_probability = 5\n", "display_step = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_path = 'checkpoints/dev'\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n", "max_target_sentence_length = max([len(sentence) for sentence in source_int_text])\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()\n", "\n", " #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')\n", " input_shape = tf.shape(input_data)\n", "\n", " train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),\n", " targets,\n", " keep_prob,\n", " batch_size,\n", " source_sequence_length,\n", " target_sequence_length,\n", " max_target_sequence_length,\n", " len(source_vocab_to_int),\n", " len(target_vocab_to_int),\n", " encoding_embedding_size,\n", " decoding_embedding_size,\n", " rnn_size,\n", " num_layers,\n", " target_vocab_to_int)\n", "\n", "\n", " training_logits = tf.identity(train_logits.rnn_output, name='logits')\n", " inference_logits = tf.identity(inference_logits.sample_id, name='predictions')\n", "\n", " masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\n", "\n", " with tf.name_scope(\"optimization\"):\n", " # Loss function\n", " cost = tf.contrib.seq2seq.sequence_loss(\n", " training_logits,\n", " targets,\n", " masks)\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batch and pad the source and target sequences" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "def pad_sentence_batch(sentence_batch, pad_int):\n", " \"\"\"Pad sentences with <PAD> so that each sentence of a batch has the same length\"\"\"\n", " max_sentence = max([len(sentence) for sentence in sentence_batch])\n", " return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]\n", "\n", "\n", "def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):\n", " \"\"\"Batch targets, sources, and the lengths of their sentences together\"\"\"\n", " for batch_i in range(0, len(sources)//batch_size):\n", " start_i = batch_i * batch_size\n", "\n", " # Slice the right amount for the batch\n", " sources_batch = sources[start_i:start_i + batch_size]\n", " targets_batch = targets[start_i:start_i + batch_size]\n", "\n", " # Pad\n", " pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\n", " pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\n", "\n", " # Need the lengths for the _lengths parameters\n", " pad_targets_lengths = []\n", " for target in pad_targets_batch:\n", " pad_targets_lengths.append(len(target))\n", "\n", " pad_source_lengths = []\n", " for source in pad_sources_batch:\n", " pad_source_lengths.append(len(source))\n", "\n", " yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 10/538 - Train Accuracy: 0.3256, Validation Accuracy: 0.4098, Loss: 3.1298\n", "Epoch 0 Batch 20/538 - Train Accuracy: 0.4321, Validation Accuracy: 0.4693, Loss: 2.4952\n", "Epoch 0 Batch 30/538 - Train Accuracy: 0.4361, Validation Accuracy: 0.4952, Loss: 2.3096\n", "Epoch 0 Batch 40/538 - Train Accuracy: 0.5059, Validation Accuracy: 0.5071, Loss: 1.8389\n", "Epoch 0 Batch 50/538 - Train Accuracy: 0.4650, Validation Accuracy: 0.4956, Loss: 1.7285\n", "Epoch 0 Batch 60/538 - Train Accuracy: 0.4404, Validation Accuracy: 0.4792, Loss: 1.5384\n", "Epoch 0 Batch 70/538 - Train Accuracy: 0.4758, Validation Accuracy: 0.5021, Loss: 1.3440\n", "Epoch 0 Batch 80/538 - Train Accuracy: 0.4463, Validation Accuracy: 0.5154, Loss: 1.3046\n", "Epoch 0 Batch 90/538 - Train Accuracy: 0.4877, Validation Accuracy: 0.5057, Loss: 1.1582\n", "Epoch 0 Batch 100/538 - Train Accuracy: 0.5137, Validation Accuracy: 0.5412, Loss: 1.0433\n", "Epoch 0 Batch 110/538 - Train Accuracy: 0.5189, Validation Accuracy: 0.5607, Loss: 1.0033\n", "Epoch 0 Batch 120/538 - Train Accuracy: 0.5525, Validation Accuracy: 0.5655, Loss: 0.8887\n", "Epoch 0 Batch 130/538 - Train Accuracy: 0.5675, Validation Accuracy: 0.5866, Loss: 0.8162\n", "Epoch 0 Batch 140/538 - Train Accuracy: 0.5316, Validation Accuracy: 0.5666, Loss: 0.9703\n", "Epoch 0 Batch 150/538 - Train Accuracy: 0.5941, Validation Accuracy: 0.5845, Loss: 0.7902\n", "Epoch 0 Batch 160/538 - Train Accuracy: 0.6053, Validation Accuracy: 0.6067, Loss: 0.7047\n", "Epoch 0 Batch 170/538 - Train Accuracy: 0.6155, Validation Accuracy: 0.6088, Loss: 0.6753\n", "Epoch 0 Batch 180/538 - Train Accuracy: 0.6388, Validation Accuracy: 0.6028, Loss: 0.6502\n", "Epoch 0 Batch 190/538 - Train Accuracy: 0.6045, Validation Accuracy: 0.6152, Loss: 0.6521\n", "Epoch 0 Batch 200/538 - Train Accuracy: 0.6188, Validation Accuracy: 0.6294, Loss: 0.6103\n", "Epoch 0 Batch 210/538 - Train Accuracy: 0.6135, Validation Accuracy: 0.6268, Loss: 0.5747\n", "Epoch 0 Batch 220/538 - Train Accuracy: 0.6291, Validation Accuracy: 0.6451, Loss: 0.5471\n", "Epoch 0 Batch 230/538 - Train Accuracy: 0.6697, Validation Accuracy: 0.6383, Loss: 0.5546\n", "Epoch 0 Batch 240/538 - Train Accuracy: 0.6535, Validation Accuracy: 0.6655, Loss: 0.5421\n", "Epoch 0 Batch 250/538 - Train Accuracy: 0.6828, Validation Accuracy: 0.6822, Loss: 0.5011\n", "Epoch 0 Batch 260/538 - Train Accuracy: 0.6698, Validation Accuracy: 0.6774, Loss: 0.4660\n", "Epoch 0 Batch 270/538 - Train Accuracy: 0.6977, Validation Accuracy: 0.6889, Loss: 0.4600\n", "Epoch 0 Batch 280/538 - Train Accuracy: 0.7344, Validation Accuracy: 0.7287, Loss: 0.4112\n", "Epoch 0 Batch 290/538 - Train Accuracy: 0.7445, Validation Accuracy: 0.7312, Loss: 0.3946\n", "Epoch 0 Batch 300/538 - Train Accuracy: 0.7424, Validation Accuracy: 0.7424, Loss: 0.3560\n", "Epoch 0 Batch 310/538 - Train Accuracy: 0.7898, Validation Accuracy: 0.7706, Loss: 0.3444\n", "Epoch 0 Batch 320/538 - Train Accuracy: 0.7766, Validation Accuracy: 0.7935, Loss: 0.3067\n", "Epoch 0 Batch 330/538 - Train Accuracy: 0.8298, Validation Accuracy: 0.7905, Loss: 0.2723\n", "Epoch 0 Batch 340/538 - Train Accuracy: 0.8113, Validation Accuracy: 0.8018, Loss: 0.2655\n", "Epoch 0 Batch 350/538 - Train Accuracy: 0.8545, Validation Accuracy: 0.8082, Loss: 0.2501\n", "Epoch 0 Batch 360/538 - Train Accuracy: 0.8477, Validation Accuracy: 0.8180, Loss: 0.2348\n", "Epoch 0 Batch 370/538 - Train Accuracy: 0.8445, Validation Accuracy: 0.8493, Loss: 0.2062\n", "Epoch 0 Batch 380/538 - Train Accuracy: 0.8820, Validation Accuracy: 0.8610, Loss: 0.1796\n", "Epoch 0 Batch 390/538 - Train Accuracy: 0.8890, Validation Accuracy: 0.8452, Loss: 0.1567\n", "Epoch 0 Batch 400/538 - Train Accuracy: 0.8811, Validation Accuracy: 0.8707, Loss: 0.1591\n", "Epoch 0 Batch 410/538 - Train Accuracy: 0.8742, Validation Accuracy: 0.8633, Loss: 0.1621\n", "Epoch 0 Batch 420/538 - 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Train Accuracy: 0.9833, Validation Accuracy: 0.9718, Loss: 0.0095\n", "Epoch 4 Batch 50/538 - Train Accuracy: 0.9848, Validation Accuracy: 0.9735, Loss: 0.0106\n", "Epoch 4 Batch 60/538 - Train Accuracy: 0.9896, Validation Accuracy: 0.9751, Loss: 0.0129\n", "Epoch 4 Batch 70/538 - Train Accuracy: 0.9853, Validation Accuracy: 0.9680, Loss: 0.0092\n", "Epoch 4 Batch 80/538 - Train Accuracy: 0.9822, Validation Accuracy: 0.9664, Loss: 0.0092\n", "Epoch 4 Batch 90/538 - Train Accuracy: 0.9929, Validation Accuracy: 0.9679, Loss: 0.0109\n", "Epoch 4 Batch 100/538 - Train Accuracy: 0.9939, Validation Accuracy: 0.9661, Loss: 0.0082\n", "Epoch 4 Batch 110/538 - Train Accuracy: 0.9904, Validation Accuracy: 0.9714, Loss: 0.0109\n", "Epoch 4 Batch 120/538 - Train Accuracy: 0.9902, Validation Accuracy: 0.9679, Loss: 0.0097\n", "Epoch 4 Batch 130/538 - Train Accuracy: 0.9953, Validation Accuracy: 0.9743, Loss: 0.0098\n", "Epoch 4 Batch 140/538 - Train Accuracy: 0.9885, Validation Accuracy: 0.9654, Loss: 0.0132\n", "Epoch 4 Batch 150/538 - Train Accuracy: 0.9908, Validation Accuracy: 0.9716, Loss: 0.0109\n", "Epoch 4 Batch 160/538 - Train Accuracy: 0.9827, Validation Accuracy: 0.9656, Loss: 0.0119\n", "Epoch 4 Batch 170/538 - Train Accuracy: 0.9842, Validation Accuracy: 0.9680, Loss: 0.0117\n", "Epoch 4 Batch 180/538 - Train Accuracy: 0.9754, Validation Accuracy: 0.9723, Loss: 0.0139\n", "Epoch 4 Batch 190/538 - Train Accuracy: 0.9927, Validation Accuracy: 0.9744, Loss: 0.0141\n", "Epoch 4 Batch 200/538 - Train Accuracy: 0.9916, Validation Accuracy: 0.9670, Loss: 0.0087\n", "Epoch 4 Batch 210/538 - Train Accuracy: 0.9849, Validation Accuracy: 0.9716, Loss: 0.0125\n", "Epoch 4 Batch 220/538 - Train Accuracy: 0.9853, Validation Accuracy: 0.9595, Loss: 0.0127\n", "Epoch 4 Batch 230/538 - Train Accuracy: 0.9896, Validation Accuracy: 0.9622, Loss: 0.0126\n", "Epoch 4 Batch 240/538 - Train Accuracy: 0.9729, Validation Accuracy: 0.9643, Loss: 0.0118\n", "Epoch 4 Batch 250/538 - Train Accuracy: 0.9850, Validation Accuracy: 0.9549, Loss: 0.0115\n", "Epoch 4 Batch 260/538 - Train Accuracy: 0.9879, Validation Accuracy: 0.9632, Loss: 0.0139\n", "Epoch 4 Batch 270/538 - Train Accuracy: 0.9879, Validation Accuracy: 0.9648, Loss: 0.0103\n", "Epoch 4 Batch 280/538 - Train Accuracy: 0.9885, Validation Accuracy: 0.9718, Loss: 0.0088\n", "Epoch 4 Batch 290/538 - Train Accuracy: 0.9906, Validation Accuracy: 0.9675, Loss: 0.0107\n", "Epoch 4 Batch 300/538 - Train Accuracy: 0.9894, Validation Accuracy: 0.9737, Loss: 0.0107\n", "Epoch 4 Batch 310/538 - Train Accuracy: 0.9842, Validation Accuracy: 0.9670, Loss: 0.0140\n", "Epoch 4 Batch 320/538 - Train Accuracy: 0.9864, Validation Accuracy: 0.9815, Loss: 0.0099\n", "Epoch 4 Batch 330/538 - Train Accuracy: 0.9898, Validation Accuracy: 0.9672, Loss: 0.0091\n", "Epoch 4 Batch 340/538 - Train Accuracy: 0.9908, Validation Accuracy: 0.9728, Loss: 0.0096\n", "Epoch 4 Batch 350/538 - Train Accuracy: 0.9870, Validation Accuracy: 0.9723, Loss: 0.0120\n", "Epoch 4 Batch 360/538 - Train Accuracy: 0.9906, Validation Accuracy: 0.9645, Loss: 0.0092\n", "Epoch 4 Batch 370/538 - Train Accuracy: 0.9910, Validation Accuracy: 0.9751, Loss: 0.0108\n", "Epoch 4 Batch 380/538 - Train Accuracy: 0.9914, Validation Accuracy: 0.9581, Loss: 0.0095\n", "Epoch 4 Batch 390/538 - Train Accuracy: 0.9767, Validation Accuracy: 0.9707, Loss: 0.0142\n", "Epoch 4 Batch 400/538 - Train Accuracy: 0.9849, Validation Accuracy: 0.9833, Loss: 0.0121\n", "Epoch 4 Batch 410/538 - Train Accuracy: 0.9891, Validation Accuracy: 0.9700, Loss: 0.0098\n", "Epoch 4 Batch 420/538 - Train Accuracy: 0.9850, Validation Accuracy: 0.9686, Loss: 0.0115\n", "Epoch 4 Batch 430/538 - Train Accuracy: 0.9902, Validation Accuracy: 0.9673, Loss: 0.0099\n", "Epoch 4 Batch 440/538 - Train Accuracy: 0.9893, Validation Accuracy: 0.9766, Loss: 0.0109\n", "Epoch 4 Batch 450/538 - Train Accuracy: 0.9820, Validation Accuracy: 0.9703, Loss: 0.0161\n", "Epoch 4 Batch 460/538 - Train Accuracy: 0.9829, Validation Accuracy: 0.9735, Loss: 0.0121\n", "Epoch 4 Batch 470/538 - Train Accuracy: 0.9875, Validation Accuracy: 0.9775, Loss: 0.0094\n", "Epoch 4 Batch 480/538 - Train Accuracy: 0.9862, Validation Accuracy: 0.9663, Loss: 0.0083\n", "Epoch 4 Batch 490/538 - Train Accuracy: 0.9855, Validation Accuracy: 0.9780, Loss: 0.0091\n", "Epoch 4 Batch 500/538 - Train Accuracy: 0.9954, Validation Accuracy: 0.9762, Loss: 0.0061\n", "Epoch 4 Batch 510/538 - Train Accuracy: 0.9918, Validation Accuracy: 0.9757, Loss: 0.0079\n", "Epoch 4 Batch 520/538 - Train Accuracy: 0.9871, Validation Accuracy: 0.9721, Loss: 0.0148\n", "Epoch 4 Batch 530/538 - Train Accuracy: 0.9840, Validation Accuracy: 0.9728, Loss: 0.0121\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "def get_accuracy(target, logits):\n", " \"\"\"\n", " Calculate accuracy\n", " \"\"\"\n", " max_seq = max(target.shape[1], logits.shape[1])\n", " if max_seq - target.shape[1]:\n", " target = np.pad(\n", " target,\n", " [(0,0),(0,max_seq - target.shape[1])],\n", " 'constant')\n", " if max_seq - logits.shape[1]:\n", " logits = np.pad(\n", " logits,\n", " [(0,0),(0,max_seq - logits.shape[1])],\n", " 'constant')\n", "\n", " return np.mean(np.equal(target, logits))\n", "\n", "# Split data to training and validation sets\n", "train_source = source_int_text[batch_size:]\n", "train_target = target_int_text[batch_size:]\n", "valid_source = source_int_text[:batch_size]\n", "valid_target = target_int_text[:batch_size]\n", "(valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source,\n", " valid_target,\n", " batch_size,\n", " source_vocab_to_int['<PAD>'],\n", " target_vocab_to_int['<PAD>'])) \n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(epochs):\n", " for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate(\n", " get_batches(train_source, train_target, batch_size,\n", " source_vocab_to_int['<PAD>'],\n", " target_vocab_to_int['<PAD>'])):\n", "\n", " _, loss = sess.run(\n", " [train_op, cost],\n", " {input_data: source_batch,\n", " targets: target_batch,\n", " lr: learning_rate,\n", " target_sequence_length: targets_lengths,\n", " source_sequence_length: sources_lengths,\n", " keep_prob: keep_probability})\n", "\n", "\n", " if batch_i % display_step == 0 and batch_i > 0:\n", "\n", "\n", " batch_train_logits = sess.run(\n", " inference_logits,\n", " {input_data: source_batch,\n", " source_sequence_length: sources_lengths,\n", " target_sequence_length: targets_lengths,\n", " keep_prob: 1.0})\n", "\n", "\n", " batch_valid_logits = sess.run(\n", " inference_logits,\n", " {input_data: valid_sources_batch,\n", " source_sequence_length: valid_sources_lengths,\n", " target_sequence_length: valid_targets_lengths,\n", " keep_prob: 1.0})\n", "\n", " train_acc = get_accuracy(target_batch, batch_train_logits)\n", "\n", " valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits)\n", "\n", " print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'\n", " .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_path)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save Parameters\n", "Save the `batch_size` and `save_path` parameters for inference." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params(save_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n", "load_path = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sentence to Sequence\n", "To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n", "\n", "- Convert the sentence to lowercase\n", "- Convert words into ids using `vocab_to_int`\n", " - Convert words not in the vocabulary, to the `<UNK>` word id." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def sentence_to_seq(sentence, vocab_to_int):\n", " \"\"\"\n", " Convert a sentence to a sequence of ids\n", " :param sentence: String\n", " :param vocab_to_int: Dictionary to go from the words to an id\n", " :return: List of word ids\n", " \"\"\"\n", " # TODO: Implement Function\n", " sentence = sentence.lower()\n", " words = sentence.split(' ')\n", " ids = []\n", " for w in words:\n", " if(w in vocab_to_int):\n", " ids.append(vocab_to_int[w])\n", " else:\n", " ids.append(vocab_to_int['<UNK>'])\n", " return ids\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_sentence_to_seq(sentence_to_seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Translate\n", "This will translate `translate_sentence` from English to French." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/dev\n", "Input\n", " Word Ids: [18, 26, 195, 101, 62, 219, 75]\n", " English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n", "\n", "Prediction\n", " Word Ids: [90, 118, 269, 293, 308, 98, 298, 1]\n", " French Words: il vu un vieux camion jaune . <EOS>\n" ] } ], "source": [ "translate_sentence = 'he saw a old yellow truck .'\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n", "\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_path + '.meta')\n", " loader.restore(sess, load_path)\n", "\n", " input_data = loaded_graph.get_tensor_by_name('input:0')\n", " logits = loaded_graph.get_tensor_by_name('predictions:0')\n", " target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\n", " source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\n", " keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", "\n", " translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size,\n", " target_sequence_length: [len(translate_sentence)*2]*batch_size,\n", " source_sequence_length: [len(translate_sentence)]*batch_size,\n", " keep_prob: 1.0})[0]\n", "\n", "print('Input')\n", "print(' Word Ids: {}'.format([i for i in translate_sentence]))\n", "print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n", "\n", "print('\\nPrediction')\n", "print(' Word Ids: {}'.format([i for i in translate_logits]))\n", "print(' French Words: {}'.format(\" \".join([target_int_to_vocab[i] for i in translate_logits])))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imperfect Translation\n", "You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n", "\n", "You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

yuhangwang/MirrorAI

prototype/1DLearner/coarse_mirror_samples.ipynb

1

8329

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mirror_label(isTrue):\n", " if isTrue:\n", " return [1, 0]\n", " else:\n", " return [0, 1]" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def one_row(n, low, high):\n", " \"\"\"Return one row of numbers\n", " \n", " Args:\n", " n (int): length\n", " low (int): lowest possible number\n", " high (int): highest possible number\n", " Returns:\n", " 1D numpy array\n", " \"\"\"\n", " return numpy.random.randint(low, high=high+1, size=n)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def half_mirror(m, n, low, high):\n", " \"\"\"Return a matrix\n", " \n", " Args:\n", " m (int): number of rows\n", " n (int): number of columns\n", " low (int): lowest possible number\n", " high (int): highest possible number\n", " Returns:\n", " 2D numpy array\n", " \"\"\"\n", " return numpy.vstack([one_row(n, low, high) for i in range(m)])\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def flatten(a):\n", " \"\"\"Flatten a 2D array\"\"\"\n", " size = numpy.prod(numpy.shape(a))\n", " return numpy.reshape(a, (size,))" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def full_mirror(height, width, low, high):\n", " \"\"\"Return a matrix\n", " Returns a symmetric image with 50% probability\n", " \n", " Args:\n", " height (int): image height\n", " width (int): image width\n", " low (int): lowest possible number\n", " high (int): highest possible number\n", " Returns:\n", " 2D numpy array\n", " \"\"\"\n", " m = (height - 1) // 2\n", " coin = numpy.random.random()\n", " if coin > 0.5:\n", " return numpy.vstack([\n", " half_mirror(m, width, low, high),\n", " numpy.zeros(width),\n", " half_mirror(m, width, low, high)\n", " ])\n", " else:\n", " half = half_mirror(m, width, low, high)\n", " return numpy.vstack([\n", " half,\n", " numpy.zeros(width),\n", " half[::-1, :]\n", " ])" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def label_full_mirror(a):\n", " m, n = numpy.shape(a)\n", " top = a[0:(m-1)//2, :]\n", " bottom = a[(m+1)//2:, :]\n", " isMirror = bool(numpy.amax(numpy.absolute(top - bottom[-1::-1])) < 1.0e-5)\n", " label = mirror_label(isMirror)\n", " return (a.reshape((m*n,)), label)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def reflection_samples(N, height, width, low, high):\n", " \"\"\"Generate reflection samples\n", " Args:\n", " N (int): total number of samples\n", " height (int): height of each image\n", " width (int): width of each image\n", " low (int): lowest number\n", " high (int): highest number\n", " Returns:\n", " a list of pairs, i.e., flattened image and its label\n", " \"\"\"\n", " raw = [label_full_mirror(full_mirror(height, width, low, high))\n", " for i in range(N)]\n", " return {\n", " \"data\": numpy.array([x[0] for x in raw]),\n", " \"labels\": numpy.array([x[1] for x in raw]),\n", " }" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'data': array([[ 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1.,\n", " 1., 0.],\n", " [ 1., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1.,\n", " 1., 1.],\n", " [ 1., 0., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1.,\n", " 0., 1.],\n", " [ 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 0.,\n", " 1., 1.],\n", " [ 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1.,\n", " 0., 0.],\n", " [ 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 0.,\n", " 1., 1.],\n", " [ 1., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1.,\n", " 0., 1.],\n", " [ 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n", " 0., 1.],\n", " [ 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 1.,\n", " 1., 1.],\n", " [ 1., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0., 1., 1.,\n", " 1., 1.]]), 'labels': array([[1, 0],\n", " [1, 0],\n", " [1, 0],\n", " [0, 1],\n", " [1, 0],\n", " [1, 0],\n", " [0, 1],\n", " [1, 0],\n", " [0, 1],\n", " [0, 1]])}" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = reflection_samples(10, 5, 3, 0, 1)\n", "a" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 0]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mirror_label(True)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 1]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mirror_label(False)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "if numpy.amax([1,0]) < 0:\n", " print(True)\n", "else:\n", " print(False)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.random.randint(0, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

peap/notebooks

pycon-2015/d00t01.django-in-depth.ipynb

1

16094

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Django in Depth\n", "- Tutorial, updated for Django 1.8\n", "- James Bennet\n", "- 2015-04-09" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The ORM\n", "\n", "### Database Backends\n", "There are a set of classes used to support the four built-in backends, plus a base class that can be used to write your own backend for un-supported databases or database drivers.\n", "\n", "### `SQLCompiler`\n", "This is the bridge between high-level queries and the database backend.\n", "\n", "It's very complex code.\n", "\n", "All the magic happens in `as_sql()`.\n", "\n", "### `Query`\n", "Data structure and methods representing a database query. It's a tree-like data structure. \n", "\n", "Two flavors: \n", "- `Query`: normal ORM operations\n", "- `RawQuery`: real, custom SQL queries, that returns something resembling what a normal `Query` would expect\n", "\n", "Also scary, complex code. It has to support infinite and arbitrary chaining of ORM methods (`.filter()`, `.distinct()`, etc.). Most of the code comes from this merging process.\n", "\n", "You shouldn't often need to subclass `Query`, but rather `QuerySet`.\n", "\n", "### Custom Lookups\n", "All your custom stuff gets access to the Django internals, like `as_sql()` and the `SQLCompiler` instance. Some ways to do custom stuff:\n", "- `F objects`\n", "- `Q objects`\n", "- `django.db.models.Lookup`\n", "- `django.db.models.Transform`\n", "\n", "### `QuerySet`\n", "Wraps `Query` in a nice API. It's lazy; doesn't touch the database until you force it to, by calling a method that doesn't return a `QuerySet`. \n", "\n", "Printing a `QuerySet`, say, in the Django shell, it'll only show the first 21 objects... `__repr__` adds a `LIMIT 21` to the query, so that you don't accidentally run out of memory trying to build a string with a million results in it.\n", "\n", "It's a container - it stores results to a cache that is only every populated once.\n", "\n", "All operations except for iteration and length/existence checks perform a new query and return a new QuerySet. So you can't get a `QuerySet` of several objects, update an attribute on one object, save it, and see the result in the same `QuerySet`.\n", "\n", "There's an `update()` method on `QuerySet`, but it doesn't call custom `save()` methods or send `pre_save` or `post_save` signals.\n", "\n", "The `defer()` and `only()` methods allow you get only the specified fields, but you get actual `Model` instances. If you access other fields, then a new query is performed. \n", "\n", "The `values()` and `values_list()` get you field values, not `Model` instances. With `flat=True`, `values_list()` gets you a flat list of all values in the `QuerySet`.\n", "\n", "The `select_related()` method solves the N+1 problem for `ForeignKey` and `OneToOneField` relations - just one SQL query, instead of one for the first model, and one for each of the N results.\n", "\n", "The `prefetch_related()` method solves it for `ManyToManyField`s and `GenericRelation`s - one query for model, relations resolved in python code.\n", "\n", "### `Manager`\n", "The high-level interaface. The `Model` that the `Manager` is attached to is available in the `Manager` as `self.model`.\n", "\n", "If a model has multiple `Manager`s, the first one defined is the default (`Model._default_manager`).\n", "\n", "Should always have a `Manager` on your model that can get alllll the objects.\n", "\n", "### `Model`\n", "Representation of the data and the associated logic.\n", "- one class = one table\n", "- one field = one column\n", "- one instance = one row\n", "\n", "Uses python metaclasses, via `django.db.models.base.ModelBase`. This is how the `Meta` class property is created. It also creates all those extra methods and properties on the class, like `Model.DoesNotExist`.\n", "\n", "`ModelBase` calls `contribute_to_class()` on each thing in the attribute dictionary of the new class. For example, `DateTimeField` has a `contribute_to_class()` method that adds the `get_next_by_<field-name>` method.\n", "\n", "### `Field`\n", "Custom fields can pretend to be another field by setting internal type to something that exists already.\n", "\n", "`to_python()` converts from DB-level type to correct Python type.\n", "\n", "`value_to_string()` converts to string for serialization purposes.\n", "\n", "Multiple other methods for preparing values for various kinds of DB-level usage - querying, storage, etc.\n", "\n", "### Model Inheritance\n", "Multiple types available:\n", "- abstract parents\n", " - Indicated by `abstract = True` in model's `Meta` declaration\n", " - doesn't create a database table\n", " - subclasses, if not abstract, generate a table with both their own fields and those of the abstract parent\n", "- multi-table\n", " - no special syntax; just subclass the parent model\n", "- proxy models\n", " - set `proxy = True` in `Meta`\n", " - will reuse parent's table and fields; only allowed changes are at the python level\n", " - can define additional methods, manager, etc.\n", " - queries return instances of the model queried\n", " \n", "#### Unmanaged models\n", "Related to proxy models. Set `managed = False` in `Meta`. Wraps a database or view that you don't want Django to... manage. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Forms Library\n", "Major components:\n", "- forms\n", "- fields \n", "- components\n", "- media support\n", "\n", "### Widgets\n", "The low-level Form components:\n", "- one for each type of HTML form control\n", "- handles putting data into form control, and taking it out\n", "- `value_from_datadict()` pulls out that widget's value\n", "- `render()` generates the HTML\n", "- `MultiWidget` is a special class that wraps multiple other widgets\n", " - useful for things like splitting date/time; single value in DB, often want multiple HTML controls\n", " \n", "### Fields\n", "- represent data type and validation contrainst\n", "- have widgets associated with them for rendering\n", "- calls `clean()` to validate\n", " - first calls `to_python()`: convert from what came in on HTTP to the correct python type\n", " - then calls `validate()`: Field's built-in validation\n", " - finally, calls `run_validators()`: custom validators added in the Field's `validators` kwarg\n", " - return python value, or raise `ValidationError`\n", "- Choosing a validation scheme\n", " - `to_python`: when validation constraint is tightly tied to the data type\n", " - `validate`: when validation is intrinsic to the field (like email addresses)\n", " - `validators`: when the basic field does almost all the validation you need and it's simpler than writing a whole new field\n", "- error messages\n", " - django has a lot of special ones\n", "- every field actually has two widgets: regular and hidden versions\n", "\n", "### Forms\n", "- Also uses a metaclass, but probably shouldn't. It just keeps track of the order that fields were added to the class.\n", "- Instantiating a `Form` with data will cause it to wrap its fields with instances with `BoundField`.\n", "- Validation\n", " - Form's `clean()` method\n", " - happens after field valiation\n", " - error messages are instances of `ErrorDict`, `ErrorList`\n", "- Displaying\n", " - default representation is an HTML table\n", " - can also output `as_p` or `as_ul`\n", " - if you want to customize the output, look at the `_html_output()` method\n", " - search for django forms 508 accessibility for examples/libraries of ensuring Section 508 accessibility compliance\n", " \n", "### ModelForms\n", "Introspects a `Model` and creates a `Form` out of it. Uses the model meta API to get list of fields, etc.\n", "- good place to find examples for how to use the model meta API\n", "- calls the `formfield()` method on each field\n", " - can be overridden by defining the `formfield_callback()` method on the Form\n", "- calls each field's `value_from_object()` method to get values for the given `Model` instance\n", "\n", "### Form Media\n", "Forms and Widgets support an inner class named `Media`\n", "- supports values `css` and `js`, listing CSS JavaScript assets to include when rendering the form/widget\n", "- weeds out duplicates across the form and includes all the required resources in the form\n", "- implemented via... metaclasses! `django.forms.widgets.MediaDefiningClass`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Template System\n", "Major components:\n", "- engine\n", "- loade\n", "- template\n", "- tag/filter libraries\n", "- context\n", "\n", "### Template Engines\n", "New in Django 1.8. Jinja2 is built-in now.\n", "- sublcass `django.template.backends.base.BaseEngine`\n", "- must define `get_template()`\n", "- returned template must have a `render()` method that accepts a context dictionary\n", "\n", "### Template Loaders\n", "Does the hard work of actually finding the template source.\n", "- must define `load_template_source()` that accepts a template name\n", "- can store templates in the database, the cache, etc.; doesn't have to be the filesystem\n", "- returns a `Template` object\n", "\n", "### The Django Template Language\n", "Built to be simple; they didn't want a bunch of business logic in templates when Django was created. Not fast, but wasn't supposed to be.\n", "\n", "Key classes:\n", "- `Template`\n", "- `Lexer`\n", "- `Parser`\n", "- `Token`\n", "- `Node` and `NodeList`\n", "\n", "Template lexing:\n", "- instantiate a `Lexer` with template source, then call `tokenize()`\n", "- splits the template source on a regex that looks for {% xxx %}, {{ xxx }}, and {# xxx #}\n", " - now you have `Text`, `Var`, `Block`, and `Comment` tokens\n", " \n", "Template parsing:\n", "- list of tokens from `Lexer` is used to instantiate a `Parser`, which then calls `parse()`\n", "- each tag provides a compilation process\n", "- tags have access to the parser, so they can see what comes before/after themselves\n", "- tags return a `Node` or a `NodeList`\n", "\n", "### Template Context\n", "Behaves like a dictionary, but it's actually a stack of dictionaries\n", "- supports push/pop and fall-through lookups\n", "- first match in the stack is the value\n", "- this is how for-loop tags create their forloop.counter and other variables to the context, but only inside the block\n", "- comes at a performance cost, especially with things like for-loops with nested with tags\n", "- fall-through lookups are slow\n", "\n", "`RenderContext`\n", "- thread-safety tool for simultaneous renderings of the same template instance\n", "- safe place to store state for a context; the `cycle` tag uses this\n", "- attached to every `Context`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Request/Response Processing\n", "The entry point for a Django project is `django.core.handlers.WSGIHandler`, which implements a WSGI application. See PEP 333 and PEP 3333. It's the only supported implementation.\n", "\n", "### Handler lifecycle\n", "- sets up middleware\n", "- sends `request_started` signal\n", "- intializes `HttpRequest`\n", "- calls handler's `get_response()`\n", " - apply request middleware (allowed to modify `request.url_conf`!)\n", " - resolve url\n", " - apply view middleware\n", " - call view\n", " - apply response middleware\n", " - return response\n", "- transforms `HttpResponse` into output format for `WSGIHandler`\n", "\n", "### URL Resolution\n", "High-level: `django.core.urls.RegexURLResolver`\n", "\n", "patterns: instances of `RegexURLPattern`\n", "- it's `render()` method returns a `ResolverMatch` object\n", "\n", "lifecycle\n", "- `RegexURLResolver` iterates over supplied patterns\n", "- keeps a list of patterns it tried and didn't get a match\n", "- returns the first time it gets a match\n", "- raises Resolver404 if no match\n", "\n", "each `include()` causes a nested `RegexURLResolver` to be called at resolution time, but will be ignored if the prefix doesn't match\n", "\n", "### Exception Handling\n", "It's really important that your `handler404` or `handler500`, make it as bullet-proof as possible - really don't want to raise an Exception\n", "\n", "Django never tries to catch `SystemExit`.\n", "\n", "### Request and Response Objects\n", "`HttpRequest` is technically handler-specific, but there's only one handler now\n", "\n", "`HttpResponse` comes in multiple flavors depending on status code\n", "- 301, 302, 400, 403, 404, 405, 410, 500\n", "- also `JSONResponse` for JSON data\n", "\n", "### Views\n", "requirements:\n", "- be callable\n", "- accept `HttpRequest` in first position\n", "- return `HttpResponse` or raise an `Exception`\n", "\n", "#### Class-based Generic Views\n", "Inheritance diagram is complex, but usage is not. Complexity exists to let funtionality to be composed.\n", "\n", "Basics\n", "- `self.request` is the current `HttpRequest`\n", "- dispatch is handled by `dispatch()`, based on the HTTP method\n", "- you probably want `TemplateResponseMixin` somewhere in your inheritance chain if it's not already\n", "- call `as_view()` when putting it in a URLconf\n", "\n", "Big advantage of CBVs is composability/reusability of funtionality, and the ease with which they avoid the large argument lists functions would require to support the same customization.\n", "\n", "The primary disadvantage is the proliferation of mixins and base classes needed to provide all the combinations of behaviors that Django's generic views support." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Django Admin\n", "Pulls together lots of components, but not in the way you might expect. For example, it uses CBVs, but not the generic CBVs, because it was written before those were available.\n", "\n", "You can subclass `ModelAdmin`, which is a class that dispatches almost everything you can do in the admin app to view methods. The views are methods that have \"`_view()`\" in their name.\n", "\n", "Except for `ChangeList`, which is possibly the scariest code outside of the ORM. It's survived two rewrites of the Admin.\n", "\n", "Admin urls are also included as an attribute on the `ModelAdmin` class. It's crazy.\n", "\n", "`AdminSite` represents the admin interface. Can have multiple instances in a single Django project, since you can namespace your URLs.\n", "\n", "The Django Admin is not a good reference for well-structured Django applications." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

google/iree

samples/colab/tensorflow_hub_import.ipynb

1

19002

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "tensorflow_hub_import.ipynb", "provenance": [], "collapsed_sections": [ "-V0X0E7LkEa4", "FH3IRpYTta2v" ] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "FH3IRpYTta2v" }, "source": [ "##### Copyright 2021 The IREE Authors" ] }, { "cell_type": "code", "metadata": { "id": "mWGa71_Ct2ug", "cellView": "form" }, "source": [ "#@title Licensed under the Apache License v2.0 with LLVM Exceptions.\n", "# See https://llvm.org/LICENSE.txt for license information.\n", "# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Qb3S0mSjpK7J" }, "source": [ "# IREE TensorFlow Hub Import\n", "\n", "This notebook demonstrates how to download, import, and compile models from [TensorFlow Hub](https://tfhub.dev/). It covers:\n", "\n", "* Downloading a model from TensorFlow Hub\n", "* Ensuring the model has serving signatures needed for import\n", "* Importing and compiling the model with IREE\n", "\n", "At the end of the notebook, the compilation artifacts are compressed into a .zip file for you to download and use in an application.\n", "\n", "See also https://google.github.io/iree/ml-frameworks/tensorflow/." ] }, { "cell_type": "markdown", "metadata": { "id": "9rNAJKNVkKOr" }, "source": [ "## Setup" ] }, { "cell_type": "code", "metadata": { "id": "RdVc4TbOkHM2" }, "source": [ "%%capture\n", "!python -m pip install iree-compiler iree-runtime iree-tools-tf -f https://github.com/google/iree/releases" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qRwv3qI_l5O_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "31418cc7-ecd5-4c73-c595-ff1d6caf03cc" }, "source": [ "import os\n", "import tensorflow as tf\n", "import tensorflow_hub as hub\n", "import tempfile\n", "from IPython.display import clear_output\n", "\n", "from iree.compiler import tf as tfc\n", "\n", "# Print version information for future notebook users to reference.\n", "print(\"TensorFlow version: \", tf.__version__)\n", "\n", "ARTIFACTS_DIR = os.path.join(tempfile.gettempdir(), \"iree\", \"colab_artifacts\")\n", "os.makedirs(ARTIFACTS_DIR, exist_ok=True)\n", "print(f\"Using artifacts directory '{ARTIFACTS_DIR}'\")" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "TensorFlow version: 2.5.0\n", "Using artifacts directory '/tmp/iree/colab_artifacts'\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ZZAobcAhocFE" }, "source": [ "## Import pretrained [`mobilenet_v2`](https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4) model\n", "\n", "IREE supports importing TensorFlow 2 models exported in the [SavedModel](https://www.tensorflow.org/guide/saved_model) format. This model we'll be importing is published in that format already, while other models may need to be converted first.\n", "\n", "MobileNet V2 is a family of neural network architectures for efficient on-device image classification and related tasks. This TensorFlow Hub module contains a trained instance of one particular network architecture packaged to perform image classification." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7fd0vmnloZo9", "outputId": "fcb260e8-672a-479d-c428-2a93d306d4b2" }, "source": [ "#@title Download the pretrained model\n", "\n", "# Use the `hub` library to download the pretrained model to the local disk\n", "# https://www.tensorflow.org/hub/api_docs/python/hub\n", "HUB_PATH = \"https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\"\n", "model_path = hub.resolve(HUB_PATH)\n", "print(f\"Downloaded model from tfhub to path: '{model_path}'\")" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "Downloaded model from tfhub to path: '/tmp/tfhub_modules/426589ad685896ab7954855255a52db3442cb38d'\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "CedNRSQTOE7C" }, "source": [ "### Check for serving signatures and re-export as needed\n", "\n", "IREE's compiler tools, like TensorFlow's `saved_model_cli` and other tools, require \"serving signatures\" to be defined in SavedModels.\n", "\n", "More references:\n", "\n", "* https://www.tensorflow.org/tfx/serving/signature_defs\n", "* https://blog.tensorflow.org/2021/03/a-tour-of-savedmodel-signatures.html" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qiO66oEYQmsd", "outputId": "5d7e4f7d-e77e-45f1-c37e-11e38ed05ce2" }, "source": [ "#@title Check for serving signatures\n", "\n", "# Load the SavedModel from the local disk and check if it has serving signatures\n", "# https://www.tensorflow.org/guide/saved_model#loading_and_using_a_custom_model\n", "loaded_model = tf.saved_model.load(model_path)\n", "serving_signatures = list(loaded_model.signatures.keys())\n", "print(f\"Loaded SavedModel from '{model_path}'\")\n", "print(f\"Serving signatures: {serving_signatures}\")\n", "\n", "# Also check with the saved_model_cli:\n", "print(\"\\n---\\n\")\n", "print(\"Checking for signature_defs using saved_model_cli:\\n\")\n", "!saved_model_cli show --dir {model_path} --tag_set serve --signature_def serving_default" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "Loaded SavedModel from '/tmp/tfhub_modules/426589ad685896ab7954855255a52db3442cb38d'\n", "Serving signatures: []\n", "\n", "---\n", "\n", "Checking for signature_defs using saved_model_cli:\n", "\n", "The given SavedModel SignatureDef contains the following input(s):\n", "The given SavedModel SignatureDef contains the following output(s):\n", "Method name is: \n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "kKqqX2LsReNz" }, "source": [ "Since the model we downloaded did not include any serving signatures, we'll re-export it with serving signatures defined.\n", "\n", "* https://www.tensorflow.org/guide/saved_model#specifying_signatures_during_export" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OlDG2OuqOBGC", "outputId": "044bb741-a563-4fe2-b0d9-8b47bcc5f286" }, "source": [ "#@title Look up input signatures to use when exporting\n", "\n", "# To save serving signatures we need to specify a `ConcreteFunction` with a\n", "# TensorSpec signature. We can determine what this signature should be by\n", "# looking at any documentation for the model or running the saved_model_cli.\n", "\n", "!saved_model_cli show --dir {model_path} --all \\\n", " 2> /dev/null | grep \"inputs: TensorSpec\" | tail -n 1" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ " inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=u'inputs')\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gnb4HhMmkgiT", "outputId": "3318bc8e-321c-417f-d9b0-b4cc9dcca8de" }, "source": [ "#@title Re-export the model using the known signature\n", "\n", "# Get a concrete function using the signature we found above.\n", "# \n", "# The first element of the shape is a dynamic batch size. We'll be running\n", "# inference on a single image at a time, so set it to `1`. The rest of the\n", "# shape is the fixed image dimensions [width=224, height=224, channels=3].\n", "call = loaded_model.__call__.get_concrete_function(tf.TensorSpec([1, 224, 224, 3], tf.float32))\n", "\n", "# Save the model, setting the concrete function as a serving signature.\n", "# https://www.tensorflow.org/guide/saved_model#saving_a_custom_model\n", "resaved_model_path = '/tmp/resaved_model'\n", "tf.saved_model.save(loaded_model, resaved_model_path, signatures=call)\n", "clear_output() # Skip over TensorFlow's output.\n", "print(f\"Saved model with serving signatures to '{resaved_model_path}'\")\n", "\n", "# Load the model back into memory and check that it has serving signatures now\n", "reloaded_model = tf.saved_model.load(resaved_model_path)\n", "reloaded_serving_signatures = list(reloaded_model.signatures.keys())\n", "print(f\"\\nReloaded SavedModel from '{resaved_model_path}'\")\n", "print(f\"Serving signatures: {reloaded_serving_signatures}\")\n", "\n", "# Also check with the saved_model_cli:\n", "print(\"\\n---\\n\")\n", "print(\"Checking for signature_defs using saved_model_cli:\\n\")\n", "!saved_model_cli show --dir {resaved_model_path} --tag_set serve --signature_def serving_default" ], "execution_count": 7, "outputs": [ { "output_type": "stream", "text": [ "Saved model with serving signatures to '/tmp/resaved_model'\n", "\n", "Reloaded SavedModel from '/tmp/resaved_model'\n", "Serving signatures: ['serving_default']\n", "\n", "---\n", "\n", "Checking for signature_defs using saved_model_cli:\n", "\n", "The given SavedModel SignatureDef contains the following input(s):\n", " inputs['inputs'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (1, 224, 224, 3)\n", " name: serving_default_inputs:0\n", "The given SavedModel SignatureDef contains the following output(s):\n", " outputs['output_0'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (1, 1001)\n", " name: StatefulPartitionedCall:0\n", "Method name is: tensorflow/serving/predict\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "YdmgASzwanSz" }, "source": [ "### Import and compile the SavedModel with IREE" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GLkjlHE5mdmg", "outputId": "d057d2dc-001c-4fe4-cf4b-9517d4cf598e" }, "source": [ "#@title Import from SavedModel\n", "\n", "# The main output file from compilation is a .vmfb \"VM FlatBuffer\". This file\n", "# can used to run the compiled model with IREE's runtime.\n", "output_file = os.path.join(ARTIFACTS_DIR, \"mobilenet_v2.vmfb\")\n", "# As compilation runs, dump some intermediate .mlir files for future inspection.\n", "tf_input = os.path.join(ARTIFACTS_DIR, \"mobilenet_v2_tf_input.mlir\")\n", "iree_input = os.path.join(ARTIFACTS_DIR, \"mobilenet_v2_iree_input.mlir\")\n", "\n", "# Since our SavedModel uses signature defs, we use `saved_model_tags` with\n", "# `import_type=\"SIGNATURE_DEF\"`. If the SavedModel used an object graph, we\n", "# would use `exported_names` with `import_type=\"OBJECT_GRAPH\"` instead.\n", "\n", "# We'll set `target_backends=[\"vmvx\"]` to use IREE's reference CPU backend.\n", "# We could instead use different backends here, or set `import_only=True` then\n", "# download the imported .mlir file for compilation using native tools directly.\n", "\n", "tfc.compile_saved_model(\n", " resaved_model_path,\n", " output_file=output_file,\n", " save_temp_tf_input=tf_input,\n", " save_temp_iree_input=iree_input,\n", " import_type=\"SIGNATURE_DEF\",\n", " saved_model_tags=set([\"serve\"]),\n", " target_backends=[\"vmvx\"])\n", "clear_output() # Skip over TensorFlow's output.\n", "\n", "print(f\"Saved compiled output to '{output_file}'\")\n", "print(f\"Saved tf_input to '{tf_input}'\")\n", "print(f\"Saved iree_input to '{iree_input}'\")" ], "execution_count": 8, "outputs": [ { "output_type": "stream", "text": [ "Saved compiled output to '/tmp/iree/colab_artifacts/mobilenet_v2.vmfb'\n", "Saved tf_input to '/tmp/iree/colab_artifacts/mobilenet_v2_tf_input.mlir'\n", "Saved iree_input to '/tmp/iree/colab_artifacts/mobilenet_v2_iree_input.mlir'\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 102 }, "id": "IEJAzOb5qASI", "outputId": "52da0e1f-a512-4ee8-ea65-e716de9a3cf2" }, "source": [ "#@title Download compilation artifacts\n", "\n", "ARTIFACTS_ZIP = \"/tmp/mobilenet_colab_artifacts.zip\"\n", "\n", "print(f\"Zipping '{ARTIFACTS_DIR}' to '{ARTIFACTS_ZIP}' for download...\")\n", "!cd {ARTIFACTS_DIR} && zip -r {ARTIFACTS_ZIP} .\n", "\n", "# Note: you can also download files using the file explorer on the left\n", "try:\n", " from google.colab import files\n", " print(\"Downloading the artifacts zip file...\")\n", " files.download(ARTIFACTS_ZIP)\n", "except ImportError:\n", " print(\"Missing google_colab Python package, can't download files\")" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "Zipping '/tmp/iree/colab_artifacts' to '/tmp/mobilenet_colab_artifacts.zip' for download...\n", " adding: mobilenet_v2.vmfb (deflated 8%)\n", " adding: mobilenet_v2_iree_input.mlir (deflated 46%)\n", " adding: mobilenet_v2_tf_input.mlir (deflated 47%)\n", "Downloading the artifacts zip file...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "application/javascript": [ "download(\"download_173f97e9-1e2d-401a-8aad-646463432451\", \"mobilenet_colab_artifacts.zip\", 59101356)" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": { "tags": [] } } ] } ] }

apache-2.0

vibinash/kaggle

dogs-vs-cats-redux/Dogs-vs-Cats-Redux.ipynb

1

2309061

apache-2.0

nkmk/python-snippets

notebook/random_shuffle.ipynb

1

4000

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import random" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "l = list(range(5))\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 0, 4, 3, 2]\n" ] } ], "source": [ "random.shuffle(l)\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "l = list(range(5))\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 3, 1, 4, 2]\n" ] } ], "source": [ "lr = random.sample(l, len(l))\n", "print(lr)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "s = 'abcde'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# random.shuffle(s)\n", "# TypeError: 'str' object does not support item assignment" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 1, 2, 3, 4)\n" ] } ], "source": [ "t = tuple(range(5))\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# random.shuffle(t)\n", "# TypeError: 'tuple' object does not support item assignment" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bedca\n" ] } ], "source": [ "sr = ''.join(random.sample(s, len(s)))\n", "print(sr)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 1, 2, 4, 3)\n" ] } ], "source": [ "tr = tuple(random.sample(t, len(l)))\n", "print(tr)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 1, 0, 4, 3]\n" ] } ], "source": [ "l = list(range(5))\n", "random.seed(0)\n", "random.shuffle(l)\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 1, 0, 4, 3]\n" ] } ], "source": [ "l = list(range(5))\n", "random.seed(0)\n", "random.shuffle(l)\n", "print(l)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

manoharan-lab/structural-color

polarization_tutorial.ipynb

1

59429

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial for reflectance calculations with polarization in the structural-color package\n", "\n", "Copyright 2016, Vinothan N. Manoharan, Victoria Hwang, Annie Stephenson\n", "\n", "This file is part of the structural-color python package.\n", "\n", "This package is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n", "\n", "This package is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n", "\n", "You should have received a copy of the GNU General Public License along with this package. If not, see http://www.gnu.org/licenses/." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to how polarization is implemented in the package\n", "\n", "Polarization plays a role in several calculations throughout the structural-color package:\n", "\n", "1. Cross sections\n", "<br>\n", "In the single scattering model used in this package, the cross sections are proportional to the structure factor * form factor, where the form factor comes from Mie theory. Mie theory solves Maxwell's equations for spheres, giving the scattered electric fields, which we use to calculate scattered intensity and scattering cross sections. The Mie solutions are polarization-dependent; we use Mie theory to find Jones or Stokes vectors to describe the fields, whose components reveal the polarization state. \n", "<br>\n", "<br>\n", "Most Mie solutions are performed in a basis defined by the scattering plane. So the Jones vector describes the scattered electric field components in terms of the scattered light that has polarization parallel to the scattering plane and the scattered light that is perpendicular to the scattering plane. (Remember, the scattering plane is the plane that contains the incident and scattered k-vectors of the light). Here's the way the Jones vector is written in this basis:\n", "<br>\n", "<br>\n", "$E_{scat} = \\begin{bmatrix}E_{||} \\\\ E_{\\perp}\\end{bmatrix}$\n", "<br>\n", "<br>\n", "This scattering plane basis is not ideal if you want to actually compare to any measured polarizations, because in the lab, we do not measure polarization according to its relation to the scattering plane, we measure it in the lab frame, which is not related to the scattering vectors of the light at all. And more specifically, we like to measure things in cartesian coordinates because it's intuitive. So we rotate the scattering matrix that comes from the Mie calculations, which allows us to get an expression for the Jones vector in a cartesian basis. \n", "<br>\n", "<br>\n", "$E_{scat} = \\begin{bmatrix}E_{x} \\\\ E_{y}\\end{bmatrix}$\n", "<br>\n", "<br>\n", "Now looking at this expression, you'll notice that there is no z component. The absence of z happens because we are assuming that the light is traveling in the +z direction, and under circumstances relevant to the model, polarization cannot be parallel to the direction of propagation. At this point, we can calculate the scattering cross sections in this cartesian coordinate system be doing a bit of math. To read more about this, see the docstrings of functions in mie.py. I worked very hard on them, and there is no point in repeating them here. \n", "<br>\n", "<br>\n", "2. Polarization vector\n", "<br>\n", "The polarization vector tells us the polarization direction of the the light, or in the case of the Monte Carlo model, the photon packets. It's often expressed as a Stokes or Jones vector, and it can be calculated by normalizing the above $E_{scat}$. \n", "<br>\n", "But it's not quite that simple. We have to be careful here. This Jones vector is in a cartesian coordinate system that is no longer dependent on the incident and scattered vectors as the scattering plane basis is. But this cartesian coordinate system is not the one we care about for comparing to measurements. Why? Because these coordinates are are defined in the local coordinate system of the light, or a photon packet, in the language of the Monte Carlo model. No matter the global direction of the light, it always sees itself as traveling in it's own +z direction. If it changes direction, as it will in the Monte Carlo model, it rotates it's coordinate system, so that it always is traveling in it's own +z direction. \n", "<br>\n", "<br>\n", "We experimentalists do not care what direction the photon packet thinks it is traveling in; we care about the direction relative to our own world: the global lab frame. So we do another rotation, one that brings the expression for the field into a global cartesian coordinate system that assumes the normal to a film sample is in the -z direction, meaning normally incident light travels in the +z direction. The +x, and +y directions can be found according to right hand rule. So our new expression for the E-field has 3 components:\n", "<br>\n", "<br>\n", "$ E_{scat} = \\begin{bmatrix}E_{x} \\\\ E_{y}\\\\ E_{z}\\end{bmatrix}$\n", "<br>\n", "<br>\n", "This is the format of the polarization vectors that are attributes of a Trajectory object. \n", "<br>\n", "<br>\n", "3. Phase function\n", "<br>\n", "The final place the polarization comes in is in the phase function. The phase function is the probability distribution of scattering angles, and it's calculated by just normalizing the differential cross section:\n", "<br>\n", "<br>\n", "$p(\\theta, \\phi) = \\frac{\\frac{d\\sigma(\\theta, \\phi)}{d\\Omega}}{\\sigma}$\n", "<br>\n", "<br>\n", "You'll notice that it's a function of $\\theta$ and $\\phi$. For unpolarized light, we don't have to worry about the $\\phi$ dependence in our samples because both the structure factors and the form factors for unpolarized light are constant as a function of $\\phi$. Polarized light is a different story. For polarized incident light, we do have to calculate the phase function as a function of $\\theta$ and $\\phi$.\n", "<br>\n", "<br>\n", "Having a phase function that depends on two variables means that our phase function is now a 2-dimentional array instead of a 1-dimensional array, and the sampling of the angles is a bit more complicated and time-consuming. This is all under the hood though--you (the user) won't have to fuss with the different possible dimensions of the phase function, but I think it's good to know in case you ever want to plot the phase function. \n", "\n", "**FAQ\n", "\n", "\n", "Q: If polarization is always calculated in Mie theory, what do we do with it in the calculations where we assume the light is unpolarized? \n", "<br>\n", "A: We average the parallel and perpendicular components. This is derived on pg 73 of Bohren and Huffman's $\\textit{Absorption and Scattering of Light by Small Particles}$\n", "\n", "\n", "Q: Is polarization implemented in both the single scattering model and the Monte Carlo model?\n", "<br>\n", "A: Currently, polarization is only fully implemented in the Monte Carlo model. However, it is certainly possible to add it to the single scattering model in the future" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Loading and using the package" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import structcol as sc\n", "from structcol import refractive_index as ri\n", "from structcol import montecarlo as mc\n", "from structcol import detector as det\n", "import pymie as pm\n", "from pymie import size_parameter, index_ratio \n", "import seaborn as sns\n", "import time\n", "\n", "# For Jupyter notebooks only:\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Monte Carlo model and calculate reflectance and polarization for trajectories\n", "#### for a single wavelength" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "set system parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# incident light wavelength\n", "wavelength = sc.Quantity('600 nm')\n", "\n", "# sample parameters\n", "radius = sc.Quantity('0.140 um')\n", "volume_fraction = sc.Quantity(0.55, '')\n", "n_imag = 2.1e-4\n", "n_particle = ri.n('polystyrene', wavelength) + n_imag # refractive indices can be specified as pint quantities or\n", "n_matrix = ri.n('vacuum', wavelength) # called from the refractive_index module. n_matrix is the \n", "n_medium = ri.n('vacuum', wavelength) # space within sample. n_medium is outside the sample\n", "n_sample = ri.n_eff(n_particle, # refractive index of sample, calculated using Bruggeman approximation\n", " n_matrix, \n", " volume_fraction)\n", "thickness = sc.Quantity('80 um')\n", "boundary = 'film'\n", "\n", "# Monte Carlo parameters\n", "ntrajectories = 300 # number of trajectories\n", "nevents = 300 # number of scattering events in each trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "initialize and run trajectories" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/stephenson/anaconda3/lib/python3.5/site-packages/Pint-0.7.2-py3.5.egg/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n" ] } ], "source": [ "# Calculate scattering quantities\n", "p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, \n", " volume_fraction, wavelength, polarization= True)\n", "\n", "# Initialize trajectories\n", "r0, k0, W0, p0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, polarization=True)\n", "r0 = sc.Quantity(r0, 'um')\n", "k0 = sc.Quantity(k0, '')\n", "W0 = sc.Quantity(W0, '')\n", "p0 = sc.Quantity(p0,'')\n", "\n", "trajectories = mc.Trajectory(r0, k0, W0, p0)\n", "\n", "# Sample trajectory angles\n", "sintheta, costheta, sinphi, cosphi, theta, phi= mc.sample_angles(nevents, \n", " ntrajectories,p)\n", "# Sample step sizes\n", "step = mc.sample_step(nevents, ntrajectories, mu_scat)\n", "\n", "# Update trajectories based on sampled values\n", "trajectories.scatter(sintheta, costheta, sinphi, cosphi)\n", "trajectories.polarize(theta, phi, sintheta, costheta, sinphi,cosphi,\n", " n_particle, n_sample, radius, wavelength, volume_fraction)\n", "trajectories.move(step)\n", "trajectories.absorb(mu_abs, step) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "calculate reflectance" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reflectance: 0.945385606824\n", "Transmittance: 0.0546143931759\n" ] } ], "source": [ "reflectance, transmittance = det.calc_refl_trans(trajectories, thickness, n_medium, n_sample, boundary)\n", "\n", "print('Reflectance: ' + str(reflectance))\n", "print('Transmittance: ' + str(transmittance))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Monte Carlo model and calculate reflectance and polarization for trajectories\n", "#### for the full visible spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "set system parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# incident light wavelength\n", "wavelengths = sc.Quantity(np.arange(400, 800, 20), 'nm')\n", "\n", "# sample parameters\n", "radius = sc.Quantity('0.140 um')\n", "volume_fraction = sc.Quantity(0.55, '')\n", "n_imag = 2.1e-4\n", "n_particle = ri.n('polystyrene', wavelengths) + n_imag*1j # refractive indices can be specified as pint quantities or\n", "n_matrix = ri.n('vacuum', wavelengths) # called from the refractive_index module. n_matrix is the \n", "n_medium = ri.n('vacuum', wavelengths) # space within sample. n_medium is outside the sample\n", "\n", "thickness = sc.Quantity('80 um')\n", "z_low = sc.Quantity('0 um')\n", "\n", "# Monte Carlo parameters\n", "ntrajectories = 300 # number of trajectories\n", "nevents = 300 # number of scattering events in each trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "initialize trajectories, run trajectories, and calculate reflectance for each wavelength" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wavelength: 400 nanometer\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/stephenson/anaconda3/lib/python3.5/site-packages/Pint-0.7.2-py3.5.egg/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "wavelength: 420 nanometer\n", "wavelength: 440 nanometer\n", "wavelength: 460 nanometer\n", "wavelength: 480 nanometer\n", "wavelength: 500 nanometer\n", "wavelength: 520 nanometer\n", "wavelength: 540 nanometer\n", "wavelength: 560 nanometer\n", "wavelength: 580 nanometer\n", "wavelength: 600 nanometer\n", "wavelength: 620 nanometer\n", "wavelength: 640 nanometer\n", "wavelength: 660 nanometer\n", "wavelength: 680 nanometer\n", "wavelength: 700 nanometer\n", "wavelength: 720 nanometer\n", "wavelength: 740 nanometer\n", "wavelength: 760 nanometer\n", "wavelength: 780 nanometer\n" ] } ], "source": [ "reflectance = np.zeros(wavelengths.size)\n", "pol_refl_x = np.zeros(wavelengths.size)\n", "pol_refl_y = np.zeros(wavelengths.size)\n", "pol_refl_z = np.zeros(wavelengths.size)\n", "\n", "for i in range(wavelengths.size):\n", " # print wavelength\n", " print('wavelength: ' + str(wavelengths[i]))\n", " \n", " # calculate n_sample\n", " n_sample = ri.n_eff(n_particle[i], n_matrix[i], volume_fraction)\n", " \n", " # Calculate scattering quantities\n", " p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle[i], n_sample, \n", " volume_fraction, wavelengths[i], polarization= True)\n", "\n", " # Initialize trajectories\n", " r0, k0, W0, p0 = mc.initialize(nevents, ntrajectories, n_medium[i], n_sample, boundary, polarization=True)\n", " r0 = sc.Quantity(r0, 'um')\n", " k0 = sc.Quantity(k0, '')\n", " W0 = sc.Quantity(W0, '')\n", " p0 = sc.Quantity(p0,'')\n", "\n", " trajectories = mc.Trajectory(r0, k0, W0, p0)\n", "\n", " # Sample trajectory angles\n", " sintheta, costheta, sinphi, cosphi, theta, phi= mc.sample_angles(nevents, \n", " ntrajectories,p)\n", " # Sample step sizes\n", " step = mc.sample_step(nevents, ntrajectories, mu_scat)\n", "\n", " # Update trajectories based on sampled values\n", " trajectories.scatter(sintheta, costheta, sinphi, cosphi)\n", " trajectories.polarize(theta, phi, sintheta, costheta, sinphi,cosphi,\n", " n_particle[i], n_sample, radius, wavelengths[i], volume_fraction)\n", " trajectories.move(step)\n", " trajectories.absorb(mu_abs, step) \n", " \n", " # calculate reflectance and other values of interest\n", " refl_indices, trans_indices,_,_,_,_,_,_,_,_,_,reflectance[i],_,_,_,_ = det.calc_refl_trans(trajectories, \n", " thickness, n_medium[i], n_sample,\n", " boundary, return_extra = True)\n", " \n", " # calculate reflectance contribution from each polarization component\n", " pol_refl_x[i], pol_refl_y[i], pol_refl_z[i] = det.calc_pol_frac(trajectories, refl_indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot reflectance and polarization spectra" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f754303d668>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hQ4bw7LPPdmYogm5A6eZLYNREAqMmYjY1UF2ejq7sFDrtGYeludlYQ/qht7tUJCxmPRlH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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f754301a2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style('white')\n", "plt.figure()\n", "plt.plot(wavelengths, reflectance , label = 'total', linewidth = 3.5)\n", "plt.plot(wavelengths, pol_refl_x, label = 'x-polarized', linewidth = 3)\n", "plt.plot(wavelengths, pol_refl_y, label = 'y-polarized', linewidth = 3)\n", "plt.plot(wavelengths, pol_refl_z, label = 'z-polarized', linewidth = 3)\n", "plt.plot(wavelengths, pol_refl_x + pol_refl_y + pol_refl_z, label = 'total polarized', linewidth = 3)\n", "plt.xlim([400,800])\n", "plt.ylim([0,1])\n", "plt.ylabel('Reflectance')\n", "plt.xlabel('Wavelength (nm)')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot shows the contribution to reflectance from light which each of three possible polarization vectors in the global lab coordinate system. The total reflectance nearly overlaps with the sum of the three polarization compontents. The lack of a perfect overlap is due to any extra reflectance which is calculated outside of the trajectories themselves. This includes fresnel reflections at the initial medium-sample interface and the distribution of trajectories that were stuck inside the sample at the end of the Monte Carlo calculation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

daniel-koehn/Theory-of-seismic-waves-II

05_2D_acoustic_FD_modelling/2_Optimizing_fdac2d_code.ipynb

1

160130

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook style sheet by L.A. Barba, N.C. Clementi" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<link href=\"https://fonts.googleapis.com/css?family=Merriweather:300,300i,400,400i,700,700i,900,900i\" rel='stylesheet' >\n", "<link href=\"https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,300i,400,400i,700,700i\" rel='stylesheet' >\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' >\n", "<style>\n", "\n", "@font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", "}\n", "\n", "\n", "#notebook_panel { /* main background */\n", " background: rgb(245,245,245);\n", "}\n", "\n", "div.cell { /* set cell width */\n", " width: 800px;\n", "}\n", "\n", "div #notebook { /* centre the content */\n", " background: #fff; /* white background for content */\n", " width: 1000px;\n", " margin: auto;\n", " padding-left: 0em;\n", "}\n", "\n", "#notebook li { /* More space between bullet points */\n", "margin-top:0.5em;\n", "}\n", "\n", "/* draw border around running cells */\n", "div.cell.border-box-sizing.code_cell.running { \n", " border: 1px solid #111;\n", "}\n", "\n", "/* Put a solid color box around each cell and its output, visually linking them*/\n", "div.cell.code_cell {\n", " background-color: rgb(256,256,256); \n", " border-radius: 0px; \n", " padding: 0.5em;\n", " margin-left:1em;\n", " margin-top: 1em;\n", "}\n", "\n", "\n", "div.text_cell_render{\n", " font-family: 'Source Sans Pro', sans-serif;\n", " line-height: 140%;\n", " font-size: 110%;\n", " width:680px;\n", " margin-left:auto;\n", " margin-right:auto;\n", "}\n", "\n", "/* Formatting for header cells */\n", ".text_cell_render h1 {\n", " font-family: 'Merriweather', serif;\n", " font-style:regular;\n", " font-weight: bold; \n", " font-size: 250%;\n", " line-height: 100%;\n", " color: #004065;\n", " margin-bottom: 1em;\n", " margin-top: 0.5em;\n", " display: block;\n", "}\t\n", ".text_cell_render h2 {\n", " font-family: 'Merriweather', serif;\n", " font-weight: bold; \n", " font-size: 180%;\n", " line-height: 100%;\n", " color: #0096d6;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", "}\t\n", "\n", ".text_cell_render h3 {\n", " font-family: 'Merriweather', serif;\n", "\tfont-size: 150%;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " font-style: regular;\n", " color: #008367;\n", "}\n", "\n", ".text_cell_render h4 { /*Use this for captions*/\n", " font-family: 'Merriweather', serif;\n", " font-weight: 300; \n", " font-size: 100%;\n", " line-height: 120%;\n", " text-align: left;\n", " width:500px;\n", " margin-top: 1em;\n", " margin-bottom: 2em;\n", " margin-left: 80pt;\n", " font-style: regular;\n", "}\n", "\n", ".text_cell_render h5 { /*Use this for small titles*/\n", " font-family: 'Source Sans Pro', sans-serif;\n", " font-weight: regular;\n", " font-size: 130%;\n", " color: #e31937;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 1em;\n", " display: block;\n", "}\n", "\n", ".text_cell_render h6 { /*use this for copyright note*/\n", " font-family: 'Source Code Pro', sans-serif;\n", " font-weight: 300;\n", " font-size: 9pt;\n", " line-height: 100%;\n", " color: grey;\n", " margin-bottom: 1px;\n", " margin-top: 1px;\n", "}\n", "\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", "\t\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"], \n", " equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Execute this cell to load the notebook's style sheet, then ignore it\n", "from IPython.core.display import HTML\n", "css_file = '../style/custom.css'\n", "HTML(open(css_file, \"r\").read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Performance optimization of the 2D acoustic finite difference modelling code\n", "\n", "During the [last class](http://nbviewer.jupyter.org/github/daniel-koehn/Theory-of-seismic-waves-II/blob/master/05_2D_acoustic_FD_modelling/1_From_1D_to_2D_acoustic_FD_modelling_final.ipynb), it took us only 15 minutes to develop a 2D acoustic FD code based on the 1D code. However, with a runtime of roughly 3 minutes, the performance of this \"vanilla\" Python implementation was quite underwhelming. Therefore, the aim of this lesson is to optimize the performance of this code. \n", "\n", "Let's start with a slightly modified version of the original code. Basically, I moved the computation of the analytical solution outside the main code, the discretization parameters $nx,\\; nz,\\; nt,\\; dx,\\; dz,\\; dt$ are also fixed in order to minimize the input to the FD modelling function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [ 0 ] }, "outputs": [], "source": [ "# Import Libraries \n", "# ----------------\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from pylab import rcParams\n", "\n", "# Ignore Warning Messages\n", "# -----------------------\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "code_folding": [], "scrolled": false }, "outputs": [], "source": [ "# Definition of modelling parameters\n", "# ----------------------------------\n", "xmax = 500.0 # maximum spatial extension of the 1D model in x-direction (m)\n", "zmax = xmax # maximum spatial extension of the 1D model in z-direction(m)\n", "dx = 1.0 # grid point distance in x-direction\n", "dz = dx # grid point distance in z-direction\n", "\n", "tmax = 0.502 # maximum recording time of the seismogram (s)\n", "dt = 0.0010 # time step\n", "\n", "vp0 = 580. # P-wave speed in medium (m/s)\n", "\n", "# acquisition geometry\n", "xr = 330.0 # x-receiver position (m)\n", "zr = xr # z-receiver position (m)\n", "\n", "xsrc = 250.0 # x-source position (m)\n", "zsrc = 250.0 # z-source position (m)\n", "\n", "f0 = 40. # dominant frequency of the source (Hz)\n", "t0 = 4. / f0 # source time shift (s)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nx = 500\n", "nz = 500\n", "nt = 502\n" ] } ], "source": [ "# define model discretization\n", "# ---------------------------\n", "\n", "nx = (int)(xmax/dx) # number of grid points in x-direction\n", "print('nx = ',nx)\n", "\n", "nz = (int)(zmax/dz) # number of grid points in x-direction\n", "print('nz = ',nz)\n", "\n", "nt = (int)(tmax/dt) # maximum number of time steps \n", "print('nt = ',nt)\n", "\n", "ir = (int)(xr/dx) # receiver location in grid in x-direction \n", "jr = (int)(zr/dz) # receiver location in grid in z-direction\n", "\n", "isrc = (int)(xsrc/dx) # source location in grid in x-direction\n", "jsrc = (int)(zsrc/dz) # source location in grid in x-direction\n", "\n", "# Source time function (Gaussian)\n", "# -------------------------------\n", "src = np.zeros(nt + 1)\n", "time = np.linspace(0 * dt, nt * dt, nt)\n", "\n", "# 1st derivative of a Gaussian\n", "src = -2. * (time - t0) * (f0 ** 2) * (np.exp(- (f0 ** 2) * (time - t0) ** 2))\n", "\n", "# Analytical solution\n", "# -------------------\n", "G = time * 0.\n", "\n", "# Initialize coordinates\n", "# ----------------------\n", "x = np.arange(nx)\n", "x = x * dx # coordinates in x-direction (m)\n", "\n", "z = np.arange(nz)\n", "z = z * dz # coordinates in z-direction (m)\n", "\n", "# calculate source-receiver distance\n", "r = np.sqrt((x[ir] - x[isrc])**2 + (z[jr] - z[jsrc])**2)\n", "\n", "for it in range(nt): # Calculate Green's function (Heaviside function)\n", " if (time[it] - r / vp0) >= 0:\n", " G[it] = 1. / (2 * np.pi * vp0**2) * (1. / np.sqrt(time[it]**2 - (r/vp0)**2))\n", "Gc = np.convolve(G, src * dt)\n", "Gc = Gc[0:nt]\n", "lim = Gc.max() # get limit value from the maximum amplitude\n", "\n", "# Initialize model (assume homogeneous model)\n", "# -------------------------------------------\n", "vp = np.zeros((nx,nz))\n", "vp2 = np.zeros((nx,nz))\n", "\n", "vp = vp + vp0 # initialize wave velocity in model\n", "vp2 = vp**2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "code_folding": [ 42 ] }, "outputs": [], "source": [ "# 2D Wave Propagation (Finite Difference Solution) \n", "# ------------------------------------------------\n", "def FD_2D_acoustic_vanilla(): \n", " \n", " # Initialize empty pressure arrays\n", " # --------------------------------\n", " p = np.zeros((nx,nz)) # p at time n (now)\n", " pold = np.zeros((nx,nz)) # p at time n-1 (past)\n", " pnew = np.zeros((nx,nz)) # p at time n+1 (present)\n", " d2px = np.zeros((nx,nz)) # 2nd spatial x-derivative of p\n", " d2pz = np.zeros((nx,nz)) # 2nd spatial z-derivative of p\n", "\n", " # Initialize empty seismogram\n", " # ---------------------------\n", " seis = np.zeros(nt) \n", " \n", " # Calculate Partial Derivatives\n", " # -----------------------------\n", " for it in range(nt):\n", " \n", " # FD approximation of spatial derivative by 3 point operator\n", " for i in range(1, nx - 1):\n", " for j in range(1, nz - 1):\n", " \n", " d2px[i,j] = (p[i + 1,j] - 2 * p[i,j] + p[i - 1,j]) / dx ** 2 \n", " d2pz[i,j] = (p[i,j + 1] - 2 * p[i,j] + p[i,j - 1]) / dz ** 2\n", "\n", " # Time Extrapolation\n", " # ------------------\n", " pnew = 2 * p - pold + vp ** 2 * dt ** 2 * (d2px + d2pz)\n", "\n", " # Add Source Term at isrc\n", " # -----------------------\n", " # Absolute pressure w.r.t analytical solution\n", " pnew[isrc,jsrc] = pnew[isrc,jsrc] + src[it] / (dx * dz) * dt ** 2\n", " \n", " # Remap Time Levels\n", " # -----------------\n", " pold, p = p, pnew\n", " \n", " # Output of Seismogram\n", " # -----------------\n", " seis[it] = p[ir,jr]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You know what happened the last time, we executed the cell below. We had to wait 3 minutes until the modelling run finished. So for safety reasons I commented the code execution and defined the runtime. You should adapt the value of the timing measurement `t_vanilla_python` by the value of your computer." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#%%time\n", "#FD_2D_acoustic_vanilla()\n", "t_vanilla_python = 190.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Just-In-Time (JIT) code compilation with Numba \n", "\n", "The poor performance of the vanilla Python code is due to the nested FOR loops to compute the 2nd spatial FD derivatives. We can optimize the performance using the `Numba ` library for Python ([http://numba.pydata.org/](http://numba.pydata.org/)) which turns Python functions into C-style compiled functions using [LLVM](https://en.wikipedia.org/wiki/LLVM). A nice introduction to Numba was presented at the SciPy conference 2016 by Gil Forsyth & Lorena Barba with the title \n", "\n", "**Numba: Tell those C++ bullies to get lost**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/SzBi3xdEF2Y\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x7fa63edf0898>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('SzBi3xdEF2Y')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The associated Jupyter notebooks can be cloned from [here](https://github.com/barbagroup/numba_tutorial_scipy2016).\n", "\n", "First, we have to install Numba, which is quite easy using Anaconda:\n", "\n", "`conda install numba` \n", "\n", "From the Numba library we import **jit**: " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# import JIT from Numba\n", "from numba import jit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only thing, we modify in our original Python code is to add the function decorator \n", "\n", "`@jit(nopython=True)`\n", "\n", "which tags the function `FD_2D_acoustic_JIT` to be compiled:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# FD_2D_acoustic code with JIT optimization\n", "# -----------------------------------------\n", "@jit(nopython=True) # use Just-In-Time (JIT) Compilation for C-performance\n", "def FD_2D_acoustic_JIT(): \n", " \n", " # Initialize empty pressure arrays\n", " # --------------------------------\n", " p = np.zeros((nx,nz)) # p at time n (now)\n", " pold = np.zeros((nx,nz)) # p at time n-1 (past)\n", " pnew = np.zeros((nx,nz)) # p at time n+1 (present)\n", " d2px = np.zeros((nx,nz)) # 2nd spatial x-derivative of p\n", " d2pz = np.zeros((nx,nz)) # 2nd spatial z-derivative of p\n", "\n", " # Initialize empty seismogram\n", " # ---------------------------\n", " seis = np.zeros(nt) \n", " \n", " # Calculate Partial Derivatives\n", " # -----------------------------\n", " for it in range(nt):\n", " \n", " # FD approximation of spatial derivative by 3 point operator\n", " for i in range(1, nx - 1):\n", " for j in range(1, nz - 1):\n", " \n", " d2px[i,j] = (p[i + 1,j] - 2 * p[i,j] + p[i - 1,j]) / dx**2 \n", " d2pz[i,j] = (p[i,j + 1] - 2 * p[i,j] + p[i,j - 1]) / dz**2\n", "\n", " # Time Extrapolation\n", " # ------------------\n", " pnew = 2 * p - pold + vp2 * dt**2 * (d2px + d2pz)\n", "\n", " # Add Source Term at isrc\n", " # -----------------------\n", " # Absolute pressure w.r.t analytical solution\n", " pnew[isrc,jsrc] = pnew[isrc,jsrc] + src[it] / (dx * dz) * dt ** 2\n", " \n", " # Remap Time Levels\n", " # -----------------\n", " pold, p = p, pnew\n", " \n", " # Output of Seismogram\n", " # -----------------\n", " seis[it] = p[ir,jr] \n", " \n", " return seis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's run the code:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 372 ms, sys: 0 ns, total: 372 ms\n", "Wall time: 373 ms\n" ] } ], "source": [ "%%time\n", "seis_FD_JIT = FD_2D_acoustic_JIT()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow, you don't have to wait 3 minutes, but only 747 ms. Run the cell above again ...\n", "\n", "... and you see that the runtime is suddenly further decreased to 373 ms. This performance improvement can be explained by the code compilation during the first run of the code. So by simply using the `@jit` function decorator we get a performance increase of **509x** compared to the non-optimized Python code." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "t_JIT_python = 0.373 # runtime of JIT compiled Python code (s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another approach to get rid of the nested FOR-loops is to use Numpy array operations:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# FD_2D_acoustic code with NumPy arrays\n", "# -------------------------------------\n", "def FD_2D_acoustic_numpy(): \n", " \n", " # Initialize empty pressure arrays\n", " # --------------------------------\n", " p = np.zeros((nx,nz)) # p at time n (now)\n", " pold = np.zeros((nx,nz)) # p at time n-1 (past)\n", " pnew = np.zeros((nx,nz)) # p at time n+1 (present)\n", " d2px = np.zeros((nx,nz)) # 2nd spatial x-derivative of p\n", " d2pz = np.zeros((nx,nz)) # 2nd spatial z-derivative of p\n", "\n", " # Initialize empty seismogram\n", " # ---------------------------\n", " seis = np.zeros(nt) \n", " \n", " # Calculate Partial Derivatives\n", " # -----------------------------\n", " for it in range(nt): \n", "\n", " # Old FD approximation of spatial derivative by 3-point operator\n", " # using nested FOR-loops is replaced by ...\n", " #for i in range(1, nx - 1):\n", " # for j in range(1, nz - 1):\n", " # \n", " # d2px[i,j] = (p[i + 1,j] - 2 * p[i,j] + p[i - 1,j]) / dx**2 \n", " # d2pz[i,j] = (p[i,j + 1] - 2 * p[i,j] + p[i,j - 1]) / dz**2\n", " \n", " # ... Numpy array operations:\n", " d2px[1:-2,1:-2] = (p[2:-1,1:-2] - 2 * p[1:-2,1:-2] + p[0:-3,1:-2]) / dx**2\n", " d2pz[1:-2,1:-2] = (p[1:-2,2:-1] - 2 * p[1:-2,1:-2] + p[1:-2,0:-3]) / dz**2\n", " \n", " # Time Extrapolation\n", " # ------------------\n", " pnew = 2 * p - pold + vp ** 2 * dt ** 2 * (d2px + d2pz)\n", "\n", " # Add Source Term at isrc\n", " # -----------------------\n", " # Absolute pressure w.r.t analytical solution\n", " pnew[isrc,jsrc] = pnew[isrc,jsrc] + src[it] / (dx * dz) * dt ** 2\n", " \n", " # Remap Time Levels\n", " # -----------------\n", " pold, p = p, pnew\n", " \n", " # Output of Seismogram\n", " # -----------------\n", " seis[it] = p[ir,jr] \n", " \n", " return seis" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.33 s, sys: 380 ms, total: 7.71 s\n", "Wall time: 1.93 s\n" ] } ], "source": [ "%%time\n", "seis_FD_numpy = FD_2D_acoustic_numpy()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "t_numpy_python = 1.93 # runtime of JIT compiled Python code (s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The runtime 1.93 s of the `NumPy` version is not as fast as the 0.373 s of the JIT version, but a **98x** improvement is still better than the non-optimized version. Can JIT also improve the performance of `FD_2D_acoustic_numpy`?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# FD_2D_acoustic code with NumPy arrays + JIT\n", "# -------------------------------------------\n", "@jit(nopython=True) # use Just-In-Time (JIT) Compilation for C-performance\n", "def FD_2D_acoustic_numpy_JIT(): \n", " \n", " # Initialize empty pressure arrays\n", " # --------------------------------\n", " p = np.zeros((nx,nz)) # p at time n (now)\n", " pold = np.zeros((nx,nz)) # p at time n-1 (past)\n", " pnew = np.zeros((nx,nz)) # p at time n+1 (present)\n", " d2px = np.zeros((nx,nz)) # 2nd spatial x-derivative of p\n", " d2pz = np.zeros((nx,nz)) # 2nd spatial z-derivative of p\n", "\n", " # Initialize empty seismogram\n", " # ---------------------------\n", " seis = np.zeros(nt) \n", " \n", " # Calculate Partial Derivatives\n", " # -----------------------------\n", " for it in range(nt): \n", "\n", " # Old FD approximation of spatial derivative by 3-point operator\n", " # using Numpy array operations\n", " d2px[1:-2,1:-2] = (p[2:-1,1:-2] - 2 * p[1:-2,1:-2] + p[0:-3,1:-2]) / dx**2\n", " d2pz[1:-2,1:-2] = (p[1:-2,2:-1] - 2 * p[1:-2,1:-2] + p[1:-2,0:-3]) / dz**2\n", " \n", " # Time Extrapolation\n", " # ------------------\n", " pnew = 2 * p - pold + vp ** 2 * dt ** 2 * (d2px + d2pz)\n", "\n", " # Add Source Term at isrc\n", " # -----------------------\n", " # Absolute pressure w.r.t analytical solution\n", " pnew[isrc,jsrc] = pnew[isrc,jsrc] + src[it] / (dx * dz) * dt ** 2\n", " \n", " # Remap Time Levels\n", " # -----------------\n", " pold, p = p, pnew\n", " \n", " # Output of Seismogram\n", " # -----------------\n", " seis[it] = p[ir,jr] \n", " \n", " return seis" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 484 ms, sys: 0 ns, total: 484 ms\n", "Wall time: 481 ms\n" ] } ], "source": [ "%%time\n", "seis_FD_numpy_JIT = FD_2D_acoustic_numpy_JIT()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "t_numpy_python_JIT = 0.481 # runtime of JIT compiled Python code (s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So JIT could also improve the performance of the code using `NumPy` array operations, but the performance of the compiled code with the nested FOR loops has a slight edge in terms of performance. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison with a C++ implementation\n", "\n", "How does the performance of the JIT-codes compare to a C++ bully code? I invested 1 hour to write [this C++ code](https://github.com/daniel-koehn/Theory-of-seismic-waves-II/tree/master/05_2D_acoustic_FD_modelling/cxx/2dac.cpp), which is similar to the 2D acoustic FD Python code. \n", "\n", "In order to use similar matrix data structures in C++ as in Python, I use the `Eigen` library:\n", "\n", "www.eigen.tuxfamily.org/\n", "\n", "which also allows auto-vectorization of matrix-matrix products. To compile the source code, you need a C++ compiler, e.g. `g++` and the `Eigen` library which can either be compiled from source or installed using the package manager of your Linux distribution. \n", "\n", "I also recommend to use the moderate optimization option `-O2` and Advanced Vector Extensions ([AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions)) `-mavx` during code compilation for a significant performance increase of the code. Let's compile and run the code:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 432.423 ms\r\n" ] } ], "source": [ "# Compile and run C++-version\n", "!g++ -I /usr/include/eigen3 cxx/2dac.cpp -o 2dac -O2 -mavx\n", "!./2dac\n", "\n", "# load seismogram\n", "time_Cpp, seis_FD_Cpp = np.loadtxt('seis.dat', delimiter='\\t', skiprows=0, unpack=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "t_cxx = 0.432 # runtime of C++ code (s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The C++ code performance is comparable with the JIT version of the Python code using `NumPy` operations, which is quite impressive considering the simple Python code optimization using JIT.\n", "\n", "To check if the optimized codes are not only fast but still produce reasonable modelling results, it is a good idea to check if the seismograms of the optimized codes still coincide with the analytical solution." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compare FD Seismogram with analytical solution\n", "# ----------------------------------------------\n", "# Define figure size\n", "rcParams['figure.figsize'] = 12, 5\n", "plt.plot(time, seis_FD_JIT, 'b-',lw=3,label=\"FD solution (Python + JIT)\") # plot FD seismogram\n", "plt.plot(time, seis_FD_numpy, 'g-',lw=3,label=\"FD solution (Python + NumPy)\") # plot FD seismogram\n", "plt.plot(time, seis_FD_numpy_JIT, 'k-',lw=3,label=\"FD solution (Python + NumPy + JIT)\") # plot FD seismogram\n", "plt.plot(time_Cpp, seis_FD_Cpp, 'y-',lw=3,label=\"FD solution (C++)\") # plot FD seismogram\n", "Analy_seis = plt.plot(time,Gc,'r--',lw=3,label=\"Analytical solution\") # plot analytical solution\n", "plt.xlim(time[0], time[-1])\n", "plt.title('Seismogram')\n", "plt.xlabel('Time (s)')\n", "plt.ylabel('Amplitude')\n", "plt.legend()\n", "plt.grid()\n", "plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we produce some nice bar charts to compare the performance of the different codes developed in this `Jupyter` notebook:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# define codes\n", "codes = ('Python', 'Python + JIT', 'Python + NumPy', 'Python + NumPy + JIT', 'C++')\n", "y_pos = np.arange(len(codes))\n", "\n", "# runtime\n", "performance = [t_vanilla_python,t_JIT_python,t_numpy_python,t_numpy_python_JIT,t_cxx]\n", "\n", "# speed-up with respect to the non-optimized code\n", "speedup = [t_vanilla_python/t_vanilla_python,\n", " t_vanilla_python/t_JIT_python,\n", " t_vanilla_python/t_numpy_python,\n", " t_vanilla_python/t_numpy_python_JIT,\n", " t_vanilla_python/t_cxx]\n", "\n", "# Define figure size\n", "rcParams['figure.figsize'] = 12, 8\n", "\n", "# Plot runtimes of 2D acoustic FD codes\n", "ax1 = plt.subplot(211)\n", "\n", "plt.bar(y_pos, performance, align='center', alpha=0.5)\n", "plt.xticks(y_pos, codes)\n", "plt.ylabel('Runtime (s)')\n", "plt.title('Performance comparison of 2D acoustic FD modelling codes')\n", " \n", "# make tick labels invisible\n", "plt.setp(ax1.get_xticklabels(), visible=False) \n", "\n", "# Plot speedup of 2D acoustic FD codes\n", "ax2 = plt.subplot(212, sharex=ax1) \n", "\n", "plt.bar(y_pos, speedup, align='center', alpha=0.5,color='r')\n", "plt.xticks(y_pos, codes)\n", "plt.ylabel('Speedup ()')\n", "plt.tight_layout() \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is this the best result we can achieve or are further code improvements possible? \n", "\n", "Using domain decomposition with the **Message-Passing Interface MPI** to distribute the workload over multiple CPU cores, combined with a partioning of the tasks in each domain using **Multithreading** can significantly improve the code performance. One key is the manual optimization of CPU and GPU kernels, especially regarding memory access times or communication between MPI processes. As an example I plotted the runtime and speedup for the same homogeneous acoustic problem from this Jupyter notebook using the 2D acoustic modelling code [DENISE Black-Edition](https://github.com/daniel-koehn/DENISE-Black-Edition) which only relies on MPI:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define figure size\n", "rcParams['figure.figsize'] = 12, 6\n", "\n", "# number of cores and runtime\n", "cores = np.array([1, 2, 4, 8, 16, 25])\n", "t_denise = np.array([0.926, 0.482, 0.234, 0.123, 0.067, 0.055])\n", "\n", "# speed-up with respect to the runtime of the 1st core\n", "# and linear speedup\n", "speedup_denise = t_denise[0] / t_denise\n", "linear_speedup = cores\n", "\n", "# plot runtime\n", "ax2 = plt.subplot(121)\n", "plt.plot(cores, t_denise, 'rs-',lw=3,label=\"Runtime\")\n", "plt.title('Runtime DENISE Black-Edition')\n", "plt.xlabel('Number of cores')\n", "plt.ylabel('Runtime (s)')\n", "plt.legend()\n", "plt.grid()\n", "\n", "# plot speedup\n", "ax2 = plt.subplot(122)\n", "plt.plot(cores, speedup_denise, 'bs-',lw=3,label=\"Speedup DENISE\")\n", "plt.plot(cores, linear_speedup, 'k-',lw=3,label=\"Linear speedup\")\n", "plt.title('Speedup DENISE Black-Edition')\n", "plt.xlabel('Number of cores')\n", "plt.ylabel('Speedup')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using less than 2 cores, the JIT compiled Python code with a runtime of 353 ms is faster than the MPI code. Utilizing more cores, the DENISE code leads to a steady runtime decrease. However, notice that the speedup is not linear anymore when using 16 cores or more. This can be explained by excessive communication time between the MPI processes, when the domain sizes decreases. More details about MPI and Multithreading optimizations are beyond the scope of the TEW2 course, but will be the topic of a future HPC lecture ...\n", "\n", "To get an idea about the difference between JIT optimized Python codes and manually optimized codes, I recommend a SciPy 2016 talk by Andreas Klöckner:\n", "\n", "**High Performance with Python: Architectures, Approaches & Applications**" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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\n", "text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/Zz_6P5qAJck\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x7fa628b95748>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('Zz_6P5qAJck')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What we learned:\n", "\n", "* The performance of our 2D acoustic FD modelling code, developed in the previous class, suffered from the nested FOR loops\n", "* Using JIT compilation of the Python code using `Numba`, the performance could be significantly improved by a factor 509x\n", "* Alternatively, we can replace the nested FOR loops by `NumPy` array operations to improve the runtime performance\n", "* The performance of the JIT compiled Python code is comparable with a C++ implementation\n", "* Check if the modelling results of your optimized Python codes are still correct" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

jsaudino/75.06_tp1_acs

TP-1 DATOS.ipynb

2

1796313

mit

jbarnoud/PBxplore

doc/source/notebooks/Assignement.ipynb

1

33117

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PB assignation\n", "\n", "We hereby demonstrate how to use the API to assign PB sequences." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "from pprint import pprint\n", "import os" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pbxplore as pbx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use the built-in structure parser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assign PB for a single structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The :func:`pbxplore.chains_from_files` function is the prefered way to read PDB and PDBx/mmCIF files using PBxplore. This function takes a list of file path as argument, and yield each chain it can read from these files. It provides a single interface to read PDB and PDBx/mmCIF files, to read single model and multimodel files, and to read a single file of a collection of files.\n", "\n", "Here we want to read a single file with a single model and a single chain. Therefore, we need the first and only record that is yield by :func:`pbxplore.chains_from_files`. This record contains a name for the chain, and the chain itself as a :class:`pbxplore.structure.structure.Chain` object. Note that, even if we want to read a single file, we need to provide it as a list to :func:`pbxplore.chains_from_files`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Read 1 chain(s) in /Users/jon/dev/PBtest/pbxplore/demo/1BTA.pdb\n", "/Users/jon/dev/PBtest/pbxplore/demo/1BTA.pdb | chain A\n", "Chain A / model : 1434 atoms\n" ] } ], "source": [ "pdb_path = os.path.join(pbx.DEMO_DATA_PATH, '1BTA.pdb')\n", "structure_reader = pbx.chains_from_files([pdb_path])\n", "chain_name, chain = next(structure_reader)\n", "print(chain_name)\n", "print(chain)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Protein Blocks are assigned based on the dihedral angles of the backbone. So we need to calculate them. The :meth:`pbxplore.structure.structure.Chain.get_phi_psi_angles` methods calculate these angles and return them in a form that can be directly provided to the assignement function.\n", "\n", "The dihedral angles are returned as a dictionnary. Each key of this dictionary is a residue number, and each value is a dictionary with the phi and psi angles." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: {'phi': None, 'psi': -171.6556313444855},\n", " 2: {'phi': -133.80467711845586, 'psi': 153.74322760775027},\n", " 3: {'phi': -134.66175688926953, 'psi': 157.30476083095584},\n", " 4: {'phi': -144.49159910635186, 'psi': 118.59706956501037},\n", " 5: {'phi': -100.12866913978127, 'psi': 92.98634825528089},\n", " 6: {'phi': -83.48980457968895, 'psi': 104.23730726195485},\n", " 7: {'phi': -64.77163869310709, 'psi': -43.25159835828049},\n", " 8: {'phi': -44.47885842536948, 'psi': -25.89184262616925},\n", " 9: {'phi': -94.90790101955957, 'psi': -47.182577907117775},\n", " 10: {'phi': -41.31267169232996, 'psi': 133.73743399231304},\n", " 11: {'phi': -119.15122785547305, 'psi': -11.82789586402356},\n", " 12: {'phi': -174.21196552933984, 'psi': 175.87239770676175},\n", " 13: {'phi': -56.61341695443224, 'psi': -45.74767617535588},\n", " 14: {'phi': -50.78226415072095, 'psi': -45.3742585970337},\n", " 15: {'phi': -57.93584481869442, 'psi': -43.329444361460844},\n", " 16: {'phi': -55.209603541130434, 'psi': -56.47559202715399},\n", " 17: {'phi': -64.51979885245254, 'psi': -18.577118068149446},\n", " 18: {'phi': -70.24273354141468, 'psi': -55.153744337676926},\n", " 19: {'phi': -65.20648546633561, 'psi': -41.28370221159946},\n", " 20: {'phi': -58.98821952110768, 'psi': -35.78957701447905},\n", " 21: {'phi': -66.8659714296852, 'psi': -42.14634696303375},\n", " 22: {'phi': -67.34201665142825, 'psi': -57.40438549689628},\n", " 23: {'phi': -52.29793609141382, 'psi': -66.09120830346023},\n", " 24: {'phi': -61.19010445362886, 'psi': -14.807316930892569},\n", " 25: {'phi': 54.951586944206355, 'psi': 47.59528477656777},\n", " 26: {'phi': -69.51531755580697, 'psi': 161.10806531443862},\n", " 27: {'phi': -57.36300935545188, 'psi': -179.66365615297644},\n", " 28: {'phi': -79.91369005407893, 'psi': -18.494472196394668},\n", " 29: {'phi': -93.51717329199727, 'psi': 13.80253054655975},\n", " 30: {'phi': -38.40653214238887, 'psi': 105.85297788366393},\n", " 31: {'phi': -64.01559307951965, 'psi': -5.507357757886837},\n", " 32: {'phi': 50.06519606710964, 'psi': 3.604730286754302},\n", " 33: {'phi': -84.83560576923662, 'psi': -176.04877012701309},\n", " 34: {'phi': -76.65985981150652, 'psi': -36.89428882663367},\n", " 35: {'phi': -66.20745817863622, 'psi': -36.19018119951471},\n", " 36: {'phi': -80.76844188891471, 'psi': -55.88509876949212},\n", " 37: {'phi': -45.0995601497454, 'psi': -50.82304368319501},\n", " 38: {'phi': -58.512419169182465, 'psi': -56.4318511704347},\n", " 39: {'phi': -44.00775783983471, 'psi': -26.06209153795419},\n", " 40: {'phi': -79.6799641005731, 'psi': -51.3827703817916},\n", " 41: {'phi': -58.80532943671335, 'psi': -49.46425322450557},\n", " 42: {'phi': -75.73059711071141, 'psi': 3.9162670655634235},\n", " 43: {'phi': -177.14613562249534, 'psi': 60.46495675947551},\n", " 44: {'phi': 177.12658169328853, 'psi': -66.62887199130637},\n", " 45: {'phi': -58.436100708193806, 'psi': 149.59997847317612},\n", " 46: {'phi': -102.66050573267097, 'psi': 132.43212727859543},\n", " 47: {'phi': -114.52132755246623, 'psi': 169.33012343233455},\n", " 48: {'phi': -61.39150617820462, 'psi': 136.7035538929314},\n", " 49: {'phi': -113.17589693608565, 'psi': 156.54195412530404},\n", " 50: {'phi': -117.26440335376822, 'psi': 138.51305036902693},\n", " 51: {'phi': -120.03410170277817, 'psi': 81.75707989178757},\n", " 52: {'phi': -77.60981590398819, 'psi': 83.18451037698443},\n", " 53: {'phi': -79.65858964180552, 'psi': 111.40143302647459},\n", " 54: {'phi': -100.37011629225776, 'psi': 150.03395825502497},\n", " 55: {'phi': 49.87330458406237, 'psi': 68.74199803405018},\n", " 56: {'phi': -73.87938409722335, 'psi': -66.7355521840301},\n", " 57: {'phi': -56.20534388077749, 'psi': -35.207843043514686},\n", " 58: {'phi': -66.38284564180043, 'psi': -32.21866387324769},\n", " 59: {'phi': -94.6778115344365, 'psi': 17.686140221665553},\n", " 60: {'phi': -111.48538994784963, 'psi': -38.09776457861392},\n", " 61: {'phi': -70.64502750557983, 'psi': -62.8582975880629},\n", " 62: {'phi': -33.50588994671665, 'psi': -32.02546270762559},\n", " 63: {'phi': -128.57384077349852, 'psi': 62.57927537310066},\n", " 64: {'phi': -12.365761900396365, 'psi': 106.99327496259977},\n", " 65: {'phi': 73.68588813063124, 'psi': 32.131558860201714},\n", " 66: {'phi': -89.05260862755028, 'psi': -69.16778908477181},\n", " 67: {'phi': -77.83088301001709, 'psi': -21.564910924673597},\n", " 68: {'phi': -71.32122280651765, 'psi': -21.859413182600065},\n", " 69: {'phi': -81.4118653034867, 'psi': -55.2935117883826},\n", " 70: {'phi': -52.047970110313145, 'psi': -43.22593946145588},\n", " 71: {'phi': -59.215594114973726, 'psi': -45.283196644537554},\n", " 72: {'phi': -52.67186926130671, 'psi': -38.127901315075064},\n", " 73: {'phi': -75.00963018964649, 'psi': -30.83999517691734},\n", " 74: {'phi': -74.69878930178584, 'psi': -35.042954979175136},\n", " 75: {'phi': -80.22740138668189, 'psi': -37.2721834868002},\n", " 76: {'phi': -63.3253002341084, 'psi': -46.736848174955014},\n", " 77: {'phi': -62.577975558265166, 'psi': -38.836376804396195},\n", " 78: {'phi': -58.4371262613883, 'psi': -30.932534133630554},\n", " 79: {'phi': -77.25603045197096, 'psi': -28.810984281581455},\n", " 80: {'phi': -65.77402807318447, 'psi': -6.587861693755428},\n", " 81: {'phi': 113.27162201541087, 'psi': -14.067924223417435},\n", " 82: {'phi': -63.856071155072016, 'psi': 160.46313493362334},\n", " 83: {'phi': -109.29442965951228, 'psi': 65.33016925110071},\n", " 84: {'phi': -94.29902268445335, 'psi': 87.93029438989075},\n", " 85: {'phi': -52.91938395571083, 'psi': 98.897475962567},\n", " 86: {'phi': -73.44769372512917, 'psi': 114.6488125441093},\n", " 87: {'phi': -114.16119204550668, 'psi': 101.24805765454327},\n", " 88: {'phi': -96.78933556699712, 'psi': 106.74340425527281},\n", " 89: {'phi': -109.02775603395975, 'psi': None}}\n" ] } ], "source": [ "dihedrals = chain.get_phi_psi_angles()\n", "pprint(dihedrals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dihedral angles can be provided to the :func:`pbxplore.assign` function that assigns a Protein Block to each residue, and that returns the PB sequence as a string. Note that the first and last two residues are assigned to the `Z` jocker block as some dihedral angles cannot be calculated." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ZZdddfklonbfklmmmmmmmmnopafklnoiaklmmmmmnoopacddddddehkllmmmmngoilmmmmmmmmmmmmnopacdcddZZ\n" ] } ], "source": [ "pb_seq = pbx.assign(dihedrals)\n", "print(pb_seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assign PB for several models of a single file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A single PDB file can contain several models. Then, we do not want to read only the first chain. Instead, we want to iterate over all the chains." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Read 10 chain(s) in /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 1 | chain A\n", " ZZbghiacfkbccdddddehiadddddddddddfklggcdddddddddddddehifbdcddddddddddfklopadddddfhpamlnopcddddddehjadddddehjacbddddddddfklmaccddddddfbgniaghiapaddddddfklnoambZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 2 | chain A\n", " ZZpcfblcffbccdddddeehjacdddddddddfklggcddddddddddddddfblghiadddddddddfklopadddddehpmmmnopcddddddeehiacdddfblopadcddddddfklpaccdddddfklmlmgcdehiaddddddfklmmgopZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 3 | chain A\n", " ZZmgghiafbbccdddddehjbdcdddddddddfklggcddddddddddddddfbfghpacddddddddfklopadddddehiaklmmmgcdddddeehiaddddfkbgciacdddddefklpaccddddddfkgojbdfehpaddddddfkbccfbgZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 4 | chain A\n", " ZZcghiacfkbacdddddfbhpacdddddddddfklmcfdddddddddddddehiacddddddddddddfknopadddddfkpamlnopaddddddehjaccdddfklnopacddddddfklmpccdddddddehiabghehiaddddddfklpccfkZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 5 | chain A\n", " ZZpaehiehkaccdddddehjbccdddddddddfklggcddddddddddddddfbhpadddddddddddfklopadddddehiamlmmpccdddddeehiadddddfbacddcddddddfklmaccddddddfbgghiafehiadddddddfklpacfZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 6 | chain A\n", " ZZmghbacfkbccdddddeehpacdddddddddfklggcdddddddddddddehiacadddddddddddfklopadddddehiaklnopcddddddeehiadddehjlnopacddddddfklmaccddddehiaehbgcdehiadddddddfehjlpcZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 7 | chain A\n", " ZZcchbacfkbccdddddfehpacdddddddddfklggcdddddddddddddddehjapadddddddddfknopadddddfklmmmnopcddddddehjiddddddfknopacddddddfklpaccdddddfklmaacdfehpadddddehjblckknZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 8 | chain A\n", " ZZcehjdeehiacdjdddedjbdcdddddddddfklggcdddddddddddddddbfblbacddddddddfklopacddddehiamlnopaddddddehjacddddfehpaaccdddddefklpaccdddddfklmbfbehehiaddddddffkgoiehZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 9 | chain A\n", " ZZpccdjdfkbccdddddehhpacdddddddddfklggcdddddddddddddehiacbdcdddddddddfklopadddddehiammnopcddddddeejiadddehjlgobacddddddfklmpccddddehiacbcbdfehpadddddehjklmklmZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 10 | chain A\n", " ZZccfklcfkbccdddddehjbdcdddddddddfklggcdddddddddddddehiapaccdddddddddfklopadddddehjamlnopaddddddehjddcdddfbfghpacddddddfklpaccddddddfbcfbacfehpadddddddekpghiaZZ\n" ] } ], "source": [ "pdb_path = os.path.join(pbx.DEMO_DATA_PATH, '2LFU.pdb')\n", "for chain_name, chain in pbx.chains_from_files([pdb_path]):\n", " dihedrals = chain.get_phi_psi_angles()\n", " pb_seq = pbx.assign(dihedrals)\n", " print('* {}'.format(chain_name))\n", " print(' {}'.format(pb_seq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assign PB for a set of structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The :func:`pbxplore.chains_from_files` function can also handle several chains from several files." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The following files will be used:\n", "['/Users/jon/dev/PBtest/pbxplore/demo/1BTA.pdb',\n", " '/Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb',\n", " '/Users/jon/dev/PBtest/pbxplore/demo/3ICH.pdb']\n", "Read 1 chain(s) in /Users/jon/dev/PBtest/pbxplore/demo/1BTA.pdb\n", "* /Users/jon/dev/PBtest/pbxplore/demo/1BTA.pdb | chain A\n", " ZZdddfklonbfklmmmmmmmmnopafklnoiaklmmmmmnoopacddddddehkllmmmmngoilmmmmmmmmmmmmnopacdcddZZ\n", "Read 10 chain(s) in /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 1 | chain A\n", " ZZbghiacfkbccdddddehiadddddddddddfklggcdddddddddddddehifbdcddddddddddfklopadddddfhpamlnopcddddddehjadddddehjacbddddddddfklmaccddddddfbgniaghiapaddddddfklnoambZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 2 | chain A\n", " ZZpcfblcffbccdddddeehjacdddddddddfklggcddddddddddddddfblghiadddddddddfklopadddddehpmmmnopcddddddeehiacdddfblopadcddddddfklpaccdddddfklmlmgcdehiaddddddfklmmgopZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 3 | chain A\n", " ZZmgghiafbbccdddddehjbdcdddddddddfklggcddddddddddddddfbfghpacddddddddfklopadddddehiaklmmmgcdddddeehiaddddfkbgciacdddddefklpaccddddddfkgojbdfehpaddddddfkbccfbgZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 4 | chain A\n", " ZZcghiacfkbacdddddfbhpacdddddddddfklmcfdddddddddddddehiacddddddddddddfknopadddddfkpamlnopaddddddehjaccdddfklnopacddddddfklmpccdddddddehiabghehiaddddddfklpccfkZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 5 | chain A\n", " ZZpaehiehkaccdddddehjbccdddddddddfklggcddddddddddddddfbhpadddddddddddfklopadddddehiamlmmpccdddddeehiadddddfbacddcddddddfklmaccddddddfbgghiafehiadddddddfklpacfZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 6 | chain A\n", " ZZmghbacfkbccdddddeehpacdddddddddfklggcdddddddddddddehiacadddddddddddfklopadddddehiaklnopcddddddeehiadddehjlnopacddddddfklmaccddddehiaehbgcdehiadddddddfehjlpcZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 7 | chain A\n", " ZZcchbacfkbccdddddfehpacdddddddddfklggcdddddddddddddddehjapadddddddddfknopadddddfklmmmnopcddddddehjiddddddfknopacddddddfklpaccdddddfklmaacdfehpadddddehjblckknZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 8 | chain A\n", " ZZcehjdeehiacdjdddedjbdcdddddddddfklggcdddddddddddddddbfblbacddddddddfklopacddddehiamlnopaddddddehjacddddfehpaaccdddddefklpaccdddddfklmbfbehehiaddddddffkgoiehZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 9 | chain A\n", " ZZpccdjdfkbccdddddehhpacdddddddddfklggcdddddddddddddehiacbdcdddddddddfklopadddddehiammnopcddddddeejiadddehjlgobacddddddfklmpccddddehiacbcbdfehpadddddehjklmklmZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/2LFU.pdb | model 10 | chain A\n", " ZZccfklcfkbccdddddehjbdcdddddddddfklggcdddddddddddddehiapaccdddddddddfklopadddddehjamlnopaddddddehjddcdddfbfghpacddddddfklpaccddddddfbcfbacfehpadddddddekpghiaZZ\n", "Read 1 chain(s) in /Users/jon/dev/PBtest/pbxplore/demo/3ICH.pdb\n", "* /Users/jon/dev/PBtest/pbxplore/demo/3ICH.pdb | chain A\n", " ZZccdfbdcdddddehjbdebjcdddddfklmmmlmmmmmmmmnopnopajeopacfbdcehibacehiamnonopgocdfkbjbdcdfblmbccfbghiacdddebehiafkbccddfbdcfklgokaccfbdcfbhklmmmmmmmpccdfkopafbacddfbgcddddfbacddddZZ\n" ] } ], "source": [ "import glob\n", "files = [os.path.join(pbx.DEMO_DATA_PATH, pdb_name)\n", " for pdb_name in ('1BTA.pdb', '2LFU.pdb', '3ICH.pdb')]\n", "print('The following files will be used:')\n", "pprint(files)\n", "for chain_name, chain in pbx.chains_from_files(files):\n", " dihedrals = chain.get_phi_psi_angles()\n", " pb_seq = pbx.assign(dihedrals)\n", " print('* {}'.format(chain_name))\n", " print(' {}'.format(pb_seq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assign PB for frames in a trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PB sequences can be assigned from a trajectory. To do so, we use the :func:`pbxplore.chains_from_trajectory` function that takes the path to a trajectory and the path to the corresponding topology as argument. Any file formats readable by MDAnalysis can be used. Except for its arguments, :func:`pbxplore.chains_from_trajectory` works the same as :func:`pbxplore.chains_from_files`.\n", "\n", "** Note that MDAnalysis is required to use this feature. **" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 0\n", " ZZdddfklpmbfklmmmmmmmmnopafklgoiaklmmmmmmmmpacddddddehklmmmmmoghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 1\n", " ZZdddfklpcbfklmmmmmmmmnopafkbghiaklmmmmmmmmpccddddddehklmmmmmcehilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 2\n", " ZZdddfklpcbfklmmmmmmmmnopafklgoiaklmmmmmmmmpccddddddehklmmmmnpghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 3\n", " ZZdddfklccbfklmmmmmmmmnopafkbghiaklmmmmmnopaccddddddehkllmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 4\n", " ZZdddfklpmbfklmmmmmmmmnopafklgoiaklmmmmmnopaccddddddehklmmmmmpghjllmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 5\n", " ZZdddfklpmbfklmmmmmmmmnopafkbgoiaklmmmmmnopbacddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 6\n", " ZZdddfklpmblmlmmmmmmmmnopafkbghiaklmmmmmnopbacddddddehjllmmmnoghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 7\n", " ZZdddfklcfblmlmmmmmmmmnopafkbgoiaklmmmmmmmmcacddddddehklmmmmnpghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 8\n", " ZZdddfklpgbfklmmmmmmmmnopafklgoiaklmmmmmnojaccddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 9\n", " ZZdddfklpcbfklmmmmmmmmnopafklgoiaklmmmmmmombacddddddehkllmmmnbghilkmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 10\n", " ZZdddfklmmblklmmmmmmmmnopafkbgoiaklmmmmmmmmppcddddddehkllmmmmbghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 11\n", " ZZddefklpcbfklmmmmmmmmnopafklghiaklmmmmmnopbacddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 12\n", " ZZdddfklcfbfklmmmmmmmmnopafklgoiaklmmmmmmmmpacddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 13\n", " ZZdddfklpmbfklmmmmmmmmnopafklgoiaklmmmmmnopbacddddddehklmmmmmpghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 14\n", " ZZdddfklpmbfklmmmmmmmmnopafklghiaklmmmmmmmmpccddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* 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ZZdddfklcfbfklmmmmmmmmnopafklgoiaklmmmmmmmmpacddddddehkllmmmmmghijklmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 21\n", " ZZdddfklpcbfklmmmmmmmmnopafklghiaklmmmmmmmmpccddddddehklmmmmmpghijklmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 22\n", " ZZdddfklpmbfklmmmmmmmmnopafkbghiaklmmmmmmoopacddddddehklmmmmmmghijmlmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 23\n", " ZZdddfklpfblmlmmmmmmmmnopafkbgoiaklmmmmmmombacddddddehklmmmmmmghijmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 24\n", " ZZdddfklpmbfklmmmmmmmmnopafkbccdfklmmmmmnopaccddddddehklmmmmmbghijklmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 25\n", " ZZdddfklpgbfklmmmmmmmmnopafkbccdfklmmmmmmmmpacddddddehklmmmmmpghklmmmmmmmmmmmmnopadddddZZ\n", "* 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ZZdddfklpmbfklmmmmmmmmnopafklccdfklmmmmmnopaacddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 32\n", " ZZdddfklcfbfklmmmmmmmmnopafkbcbdfklmmmmmmmmpacddddddehklmmmmmpghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 33\n", " ZZdddfklpcbfmlmmmmmmmmnopafkbckbfklmmmmmmmmmccddddddehjlmmmmmoghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 34\n", " ZZdddfklcfbfklmmmmmmmmnopafklccdfklmmmmmmmmpccddddddehklmmmmmpghilmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 35\n", " ZZdddfklcfbfklmmmmmmmmnopafkbccdfklmmmmmmmmppcddddddehklmmmmmpghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 36\n", " ZZdddfklpgbjklmmmmmmmmnopafkbccbfklmmmmmmmmpacddddddehklmmmmmoghjlmmmmmmmmmmmmnopacddddZZ\n", "* 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ZZdddfklpmblmlmmmmmmmmnopafkbccdfklmmmmmmgoiacddddddehklmmmmmmgoklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 43\n", " ZZdddfklpcbfklmmmmmmmmnopafklccdfklmmmmmmmmpacddddddehklmmmmmmghklmmmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 44\n", " ZZdddfklcfbfklmmmmmmmmnopafkbccdfklmmmmmmmmgccddddddehjllmmmmmghiaklmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 45\n", " ZZdddfklcfbfmlmmmmmmmmnopafkbccbfklmmmmmmmmcccddddddehklmmmmmpghiamlmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 46\n", " ZZdddfkbcfbfklmmmmmmmmnopafkbccdfklmmmmmmmmpacddddddehkllmmmmpghijmlmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 47\n", " ZZdddfkbcfbfklmmmmmmmmnopafkbccdfklmmmmmmmmpccddddddehklmmmmmpghijklmmmmmmmmmmnopadddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 48\n", " ZZdddfkbcfbfklmmmmmmmmnopafkbccdfklmmmmmmomcacddddddehkllmmmmpghilmlmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 49\n", " ZZdddffbcfbfklmmmmmmmmnopafkbccbfklmmmmmmnmpacddddddehklmmmmmpghijklmmmmmmmmmmnopacddddZZ\n", "* /Users/jon/dev/PBtest/pbxplore/demo/barstar_md_traj.xtc | frame 50\n", " ZZdddfklgfbfklmmmmmmmmnopafklccdfklmmmmmmmmmccddddddehklmmmmmpghijmmmmmmmmmmmmnopacddddZZ\n" ] } ], "source": [ "trajectory = os.path.join(pbx.DEMO_DATA_PATH, 'barstar_md_traj.xtc')\n", "topology = os.path.join(pbx.DEMO_DATA_PATH, 'barstar_md_traj.gro')\n", "for chain_name, chain in pbx.chains_from_trajectory(trajectory, topology):\n", " dihedrals = chain.get_phi_psi_angles()\n", " pb_seq = pbx.assign(dihedrals)\n", " print('* {}'.format(chain_name))\n", " print(' {}'.format(pb_seq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use a different structure parser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Providing the dihedral angles can be formated as expected by :func:`pbxplore.assign`, the source of these angles does not matter. For instance, other PDB parser can be used with PBxplore." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BioPython" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<Model id=0> <Chain id=A>\n", "ZZbghiacfkbccdddddehiadddddddddddfklggcdddddddddddddehifbdcddddddddddfklopadddddfhpamlnopcddddddehjadddddehjacbddddddddfklmaccddddddfbgniaghiapaddddddfklnoambZZ\n", "<Model id=1> <Chain id=A>\n", "ZZpcfblcffbccdddddeehjacdddddddddfklggcddddddddddddddfblghiadddddddddfklopadddddehpmmmnopcddddddeehiacdddfblopadcddddddfklpaccdddddfklmlmgcdehiaddddddfklmmgopZZ\n", "<Model id=2> <Chain id=A>\n", "ZZmgghiafbbccdddddehjbdcdddddddddfklggcddddddddddddddfbfghpacddddddddfklopadddddehiaklmmmgcdddddeehiaddddfkbgciacdddddefklpaccddddddfkgojbdfehpaddddddfkbccfbgZZ\n", "<Model id=3> <Chain id=A>\n", "ZZcghiacfkbacdddddfbhpacdddddddddfklmcfdddddddddddddehiacddddddddddddfknopadddddfkpamlnopaddddddehjaccdddfklnopacddddddfklmpccdddddddehiabghehiaddddddfklpccfkZZ\n", "<Model id=4> <Chain id=A>\n", "ZZpaehiehkaccdddddehjbccdddddddddfklggcddddddddddddddfbhpadddddddddddfklopadddddehiamlmmpccdddddeehiadddddfbacddcddddddfklmaccddddddfbgghiafehiadddddddfklpacfZZ\n", "<Model id=5> <Chain id=A>\n", "ZZmghbacfkbccdddddeehpacdddddddddfklggcdddddddddddddehiacadddddddddddfklopadddddehiaklnopcddddddeehiadddehjlnopacddddddfklmaccddddehiaehbgcdehiadddddddfehjlpcZZ\n", "<Model id=6> <Chain id=A>\n", "ZZcchbacfkbccdddddfehpacdddddddddfklggcdddddddddddddddehjapadddddddddfknopadddddfklmmmnopcddddddehjiddddddfknopacddddddfklpaccdddddfklmaacdfehpadddddehjblckknZZ\n", "<Model id=7> <Chain id=A>\n", "ZZcehjdeehiacdjdddedjbdcdddddddddfklggcdddddddddddddddbfblbacddddddddfklopacddddehiamlnopaddddddehjacddddfehpaaccdddddefklpaccdddddfklmbfbehehiaddddddffkgoiehZZ\n", "<Model id=8> <Chain id=A>\n", "ZZpccdjdfkbccdddddehhpacdddddddddfklggcdddddddddddddehiacbdcdddddddddfklopadddddehiammnopcddddddeejiadddehjlgobacddddddfklmpccddddehiacbcbdfehpadddddehjklmklmZZ\n", "<Model id=9> <Chain id=A>\n", "ZZccfklcfkbccdddddehjbdcdddddddddfklggcdddddddddddddehiapaccdddddddddfklopadddddehjamlnopaddddddehjddcdddfbfghpacddddddfklpaccddddddfbcfbacfehpadddddddekpghiaZZ\n" ] } ], "source": [ "import Bio.PDB\n", "import math\n", "\n", "pdb_path = os.path.join(pbx.DEMO_DATA_PATH, \"2LFU.pdb\")\n", "for model in Bio.PDB.PDBParser().get_structure(\"2LFU\", pdb_path):\n", " for chain in model:\n", " polypeptides = Bio.PDB.PPBuilder().build_peptides(chain)\n", " for poly_index, poly in enumerate(polypeptides):\n", " dihedral_list = poly.get_phi_psi_list()\n", " dihedrals = {}\n", " for resid, (phi, psi) in enumerate(dihedral_list, start=1):\n", " if not phi is None:\n", " phi = 180 * phi / math.pi\n", " if not psi is None:\n", " psi = 180 * psi / math.pi\n", " dihedrals[resid] = {'phi': phi, 'psi': psi}\n", " print(model, chain)\n", " pb_seq = pbx.assign(dihedrals)\n", " print(pb_seq)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

julianogalgaro/udacity

nd101/p2-image-classification/dlnd_image_classification.ipynb

1

116786

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Image Classification\n", "In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.\n", "## Get the Data\n", "Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All files found!\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import problem_unittests as tests\n", "import tarfile\n", "\n", "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n", "\n", "# Use Floyd's cifar-10 dataset if present\n", "floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n", "if isfile(floyd_cifar10_location):\n", " tar_gz_path = floyd_cifar10_location\n", "else:\n", " tar_gz_path = 'cifar-10-python.tar.gz'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(tar_gz_path):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n", " urlretrieve(\n", " 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n", " tar_gz_path,\n", " pbar.hook)\n", "\n", "if not isdir(cifar10_dataset_folder_path):\n", " with tarfile.open(tar_gz_path) as tar:\n", " tar.extractall()\n", " tar.close()\n", "\n", "\n", "tests.test_folder_path(cifar10_dataset_folder_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Explore the Data\n", "The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n", "* airplane\n", "* automobile\n", "* bird\n", "* cat\n", "* deer\n", "* dog\n", "* frog\n", "* horse\n", "* ship\n", "* truck\n", "\n", "Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n", "\n", "Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\". Answers to questions like these will help you preprocess the data and end up with better predictions." ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Stats of batch 1:\n", "Samples: 10000\n", "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n", "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n", "\n", "Example of Image 890:\n", "Image - Min Value: 1 Max Value: 255\n", "Image - Shape: (32, 32, 3)\n", "Label - Label Id: 9 Name: truck\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f571a673b38>" ] }, "metadata": { "image/png": { "height": 250, "width": 253 } }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import helper\n", "import numpy as np\n", "\n", "# Explore the dataset\n", "batch_id = 1\n", "sample_id = 890\n", "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Preprocess Functions\n", "### Normalize\n", "In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as `x`." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def normalize(x):\n", " \"\"\"\n", " Normalize a list of sample image data in the range of 0 to 1\n", " : x: List of image data. The image shape is (32, 32, 3)\n", " : return: Numpy array of normalize data\n", " \"\"\"\n", " # TODO: Implement Function\n", " return x / 255\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_normalize(normalize)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### One-hot encode\n", "Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.\n", "\n", "Hint: Don't reinvent the wheel." ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "labels_one_hot=np.zeros(10)\n", "def one_hot_encode(x):\n", " \"\"\"\n", " One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n", " : x: List of sample Labels\n", " : return: Numpy array of one-hot encoded labels\n", " \"\"\"\n", " \n", " # TODO: Implement Function\n", " result=[]\n", " for i in x:\n", " temp=labels_one_hot*0\n", " temp[i]=1\n", " result.append(temp)\n", " return np.array(result)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_one_hot_encode(one_hot_encode)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Randomize Data\n", "As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation." ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import pickle\n", "import problem_unittests as tests\n", "import helper\n", "\n", "# Load the Preprocessed Validation data\n", "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build the network\n", "For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.\n", "\n", ">**Note:** If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages to build each layer, except the layers you build in the \"Convolutional and Max Pooling Layer\" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n", "\n", ">However, if you would like to get the most out of this course, try to solve all the problems _without_ using anything from the TF Layers packages. You **can** still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the `conv2d` class, [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d), you would want to use the TF Neural Network version of `conv2d`, [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d). \n", "\n", "Let's begin!\n", "\n", "### Input\n", "The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions\n", "* Implement `neural_net_image_input`\n", " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n", " * Set the shape using `image_shape` with batch size set to `None`.\n", " * Name the TensorFlow placeholder \"x\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n", "* Implement `neural_net_label_input`\n", " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n", " * Set the shape using `n_classes` with batch size set to `None`.\n", " * Name the TensorFlow placeholder \"y\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n", "* Implement `neural_net_keep_prob_input`\n", " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) for dropout keep probability.\n", " * Name the TensorFlow placeholder \"keep_prob\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n", "\n", "These names will be used at the end of the project to load your saved model.\n", "\n", "Note: `None` for shapes in TensorFlow allow for a dynamic size." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Image Input Tests Passed.\n", "Label Input Tests Passed.\n", "Keep Prob Tests Passed.\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "def neural_net_image_input(image_shape):\n", " \"\"\"\n", " Return a Tensor for a batch of image input\n", " : image_shape: Shape of the images\n", " : return: Tensor for image input.\n", " \"\"\"\n", " # TODO: Implement Function\n", " x = tf.placeholder(tf.float32, shape=(None, image_shape[0], image_shape[1],image_shape[2]), name=\"x\")\n", " return x\n", "\n", "\n", "def neural_net_label_input(n_classes):\n", " \"\"\"\n", " Return a Tensor for a batch of label input\n", " : n_classes: Number of classes\n", " : return: Tensor for label input.\n", " \"\"\"\n", " # TODO: Implement Function\n", " y = tf.placeholder(tf.float32, shape=(None, n_classes), name=\"y\")\n", " return y\n", "\n", "\n", "def neural_net_keep_prob_input():\n", " \"\"\"\n", " Return a Tensor for keep probability\n", " : return: Tensor for keep probability.\n", " \"\"\"\n", " # TODO: Implement Function\n", " return tf.placeholder(tf.float32, name=\"keep_prob\")\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tf.reset_default_graph()\n", "tests.test_nn_image_inputs(neural_net_image_input)\n", "tests.test_nn_label_inputs(neural_net_label_input)\n", "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Convolution and Max Pooling Layer\n", "Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:\n", "* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.\n", "* Apply a convolution to `x_tensor` using weight and `conv_strides`.\n", " * We recommend you use same padding, but you're welcome to use any padding.\n", "* Add bias\n", "* Add a nonlinear activation to the convolution.\n", "* Apply Max Pooling using `pool_ksize` and `pool_strides`.\n", " * We recommend you use same padding, but you're welcome to use any padding.\n", "\n", "**Note:** You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for **this** layer, but you can still use TensorFlow's [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) package. You may still use the shortcut option for all the **other** layers." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n", " \"\"\"\n", " Apply convolution then max pooling to x_tensor\n", " :param x_tensor: TensorFlow Tensor\n", " :param conv_num_outputs: Number of outputs for the convolutional layer\n", " :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n", " :param conv_strides: Stride 2-D Tuple for convolution\n", " :param pool_ksize: kernal size 2-D Tuple for pool\n", " :param pool_strides: Stride 2-D Tuple for pool\n", " : return: A tensor that represents convolution and max pooling of x_tensor\n", " \"\"\"\n", " # TODO: Implement Function\n", " i_width=x_tensor.shape[1].value\n", " i_height=x_tensor.shape[2].value\n", " i_depth=x_tensor.shape[3].value\n", "\n", " f_w=tf.Variable(tf.truncated_normal([conv_ksize[0],conv_ksize[1],i_depth, conv_num_outputs],stddev=1/np.sqrt(conv_ksize[0]*conv_ksize[1]*i_depth)))\n", " f_b=tf.zeros(conv_num_outputs)\n", " \n", " padding='SAME'\n", " conv = tf.nn.conv2d(x_tensor, f_w, strides=[1,conv_strides[0],conv_strides[1],1], padding=padding)\n", " conv = tf.nn.bias_add(conv, f_b)\n", " conv = tf.nn.relu(conv)\n", " \n", " return tf.nn.max_pool(conv, ksize=[1,pool_ksize[0],pool_ksize[1],1], strides=[1,pool_strides[0],pool_strides[1],1], padding=padding)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_con_pool(conv2d_maxpool)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Flatten Layer\n", "Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def flatten(x_tensor):\n", " \"\"\"\n", " Flatten x_tensor to (Batch Size, Flattened Image Size)\n", " : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n", " : return: A tensor of size (Batch Size, Flattened Image Size).\n", " \"\"\"\n", " # TODO: Implement Function\n", " new_dim=1\n", " for i in x_tensor.shape[1:]:\n", " new_dim*=i.value\n", "\n", " return tf.reshape(x_tensor,[-1,new_dim])\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_flatten(flatten)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Fully-Connected Layer\n", "Implement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages." ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def fully_conn(x_tensor, num_outputs):\n", " \"\"\"\n", " Apply a fully connected layer to x_tensor using weight and bias\n", " : x_tensor: A 2-D tensor where the first dimension is batch size.\n", " : num_outputs: The number of output that the new tensor should be.\n", " : return: A 2-D tensor where the second dimension is num_outputs.\n", " \"\"\"\n", " # TODO: Implement Function\n", " n=x_tensor.shape[1].value\n", "\n", " w=tf.Variable(tf.truncated_normal([n, num_outputs],stddev=(1/np.sqrt(n))))\n", " b=tf.zeros(num_outputs)\n", " \n", " return tf.nn.relu(tf.add(tf.matmul(x_tensor,w),b))\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_fully_conn(fully_conn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Output Layer\n", "Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.\n", "\n", "**Note:** Activation, softmax, or cross entropy should **not** be applied to this." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def output(x_tensor, num_outputs):\n", " \"\"\"\n", " Apply a output layer to x_tensor using weight and bias\n", " : x_tensor: A 2-D tensor where the first dimension is batch size.\n", " : num_outputs: The number of output that the new tensor should be.\n", " : return: A 2-D tensor where the second dimension is num_outputs.\n", " \"\"\"\n", " # TODO: Implement Function\n", " n=x_tensor.shape[1].value\n", " w=tf.Variable(tf.truncated_normal([n, num_outputs],stddev=(1/np.sqrt(n))))\n", " b=tf.zeros(num_outputs)\n", " \n", " return tf.add(tf.matmul(x_tensor,w),b)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_output(output)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Create Convolutional Model\n", "Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:\n", "\n", "* Apply 1, 2, or 3 Convolution and Max Pool layers\n", "* Apply a Flatten Layer\n", "* Apply 1, 2, or 3 Fully Connected Layers\n", "* Apply an Output Layer\n", "* Return the output\n", "* Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`. " ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Neural Network Built!\n" ] } ], "source": [ "def conv_net(x, keep_prob):\n", " \"\"\"\n", " Create a convolutional neural network model\n", " : x: Placeholder tensor that holds image data.\n", " : keep_prob: Placeholder tensor that hold dropout keep probability.\n", " : return: Tensor that represents logits\n", " \"\"\"\n", " # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n", " # Play around with different number of outputs, kernel size and stride\n", " # Function Definition from Above:\n", " \n", " out=conv2d_maxpool(x, conv_num_outputs=64, conv_ksize=[9,9], conv_strides=[2,2], pool_ksize=[6,6], pool_strides=[2,2])\n", " out=conv2d_maxpool(out, conv_num_outputs=128, conv_ksize=[7,7], conv_strides=[2,2], pool_ksize=[4,4], pool_strides=[3,3])\n", " out=conv2d_maxpool(out, conv_num_outputs=512, conv_ksize=[4,4], conv_strides=[2,2], pool_ksize=[3,3], pool_strides=[2,2])\n", " \n", " \n", " \n", " \n", " # TODO: Apply a Flatten Layer\n", " # Function Definition from Above:\n", " out=flatten(out)\n", " \n", " # TODO: Apply 1, 2, or 3 Fully Connected Layers\n", " # Play around with different number of outputs\n", " # Function Definition from Above:\n", " # fully_conn(x_tensor, num_outputs)\n", " out=tf.nn.dropout(out, keep_prob=keep_prob)\n", " out=fully_conn(out, num_outputs=128)\n", "\n", " \n", " \n", " # TODO: Apply an Output Layer\n", " # Set this to the number of classes\n", " # Function Definition from Above:\n", " # output(x_tensor, num_outputs)\n", " out=output(out, num_outputs=10)\n", " \n", " # TODO: return output\n", " return out\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "\n", "##############################\n", "## Build the Neural Network ##\n", "##############################\n", "\n", "# Remove previous weights, bias, inputs, etc..\n", "tf.reset_default_graph()\n", "\n", "# Inputs\n", "x = neural_net_image_input((32, 32, 3))\n", "y = neural_net_label_input(10)\n", "keep_prob = neural_net_keep_prob_input()\n", "\n", "# Model\n", "logits = conv_net(x, keep_prob)\n", "\n", "# Name logits Tensor, so that is can be loaded from disk after training\n", "logits = tf.identity(logits, name='logits')\n", "\n", "# Loss and Optimizer\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n", "optimizer = tf.train.AdamOptimizer().minimize(cost)\n", "\n", "# Accuracy\n", "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n", "\n", "tests.test_conv_net(conv_net)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train the Neural Network\n", "### Single Optimization\n", "Implement the function `train_neural_network` to do a single optimization. The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:\n", "* `x` for image input\n", "* `y` for labels\n", "* `keep_prob` for keep probability for dropout\n", "\n", "This function will be called for each batch, so `tf.global_variables_initializer()` has already been called.\n", "\n", "Note: Nothing needs to be returned. This function is only optimizing the neural network." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n", " \"\"\"\n", " Optimize the session on a batch of images and labels\n", " : session: Current TensorFlow session\n", " : optimizer: TensorFlow optimizer function\n", " : keep_probability: keep probability\n", " : feature_batch: Batch of Numpy image data\n", " : label_batch: Batch of Numpy label data\n", " \"\"\"\n", " # TODO: Implement Function\n", " session.run(optimizer, feed_dict={\n", " x: feature_batch,\n", " y: label_batch,\n", " keep_prob: keep_probability})\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_train_nn(train_neural_network)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Show Stats\n", "Implement the function `print_stats` to print loss and validation accuracy. Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy. Use a keep probability of `1.0` to calculate the loss and validation accuracy." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n", " \"\"\"\n", " Print information about loss and validation accuracy\n", " : session: Current TensorFlow session\n", " : feature_batch: Batch of Numpy image data\n", " : label_batch: Batch of Numpy label data\n", " : cost: TensorFlow cost function\n", " : accuracy: TensorFlow accuracy function\n", " \"\"\"\n", " # TODO: Implement Function\n", " loss = session.run(cost, feed_dict={\n", " x: feature_batch,\n", " y: label_batch,\n", " keep_prob: 1.})\n", " valid_acc = session.run(accuracy, feed_dict={\n", " x: valid_features,\n", " y: valid_labels,\n", " keep_prob: 1.})\n", "\n", " print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(\n", " loss,\n", " valid_acc))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Hyperparameters\n", "Tune the following parameters:\n", "* Set `epochs` to the number of iterations until the network stops learning or start overfitting\n", "* Set `batch_size` to the highest number that your machine has memory for. Most people set them to common sizes of memory:\n", " * 64\n", " * 128\n", " * 256\n", " * ...\n", "* Set `keep_probability` to the probability of keeping a node using dropout" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# TODO: Tune Parameters\n", "epochs = 15\n", "batch_size = 256\n", "keep_probability = 0.7" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Train on a Single CIFAR-10 Batch\n", "Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section." ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking the Training on a Single Batch...\n", "Epoch 1, CIFAR-10 Batch 1: Loss: 2.0416 Validation Accuracy: 0.290400\n", "Epoch 2, CIFAR-10 Batch 1: Loss: 1.7830 Validation Accuracy: 0.363600\n", "Epoch 3, CIFAR-10 Batch 1: Loss: 1.5417 Validation Accuracy: 0.426600\n", "Epoch 4, CIFAR-10 Batch 1: Loss: 1.3733 Validation Accuracy: 0.440400\n", "Epoch 5, CIFAR-10 Batch 1: Loss: 1.2697 Validation Accuracy: 0.463800\n", "Epoch 6, CIFAR-10 Batch 1: Loss: 1.1328 Validation Accuracy: 0.484600\n", "Epoch 7, CIFAR-10 Batch 1: Loss: 0.9780 Validation Accuracy: 0.499200\n", "Epoch 8, CIFAR-10 Batch 1: Loss: 0.8970 Validation Accuracy: 0.476600\n", "Epoch 9, CIFAR-10 Batch 1: Loss: 0.7501 Validation Accuracy: 0.516000\n", "Epoch 10, CIFAR-10 Batch 1: Loss: 0.6390 Validation Accuracy: 0.532000\n", "Epoch 11, CIFAR-10 Batch 1: Loss: 0.5710 Validation Accuracy: 0.538800\n", "Epoch 12, CIFAR-10 Batch 1: Loss: 0.4704 Validation Accuracy: 0.536000\n", "Epoch 13, CIFAR-10 Batch 1: Loss: 0.4276 Validation Accuracy: 0.535600\n", "Epoch 14, CIFAR-10 Batch 1: Loss: 0.3966 Validation Accuracy: 0.547200\n", "Epoch 15, CIFAR-10 Batch 1: Loss: 0.3306 Validation Accuracy: 0.560600\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "print('Checking the Training on a Single Batch...')\n", "with tf.Session() as sess:\n", " # Initializing the variables\n", " sess.run(tf.global_variables_initializer())\n", " \n", " # Training cycle\n", " for epoch in range(epochs):\n", " batch_i = 1\n", " for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n", " train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n", " print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n", " print_stats(sess, batch_features, batch_labels, cost, accuracy)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Fully Train the Model\n", "Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches." ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training...\n", "Epoch 1, CIFAR-10 Batch 1: Loss: 2.1114 Validation Accuracy: 0.255400\n", "Epoch 1, CIFAR-10 Batch 2: Loss: 1.6621 Validation Accuracy: 0.318800\n", "Epoch 1, CIFAR-10 Batch 3: Loss: 1.4704 Validation Accuracy: 0.403000\n", "Epoch 1, CIFAR-10 Batch 4: Loss: 1.5213 Validation Accuracy: 0.431600\n", "Epoch 1, CIFAR-10 Batch 5: Loss: 1.4146 Validation Accuracy: 0.478400\n", "Epoch 2, CIFAR-10 Batch 1: Loss: 1.5334 Validation Accuracy: 0.477400\n", "Epoch 2, CIFAR-10 Batch 2: Loss: 1.3400 Validation Accuracy: 0.452200\n", "Epoch 2, CIFAR-10 Batch 3: Loss: 1.0502 Validation Accuracy: 0.495600\n", "Epoch 2, CIFAR-10 Batch 4: Loss: 1.1369 Validation Accuracy: 0.518800\n", "Epoch 2, CIFAR-10 Batch 5: Loss: 1.1763 Validation Accuracy: 0.525200\n", "Epoch 3, CIFAR-10 Batch 1: Loss: 1.2072 Validation Accuracy: 0.544000\n", "Epoch 3, CIFAR-10 Batch 2: Loss: 0.9699 Validation Accuracy: 0.515600\n", "Epoch 3, CIFAR-10 Batch 3: Loss: 0.7951 Validation Accuracy: 0.525400\n", "Epoch 3, CIFAR-10 Batch 4: Loss: 0.9368 Validation Accuracy: 0.583200\n", "Epoch 3, CIFAR-10 Batch 5: Loss: 0.9819 Validation Accuracy: 0.558000\n", "Epoch 4, CIFAR-10 Batch 1: Loss: 0.9912 Validation Accuracy: 0.577000\n", "Epoch 4, CIFAR-10 Batch 2: Loss: 0.7726 Validation Accuracy: 0.584800\n", "Epoch 4, CIFAR-10 Batch 3: Loss: 0.6478 Validation Accuracy: 0.551400\n", "Epoch 4, CIFAR-10 Batch 4: Loss: 0.7553 Validation Accuracy: 0.604000\n", "Epoch 4, CIFAR-10 Batch 5: Loss: 0.7292 Validation Accuracy: 0.607800\n", "Epoch 5, CIFAR-10 Batch 1: Loss: 0.8663 Validation Accuracy: 0.566200\n", "Epoch 5, CIFAR-10 Batch 2: Loss: 0.6551 Validation Accuracy: 0.590200\n", "Epoch 5, CIFAR-10 Batch 3: Loss: 0.5248 Validation Accuracy: 0.584400\n", "Epoch 5, CIFAR-10 Batch 4: Loss: 0.6010 Validation Accuracy: 0.622200\n", "Epoch 5, CIFAR-10 Batch 5: Loss: 0.5684 Validation Accuracy: 0.617800\n", "Epoch 6, CIFAR-10 Batch 1: Loss: 0.6451 Validation Accuracy: 0.590400\n", "Epoch 6, CIFAR-10 Batch 2: Loss: 0.5265 Validation Accuracy: 0.610400\n", "Epoch 6, CIFAR-10 Batch 3: Loss: 0.4242 Validation Accuracy: 0.607200\n", "Epoch 6, CIFAR-10 Batch 4: Loss: 0.5402 Validation Accuracy: 0.610000\n", "Epoch 6, CIFAR-10 Batch 5: Loss: 0.4996 Validation Accuracy: 0.619000\n", "Epoch 7, CIFAR-10 Batch 1: Loss: 0.5113 Validation Accuracy: 0.627600\n", "Epoch 7, CIFAR-10 Batch 2: Loss: 0.3948 Validation Accuracy: 0.647200\n", "Epoch 7, CIFAR-10 Batch 3: Loss: 0.3454 Validation Accuracy: 0.629800\n", "Epoch 7, CIFAR-10 Batch 4: Loss: 0.4731 Validation Accuracy: 0.616400\n", "Epoch 7, CIFAR-10 Batch 5: Loss: 0.4121 Validation Accuracy: 0.625600\n", "Epoch 8, CIFAR-10 Batch 1: Loss: 0.4313 Validation Accuracy: 0.636000\n", "Epoch 8, CIFAR-10 Batch 2: Loss: 0.3741 Validation Accuracy: 0.641000\n", "Epoch 8, CIFAR-10 Batch 3: Loss: 0.2568 Validation Accuracy: 0.639800\n", "Epoch 8, CIFAR-10 Batch 4: Loss: 0.4255 Validation Accuracy: 0.601600\n", "Epoch 8, CIFAR-10 Batch 5: Loss: 0.3319 Validation Accuracy: 0.632000\n", "Epoch 9, CIFAR-10 Batch 1: Loss: 0.3885 Validation Accuracy: 0.647800\n", "Epoch 9, CIFAR-10 Batch 2: Loss: 0.3106 Validation Accuracy: 0.643400\n", "Epoch 9, CIFAR-10 Batch 3: Loss: 0.2768 Validation Accuracy: 0.619600\n", "Epoch 9, CIFAR-10 Batch 4: Loss: 0.3688 Validation Accuracy: 0.640800\n", "Epoch 9, CIFAR-10 Batch 5: Loss: 0.2523 Validation Accuracy: 0.633400\n", "Epoch 10, CIFAR-10 Batch 1: Loss: 0.3416 Validation Accuracy: 0.637400\n", "Epoch 10, CIFAR-10 Batch 2: Loss: 0.2748 Validation Accuracy: 0.628400\n", "Epoch 10, CIFAR-10 Batch 3: Loss: 0.1987 Validation Accuracy: 0.645600\n", "Epoch 10, CIFAR-10 Batch 4: Loss: 0.3145 Validation Accuracy: 0.638000\n", "Epoch 10, CIFAR-10 Batch 5: Loss: 0.2235 Validation Accuracy: 0.636600\n", "Epoch 11, CIFAR-10 Batch 1: Loss: 0.3202 Validation Accuracy: 0.648400\n", "Epoch 11, CIFAR-10 Batch 2: Loss: 0.2266 Validation Accuracy: 0.640400\n", "Epoch 11, CIFAR-10 Batch 3: Loss: 0.1674 Validation Accuracy: 0.632200\n", "Epoch 11, CIFAR-10 Batch 4: Loss: 0.2009 Validation Accuracy: 0.653600\n", "Epoch 11, CIFAR-10 Batch 5: Loss: 0.1658 Validation Accuracy: 0.655600\n", "Epoch 12, CIFAR-10 Batch 1: Loss: 0.2527 Validation Accuracy: 0.654400\n", "Epoch 12, CIFAR-10 Batch 2: Loss: 0.2024 Validation Accuracy: 0.643400\n", "Epoch 12, CIFAR-10 Batch 3: Loss: 0.2014 Validation Accuracy: 0.638400\n", "Epoch 12, CIFAR-10 Batch 4: Loss: 0.1871 Validation Accuracy: 0.655200\n", "Epoch 12, CIFAR-10 Batch 5: Loss: 0.1394 Validation Accuracy: 0.659400\n", "Epoch 13, CIFAR-10 Batch 1: Loss: 0.2246 Validation Accuracy: 0.638800\n", "Epoch 13, CIFAR-10 Batch 2: Loss: 0.1639 Validation Accuracy: 0.653000\n", "Epoch 13, CIFAR-10 Batch 3: Loss: 0.1412 Validation Accuracy: 0.638400\n", "Epoch 13, CIFAR-10 Batch 4: Loss: 0.1975 Validation Accuracy: 0.641600\n", "Epoch 13, CIFAR-10 Batch 5: Loss: 0.1466 Validation Accuracy: 0.670400\n", "Epoch 14, CIFAR-10 Batch 1: Loss: 0.1832 Validation Accuracy: 0.644600\n", "Epoch 14, CIFAR-10 Batch 2: Loss: 0.1153 Validation Accuracy: 0.646200\n", "Epoch 14, CIFAR-10 Batch 3: Loss: 0.1062 Validation Accuracy: 0.662400\n", "Epoch 14, CIFAR-10 Batch 4: Loss: 0.1244 Validation Accuracy: 0.663600\n", "Epoch 14, CIFAR-10 Batch 5: Loss: 0.1395 Validation Accuracy: 0.664600\n", "Epoch 15, CIFAR-10 Batch 1: Loss: 0.1659 Validation Accuracy: 0.643400\n", "Epoch 15, CIFAR-10 Batch 2: Loss: 0.0862 Validation Accuracy: 0.662200\n", "Epoch 15, CIFAR-10 Batch 3: Loss: 0.0849 Validation Accuracy: 0.665000\n", "Epoch 15, CIFAR-10 Batch 4: Loss: 0.1418 Validation Accuracy: 0.672200\n", "Epoch 15, CIFAR-10 Batch 5: Loss: 0.1960 Validation Accuracy: 0.652600\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_model_path = './image_classification'\n", "\n", "print('Training...')\n", "with tf.Session() as sess:\n", " # Initializing the variables\n", " sess.run(tf.global_variables_initializer())\n", " \n", " # Training cycle\n", " for epoch in range(epochs):\n", " # Loop over all batches\n", " n_batches = 5\n", " for batch_i in range(1, n_batches + 1):\n", " for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n", " train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n", " print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n", " print_stats(sess, batch_features, batch_labels, cost, accuracy)\n", " \n", " # Save Model\n", " saver = tf.train.Saver()\n", " save_path = saver.save(sess, save_model_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint\n", "The model has been saved to disk.\n", "## Test Model\n", "Test your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters." ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing Accuracy: 0.651953125\n", "\n" ] }, { "data": { "image/png": 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imGMRERERkUSdYxERERGRRJ1jEREREZFEnWMRERERkaRutBsgIjIRmdljwEyg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\ni1AnOcYWy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"text/plain": [ "<matplotlib.figure.Figure at 0x7f80b190c630>" ] }, "metadata": { "image/png": { "height": 319, "width": 355 } }, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import tensorflow as tf\n", "import pickle\n", "import helper\n", "import random\n", "\n", "# Set batch size if not already set\n", "try:\n", " if batch_size:\n", " pass\n", "except NameError:\n", " batch_size = 64\n", "\n", "save_model_path = './image_classification'\n", "n_samples = 4\n", "top_n_predictions = 3\n", "\n", "def test_model():\n", " \"\"\"\n", " Test the saved model against the test dataset\n", " \"\"\"\n", "\n", " test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n", " loaded_graph = tf.Graph()\n", "\n", " with tf.Session(graph=loaded_graph) as sess:\n", " # Load model\n", " loader = tf.train.import_meta_graph(save_model_path + '.meta')\n", " loader.restore(sess, save_model_path)\n", "\n", " # Get Tensors from loaded model\n", " loaded_x = loaded_graph.get_tensor_by_name('x:0')\n", " loaded_y = loaded_graph.get_tensor_by_name('y:0')\n", " loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", " loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n", " loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n", " \n", " # Get accuracy in batches for memory limitations\n", " test_batch_acc_total = 0\n", " test_batch_count = 0\n", " \n", " for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n", " test_batch_acc_total += sess.run(\n", " loaded_acc,\n", " feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n", " test_batch_count += 1\n", "\n", " print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n", "\n", " # Print Random Samples\n", " random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n", " random_test_predictions = sess.run(\n", " tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n", " feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n", " helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n", "\n", "\n", "test_model()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Why 50-80% Accuracy?\n", "You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores [well above 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130). That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_image_classification.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

duane-edgington/stoqs

stoqs/contrib/notebooks/create_timeline_plot.ipynb

4

132275

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Timeline Plot of Platforms in a STOQS Database\n", "*Walk through activities of the database and summarize platform time in water*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Executing this Notebook requires a personal STOQS server. Follow the [steps to build your own development system](https://github.com/stoqs/stoqs/blob/master/README.md) &mdash; this will take a few hours and depends on a good connection to the Internet. Once your server is up log into it (after a `cd ~/Vagrants/stoqsvm`) and activate your virtual environment with the usual commands:\n", "\n", " vagrant ssh -- -X\n", " cd /vagrant/dev/stoqsgit\n", " source venv-stoqs/bin/activate\n", " \n", "Connect to your Institution's STOQS database server using read-only credentials. (Note: firewalls typically limit unprivileged access to such resources.)\n", "\n", " cd stoqs\n", " ln -s mbari_campaigns.py campaigns.py\n", " export DATABASE_URL=postgis://everyone:[email protected]:5433/stoqs\n", " \n", "Launch Jupyter Notebook on your system with:\n", "\n", " cd contrib/notebooks\n", " ../../manage.py shell_plus --notebook\n", " \n", "navigate to this file and open it. You will then be able to execute the cells and experiment with this notebook.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the KISS April 2017 campaign add up the hours and collect in a dictionary all the platform start and end times using Matplotlib mdate format:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Platform Name # Deployments Hours\n", "------------- ------------- -----\n", "tethys 35 341.9\n", "NPS_Glider_34 1 910.0\n", "SG_Glider_621 335 703.6\n", "dorado 3 65.3\n", "Slocum_nemesis 127 173.5\n", "SG_Glider_539 322 686.5\n", "opah 306 282.4\n", "WesternFlyer_PCTD 37 10.1\n", "OA1_Mooring 1 912.0\n", "OA2_Mooring 1 912.0\n", "M1_Mooring 1 912.0\n", "wg_Tiny_Glider 1 912.0\n", "SPRAY_L66a_Glider 1 912.0\n", "daphne 41 291.3\n", "aku 45 254.1\n", "ahi 2 1.6\n", "WesternFlyer_UCTD 26 90.1\n" ] } ], "source": [ "from collections import defaultdict\n", "from datetime import timedelta\n", "import matplotlib.dates as mdates\n", "import operator\n", "\n", "db = 'stoqs_canon_april2017'\n", "\n", "plat_start_ends = defaultdict(list)\n", "plat_depl_dur = {}\n", "print('Platform Name # Deployments Hours')\n", "print('------------- ------------- -----')\n", "for plat in Platform.objects.using(db).all():\n", " time_diff_sum = timedelta(0)\n", " for act in Activity.objects.using(db).filter(platform=plat):\n", " time_diff = act.enddate - act.startdate\n", " time_diff_sum += time_diff\n", " plat_start_ends[plat].append((mdates.date2num(act.startdate), \n", " mdates.date2num(act.enddate)))\n", "\n", " plat_depl_dur[plat] = (len(plat_start_ends[plat]), \n", " time_diff_sum.total_seconds() / 3600)\n", " print(f'{plat.name:20s} {plat_depl_dur[plat][0]:10d} {plat_depl_dur[plat][1]:7.1f}')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Activity start and end date values may be incorrect in legacy databases. To correct them this script can be run by the database admininstrator to update them based on the actual InstantPoint timevalues:\n", "\n", "```python\n", "db = 'stoqs_canon_april2017'\n", "for act in Activity.objects.using(db):\n", " ip_qs = (InstantPoint.objects.using(db)\n", " .filter(activity=act)\n", " .aggregate(Max('timevalue'), Min('timevalue')))\n", " if ip_qs['timevalue__min'] and ip_qs['timevalue__max']:\n", " act.startdate = ip_qs['timevalue__min']\n", " act.enddate = ip_qs['timevalue__max']\n", " act.save(using=db)\n", " else:\n", " act.delete(using=db)\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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UwN/PvkTL5L0se9ZV9YyI/I7To70EWI8zPL0KsAmoepHaldYbnZLWRlU9KSI/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u0nPdM0dDuGtmTUd5MJHd+Ub/asZXaniWmelU7xXuu3yOXlR6NpHWk8/Sny+uRmYuvUXzX3RKUm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\nRxVq2VyrYshezMzSgVeAfwLz3X1GtL018Br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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdc1641bf98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "from matplotlib import pylab\n", "from numpy import arange\n", "\n", "pylab.rcParams['figure.figsize'] = (15, 9);\n", "fig, ax = plt.subplots()\n", "plat_labels = []\n", "\n", "# Annotate at max x value and plot in order by platformtype name \n", "xpos = max([dr[1] for dr in list(plat_start_ends.values())[0]])\n", "for ypos, plat in enumerate(\n", " sorted(plat_start_ends.keys(),\n", " key=operator.attrgetter('platformtype.name'))):\n", " plat_labels.append(f'{plat.name} ({plat.platformtype.name})') \n", " for bdate, edate in plat_start_ends[plat]:\n", " ax.barh(ypos+0.5, edate - bdate, left=bdate, height=0.8, \n", " align='center', color='#' + plat.color, alpha=0.5)\n", " if plat_depl_dur[plat][0] == 1:\n", " fmt = '{:10d} deployment {:7.1f} hours'\n", " else:\n", " fmt = '{:10d} deployments {:7.1f} hours'\n", " ax.annotate(fmt.format(plat_depl_dur[plat][0], plat_depl_dur[plat][1]),\n", " verticalalignment='center', horizontalalignment='right', \n", " xy=(xpos, ypos+0.5), fontsize=13, ) \n", "\n", "ax.set_title(Campaign.objects.using(db).get(id=1).description)\n", "ax.set_ylim(-0.5, len(plat_labels) + 0.5)\n", "ax.set_yticks(arange(len(plat_labels)) + 0.5)\n", "ax.set_yticklabels(plat_labels)\n", "ax.grid(True)\n", "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))\n", "plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MO))\n", "plt.gca().xaxis.set_minor_locator(mdates.DayLocator())\n", "plt.gcf().autofmt_xdate()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

adonese/array2latex

.ipynb_checkpoints/testing_arraytolatex-checkpoint.ipynb

1

12146

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import warnings\n", "import numpy as np\n", "import pandas\n", "import csv\n", "import re" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_type(data):\n", " return type(data)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = np.array([[1,2,3], [3, 5, 6]])\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'[[1 2 3]\\n [3 5 6]]'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_string = np.array2string(a)\n", "a_string" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1 2 3]\\n [3 5 6'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_string[2:][:-2]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "regex_1 = r\"\\[|\\]\"\n", "regex_2 = r\"\\n \"\n", "regex_3 = r\" \"\n", "\n", "repl_1 = r\"\"\n", "repl_2 = r\"\\\\\"\n", "repl_3 = r\",\"\n", "\n", "for regex_repl in zip((regex_1, regex_2, regex_3), (repl_1, repl_2, repl_3)):\n", " a_string = re.sub(regex_repl[0], regex_repl[1], a_string)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1&2&3]\\n&[3&5&6'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.sub(regex_3, r\"&\", a_string[2:][:-2])" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'[[ 1 3 505 42 434]\\n [ 2 43 43 43 -4343]\\n [ 432 42 43 42 424042]]'" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 215, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 & 3 & 505 & 42 & 434\\\\\n", " & 2 & 43 & 43 & 43 & -4343\\\\\n", " & 432 & 42 & 43 & 42 & 424042\n" ] } ], "source": [ "# Getting the largest number\n", "a = np.array([[1, 3, 505, 42, 434], [2, 43, 43, 43, -4343], [432, 42, 43, 42, 424042]])\n", "string_array = np.array2string(a)\n", "\n", "a_string = a_string[2:][:-2]\n", "regex_1 = r\"\\[|\\]\"\n", "regex_2 = \"\\n \"\n", "regex_3 = r\" \"\n", "repl_1 = r\"\"\n", "repl_2 = r\"\\\\\\\\\\n\"\n", "repl_3 = \" & \"\n", "for regex_repl in zip((regex_1, regex_2), (repl_1, repl_2)):\n", " string_array = re.sub(regex_repl[0], regex_repl[1], string_array)\n", "# We need to do second pass. I really wish if there is an easy way of doing that\n", "array_removed_spaces = \" \".join(string_array.split())\n", "string_array = re.sub(regex_3, repl_3, array_removed_spaces)\n", "# Adding the extra \\n\n", "print(\"\\\\\\\\\\n\".join(string_array.split(r\"\\\\\")))" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "' & & 50& 4& 434\\\\\\\\\\n & 4& 4& 4& -4343\\\\\\\\\\n 43& 4& 4& 4&424042'" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'sssss'" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5*\"s\"" ] }, { "cell_type": "code", "execution_count": 331, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def tolatex(array, header, centring=True, caption=\"My table\", label=\"table_something\"):\n", " \"\"\"\n", " This function converts numpy ndarray into a valid latex table.\n", " \"\"\"\n", " string_array = np.array2string(array)[2:][:-2] # I use this for tables items\n", " regex_1 = r\"\\[|\\]\"\n", " regex_2 = \"\\n \"\n", " regex_3 = r\" \"\n", " repl_1 = r\"\"\n", " repl_2 = r\"\\\\\\\\\\n\"\n", " repl_3 = \" & \"\n", " \n", " a_string = re.sub(regex_1, repl_1, string_array) # I use this to compute the largest number length\n", " string_table_items = a_string.replace(\"\\n\", \"\").split(\" \")\n", " length_table_items = max(len(str(i)) for i in string_table_items)\n", " \n", " template = \"\"\"\\\\begin{table}[]\n", " %s\n", " \\\\caption{%s}\n", " \\\\label{%s}\n", " \\\\begin{tabular}{@{%s}@{}}\n", " \\\\toprule\n", " \"\"\" % (centring, caption, label, len(header) * \"l\")\n", " length_header_items = max(len(i) for i in header)\n", " padding = \" \" * (max(length_table_items, length_header_items) // 2) + \"&\" + \" \" * (max(length_table_items, length_header_items) // 2)\n", " midrule = \" \" + padding.join(header) + r\"\\\\\" + \"\\n\" + \" \"* (max(length_table_items, length_header_items) // 2 + 2) + \"\\midrule\" + \"\\n\"\n", " template += midrule\n", " columns, rows = array.shape\n", " if columns != header:\n", " warnings.warn(\"The length of header doesn't match with the length of \\\n", " the column. We will placeholder it with an empty string.\")\n", " for regex_repl in zip((regex_1, regex_2), (repl_1, repl_2)):\n", " string_array = re.sub(regex_repl[0], regex_repl[1], string_array)\n", " # We need to do second pass. I really wish if there is an easy way of doing that\n", " array_removed_spaces = \" \".join(string_array.strip().split())\n", " string_array = re.sub(regex_3, repl_3, array_removed_spaces)\n", " # Adding the extra \\n\n", " string_array = \"\\t\\\\\\\\\\n\\t\".join(string_array.split(r\"\\\\\"))\n", " string_array = \"\\t\" + string_array + \"\\\\\\\\\\n\"+ \" \\\\bottomrule\" + \"\\n\" + \" \\\\end{tabular}\" + \"\\n\" + \"\\\\end{table}\"\n", " \n", " return template + string_array" ] }, { "cell_type": "code", "execution_count": 332, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\begin{table}[]\n", " True\n", " \\caption{My table}\n", " \\label{table_something}\n", " \\begin{tabular}{@{lllll}@{}}\n", " \\toprule\n", " One & Two & Three & Four & Five\\\\\n", " \\midrule\n", "\t0.16631292 & 0.97702809\t\\\\\n", "\t & 0.20791699 & 0.67693387\t\\\\\n", "\t & 0.51874648 & 0.57966175\t\\\\\n", "\t & 0.77753471 & 0.4093189 & \t\\\\\n", "\t & 0.51863438 & 0.88115219\t\\\\\n", "\t & 0.60895347 & 0.01832751\t\\\\\n", "\t & 0.80437334 & 0.62710777\\\\\n", " \\bottomrule\n", " \\end{tabular}\n", "\\end{table}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/adonese/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:30: UserWarning: The length of header doesn't match with the length of the column. We will placeholder it with an empty string.\n" ] } ], "source": [ "a = np.random.rand(7, 2)\n", "print(tolatex(a, header=[\"One\", \"Two\", \"Three\", \"Four\", \"Five\"]))" ] }, { "cell_type": "code", "execution_count": 375, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['0.06021033 & 0.33597086', '0.507968750.50427236', '0.645672580.16201692', '0.840258750.43529734', '0.519282170.8519447', '0.975711230.55873163']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/adonese/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:27: UserWarning: The length of header doesn't match with the length of the column. We will placeholder it with an empty string.\n" ] } ], "source": [ "array = np.random.rand(6, 2)\n", "string_array = np.array2string(array)[2:][:-2] # I use this for tables items\n", "regex_1 = r\"\\[|\\]\"\n", "regex_2 = \"\\n \"\n", "regex_3 = r\" \"\n", "repl_1 = r\"\"\n", "repl_2 = r\"\\\\\\\\\\n\"\n", "repl_3 = \" & \"\n", "\n", "a_string = re.sub(regex_1, repl_1, string_array) # I use this to compute the largest number length\n", "string_table_items = a_string.replace(\"\\n\", \"\").split(\" \")\n", "length_table_items = max(len(str(i)) for i in string_table_items)\n", "\n", "template = \"\"\"\\\\begin{table}[]\n", " %s\n", " \\\\caption{%s}\n", " \\\\label{%s}\n", " \\\\begin{tabular}{@{%s}@{}}\n", " \\\\toprule\n", "\"\"\" % (centring, caption, label, len(header) * \"l\")\n", "length_header_items = max(len(i) for i in header)\n", "padding = \" \" * (max(length_table_items, length_header_items) // 2) + \"&\" + \" \" * (max(length_table_items, length_header_items) // 2)\n", "midrule = \" \" + padding.join(header) + r\"\\\\\" + \"\\n\" + \" \"* (max(length_table_items, length_header_items) // 2 + 2) + \"\\midrule\" + \"\\n\"\n", "template += midrule\n", "columns, rows = array.shape\n", "if columns != header:\n", " warnings.warn(\"The length of header doesn't match with the length of \\\n", " the column. We will placeholder it with an empty string.\")\n", "for regex_repl in zip((regex_1, regex_2), (repl_1, repl_2)):\n", " string_array = re.sub(regex_repl[0], regex_repl[1], string_array)\n", "# We need to do second pass. I really wish if there is an easy way of doing that\n", "array_removed_spaces = \" \".join(string_array.split())\n", "\n", "string_array = re.sub(regex_3, repl_3, array_removed_spaces)\n", "string_array = string_array.split(\"\\\\\\\\\")\n" ] }, { "cell_type": "code", "execution_count": 362, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(7, 2)" ] }, "execution_count": 362, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

marwin-ko/projects

all_state-kaggle_competition/run_prediction.ipynb

1

10604

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## RUN PREDICTION (Gradient Boosting Model)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import pickle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAD TEST DATA" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cont1</th>\n", " <th>cont2</th>\n", " <th>cont3</th>\n", " <th>cont4</th>\n", " <th>cont5</th>\n", " <th>cont6</th>\n", " <th>cont7</th>\n", " <th>cont8</th>\n", " <th>cont9</th>\n", " <th>cont10</th>\n", " <th>...</th>\n", " <th>cat116_MX</th>\n", " <th>cat116_N</th>\n", " <th>cat116_O</th>\n", " <th>cat116_Q</th>\n", " <th>cat116_R</th>\n", " <th>cat116_S</th>\n", " <th>cat116_T</th>\n", " <th>cat116_U</th>\n", " <th>cat116_Y</th>\n", " <th>cat116_W</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.321594</td>\n", " <td>0.299102</td>\n", " <td>0.246911</td>\n", " <td>0.402922</td>\n", " <td>0.281143</td>\n", " <td>0.466591</td>\n", " <td>0.317681</td>\n", " <td>0.61229</td>\n", " <td>0.34365</td>\n", " <td>0.38016</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.634734</td>\n", " <td>0.620805</td>\n", " <td>0.654310</td>\n", " <td>0.946616</td>\n", " <td>0.836443</td>\n", " <td>0.482425</td>\n", " <td>0.443760</td>\n", " <td>0.71330</td>\n", " <td>0.51890</td>\n", " <td>0.60401</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.290813</td>\n", " <td>0.737068</td>\n", " <td>0.711159</td>\n", " <td>0.412789</td>\n", " <td>0.718531</td>\n", " <td>0.212308</td>\n", " <td>0.325779</td>\n", " <td>0.29758</td>\n", " <td>0.34365</td>\n", " <td>0.30529</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.268622</td>\n", " <td>0.681761</td>\n", " <td>0.592681</td>\n", " <td>0.354893</td>\n", " <td>0.397069</td>\n", " <td>0.369930</td>\n", " <td>0.342355</td>\n", " <td>0.40028</td>\n", " <td>0.33237</td>\n", " <td>0.31480</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.553846</td>\n", " <td>0.299102</td>\n", " <td>0.263570</td>\n", " <td>0.696873</td>\n", " <td>0.302678</td>\n", " <td>0.398862</td>\n", " <td>0.391833</td>\n", " <td>0.23688</td>\n", " <td>0.43731</td>\n", " <td>0.50556</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 1117 columns</p>\n", "</div>" ], "text/plain": [ " cont1 cont2 cont3 cont4 cont5 cont6 cont7 \\\n", "0 0.321594 0.299102 0.246911 0.402922 0.281143 0.466591 0.317681 \n", "1 0.634734 0.620805 0.654310 0.946616 0.836443 0.482425 0.443760 \n", "2 0.290813 0.737068 0.711159 0.412789 0.718531 0.212308 0.325779 \n", "3 0.268622 0.681761 0.592681 0.354893 0.397069 0.369930 0.342355 \n", "4 0.553846 0.299102 0.263570 0.696873 0.302678 0.398862 0.391833 \n", "\n", " cont8 cont9 cont10 ... cat116_MX cat116_N cat116_O \\\n", "0 0.61229 0.34365 0.38016 ... 0.0 0.0 0.0 \n", "1 0.71330 0.51890 0.60401 ... 0.0 0.0 0.0 \n", "2 0.29758 0.34365 0.30529 ... 0.0 0.0 0.0 \n", "3 0.40028 0.33237 0.31480 ... 0.0 0.0 0.0 \n", "4 0.23688 0.43731 0.50556 ... 0.0 0.0 0.0 \n", "\n", " cat116_Q cat116_R cat116_S cat116_T cat116_U cat116_Y cat116_W \n", "0 0.0 0.0 0.0 0.0 0.0 0.0 0 \n", "1 0.0 0.0 0.0 0.0 0.0 0.0 0 \n", "2 0.0 0.0 0.0 0.0 0.0 0.0 0 \n", "3 0.0 0.0 0.0 0.0 0.0 0.0 0 \n", "4 0.0 0.0 0.0 0.0 0.0 0.0 0 \n", "\n", "[5 rows x 1117 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('https://s3.amazonaws.com/marweezys-bucket/all_state_insurance_prediction/test.csv')\n", "ids = list(df['id'])\n", "df.drop(labels='id', axis=1, inplace=True)\n", "one_hot_df = pd.get_dummies(df.ix[:,:])\n", "one_hot_df['cat116_W'] = 0\n", "one_hot_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAD MODEL" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('GB_feats.plk','rb') as f:\n", " feats = pickle.load(f)\n", "with open('GB_model.plk','rb') as f:\n", " model = pickle.load(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MAKE PREDICTIONS" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = one_hot_df[feats]\n", "y_pred = model.predict(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SAVE/SHOW PREDICTIONS" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>loss</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4</td>\n", " <td>1579.628558</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>2265.133652</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>9</td>\n", " <td>9989.328296</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>12</td>\n", " <td>6191.534165</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>15</td>\n", " <td>1151.990087</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id loss\n", "0 4 1579.628558\n", "1 6 2265.133652\n", "2 9 9989.328296\n", "3 12 6191.534165\n", "4 15 1151.990087" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GB_d = {}\n", "GB_d['id'] = ids\n", "GB_d['loss'] = y_pred\n", "GB_df = pd.DataFrame(GB_d)\n", "GB_df.to_csv('GB_preds.csv', sep=',', index=False)\n", "GB_df.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ellenjunghyunkim/MyMatching.jl

.ipynb_checkpoints/multimatching-checkpoint.ipynb

1

4321

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "my_deferred_acceptance (generic function with 2 methods)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function my_deferred_acceptance(prop_prefs::Vector{Vector{Int}},\n", " resp_prefs::Vector{Vector{Int}},\n", " caps::Vector{Int})\n", " num_prop = length(prop_prefs)\n", " num_resp = length(resp_prefs)\n", " \n", " prop_matched = zeros(Int,num_prop)\n", " resp_matched = zeros(Int, sum(caps))\n", " \n", " prop_pointers = ones(Int, num_prop)\n", " accept = zeros(Int,num_resp)\n", " #making indptr\n", " indptr = Array{Int}(num_resp+1)\n", " indptr[1] = 1\n", " for i in 1:num_resp\n", " indptr[i+1] = indptr[i] + caps[i]\n", " end\n", " #main loop\n", " \n", " for j in 1:num_resp\n", " for i in 1:num_prop\n", " if prop_matched[i] == 0 && prop_pointers[i] <= length(prop_prefs[i])\n", " k = prop_prefs[i][prop_pointers[i]]\n", " if findfirst(resp_prefs[k],i) != 0\n", " if accept[k] < caps[k]\n", " resp_matched[indptr[k+1]-1 - accept[k]]=i\n", " prop_matched[i]=k\n", " accept[k]+=1\n", " \n", " else\n", " list= resp_matched[indptr[k]:indptr[k+1]-1]\n", " ranking = zeros(Int, caps[k])\n", " for e in e:caps[k]\n", " ranking[e]= findfirst(resp_prefs[k],list[e])\n", " end\n", " if 0< findfirst(resp_prefs[k],i)<maximum(ranking)\n", " resp_matched[indptr[k]+indmax(ranking)-1]=i\n", " prop_matched[list[indmax(ranking)]]=0\n", " prop_matched[i]=k\n", " end\n", " end\n", " end\n", " prop_pointers[i]+=1\n", " end\n", " end\n", " end\n", " return prop_matched, resp_matched, indptr\n", "end\n", "\n", "function my_deferred_acceptance(prop_prefs::Matrix{Int},\n", " resp_prefs::Matrix{Int})\n", " caps = ones(Int, length(resp_prefs))\n", " \n", " prop_matched, resp_matched, indptr =\n", " my_deferred_acceptance(prop_prefs, resp_prefs, caps)\n", " return prop_matched, resp_matched\n", "end" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[34mINFO: Testing MyMatching\n", "\u001b[0m" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[37mTest Summary: | \u001b[0m\u001b[1m\u001b[32mPass \u001b[0m\u001b[1m\u001b[34mTotal\u001b[0m\n", " Testing deferred acceptance | \u001b[1m\u001b[32m 12 \u001b[0m\u001b[1m\u001b[34m 12\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[34mINFO: MyMatching tests passed\n", "\u001b[0m" ] } ], "source": [ "Pkg.test(\"MyMatching\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.5.1", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

a301-teaching/a301_code

notebooks/radiance_conservation_solution.ipynb

1

216422

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Radiance conservation solution -- discussed on day 9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question for Monday, September 26, hand in at beginning of class on paper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure below shows three different fields of view in the form of circular cones with different spreading angles $\\theta$, lengths $R$ and diameters $D$.\n", "\n", "The cones are thin enough so that $R \\approx H$, where H is central length of the cone, and \n", "\n", "$$sin \\theta = \\text{opposite/hypotenuse} \\approx \\theta = \\frac{D/2}{R}$$\n", "\n", "where D is the diameter of the cone.\n", "\n", "Note that cones a) and b) have the same spreading angle $\\theta1$ but different lengths $R1$ and $R2$, and that cones a) and c) have the same length $R1$ but different spreading angles $\\theta1$ and $\\theta 2$\n", "\n", "Suppose the red line is a wall with uniform temperature emitting blackbody irradiance $E^*$. Find:\n", "\n", "1) The irradiance E reaching a), b) and c) (the cone tips) assuming the power is spreading out into a hemisphere of radius $R$ in each case.\n", "\n", "2) The radiance L = E/$\\Delta \\omega$ at a), b) and c).\n", "\n", "If you do it right, you should find that the three radiances are identical, i.e. that for small angles and uniform emitters, L is independent of distance to the target." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AAQTSCTQ3N8uHH34oU6ZMSbcK8xFAAAEEEEAAAUkEbvPmVUhbu0uWrVjqtJMM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\nBBBAAAEER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/T5MYUF9fn3gef7Jly5b4Ls8RQAABBBBAAAEEEEAAAQQQQAABBBBAYIACPjDTYMsH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\nnlk4ps+1j7ZFfeW4rl+mfXRrbm62x6amJnvk/xBAAAE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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('figures/three_cones.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Approximate answer assuming power spreads uniformly into hemisphere" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) power at D1 = $E^* \\times \\pi (D_1/2)^2$ (Watts)\n", " $E_a$ at tip of cone =\n", " $$\\frac{E^* \\pi (D_1/2)^2}{2 \\pi R_1^2}$$\n", " \n", "a) solid angle = $\\Delta \\omega = A/R^2 = (\\pi (D_1/2)^2)/R_1^2$\n", "\n", "a) Radiance = $L_a = E_a/\\Delta \\omega = E^*/(2 \\pi)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", "b) power at D2 = $E^* \\times \\pi (D_2/2)^2$ (Watts)\n", " $E_b$ at tip of cone =\n", " $$\\frac{E^* \\pi (D_2/2)^2}{2 \\pi R_2^2}$$\n", " \n", "b) solid angle = $\\Delta \\omega = A/R^2 = (\\pi (D_2/2)^2)/R_2^2$\n", "\n", "b) Radiance = $L_b = E_b/\\Delta \\omega = E^*/(2 \\pi)$\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "c) power at D2 = $E^* \\times \\pi (D_2/2)^2$ (Watts)\n", " $E_c$ at tip of cone =\n", " $$\\frac{E^* \\pi (D_2/2)^2}{2 \\pi R_1^2}$$\n", " \n", "c) solid angle = $\\Delta \\omega = A/R^2_1 = (\\pi (D_2/2)^2)/R_1^2$\n", "\n", "c) Radiance = $L_c=E_c/\\Delta \\omega = E^*/(2 \\pi)$\n", " \n", " \n", " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Solution, second take" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like $L_a = L_b = L_c$ which shows that L is independent of distance for these examples. Note that this isn't the reason we know that L is conserved -- **it's because Maxwell's equations show that conservation of L is a property of electromagnetic radiation.** Given this, we can check the approximations we made in estimating the flux for this problem. How close is our solution to what we get using the correct formula for flux?\n", "\n", "We saw in the [flux_to_radiance](http://clouds.eos.ubc.ca/~phil/courses/atsc301/html/flux_to_radiance.html#EfromL) notebook that the exact formula to convert from radiance to flux for a hemisphere was:\n", "\n", "$$ E = \\int_0^{2\\pi} \\int_0^{\\pi/2} L \\cos \\theta \\sin \\theta \\, d\\theta \\, d \\phi $$\n", "\n", "The only difference for a cone is the limited zenith angle, if the half-angle (that is the angle from the centerline to the side) of the cone is $\\theta$, the we've got:\n", "\n", "$$ E = \\int_0^{2\\pi} \\int_0^{\\theta} L \\cos \\theta \\sin \\theta \\, d\\theta \\, d \\phi $$\n", "\n", "(note that I've changed my definition of $\\theta$ from the figure above by dividing by 2. Here's the new setup for a cone of radius $r$ and length $l$.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TBBBwr0BK43e9J5LS3pGQllbtjtuasMpxT57WiJ8Hl3VrArJjvIlGxGTtlYek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\nmIQoLAggsEUE6OG3RR40t4kAA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CW3UhazBl8QN7Nm4RdfP7ea3b9YNCRTOgLQhIRMTZrR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\nCCCAAAIIIIAAAggggAACCHSQwJ8AWdsUlnthSVAAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(\"figures/full_triangle.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what is $\\theta$? It has to be $\\theta = \\arctan(r/l)$.\n", "[Wikipedia](https://en.wikipedia.org/wiki/Inverse_trigonometric_functions) says that:\n", "\n", "$$\\cos \\theta = \\cos\\left ( \\arctan(r/l) \\right ) = \\frac{1}{\\sqrt{1 + (r/l)^2}}$$\n", "\n", "So try a change of variables to do the integral: let $\\mu = \\cos \\theta$\n", "so $d\\mu = - \\sin \\theta \\, \\cos \\theta\\, d\\theta$\n", "\n", "Since $L$ is independent of angle, we can integrate out $\\phi$ and do the transformation to get:\n", "\n", "$$ E = 2 \\pi \\int_0^{\\theta} L \\cos \\theta \\sin \\theta \\, d\\theta = \n", "2 \\pi \\int_{\\cos\\theta}^1 L \\mu d\\mu$$\n", "since $\\cos 0 = 1$.\n", "\n", "Solving this yields: \n", "\n", "$$E = \\pi L \\left [ \\frac{\\left ( r^2/l^2 \\right )}{1 + \\left( r^2/l^2 \\right )} \\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the limits work correctly -- as we move the cone tip towards the surface, $r/l \\to \\infty$ and we get the fat cone limit \n", "\n", "$$E = \\pi L \\frac{\\infty}{\\infty} = \\pi L$$\n", "\n", "In agreement with the equation in the [flux_to_radiance](http://clouds.eos.ubc.ca/~phil/courses/atsc301/html/flux_to_radiance.html#EfromL) notebook\n", "\n", "And as we zoom out, $r/l \\to 0$ and the thin cone limit becomes:\n", "\n", "$$ E = \\pi L \\left ( \\frac{r^2}{l^2} \\right )$$\n", "\n", "or rearranging:\n", "\n", "$$ E = L \\left ( \\frac{\\pi r^2}{l^2} \\right ) = L \\left ( \\frac{\\Delta A}{l^2} \\right ) \\approx L \\Delta \\omega$$\n", "\n", "as expected." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How does this limit compare with the answers we got to the question assuming spreading into a hemisphere? Let's *assume* that L doesn't depend on distance, so that $L = L^* = E^*/\\pi$. If we insert that into our last equation we get:\n", "\n", "$$E = L \\left ( \\frac{\\Delta A}{l^2} \\right ) = \\frac{E^*}{\\pi}\n", "\\left ( \\frac{\\Delta A}{l^2} \\right ) = \\left ( \\frac{E^* \\Delta A}{\\pi l^2} \\right )$$\n", "\n", "So we're off by a factor of 2 in the denominator. That's because the power isn't being evenly distributed into a hemisphere from the circular patch on the surface, so our assumption above that the power spreading out evenly into $2 \\pi l^2$ above isn't accurate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

tensorflow/recommenders

docs/examples/ranking_tfx.ipynb

1

24693

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DjUA6S30k52h" }, "source": [ "##### Copyright 2022 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SpNWyqewk8fE" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "6x1ypzczQCwy" }, "source": [ "# Using TensorFlow Recommenders with TFX\n", "\n", "***A tutorial to train a TensorFlow Recommenders ranking model as a [TFX pipeline](https://www.tensorflow.org/tfx).***" ] }, { "cell_type": "markdown", "metadata": { "id": "HU9YYythm0dx" }, "source": [ "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/recommenders/examples/ranking_tfx\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/ranking_tfx.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/recommenders/blob/main/docs/examples/ranking_tfx.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/ranking_tfx.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n", " \u003c/td\u003e\n", "\u003c/table\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "_VuwrlnvQJ5k" }, "source": [ "In this notebook-based tutorial, we will create and run a [TFX pipeline](https://www.tensorflow.org/tfx)\n", "to train a ranking model to predict movie ratings using TensorFlow Recommenders (TFRS).\n", "The pipeline will consist of three essential TFX components: ExampleGen,\n", "Trainer and Pusher. The pipeline includes the most minimal ML workflow like\n", "importing data, training a model and exporting the trained TFRS ranking model." ] }, { "cell_type": "markdown", "metadata": { "id": "Fmgi8ZvQkScg" }, "source": [ "## Set Up\n", "We first need to install the TFX Python package and download\n", "the dataset which we will use for our model.\n", "\n", "### Upgrade Pip\n", "\n", "To avoid upgrading Pip in a system when running locally,\n", "check to make sure that we are running in Colab.\n", "Local systems can of course be upgraded separately." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "as4OTe2ukSqm" }, "outputs": [], "source": [ "import sys\n", "if 'google.colab' in sys.modules:\n", " !pip install --upgrade pip" ] }, { "cell_type": "markdown", "metadata": { "id": "MZOYTt1RW4TK" }, "source": [ "### Install TFX\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iyQtljP-qPHY" }, "outputs": [], "source": [ "!pip install -U tfx\n", "!pip install -U tensorflow-recommenders" ] }, { "cell_type": "markdown", "metadata": { "id": "EwT0nov5QO1M" }, "source": [ "### Did you restart the runtime?\n", "\n", "If you are using Google Colab, the first time that you run\n", "the cell above, you must restart the runtime by clicking\n", "above \"RESTART RUNTIME\" button or using \"Runtime \u003e Restart\n", "runtime ...\" menu. This is because of the way that Colab\n", "loads packages.\n", "\n", "Before we define the pipeline, we need to write the model code for the\n", "Trainer component and save it in a file." ] }, { "cell_type": "markdown", "metadata": { "id": "BDnPgN8UJtzN" }, "source": [ "Check the TensorFlow and TFX versions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6jh7vKSRqPHb" }, "outputs": [], "source": [ "import tensorflow as tf\n", "print('TensorFlow version: {}'.format(tf.__version__))\n", "from tfx import v1 as tfx\n", "print('TFX version: {}'.format(tfx.__version__))" ] }, { "cell_type": "markdown", "metadata": { "id": "aDtLdSkvqPHe" }, "source": [ "### Set up variables\n", "\n", "There are some variables used to define a pipeline. You can customize these\n", "variables as you want. By default all output from the pipeline will be\n", "generated under the current directory. Instead of using the SchemaGen component to generate a schema, for this\n", "tutorial we will create a hardcoded schema." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EcUseqJaE2XN" }, "outputs": [], "source": [ "import os\n", "\n", "PIPELINE_NAME = 'TFRS-ranking'\n", "\n", "# Directory where MovieLens 100K rating data lives\n", "DATA_ROOT = os.path.join('data', PIPELINE_NAME)\n", "# Output directory to store artifacts generated from the pipeline.\n", "PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)\n", "# Path to a SQLite DB file to use as an MLMD storage.\n", "METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')\n", "# Output directory where created models from the pipeline will be exported.\n", "SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)\n", "\n", "from absl import logging\n", "logging.set_verbosity(logging.INFO) # Set default logging level." ] }, { "cell_type": "markdown", "metadata": { "id": "8F2SRwRLSYGa" }, "source": [ "### Prepare example data\n", "Since TFX does not currently support TensorFlow Datasets API, we will download the MovieLens 100K dataset manually for use in our TFX pipeline. The dataset we\n", "are using is\n", "[MovieLens 100K Dataset](https://grouplens.org/datasets/movielens/100k/).\n", "\n", "There are four numeric features in this dataset:\n", "\n", "- userId\n", "- movieId\n", "- rating\n", "- timestamp\n", "\n", "We will build a ranking model which predicts the `rating` of the movies. We will not use the `timestamp` feature." ] }, { "cell_type": "markdown", "metadata": { "id": "11J7XiCq6AFP" }, "source": [ "Because TFX ExampleGen reads inputs from a directory, we need to create a\n", "directory and copy dataset to it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4fxMs6u86acP" }, "outputs": [], "source": [ "!wget https://files.grouplens.org/datasets/movielens/ml-100k.zip\n", "!mkdir -p {DATA_ROOT}\n", "!unzip ml-100k.zip\n", "!echo 'userId,movieId,rating,timestamp' \u003e {DATA_ROOT}/ratings.csv\n", "!sed 's/\\t/,/g' ml-100k/u.data \u003e\u003e {DATA_ROOT}/ratings.csv" ] }, { "cell_type": "markdown", "metadata": { "id": "ASpoNmxKSQjI" }, "source": [ "Take a quick look at the CSV file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-eSz28UDSnlG" }, "outputs": [], "source": [ "!head {DATA_ROOT}/ratings.csv" ] }, { "cell_type": "markdown", "metadata": { "id": "OTtQNq1DdVvG" }, "source": [ "You should be able to see four values. For example, the first example means user '196' gives a rating of 3 to movie '242'." ] }, { "cell_type": "markdown", "metadata": { "id": "nH6gizcpSwWV" }, "source": [ "## Create a pipeline\n", "\n", "TFX pipelines are defined using Python APIs. We will define a pipeline which\n", "consists of following three components.\n", "- CsvExampleGen: Reads in data files and convert them to TFX internal format\n", "for further processing. There are multiple\n", "[ExampleGen](https://www.tensorflow.org/tfx/guide/examplegen)s for various\n", "formats. In this tutorial, we will use CsvExampleGen which takes CSV file input.\n", "- Trainer: Trains an ML model.\n", "[Trainer component](https://www.tensorflow.org/tfx/guide/trainer) requires a\n", "model definition code from users. You can use TensorFlow APIs to specify how to\n", "train a model and save it in a _saved_model_ format.\n", "- Pusher: Copies the trained model outside of the TFX pipeline.\n", "[Pusher component](https://www.tensorflow.org/tfx/guide/pusher) can be thought\n", "of an deployment process of the trained ML model.\n", "\n", "Before actually define the pipeline, we need to write a model code for the\n", "Trainer component first." ] }, { "cell_type": "markdown", "metadata": { "id": "lOjDv93eS5xV" }, "source": [ "### Write model training code\n", "\n", "We will build a simple ranking model to predict movie ratings. This model training code will be saved to a separate file.\n", "\n", "In this tutorial we will use\n", "[Generic Trainer](https://www.tensorflow.org/tfx/guide/trainer#generic_trainer)\n", "of TFX which support Keras-based models. You need to write a Python file\n", "containing `run_fn` function, which is the entrypoint for the `Trainer`\n", "component." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aES7Hv5QTDK3" }, "outputs": [], "source": [ "_trainer_module_file = 'tfrs_ranking_trainer.py'" ] }, { "cell_type": "markdown", "metadata": { "id": "HFsQCOytiidq" }, "source": [ "The ranking model we use is almost exactly the same as in the [Basic Ranking](https://www.tensorflow.org/recommenders/examples/basic_ranking) tutorial. The only difference is that we use movie IDs instead of movie titles in the candidate tower." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Gnc67uQNTDfW" }, "outputs": [], "source": [ "%%writefile {_trainer_module_file}\n", "\n", "from typing import Dict, Text\n", "from typing import List\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "from tensorflow_metadata.proto.v0 import schema_pb2\n", "import tensorflow_recommenders as tfrs\n", "from tensorflow_transform.tf_metadata import schema_utils\n", "from tfx import v1 as tfx\n", "from tfx_bsl.public import tfxio\n", "\n", "_FEATURE_KEYS = ['userId', 'movieId']\n", "_LABEL_KEY = 'rating'\n", "\n", "_FEATURE_SPEC = {\n", " **{\n", " feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)\n", " for feature in _FEATURE_KEYS\n", " }, _LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)\n", "}\n", "\n", "\n", "class RankingModel(tf.keras.Model):\n", "\n", " def __init__(self):\n", " super().__init__()\n", " embedding_dimension = 32\n", "\n", " unique_user_ids = np.array(range(943)).astype(str)\n", " unique_movie_ids = np.array(range(1682)).astype(str)\n", "\n", " # Compute embeddings for users.\n", " self.user_embeddings = tf.keras.Sequential([\n", " tf.keras.layers.Input(shape=(1,), name='userId', dtype=tf.int64),\n", " tf.keras.layers.Lambda(lambda x: tf.as_string(x)),\n", " tf.keras.layers.StringLookup(\n", " vocabulary=unique_user_ids, mask_token=None),\n", " tf.keras.layers.Embedding(\n", " len(unique_user_ids) + 1, embedding_dimension)\n", " ])\n", "\n", " # Compute embeddings for movies.\n", " self.movie_embeddings = tf.keras.Sequential([\n", " tf.keras.layers.Input(shape=(1,), name='movieId', dtype=tf.int64),\n", " tf.keras.layers.Lambda(lambda x: tf.as_string(x)),\n", " tf.keras.layers.StringLookup(\n", " vocabulary=unique_movie_ids, mask_token=None),\n", " tf.keras.layers.Embedding(\n", " len(unique_movie_ids) + 1, embedding_dimension)\n", " ])\n", "\n", " # Compute predictions.\n", " self.ratings = tf.keras.Sequential([\n", " tf.keras.layers.Dense(256, activation='relu'),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.Dense(1)\n", " ])\n", "\n", " def call(self, inputs):\n", "\n", " user_id, movie_id = inputs\n", "\n", " user_embedding = self.user_embeddings(user_id)\n", " movie_embedding = self.movie_embeddings(movie_id)\n", "\n", " return self.ratings(tf.concat([user_embedding, movie_embedding], axis=2))\n", "\n", "\n", "class MovielensModel(tfrs.models.Model):\n", "\n", " def __init__(self):\n", " super().__init__()\n", " self.ranking_model: tf.keras.Model = RankingModel()\n", " self.task: tf.keras.layers.Layer = tfrs.tasks.Ranking(\n", " loss=tf.keras.losses.MeanSquaredError(),\n", " metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", "\n", " def call(self, features: Dict[str, tf.Tensor]) -\u003e tf.Tensor:\n", " return self.ranking_model((features['userId'], features['movieId']))\n", "\n", " def compute_loss(self,\n", " features: Dict[Text, tf.Tensor],\n", " training=False) -\u003e tf.Tensor:\n", "\n", " labels = features[1]\n", " rating_predictions = self(features[0])\n", "\n", " # The task computes the loss and the metrics.\n", " return self.task(labels=labels, predictions=rating_predictions)\n", "\n", "\n", "def _input_fn(file_pattern: List[str],\n", " data_accessor: tfx.components.DataAccessor,\n", " schema: schema_pb2.Schema,\n", " batch_size: int = 256) -\u003e tf.data.Dataset:\n", " return data_accessor.tf_dataset_factory(\n", " file_pattern,\n", " tfxio.TensorFlowDatasetOptions(\n", " batch_size=batch_size, label_key=_LABEL_KEY),\n", " schema=schema).repeat()\n", "\n", "\n", "def _build_keras_model() -\u003e tf.keras.Model:\n", " return MovielensModel()\n", "\n", "\n", "# TFX Trainer will call this function.\n", "def run_fn(fn_args: tfx.components.FnArgs):\n", " \"\"\"Train the model based on given args.\n", "\n", " Args:\n", " fn_args: Holds args used to train the model as name/value pairs.\n", " \"\"\"\n", " schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)\n", "\n", " train_dataset = _input_fn(\n", " fn_args.train_files, fn_args.data_accessor, schema, batch_size=8192)\n", " eval_dataset = _input_fn(\n", " fn_args.eval_files, fn_args.data_accessor, schema, batch_size=4096)\n", "\n", " model = _build_keras_model()\n", "\n", " model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))\n", "\n", " model.fit(\n", " train_dataset,\n", " steps_per_epoch=fn_args.train_steps,\n", " epochs = 3,\n", " validation_data=eval_dataset,\n", " validation_steps=fn_args.eval_steps)\n", "\n", " model.save(fn_args.serving_model_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "blaw0rs-emEf" }, "source": [ "Now you have completed all preparation steps to build the TFX pipeline." ] }, { "cell_type": "markdown", "metadata": { "id": "w3OkNz3gTLwM" }, "source": [ "### Write a pipeline definition\n", "\n", "We define a function to create a TFX pipeline. A `Pipeline` object\n", "represents a TFX pipeline which can be run using one of pipeline\n", "orchestration systems that TFX supports.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "M49yYVNBTPd4" }, "outputs": [], "source": [ "def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,\n", " module_file: str, serving_model_dir: str,\n", " metadata_path: str) -\u003e tfx.dsl.Pipeline:\n", " \"\"\"Creates a three component pipeline with TFX.\"\"\"\n", " # Brings data into the pipeline.\n", " example_gen = tfx.components.CsvExampleGen(input_base=data_root)\n", "\n", " # Uses user-provided Python function that trains a model.\n", " trainer = tfx.components.Trainer(\n", " module_file=module_file,\n", " examples=example_gen.outputs['examples'],\n", " train_args=tfx.proto.TrainArgs(num_steps=12),\n", " eval_args=tfx.proto.EvalArgs(num_steps=24))\n", "\n", " # Pushes the model to a filesystem destination.\n", " pusher = tfx.components.Pusher(\n", " model=trainer.outputs['model'],\n", " push_destination=tfx.proto.PushDestination(\n", " filesystem=tfx.proto.PushDestination.Filesystem(\n", " base_directory=serving_model_dir)))\n", "\n", " # Following three components will be included in the pipeline.\n", " components = [\n", " example_gen,\n", " trainer,\n", " pusher,\n", " ]\n", "\n", " return tfx.dsl.Pipeline(\n", " pipeline_name=pipeline_name,\n", " pipeline_root=pipeline_root,\n", " metadata_connection_config=tfx.orchestration.metadata\n", " .sqlite_metadata_connection_config(metadata_path),\n", " components=components)" ] }, { "cell_type": "markdown", "metadata": { "id": "mJbq07THU2GV" }, "source": [ "## Run the pipeline\n", "\n", "TFX supports multiple orchestrators to run pipelines.\n", "In this tutorial we will use `LocalDagRunner` which is included in the TFX\n", "Python package and runs pipelines on local environment." ] }, { "cell_type": "markdown", "metadata": { "id": "7mp0AkmrPdUb" }, "source": [ "Now we create a `LocalDagRunner` and pass a `Pipeline` object created from the\n", "function we already defined.\n", "\n", "The pipeline runs directly and you can see logs for the progress of the pipeline including ML model training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fAtfOZTYWJu-" }, "outputs": [], "source": [ "tfx.orchestration.LocalDagRunner().run(\n", " _create_pipeline(\n", " pipeline_name=PIPELINE_NAME,\n", " pipeline_root=PIPELINE_ROOT,\n", " data_root=DATA_ROOT,\n", " module_file=_trainer_module_file,\n", " serving_model_dir=SERVING_MODEL_DIR,\n", " metadata_path=METADATA_PATH))" ] }, { "cell_type": "markdown", "metadata": { "id": "ppERq0Mj6xvW" }, "source": [ "You should see \"INFO:absl:Component Pusher is finished.\" at the end of the\n", "logs if the pipeline finished successfully. Because `Pusher` component is the\n", "last component of the pipeline.\n", "\n", "The pusher component pushes the trained model to the `SERVING_MODEL_DIR` which\n", "is the `serving_model/TFRS-ranking` directory if you did not change the\n", "variables in the previous steps. You can see the result from the file browser\n", "in the left-side panel in Colab, or using the following command:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NTHROkqX6yHx" }, "outputs": [], "source": [ "# List files in created model directory.\n", "!ls -R {SERVING_MODEL_DIR}" ] }, { "cell_type": "markdown", "metadata": { "id": "W8HQfT-ziids" }, "source": [ "Now we can test the ranking model by computing predictions for a user and a movie:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5EDMkz8Wiidt" }, "outputs": [], "source": [ "import glob\n", "# Load the latest model for testing\n", "loaded = tf.saved_model.load(max(glob.glob(os.path.join(SERVING_MODEL_DIR, '*/')), key=os.path.getmtime))\n", "print(loaded({'userId': [[42]], 'movieId': [[15]]}).numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "08R8qvweThRf" }, "source": [ "This concludes the TensorFlow Recommenders + TFX tutorial." ] } ], "metadata": { "colab": { "collapsed_sections": [ "DjUA6S30k52h" ], "name": "ranking_tfx.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

flavio-casacurta/Nat2Py

Util/split_cmd.ipynb

1

3066

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from warehouse import LINEFEED\n", "from HOFs import *" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lines = [\"\"\" IF *PF-KEY EQ 'PF3' OR= 'PF15' OR= 'PF17'\"\"\"]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "line = \"\"\" IF *PF-KEY EQ 'PF3' OR= 'PF15' OR= 'PF17'\"\"\"" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"OR= 'PF17'\"" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wrd1 = 'OR='\n", "lw = len(wrd1)\n", "idx = line[lw:].index(wrd1)+2\n", "wrd1 + ' ' + line[lw:idx]\n", "ll = len(wrd1 + ' ' + line[lw:idx])\n", "line[ll:]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'OR='" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wrd1 = word(line, 1)\n", "wrd1" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ " for line in lines:\n", " wrd = words(line)\n", " for w in xrange(1, wrd[0]):\n", " if wrd[1][w] in LINEFEED:\n", " joinLines.append(wrd[1][w] + ' ' + line[len(wrd[1][w]):]line.index(wrd[1][w])])\n", " line = line[line.index(wrd[1][w]):]\n", " joinLines.append(line)\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[\"OR= IF *PF-KEY EQ 'PF3' \",\n", " 'OR= ',\n", " \"OR= 'PF15' OR= 'PF17'\"]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joinLines" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

eds-uga/csci1360-fa16

assignments/A6/A6_Q1.ipynb

1

11519

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q1_prompt", "locked": true, "solution": false } }, "source": [ "# Q1\n", "\n", "In this question, we'll review the basics of file I/O (file input/output) and the various function calls and modes required (this will draw on material from L14)." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q1a_prompt", "locked": true, "solution": false } }, "source": [ "### A\n", "\n", "Write a function **`read_file_contents`** which takes a string pathname as an argument, and returns a single string that contains all the contents of the file. Don't import any additional packages.\n", "\n", "If I have a file `random_text.txt`, I'll give the full path to this file to the function: `contents = read_file_contents(\"random_text.txt\")`, and I should get back a single string `contents` that contains all the contents of the file.\n", "\n", "**NOTE:** Your function should be able to handle errors gracefully! If an error occurs when trying to read from the file, your function should return `None` (note the capitalization of the first letter)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "q1a", "locked": false, "solution": true } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1a_test1", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "truth = \"This is some text.\\nMore text, but on a different line!\\nInsert your favorite meme here.\\n\"\n", "pred = read_file_contents(\"q1data/file1.txt\")\n", "assert truth == pred" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1a_test2", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "retval = -1\n", "try:\n", " retval = read_file_contents(\"nonexistent/path.txt\")\n", "except:\n", " assert False\n", "else:\n", " assert retval is None" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q1b_prompt", "locked": true, "solution": false } }, "source": [ "### B\n", "\n", "This time, write a function `read_file` that takes **two** arguments: the first is the path to the file (same as before), and the second is an *optional* boolean argument `as_list` that defaults to `False`. When this flag is `False` (the default), your function should behave identically to `read_file_contents`. In fact, if `as_list` is `False`, you can just call your previous function.\n", "\n", "If `as_list` is `True`, instead of returning a single string of the file's contents, return a list of strings, where each item in the list is a line from the file.\n", "\n", "**NOTE:** Your function should be able to handle errors gracefully! If an error occurs when trying to read from the file, your function should return `None` (note the capitalization of the first letter)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "q1b", "locked": false, "solution": true } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1b_test1", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "truth = \"Yo dawg, I heard yo and yo dawg like yo-yos.\\nSo we put yo dawg in a yo-yo.\\nSo yo can yo-yo yo dawg while yo dawg yo-yos, dawg.\\nMaximum ridiculousness reached.\\n\"\n", "pred = read_file(\"q1data/file2.txt\")\n", "assert truth == pred" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1b_test2", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "truth = ['Yo dawg, I heard yo and yo dawg like yo-yos.\\n',\n", " 'So we put yo dawg in a yo-yo.\\n',\n", " 'So yo can yo-yo yo dawg while yo dawg yo-yos, dawg.\\n',\n", " 'Maximum ridiculousness reached.\\n']\n", "pred = read_file(\"q1data/file2.txt\", as_list = True)\n", "for item in truth:\n", " assert item in pred\n", "for item in pred:\n", " assert item in truth" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1b_test3", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "retval = -1\n", "try:\n", " retval = read_file(\"another/nonexistent/path.txt\")\n", "except:\n", " assert False\n", "else:\n", " assert retval is None" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q1c_prompt", "locked": true, "solution": false } }, "source": [ "### C\n", "\n", "In this question, you'll read from one file, perform a simple computation, and write the results to a new file.\n", "\n", "Write a function **`count_lines`** that takes two arguments: the first is a path to a file to read, the second is the path to an output file. Your function will count the number of lines in the file at the first argument, and write this number to a file at the second argument.\n", "\n", "Your function should return `True` on success, and `False` if an error occurred.\n", "\n", "**NOTE:** Your function should be able to handle errors gracefully! If an error occurs when trying to read from the file or write to the output file, your function should return `False`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "q1c", "locked": false, "solution": true } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1c_test1", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "import os.path\n", "assert count_lines(\"q1data/file1.txt\", \"q1data/file1_out.txt\")\n", "assert os.path.exists(\"q1data/file1_out.txt\")\n", "assert int(open(\"q1data/file1_out.txt\", \"r\").read()) == 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1c_test2", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "r1 = None\n", "try:\n", " r1 = count_lines(\"yet/another/nonexistent/path.txt\", \"meaningless\")\n", "except:\n", " assert False\n", "else:\n", " assert not r1\n", "\n", "r2 = None\n", "try:\n", " r2 = count_lines(\"q1data/file1.txt\", \"/this/should/throw/an/error.txt\")\n", "except:\n", " assert False\n", "else:\n", " assert not r2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "q1d_prompt", "locked": true, "solution": false } }, "source": [ "### D\n", "\n", "In this question, you'll write a function **`acount_lines`** that performs the same operation as before, except in the case that the output file already exists: in this case, you'll **append** the line count to the file instead of overwriting it, thus preserving any existing previous line counts.\n", "\n", "Each new appended line count should be *on its own line in the output file*. You may need to manually insert newline characters, which are a backslash followed by the letter n: **`\\n`**\n", "\n", "Your function should return `True` on success, and `False` if an error occurred.\n", "\n", "**NOTE:** Your function should be able to handle errors gracefully! If an error occurs when trying to read from the file or write to the output file, your function should return `False`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "q1d", "locked": false, "solution": true } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1d_test1", "locked": true, "points": 2.5, "solution": false } }, "outputs": [], "source": [ "if os.path.exists(\"q1data/out_again.txt\"):\n", " os.remove(\"q1data/out_again.txt\")\n", "\n", "assert acount_lines(\"q1data/file1.txt\", \"q1data/out_again.txt\")\n", "assert os.path.exists(\"q1data/out_again.txt\")\n", "assert int(open(\"q1data/out_again.txt\", \"r\").read()) == 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1d_test2", "locked": true, "points": 2.5, "solution": false } }, "outputs": [], "source": [ "assert acount_lines(\"q1data/file2.txt\", \"q1data/out_again.txt\")\n", "assert os.path.exists(\"q1data/out_again.txt\")\n", "assert int(\"\".join(open(\"q1data/out_again.txt\", \"r\").read().split(\"\\n\"))) == 34" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbgrader": { "grade": true, "grade_id": "q1d_test3", "locked": true, "points": 5, "solution": false } }, "outputs": [], "source": [ "r1 = None\n", "try:\n", " r1 = acount_lines(\"yet/another/nonexistent/path.txt\", \"meaningless\")\n", "except:\n", " assert False\n", "else:\n", " assert not r1\n", "\n", "r2 = None\n", "try:\n", " r2 = acount_lines(\"q1data/file2.txt\", \"/this/should/throw/an/error.txt\")\n", "except:\n", " assert False\n", "else:\n", " assert not r2" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Create Assignment", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

mssalvador/notebooks

notebooks/konkurs/.ipynb_checkpoints/BancruptcyDectect-checkpoint.ipynb

1

50098

{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "SQLContext.newSession(sqlContext)\n", "from pyspark.sql import functions as F\n", "from pyspark.ml.feature import VectorAssembler,StandardScaler,RFormula\n", "from pyspark.ml.classification import LogisticRegression\n", "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder, TrainValidationSplit\n", "from pyspark.ml.linalg import VectorUDT,Vectors\n", "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n", "from pyspark.sql import Window\n", "from pyspark.ml import Pipeline\n", "from pyspark.ml.regression import GeneralizedLinearRegression\n", "\n", "import pandas as pd\n", "import re\n", "from tabulate import tabulate\n", "import random\n", "import sys\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+--------------------+-----+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+-----------+-----------------+------------------+------------------+------------------+------------------+-------------+-------------+-------------+-------------+----------------+---------------+\n", "|cvrNummer| status|label|AarsVaerk_1|AarsVaerk_2|AarsVaerk_3|AarsVaerk_4|AarsVaerk_5|AarsVaerk_6|AarsVaerk_7|AarsVaerk_8|AarsVaerk_9|AarsVaerk_10|AarsVaerk_11|AarsVaerk_12|AarsVaerk_13|AarsVaerk_14|AarsVaerk_15|avgVarighed|totalAabneEnheder|totalLukketEnheder| vaerdiSlope_1| vaerdiSlope_2| vaerdiSlope_3|vaerdiSlope_4|vaerdiSlope_5|vaerdiSlope_6|vaerdiSlope_7|reklamebeskyttet|kortBeskrivelse|\n", "+---------+--------------------+-----+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+-----------+-----------------+------------------+------------------+------------------+------------------+-------------+-------------+-------------+-------------+----------------+---------------+\n", "| 10043271| [NORMAL]| 0.0| 2.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 2.0| 5.0| null| 0.0| 1.0| 36.8437212158428| null| null| null| null| null| null| 0.0| APS|\n", "| 10058511|[OPLØST EFTER ERK...| 0.0| 1.0| 2.0| 2.0| 2.0| 1.0| null| null| null| null| null| null| null| null| null| null| 1281.25| 4.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10058511|[OPLØST EFTER ERK...| 0.0| 1.0| 2.0| 2.0| 2.0| 1.0| null| null| null| null| null| null| null| null| null| null| 1281.25| 4.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10063027|[TVANGSOPLØST, UN...| 0.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| null| 1356.0| 1.0| 0.0|105.02173913043478|301.92857142857144| null| null| null| null| null| 0.0| APS|\n", "| 10063027|[TVANGSOPLØST, UN...| 0.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| null| 1356.0| 1.0| 0.0|105.02173913043478|301.92857142857144| null| null| null| null| null| 0.0| APS|\n", "| 10063027|[TVANGSOPLØST, UN...| 0.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| null| 1356.0| 1.0| 0.0|105.02173913043478|301.92857142857144| null| null| null| null| null| 0.0| APS|\n", "| 10063310|[TVANGSOPLØST, UN...| 0.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| null| null| 712.0| 2.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10063310|[TVANGSOPLØST, UN...| 0.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| null| null| 712.0| 2.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10063310|[TVANGSOPLØST, UN...| 0.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| null| null| 712.0| 2.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10065615| [NORMAL]| 0.0| 1.0| 1.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| null| null| null| 0.0| 1.0|-612.4527363184079| 0.0| null| null| null| null| null| 0.0| APS|\n", "| 10083869|[OPLØST EFTER FRI...| 0.0| 1.0| 10.0| 10.0| 5.0| 5.0| 5.0| 5.0| null| null| null| null| null| null| null| null| 3023.0| 1.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10083869|[OPLØST EFTER FRI...| 0.0| 1.0| 10.0| 10.0| 5.0| 5.0| 5.0| 5.0| null| null| null| null| null| null| null| null| 3023.0| 1.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10083869|[OPLØST EFTER FRI...| 0.0| 1.0| 10.0| 10.0| 5.0| 5.0| 5.0| 5.0| null| null| null| null| null| null| null| null| 3023.0| 1.0| 0.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10097924| [NORMAL]| 0.0| 50.0| 50.0| 20.0| 20.0| 10.0| 10.0| 10.0| 10.0| 10.0| 10.0| null| null| null| null| null| 0.0| 1.0| 1.0|3214.7017543859647| 2.399234693877551|1.5797788309636651| null| null| null| null| 0.0| APS|\n", "| 10117305| [NORMAL]| 0.0| 2.0| 2.0| 2.0| 5.0| 2.0| 2.0| 2.0| 2.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| null| 0.0| 1.0| 38.47386983007374| null| null| null| null| null| null| 0.0| APS|\n", "| 10118859|[OPLØST EFTER FRI...| 0.0| 1.0| 2.0| 2.0| 1.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| 4051.0| 1.0| 0.0| 897.1291866028708| null| null| null| null| null| null| 0.0| APS|\n", "| 10118859|[OPLØST EFTER FRI...| 0.0| 1.0| 2.0| 2.0| 1.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| 4051.0| 1.0| 0.0| 897.1291866028708| null| null| null| null| null| null| 0.0| APS|\n", "| 10118859|[OPLØST EFTER FRI...| 0.0| 1.0| 2.0| 2.0| 1.0| 1.0| 1.0| null| null| null| null| null| null| null| null| null| 4051.0| 1.0| 0.0| 897.1291866028708| null| null| null| null| null| null| 0.0| APS|\n", "| 10127467| [NORMAL]| 0.0| 1.0| 1.0| 1.0| 1.0| 1.0| 1.0| 1.0| 1.0| 1.0| null| null| null| null| null| null| null| 0.0| 1.0| null| null| null| null| null| null| null| 0.0| APS|\n", "| 10144213|[UNDER KONKURS, N...| 1.0| 1.0| 50.0| 100.0| 100.0| 100.0| null| null| null| null| null| null| null| null| null| null| 593.5| 2.0| 1.0| 833333.3333333334| 17574.69244288225|17055.267702936097| null| null| null| null| 0.0| APS|\n", "+---------+--------------------+-----+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+-----------+-----------------+------------------+------------------+------------------+------------------+-------------+-------------+-------------+-------------+----------------+---------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "#import data and rename bad name rank into vaerdiSlope\n", "#RAW DATA!!! \n", "\n", "\n", "#exclude some of the variables, and cast all variables to double\n", "excludeCols = [\"medArb_\"+str(i) for i in range(1,16)] # we don't need the medarbejders \n", "includeCols = [i for i in df.columns if i not in excludeCols]\n", "\n", "rankCols = [re.sub(pattern=\"rank_\",repl=\"vaerdiSlope_\",string=i) for i in includeCols]\n", "finalCols = [F.col(i) for i in includeCols[:2]]+[\"kortBeskrivelse\"]+[F.col(i).cast(\"double\") for i in includeCols[2:] if i not in [\"kortBeskrivelse\"]]\n", "\n", "\n", "df = sqlContext.read.parquet(\"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/featureDataCvr\")\n", "df.select([\"cvrNummer\"])\n", "rankCols = [re.sub(pattern=\"rank_\",repl=\"vaerdiSlope_\",string=i) for i in includeCols ]\n", "renamedDf = (df\n", " .select(*finalCols)\n", " .select([F.col(val).alias(rankCols[idx]) for idx,val in enumerate(includeCols)])\n", " .filter((F.col(\"kortBeskrivelse\") == \"APS\") | (F.col(\"kortBeskrivelse\") == \"AS\"))\n", " )\n", "renamedDf.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "windowSpecRank =(Window.partitionBy(F.col(\"cvrNummer\"))).orderBy(F.col(\"periode_gyldigFra\").desc())\n", "groupCols = [\"cvrNummer\",\"vaerdi\"]\n", "\n", "companyNameDf = (sqlContext\n", " .read\n", " .parquet(\"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/companyCvrData\")\n", " .withColumn(colName=\"rank\",col=F.rank().over(windowSpecRank))\n", " .filter((F.col(\"rank\")==1) & (F.col(\"sekvensnr\")==0))\n", " .select([F.col(i) for i in groupCols])\n", " .withColumnRenamed(existing=\"vaerdi\",new=\"navn\")\n", " .orderBy(F.col(\"cvrNummer\"))\n", " .cache()\n", " )" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+---------+-----+--------------------+---------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+-----------+-----------------+------------------+--------------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+\n", "| navn|cvrNummer|label| status|kortBeskrivelse|AarsVaerk_1|AarsVaerk_2|AarsVaerk_3|AarsVaerk_4|AarsVaerk_5|AarsVaerk_6|AarsVaerk_7|AarsVaerk_8|AarsVaerk_9|AarsVaerk_10|AarsVaerk_11|AarsVaerk_12|AarsVaerk_13|AarsVaerk_14|AarsVaerk_15|avgVarighed|totalAabneEnheder|totalLukketEnheder| vaerdiSlope_1|vaerdiSlope_2|vaerdiSlope_3|vaerdiSlope_4|vaerdiSlope_5|vaerdiSlope_6|vaerdiSlope_7|reklamebeskyttet|\n", "+--------------------+---------+-----+--------------------+---------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+-----------+-----------------+------------------+--------------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+\n", "|KØBMANDSSELSKABET...| 10033764| 0.0| [NORMAL]| APS| 5.0| 5.0| 5.0| 5.0| 5.0| 2.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 276.0| 2.0| 1.0|1.0000002802690583E8| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|\n", "|JB PROFESSIONEL B...| 10045126| 1.0|[OPLØST EFTER KON...| APS| 1.0| 5.0| 1.0| 2.0| 2.0| 2.0| 2.0| 2.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 2577.5| 2.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 1.0|\n", "|AGENDA MANAGEMENT...| 10052262| 1.0|[OPLØST EFTER KON...| APS| 1.0| 5.0| 5.0| 5.0| 10.0| 10.0| 10.0| 10.0| 10.0| 2.0| 0.0| 0.0| 0.0| 0.0| 0.0| 4928.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|\n", "|TORNUM SNEDKER- O...| 10059968| 0.0| [NORMAL]| APS| 2.0| 2.0| 2.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 5.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|\n", "+--------------------+---------+-----+--------------------+---------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+------------+------------+------------+------------+------------+-----------+-----------------+------------------+--------------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+\n", "only showing top 4 rows\n", "\n" ] } ], "source": [ "labelCols = [\"navn\",\"cvrNummer\",\"label\",\"status\",\"kortBeskrivelse\"]\n", "featCols = [i for i in companyNameDf.columns+renamedDf.columns if i not in labelCols]\n", "\n", "#get minimum values from each column\n", "minCols = [F.min(i).alias(i) for i in featCols]\n", "minValsRdd = renamedDf.groupby().agg(*minCols).rdd\n", "broadcastedmin = sc.broadcast(minValsRdd.first().asDict())\n", "\n", "#create array that subtracts minimum value in the numeric columns.\n", "logColsSelected = [F.col(i).alias(i) for i in labelCols]+[(F.col(i)-F.lit(broadcastedmin.value[i])).alias(i) for i in featCols]\n", "\n", "#takes log(x+1) to the numeric columns and fills the blanks with 0.0 \n", "logDf = (renamedDf\n", " .join(companyNameDf,(companyNameDf[\"cvrNummer\"]==renamedDf[\"cvrNummer\"]),\"inner\")\n", " .drop(companyNameDf[\"cvrNummer\"])\n", " .select(*logColsSelected)\n", " #.select([F.col(i).alias(i) for i in labelCols]+[F.log1p(F.col(i)).alias(i) for i in featCols])\n", " .distinct()\n", " .na\n", " .fill(0.0,featCols)\n", " .cache()\n", " )\n", "logDf.show(4)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "strs = \"\"\n", "excludedCols = [\"medArb_\"+str(i) for i in range(1,16)]+[\"cvrNummer\",\"label\",\"status\",\"navn\",\"kortBeskrivelse\"]\n", "for i in logDf.columns:\n", " if i not in excludedCols:\n", " strs += i+\" + \"\n", "\n", "#excludedCols \n", "imputedDf = logDf.fillna(value=0.0)\n", "formula = RFormula(formula=\"label ~ \"+strs[:-3],labelCol=\"label\")\n", "\n", "glr = GeneralizedLinearRegression(family=\"binomial\", link=\"logit\", maxIter=10, regParam=0.3)\n", "standardScale = StandardScaler(withMean=True,withStd=True,inputCol=glr.getFeaturesCol(),outputCol=\"scaledFeatures\")\n", "\n", "\n", "pipeline = Pipeline(stages=[formula,standardScale,glr])\n", "\n", "grid = (ParamGridBuilder()\n", " .baseOn({lr.predictionCol:\"prediction\"})\n", " .baseOn({lr.rawPredictionCol:\"rawPrediction\"})\n", " .baseOn({lr.probabilityCol:\"probability\"})\n", " .baseOn({lr.labelCol:\"label\"})\n", " .baseOn({lr.featuresCol:\"features\"})\n", " .addGrid(param=lr.elasticNetParam,values=[0.1,1.0])\n", " .addGrid(param=lr.getMaxIter,values=[10])\n", " .build()\n", " )\n", "\n", "evaluate = BinaryClassificationEvaluator()\n", "\n", "trainEvalModel = TrainValidationSplit(estimator=pipeline,estimatorParamMaps=grid,evaluator=evaluate,trainRatio=0.8)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cols = [i for i in logDf.columns if i not in excludedCols]+[\"label\"]\n", "\n", "model = pipeline.fit(imputedDf.select(*cols).filter(F.col(\"label\") <= 1))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predict = model.transform(imputedDf.select(*cols).filter(F.col(\"label\") <= 1))\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficient Standard Errors: [0.0027153657861677017, 0.0033576183070386195, 0.0036618804887292426, 0.0035754531498949087, 0.004800442480010473, 0.0053070346268332775, 0.006044648209200047, 0.006661858012195807, 0.007406240336102599, 0.007567900680939748, 0.008093150133059149, 0.008512938676244109, 0.008348498511501063, 0.009076922598213258, 0.009345658737006969, 3.570481466497785e-06, 0.0077130328315039385, 0.01130608609950618, 2.2163928182556967e-10, 4.2738460918044414e-08, 1.7226360583964896e-08, 1.5410312395327676e-09, 1.1484656996653029e-07, 1.2769605473113265e-07, 3.5900730800140656e-07, 0.012775368232153424, 0.013846855355999527]\n", "T Values: [-11.13953124016816, 19.273340427415945, -6.0592684049208865, 0.9099659049882795, -2.4512213897006436, -1.3093167681591893, -1.8914994368699147, -2.1100010055623724, -3.195706289762549, 0.34554268948276684, 0.7313830924481478, -1.3424645212472244, 2.7347575160967175, -2.395285013922758, -3.397615083227601, 19.49128313879151, 44.36488143421366, -68.92167133560662, -31.8529021339281, -0.2222128572690277, 1.5991730937620523, -0.4287430069091245, -0.6585598419860235, 0.34954974901357716, -0.8366288434819199, -9.752079731824601, -87.10633918581522]\n", "P Values: [0.0, 0.0, 1.3674203991342893e-09, 0.36284049095630877, 0.01423723563300916, 0.1904270719448573, 0.058557706085733185, 0.0348582692614563, 0.001394890275855598, 0.7296864227022648, 0.4645451895952988, 0.1794454265400829, 0.006242624418354437, 0.01660745022866772, 0.0006797599000241128, 0.0, 0.0, 0.0, 0.0, 0.8241481856625836, 0.10978214704359468, 0.6681102590719781, 0.5101784575418398, 0.7266766289362669, 0.40280122745020397, 0.0, 0.0]\n", "Dispersion: 1.0\n", "Null Deviance: 121055.98568276048\n", "Residual Degree Of Freedom Null: 120237\n", "Deviance: 101184.7918554938\n", "Residual Degree Of Freedom: 120211\n", "AIC: 101238.79185549381\n", "Deviance Residuals: \n", "+--------------------+\n", "| devianceResiduals|\n", "+--------------------+\n", "| -0.505190997906927|\n", "| 1.3078044644641424|\n", "| 1.6187928357391999|\n", "| -0.4495402012605015|\n", "|-0.33229486966967725|\n", "| 1.8426902937742191|\n", "| 1.5254278324294124|\n", "| -0.471613616639455|\n", "| -0.4950018560652388|\n", "| -0.8640582707493624|\n", "| 1.5853489569577377|\n", "|-0.35961459244144967|\n", "| -0.7359717752958044|\n", "|-0.30842335391405723|\n", "| -0.7722726367062537|\n", "| -0.3493557842750053|\n", "| -0.4483736687291667|\n", "| -0.8236446082029221|\n", "| -0.7976553154273766|\n", "| -0.3346202882235235|\n", "+--------------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "p = model.stages[-1].summary\n", "\n", "print(\"Coefficient Standard Errors: \" + str(p.coefficientStandardErrors))\n", "print(\"T Values: \" + str(p.tValues))\n", "print(\"P Values: \" + str(p.pValues))\n", "print(\"Dispersion: \" + str(p.dispersion))\n", "print(\"Null Deviance: \" + str(p.nullDeviance))\n", "print(\"Residual Degree Of Freedom Null: \" + str(p.residualDegreeOfFreedomNull))\n", "print(\"Deviance: \" + str(p.deviance))\n", "print(\"Residual Degree Of Freedom: \" + str(p.residualDegreeOfFreedom))\n", "print(\"AIC: \" + str(p.aic))\n", "print(\"Deviance Residuals: \")\n", "p.residuals().show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-46-f97013934ad1>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-46-f97013934ad1>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m summary = {\"Labels\":imputedDf.columns[3:,\"coefficient Std Err\":p.coefficientStandardErrors,\"T Values\":p.tValues,\"P Values\":p.pValues}\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "summary = {\"Labels\":cols,\"coefficient Std Err\":p.coefficientStandardErrors,\"T Values\":p.tValues,\"P Values\":p.pValues}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "pd.DataFrame(summary,columns=[\"Labels\",\"coefficient Std Err\",\"T Values\",\"P Values\"])\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------+-------------------+------------------+------------------+------------------+-----------------+-----------------+------------------+------------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+------------------+------------------+-----------------+------------------+------------------+-------------------+-----------------+------------------+-----------------+-------------------+------------------+------------------+------------------+-------------------+\n", "|summary| cvrNummer| label| AarsVaerk_1| AarsVaerk_2| AarsVaerk_3| AarsVaerk_4| AarsVaerk_5| AarsVaerk_6| AarsVaerk_7| AarsVaerk_8| AarsVaerk_9| AarsVaerk_10| AarsVaerk_11| AarsVaerk_12| AarsVaerk_13| AarsVaerk_14| AarsVaerk_15| medArb_1| medArb_2| medArb_3| medArb_4| medArb_5| medArb_6| medArb_7| medArb_8| medArb_9| medArb_10| medArb_11| medArb_12| medArb_13| medArb_14| medArb_15| avgVarighed| totalAabneEnheder| totalLukketEnheder| vaerdiSlope_1| vaerdiSlope_2| vaerdiSlope_3| vaerdiSlope_4| vaerdiSlope_5| vaerdiSlope_6| vaerdiSlope_7| reklamebeskyttet|\n", "+-------+-------------------+------------------+------------------+------------------+-----------------+-----------------+------------------+------------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+------------------+------------------+-----------------+------------------+------------------+-------------------+-----------------+------------------+-----------------+-------------------+------------------+------------------+------------------+-------------------+\n", "| mean|2.992587977176892E7|0.6770318443681625| 4.963124592938926| 6.599220488736471|7.883416689601503|9.083146093176817| 9.771213165424054|10.298917308714694|10.77020146114678|11.333662682729393|11.979418607991267|12.565540744720142|13.190747344984533|13.685712327515306|14.092008168822328|15.306301524961965|15.918722786647315| 5.503519210136332|8.213265810368465|9.818617076804813|11.16323711482025|12.375860604668324|12.991983190015274|13.533087125405036|13.999154802152278|14.809654439433546|15.498606587650674| 16.10517604870648|16.86322306497304|17.443267528931244|18.770017337154584|19.24664643532568|3104.0394283223163|1.1975634543320737|0.42037334776466445|9367.222088225697|14773.935940526042|37270.54804289701| 534722.7367923224| 69479.75111113006| 79309.96883183606|246815.61845150983|0.05565606446274231|\n", "| stddev|1.595872029695645E7|0.4815407404508707|28.219409782015937|31.349174803319965|36.38636236195736|40.84287153372559|43.053166612742295| 43.50716762822306|44.69115651768418| 46.24885171030284| 48.03721081702004|49.663482624596746| 50.952745810432|51.757992157941516| 51.79592907581495|57.952550432313735|59.896979275308205|31.469466071930402| 35.7271436211818|40.94583736584511| 45.5828092120186| 48.68024770176678| 49.50427185113245|50.816539411344706| 52.01619572491007|54.732773025784745| 56.2466055958348|56.478429021321915|58.18250553629226| 59.22942233650709| 64.22850620145455|64.30084551221002| 2826.832782887831| 4.297482547820878| 2.316930888416194|1104747.513966895|485532.34538503713|947528.7880333586|4.535843433554792E7|1192985.3794908465|1020811.2995945786| 4224380.725125994|0.22925669525652828|\n", "+-------+-------------------+------------------+------------------+------------------+-----------------+-----------------+------------------+------------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+------------------+------------------+------------------+------------------+-----------------+------------------+------------------+-----------------+------------------+------------------+-------------------+-----------------+------------------+-----------------+-------------------+------------------+------------------+------------------+-------------------+\n", "\n" ] } ], "source": [ "#check mean and stddev\n", "descriptionCVR.filter((F.col(\"summary\") ==\"mean\") | (F.col(\"summary\") ==\"stddev\")).show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+--------------------+\n", "|cvrNummer| navn|\n", "+---------+--------------------+\n", "| 10000009| YELLOW|\n", "| 10000025|WATERFRONT CONNEC...|\n", "+---------+--------------------+\n", "only showing top 2 rows\n", "\n" ] } ], "source": [ "windowSpecRank =(Window.partitionBy(F.col(\"cvrNummer\"))).orderBy(F.col(\"gyldigFra\").desc())\n", "\n", "groupCols = [\"cvrNummer\",\"vaerdi\"]\n", "\n", "companyNameDf = (sqlContext\n", " .read\n", " .parquet(\"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/\"+\"companyCvrData\")\n", " .withColumn(colName=\"rank\",col=F.rank().over(windowSpecRank))\n", " .filter((F.col(\"rank\")==1) & (F.col(\"sekvensnr\")==0))\n", " .select([F.col(i) for i in groupCols])\n", " .withColumnRenamed(existing=\"vaerdi\",new=\"navn\")\n", " .orderBy(F.col(\"cvrNummer\"))\n", " .cache()\n", " )\n", "companyNameDf.show(2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+---------+-----+--------------------+--------------------+\n", "| navn|cvrNummer|label| status| features|\n", "+--------------------+---------+-----+--------------------+--------------------+\n", "| EMPAKA KARTONNAGE| 10016533| 1| [NORMAL]|[0.12721405809989...|\n", "| DET GAMLE GODS| 10016606| 1| [NORMAL]|[-0.1483813281958...|\n", "| DIXEN| 10018064| 0|[OPLØST EFTER KON...|[-0.1483813281958...|\n", "| TRELBORG VVS| 10063760| 1| [NORMAL]|[-0.1483813281958...|\n", "| CYBERSUN| 10065917| 0|[OPLØST EFTER KON...|[-0.0258944898421...|\n", "| HME2| 10080207| 1| [NORMAL]|[-0.1483813281958...|\n", "|TØMRERFIRMAET HER...| 10082528| 1| [NORMAL]|[-0.1177596186074...|\n", "| MØRKHOLT VINDUER| 10096227| 1| [NORMAL]|[-0.1483813281958...|\n", "| KISØ| 10108993| 1| [NORMAL]|[-0.1177596186074...|\n", "| C-CUT| 10117267| 1|[OPLØST EFTER ERK...|[-0.1483813281958...|\n", "| HLS INVEST| 10120829| 0|[OPLØST EFTER KON...|[-0.1483813281958...|\n", "| ELKA RAINWEAR| 10138698| 1| [NORMAL]|[0.12721405809989...|\n", "| BØMLER| 10142539| 0|[OPLØST EFTER KON...|[-0.1483813281958...|\n", "| NORDISKE MEDIER| 10150825| 1| [NORMAL]|[-0.1177596186074...|\n", "| SBR POUL-ERIK KJÆR| 10178509| 0|[OPLØST EFTER KON...|[-0.1483813281958...|\n", "| SCAN ORIENT| 10228476| 1|[TVANGSOPLØST, UN...|[-0.1483813281958...|\n", "| AQUA-NORD| 10310989| 1|[OPLØST EFTER FRI...|[-0.1177596186074...|\n", "| IKAST ETIKET| 10402980| 1| [NORMAL]|[-0.1177596186074...|\n", "|BRDR. JØRGENSEN C...| 10518431| 1| [NORMAL]|[0.43343115398404...|\n", "| AXEL MADSEN| 10553490| 1| [NORMAL]|[-0.0258944898421...|\n", "+--------------------+---------+-----+--------------------+--------------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "#take ln(x+1) of features\n", "\n", "labelCols = [\"cvrNummer\",\"label\",\"status\"]\n", "logFeatCols = [i for i in renamedDf.columns if i not in labelCols]\n", "#print(logFeatCols)\n", "mininum = descriptionCVR.filter(F.col(\"summary\")==\"min\").collect()[0]\n", "#print(mininum)\n", "\n", "\n", "logDf = (renamedDf\n", " .select([F.col(\"cvrNummer\"),F.col(\"label\")]+[F.log1p(F.col(i)-F.lit(mininum[i])).alias(i) for i in logFeatCols])\n", " .na\n", " .fill(0.0,logFeatCols)\n", " \n", " )\n", "#logDf.show(2)\n", "\n", "\n", "#First convert features to vetor\n", "toDenseUDf = F.udf(lambda x: Vectors.dense(x.toArray()),VectorUDT())\n", "vectorizer = VectorAssembler(inputCols=logFeatCols,outputCol=\"features\")\n", "\n", "rawVectorDataDf = (vectorizer.transform(renamedDf \n", " .join(companyNameDf,(companyNameDf[\"cvrNummer\"]==renamedDf[\"cvrNummer\"]),\"inner\")\n", " .drop(companyNameDf[\"cvrNummer\"])\n", " #.select(*logColsSelected) \n", " .na\n", " .fill(0.0,logFeatCols)\n", " .distinct()\n", " )\n", " .select([\"navn\"]+labelCols+[toDenseUDf(vectorizer.getOutputCol()).alias(vectorizer.getOutputCol())])\n", " )\n", "\n", "standardScale = StandardScaler(withMean=True,withStd=True,inputCol=vectorizer.getOutputCol(),outputCol=\"scaledFeatures\")\n", "standardScaleModel = standardScale.fit(rawVectorDataDf)\n", "scaledFeaturesDf = (standardScaleModel\n", " .transform(rawVectorDataDf)\n", " .drop(\"features\")\n", " .withColumnRenamed(existing=\"scaledFeatures\",new=\"features\")\n", " )\n", "\n", "scaledFeaturesDf.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----+-----+\n", "|label|count|\n", "+-----+-----+\n", "| 0| 6078|\n", "| 1|25756|\n", "+-----+-----+\n", "\n", "+-----+------+\n", "|label| count|\n", "+-----+------+\n", "| 0| 24465|\n", "| 1|103472|\n", "+-----+------+\n", "\n", "Number of data points: 160648\n", "Number of data points train: 127937\n", "Number of data points test: 31834\n" ] } ], "source": [ "#put them into a feature vecto\n", "vectorizedTestDf = scaledFeaturesDf.filter(F.col(\"label\") <= 1).sampleBy(\"label\", fractions={0: 0.2, 1: 0.2}, seed=42)\n", "vectorizedTestDf.groupBy(\"label\").count().show()\n", "\n", "scaledCvrDf = scaledFeaturesDf.select(F.col(\"cvrNummer\"))\n", "cvrTestDf = vectorizedTestDf.select(\"cvrNummer\")\n", "cvrTrainDf = scaledCvrDf.subtract(cvrTestDf) #take the other partion as training set\n", "\n", "vectorizedTrainDf = (scaledFeaturesDf\n", " .filter(F.col(\"label\") <= 1)\n", " .join(cvrTrainDf,(scaledFeaturesDf[\"cvrNummer\"] == cvrTrainDf[\"cvrNummer\"]),\"inner\")\n", " .drop(cvrTrainDf[\"cvrNummer\"])\n", " )\n", "vectorizedTrainDf.groupBy(\"label\").count().show()\n", "print(\"Number of data points: \"+str(scaledFeaturesDf.count()))\n", "print(\"Number of data points train: \"+str(vectorizedTrainDf.select(\"cvrNummer\").count()))\n", "print(\"Number of data points test: \"+str(vectorizedTestDf.select(\"cvrNummer\").count()))\n", "#vectorizedTrainDf.printSchema()\n", "#print(vectorizedTrainDf.first())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+--------------------+--------------------+---------+\n", "| navn|label| status| features|cvrNummer|\n", "+--------------------+-----+--------------------+--------------------+---------+\n", "|SKYTTENS HANDEL O...| 1|[OPLØST EFTER FUS...|[-0.1483813281958...| 10019052|\n", "| DIKI.NET| 0|[OPLØST EFTER KON...|[-0.1483813281958...| 10026113|\n", "| CTEK| 1|[OPLØST EFTER FUS...|[-0.1177596186074...| 10040523|\n", "| VG ENTREPRENØR| 1| [NORMAL]|[0.43343115398404...| 10057426|\n", "|NORDBYENS OLIEFYR...| 1| [NORMAL]|[-0.1483813281958...| 10089514|\n", "|EXPRESS LABELLING...| 1|[TVANGSOPLØST, UN...|[-0.1483813281958...| 10091713|\n", "|PSYKOLOGERNE VED ...| 1|[OPLØST EFTER FUS...|[-0.1790030377842...| 10108624|\n", "| RAH HOLDING| 1| [NORMAL]|[-0.1483813281958...| 10127351|\n", "|AMAGER BROLÆGGERF...| 0|[OPLØST EFTER KON...|[-0.1177596186074...| 10128587|\n", "| ART OF JEWEL| 1| [NORMAL]|[-0.1177596186074...| 10145619|\n", "| FRONT FREDERICIA| 1|[OPLØST EFTER FUS...|[0.12721405809989...| 10156521|\n", "|M.L.R. AF 16. APR...| 1|[OPLØST EFTER ERK...|[-0.1177596186074...| 10200288|\n", "| FOLKE LARSENS EFTF.| 1| [NORMAL]|[-0.0258944898421...| 10351235|\n", "| TAORA| 1|[OPLØST EFTER FUS...|[-0.1790030377842...| 10366380|\n", "| CH AF 24/8 2000| 1|[OPLØST EFTER FRI...|[0.12721405809989...| 10412986|\n", "| SIBA-ØST| 1|[OPLØST EFTER FRI...|[-0.1483813281958...| 10481171|\n", "| STEVNS TRYK| 1|[OPLØST EFTER ERK...|[-0.1483813281958...| 10577675|\n", "| PERSPEKTIV HOLDING| 1| [NORMAL]|[-0.1483813281958...| 10606683|\n", "| INTRUM JUSTITIA| 1| [NORMAL]|[1.35208244163648...| 10613779|\n", "| LOBO MØBLER| 1| [NORMAL]|[-0.1177596186074...| 10629098|\n", "+--------------------+-----+--------------------+--------------------+---------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "vectorizedTrainDf.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8090805096973791" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Train the logistic regressionmodel\n", "lr = LogisticRegression()\n", "grid = (ParamGridBuilder()\n", " .baseOn({lr.predictionCol:\"prediction\"})\n", " .baseOn({lr.rawPredictionCol:\"rawPrediction\"})\n", " .baseOn({lr.probabilityCol:\"probability\"})\n", " .baseOn({lr.labelCol:\"label\"})\n", " .baseOn({lr.featuresCol:\"features\"})\n", " .addGrid(param=lr.elasticNetParam,values=[0.1,1.0])\n", " .addGrid(param=lr.getMaxIter,values=[10])\n", " .build()\n", " )\n", "evaluate = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\n", "crossVal = CrossValidator(estimator=lr,estimatorParamMaps=grid,evaluator=evaluate,numFolds=10)\n", "\n", "crossValModel = crossVal.fit(dataset=vectorizedTrainDf)\n", "evaluate.evaluate(crossValModel.transform(vectorizedTestDf))\n", "#coef = lrModel.coefficients" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bestModel = crossValModel.bestModel" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#test the values\n", "result = bestModel.transform(vectorizedTestDf)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----+-----+\n", "|cases|count|\n", "+-----+-----+\n", "| TP|25710|\n", "| TN| 36|\n", "| FN| 46|\n", "| FP| 6042|\n", "+-----+-----+\n", "\n" ] } ], "source": [ "#result.orderBy(\"prediction\").show(100)\n", "confCols = [F.col(i) for i in [\"TP\",\"TN\",\"FP\",\"FN\"]]\n", "\n", "\n", "csCols = [F.when((F.col(\"label\")==1) & (F.col(\"difference\") == 0),\"TP\")\n", " ,F.when((F.col(\"label\")==0) & (F.col(\"difference\") == 0),\"TN\")\n", " ,F.when(F.col(\"difference\") == 1,\"FN\")\n", " ,F.when(F.col(\"difference\") == -1,\"FP\")\n", " ]\n", "\n", "confusionDf = result.select(F.col(\"label\"),F.col(\"prediction\"),(F.col(\"label\")-F.col(\"prediction\")).alias(\"difference\"))\n", "(confusionDf\n", " .select(F.coalesce(*csCols).alias(\"cases\") \n", " #,.otherwise(0).alias(\"FP\")\n", " #,.otherwise(0).alias(\"FN\")\n", " )\n", " .groupBy(\"cases\").count()\n", ").show()\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crossValModel.bestModel.hasSummary" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "summary = crossValModel.bestModel.summary" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+--------------------+--------------------+---------+--------------------+--------------------+----------+\n", "| navn|label| status| features|cvrNummer| rawPrediction| probability|prediction|\n", "+--------------------+-----+--------------------+--------------------+---------+--------------------+--------------------+----------+\n", "|SKYTTENS HANDEL O...| 1|[OPLØST EFTER FUS...|[-0.1483813281958...| 10019052|[-0.1436615979228...|[0.46414624375980...| 1.0|\n", "| DIKI.NET| 0|[OPLØST EFTER KON...|[-0.1483813281958...| 10026113|[-0.2470744652013...|[0.43854370342953...| 1.0|\n", "| CTEK| 1|[OPLØST EFTER FUS...|[-0.1177596186074...| 10040523|[-0.6803097893868...|[0.33619216432178...| 1.0|\n", "| VG ENTREPRENØR| 1| [NORMAL]|[0.43343115398404...| 10057426|[-3.7227933151943...|[0.02359613625108...| 1.0|\n", "|NORDBYENS OLIEFYR...| 1| [NORMAL]|[-0.1483813281958...| 10089514|[-2.8801667669252...|[0.05314274429565...| 1.0|\n", "|EXPRESS LABELLING...| 1|[TVANGSOPLØST, UN...|[-0.1483813281958...| 10091713|[-0.1715003567869...|[0.45722969080531...| 1.0|\n", "|PSYKOLOGERNE VED ...| 1|[OPLØST EFTER FUS...|[-0.1790030377842...| 10108624|[-0.6485674484631...|[0.34331243211346...| 1.0|\n", "| RAH HOLDING| 1| [NORMAL]|[-0.1483813281958...| 10127351|[-2.8804422421872...|[0.05312888447426...| 1.0|\n", "|AMAGER BROLÆGGERF...| 0|[OPLØST EFTER KON...|[-0.1177596186074...| 10128587|[-1.1369952588026...|[0.24287246215155...| 1.0|\n", "| ART OF JEWEL| 1| [NORMAL]|[-0.1177596186074...| 10145619|[-6.3724232788323...|[0.00170510286780...| 1.0|\n", "| FRONT FREDERICIA| 1|[OPLØST EFTER FUS...|[0.12721405809989...| 10156521|[-0.3793527083137...|[0.40628302543127...| 1.0|\n", "|M.L.R. AF 16. APR...| 1|[OPLØST EFTER ERK...|[-0.1177596186074...| 10200288|[-1.4990290785923...|[0.18257037794247...| 1.0|\n", "| FOLKE LARSENS EFTF.| 1| [NORMAL]|[-0.0258944898421...| 10351235|[-3.6327245272056...|[0.02576276706084...| 1.0|\n", "| TAORA| 1|[OPLØST EFTER FUS...|[-0.1790030377842...| 10366380|[-0.9451119361175...|[0.27986891278054...| 1.0|\n", "| CH AF 24/8 2000| 1|[OPLØST EFTER FRI...|[0.12721405809989...| 10412986|[-0.5220910962423...|[0.37236339559707...| 1.0|\n", "| SIBA-ØST| 1|[OPLØST EFTER FRI...|[-0.1483813281958...| 10481171|[-0.6766429736895...|[0.33701096745397...| 1.0|\n", "| STEVNS TRYK| 1|[OPLØST EFTER ERK...|[-0.1483813281958...| 10577675|[-0.9080179077065...|[0.28740560581535...| 1.0|\n", "| PERSPEKTIV HOLDING| 1| [NORMAL]|[-0.1483813281958...| 10606683|[-4.1379135688213...|[0.01570550931538...| 1.0|\n", "| INTRUM JUSTITIA| 1| [NORMAL]|[1.35208244163648...| 10613779|[-2.8641158595488...|[0.05395622053629...| 1.0|\n", "| LOBO MØBLER| 1| [NORMAL]|[-0.1177596186074...| 10629098|[-3.4536192522129...|[0.03066110863007...| 1.0|\n", "+--------------------+-----+--------------------+--------------------+---------+--------------------+--------------------+----------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "summary.predictions.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0