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The exponential function $\mathrm{exp}(x) := e^x$ is computed as follows: | math.exp(1) | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The natural logarithm $\ln(x)$, which is defined as the inverse of the function $\exp(x)$, is called `log` (instead of `ln`): | math.log(math.e * math.e) | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The square root $\sqrt{x}$ of a number $x$ is computed using the function `sqrt`: | math.sqrt(2) | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The flooring function $\texttt{floor}(x)$ truncates a floating point number $x$ down to the biggest integer number less or equal to $x$:$$ \texttt{floor}(x) = \max(\{ n \in \mathbb{Z} \mid n \leq x \} $$ | math.floor(1.999) | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The ceiling function $\texttt{ceil}(x)$ rounds a floating point number $x$ up to the next integer number bigger or equal to $x$:$$ \texttt{ceil}(x) = \min(\{ n \in \mathbb{Z} \mid x \leq n \} $$ | math.ceil(1.001)
round(1.5), round(1.39), round(1.51)
abs(-1) | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The Help System Typing a single question mark '?' starts the help system of *Jupyter*. | ? | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
If the name of a module is followed by a question mark, a description of the module is printed. | math? | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
This also works for functions defined in a module. | math.sin? | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The question mark operator only works inside a Jupyter notebook. If you are using an interpreted in the command line for executing *Python* commands, use the function `help` instead. | help(math)
help(math.sin)
help(dir) | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
We can use the function dir() to print the names of the variables that have been defined. | dir()
2 * 3
_ | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
The magic command %quickref prints an overview of the so called magic commands that are available in Jupyter notebooks. | %quickref | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
We can use the command ls to list the files in the current directory. | ls -al | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
Prefixing a shell command with a `!` executes this command in a shell. Below, I have used the Windows command `dir`. On Linux, the corresponding command is called `ls`. | !ls
!dir | _____no_output_____ | MIT | Python/Introduction.ipynb | BuserLukas/Logic |
Extracting training data from the ODC * [**Sign up to the DEA Sandbox**](https://docs.dea.ga.gov.au/setup/sandbox.html) to run this notebook interactively from a browser* **Compatibility:** Notebook currently compatible with the `DEA Sandbox` environment* **Products used:** [ls8_nbart_geomedian_annual](https://explorer.sandbox.dea.ga.gov.au/products/ls8_nbart_geomedian_annual/extents),[ls8_nbart_tmad_annual](https://explorer.sandbox.dea.ga.gov.au/products/ls8_nbart_tmad_annual/extents),[fc_percentile_albers_annual](https://explorer.sandbox.dea.ga.gov.au/products/fc_percentile_albers_annual/extents) Background**Training data** is the most important part of any supervised machine learning workflow. The quality of the training data has a greater impact on the classification than the algorithm used. Large and accurate training data sets are preferable: increasing the training sample size results in increased classification accuracy ([Maxell et al 2018](https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1433343)). A review of training data methods in the context of Earth Observation is available [here](https://www.mdpi.com/2072-4292/12/6/1034) When creating training labels, be sure to capture the **spectral variability** of the class, and to use imagery from the time period you want to classify (rather than relying on basemap composites). Another common problem with training data is **class imbalance**. This can occur when one of your classes is relatively rare and therefore the rare class will comprise a smaller proportion of the training set. When imbalanced data is used, it is common that the final classification will under-predict less abundant classes relative to their true proportion.There are many platforms to use for gathering training labels, the best one to use depends on your application. GIS platforms are great for collection training data as they are highly flexible and mature platforms; [Geo-Wiki](https://www.geo-wiki.org/) and [Collect Earth Online](https://collect.earth/home) are two open-source websites that may also be useful depending on the reference data strategy employed. Alternatively, there are many pre-existing training datasets on the web that may be useful, e.g. [Radiant Earth](https://www.radiant.earth/) manages a growing number of reference datasets for use by anyone. DescriptionThis notebook will extract training data (feature layers, in machine learning parlance) from the `open-data-cube` using labelled geometries within a geojson. The default example will use the crop/non-crop labels within the `'data/crop_training_WA.geojson'` file. This reference data was acquired and pre-processed from the USGS's Global Food Security Analysis Data portal [here](https://croplands.org/app/data/search?page=1&page_size=200) and [here](https://e4ftl01.cr.usgs.gov/MEASURES/GFSAD30VAL.001/2008.01.01/).To do this, we rely on a custom `dea-notebooks` function called `collect_training_data`, contained within the [dea_tools.classification](../../Tools/dea_tools/classification.py) script. The principal goal of this notebook is to familarise users with this function so they can extract the appropriate data for their use-case. The default example also highlights extracting a set of useful feature layers for generating a cropland mask forWA.1. Preview the polygons in our training data by plotting them on a basemap2. Define a feature layer function to pass to `collect_training_data`3. Extract training data from the datacube using `collect_training_data`4. Export the training data to disk for use in subsequent scripts*** Getting startedTo run this analysis, run all the cells in the notebook, starting with the "Load packages" cell. Load packages | %matplotlib inline
import os
import sys
import datacube
import numpy as np
import xarray as xr
import subprocess as sp
import geopandas as gpd
from odc.io.cgroups import get_cpu_quota
from datacube.utils.geometry import assign_crs
sys.path.append('../../Scripts')
from dea_plotting import map_shapefile
from dea_bandindices import calculate_indices
from dea_classificationtools import collect_training_data
import warnings
warnings.filterwarnings("ignore") | /env/lib/python3.6/site-packages/geopandas/_compat.py:88: UserWarning: The Shapely GEOS version (3.7.2-CAPI-1.11.0 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.
shapely_geos_version, geos_capi_version_string
/env/lib/python3.6/site-packages/datacube/storage/masking.py:8: DeprecationWarning: datacube.storage.masking has moved to datacube.utils.masking
category=DeprecationWarning)
| Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Analysis parameters* `path`: The path to the input vector file from which we will extract training data. A default geojson is provided.* `field`: This is the name of column in your shapefile attribute table that contains the class labels. **The class labels must be integers** | path = 'data/crop_training_WA.geojson'
field = 'class' | _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Find the number of CPUs | ncpus = round(get_cpu_quota())
print('ncpus = ' + str(ncpus)) | ncpus = 7
| Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Preview input dataWe can load and preview our input data shapefile using `geopandas`. The shapefile should contain a column with class labels (e.g. 'class'). These labels will be used to train our model. > Remember, the class labels **must** be represented by `integers`. | # Load input data shapefile
input_data = gpd.read_file(path)
# Plot first five rows
input_data.head()
# Plot training data in an interactive map
map_shapefile(input_data, attribute=field) | _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Extracting training dataThe function `collect_training_data` takes our geojson containing class labels and extracts training data (features) from the datacube over the locations specified by the input geometries. The function will also pre-process our training data by stacking the arrays into a useful format and removing any `NaN` or `inf` values. The below variables can be set within the `collect_training_data` function:* `zonal_stats`: An optional string giving the names of zonal statistics to calculate across each polygon (if the geometries in the vector file are polygons and not points). Default is `None` (all pixel values are returned). Supported values are 'mean', 'median', 'max', and 'min'.* `return_coords`: If `True`, then the training data will contain two extra columns 'x_coord' and 'y_coord' corresponding to the x,y coordinate of each sample. This variable can be useful for handling spatial autocorrelation between samples later on in the ML workflow when we conduct k-fold cross validation.* `dc_query`: a datacube dictionary query object, This should not contain lat and long (x or y) variables as these are supplied by the 'gdf' geometries.> Note: `collect_training_data` also has a number of additional parameters for handling ODC I/O read failures, where polygons that return an excessive number of null values can be resubmitted to the multiprocessing queue. Check out the [docs](https://github.com/GeoscienceAustralia/dea-notebooks/blob/68d3526f73779f3316c5e28001c69f556c0d39ae/Tools/dea_tools/classification.pyL661) to learn more. In addition to the parameters required for `collect_training_data`, we also need to set up a few parameters for the `dc_query` parameter, such as `measurements` (the bands to load from the satellite), the `resolution` (the cell size), and the `output_crs` (the output projection). | # Set up our inputs to collect_training_data
time = ('2014')
zonal_stats = None
return_coords = True
# Set up the inputs for the ODC query
measurements = ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']
resolution = (-30, 30)
output_crs = 'epsg:3577'
# Generate a new datacube query object
query = {
'time': time,
'measurements': measurements,
'resolution': resolution,
'output_crs': output_crs,
} | _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Defining feature layersTo create the desired feature layers, we pass instructions to `collect training data` through the `feature_func` parameter. * `feature_func`: A function for generating feature layers that is applied to the data within the bounds of the input geometry. The 'feature_func' must accept a 'dc_query' object, and return a single xarray.Dataset or xarray.DataArray containing 2D coordinates (i.e x, y - no time dimension). e.g. def feature_function(query): dc = datacube.Datacube(app='feature_layers') ds = dc.load(**query) ds = ds.mean('time') return dsBelow, we will define a more complicated feature layer function than the brief example shown above. We will calculate some band indices on the Landsat 8 geomedian, append the ternary median aboslute deviation dataset from the same year: [ls8_nbart_tmad_annual](https://explorer.sandbox.dea.ga.gov.au/products/ls8_nbart_tmad_annual/extents), and append fractional cover percentiles for the photosynthetic vegetation band, also from the same year: [fc_percentile_albers_annual](https://explorer.sandbox.dea.ga.gov.au/products/fc_percentile_albers_annual/extents). | def feature_layers(query):
#connect to the datacube
dc = datacube.Datacube(app='custom_feature_layers')
#load ls8 geomedian
ds = dc.load(product='ls8_nbart_geomedian_annual',
**query)
# Calculate some band indices
da = calculate_indices(ds,
index=['NDVI', 'LAI', 'MNDWI'],
drop=False,
collection='ga_ls_2')
# Add TMADs dataset
tmad = dc.load(product='ls8_nbart_tmad_annual',
measurements=['sdev','edev','bcdev'],
like=ds.geobox, #will match geomedian extent
time='2014' #same as geomedian
)
# Add Fractional cover percentiles
fc = dc.load(product='fc_percentile_albers_annual',
measurements=['PV_PC_10','PV_PC_50','PV_PC_90'], #only the PV band
like=ds.geobox, #will match geomedian extent
time='2014' #same as geomedian
)
# Merge results into single dataset
result = xr.merge([da, tmad, fc],compat='override')
return result | _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Now, we can pass this function to `collect_training_data`. This will take a few minutes to run all 430 samples on the default sandbox as it only has two cpus. | %%time
column_names, model_input = collect_training_data(
gdf=input_data,
dc_query=query,
ncpus=ncpus,
return_coords=return_coords,
field=field,
zonal_stats=zonal_stats,
feature_func=feature_layers)
print(column_names)
print('')
print(np.array_str(model_input, precision=2, suppress_small=True)) | ['class', 'blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'NDVI', 'LAI', 'MNDWI', 'sdev', 'edev', 'bcdev', 'PV_PC_10', 'PV_PC_50', 'PV_PC_90', 'x_coord', 'y_coord']
[[ 1. 809. 1249. ... 70. -1447515. -3510225.]
[ 1. 1005. 1464. ... 68. -1393035. -3614685.]
[ 1. 950. 1506. ... 70. -1430025. -3532245.]
...
[ 0. 519. 744. ... 48. -698085. -1657005.]
[ 0. 667. 1049. ... 22. -516825. -3463935.]
[ 0. 232. 344. ... 71. -1468095. -3805095.]]
| Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Separate coordinate dataBy setting `return_coords=True` in the `collect_training_data` function, our training data now has two extra columns called `x_coord` and `y_coord`. We need to separate these from our training dataset as they will not be used to train the machine learning model. Instead, these variables will be used to help conduct Spatial K-fold Cross validation (SKVC) in the notebook `3_Evaluate_optimize_fit_classifier`. For more information on why this is important, see this [article](https://www.tandfonline.com/doi/abs/10.1080/13658816.2017.1346255?journalCode=tgis20). | # Select the variables we want to use to train our model
coord_variables = ['x_coord', 'y_coord']
# Extract relevant indices from the processed shapefile
model_col_indices = [column_names.index(var_name) for var_name in coord_variables]
# Export to coordinates to file
np.savetxt("results/training_data_coordinates.txt", model_input[:, model_col_indices])
| _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Export training dataOnce we've collected all the training data we require, we can write the data to disk. This will allow us to import the data in the next step(s) of the workflow. | # Set the name and location of the output file
output_file = "results/test_training_data.txt"
# Grab all columns except the x-y coords
model_col_indices = [column_names.index(var_name) for var_name in column_names[0:-2]]
# Export files to disk
np.savetxt(output_file, model_input[:, model_col_indices], header=" ".join(column_names[0:-2]), fmt="%4f") | _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
Recommended next stepsTo continue working through the notebooks in this `Scalable Machine Learning on the ODC` workflow, go to the next notebook `2_Inspect_training_data.ipynb`.1. **Extracting training data from the ODC (this notebook)**2. [Inspecting training data](2_Inspect_training_data.ipynb)3. [Evaluate, optimize, and fit a classifier](3_Evaluate_optimize_fit_classifier.ipynb)4. [Classifying satellite data](4_Classify_satellite_data.ipynb)5. [Object-based filtering of pixel classifications](5_Object-based_filtering.ipynb) *** Additional information**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.**Contact:** If you need assistance, please post a question on the [Open Data Cube Slack channel](http://slack.opendatacube.org/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/GeoscienceAustralia/dea-notebooks).**Last modified:** March 2021**Compatible datacube version:** | print(datacube.__version__) | 1.8.4.dev52+g07bc51a5
| Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
TagsBrowse all available tags on the DEA User Guide's [Tags Index](https://docs.dea.ga.gov.au/genindex.html) | **Tags** :index:`Landsat 8 geomedian`, :index:`Landsat 8 TMAD`, :index:`machine learning`, :index:`collect_training_data`, :index:`Fractional Cover` | _____no_output_____ | Apache-2.0 | Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb | anchor228/dea-notebooks |
说明： 给定两个数组arr1和arr2，arr2的元素是不同的，arr2中的所有元素也在arr1中。 对arr1的元素进行排序，以使arr1中项目的相对顺序与arr2中的相同。 不在arr2中出现的元素应按升序放置在arr1的末尾。Example 1: Input: arr1 = [2,3,1,3,2,4,6,7,9,2,19], arr2 = [2,1,4,3,9,6] Output: [2,2,2,1,4,3,3,9,6,7,19]Constraints: 1、arr1.length, arr2.length <= 1000 2、0 <= arr1[i], arr2[i] <= 1000 3、Each arr2[i] is distinct. 4、Each arr2[i] is in arr1. | class Solution:
def relativeSortArray(self, arr1, arr2):
res = sorted(arr1)
idx_r = 0
idx_2 = 0
print(res)
while idx_r < len(res) and idx_2 < len(arr2):
if res[idx_r] == arr2[idx_2]:
if idx_r < len(res) - 1 and res[idx_r + 1] != res[idx_r]:
idx_2 += 1
idx_r += 1
else:
while idx_r < len(res) - 1 and res[idx_r] == res[idx_r + 1]:
idx_r += 1
print(res)
res[idx_r], res[idx_r+1] = res[idx_r+1], res[idx_r]
return res
class Solution:
def relativeSortArray(self, arr1, arr2):
res = []
arr1.sort()
for n_2 in arr2:
while n_2 in arr1:
res.append(n_2)
arr1.remove(n_2)
res.extend(arr1)
return res
class Solution:
def relativeSortArray(self, arr1, arr2):
frequency_dict = {}
for i in range(len(arr1)):
if arr1[i] in frequency_dict:
frequency_dict[arr1[i]] += 1
else:
frequency_dict[arr1[i]] = 1
result = []
for i in range(len(arr2)):
if arr2[i] in frequency_dict:
for j in range(0,frequency_dict[arr2[i]]):
result.append(arr2[i])
frequency_dict.pop(arr2[i])
if len(frequency_dict.keys()) == 0:
return result
else:
excess = list(frequency_dict.keys())
excess.sort()
for i in range(len(excess)):
for j in range(0,frequency_dict[excess[i]]):
result.append(excess[i])
return result
solution = Solution()
solution.relativeSortArray([2,3,1,3,2,4,6,7,9,2,19], [2,1,4,3,9,6]) | {2: 3, 3: 2, 1: 1, 4: 1, 6: 1, 7: 1, 9: 1, 19: 1}
| Apache-2.0 | Sort/0926/1122. Relative Sort Array.ipynb | YuHe0108/Leetcode |
import tensorflow as tf
import tensorflow.feature_column as fc
import os
import sys
import matplotlib.pyplot as plt
from IPython.display import clear_output
tf.enable_eager_execution()
!pip install -q requests
!git clone --depth 1 https://github.com/tensorflow/models
# add the root directory of the repo to your python path
models_path = os.path.join(os.getcwd(), 'models')
sys.path.append(models_path)
# download the dataset
from official.wide_deep import census_dataset, census_main
census_dataset.download("/tmp/census_data/")
# for using in Command line
if 'PYTHONPATH' in os.environ:
os.environ['PYTHONPATH'] += os.pathsep + models_path
else:
os.environ['PYTHONPATH'] = models_path
# Use --help to see what command line options are available:
!python -m official.wide_deep.census_main --help
# run model
!python -m official.wide_deep.census_main --model_type=wide --train_epochs=2
# Read US census data
!ls /tmp/census_data/
train_file = '/tmp/census_data/adult.data'
test_file = '/tmp/census_data/adult.test'
import pandas as pd
train_df = pd.read_csv(train_file, names=census_dataset._CSV_COLUMNS)
test_df = pd.read_csv(test_file, names=census_dataset._CSV_COLUMNS)
train_df.tail()
# 위에서 eager execution enable했기 때문에 쉽게 데이터셋 보기 가능
# tf.data.Dataset으로 slicing하는건 데이터가 작기때문에 가능
def easy_input_function(df, label_key, num_epochs, shuffle, batch_size):
label = df[label_key]
ds = tf.data.Dataset.from_tensor_slices((dict(df),label))
if shuffle:
ds = ds.shuffle(10000)
ds = ds.batch(batch_size).repeat(num_epochs)
return ds
ds = easy_input_function(train_df, label_key='income_bracket', num_epochs=5, shuffle=True, batch_size=10)
for feature_batch, label_batch in ds.take(1):
print('Some feature keys:', list(feature_batch.keys())[:5])
print()
print('A batch of Ages :', feature_batch['age'])
print()
print('A batch of Labels:', label_batch )
# 데이터가 커지면....tf.decode_csv와 tf.data.TextLineDataset를 사용한다. (이 둘을 사용한게 input_fn)
# 위와 같은 결과가 나온다.
# input_fn을 사용하여 batch와 epochs를 지정하여 계속 뽑을 수 있다.
import inspect
print(inspect.getsource(census_dataset.input_fn))
ds = census_dataset.input_fn(train_file, num_epochs=5, shuffle=True, batch_size=10)
for feature_batch, label_batch in ds.take(1):
print('Feature keys:', list(feature_batch.keys())[:5])
print()
print('Age batch :', feature_batch['age'])
print()
print('Label batch :', label_batch )
# Because Estimators expect an input_fn that takes no arguments,
# we typically wrap configurable input function into an obejct with the expected signature.
# For this notebook configure the train_inpf to iterate over the data twice:
import functools
train_inpf = functools.partial(census_dataset.input_fn, train_file, num_epochs=2, shuffle=True, batch_size=64)
test_inpf = functools.partial(census_dataset.input_fn, test_file, num_epochs=1, shuffle=False, batch_size=64)
# model only using a column
classifier = tf.estimator.LinearClassifier(feature_columns=[fc.numeric_column('age')])
classifier.train(train_inpf)
result = classifier.evaluate(test_inpf)
clear_output()
print(result)
# Similarly, we can define a NumericColumn for each continuous feature column that we want to use in the model:
# 이런식으로 numerical columns들만 뽑을 수도 있음.
age = fc.numeric_column('age')
education_num = tf.feature_column.numeric_column('education_num')
capital_gain = tf.feature_column.numeric_column('capital_gain')
capital_loss = tf.feature_column.numeric_column('capital_loss')
hours_per_week = tf.feature_column.numeric_column('hours_per_week')
my_numeric_columns = [age,education_num, capital_gain, capital_loss, hours_per_week]
fc.input_layer(feature_batch, my_numeric_columns).numpy()
classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns)
classifier.train(train_inpf)
result = classifier.evaluate(test_inpf)
clear_output()
for key,value in sorted(result.items()):
print('%s: %s' % (key, value))
# categorical columns들에 대해선...
# 다음과 같이 뽑을 수 있음.
relationship = fc.categorical_column_with_vocabulary_list(
'relationship',
['Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried', 'Other-relative'])
fc.input_layer(feature_batch, [age, fc.indicator_column(relationship)])
# 위는 category갯수를 아는 경우에 해당되고,
# 모를 땐 다음을 활용한다. categorical_column_with_hash_bucket
occupation = tf.feature_column.categorical_column_with_hash_bucket(
'occupation', hash_bucket_size=1000)
for item in feature_batch['occupation'].numpy():
print(item.decode())
education = tf.feature_column.categorical_column_with_vocabulary_list(
'education', [
'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college',
'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school',
'5th-6th', '10th', '1st-4th', 'Preschool', '12th'])
marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
'marital_status', [
'Married-civ-spouse', 'Divorced', 'Married-spouse-absent',
'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed'])
workclass = tf.feature_column.categorical_column_with_vocabulary_list(
'workclass', [
'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov',
'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked'])
my_categorical_columns = [relationship, occupation, education, marital_status, workclass]
classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns+my_categorical_columns)
classifier.train(train_inpf)
result = classifier.evaluate(test_inpf)
clear_output()
for key,value in sorted(result.items()):
print('%s: %s' % (key, value))
# to be continue.... | _____no_output_____ | MIT | tf_estimator_linearmodel.ipynb | Junhojuno/DeepLearning-Beginning |
|
Pre-processing the text for Object2VecProcessing the text to fit Object2Vec algorithm. | import boto3
import pandas as pd
import re
from sklearn import preprocessing
import numpy as np
import json
import os
from sklearn.feature_extraction.text import CountVectorizer
import random
random.seed(42)
from random import sample
from sklearn.utils import shuffle
from nltk import word_tokenize | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Functions | def get_filtered_objects(bucket_name, prefix):
"""filter objects based on bucket and prefix"""
s3 = boto3.client("s3")
files = s3.list_objects_v2(Bucket = bucket_name, Prefix =prefix)
return files
def download_object(bucket_name, key, local_path):
"""Download S3 object to local"""
s3 = boto3.resource('s3')
try:
s3.Bucket(bucket_name).download_file(key,local_path)
except botocore.exceptions.ClientError as e:
if e.response['Error']['Code'] == "404":
print("The object does not exist")
else:
raise
def get_csv(files):
"""Filter the files by selecting .csv extension"""
paths = []
for file in files:
if file['Key'].endswith(".csv"):
paths.append(file['Key'])
return paths
def sentence_to_tokens(sentence, vocab_to_tokens):
"""converts sentences to tokens"""
words = word_tokenize(sentence)
return [ vocab_to_tokens[w] for w in words if w in vocab_to_tokens]
def create_dir(directory):
"""Create a directory"""
if not os.path.exists(directory):
os.makedirs(directory)
def remove_file(file_path):
"""Remove locally the specified path"""
if os.path.isfile(file_path):
os.remove(file_path)
else:
print("Error, file not found.")
def build_sentence_pairs(data):
"""transform the dataframe into sentence pairs for Object2Vec algorithm."""
sentence_pairs = []
for r in range(len(data)):
row = data.iloc[r]
sentence_pairs.append({'in0': row['encoded_content'], \
'in1': row['labels'],\
'label':1})
return sentence_pairs
def build_negative_pairs(data, negative_labels_to_sample,sentence_pairs, n_neg_pairs_per_label=10):
"""build negative pairs for training dataframe"""
for r in negative_labels_to_sample:
#news that have that label as tag
selection = data.loc[data.labels.apply(lambda x: x is not None and r in x)]
#news that do not have that label as tag.
wrong_selection = data.loc[data.labels.apply(lambda x: x is not None and r not in x)]
if len(wrong_selection)>0:
for p in range(n_neg_pairs_per_label):
negative_pair = {}
negative_pair['in0'] = selection.sample(1)['encoded_content'].iloc[0]
negative_pair['in1'] = wrong_selection.sample(1)['labels'].iloc[0]
negative_pair['label'] = 0
sentence_pairs.append(negative_pair)
return sentence_pairs | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Download the data locally | bucket_name = "YOUR_BUCKET_HERE"
prefix = "connect/"
#save the files locally.
create_dir("./data")
files = get_filtered_objects(bucket_name, prefix)['Contents']
files = get_csv(files)
local_files=[]
print(files)
for file in files:
full_prefix = "/".join(file.split("/")[:-1])
inner_folder = full_prefix.replace(prefix,'')
local_path = "./data/" +file.split("/")[-1]
download_object(bucket_name, file, local_path)
local_files.append(local_path)
local_files | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Concatenate the .csv files | import pandas.errors
content = []
for filename in local_files:
try:
df = pd.read_csv(filename, sep=";")
print(df.columns)
content.append(df)
except pandas.errors.ParserError:
print("File", filename, "cannot be parsed. Check its format")
data = pd.concat(content)
customer_text = data.loc[data.ParticipantId=='CUSTOMER']
customer_text.shape | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Create random labelsChange this to use your own labelsAlso: we are here replicating the texts to increase statistics | customer_text = pd.concat([customer_text]*300, ignore_index=True)
customer_text['labels']=np.random.randint(low=0, high=5, size=len(customer_text))
customer_text.labels.hist() | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Get vocabulary from the corpus using sklearn for the heavy liftingThe vocabulary will be built only taking into account words that belong to news related to crimes. | counts = CountVectorizer(min_df=5, max_df=0.95, token_pattern=r'(?u)\b[A-Za-z]{2,}\b').fit(customer_text['Content'].values.tolist())
vocab = counts.get_feature_names()
vocab_to_token_dict = dict(zip(vocab, range(len(vocab))))
token_to_vocab_dict = dict(zip(range(len(vocab)), vocab))
len(vocab)
create_dir("./vocab")
vocab_filename = './vocab/vocab.json'
with open(vocab_filename, "w") as write_file:
json.dump(vocab_to_token_dict, write_file) | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Encode data bodyTransform the texts in the data to encodings from the vocabulary created. | import nltk
nltk.download('punkt')
customer_text['encoded_content'] = customer_text['Content'].apply(lambda x: sentence_to_tokens(x, vocab_to_token_dict))
customer_text['labels']
customer_text['labels']=customer_text['labels'].apply(lambda x: [x])
customer_text[['labels','encoded_content']]
# remove entriews with no text
customer_text = customer_text.loc[customer_text['encoded_content'].apply(lambda x: len(x)>0)]
customer_text[['labels','encoded_content', 'Content']] | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Build sentence pairs Object2Vec | #negative pairs for the algorithm: need to decide which lables we want to sample *against*.
negative_labels_to_sample = range(5)
sentence_pairs = build_sentence_pairs(customer_text)
| _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
Build negative sentence pairs for training Object2VecNegative sampling for the Object2Vec algorithm - add negative and positive pairs (document,label) | sentence_pairs = build_negative_pairs(customer_text,negative_labels_to_sample,sentence_pairs)
print("Sample of input for Object2vec algorith: {}".format(sentence_pairs[1]))
!pip install jsonlines | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
train/test/val split, save to file | # shuffle and split test/train/val
random.seed(42)
random.shuffle(sentence_pairs)
n_train = int(0.7 * len(sentence_pairs))
# split train and test
sentence_pairs_train = sentence_pairs[:n_train]
sentence_pairs_test = sentence_pairs[n_train:]
# further split test set into validation set (val_vectors) and test set (test_vectors)
n_test = len(sentence_pairs_test)
sentence_pairs_val = sentence_pairs_test[:n_test//2]
sentence_pairs_test = sentence_pairs_test[n_test//2:]
import jsonlines
with jsonlines.open('./data/train.jsonl', mode='w') as writer:
writer.write_all(sentence_pairs_train)
with jsonlines.open('./data/test.jsonl', mode='w') as writer:
writer.write_all(sentence_pairs_test)
with jsonlines.open('./data/val.jsonl', mode='w') as writer:
writer.write_all(sentence_pairs_val) | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
8. Upload to S3 | import os
s3_client = boto3.client('s3')
out_prefix = "connect/O2VInput"
for n in ['train', 'test', 'val',]:
s3_client.upload_file("./data/"+n+'.jsonl', bucket_name, \
os.path.join(out_prefix, n, n+'.jsonl'),\
ExtraArgs = {'ServerSideEncryption':'AES256'}) #upload input files
print(vocab_filename)
print(out_prefix)
print( os.path.join(out_prefix, "auxiliary/vocab.json"))
s3_client.upload_file(vocab_filename,
bucket_name, os.path.join(out_prefix, "auxiliary/vocab.json"),
ExtraArgs = {'ServerSideEncryption':'AES256'}) #upload vocab file
import pickle
pickle.dump(vocab_to_token_dict, open('./vocab/vocab_to_token_dict.p', 'wb'))
pickle.dump(token_to_vocab_dict, open('./vocab/token_to_vocab_dict.p', 'wb'))
for f in ['vocab_to_token_dict.p','token_to_vocab_dict.p']:
s3_client.upload_file("./vocab/"+f, bucket_name, \
os.path.join(out_prefix, 'meta', f),ExtraArgs = {'ServerSideEncryption':'AES256'})
for f in local_files:
remove_file(f) | _____no_output_____ | MIT-0 | notebooks/connect_01_text_processing.ipynb | aws-samples/contact-lens-for-amazon-connect-data-gathering-mechanism |
1. American Sign Language (ASL)American Sign Language (ASL) is the primary language used by many deaf individuals in North America, and it is also used by hard-of-hearing and hearing individuals. The language is as rich as spoken languages and employs signs made with the hand, along with facial gestures and bodily postures.A lot of recent progress has been made towards developing computer vision systems that translate sign language to spoken language. This technology often relies on complex neural network architectures that can detect subtle patterns in streaming video. However, as a first step, towards understanding how to build a translation system, we can reduce the size of the problem by translating individual letters, instead of sentences.In this notebook, we will train a convolutional neural network to classify images of American Sign Language (ASL) letters. After loading, examining, and preprocessing the data, we will train the network and test its performance.In the code cell below, we load the training and test data. x_train and x_test are arrays of image data with shape (num_samples, 3, 50, 50), corresponding to the training and test datasets, respectively.y_train and y_test are arrays of category labels with shape (num_samples,), corresponding to the training and test datasets, respectively. | # Import packages and set numpy random seed
import numpy as np
np.random.seed(5)
import tensorflow as tf
tf.set_random_seed(2)
from datasets import sign_language
import matplotlib.pyplot as plt
%matplotlib inline
# Load pre-shuffled training and test datasets
(x_train, y_train), (x_test, y_test) = sign_language.load_data() | _____no_output_____ | MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
2. Visualize the training dataNow we'll begin by creating a list of string-valued labels containing the letters that appear in the dataset. Then, we visualize the first several images in the training data, along with their corresponding labels. | # Store labels of dataset
labels = ['A','B','C']
# Print the first several training images, along with the labels
fig = plt.figure(figsize=(20,5))
for i in range(36):
ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])
ax.imshow(np.squeeze(x_train[i]))
ax.set_title("{}".format(labels[y_train[i]]))
plt.show() | _____no_output_____ | MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
3. Examine the datasetLet's examine how many images of each letter can be found in the dataset.Remember that dataset has already been split into training and test sets for you, where x_train and x_test contain the images, and y_train and y_test contain their corresponding labels.Each entry in y_train and y_test is one of 0, 1, or 2, corresponding to the letters 'A', 'B', and 'C', respectively.We will use the arrays y_train and y_test to verify that both the training and test sets each have roughly equal proportions of each letter. | # Number of A's in the training dataset
num_A_train = sum(y_train==0)
# Number of B's in the training dataset
num_B_train = sum(y_train==1)
# Number of C's in the training dataset
num_C_train = sum(y_train==2)
# Number of A's in the test dataset
num_A_test = sum(y_test==0)
# Number of B's in the test dataset
num_B_test = sum(y_test==1)
# Number of C's in the test dataset
num_C_test = sum(y_test==2)
# Print statistics about the dataset
print("Training set:")
print("\tA: {}, B: {}, C: {}".format(num_A_train, num_B_train, num_C_train))
print("Test set:")
print("\tA: {}, B: {}, C: {}".format(num_A_test, num_B_test, num_C_test)) | Training set:
A: 540, B: 528, C: 532
Test set:
A: 118, B: 144, C: 138
| MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
4. One-hot encode the dataCurrently, our labels for each of the letters are encoded as categorical integers, where 'A', 'B' and 'C' are encoded as 0, 1, and 2, respectively. However, recall that Keras models do not accept labels in this format, and we must first one-hot encode the labels before supplying them to a Keras model.This conversion will turn the one-dimensional array of labels into a two-dimensional array.Each row in the two-dimensional array of one-hot encoded labels corresponds to a different image. The row has a 1 in the column that corresponds to the correct label, and 0 elsewhere. For instance, 0 is encoded as [1, 0, 0], 1 is encoded as [0, 1, 0], and 2 is encoded as [0, 0, 1]. | from keras.utils import np_utils
# One-hot encode the training labels
y_train_OH = np_utils.to_categorical(y_train, 3)
# One-hot encode the test labels
y_test_OH = np_utils.to_categorical(y_test, 3) | _____no_output_____ | MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
5. Define the modelNow it's time to define a convolutional neural network to classify the data.This network accepts an image of an American Sign Language letter as input. The output layer returns the network's predicted probabilities that the image belongs in each category. | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Flatten, Dense
from keras.models import Sequential
model = Sequential()
# First convolutional layer accepts image input
model.add(Conv2D(filters=5, kernel_size=5, padding='same', activation='relu',
input_shape=(50, 50, 3)))
# Add a max pooling layer
model.add(MaxPooling2D(pool_size=4))
# Add a convolutional layer
model.add(Conv2D(filters=15, kernel_size=5, padding='same', activation='relu',
input_shape=(50, 50, 3)))
# Add another max pooling layer
model.add(MaxPooling2D(pool_size=4))
# Flatten and feed to output layer
model.add(Flatten())
model.add(Dense(3, activation='softmax'))
# Summarize the model
model.summary() | _________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_10 (Conv2D) (None, 50, 50, 5) 380
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 12, 12, 5) 0
_________________________________________________________________
conv2d_11 (Conv2D) (None, 12, 12, 15) 1890
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 3, 3, 15) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 135) 0
_________________________________________________________________
dense_4 (Dense) (None, 3) 408
=================================================================
Total params: 2,678
Trainable params: 2,678
Non-trainable params: 0
_________________________________________________________________
| MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
6. Compile the modelAfter we have defined a neural network in Keras, the next step is to compile it! | # Compile the model
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy']) | _____no_output_____ | MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
7. Train the modelOnce we have compiled the model, we're ready to fit it to the training data. | # Train the model
hist = model.fit(x_train, y_train_OH,
validation_split=0.2,
epochs=2,
batch_size=32) | Train on 1280 samples, validate on 320 samples
Epoch 1/2
1280/1280 [==============================] - 4s 3ms/step - loss: 0.9623 - acc: 0.6102 - val_loss: 0.7729 - val_acc: 0.8688
Epoch 2/2
1280/1280 [==============================] - 3s 3ms/step - loss: 0.6252 - acc: 0.8656 - val_loss: 0.4826 - val_acc: 0.9406
| MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
8. Test the modelTo evaluate the model, we'll use the test dataset. This will tell us how the network performs when classifying images it has never seen before!If the classification accuracy on the test dataset is similar to the training dataset, this is a good sign that the model did not overfit to the training data. | # Obtain accuracy on test set
score = model.evaluate(x=x_test,
y=y_test_OH,
verbose=0)
print('Test accuracy:', score[1]) | Test accuracy: 0.9475
| MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
9. Visualize mistakesHooray! Our network gets very high accuracy on the test set! The final step is to take a look at the images that were incorrectly classified by the model. Do any of the mislabeled images look relatively difficult to classify, even to the human eye? Sometimes, it's possible to review the images to discover special characteristics that are confusing to the model. However, it is also often the case that it's hard to interpret what the model had in mind! | # Get predicted probabilities for test dataset
y_probs = ...
# Get predicted labels for test dataset
y_preds = ...
# Indices corresponding to test images which were mislabeled
bad_test_idxs = ...
# Print mislabeled examples
fig = plt.figure(figsize=(25,4))
for i, idx in enumerate(bad_test_idxs):
ax = fig.add_subplot(2, np.ceil(len(bad_test_idxs)/2), i + 1, xticks=[], yticks=[])
ax.imshow(np.squeeze(x_test[idx]))
ax.set_title("{} (pred: {})".format(labels[y_test[idx]], labels[y_preds[idx]])) | _____no_output_____ | MIT | ASL Recognition with Deep Learning/notebook.ipynb | Shogun89/DataCamp-Python |
def fibonaci(n):
print("Llamada",n)
if n== 1 or n ==0:
return n
else:
return (fibonaci(n-1) + fibonaci (n-2))
print(fibonaci(6)) | Llamada 6
Llamada 5
Llamada 4
Llamada 3
Llamada 2
Llamada 1
Llamada 0
Llamada 1
Llamada 2
Llamada 1
Llamada 0
Llamada 3
Llamada 2
Llamada 1
Llamada 0
Llamada 1
Llamada 4
Llamada 3
Llamada 2
Llamada 1
Llamada 0
Llamada 1
Llamada 2
Llamada 1
Llamada 0
8
| MIT | 7Diciembre.ipynb | samuelgh15/daa_2021_1 |
|
Printing basic types | print("Hello World!")
print("Welcome to Foundations of Data Science!")
print(2020)
print(1.314)
print(True)
print(False)
print(True or False)
print(True and False) | _____no_output_____ | Apache-2.0 | 1-Python-Basics.ipynb | aktgitrepo/python-for-datascience |
Variables and inputs | var = "value"
print(var)
print(type(var))
print(id(var))
var_2 = "_2"
print(var_2)
print(id(var_2))
print("variable " + var)
num = 2020
print(type(num))
print(type(num))
is_good = True
print(type(is_good))
my_name = input("What is your name ")
print(my_name, type(my_name))
hours_per_week = 24 * 7
print("hours_per_week", hours_per_week) | _____no_output_____ | Apache-2.0 | 1-Python-Basics.ipynb | aktgitrepo/python-for-datascience |
String Processing | topic = "Foundations of Data Science"
print(topic)
print(topic[0])
print(topic[1])
print(topic[10])
print(topic[-1])
print(topic[-2])
print(topic[0:10])
print(topic[12:16])
print(topic.lower())
print(topic)
topic = topic.lower()
print(topic.upper())
print(topic.islower())
topic = topic.upper()
print(topic.islower())
print(topic.isupper())
print(topic.find("DATA"))
print(topic[15])
print(topic.find("AI"))
print(topic.replace("SCIENCE", "ENGINEERING"))
print(55/34)
golden_ratio = 55/34
print(type(golden_ratio))
print(golden_ratio.is_integer())
print(golden_ratio.as_integer_ratio())
print(3642617345667313/2251799813685248)
print(55 // 34, 55/34)
print(55 % 34)
print(2 ** 3) | _____no_output_____ | Apache-2.0 | 1-Python-Basics.ipynb | aktgitrepo/python-for-datascience |
If, For, While Blocks | hours_per_week = 5
if hours_per_week > 10:
print(my_name + " you are doing well")
if hours_per_week > 10:
print(my_name + " you are doing well")
print("Outside If")
if hours_per_week > 10:
print(my_name + " you are doing well")
else:
print(my_name + " you need to study more")
for i in range(5):
print(i)
range?
for i in range(2, 5):
print(i, i**2)
a = 1
b = 1
print(a)
print(b)
for i in range(10):
temp = a + b
a = b
b = temp
print(temp)
a = 1
b = 1
print(a)
print(b)
i = 2
while b < 1729:
temp = a + b
i += 1
a = b
b = temp
print(temp)
print("Printed", i, "numbers") | _____no_output_____ | Apache-2.0 | 1-Python-Basics.ipynb | aktgitrepo/python-for-datascience |
Functions | def fibonacci(pos):
a = 1
b = 1
for i in range(pos):
temp = a + b
a = b
b = temp
return temp
print(fibonacci(3))
print(fibonacci(8), fibonacci(7))
for i in range(2, 20):
ratio = fibonacci(i) / fibonacci(i - 1)
print(i, ratio)
def fibonacci_relative(pos, a, b):
for i in range(pos):
temp = a + b
a = b
b = temp
return temp
print(fibonacci_relative(3, 34, 55))
def fibonacci_ol(pos, a = 1, b = 1):
for i in range(pos):
temp = a + b
a = b
b = temp
return temp
print(fibonacci_ol(3, 34, 55))
print(fibonacci_relative(3))
def fibonacci_recursive(n, a = 1, b = 1):
if n > 1:
return fibonacci_recursive(n - 1, b, a + b)
else:
return a + b
print(fibonacci_recursive(3, 34, 55))
def fibonacci(pos):
a = 1
b = 1
for i in range(pos):
temp = a + b
a = b
b = temp
return temp
print(fibonacci(3))
print(fibonacci(8), fibonacci(7))
for i in range(2, 20):
ratio = fibonacci(i) / fibonacci(i - 1)
print(i, ratio)
def fibonacci_relative(pos, a, b):
for i in range(pos):
temp = a + b
a = b
b = temp
return temp
print(fibonacci_relative(3, 34, 55))
def fibonacci_ol(pos, a = 1, b = 1):
for i in range(pos):
temp = a + b
a = b
b = temp
return temp
print(fibonacci_ol(3, 34, 55))
print(fibonacci_relative(3))
def fibonacci_recursive(n, a = 1, b = 1):
if n > 1:
return fibonacci_recursive(n - 1, b, a + b)
else:
return a + b
print(fibonacci_recursive(3, 34, 55)) | _____no_output_____ | Apache-2.0 | 1-Python-Basics.ipynb | aktgitrepo/python-for-datascience |
KMeans | X_cluster = pd.DataFrame(data['LotArea'])
X_cluster['OverallCond'] = data['OverallCond']
X_cluster['OverallQual'] = data['OverallQual']
#X_svm['FullBath'] = data['FullBath']
X_cluster['TotRmsAbvGrd'] = data['TotRmsAbvGrd']
#X_cluster['SalePrice'] = data['SalePrice']
#X_svm['GarageArea'] = data['GarageArea']
k_range = np.arange(2,10)
SSE = []
for i in k_range:
kmeans_model = KMeans(i)
clusters = kmeans_model.fit_predict(X_cluster)
SSE.append(kmeans_model.inertia_)
plt.scatter(k_range, SSE)
kmeans_model = KMeans(4)
clusters = kmeans_model.fit_predict(X_cluster)
plt.title('SalePrice Distribution for each cluster')
plt.scatter(clusters,data['SalePrice'])
"""plt.plot(clusters[0]['OverallCond'],clusters[0]['SalePrice'],'g',
clusters[1]['OverallCond'],clusters[1]['SalePrice'],'b',
clusters[2]['OverallCond'],clusters[2]['SalePrice'],'r',
clusters[3]['OverallCond'],clusters[3]['SalePrice'],'b')"""
plt.show()
clust_0 = [i for i,d in enumerate(clusters) if d == 0]
clust_1 = [i for i,d in enumerate(clusters) if d == 1]
clust_2 = [i for i,d in enumerate(clusters) if d == 2]
clust_3 = [i for i,d in enumerate(clusters) if d == 3]
plt.title('SalePrice vs. OverallCond')
plt.plot(data.iloc[clust_0]['OverallCond'],data.iloc[clust_0]['SalePrice'],'go',
data.iloc[clust_1]['OverallCond'],data.iloc[clust_1]['SalePrice'],'bx',
data.iloc[clust_2]['OverallCond'],data.iloc[clust_2]['SalePrice'],'ro',
data.iloc[clust_3]['OverallCond'],data.iloc[clust_3]['SalePrice'],'m^')
plt.show()
plt.title('SalePrice vs. 1stFlrSF')
plt.plot(data.iloc[clust_0]['1stFlrSF'],data.iloc[clust_0]['SalePrice'],'go',
data.iloc[clust_1]['1stFlrSF'],data.iloc[clust_1]['SalePrice'],'bo',
data.iloc[clust_2]['1stFlrSF'],data.iloc[clust_2]['SalePrice'],'ro',
data.iloc[clust_3]['1stFlrSF'],data.iloc[clust_3]['SalePrice'],'m^')
plt.rcParams['figure.figsize'] = (16, 16)
plt.show() | _____no_output_____ | MIT | Intro to Python Class Projects/Intro to Python ML Project D.ipynb | Ddottsai/Code-Storage |
We decided to cluster using several features that we previously saw had significant relationships with SalePrice. The clustering revealed several unique properties. In the first graph, which shows the distribution of SalePrice within each cluster, indicates that the variables we chose do indeed have a strong relationship with SalePrice; the distributions and means are notably different. The second graph compares SalePrice to one specific feature that was used in our KMeans, OverallCond. It appears that KMeans was able to separate out one cluster that had a large spread in OverallCond and a small spread in SalePrice (primarily in the low range of SalePrice). The third graph looks at two features that were both not included in our KMeans: 1stFlrSF and SalePrice. It appears that 1stFlrSF tends to have a positive relationship with SalePrice; however, that is not true for one cluster. For one cluster, the shape of the points has the shape of an inverse relationship. | <font color=blue size = 40>**Ensemble (Stacking) Model**</font>
init_prior = []
data['BldgType'] = LabelEncoder().fit_transform(data['BldgType'])
init_prior.append('OverallQual')
init_prior.append('BldgType')
init_prior.append('TotalBsmtSF')
init_prior.append('1stFlrSF')
init_prior.append('GrLivArea')
init_prior.append('GarageCars')
init_prior.append('GarageArea')
init_prior.append('2ndFlrSF')
init_prior.append('FullBath')
init_prior.append('TotRmsAbvGrd')
init_prior.append('LotArea')
init_prior.append('YearRemodAdd')
init_prior.append('YearBuilt')
init_prior.append('GarageYrBlt')
init_prior.append('FireplaceQu')
init_prior.append('MSSubClass')
init_prior.append('WoodDeckSF')
# eliminate some dependent/redundant features
corr_within_prior = data[init_prior].corr()
for row_name,cols in corr_within_prior.iterrows():
print(row_name)
for (name,val) in cols.iteritems():
if (row_name != name and ((val > 0.6) | (val < -0.6))):
print(" %s: %0.3f" % (name,val))
print() | _____no_output_____ | MIT | Intro to Python Class Projects/Intro to Python ML Project D.ipynb | Ddottsai/Code-Storage |
Sets of somewhat correlated variables:['GarageCars', 'OverallQual', 'GarageArea']['TotalBsmtSF', '1stFlrSF']['GrLivArea', '2ndFlrSF', 'FullBath', 'TotRmsAbvGrd']['GarageYrBlt', 'YearRemodAdd', 'YearBuilt'] **Feature Selection** | new_prior = list(init_prior)
names = ['Garage','LowerSF','Living','Year']
new_prior.extend(names)
for dependents in [['GarageCars', 'OverallQual', 'GarageArea'],['TotalBsmtSF','1stFlrSF'],
['GrLivArea', '2ndFlrSF', 'FullBath', 'TotRmsAbvGrd'],['GarageYrBlt', 'YearRemodAdd', 'YearBuilt']]:
pca = PCA(1).fit(data[dependents])
factors = pca.components_
data[names.pop()] = np.sum(np.multiply(factors, data[dependents]),axis=1)
#plt.bar(np.arrange(len(dependents)),pca.explained_variance_ratio_)
#plt.xticks(np.arrange(len(dependents)),dependents)
#plt.title('Fraction of Variance Explained by Each Component')
#plt.xlabel('Component')
#plt.ylabel('Fraction of Total Variance')
#plt.show()
for i in dependents:
new_prior.remove(i)
print(new_prior)
sale_corr = data.corr()['SalePrice']
sale_corr.drop('SalePrice')
display(Markdown("**Correlation with SalePrice**"))
display(sale_corr[new_prior])
new_prior_2 = list(new_prior)
new_prior_2.remove('BldgType')
new_prior_2.remove('LotArea')
new_prior_2.remove('FireplaceQu')
new_prior_2.remove('MSSubClass')
new_prior_2.remove('WoodDeckSF')
display(new_prior_2) | _____no_output_____ | MIT | Intro to Python Class Projects/Intro to Python ML Project D.ipynb | Ddottsai/Code-Storage |
**Preprocessing** | plt.rcParams['figure.figsize'] = (16,4)
for feature_name in new_prior_2:
plt.title(feature_name)
plt.subplot(1, 3, 1)
plt.hist(data[feature_name],density=True)
plt.xlabel(feature_name)
plt.ylabel('Frequency')
stdev = np.std(data[feature_name])
mean = np.mean(data[feature_name])
col_distr = ((data[feature_name] - mean) / stdev)
plt.subplot(1, 3, 2)
plt.hist(col_distr,density=True)
plt.xlabel('std. dev. distance of ' + feature_name)
plt.ylabel('Frequency')
plt.subplot(1, 3, 3)
plt.boxplot(data[feature_name])
plt.xlabel(feature_name)
plt.ylabel('Value')
plt.show()
scaler = RobustScaler()
data["Garage_scaled"] = scaler.fit_transform(data['Garage'].values.reshape(-1,1))
data["LowerSF_scaled"] = scaler.fit_transform(data['LowerSF'].values.reshape(-1,1))
data["Living_scaled"] = scaler.fit_transform(data['Living'].values.reshape(-1,1))
data["Year_scaled"] = scaler.fit_transform(data['Year'].values.reshape(-1,1))
for feature_name in new_prior_2:
plt.title(feature_name + "_scaled")
plt.subplot(1, 3, 1)
plt.hist(data[feature_name + "_scaled"],density=True)
plt.xlabel(feature_name + "_scaled")
plt.ylabel('Frequency')
plt.show()
final_features = ['Garage_scaled','LowerSF_scaled','Living_scaled','Year_scaled'] | _____no_output_____ | MIT | Intro to Python Class Projects/Intro to Python ML Project D.ipynb | Ddottsai/Code-Storage |
Clustering Algorithm for part of Stack | def cluster_from_stack_of_KMeans(data, K=8, num_weak_learners = 5,num_neighbors=1,
distance_penalty= lambda x: x):
assert K < len(data)
assert num_neighbors <= len(data)
C = np.empty((num_weak_learners,data.shape[0]))
SSE = np.empty((num_weak_learners,))
for iter_num in range(num_weak_learners):
clustering = KMeans(K,init='random', n_init=1, n_jobs=-1)
clustering.fit(data)
#np.put(C,[iter_num,],clustering.labels_)
C[iter_num] = clustering.labels_
SSE[iter_num] = clustering.inertia_
# calculate node connections
C_scores = distance_penalty((1 - SSE)/ np.max(SSE))
connections = [[] for _ in range(len(data))]
for i in range(len(connections)):
connections[i] = np.argsort([np.sum([d for iter_num,d in enumerate(C_scores) if
i != j and C[iter_num][i] == C[iter_num][j]])
for j in range(len(data))])[-num_neighbors:]
for _ in range(num_neighbors-1):
if np.random.ranf() > 0.7:
connections[i] = connections[i][:-1]
# group together linked nodes to form (unequal) clusters
cluster_sets = []
cluster_dict = {} # set of (element index -> index in clusters)
to_merge = {} # lowest cluster_set index in sets to be merged -> the rest of the sets that should be merged together
#to_merge_not_keys = set()
for i1,links in enumerate(connections):
for i2 in links:
if i2 not in cluster_dict:
if i1 in cluster_dict:
c = cluster_dict[i1]
cluster_sets[c].add(i2)
cluster_dict[i2]= c
else:
cluster_sets.append({i1,i2})
cluster_dict.update({i1 : len(cluster_sets)-1, i2 : len(cluster_sets)-1})
else:
if i1 in cluster_dict:
in_to_merge = 0
temp_key = None
for merge_key,merge_set in to_merge.items():
if in_to_merge != 1 and (cluster_dict[i1] in merge_set or cluster_dict[i1] == merge_key):
if in_to_merge == 0:
in_to_merge = 1
temp_key = merge_key
else:
merge_set.add(merge_key)
to_merge[temp_key].add(merge_key)
comb = merge_set.union(to_merge[temp_key])
comb.add(cluster_dict[i2])
comb.add(cluster_dict[i1])
min_key = min(comb)
comb.remove(min_key)
to_merge.update({min_key:comb})
del to_merge[merge_key]
del to_merge[temp_key]
in_to_merge=3
break
elif in_to_merge != 2 and (cluster_dict[i2] in merge_set or cluster_dict[i2] == merge_key):
if in_to_merge == 0:
in_to_merge = 2
temp_key = merge_key
else:
merge_set.add(merge_key)
to_merge[temp_key].add(merge_key)
comb = merge_set.union(to_merge[temp_key])
comb.add(cluster_dict[i2])
comb.add(cluster_dict[i1])
min_key = min(comb)
comb.remove(min_key)
to_merge.update({min_key:comb})
del to_merge[merge_key]
del to_merge[temp_key]
in_to_merge=3
break
if in_to_merge==1:
if cluster_dict[i2] > temp_key:
to_merge[temp_key].add(cluster_dict[i2])
else:
to_merge[temp_key].add(temp_key)
to_merge.update({cluster_dict[i2]:to_merge[temp_key]})
del to_merge[temp_key]
elif in_to_merge==2:
if cluster_dict[i1] > temp_key:
to_merge[temp_key].add(cluster_dict[i1])
else:
to_merge[temp_key].add(temp_key)
to_merge.update({cluster_dict[i1]:to_merge[temp_key]})
del to_merge[temp_key]
elif in_to_merge==0:
if cluster_dict[i1] < cluster_dict[i2]:
to_merge.update({cluster_dict[i1]:{cluster_dict[i2]}})
else:
to_merge.update({cluster_dict[i2]:{cluster_dict[i1]}})
#to_merge_not_keys.add(i2)
else:
c = cluster_dict[i2]
cluster_dict[i1] = c
cluster_sets[c].add(i1)
combined_clusters = set()
for i,c_set in enumerate(cluster_sets):
if c_set is not None:
if i not in to_merge:
combined_clusters.add(frozenset(c_set))
else:
total_set = c_set
for cluster_sets_index in to_merge[i]:
total_set = total_set.union(cluster_sets[cluster_sets_index])
cluster_sets[cluster_sets_index] = None
combined_clusters.add(frozenset(total_set))
return combined_clusters | _____no_output_____ | MIT | Intro to Python Class Projects/Intro to Python ML Project D.ipynb | Ddottsai/Code-Storage |
Finding Stable Parameters for clustering | k_ticks = [3,7,20]
w_ticks = [5,20]
n_ticks = [1,2,3]
def sample_clustering_params():
cluster_sizes = {}
for k in k_ticks:
for w in w_ticks:
for n in n_ticks:
c_sizes = []
for i in range(5):
train, valid, _, _ = train_test_split(
data[final_features],data['SalePrice'],test_size=0.2)
clusters = cluster_from_stack_of_KMeans(train, K=k, num_weak_learners=w, num_neighbors=n)
temp = []
for c in clusters:
temp.append(len(c))
c_sizes.append(temp)
print(' ...')
cluster_sizes[(k,w,n)]= c_sizes
print("----------" + str(n))
print(cluster_sizes)
print("----" + str(w))
print(k)
return cluster_sizes
cluster_sizes = sample_clustering_params()
temp = cluster_sizes
other = sample_clustering_params()
# data from runnning sample_clustering_params
cluster_sizes={(3, 5, 1): [[1136], [9, 1041, 81, 5], [1, 292, 842, 1], [1, 2, 382, 751], [6, 4, 2, 13, 1111]], (3, 5, 2): [[2, 3, 9, 11, 54, 9, 520, 4, 43, 28, 1, 54, 36, 290, 1, 70, 1], [436, 302, 7, 1, 1, 142, 53, 95, 30, 7, 5, 57], [12, 12, 2, 4, 3, 23, 65, 2, 99, 84, 61, 2, 75, 207, 2, 346, 2, 2, 124, 2, 7], [166, 3, 112, 260, 25, 1, 2, 377, 50, 5, 53, 30, 8, 2, 42], [72, 619, 1, 120, 17, 4, 2, 5, 296]], (3, 5, 3): [[72, 5, 3, 1, 513, 1, 534, 1, 6], [246, 1, 362, 88, 432, 3, 4], [40, 2, 318, 1, 6, 73, 133, 36, 1, 15, 1, 1, 1, 148, 21, 3, 1, 3, 7, 230, 2, 1, 87, 4, 1], [671, 11, 278, 3, 9, 67, 93, 4], [4, 235, 9, 4, 111, 2, 10, 6, 3, 83, 4, 1, 1, 31, 321, 1, 4, 15, 1, 1, 166, 53, 6, 1, 3, 23, 37]], (3, 5, 6): [[17, 11, 115, 7, 74, 62, 40, 1, 1, 101, 240, 1, 118, 348], [13, 130, 245, 12, 24, 92, 4, 269, 137, 64, 146], [6, 15, 41, 314, 20, 1, 11, 207, 65, 12, 10, 6, 183, 88, 144, 13], [60, 9, 5, 188, 191, 1, 14, 7, 89, 219, 1, 8, 210, 134], [6, 1, 542, 3, 130, 81, 18, 11, 344]], (3, 20, 1): [[1134, 1, 1], [691, 2, 4, 439], [1134, 1, 1], [2, 708, 11, 7, 2, 406], [11, 1, 75, 687, 3, 20, 3, 37, 1, 151, 1, 7, 1, 112, 7, 19]], (3, 20, 2): [[83, 6, 3, 2, 3, 2, 118, 1, 268, 1, 133, 2, 94, 114, 50, 3, 8, 2, 2, 239, 2], [94, 5, 1, 12, 2, 4, 1, 693, 16, 1, 72, 46, 1, 1, 9, 121, 57], [3, 2, 8, 13, 4, 4, 2, 85, 5, 250, 7, 4, 106, 37, 63, 92, 20, 3, 2, 392, 18, 16], [93, 4, 106, 2, 2, 307, 3, 3, 49, 2, 22, 1, 4, 7, 50, 2, 1, 2, 24, 3, 49, 58, 116, 151, 2, 3, 68, 2], [75, 61, 7, 88, 1, 1, 6, 2, 15, 6, 8, 75, 2, 38, 7, 17, 2, 2, 121, 8, 3, 3, 171, 6, 83, 275, 53]], (3, 20, 3): [[3, 4, 1, 1, 4, 96, 1, 1, 149, 3, 598, 3, 46, 2, 17, 102, 1, 3, 3, 8, 6, 73, 6, 1, 4], [652, 7, 15, 3, 7, 2, 3, 81, 60, 15, 6, 52, 3, 1, 50, 7, 1, 1, 1, 4, 13, 23, 10, 8, 111], [3, 105, 3, 6, 4, 4, 1, 27, 84, 5, 3, 44, 53, 1, 442, 169, 4, 4, 18, 5, 3, 6, 2, 95, 35, 4, 6], [5, 8, 18, 104, 7, 3, 4, 57, 5, 41, 1, 22, 3, 1, 21, 53, 11, 19, 9, 72, 8, 33, 306, 174, 6, 145], [127, 3, 2, 126, 4, 81, 4, 4, 26, 5, 16, 270, 28, 50, 1, 228, 161]], (3, 20, 6): [[326, 8, 6, 23, 39, 1, 10, 99, 1, 1, 401, 11, 7, 1, 17, 34, 151], [2, 8, 12, 2, 177, 33, 5, 50, 1, 132, 4, 220, 7, 4, 5, 125, 1, 348], [5, 315, 4, 9, 6, 2, 173, 7, 186, 68, 14, 7, 37, 25, 7, 194, 77], [8, 30, 4, 119, 34, 6, 27, 40, 14, 112, 9, 164, 316, 12, 241], [7, 1, 85, 8, 12, 3, 328, 176, 4, 11, 128, 102, 10, 10, 16, 84, 14, 124, 5, 7, 1]], (3, 40, 1): [[542, 2, 6, 3, 142, 4, 3, 19, 1, 13, 401], [9, 4, 2, 392, 2, 5, 6, 2, 714], [147, 18, 860, 2, 109], [904, 1, 1, 81, 102, 7, 2, 3, 2, 1, 17, 3, 2, 1, 1, 8], [13, 2, 1, 2, 1114, 2, 2]], (3, 40, 2): [[3, 3, 8, 5, 56, 29, 9, 2, 3, 458, 194, 4, 1, 3, 9, 1, 97, 3, 105, 7, 28, 32, 42, 2, 2, 3, 3, 3, 21], [228, 7, 5, 3, 54, 1, 533, 2, 4, 3, 3, 21, 170, 2, 2, 14, 1, 64, 3, 16], [55, 1, 2, 1, 1, 2, 54, 4, 207, 19, 8, 16, 2, 4, 36, 3, 145, 8, 251, 24, 141, 2, 1, 2, 3, 19, 11, 4, 2, 6, 6, 3, 2, 4, 41, 2, 7, 22, 2, 3, 6, 4], [4, 10, 66, 17, 6, 93, 227, 66, 125, 20, 41, 2, 2, 27, 4, 135, 2, 5, 3, 3, 4, 8, 46, 3, 177, 9, 7, 3, 21], [2, 16, 4, 3, 1, 4, 2, 11, 8, 3, 183, 70, 141, 11, 3, 67, 4, 2, 2, 3, 3, 570, 23]], (3, 40, 3): [[26, 13, 40, 26, 1, 3, 4, 5, 37, 2, 101, 32, 6, 93, 3, 2, 4, 1, 1, 4, 6, 106, 538, 1, 1, 6, 4, 4, 66], [1, 1, 4, 58, 5, 35, 165, 24, 331, 39, 42, 109, 3, 2, 2, 7, 1, 5, 1, 4, 111, 4, 4, 2, 16, 81, 6, 1, 72], [2, 4, 79, 3, 112, 120, 12, 20, 6, 1, 1, 1, 50, 28, 1, 5, 162, 1, 60, 162, 306], [3, 1, 3, 4, 5, 2, 2, 3, 113, 421, 25, 4, 114, 55, 122, 4, 6, 70, 166, 13], [166, 13, 31, 10, 36, 89, 2, 1, 1, 3, 6, 45, 1, 5, 262, 52, 91, 1, 1, 5, 223, 46, 8, 1, 4, 6, 3, 24]], (3, 40, 6): [[187, 228, 12, 57, 7, 1, 331, 16, 209, 88], [1, 136, 10, 95, 215, 11, 51, 38, 6, 107, 230, 55, 5, 1, 1, 156, 1, 17], [102, 20, 4, 5, 5, 365, 10, 13, 14, 15, 148, 168, 15, 6, 28, 39, 25, 5, 1, 3, 7, 1, 104, 33], [841, 4, 22, 85, 35, 8, 7, 6, 51, 1, 74, 1, 1], [7, 6, 112, 68, 1, 8, 1, 6, 12, 13, 7, 76, 1, 11, 2, 154, 1, 5, 338, 205, 8, 1, 1, 2, 50, 1, 1, 38]], (7, 5, 1): [[431, 705], [1136], [1010, 126], [5, 1131], [342, 794]], (7, 5, 2): [[33, 3, 16, 4, 2, 25, 1, 8, 3, 4, 211, 3, 2, 9, 20, 394, 5, 8, 165, 4, 12, 104, 14, 69, 2, 3, 9, 3], [9, 2, 2, 2, 64, 33, 6, 4, 2, 26, 43, 2, 2, 2, 3, 40, 39, 6, 9, 201, 3, 9, 2, 8, 26, 2, 2, 6, 49, 2, 9, 8, 2, 64, 18, 50, 269, 2, 2, 13, 2, 89, 2], [45, 28, 6, 2, 5, 4, 4, 4, 116, 237, 4, 210, 20, 2, 53, 135, 3, 2, 47, 4, 127, 1, 6, 1, 2, 12, 41, 2, 10, 1, 2], [3, 3, 85, 2, 221, 2, 2, 1, 155, 169, 60, 4, 5, 1, 67, 3, 2, 5, 4, 48, 11, 2, 26, 11, 2, 1, 62, 32, 6, 76, 65], [3, 50, 2, 238, 11, 2, 30, 2, 68, 57, 8, 31, 13, 7, 30, 15, 137, 70, 191, 4, 9, 7, 28, 31, 3, 23, 4, 8, 26, 5, 6, 13, 4]], (7, 5, 3): [[81, 18, 10, 49, 24, 113, 109, 3, 4, 27, 3, 3, 145, 6, 43, 4, 311, 116, 8, 2, 2, 3, 52], [11, 10, 8, 3, 6, 157, 166, 4, 63, 7, 7, 1, 99, 32, 20, 12, 12, 4, 16, 5, 7, 73, 148, 265], [6, 17, 24, 26, 14, 147, 23, 52, 151, 75, 91, 4, 5, 3, 24, 5, 4, 7, 8, 12, 4, 4, 140, 90, 3, 37, 3, 101, 9, 41, 6], [3, 13, 1, 3, 23, 2, 368, 41, 5, 116, 13, 4, 3, 67, 3, 8, 26, 3, 310, 111, 7, 4, 2], [2, 51, 3, 4, 106, 6, 3, 33, 14, 1, 18, 85, 16, 28, 11, 8, 12, 101, 57, 5, 15, 44, 129, 3, 63, 2, 5, 48, 3, 8, 128, 42, 82]], (7, 5, 6): [[61, 1, 11, 11, 6, 9, 346, 42, 14, 6, 157, 4, 154, 12, 9, 20, 85, 117, 9, 62], [10, 12, 7, 35, 227, 8, 7, 6, 5, 96, 6, 5, 14, 11, 222, 44, 4, 78, 71, 268], [45, 8, 46, 2, 5, 98, 65, 24, 4, 3, 50, 109, 41, 5, 246, 20, 6, 5, 124, 6, 7, 180, 10, 21, 6], [5, 7, 46, 125, 3, 104, 9, 164, 19, 68, 47, 60, 1, 179, 36, 30, 66, 56, 111], [28, 12, 15, 195, 181, 86, 43, 221, 350, 5]], (7, 20, 1): [[1123, 13], [242, 894], [1046, 90], [380, 756], [280, 854, 2]], (7, 20, 2): [[4, 6, 140, 87, 17, 82, 2, 2, 4, 17, 4, 139, 15, 4, 35, 2, 1, 41, 23, 4, 2, 4, 2, 3, 40, 12, 5, 3, 11, 2, 3, 4, 2, 3, 62, 4, 2, 3, 3, 6, 2, 4, 27, 2, 11, 11, 128, 43, 15, 3, 4, 2, 11, 3, 8, 4, 2, 4, 3, 21, 23], [11, 146, 131, 8, 2, 9, 16, 3, 2, 23, 25, 43, 3, 2, 2, 34, 7, 12, 14, 44, 13, 40, 5, 3, 2, 4, 12, 4, 2, 5, 7, 20, 4, 2, 2, 3, 3, 7, 6, 2, 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23, 3, 7, 6, 3, 6, 34, 24, 13, 3, 10, 3, 8, 11, 2, 3, 9, 17, 30, 4, 3, 29, 38, 4, 7, 21, 19, 3, 4, 11, 22, 7, 26, 4, 3, 37, 3, 4, 5, 49], [4, 3, 6, 28, 14, 3, 3, 46, 58, 17, 27, 3, 40, 48, 46, 5, 43, 15, 3, 5, 18, 29, 3, 4, 4, 5, 3, 59, 6, 2, 43, 3, 66, 6, 13, 6, 14, 3, 4, 44, 4, 2, 5, 60, 2, 15, 3, 3, 20, 12, 22, 6, 4, 71, 42, 7, 36, 4, 23, 43]], (10, 20, 6): [[19, 25, 69, 6, 7, 5, 119, 24, 25, 42, 133, 23, 13, 136, 6, 6, 5, 28, 173, 73, 102, 78, 6, 3, 10], [16, 18, 10, 93, 17, 5, 4, 7, 3, 12, 4, 23, 2, 21, 34, 51, 1, 221, 11, 19, 6, 109, 11, 16, 17, 10, 119, 28, 39, 188, 6, 15], [5, 10, 65, 4, 2, 151, 17, 5, 19, 5, 22, 12, 8, 79, 6, 1, 13, 4, 85, 184, 8, 11, 5, 4, 6, 21, 7, 148, 4, 8, 12, 72, 3, 24, 12, 5, 3, 17, 4, 3, 5, 9, 48], [9, 6, 33, 23, 6, 2, 7, 10, 8, 63, 277, 31, 6, 157, 6, 7, 6, 26, 10, 35, 6, 10, 35, 15, 7, 18, 86, 25, 6, 36, 8, 53, 29, 4, 37, 5, 16, 12], [51, 4, 15, 34, 8, 42, 11, 15, 134, 13, 90, 55, 107, 58, 31, 83, 89, 99, 4, 34, 40, 8, 13, 8, 23, 7, 7, 53]], (10, 40, 1): [[1135, 1], [322, 814], [882, 254], [254, 882], [252, 882, 2]], (10, 40, 2): [[6, 6, 3, 26, 4, 54, 6, 2, 2, 5, 14, 3, 2, 5, 35, 9, 47, 7, 2, 15, 6, 7, 2, 3, 9, 6, 4, 3, 3, 3, 3, 2, 5, 10, 31, 2, 22, 19, 27, 3, 18, 70, 19, 17, 6, 6, 3, 37, 6, 2, 5, 16, 3, 2, 31, 35, 44, 2, 11, 27, 20, 12, 2, 4, 80, 8, 14, 5, 25, 2, 2, 8, 2, 16, 10, 5, 7, 5, 11, 7, 5, 44, 4, 17, 2, 4, 25, 7], [14, 38, 19, 8, 38, 8, 2, 2, 3, 4, 4, 12, 13, 23, 44, 5, 2, 6, 18, 7, 3, 53, 6, 5, 6, 4, 12, 3, 2, 2, 7, 4, 5, 13, 12, 20, 10, 3, 14, 26, 30, 5, 38, 11, 81, 3, 23, 24, 9, 9, 2, 28, 5, 2, 2, 2, 11, 2, 9, 2, 8, 7, 24, 48, 5, 26, 39, 3, 17, 2, 5, 5, 2, 28, 4, 4, 8, 12, 2, 14, 3, 2, 8, 35, 4, 2, 4, 3, 1, 11, 5, 3, 12, 2], [2, 14, 3, 4, 9, 3, 16, 2, 2, 35, 5, 14, 14, 8, 2, 5, 2, 7, 2, 10, 2, 10, 19, 13, 5, 17, 2, 8, 15, 3, 48, 7, 8, 3, 4, 3, 34, 13, 96, 16, 3, 3, 5, 12, 2, 6, 19, 17, 26, 10, 11, 61, 7, 14, 69, 20, 5, 3, 10, 27, 2, 4, 11, 3, 8, 5, 5, 22, 5, 3, 4, 30, 16, 2, 4, 74, 12, 2, 43, 3, 2, 25, 2, 3, 5, 2, 6, 2, 2, 2, 2, 2, 8], [22, 53, 8, 3, 4, 23, 2, 10, 3, 65, 3, 6, 3, 3, 170, 2, 2, 14, 64, 11, 4, 20, 2, 3, 56, 4, 11, 2, 6, 24, 5, 29, 4, 19, 3, 4, 12, 8, 7, 6, 6, 4, 11, 2, 2, 8, 2, 4, 19, 11, 12, 50, 4, 18, 15, 25, 8, 46, 2, 28, 2, 16, 6, 11, 1, 2, 4, 3, 4, 5, 2, 43, 16, 44], [28, 2, 43, 14, 8, 77, 29, 16, 3, 4, 5, 3, 4, 9, 3, 2, 2, 4, 2, 12, 8, 14, 3, 7, 6, 12, 4, 4, 3, 23, 3, 3, 21, 32, 30, 2, 6, 2, 6, 2, 26, 21, 14, 43, 4, 6, 2, 35, 17, 5, 40, 32, 5, 5, 2, 4, 2, 8, 5, 4, 8, 8, 2, 8, 59, 15, 23, 7, 15, 36, 13, 3, 7, 2, 3, 12, 4, 46, 2, 12, 7, 3, 2, 5, 2, 3, 62, 7, 15, 4]], (10, 40, 3): [[31, 15, 13, 5, 9, 35, 9, 19, 40, 3, 52, 51, 8, 9, 11, 16, 121, 17, 3, 25, 11, 25, 6, 10, 5, 8, 55, 5, 21, 112, 3, 3, 8, 27, 4, 12, 4, 13, 3, 13, 25, 9, 4, 5, 6, 40, 26, 20, 3, 41, 3, 5, 2, 2, 9, 6, 8, 30, 7, 16, 3, 3, 4, 19], [11, 6, 2, 20, 7, 10, 23, 26, 14, 4, 16, 3, 2, 11, 33, 26, 23, 36, 14, 14, 2, 23, 28, 2, 4, 3, 70, 6, 3, 4, 24, 6, 2, 24, 27, 3, 4, 4, 5, 3, 2, 9, 2, 65, 10, 7, 2, 9, 5, 4, 10, 43, 3, 3, 4, 8, 3, 3, 4, 10, 13, 3, 16, 4, 6, 3, 66, 22, 20, 3, 16, 17, 7, 10, 13, 3, 4, 87, 5, 37, 15, 12], [21, 4, 22, 6, 3, 42, 26, 12, 39, 6, 6, 3, 10, 9, 8, 41, 3, 17, 24, 24, 2, 3, 3, 12, 7, 24, 7, 7, 11, 13, 1, 3, 162, 93, 21, 10, 19, 31, 12, 13, 40, 5, 3, 25, 3, 26, 17, 15, 6, 3, 20, 3, 9, 30, 3, 2, 8, 7, 22, 2, 27, 32, 48], [9, 46, 6, 3, 12, 57, 22, 29, 21, 3, 4, 4, 9, 4, 23, 2, 64, 6, 11, 5, 7, 9, 3, 16, 3, 10, 3, 42, 31, 4, 3, 4, 4, 28, 6, 15, 3, 13, 5, 32, 3, 17, 15, 60, 6, 34, 3, 23, 12, 12, 27, 6, 4, 4, 61, 28, 43, 12, 31, 9, 56, 2, 7, 2, 10, 10, 19, 17, 5, 2, 4, 11], [2, 61, 8, 2, 3, 6, 59, 37, 4, 5, 27, 11, 5, 7, 21, 22, 6, 3, 15, 3, 5, 2, 15, 4, 19, 26, 3, 9, 4, 25, 72, 22, 30, 41, 7, 7, 11, 3, 5, 8, 12, 30, 8, 8, 6, 5, 56, 5, 3, 7, 12, 22, 60, 4, 11, 21, 34, 44, 2, 11, 12, 62, 14, 13, 6, 30, 2, 3, 3, 5]], (10, 40, 6): [[36, 46, 130, 3, 6, 29, 6, 3, 10, 46, 6, 67, 6, 5, 4, 5, 76, 26, 4, 7, 4, 9, 2, 11, 14, 6, 7, 236, 4, 22, 140, 10, 4, 6, 57, 7, 14, 62], [53, 22, 11, 12, 17, 18, 96, 11, 2, 43, 3, 15, 8, 15, 4, 2, 263, 6, 6, 3, 9, 4, 26, 9, 72, 96, 66, 3, 2, 3, 19, 45, 74, 39, 52, 7], [7, 46, 55, 15, 9, 41, 12, 3, 3, 6, 13, 94, 127, 11, 4, 225, 101, 51, 9, 4, 13, 11, 13, 30, 88, 5, 24, 19, 90, 1, 6], [67, 4, 12, 14, 60, 18, 63, 19, 8, 2, 41, 108, 22, 4, 90, 70, 84, 54, 90, 42, 16, 7, 17, 58, 22, 26, 3, 110, 5], [5, 74, 13, 52, 45, 71, 62, 6, 8, 7, 31, 216, 6, 5, 12, 4, 72, 31, 20, 122, 46, 5, 117, 106]], (20, 5, 1): [[1136], [127, 1009], [118, 1018], [1031, 102, 3], [93, 1043]], (20, 5, 2): [[74, 19, 16, 6, 64, 2, 18, 5, 2, 24, 47, 20, 6, 9, 62, 2, 57, 22, 3, 44, 19, 46, 7, 3, 24, 6, 32, 39, 14, 76, 78, 50, 26, 47, 65, 20, 12, 12, 15, 16, 14, 2, 11], [2, 59, 12, 26, 3, 5, 4, 2, 12, 56, 2, 2, 28, 57, 5, 33, 2, 60, 13, 36, 3, 21, 22, 11, 17, 7, 26, 44, 2, 32, 2, 17, 49, 18, 4, 4, 34, 81, 8, 12, 49, 2, 120, 10, 18, 28, 8, 65, 3], [3, 12, 118, 5, 48, 84, 9, 91, 8, 45, 42, 8, 8, 13, 2, 12, 16, 38, 7, 3, 23, 29, 4, 34, 34, 83, 3, 49, 15, 18, 6, 5, 11, 27, 44, 2, 3, 58, 17, 4, 3, 28, 26, 24, 14], [2, 4, 41, 13, 3, 10, 38, 49, 6, 15, 48, 14, 53, 40, 8, 54, 29, 6, 5, 3, 2, 7, 3, 4, 1, 6, 35, 4, 13, 5, 2, 25, 10, 18, 90, 27, 24, 4, 2, 3, 20, 2, 50, 9, 2, 103, 22, 20, 24, 62, 16, 5, 6, 6, 10, 2, 9, 2, 10, 22, 5, 3], [11, 20, 22, 9, 4, 82, 29, 12, 14, 6, 38, 2, 3, 18, 6, 2, 5, 21, 41, 58, 3, 6, 8, 23, 47, 72, 3, 19, 40, 8, 28, 7, 2, 14, 135, 10, 184, 5, 6, 54, 16, 3, 40]], (20, 5, 3): [[17, 42, 6, 10, 30, 19, 32, 2, 20, 3, 25, 22, 60, 19, 16, 32, 5, 52, 98, 32, 28, 17, 9, 2, 13, 22, 39, 3, 53, 15, 92, 22, 19, 37, 4, 11, 97, 46, 65], [3, 6, 77, 21, 2, 53, 22, 5, 7, 63, 37, 7, 38, 33, 8, 13, 48, 73, 10, 35, 18, 69, 126, 23, 8, 40, 86, 116, 7, 82], [25, 96, 5, 21, 33, 44, 2, 17, 63, 4, 11, 22, 16, 26, 4, 64, 79, 4, 120, 55, 55, 13, 21, 33, 47, 3, 76, 16, 6, 5, 16, 10, 8, 32, 56, 28], [50, 20, 3, 102, 78, 38, 15, 2, 46, 3, 23, 7, 65, 69, 2, 86, 44, 36, 69, 32, 15, 9, 74, 12, 40, 22, 25, 36, 23, 6, 9, 22, 51, 2], [16, 35, 49, 8, 15, 12, 119, 36, 31, 30, 9, 2, 4, 7, 5, 42, 30, 2, 5, 2, 11, 52, 355, 150, 45, 11, 39, 14]], (20, 5, 6): [[143, 6, 9, 20, 17, 25, 268, 35, 102, 134, 98, 201, 25, 53], [251, 34, 61, 57, 443, 10, 88, 5, 67, 34, 25, 5, 56], [7, 10, 29, 6, 19, 39, 26, 13, 151, 106, 63, 100, 14, 12, 131, 13, 16, 11, 365, 5], [15, 6, 188, 262, 95, 432, 119, 19], [329, 274, 140, 63, 54, 15, 95, 84, 29, 2, 20, 31]], (20, 20, 1): [[187, 949], [955, 181], [1031, 105], [2, 239, 2, 891, 2], [905, 2, 204, 3, 3, 3, 2, 4, 2, 6, 2]], (20, 20, 2): [[74, 2, 2, 2, 2, 16, 3, 3, 17, 3, 2, 27, 10, 6, 5, 8, 7, 19, 4, 4, 14, 17, 14, 3, 3, 11, 9, 7, 27, 11, 17, 50, 4, 43, 13, 3, 9, 15, 13, 3, 19, 32, 13, 2, 15, 3, 31, 5, 4, 18, 8, 5, 12, 32, 6, 10, 2, 7, 5, 16, 42, 3, 4, 5, 4, 3, 3, 11, 10, 4, 11, 36, 4, 10, 32, 8, 3, 24, 17, 16, 6, 10, 19, 2, 6, 3, 34, 7, 5, 4, 2, 12, 11, 3, 10, 5], [9, 2, 5, 7, 17, 4, 6, 29, 49, 39, 15, 4, 2, 3, 2, 2, 32, 56, 7, 13, 4, 2, 18, 6, 3, 4, 11, 3, 25, 2, 4, 3, 2, 53, 20, 25, 10, 29, 4, 17, 24, 19, 2, 9, 16, 24, 16, 2, 55, 2, 10, 2, 13, 2, 6, 9, 9, 2, 4, 4, 5, 79, 33, 57, 54, 7, 29, 12, 7, 2, 31, 4, 10, 28], [3, 13, 11, 18, 10, 26, 25, 14, 22, 6, 33, 4, 10, 20, 17, 32, 31, 5, 6, 14, 2, 3, 3, 10, 2, 9, 26, 27, 17, 9, 6, 12, 3, 15, 5, 10, 35, 6, 30, 32, 4, 34, 2, 7, 4, 15, 2, 3, 27, 32, 19, 43, 4, 3, 2, 32, 3, 20, 18, 17, 8, 15, 3, 13, 13, 4, 8, 2, 11, 4, 25, 13, 2, 34, 8, 5, 8, 25, 5, 5, 5, 27, 6, 2, 12], [10, 3, 2, 17, 23, 45, 11, 16, 54, 16, 10, 2, 28, 15, 2, 25, 33, 2, 2, 2, 7, 39, 5, 4, 11, 9, 13, 17, 57, 7, 8, 32, 8, 47, 53, 30, 6, 13, 39, 2, 3, 9, 7, 3, 2, 46, 2, 8, 4, 9, 3, 24, 19, 3, 2, 16, 12, 3, 12, 5, 5, 5, 23, 13, 6, 21, 19, 9, 10, 19, 2, 2, 7, 8, 4, 35, 2, 13, 14, 2], [4, 26, 15, 13, 7, 1, 12, 3, 33, 3, 11, 38, 2, 49, 10, 57, 13, 2, 26, 6, 23, 38, 21, 3, 14, 25, 18, 6, 13, 5, 44, 4, 7, 2, 13, 11, 7, 21, 2, 4, 5, 66, 7, 53, 13, 16, 3, 2, 15, 5, 10, 6, 2, 8, 36, 5, 6, 28, 8, 17, 2, 17, 2, 35, 31, 10, 4, 82, 30]], (20, 20, 3): [[34, 64, 29, 2, 101, 11, 11, 3, 4, 10, 7, 11, 28, 66, 27, 115, 7, 16, 4, 5, 13, 3, 43, 8, 3, 21, 56, 4, 6, 53, 12, 16, 5, 23, 4, 9, 14, 10, 4, 16, 13, 39, 31, 13, 2, 86, 6, 7, 53, 2, 3, 3], [32, 3, 61, 4, 3, 17, 18, 24, 2, 27, 4, 32, 21, 31, 8, 17, 3, 15, 4, 14, 22, 4, 7, 11, 9, 109, 18, 106, 3, 13, 5, 27, 109, 22, 8, 21, 17, 36, 57, 20, 25, 10, 29, 59, 13, 9, 7, 20], [11, 86, 3, 8, 25, 20, 19, 46, 4, 6, 62, 22, 8, 1, 4, 31, 16, 9, 2, 23, 2, 8, 19, 46, 44, 42, 6, 3, 60, 3, 37, 11, 15, 4, 8, 69, 24, 28, 34, 50, 7, 5, 10, 11, 11, 49, 2, 4, 2, 16, 4, 2, 3, 36, 11, 6, 29, 6, 3], [48, 28, 75, 3, 12, 20, 37, 8, 5, 24, 21, 2, 56, 18, 110, 11, 54, 18, 10, 22, 68, 10, 46, 97, 48, 19, 37, 6, 41, 106, 8, 8, 60], [63, 12, 2, 15, 4, 3, 16, 30, 130, 45, 8, 9, 26, 5, 3, 44, 5, 2, 84, 2, 5, 4, 36, 4, 7, 18, 9, 4, 4, 37, 8, 31, 70, 11, 6, 73, 3, 6, 104, 3, 10, 47, 76, 6, 46]], (20, 20, 6): [[59, 14, 115, 244, 41, 57, 9, 265, 60, 21, 35, 147, 69], [31, 4, 97, 5, 237, 105, 8, 10, 8, 6, 7, 6, 6, 6, 4, 106, 110, 322, 4, 6, 7, 4, 12, 16, 9], [186, 38, 7, 21, 9, 110, 12, 26, 3, 8, 15, 157, 12, 115, 310, 8, 81, 9, 2, 3, 4], [5, 69, 12, 35, 9, 82, 37, 29, 26, 79, 16, 737], [261, 5, 128, 19, 67, 34, 3, 145, 98, 19, 49, 6, 302]], (20, 40, 1): [[1019, 117], [1136], [915, 217, 2, 2], [89, 1047], [55, 1081]], (20, 40, 2): [[5, 3, 6, 3, 3, 4, 2, 37, 8, 2, 16, 19, 6, 3, 2, 7, 3, 15, 12, 2, 3, 2, 15, 76, 12, 13, 18, 13, 9, 6, 8, 33, 2, 12, 3, 5, 4, 2, 2, 8, 5, 4, 38, 23, 4, 6, 3, 46, 3, 3, 13, 58, 13, 30, 3, 21, 4, 42, 7, 2, 11, 4, 12, 18, 2, 3, 4, 3, 34, 18, 11, 33, 17, 23, 2, 8, 2, 2, 3, 2, 23, 12, 9, 19, 21, 15, 7, 2, 3, 2, 33, 3, 5, 2, 17, 8, 6, 5], [2, 5, 44, 38, 24, 3, 10, 34, 2, 6, 3, 11, 5, 25, 4, 9, 9, 2, 38, 15, 4, 24, 25, 2, 6, 4, 15, 2, 4, 8, 8, 6, 6, 4, 4, 13, 4, 6, 11, 4, 34, 3, 2, 10, 9, 6, 10, 43, 2, 21, 6, 11, 24, 36, 4, 5, 2, 3, 8, 9, 2, 31, 26, 18, 2, 4, 15, 21, 2, 14, 23, 4, 2, 18, 51, 6, 2, 11, 14, 6, 9, 6, 3, 5, 24, 2, 21, 2, 5, 31, 2, 3, 8, 22, 10, 5, 7, 2], [16, 2, 3, 10, 3, 20, 10, 2, 36, 70, 2, 3, 5, 11, 10, 6, 5, 4, 23, 3, 6, 13, 3, 11, 5, 4, 29, 3, 2, 12, 6, 6, 4, 13, 11, 13, 2, 4, 3, 3, 16, 5, 26, 9, 37, 5, 3, 25, 12, 26, 7, 2, 4, 37, 29, 18, 6, 16, 4, 7, 2, 6, 6, 5, 6, 6, 3, 11, 2, 23, 8, 12, 2, 3, 23, 8, 27, 7, 8, 4, 5, 3, 22, 16, 5, 6, 3, 10, 7, 8, 11, 6, 32, 3, 5, 2, 20, 8, 8, 3, 12, 4, 12, 12, 14, 22, 14], [89, 4, 4, 14, 2, 6, 4, 3, 6, 7, 8, 2, 35, 3, 6, 8, 27, 12, 2, 15, 8, 26, 44, 14, 6, 27, 3, 4, 10, 3, 2, 2, 19, 18, 6, 42, 30, 6, 7, 3, 2, 5, 16, 12, 4, 2, 37, 6, 12, 63, 8, 37, 29, 32, 24, 3, 3, 5, 15, 14, 11, 5, 9, 2, 35, 20, 45, 10, 44, 9, 5, 12, 18, 4, 8, 10, 2, 8, 13], [2, 24, 5, 7, 40, 34, 9, 2, 24, 2, 7, 3, 3, 20, 8, 2, 2, 6, 11, 3, 5, 70, 3, 2, 17, 37, 7, 7, 13, 5, 12, 6, 22, 4, 13, 2, 15, 2, 23, 31, 6, 8, 5, 20, 15, 7, 7, 4, 26, 10, 3, 12, 26, 4, 5, 5, 2, 12, 8, 5, 4, 8, 3, 26, 3, 4, 25, 10, 5, 13, 12, 17, 17, 5, 2, 13, 2, 26, 6, 13, 31, 2, 4, 6, 11, 5, 39, 3, 4, 12, 5, 11, 33, 2, 2, 8, 10, 3, 9, 5, 6, 11]], (20, 40, 3): [[13, 39, 5, 7, 36, 3, 21, 50, 36, 58, 3, 80, 13, 10, 50, 20, 46, 52, 5, 58, 3, 48, 23, 9, 30, 5, 32, 7, 3, 13, 100, 3, 4, 24, 3, 16, 3, 4, 22, 28, 5, 62, 8, 32, 44], [45, 46, 86, 51, 62, 53, 23, 10, 15, 36, 7, 7, 23, 28, 7, 5, 63, 31, 34, 4, 14, 14, 13, 2, 77, 21, 2, 3, 4, 4, 71, 78, 3, 27, 8, 14, 55, 7, 72, 11], [20, 10, 60, 3, 46, 4, 43, 16, 22, 3, 25, 61, 10, 4, 119, 11, 32, 9, 60, 13, 2, 13, 18, 11, 13, 3, 83, 19, 45, 58, 5, 32, 3, 9, 61, 22, 40, 11, 5, 49, 3, 29, 31], [41, 9, 10, 51, 52, 45, 5, 6, 11, 8, 4, 3, 3, 15, 44, 21, 86, 34, 63, 28, 3, 83, 9, 58, 64, 5, 7, 9, 32, 17, 54, 24, 30, 25, 38, 3, 3, 28, 3, 13, 69, 11, 4, 5], [11, 11, 9, 12, 3, 11, 16, 16, 17, 27, 11, 14, 5, 19, 4, 4, 6, 9, 11, 14, 3, 11, 8, 19, 6, 6, 15, 23, 19, 15, 4, 4, 3, 45, 67, 7, 40, 24, 10, 2, 29, 2, 7, 6, 5, 56, 4, 43, 4, 5, 8, 4, 4, 17, 128, 4, 17, 77, 32, 64, 8, 13, 12, 4, 11, 11]], (20, 40, 6): [[11, 21, 7, 105, 63, 8, 11, 86, 4, 26, 12, 13, 7, 19, 40, 18, 6, 123, 4, 3, 40, 9, 4, 11, 31, 24, 6, 9, 63, 5, 6, 47, 75, 8, 11, 7, 26, 112, 6, 6, 43], [3, 15, 9, 16, 9, 23, 10, 410, 45, 5, 28, 6, 6, 12, 16, 25, 9, 9, 15, 121, 14, 29, 25, 33, 24, 18, 8, 10, 15, 13, 24, 25, 62, 44], [9, 2, 12, 3, 47, 227, 20, 29, 5, 32, 461, 219, 39, 24, 7], [368, 45, 10, 43, 124, 171, 44, 28, 27, 7, 220, 44, 5], [5, 78, 98, 49, 25, 42, 54, 81, 3, 19, 53, 43, 3, 34, 14, 7, 357, 171]]}
# results of runnning sample_clustering_params
cluster_sizes= {(3, 5, 3): [[41, 179, 27, 61, 34, 209, 69, 2, 2, 23, 168, 55, 4, 2, 274], [6, 107, 23, 751, 130, 124], [489, 2, 355, 449, 187]], (3, 5, 6): [[16, 96, 410, 2, 1, 306, 59, 238, 7, 9], [240, 2, 543, 2, 56, 29, 2, 269], [13, 2, 2, 240, 2, 356, 8, 2, 118, 44, 7, 284, 2, 2, 8, 7, 9, 45, 2, 2]], (3, 5, 10): [[2, 301, 555, 12, 169, 2, 90, 12], [2, 136, 2, 331, 156, 2, 512, 2], [199, 267, 2, 2, 105, 1, 12, 379, 718, 1, 131, 1, 2, 2]], (3, 5, 40): [[211, 434, 128, 157, 210], [1, 2, 1, 68, 1, 238, 630, 325, 41, 2, 1, 1, 32, 250, 177], [1, 322, 33, 949, 269, 262]], (3, 5, 100): [[179, 221, 302, 435, 2], [2, 198, 2, 139, 2, 121, 129, 549], [371, 156, 341, 867, 1, 133, 75]], (3, 20, 3): [[10, 7, 9, 2, 3, 420, 180, 4, 236, 7, 266], [117, 835, 2, 2, 2, 180, 4], [2, 2, 102, 8, 436, 2, 2, 2, 106, 480, 4]], (3, 20, 6): [[186, 576, 2, 315, 61], [7, 22, 4, 131, 219, 9, 14, 382, 48, 19, 7, 15, 91, 7, 145, 2, 28], [8, 194, 6, 744, 213, 61, 2, 8, 155, 409, 31]], (3, 20, 10): [[553, 74, 301, 210, 2], [11, 149, 660, 2, 384, 82, 497, 12, 46, 108, 27], [11, 2, 316, 30, 8, 26, 68, 144, 105, 11, 110, 107, 4, 77, 2, 129]], (3, 20, 40): [[105, 40, 309, 934, 145], [42, 695, 36, 7, 136, 191, 35], [1, 917, 209, 51, 69, 443, 1, 1]], (3, 20, 100): [[64, 160, 20, 376, 103, 1000, 227], [2, 386, 102, 387, 2, 2, 260, 2], [44, 997, 129, 703, 48]], (3, 80, 3): [[12, 4, 7, 200, 249, 7, 601, 63], [9, 415, 77, 2, 21, 243, 107, 5, 4, 5, 257], [245, 20, 161, 64, 219, 2, 4, 4, 8, 7, 188, 234]], (3, 80, 6): [[542, 2, 2, 7, 2, 317, 2, 2, 2, 267], [11, 44, 14, 90, 41, 1, 49, 10, 1, 13, 1, 1, 13, 7, 12, 7, 1, 7, 1, 8, 27, 792, 2], [48, 8, 19, 40, 390, 144, 6, 4, 6, 198, 3, 163, 8, 84, 7, 9, 7, 9]], (3, 80, 10): [[38, 14, 6, 20, 106, 881, 370, 29, 68, 3, 108, 13, 26, 32, 76, 1, 5, 43, 182, 1], [76, 100, 2, 18, 76, 8, 139, 90, 2, 181, 93, 132, 46, 15, 124, 15, 34], [38, 10, 64, 148, 312, 2, 177, 33, 106, 2, 13, 24, 16, 16, 8, 654, 11, 5]], (3, 80, 40): [[415, 32, 62, 232, 103, 119, 26, 152], [40, 104, 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1, 15, 3, 3, 17, 3, 4, 22, 15, 8, 751, 13, 5, 62, 24, 6, 38, 10, 65, 21, 22, 3, 5, 21, 39]], (13, 20, 6): [[15, 31, 3, 17, 5, 78, 39, 9, 14, 14, 19, 29, 75, 90, 20, 4, 11, 52, 48, 100, 42, 12, 33, 198, 17, 74, 22, 75, 5, 12, 3], [13, 18, 37, 57, 19, 7, 21, 115, 63, 22, 9, 130, 4, 85, 142, 259, 144, 7], [17, 11, 207, 16, 7, 34, 9, 47, 82, 7, 12, 14, 13, 9, 76, 200, 5, 5, 21, 67, 13, 99, 11, 16, 27, 104, 14, 8, 13]], (13, 20, 10): [[11, 152, 849, 34, 9, 287, 96, 11, 5, 5, 28, 23, 259], [41, 3, 21, 4, 9, 18, 9, 108, 275, 579, 40, 10, 15, 15, 3], [285, 9, 24, 16, 287, 19, 6, 8, 231, 47, 5, 14, 783, 5, 16, 8, 18, 119, 16]], (13, 20, 40): [[18, 7, 136, 56, 79, 77, 517, 238, 16], [98, 887, 17, 50, 19, 75, 619], [43, 14, 44, 37, 20, 458, 118, 15, 63, 142, 194]], (13, 20, 100): [[27, 21, 1087, 4], [39, 18, 32, 1064, 702, 34, 17, 80, 48], [9, 10, 186, 850, 22, 64]], (13, 80, 3): [[3, 14, 27, 21, 10, 76, 119, 30, 63, 29, 24, 5, 4, 47, 37, 22, 60, 5, 25, 23, 24, 113, 3, 4, 25, 4, 33, 36, 8, 12, 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6, 9, 20, 19, 4, 267]], (13, 80, 40): [[8, 133, 86, 42, 124, 29, 60, 76, 333, 1081, 139, 75], [49, 22, 922, 79, 324, 48, 24, 228, 11, 30, 181, 51], [54, 35, 302, 24, 272, 384, 71]], (13, 80, 100): [[29, 5, 38, 7, 146, 12, 794, 14, 9, 8, 84], [5, 11, 15, 1108], [5, 54, 744, 1058, 15, 178, 26]], (20, 5, 3): [[23, 19, 24, 67, 106, 7, 15, 20, 48, 127, 4, 16, 38, 34, 28, 21, 12, 7, 4, 99, 12, 12, 15, 79, 237, 8, 46, 12, 6, 19], [90, 43, 48, 14, 107, 122, 10, 17, 5, 7, 5, 9, 5, 23, 8, 109, 66, 30, 15, 6, 37, 78, 19, 17, 7, 48, 16, 13, 21, 3, 10, 31, 13, 3, 56, 20, 41], [79, 6, 8, 19, 13, 3, 9, 8, 23, 21, 48, 29, 98, 4, 206, 17, 91, 18, 50, 52, 5, 5, 9, 8, 10, 3, 25, 14, 15, 14, 5, 7, 13, 39, 11, 20, 8, 49, 31, 17, 49, 18]], (20, 5, 6): [[45, 15, 7, 32, 298, 53, 27, 63, 7, 155, 160, 20, 107, 111, 36, 17], [31, 10, 2, 325, 165, 33, 6, 88, 128, 138, 22, 178, 22], [76, 7, 8, 9, 229, 104, 37, 17, 16, 446, 24, 54, 121]], (20, 5, 10): [[293, 14, 8, 27, 25, 6, 33, 10, 681, 30, 13, 7], [14, 791, 4, 37, 10, 23, 9, 334, 165, 31, 4, 39, 24, 401], [9, 71, 11, 3, 74, 18, 11, 83, 11, 108, 6, 462, 43, 54, 8, 50, 20, 13, 99]], (20, 5, 40): [[81, 20, 433, 331, 25, 29, 222], [875, 472, 472, 106, 64, 5], [32, 207, 65, 185, 234, 53, 208, 159]], (20, 5, 100): [[629, 315, 194], [30, 88, 524, 476, 22], [612, 96, 3, 272, 157]], (20, 20, 3): [[78, 8, 66, 83, 35, 14, 7, 17, 42, 20, 36, 6, 37, 78, 6, 7, 15, 30, 43, 60, 3, 4, 6, 26, 36, 10, 53, 13, 3, 12, 18, 4, 40, 52, 9, 14, 4, 21, 11, 24, 16, 12, 4, 14, 18, 50, 16], [5, 41, 5, 6, 6, 30, 4, 21, 6, 36, 5, 5, 8, 8, 32, 95, 17, 12, 18, 5, 25, 10, 5, 8, 5, 12, 42, 15, 66, 4, 151, 70, 16, 8, 4, 8, 63, 38, 25, 40, 6, 46, 40, 6, 17, 7, 5, 24, 7, 3, 22, 17, 8], [19, 4, 40, 3, 12, 15, 5, 28, 22, 9, 4, 34, 62, 34, 33, 12, 19, 3, 3, 17, 79, 78, 10, 30, 16, 4, 4, 37, 22, 17, 8, 30, 19, 17, 4, 6, 28, 16, 8, 12, 12, 17, 27, 12, 4, 4, 4, 11, 16, 76, 4, 17, 5, 7, 4, 9, 36, 5, 16, 35, 10, 43]], (20, 20, 6): [[7, 66, 130, 62, 5, 12, 144, 70, 16, 5, 6, 41, 16, 423, 53, 21, 42, 6, 5, 8, 7, 6, 8], [39, 7, 223, 9, 36, 6, 62, 801, 6, 7, 4, 29, 42, 15, 7, 21, 115, 12, 6, 357, 49, 55, 14], [28, 179, 5, 308, 28, 7, 5, 105, 40, 29, 58, 12, 14, 5, 13, 6, 52, 43, 20, 13, 782, 18, 34, 7, 102]], (20, 20, 10): [[23, 11, 10, 13, 18, 9, 3, 143, 103, 6, 19, 43, 39, 74, 47, 16, 10, 30, 300, 4, 199, 9, 3, 8, 12, 9], [15, 11, 11, 3, 200, 26, 16, 23, 8, 90, 304, 29, 7, 32, 368, 8], [481, 6, 5, 3, 9, 133, 15, 161, 38, 112, 164, 19]], (20, 20, 40): [[15, 117, 509, 115, 354, 31], [221, 91, 107, 78, 189, 70, 22, 327, 23, 11, 7], [125, 49, 432, 14, 271, 17, 96, 43, 58, 40]], (20, 20, 100): [[88, 426, 76, 531, 19], [22, 459, 206, 221, 108, 59, 11, 57], [355, 18, 1131, 8]], (20, 80, 3): [[27, 74, 8, 9, 3, 9, 26, 3, 4, 54, 12, 83, 47, 66, 60, 14, 9, 63, 4, 10, 7, 10, 7, 27, 15, 7, 4, 7, 42, 3, 24, 48, 12, 5, 44, 10, 25, 11, 65, 26, 4, 7, 3, 106, 61, 15], [35, 100, 44, 5, 5, 47, 66, 68, 13, 10, 18, 4, 13, 26, 12, 3, 9, 16, 49, 3, 84, 5, 83, 4, 4, 13, 58, 15, 111, 7, 13, 7, 3, 6, 3, 19, 65, 52, 5, 36, 36], [15, 7, 31, 15, 14, 16, 22, 48, 8, 9, 23, 7, 5, 6, 7, 14, 30, 4, 5, 36, 6, 27, 12, 30, 3, 112, 21, 13, 11, 18, 19, 50, 3, 9, 7, 7, 37, 51, 4, 5, 3, 13, 3, 8, 3, 27, 31, 67, 61, 4, 4, 25, 23, 3, 4, 6, 9, 4, 37, 42, 4, 29, 7, 15]], (20, 80, 6): [[77, 13, 71, 10, 114, 30, 10, 15, 28, 11, 41, 25, 39, 8, 29, 6, 62, 33, 64, 9, 3, 85, 11, 8, 7, 41, 88, 61, 4, 8, 57, 8, 37, 14, 7, 15, 12, 11], [8, 22, 24, 6, 91, 58, 85, 10, 34, 4, 7, 27, 4, 48, 35, 7, 12, 81, 9, 6, 6, 54, 179, 260, 8, 3, 71, 4], [65, 45, 14, 11, 8, 7, 33, 7, 19, 20, 14, 370, 3, 13, 8, 16, 791, 22, 30, 5, 39, 248, 40, 34, 12, 10, 9, 3, 5, 8]], (20, 80, 10): [[6, 3, 219, 53, 7, 584, 11, 54, 11, 11, 154, 9, 14, 13], [883, 150, 4, 7, 7, 16, 19, 3, 13, 115, 17, 420, 7, 17, 19, 9, 7, 3, 7, 11], [69, 694, 49, 16, 104, 18, 10, 121, 7, 8, 7, 38, 7]], (20, 80, 40): [[899, 747, 43, 52, 15], [12, 164, 4, 425, 60, 21, 181, 96, 54, 28, 101], [45, 216, 39, 12, 328, 75, 357, 70]], (20, 80, 100): [[71, 33, 1095, 128, 51, 482], [10, 10, 10, 63, 1047], [31, 969, 241, 834]]}
plt.rcParams['figure.figsize'] = (16,16)
k_stats = [[] for i in range(4)] # rows are: k,mean,var,var of var
weak_stats = [[] for i in range(4)]
neighbor_stats = [[] for i in range(4)]
for params,c_sizes in cluster_sizes.items():
stats = (np.mean([len(a) for a in c_sizes]),np.mean([np.var(a) for a in c_sizes]),
np.var([np.var(a) for a in c_sizes]))
k_stats[0].append(params[0])
weak_stats[0].append(params[1])
neighbor_stats[0].append(params[2])
for i in [0,1,2]:
k_stats[i+1].append(stats[i])
weak_stats[i+1].append(stats[i])
neighbor_stats[i+1].append(stats[i])
_,subplt = plt.subplots(3, 3,gridspec_kw = {'wspace': 0.5,'hspace': 0.25}, sharey='col')
for row,d in enumerate([k_stats,weak_stats,neighbor_stats]):
for col in range(3):
subplt[row,col].scatter(d[0],d[col+1])
for i in [0,1,2]:
subplt[0][i].set_xlabel('# clusters for each weak kNN (k)')
subplt[1][i].set_xlabel('# weak kNN\'s (w)')
subplt[2][i].set_xlabel("# connections per node considered 'strong' (n)")
for i in [0,1,2]:
subplt[i][0].set_ylabel('mean of c_sizes')
subplt[i][1].set_ylabel('var of c_size')
subplt[i][2].set_ylabel('var of var of c_size')
#k_stats_2 = [d[1] for k in [3,5,7,13,20] for i,d in enumerate(k_stats) if k_stats[0]==k ]#,
k_stats_2 = [[[k_stats[1][i] for i in range(len(k_stats[1])) if k_stats[0][i]==k] for k in k_ticks],
[[k_stats[2][i] for i in range(len(k_stats[1])) if k_stats[0][i]==k] for k in k_ticks],
[[k_stats[3][i] for i in range(len(k_stats[1])) if k_stats[0][i]==k] for k in k_ticks]]
weak_stats_2 = [[[weak_stats[1][i] for i in range(len(weak_stats[1])) if weak_stats[0][i]==w] for w in w_ticks],
[[weak_stats[2][i] for i in range(len(weak_stats[1])) if weak_stats[0][i]==w] for w in w_ticks],
[[weak_stats[3][i] for i in range(len(weak_stats[1])) if weak_stats[0][i]==w] for w in w_ticks]]
neighbor_stats_2 = [[[neighbor_stats[1][i] for i in range(len(neighbor_stats[1])) if neighbor_stats[0][i]==n] for n in n_ticks],
[[neighbor_stats[2][i] for i in range(len(neighbor_stats[1])) if neighbor_stats[0][i]==n] for n in n_ticks],
[[neighbor_stats[3][i] for i in range(len(neighbor_stats[1])) if neighbor_stats[0][i]==n] for n in n_ticks]]
_,subplt = plt.subplots(3, 3,gridspec_kw = {'wspace': 0.5,'hspace': 0.25}, sharey='col')
for row,d in enumerate([k_stats_2,weak_stats_2,neighbor_stats_2]):
for col in range(3):
subplt[row,col].boxplot(d[col])
for i in [0,1,2]:
subplt[0][i].set_xlabel('# clusters for each weak kNN (k)')
subplt[0][i].set_xticklabels(k_ticks)
subplt[1][i].set_xlabel('# weak kNN\'s (w)')
subplt[1][i].set_xticklabels(w_ticks)
subplt[2][i].set_xlabel("# connections per node considered 'strong' (n)")
subplt[2][i].set_xticklabels(n_ticks)
for i in [0,1,2]:
subplt[i][0].set_ylabel('mean of c_sizes')
subplt[i][1].set_ylabel('var of c_size')
subplt[i][2].set_ylabel('var of var of c_size')
def get_regression_performance(predict_func,args,iterations,bias_factor=1):
"""
predict_func: function that implements model. must return 3-tuple of rank 1 ndarrays,
(_, test true values, test predicted values)
args: tuple of arguments for predict_func
iterations: number of validation iterations desired
"""
#cum_stats = np.zeros((3,testSize)) # rows: 0.sum(prediction); 1.sum(prediction^2); 2.sum(pred-real)
#bias = ( sum(pred - real) )^2 = ( sum(pred) - sum(real) )^2
#var = sum(pred^2) - sum(pred) ^2
for i in range(iterations):
(y_true,y_pred,_) = predict_func(*args)
if i == 0:
cum_stats = np.zeros((3,len(y_pred)))
cum_stats[0] += y_pred
cum_stats[1] += (y_pred - y_true)
cum_stats[2] += y_pred**2
mean_y_pred = np.sum(cum_stats[0]) / cum_stats.shape[1]
bias = np.sum(cum_stats[1]) / (cum_stats.shape[1]-1)
variance = (np.sum(cum_stats[2]) - mean_y_pred**2) / (cum_stats.shape[1]-1)
return (mean_y_pred, predict_func(*args)[2],(bias*bias_factor)**2 + variance)
def get_regression_performance_shuffle_split(predict_func,data,goal,args,test_size=0.2,iterations=5,bias_factor=1):
"""
predict_func: function that implements model. must return 3-tuple of rank 1 ndarrays,
(_, test true values, test predicted values)
args: tuple of arguments for predict_func
"""
for train_index, test_index in ShuffleSplit(n_splits=5, test_size=.2).split(data):
(y_true,y_pred,_) = predict_func(data[train_index],data[test_index],goal[train_index],goal[test_index],*args)
if i == 0:
cum_stats = np.zeros((3,len(y_pred)))
cum_stats[0] += y_pred
cum_stats[1] += (y_pred - y_true)
cum_stats[2] += y_pred**2
mean_y_pred = np.sum(cum_stats[0]) / cum_stats.shape[1]
bias = np.sum(cum_stats[1]) / (cum_stats.shape[1]-1)
variance = (np.sum(cum_stats[2]) - mean_y_pred**2) / (cum_stats.shape[1]-1)
return (mean_y_pred, predict_func(*args)[2],(bias*bias_factor)**2 + variance)
def lasso_prediction(x,y,alpha=1.0,rand_state=(np.random.rand(1) * 10000).astype(int),silent=True):
train_x, valid_x, train_y, valid_y = train_test_split(
x, y, test_size=0.2, random_state=rand_state)
model = Lasso(alpha=alpha)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def lasso_prediction_pre_split(train_x, valid_x, train_y, valid_y,alpha=1.0,silent=True):
model = Lasso(alpha=alpha)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def ridge_prediction(x,y,alpha=1.0,rand_state=(np.random.rand(1) * 10000).astype(int),silent=True):
train_x, valid_x, train_y, valid_y = train_test_split(
x, y, test_size=0.2, random_state=rand_state)
model = Ridge(alpha=alpha)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def ridge_prediction_pre_split(train_x, valid_x, train_y, valid_y,alpha=1.0,silent=True):
model = Ridge(alpha=alpha)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def kNN_prediction(x,y,n_neighbors=5,rand_state=(np.random.rand(1) * 10000).astype(int),silent=True):
train_x, valid_x, train_y, valid_y = train_test_split(
x, y, test_size=0.2, random_state=rand_state)
model = KNeighborsRegressor(n_neighbors=n_neighbors)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def kNN_prediction_pre_split(train_x, valid_x, train_y, valid_y,silent=True):
model = KNeighborsRegressor(n_neighbors=n_neighbors)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def svr_prediction(x,y,kernel='rbf',rand_state=(np.random.rand(1) * 10000).astype(int), C=0.8,
epsilon=0.1,gamma='auto',silent=True):
train_x, valid_x, train_y, valid_y = train_test_split(
x, y, test_size=0.2, random_state=rand_state)
model = SVR(kernel=kernel,C=C,epsilon=epsilon,gamma=gamma)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def adatree_prediction(x,y,depth=4,n_estimators=50,,rand_state=(np.random.rand(1) * 10000).astype(int),silent=True):
train_x, valid_x, train_y, valid_y = train_test_split(
x, y, test_size=0.2, random_state=rand_state)
model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=depth),
n_estimators=n_estimators,random_state=rand_state)
model.fit(train_x,train_y)
y_pred = model.predict(valid_x)
if not silent:
plt.rcParams['figure.figsize'] = (5,2)
plt.title(str(alpha))
plt.violinplot(y_pred-valid_y, vert = False, showmeans=True, showextrema=True, showmedians=True)
return(valid_y, y_pred, pickle.dumps(model))
def get_regression(data, goal, bias_factor, lasso=True, ridge=True,
kNN=True, svr=True, adatree=True):
#rand_states = [1213,239485,7298345,143542,535]
regression_options = [] # store tuples of (pickled model, error)
if lasso:
lst_alpha = [0.4,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,1,1.1]
for alpha in lst_alpha:
best = (None,1000000000000000000)
args = (alpha,)
_,pickled_model, model_score = get_regression_performance_shuffle_split(lasso_prediction_pre_split,data,
goal,args,
bias_factor=bias_factor)
best = (best if (best[1] <= model_score) else (pickled_model,model_score))
regression_options.append(best)
if ridge:
lst_alpha = [0.4,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,1,1.1]
for alpha in lst_alpha:
best = (None,1000000000000000000)
args = (alpha,)
_, pickled_model, model_score = get_regression_performance_shuffle_split(ridge_prediction_pre_split,args,
bias_factor=bias_factor)
best = (best if (best[1] <= model_score) else (pickled_model,model_score))
regression_options.append(best)
if kNN:
if len(data) > 75: # otherwise kNN regression is probably not effective
lst_n = np.arange(3,10)
for n in lst_n:
best = (None,1000000000000000000)
args = (n,)
_, pickled_model, model_score = get_regression_performance_shuffle_split(kNN_prediction_pre_split,data,
goal,args,
bias_factor=bias_factor)
best = (best if (best[1] <= model_score) else (pickled_model,model_score))
regression_options.append(best)
if svr:
lst_kernel=['rbf','linear']
lst_C= [0.1,0.5,1,5,10,50,100]
lst_epsilon = [0.01,0.03,0.05,0.07,0.1]
lst_gamma = [0.001,0.005,0.01,0.05,0.1]
combos = itertools.product(lst_kernel,lst_C,lst_epsilon)
for comb in combos:
args = (data,goal,*comb)
_, pickled_model, model_score = get_regression_performance(svr_prediction,args,
iterations=5,bias_factor=bias_factor)
best = (best if (best[1] <= model_score) else (pickled_model,model_score))
regression_options.append(best)
if adatree:
lst_depth=np.arange(2,6)
lst_weak=[10,50,100]
combos = itertools.product(lst_depth,lst_weak)
for comb in combos:
args = (data,goal,*comb)
_, pickled_model, model_score = get_regression_performance(adatree_prediction,args,
iterations=5,bias_factor=bias_factor)
best = (best if (best[1] <= model_score) else (pickled_model,model_score))
regression_options.append(best)
return regression_options, regression_options[np.argmin(tup[1] for tup in regression_options)]
# estimate most similar cluster (using purely intuition)
def find_cluster(d,cluster_stats):
return np.argmin([np.sum( (d - stat[0]) / (stat[1] * (d-stat[0])**(stat[2]*stat[3])) ) for stat in cluster_stats])
def regress_overall_stacked(features, goal, test_size, r_state=(np.random.rand(1) * 10000).astype(int),
lasso=True, ridge=True, kNN=True, svm=True, adatree=True,silent=False):
_, pickled_overall_weak = get_regression(data,goal,1,ridge=False)
overall_weak = [pickle.loads(d) for d in pickled_overall_weak]
train, valid, train_goal_true, valid_goal_true = train_test_split(
features,goal,test_size=test_size,random_state=r_state)
overall_weak_pred_train = []
for weak in overall_weak:
weak.fit_predict(train,train_goal)
overall_weak_pred_train.append(weak.predict(train))
pred_input = np.vstack(overall_weak_pred_train)
overall_stack = LogisticRegression()
overall_stack.fit(pred_input, train_goal)
overall_pred = overall_stack.predict(valid)
overall_SSE += mean_squared_error(valid_goal.values, overall_pred)
overall_r2 += r2_score(valid_goal.values, overall_pred)
if not silent:
print("Ensemble model without clustering SSE: %f" % overall_SSE)
print("Ensemble model without clustering r2: %0.4f" % overall_r2)
print("")
def regress_by_cluster(features, goal, test_size, r_state=rand_state=(np.random.rand(1) * 10000).astype(int)
lasso=True, ridge=True, kNN=True, svm=True, adatree=True,silent=False):
_, pickled_overall_weak = get_regression(data,goal,1,ridge=False)
overall_weak = [pickle.loads(d) for d in pickled_overall_weak]
train, valid, train_goal_true, valid_goal_true = train_test_split(
features,goal,test_size=test_size,random_state=r_state)
overall_weak_pred_train = []
for weak in overall_weak:
overall_weak_pred_train.append(weak.fit_predict(train,train_goal))
pred_input = np.vstack(overall_weak_pred_train)
overall_stack = LogisticRegression()
overall_stack.fit(pred_input, train_goal)
overall_pred = overall_stack.predict(valid)
overall_weak_pred = [weak.predict(valid) for weak in overall_weak]
overall_weak_scores = [(type(weak), mean_squared_error(valid_goal.values, overall_weak_pred[i]),
r2_score(valid_goal.values, overall_weak_pred[i])) for i,weak in enumerate(overall_weak)]
overall_SSE += mean_squared_error(valid_goal.values, overall_pred)
overall_r2 += r2_score(valid_goal.values, overall_pred)
clusters = list(cluster_from_stack_of_KMeans(train, K=3, num_weak_learners = 20,num_neighbors=20))
cluster_stats = np.empty((len(clusters),4,len(train.columns))) #dim2=mean,var,skew,kurtosis
for i,cluster in enumerate(clusters):
lst = list(cluster)
cluster_stats[i,0,:] = np.mean(lst)
cluster_stats[i,1,:] = np.std(lst)
cluster_stats[i,2,:] = skew(lst)
cluster_stats[i,3,:] = kurtosis(lst)
#####
if not silent:
for i,d in enumerate(clusters):
print("cluster size: " + str(len(d)))
display (pd.DataFrame(cluster_stats[i],columns=['mean','var','skew','kurtosis']))
print('\n')
#####
cluster_regressions = []
for c in clusters:
if (len(c) > 30):
bias_factor=1
#bias_factor = (1 if len(c) > 100 else (len(c)+100)/200)
lst_c = list(c)
#reg_options=get_regression(train.iloc[lst_c], train_goal.iloc[lst_c], bias_factor)
#cluster_regressions.append(reg_options[np.argmin(tup[1] for tup in reg_options)])
cluster_regressions.append(get_regression(train.iloc[lst_c], train_goal.iloc[lst_c],
bias_factor,ridge=False))
else:
cluster_regressions.append(None)
valid_clusters = [find_cluster(i[1],cluster_stats) for i in valid.iterrows()]
y_pred = []
for i,d in enumerate(valid.iterrows()):
if not cluster_regressions[valid_clusters[i]] is None:
cluster_prediction = pickle.loads((cluster_regressions[valid_clusters[i]])[0]).predict(d[1].values.reshape(1,-1))
overall_prediction = overall_model.predict(d[1].values.reshape(1,-1))
cluster_factor = 1 / (2 + 5 * math.exp(-len(clusters[valid_clusters[i]])/100))
y_pred.append(cluster_prediction*cluster_factor + overall_prediction*(1-cluster_factor))
else:
y_pred.append(overall_model.predict(d[1].values.reshape(1,-1)))
total_ensemble_SSE = mean_squared_error(valid_goal.values, y_pred)
total_ensemble_r2 = r2_score(valid_goal.values, y_pred)
if not silent:
print("Overall weak, SSE, r2:")
print(overall_weak_scores)
print("Ensemble model without clustering SSE: %f" % overall_SSE)
print("Ensemble model without clustering r2: %0.4f" % overall_r2)
print("Total Ensemble model SSE: %f" % total_ensemble_SSE)
print("Total Ensemble model r2: %0.4f" % total_ensemble_r2)
print("")
print(get_regression_performance(regress_overall_stacked,(data[final_features], data['SalePrice'], 0.2),iterations=5,bias_factor=1)):
print(get_regression_performance(regress_by_cluster,(data[final_features], data['SalePrice'], 0.2),iterations=5,bias_factor=1): | 190325167167.65564
| MIT | Intro to Python Class Projects/Intro to Python ML Project D.ipynb | Ddottsai/Code-Storage |
Задание 1.2 - Линейный классификатор (Linear classifier)В этом задании мы реализуем другую модель машинного обучения - линейный классификатор. Линейный классификатор подбирает для каждого класса веса, на которые нужно умножить значение каждого признака и потом сложить вместе.Тот класс, у которого эта сумма больше, и является предсказанием модели.В этом задании вы:- потренируетесь считать градиенты различных многомерных функций- реализуете подсчет градиентов через линейную модель и функцию потерь softmax- реализуете процесс тренировки линейного классификатора- подберете параметры тренировки на практикеНа всякий случай, еще раз ссылка на туториал по numpy: http://cs231n.github.io/python-numpy-tutorial/ | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
from dataset import load_svhn, random_split_train_val
from gradient_check import check_gradient
from metrics import multiclass_accuracy
import linear_classifer | _____no_output_____ | MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Как всегда, первым делом загружаем данныеМы будем использовать все тот же SVHN. | def prepare_for_linear_classifier(train_X, test_X):
train_flat = train_X.reshape(train_X.shape[0], -1).astype(np.float) / 255.0
test_flat = test_X.reshape(test_X.shape[0], -1).astype(np.float) / 255.0
# Subtract mean
mean_image = np.mean(train_flat, axis = 0)
train_flat -= mean_image
test_flat -= mean_image
# Add another channel with ones as a bias term
train_flat_with_ones = np.hstack([train_flat, np.ones((train_X.shape[0], 1))])
test_flat_with_ones = np.hstack([test_flat, np.ones((test_X.shape[0], 1))])
return train_flat_with_ones, test_flat_with_ones
train_X, train_y, test_X, test_y = load_svhn("data", max_train=10000, max_test=1000)
train_X, test_X = prepare_for_linear_classifier(train_X, test_X)
# Split train into train and val
train_X, train_y, val_X, val_y = random_split_train_val(train_X, train_y, num_val = 1000) | _____no_output_____ | MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Играемся с градиентами!В этом курсе мы будем писать много функций, которые вычисляют градиенты аналитическим методом.Все функции, в которых мы будем вычислять градиенты будут написаны по одной и той же схеме. Они будут получать на вход точку, где нужно вычислить значение и градиент функции, а на выходе будут выдавать кортеж (tuple) из двух значений - собственно значения функции в этой точке (всегда одно число) и аналитического значения градиента в той же точке (той же размерности, что и вход).```def f(x): """ Computes function and analytic gradient at x x: np array of float, input to the function Returns: value: float, value of the function grad: np array of float, same shape as x """ ... return value, grad```Необходимым инструментом во время реализации кода, вычисляющего градиенты, является функция его проверки. Эта функция вычисляет градиент численным методом и сверяет результат с градиентом, вычисленным аналитическим методом.Мы начнем с того, чтобы реализовать вычисление численного градиента (numeric gradient) в функции `check_gradient` в `gradient_check.py`. Эта функция будет принимать на вход функции формата, заданного выше, использовать значение `value` для вычисления численного градиента и сравнит его с аналитическим - они должны сходиться.Напишите часть функции, которая вычисляет градиент с помощью численной производной для каждой координаты. Для вычисления производной используйте так называемую two-point formula (https://en.wikipedia.org/wiki/Numerical_differentiation):Все функции приведенные в следующей клетке должны проходить gradient check. | # TODO: Implement check_gradient function in gradient_check.py
# All the functions below should pass the gradient check
def square(x):
return float(x*x), 2*x
check_gradient(square, np.array([3.0]))
def array_sum(x):
assert x.shape == (2,), x.shape
return np.sum(x), np.ones_like(x)
check_gradient(array_sum, np.array([3.0, 2.0]))
def array_2d_sum(x):
assert x.shape == (2,2)
return np.sum(x), np.ones_like(x)
check_gradient(array_2d_sum, np.array([[3.0, 2.0], [1.0, 0.0]])) | Gradient check passed!
Gradient check passed!
Gradient check passed!
| MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Начинаем писать свои функции, считающие аналитический градиентТеперь реализуем функцию softmax, которая получает на вход оценки для каждого класса и преобразует их в вероятности от 0 до 1:**Важно:** Практический аспект вычисления этой функции заключается в том, что в ней учавствует вычисление экспоненты от потенциально очень больших чисел - это может привести к очень большим значениям в числителе и знаменателе за пределами диапазона float.К счастью, у этой проблемы есть простое решение -- перед вычислением softmax вычесть из всех оценок максимальное значение среди всех оценок:```predictions -= np.max(predictions)```(подробнее здесь - http://cs231n.github.io/linear-classify/softmax, секция `Practical issues: Numeric stability`) | # TODO Implement softmax and cross-entropy for single sample
probs = linear_classifer.softmax(np.array([-10, 0, 10]))
# Make sure it works for big numbers too!
probs = linear_classifer.softmax(np.array([1000, 0, 0]))
assert np.isclose(probs[0], 1.0) | _____no_output_____ | MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Кроме этого, мы реализуем cross-entropy loss, которую мы будем использовать как функцию ошибки (error function).В общем виде cross-entropy определена следующим образом:где x - все классы, p(x) - истинная вероятность принадлежности сэмпла классу x, а q(x) - вероятность принадлежности классу x, предсказанная моделью. В нашем случае сэмпл принадлежит только одному классу, индекс которого передается функции. Для него p(x) равна 1, а для остальных классов - 0. Это позволяет реализовать функцию проще! | probs = linear_classifer.softmax(np.array([-5, 0, 5]))
display(probs)
linear_classifer.cross_entropy_loss(probs, 1) | _____no_output_____ | MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
После того как мы реализовали сами функции, мы можем реализовать градиент.Оказывается, что вычисление градиента становится гораздо проще, если объединить эти функции в одну, которая сначала вычисляет вероятности через softmax, а потом использует их для вычисления функции ошибки через cross-entropy loss.Эта функция `softmax_with_cross_entropy` будет возвращает и значение ошибки, и градиент по входным параметрам. Мы проверим корректность реализации с помощью `check_gradient`. | # TODO Implement combined function or softmax and cross entropy and produces gradient
loss, grad = linear_classifer.softmax_with_cross_entropy(np.array([1, 0, 0]), 1)
check_gradient(lambda x: linear_classifer.softmax_with_cross_entropy(x, 1), np.array([1, 0, 0], np.float)) | Gradient check passed!
| MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
В качестве метода тренировки мы будем использовать стохастический градиентный спуск (stochastic gradient descent или SGD), который работает с батчами сэмплов. Поэтому все наши фукнции будут получать не один пример, а батч, то есть входом будет не вектор из `num_classes` оценок, а матрица размерности `batch_size, num_classes`. Индекс примера в батче всегда будет первым измерением.Следующий шаг - переписать наши функции так, чтобы они поддерживали батчи.Финальное значение функции ошибки должно остаться числом, и оно равно среднему значению ошибки среди всех примеров в батче. Наконец, реализуем сам линейный классификатор!softmax и cross-entropy получают на вход оценки, которые выдает линейный классификатор.Он делает это очень просто: для каждого класса есть набор весов, на которые надо умножить пиксели картинки и сложить. Получившееся число и является оценкой класса, идущей на вход softmax.Таким образом, линейный классификатор можно представить как умножение вектора с пикселями на матрицу W размера `num_features, num_classes`. Такой подход легко расширяется на случай батча векторов с пикселями X размера `batch_size, num_features`:`predictions = X * W`, где `*` - матричное умножение.Реализуйте функцию подсчета линейного классификатора и градиентов по весам `linear_softmax` в файле `linear_classifer.py` | # TODO Extend combined function so it can receive a 2d array with batch of samples
np.random.seed(42)
# Test batch_size = 1
num_classes = 4
batch_size = 1
predictions = np.random.randint(-1, 3, size=(num_classes, batch_size)).astype(np.float)
target_index = np.random.randint(0, num_classes, size=(batch_size, 1)).astype(np.int)
check_gradient(lambda x: linear_classifer.softmax_with_cross_entropy(x, target_index), predictions)
# Test batch_size = 3
num_classes = 4
batch_size = 3
predictions = np.random.randint(-1, 3, size=(num_classes, batch_size)).astype(np.float)
target_index = np.random.randint(0, num_classes, size=(batch_size, 1)).astype(np.int)
check_gradient(lambda x: linear_classifer.softmax_with_cross_entropy(x, target_index), predictions) | Gradient check passed!
Gradient check passed!
| MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
И теперь регуляризацияМы будем использовать L2 regularization для весов как часть общей функции ошибки.Напомним, L2 regularization определяется какl2_reg_loss = regularization_strength * sumij W[i, j]2Реализуйте функцию для его вычисления и вычисления соотвествующих градиентов. | # TODO Implement linear_softmax function that uses softmax with cross-entropy for linear classifier
batch_size = 2
num_classes = 2
num_features = 3
np.random.seed(42)
W = np.random.randint(-1, 3, size=(num_features, num_classes)).astype(np.float)
X = np.random.randint(-1, 3, size=(batch_size, num_features)).astype(np.float)
target_index = np.ones(batch_size, dtype=np.int)
loss, dW = linear_classifer.linear_softmax(X, W, target_index)
check_gradient(lambda w: linear_classifer.linear_softmax(X, w, target_index), W)
# TODO Implement l2_regularization function that implements loss for L2 regularization
linear_classifer.l2_regularization(W, 0.01)
check_gradient(lambda w: linear_classifer.l2_regularization(w, 0.01), W) | Gradient check passed!
| MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Тренировка! Градиенты в порядке, реализуем процесс тренировки! | # TODO: Implement LinearSoftmaxClassifier.fit function
classifier = linear_classifer.LinearSoftmaxClassifier()
loss_history = classifier.fit(train_X, train_y, epochs=10, learning_rate=1e-3, batch_size=300, reg=1e1)
# let's look at the loss history!
plt.plot(loss_history)
# Let's check how it performs on validation set
pred = classifier.predict(val_X)
accuracy = multiclass_accuracy(pred, val_y)
print("Accuracy: ", accuracy)
# Now, let's train more and see if it performs better
classifier.fit(train_X, train_y, epochs=100, learning_rate=1e-3, batch_size=300, reg=1e1)
pred = classifier.predict(val_X)
accuracy = multiclass_accuracy(pred, val_y)
print("Accuracy after training for 100 epochs: ", accuracy) | Accuracy: 0.09399999999999997
Epoch 0, loss: 2.609400
Epoch 1, loss: 2.609363
Epoch 2, loss: 2.609327
Epoch 3, loss: 2.609292
Epoch 4, loss: 2.609257
Epoch 5, loss: 2.609224
Epoch 6, loss: 2.609191
Epoch 7, loss: 2.609159
Epoch 8, loss: 2.609128
Epoch 9, loss: 2.609097
Epoch 10, loss: 2.609068
Epoch 11, loss: 2.609039
Epoch 12, loss: 2.609011
Epoch 13, loss: 2.608983
Epoch 14, loss: 2.608957
Epoch 15, loss: 2.608931
Epoch 16, loss: 2.608906
Epoch 17, loss: 2.608882
Epoch 18, loss: 2.608859
Epoch 19, loss: 2.608836
Epoch 20, loss: 2.608814
Epoch 21, loss: 2.608793
Epoch 22, loss: 2.608773
Epoch 23, loss: 2.608753
Epoch 24, loss: 2.608735
Epoch 25, loss: 2.608717
Epoch 26, loss: 2.608699
Epoch 27, loss: 2.608683
Epoch 28, loss: 2.608667
Epoch 29, loss: 2.608652
Epoch 30, loss: 2.608637
Epoch 31, loss: 2.608624
Epoch 32, loss: 2.608611
Epoch 33, loss: 2.608599
Epoch 34, loss: 2.608587
Epoch 35, loss: 2.608577
Epoch 36, loss: 2.608567
Epoch 37, loss: 2.608557
Epoch 38, loss: 2.608549
Epoch 39, loss: 2.608541
Epoch 40, loss: 2.608534
Epoch 41, loss: 2.608527
Epoch 42, loss: 2.608521
Epoch 43, loss: 2.608516
Epoch 44, loss: 2.608512
Epoch 45, loss: 2.608508
Epoch 46, loss: 2.608505
Epoch 47, loss: 2.608503
Epoch 48, loss: 2.608502
Epoch 49, loss: 2.608501
Epoch 50, loss: 2.608501
Epoch 51, loss: 2.608501
Epoch 52, loss: 2.608502
Epoch 53, loss: 2.608504
Epoch 54, loss: 2.608507
Epoch 55, loss: 2.608510
Epoch 56, loss: 2.608514
Epoch 57, loss: 2.608518
Epoch 58, loss: 2.608524
Epoch 59, loss: 2.608530
Epoch 60, loss: 2.608536
Epoch 61, loss: 2.608543
Epoch 62, loss: 2.608551
Epoch 63, loss: 2.608560
Epoch 64, loss: 2.608569
Epoch 65, loss: 2.608579
Epoch 66, loss: 2.608590
Epoch 67, loss: 2.608601
Epoch 68, loss: 2.608613
Epoch 69, loss: 2.608625
Epoch 70, loss: 2.608639
Epoch 71, loss: 2.608652
Epoch 72, loss: 2.608667
Epoch 73, loss: 2.608682
Epoch 74, loss: 2.608698
Epoch 75, loss: 2.608714
Epoch 76, loss: 2.608731
Epoch 77, loss: 2.608749
Epoch 78, loss: 2.608767
Epoch 79, loss: 2.608786
Epoch 80, loss: 2.608806
Epoch 81, loss: 2.608826
Epoch 82, loss: 2.608847
Epoch 83, loss: 2.608869
Epoch 84, loss: 2.608891
Epoch 85, loss: 2.608914
Epoch 86, loss: 2.608937
Epoch 87, loss: 2.608961
Epoch 88, loss: 2.608986
Epoch 89, loss: 2.609011
Epoch 90, loss: 2.609037
Epoch 91, loss: 2.609064
Epoch 92, loss: 2.609091
Epoch 93, loss: 2.609119
Epoch 94, loss: 2.609147
Epoch 95, loss: 2.609176
Epoch 96, loss: 2.609206
Epoch 97, loss: 2.609236
Epoch 98, loss: 2.609267
Epoch 99, loss: 2.609299
Accuracy after training for 100 epochs: 0.125
| MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Как и раньше, используем кросс-валидацию для подбора гиперпараметтов.В этот раз, чтобы тренировка занимала разумное время, мы будем использовать только одно разделение на тренировочные (training) и проверочные (validation) данные.Теперь нам нужно подобрать не один, а два гиперпараметра! Не ограничивайте себя изначальными значениями в коде. Добейтесь точности более чем **20%** на проверочных данных (validation data). | num_epochs = 200
batch_size = 300
learning_rates = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]
reg_strengths = [1e-3, 1e-2, 1e-4, 1e-5, 1e-6, 1e-7]
best_val_accuracy = 0
for learning_rate in learning_rates:
for reg_strength in reg_strengths:
classifier.fit(train_X, train_y, batch_size, learning_rate, reg_strength, num_epochs)
pred = classifier.predict(val_X)
accuracy = multiclass_accuracy(pred, val_y)
print('Accuracy %f reg_strength %f learning rate %f' % (accuracy, reg_strength, learning_rate))
print('Accuracy %f reg_strength %f learning rate %f' % (accuracy, reg_strength, learning_rate), file=open("accuracy.txt", "a"))
best_val_accuracy = max(best_val_accuracy, accuracy)
best_classifier = None
# TODO use validation set to find the best hyperparameters
# hint: for best results, you might need to try more values for learning rate and regularization strength
# than provided initially
print('best validation accuracy achieved: %f' % best_val_accuracy) | Epoch 0, loss: 2.299253
Epoch 1, loss: 2.296205
Epoch 2, loss: 2.293332
Epoch 3, loss: 2.290550
Epoch 4, loss: 2.287845
Epoch 5, loss: 2.285214
Epoch 6, loss: 2.282651
Epoch 7, loss: 2.280155
Epoch 8, loss: 2.277721
Epoch 9, loss: 2.275346
Epoch 10, loss: 2.273029
Epoch 11, loss: 2.270765
Epoch 12, loss: 2.268554
Epoch 13, loss: 2.266393
Epoch 14, loss: 2.264280
Epoch 15, loss: 2.262214
Epoch 16, loss: 2.260192
Epoch 17, loss: 2.258214
Epoch 18, loss: 2.256277
Epoch 19, loss: 2.254381
Epoch 20, loss: 2.252524
Epoch 21, loss: 2.250705
Epoch 22, loss: 2.248923
Epoch 23, loss: 2.247177
Epoch 24, loss: 2.245466
Epoch 25, loss: 2.243788
Epoch 26, loss: 2.242144
Epoch 27, loss: 2.240532
Epoch 28, loss: 2.238952
Epoch 29, loss: 2.237402
Epoch 30, loss: 2.235882
Epoch 31, loss: 2.234391
Epoch 32, loss: 2.232929
Epoch 33, loss: 2.231495
Epoch 34, loss: 2.230087
Epoch 35, loss: 2.228706
Epoch 36, loss: 2.227352
Epoch 37, loss: 2.226022
Epoch 38, loss: 2.224717
Epoch 39, loss: 2.223437
Epoch 40, loss: 2.222180
Epoch 41, loss: 2.220946
Epoch 42, loss: 2.219734
Epoch 43, loss: 2.218545
Epoch 44, loss: 2.217378
Epoch 45, loss: 2.216231
Epoch 46, loss: 2.215105
Epoch 47, loss: 2.214000
Epoch 48, loss: 2.212914
Epoch 49, loss: 2.211847
Epoch 50, loss: 2.210800
Epoch 51, loss: 2.209770
Epoch 52, loss: 2.208759
Epoch 53, loss: 2.207766
Epoch 54, loss: 2.206789
Epoch 55, loss: 2.205830
Epoch 56, loss: 2.204887
Epoch 57, loss: 2.203960
Epoch 58, loss: 2.203049
Epoch 59, loss: 2.202154
Epoch 60, loss: 2.201273
Epoch 61, loss: 2.200408
Epoch 62, loss: 2.199556
Epoch 63, loss: 2.198719
Epoch 64, loss: 2.197896
Epoch 65, loss: 2.197086
Epoch 66, loss: 2.196289
Epoch 67, loss: 2.195505
Epoch 68, loss: 2.194734
Epoch 69, loss: 2.193975
Epoch 70, loss: 2.193228
Epoch 71, loss: 2.192493
Epoch 72, loss: 2.191770
Epoch 73, loss: 2.191058
Epoch 74, loss: 2.190357
Epoch 75, loss: 2.189666
Epoch 76, loss: 2.188987
Epoch 77, loss: 2.188317
Epoch 78, loss: 2.187658
Epoch 79, loss: 2.187008
Epoch 80, loss: 2.186369
Epoch 81, loss: 2.185738
Epoch 82, loss: 2.185117
Epoch 83, loss: 2.184505
Epoch 84, loss: 2.183902
Epoch 85, loss: 2.183308
Epoch 86, loss: 2.182722
Epoch 87, loss: 2.182144
Epoch 88, loss: 2.181574
Epoch 89, loss: 2.181013
Epoch 90, loss: 2.180459
Epoch 91, loss: 2.179913
Epoch 92, loss: 2.179374
Epoch 93, loss: 2.178842
Epoch 94, loss: 2.178318
Epoch 95, loss: 2.177801
Epoch 96, loss: 2.177290
Epoch 97, loss: 2.176786
Epoch 98, loss: 2.176289
Epoch 99, loss: 2.175799
Epoch 100, loss: 2.175314
Epoch 101, loss: 2.174836
Epoch 102, loss: 2.174363
Epoch 103, loss: 2.173897
Epoch 104, loss: 2.173437
Epoch 105, loss: 2.172982
Epoch 106, loss: 2.172532
Epoch 107, loss: 2.172089
Epoch 108, loss: 2.171650
Epoch 109, loss: 2.171217
Epoch 110, loss: 2.170789
Epoch 111, loss: 2.170366
Epoch 112, loss: 2.169947
Epoch 113, loss: 2.169534
Epoch 114, loss: 2.169125
Epoch 115, loss: 2.168722
Epoch 116, loss: 2.168322
Epoch 117, loss: 2.167927
Epoch 118, loss: 2.167537
Epoch 119, loss: 2.167151
Epoch 120, loss: 2.166769
Epoch 121, loss: 2.166391
Epoch 122, loss: 2.166017
Epoch 123, loss: 2.165647
Epoch 124, loss: 2.165281
Epoch 125, loss: 2.164919
Epoch 126, loss: 2.164561
Epoch 127, loss: 2.164206
Epoch 128, loss: 2.163855
Epoch 129, loss: 2.163508
Epoch 130, loss: 2.163164
Epoch 131, loss: 2.162824
Epoch 132, loss: 2.162486
Epoch 133, loss: 2.162153
Epoch 134, loss: 2.161822
Epoch 135, loss: 2.161495
Epoch 136, loss: 2.161171
Epoch 137, loss: 2.160850
Epoch 138, loss: 2.160532
Epoch 139, loss: 2.160217
Epoch 140, loss: 2.159904
Epoch 141, loss: 2.159595
Epoch 142, loss: 2.159289
Epoch 143, loss: 2.158985
Epoch 144, loss: 2.158684
Epoch 145, loss: 2.158386
Epoch 146, loss: 2.158090
Epoch 147, loss: 2.157797
| MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
Какой же точности мы добились на тестовых данных? | test_pred = best_classifier.predict(test_X)
test_accuracy = multiclass_accuracy(test_pred, test_y)
print('Linear softmax classifier test set accuracy: %f' % (test_accuracy, )) | _____no_output_____ | MIT | assignments/assignment1/Linear classifier.ipynb | DANTEpolaris/dlcourse_ai |
This notebook links to **ModelFlow** models and examples.Please have patience until the notebook had been loaded and executed. ModelFlow AbstractModelFlow is a Python toolkit which can handle a wide range of models from small to huge. This covers: on-boarding a model, analyze the logical structure, solve the model, analyze and compare the results. Modelflow is located on Github [here](https://github.com/IbHansen/ModelFlow2). The repo includes some models implemented in ModelFlow: - FRB/US - Federal Reserve Board - Q-JEM - Bank of Japan - ADAM - Statistics Denmark - and other models. The toolkit is fast, lean, and agile.Models and results can be analyzed and visualized using the full arsenal of Python data science tools. This is achieved by leveraging on the Python ecosystem (especially the [Pandas](https://pandas.pydata.org/) and the [numpy](https://www.nature.com/articles/s41586-020-2649-2) libraries. **A model i this context is a system of non-linear equations** specified either as: - a general model: $\textbf{0} = \textbf{G}(\textbf{y}_{t+u} \cdots \textbf{y}_t \cdots \textbf{y}_{t-r},\textbf{x}_t \cdots \textbf{x}_{t-s})$ - or as a normalized model: $\textbf{y}_t = \textbf{F}(\textbf{y}_{t+u} \cdots \textbf{y}_t \cdots \textbf{y}_{t-r},\textbf{x}_t \cdots \textbf{x}_{t-s})$ Many stress test, liquidity, macro or other models conforms to this pattern - or can easy be made to conform.Simultaneous models can be solved by - Gauss-Seidle or Newton-Raphson algorithmes if no lead - Fair-Taylor or stacked Newton-Raphson if the model contains leads and lags - The Jacobian matrice used by Newton-Raphson solvers will be calculated either by symbolic or by numerical differentiation and then handled by sparse matrix libraries. Non-simultaneous models will be sequenced and calculated. **Models can be specified in a Business logic language**. The user can concentrate on the economic content. To specify models incorporating many banks or sectors a **Macro-business logic language** is part of the library. Creating a (Macro prudential) model often entails implementing models specified in different ways: Excel, Latex, Eviews, Dynare, Python or other languages. Python''s ecosystem makes it possible to transform such model specification into ModelFlow. The core function of the library is a transpiler which first parse and analyze a model specified in the Business logic language then generates Python code which can solved the model. Jupyter notebooks with the examples can be run on a Binder virtual machine [here](https://mybinder.org/v2/gh/Ibhansen/modelflow2/tobinder?filepath=Examples%2FOverview.ipynb). Which is probably where you are just now. | from modelclass import model
model.modelflow_auto() | _____no_output_____ | X11 | Examples/Overview.ipynb | IbHansen/Modelflow2 |
Gallery Below you will find links Jupyter notebooks using ModelFlow to run different models. The purpose of the notebooks are primarily to illustrate how ModelFlow can be used to manage a fairly large range of models and to show some of the capabilities. | model.display_toc() | _____no_output_____ | X11 | Examples/Overview.ipynb | IbHansen/Modelflow2 |
**MITRE ATT&CK API FILTERS**: Python Client------------------ Import ATTACK API Client | from attackcti import attack_client | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Import Extra Libraries | from pandas import *
from pandas.io.json import json_normalize | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Initialize ATT&CK Client Variable | lift = attack_client() | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Technique by Name (TAXII)You can use a custom method in the attack_client class to get a technique across all the matrices by its name. It is case sensitive. | technique_name = lift.get_technique_by_name('Rundll32')
technique_name | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Data Sources from All Techniques (TAXII)* You can also get all the data sources available in ATT&CK* Currently the only techniques with data sources are the ones in Enterprise ATT&CK. | data_sources = lift.get_data_sources()
len(data_sources)
data_sources | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Any STIX Object by ID (TAXII)* You can get any STIX object by its id across all the matrices. It is case sensitive.* You can use the following STIX Object Types: * attack-pattern > techniques * course-of-action > mitigations * intrusion-set > groups * malware * tool | object_by_id = lift.get_object_by_attack_id('attack-pattern', 'T1307')
object_by_id | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Any Group by Alias (TAXII)You can get any Group by its Alias property across all the matrices. It is case sensitive. | group_name = lift.get_group_by_alias('Cozy Bear')
group_name | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Relationships by Any Object (TAXII)* You can get available relationships defined in ATT&CK of type **uses** and **mitigates** for specific objects across all the matrices. | groups = lift.get_groups()
one_group = groups[0]
relationships = lift.get_relationships_by_object(one_group)
relationships[0] | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get All Techniques with Mitigations (TAXII)The difference with this function and **get_all_techniques()** is that **get_techniques_mitigated_by_all_mitigations** returns techniques that have mitigations mapped to them. | techniques_mitigated = lift.get_techniques_mitigated_by_all_mitigations()
techniques_mitigated[0] | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Techniques Used by Software (TAXII)This the function returns information about a specific software STIX object. | all_software = lift.get_software()
one_software = all_software[0]
software_techniques = lift.get_techniques_used_by_software(one_software)
software_techniques[0] | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Techniques Used by Group (TAXII)If you do not provide the name of a specific **Group** (Case Sensitive), the function returns information about all the groups available across all the matrices. | groups = lift.get_groups()
one_group = groups[0]
group_techniques = lift.get_techniques_used_by_group(one_group)
group_techniques[0] | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
Get Software Used by Group (TAXII)You can retrieve every software (malware or tool) mapped to a specific Group STIX object | groups = lift.get_groups()
one_group = groups[0]
group_software = lift.get_software_used_by_group(one_group)
group_software[0] | _____no_output_____ | BSD-3-Clause | notebooks/Usage_Filters.ipynb | binaryflesh/ATTACK-Python-Client |
_*Running simulations with noise and measurement error mitigation in Aqua*_This notebook demonstrates using the [Qiskit Aer](https://qiskit.org/aer) `qasm_simulator` to run a simulation with noise, based on a noise model, in Aqua. This can be useful to investigate behavior under different noise conditions. Aer not only allows you to define your own custom noise model, but also allows a noise model to be easily created based on the properties of a real quantum device. The latter is what this notebook will demonstrate since the goal is to show how to do this in Aqua not how to build custom noise models.On the other hand, [Qiskit Ignis](https://qiskit.org/ignis) provides a solution to mitigate the measurement error when running on a noise simulation or a real quantum device.Further information on Qiskit Aer noise model can be found in the online Qiskit Aer documentation [here](https://qiskit.org/documentation/aer/device_noise_simulation.html) as well as in the [Qiskit Aer tutorials](https://github.com/Qiskit/qiskit-tutorials/tree/master/qiskit/aer).Further information on measurement error mitigation in Qiskit Ignis can be found in the [Qiskit Ignis tutorial](https://github.com/Qiskit/qiskit-tutorials/blob/master/qiskit/ignis/measurement_error_mitigation.ipynb).Note: this tutorial requires Qiskit Aer and Qiskit Ignis if you intend to run it. This can be installed using pip if you do not have it installed using `pip install qiskit-aer qiskit-ignis` | import numpy as np
import pylab
from qiskit import Aer, IBMQ
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms.adaptive import VQE
from qiskit.aqua.algorithms.classical import ExactEigensolver
from qiskit.aqua.components.optimizers import SPSA
from qiskit.aqua.components.variational_forms import RY
from qiskit.aqua.operators import WeightedPauliOperator
| _____no_output_____ | Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
Noisy simulation will be demonstrated here with VQE, finding the minimum (ground state) energy of an Hamiltonian, but the technique applies to any quantum algorithm from Aqua.So for VQE we need a qubit operator as input. Here we will take a set of paulis that were originally computed by qiskit-chemistry, for an H2 molecule, so we can quickly create an Operator. | pauli_dict = {
'paulis': [{"coeff": {"imag": 0.0, "real": -1.052373245772859}, "label": "II"},
{"coeff": {"imag": 0.0, "real": 0.39793742484318045}, "label": "ZI"},
{"coeff": {"imag": 0.0, "real": -0.39793742484318045}, "label": "IZ"},
{"coeff": {"imag": 0.0, "real": -0.01128010425623538}, "label": "ZZ"},
{"coeff": {"imag": 0.0, "real": 0.18093119978423156}, "label": "XX"}
]
}
qubit_op = WeightedPauliOperator.from_dict(pauli_dict)
num_qubits = qubit_op.num_qubits
print('Number of qubits: {}'.format(num_qubits)) | Number of qubits: 2
| Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
As the above problem is still easily tractable classically we can use ExactEigensolver to compute a reference value so we can compare later the results. _(A copy of the operator is used below as what is passed to ExactEigensolver will be converted to matrix form and we want the operator we use later, on the Aer qasm simuator, to be in paulis form.)_ | ee = ExactEigensolver(qubit_op.copy())
result = ee.run()
ref = result['energy']
print('Reference value: {}'.format(ref)) | Reference value: -1.8572750302023797
| Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
Performance *without* noiseFirst we will run on the simulator without adding noise to see the result. I have created the backend and QuantumInstance, which holds the backend as well as various other run time configuration, which are defaulted here, so it easy to compare when we get to the next section where noise is added. There is no attempt to mitigate noise or anything in this notebook so the latter setup and running of VQE is identical. | backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend=backend, seed_simulator=167, seed_transpiler=167)
counts = []
values = []
def store_intermediate_result(eval_count, parameters, mean, std):
counts.append(eval_count)
values.append(mean)
aqua_globals.random_seed = 167
optimizer = SPSA(max_trials=200)
var_form = RY(num_qubits)
vqe = VQE(qubit_op, var_form, optimizer, callback=store_intermediate_result)
vqe_result = vqe.run(quantum_instance)
print('VQE on Aer qasm simulator (no noise): {}'.format(vqe_result['energy']))
print('Delta from reference: {}'.format(vqe_result['energy']-ref)) | VQE on Aer qasm simulator (no noise): -1.8598749159580135
Delta from reference: -0.0025998857556337462
| Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
We captured the energy values above during the convergence so we can see what went on in the graph below. | pylab.rcParams['figure.figsize'] = (12, 4)
pylab.plot(counts, values)
pylab.xlabel('Eval count')
pylab.ylabel('Energy')
pylab.title('Convergence with no noise'); | _____no_output_____ | Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
Performance *with* noiseNow we will add noise. Here we will create a noise model for Aer from an actual device. You can create custom noise models with Aer but that goes beyond the scope of this notebook. Links to further information on Aer noise model, for those that may be interested in doing this, were given in instruction above.First we need to get an actual device backend and from its `configuration` and `properties` we can setup a coupling map and a noise model to match the device. While we could leave the simulator with the default all to all map, this shows how to set the coupling map too. Note: We can also use this coupling map as the entanglement map for the variational form if we choose.Note: simulation with noise takes significantly longer than without noise. | from qiskit.providers.aer import noise
provider = IBMQ.load_account()
device = provider.get_backend('ibmqx4')
coupling_map = device.configuration().coupling_map
noise_model = noise.device.basic_device_noise_model(device.properties())
basis_gates = noise_model.basis_gates
print(noise_model)
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend=backend, seed_simulator=167, seed_transpiler=167,
noise_model=noise_model,)
counts1 = []
values1 = []
def store_intermediate_result1(eval_count, parameters, mean, std):
counts1.append(eval_count)
values1.append(mean)
aqua_globals.random_seed = 167
optimizer = SPSA(max_trials=200)
var_form = RY(num_qubits)
vqe = VQE(qubit_op, var_form, optimizer, callback=store_intermediate_result1)
vqe_result1 = vqe.run(quantum_instance)
print('VQE on Aer qasm simulator (with noise): {}'.format(vqe_result1['energy']))
print('Delta from reference: {}'.format(vqe_result1['energy']-ref))
pylab.rcParams['figure.figsize'] = (12, 4)
pylab.plot(counts1, values1)
pylab.xlabel('Eval count')
pylab.ylabel('Energy')
pylab.title('Convergence with noise'); | _____no_output_____ | Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
Declarative approach and noise modelNote: if you are running an experiment using the declarative approach, with a dictionary/json, there are keywords in the `backend` section that let you define the noise model based on a device, as well as setup the coupling map too. The basis gate setup, that is shown above, will automatically be done. Here is an example of such a `backend` configuration:``` 'backend': { 'provider': 'qiskit.Aer', 'name': 'qasm_simulator', 'coupling_map_from_device': 'qiskit.IBMQ:ibmqx4', 'noise_model': 'qiskit.IBMQ:ibmqx4', 'shots': 1024 },```If you call `run_algorithm` and override the `backend` section by explicity supplying a backend instance as a parameter to run_algorithm, please note that you can provide a QuantumInstance type there instead of BaseBackend. A QuantumInstance allows you to setup and define your own custom noise model and other run time configuration. (Note when a BaseBackend type is supplied to run_algorithm it is internally wrapped into a QuantumInstance, with default values supplied for noise, run time parameters etc., so you do not get the opportunity that way to set a noise model etc. But by explicitly providing a QuantumInstance you can setup these aspects to your choosing.) Performance *with* noise and measurement error mitigationNow we will add method for measurement error mitigation, which increases the fidelity of measurement. Here we choose `CompleteMeasFitter` to mitigate the measurement error. The calibration matrix will be auto-refresh every 30 minute (default value).Note: simulation with noise takes significantly longer than without noise. | from qiskit.ignis.mitigation.measurement import CompleteMeasFitter
quantum_instance = QuantumInstance(backend=backend, seed_simulator=167, seed_transpiler=167,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter,
cals_matrix_refresh_period=30)
counts1 = []
values1 = []
def store_intermediate_result1(eval_count, parameters, mean, std):
counts1.append(eval_count)
values1.append(mean)
aqua_globals.random_seed = 167
optimizer = SPSA(max_trials=200)
var_form = RY(num_qubits)
vqe = VQE(qubit_op, var_form, optimizer, callback=store_intermediate_result1)
vqe_result1 = vqe.run(quantum_instance)
print('VQE on Aer qasm simulator (with noise and measurement error mitigation): {}'.format(vqe_result1['energy']))
print('Delta from reference: {}'.format(vqe_result1['energy']-ref))
pylab.rcParams['figure.figsize'] = (12, 4)
pylab.plot(counts1, values1)
pylab.xlabel('Eval count')
pylab.ylabel('Energy')
pylab.title('Convergence with noise, enabling measurement error mitigation'); | _____no_output_____ | Apache-2.0 | aqua/simulations_with_noise_and_measurement_error_mitigation.ipynb | lukasszz/qiskit-tutorials-community |
Omni | ptr_before = [graspy.utils.pass_to_ranks(g) for g in graphs]
lccs = get_multigraph_lcc(graphs)
tensor = np.stack(lccs)
tensor.shape
ptr = [graspy.utils.pass_to_ranks(g) for g in lccs]
omni = OmnibusEmbed()
Zhat = omni.fit_transform(lccs[:100])
Zhat = Zhat.reshape(100, 670, -1)
cmds = ClassicalMDS()
Xhat = cmds.fit_transform(Zhat)
graspy.plot.heatmap(cmds.dissimilarity_matrix_)
graspy.plot.pairplot(Xhat)
gclust = GaussianCluster(50)
gclust.fit(Xhat, labels[:100])
omni = OmnibusEmbed(n_elbows=10)
Zhat_ptr = omni.fit_transform(ptr[:100])
Zhat_ptr = Zhat_ptr.reshape(100, 670, -1)
cmds = ClassicalMDS()
Xhat_ptr = cmds.fit_transform(Zhat_ptr)
graspy.plot.heatmap(cmds.dissimilarity_matrix_)
graspy.plot.pairplot(Xhat_ptr, Y=labels[:100])
np.diag(graphs[5]).sum()
np.load('../../data/HNU1/dwi/desikan/sub-0025427_ses-10_dwi_desikan.npy')
import warnings
if True:
warnings.warn('test', Warning) | /home/j1c/graphstats/venv/lib/python3.5/site-packages/ipykernel_launcher.py:2: Warning: test
| Apache-2.0 | Experiments/20181204/Untitled.ipynb | j1c/multigraph_clustering |
*This notebook contains an excerpt instructional material from [gully](https://twitter.com/gully_) and the [K2 Guest Observer Office](https://keplerscience.arc.nasa.gov/); the content is available [on GitHub](https://github.com/gully/goldenrod).* Spot-check Everest Validation Summaries for KEGS This notebook does more spot-checking of KEGS target lightcurves with the "data validation summary" (DVS) feature in EVEREST. | import matplotlib.pyplot as plt
import numpy as np
from astropy.io import fits
import astropy
import os
import pandas as pd
import seaborn as sns
from astropy.utils.console import ProgressBar
import everest
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
everest_path = '../../everest/everest/missions/k2/tables/'
df_kegs = pd.read_csv('../metadata/KEGS_TPF_metadata.csv')
c05_everest = pd.read_csv(everest_path + 'c05.stars', names=['EPIC_ID', 'KepMag', 'Channel', 'col4'])
kegs_everest_c05 = pd.merge(df_kegs, c05_everest, how='inner', left_on='KEPLERID', right_on='EPIC_ID')
ke_list = kegs_everest_c05['KEPLERID'].values
i = 0
i | _____no_output_____ | MIT | notebooks/01.06-Everest_KEGS_DVS.ipynb | gully/goldenrod |
Decent examples to try to replicate: 12 | i+=1
star = everest.Everest(ke_list[i])
star.dvs()
for i in range(len(ke_list)):
star = everest.Everest(ke_list[i])
star.dvs() | INFO [everest.user.DownloadFile()]: Found cached file.
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INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211341772.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211346083.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211346149.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211346470.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211346668.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211348567.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211349407.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211352575.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211359991.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211362257.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211374878.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211376360.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211376898.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211377253.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211377762.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211377821.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211378205.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211378569.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211382580.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211383902.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211384920.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211385002.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211386909.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211387883.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211391030.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.DownloadFile()]: Found cached file.
INFO [everest.user.load_fits()]: Loading FITS file for 211393064.
INFO [everest.user.DownloadFile()]: Found cached file.
| MIT | notebooks/01.06-Everest_KEGS_DVS.ipynb | gully/goldenrod |
Ex2 - Getting and Knowing your DataCheck out [Chipotle Exercises Video Tutorial](https://www.youtube.com/watch?v=lpuYZ5EUyS8&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=2) to watch a data scientist go through the exercises This time we are going to pull data directly from the internet.Special thanks to: https://github.com/justmarkham for sharing the dataset and materials. Step 1. Import the necessary libraries | import pandas as pd
import numpy as np | _____no_output_____ | BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). Step 3. Assign it to a variable called chipo. | url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'
chipo = pd.read_csv(url, sep = '\t') | _____no_output_____ | BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
Step 4. See the first 10 entries | chipo.head(10) | _____no_output_____ | BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
Step 5. What is the number of observations in the dataset? | # Solution 1
chipo.shape[0] # entries <= 4622 observations
# Solution 2
chipo.info() # entries <= 4622 observations | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 4622 entries, 0 to 4621
Data columns (total 5 columns):
order_id 4622 non-null int64
quantity 4622 non-null int64
item_name 4622 non-null object
choice_description 3376 non-null object
item_price 4622 non-null object
dtypes: int64(2), object(3)
memory usage: 180.6+ KB
| BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
Step 6. What is the number of columns in the dataset? | chipo.shape[1] | _____no_output_____ | BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
Step 7. Print the name of all the columns. | chipo.columns | _____no_output_____ | BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
Step 8. How is the dataset indexed? | chipo.index | _____no_output_____ | BSD-3-Clause | 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb | ismael-araujo/pandas-exercise |
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