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gen_docker_compose.py
ganey/helium-miner-software
0
1610546
<gh_stars>0 """ Generate docker-compose.yml based on docker-compose.tempalte file. Some how balena doesn't like output of: `docker-compose -f docker-compose.template config > docker-compose.yml` That's probably because output for `docker-compose config` does not retain the order of the sections present in the file. So as a workaround this small file tries to do the same thing but without re-arranging sections and just replacing placeholder values. In future if desired this can use a proper templating system like mako or jinja2. For the time being to avoid adding dependencies to the project it just uses python format strings. """ import sys import os import argparse from typing import Union from configparser import ConfigParser from jinja2 import Environment, select_autoescape, FileSystemLoader prog_name = os.path.basename(os.path.abspath(__file__)) here = os.path.dirname(os.path.abspath(__file__)) class DockerComposer: """ A class to facilitate generation of docker-compose.yml files from templates. """ def __init__(self, templates_folder: Union[str, None] = None): if templates_folder is None: templates_folder = os.path.join(here, 'templates') self.jinja_env = Environment( loader=FileSystemLoader(templates_folder), autoescape=select_autoescape() ) config = ConfigParser(strict=True) # Suppress default behavior of converting key names to lower-case. config.optionxform = lambda option: option config.read('settings.ini') self.config = config def generate_compose_file( self, device_type: str, template_file: str, output_file: str ) -> None: """generate_compose_file Render template_file to generate compose file. Args: device_type (str): The target device for the docker compose file. This device should have a section in settings.ini. template_file (str): Template file name without folder name. Should be present in the templates folder. output_file (str): Output filename. Can be a path. Otherwise file is created in current folder. """ template = self.jinja_env.get_template(template_file) if not self.config.has_section(f'device_{device_type}'): print(f"Error: Unknown device type: {device_type}. " "No matching config section found in settings.ini") sys.exit(1) template_args = {} if 'versions' not in self.config: raise RuntimeError("Bad config, no [versions] section found") for k, v in self.config['versions'].items(): template_args[k] = v for k, v in self.config[f'device_{device_type}'].items(): template_args[k] = v output = template.render(**template_args) with open(output_file, 'w') as template_output: template_output.write(output) if __name__ == '__main__': parser = argparse.ArgumentParser("Generate docker-compose.yml.") parser.add_argument('device_type', help="Target device. Must have a section in " "settings.ini.") parser.add_argument('--template', '-t', default='docker-compose.template', help="Input template file. Should be present in " "templates folder.") parser.add_argument('--output', '-o', default='docker-compose.yml', help="Output file. Created in current folder.") args = parser.parse_args() # print(args) composer = DockerComposer() composer.generate_compose_file( args.device_type, args.template, args.output)
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tests/python/gaia-ui-tests/gaiatest/apps/phone/regions/keypad.py
pdehaan/gaia
1
132218
# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. import time from marionette.by import By from marionette.marionette import Actions from gaiatest.apps.base import Base from gaiatest.apps.phone.app import Phone from gaiatest.apps.phone.regions.call_screen import CallScreen class Keypad(Phone): #locators _keyboard_container_locator = (By.ID, 'keyboard-container') _phone_number_view_locator = (By.ID, 'phone-number-view') _call_bar_locator = (By.ID, 'keypad-callbar-call-action') _add_new_contact_button_locator = (By.ID, 'keypad-callbar-add-contact') _search_popup_locator = (By.CSS_SELECTOR, '#suggestion-bar .suggestion-item') _suggested_contact_name_locator = (By.CSS_SELECTOR, '#suggestion-bar .suggestion-item .name') _suggested_contact_phone_number_locator = (By.CSS_SELECTOR, '#suggestion-bar .suggestion-item .tel') def __init__(self, marionette): Phone.__init__(self, marionette) @property def phone_number(self): return self.marionette.find_element(*self._phone_number_view_locator).get_attribute('value') def tap_phone_number(self): self.marionette.find_element(*self._phone_number_view_locator).tap() def dial_phone_number(self, value): for i in value: if i == "+": zero_button = self.marionette.find_element(By.CSS_SELECTOR, 'div.keypad-key[data-value="0"]') Actions(self.marionette).long_press(zero_button, 1.2).perform() else: self.marionette.find_element(By.CSS_SELECTOR, 'div.keypad-key[data-value="%s"]' % i).tap() time.sleep(0.25) def call_number(self, value): self.dial_phone_number(value) return self.tap_call_button() def tap_call_button(self, switch_to_call_screen=True): self.marionette.find_element(*self._call_bar_locator).tap() if switch_to_call_screen: return CallScreen(self.marionette) def tap_add_contact(self): self.marionette.find_element(*self._add_new_contact_button_locator).tap() return AddNewNumber(self.marionette) def wait_for_search_popup_visible(self): self.wait_for_element_displayed(*self._search_popup_locator) @property def suggested_name(self): return self.marionette.find_element(*self._suggested_contact_name_locator).text @property def suggested_phone_number(self): return self.marionette.find_element(*self._suggested_contact_phone_number_locator).text def tap_search_popup(self): self.marionette.find_element(*self._search_popup_locator).tap() return CallScreen(self.marionette) def wait_for_phone_number_ready(self): # Entering dialer and expecting a phone number there is js that sets the phone value and enables this button self.wait_for_condition(lambda m: 'disabled' not in m.find_element(*self._add_new_contact_button_locator).get_attribute('class')) def switch_to_keypad_frame(self): app = self.apps.displayed_app self.marionette.switch_to_frame(app.frame) class AddNewNumber(Base): _create_new_contact_locator = (By.ID, 'create-new-contact-menuitem') _new_contact_frame_locator = (By.CSS_SELECTOR, "iframe[src^='app://communications'][src$='contacts/index.html?new']") def __init__(self, marionette): Base.__init__(self, marionette) self.wait_for_element_displayed(*self._create_new_contact_locator) def tap_create_new_contact(self): self.marionette.find_element(*self._create_new_contact_locator).tap() self.marionette.switch_to_frame() self.wait_for_element_present(*self._new_contact_frame_locator) frame = self.marionette.find_element(*self._new_contact_frame_locator) self.marionette.switch_to_frame(frame) from gaiatest.apps.contacts.regions.contact_form import NewContact return NewContact(self.marionette)
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models/account.py
allku/AllkuAccounting
0
124199
# -*- coding: utf-8 -*- import datetime from app import db from sqlalchemy import UniqueConstraint, ForeignKey class Account(db.Model): # Is used to save accounting accounts __tablename__ = 'acc_accounts' id = db.Column(db.Integer, db.Identity(start=1), primary_key=True) code = db.Column(db.String, nullable=False) name = db.Column(db.String, nullable=False) type = db.Column(db.String, nullable=False) date_created = db.Column(db.DateTime, default=datetime.datetime.now(), nullable=False) status = db.Column(db.String, nullable=False) account_id = db.Column(db.Integer, ForeignKey('acc_accounts.id')) parent = db.relationship("Account", remote_side=[id]) ledger_entries = db.relationship('LedgerEntry', backref='accounts') automatic = db.relationship('Automatic', backref='accounts') __table_args__ = (UniqueConstraint('code', name='uk_acc_accounts'),) def __init__(self, code='', name='', type='', status='', account_id=None): self.code = code self.name = name self.type = type self.status = status self.account_id = account_id def __repr__(self): return '<id {}>'.format(self.id) def serialize(self): account_data = { 'id': self.id, 'code': self.code, 'name': self.name, 'type': self.type, 'status': self.status, 'accountId': self.account_id } return account_data
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selfsupervised3d/dataset/__init__.py
jcreinhold/selfsupervised3d
5
63847
<filename>selfsupervised3d/dataset/__init__.py from .blendowski import * from .context import * from .doersch import *
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phylobook/urls.py
MullinsLab/phylobook
0
177094
"""phylobook URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.urls import path, include, re_path from django.views.generic.base import RedirectView from django.contrib import admin from django.contrib.auth import views as auth_views import phylobook.views as views #import phylobook.settings as settings from django.conf import settings admin.site.site_header = "Phylobook Admin" admin.site.site_title = "Phylobook Admin Site" admin.site.index_title = "Phylobook Admin" urlpatterns = [ path('admin/', admin.site.urls), #re_path(r"^accounts/", include("django.contrib.auth.urls")), path("password_reset", views.password_reset_request, name="password_reset"), path('password_reset/done/', auth_views.PasswordResetDoneView.as_view(), name='password_reset_done'), path('reset/<uidb64>/<token>/', auth_views.PasswordResetConfirmView.as_view(), name='password_reset_confirm'), path('reset/done/', auth_views.PasswordResetCompleteView.as_view(), name='password_reset_complete'), path('password_change/', auth_views.PasswordChangeView.as_view(), name='password_change'), path('password_change/done/', auth_views.PasswordChangeDoneView.as_view(), name='password_change_done'), path("projects/", include("phylobook.projects.urls")), re_path(r'^saml/', include('phylobook.uw_saml.urls')), re_path(r'^$', RedirectView.as_view(url='/projects')), ] if settings.LOGIN_TYPE == 'dual' or settings.LOGIN_TYPE == 'local': additional_settings =[ path('login/', auth_views.LoginView.as_view(), name='login'), path('logout/', auth_views.LogoutView.as_view(), name='logout'), ] urlpatterns += additional_settings
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deployment/TraceServer/traceServer.py
capitalch/trace
0
125961
from flask_cors import CORS import jwt import pdfkit from pdfkit.api import configuration # import asyncio # from flask_scss import Scss # import sass from werkzeug.exceptions import HTTPException import simplejson as json from flask_mail import Mail, Message from ariadne.constants import PLAYGROUND_HTML from ariadne import QueryType, graphql_sync, make_executable_schema, gql, ObjectType, load_schema_from_path from flask import Flask, jsonify, request, render_template, Response, abort, redirect import codecs from datetime import datetime # from json import JSONEncoder from allMessages import infoMessages, errorMessages from postgres import execSql from entities.authentication.sql import allSqls import util as util import entities.accounts.artifacts as accounts_artifacts import entities.sampleForms.artifacts as sample_forms_artifacts import entities.authentication.artifacts as authentication_artifacts from entities.authentication.artifactsHelper import loginHelper from loadConfig import cfg from downloadHelper import handleDownload from util import setMailConfig from entities.legacy.artifacts import trackApp from entities.accounts.artifacts import traceApp from app.link_client import connectToLinkServer from loadConfig import cfg env = cfg['env'] if(env == 'local'): linkServerUrl = cfg[env]['linkServerUrl'] else: linkServerUrl = cfg[env]['linkServerIp'] traceServerId = cfg[env].get('traceServerId', None) traceServerId = traceServerId if traceServerId is not None else 'traceServer' print('env:', env, 'linkServerUrl:', linkServerUrl) connectToLinkServer(linkServerUrl, traceServerId, token=cfg['linkServerKey']) app = Flask(__name__, template_folder="../build") app.register_blueprint(trackApp) app.register_blueprint(traceApp) # asset folder has .scss files, static folder has .css files. The Scss creates .css files from .scss files # Scss(app, asset_dir='entities/legacy/assets', # static_dir='entities/legacy/static') # sass.compile(dirname=('entities/legacy/assets','entities/legacy/static')) CORS(app, expose_headers='SELECTION-CRITERIA') setMailConfig(app) DB_NAME = 'traceEntry' # gql function validates the schema, returns error if any, otherwise returns the string itself type_defs = gql(""" type Query { menu: Menu accounts: AccountsQuery sampleForms: SampleForms authentication: AuthenticationQuery } type Mutation { accounts: AccountsMutation authentication: AuthenticationMutation } type Menu { jData: JData } """) type_defs = [type_defs, accounts_artifacts.type_defs, authentication_artifacts.type_defs, sample_forms_artifacts.type_defs] query = ObjectType('Query') mutation = ObjectType("Mutation") # this function executes only during graphql post requests. So it is executed only befor api calls. If it returns then normal execution proceeds otherwise if abort then error is thrown at client side def contextValue(request): def processError(message): error_message = json.dumps({'message': message}) abort(Response(error_message, 401)) payload = None # return # remove this line names = ['login', 'forgotPwd'] try: if (request.json is not None) and ('operationName' in request.json) and (request.json['operationName'] in names): return # for further processing else: # auth is like 'Bearer xxxxx'. the xxxxx is token. You need to take out the last word auth = request.headers.get('AUTHORIZATION') selectionCriteria = request.headers.get('SELECTION-CRITERIA') # print('selectionCriteria:', selectionCriteria) if selectionCriteria is not None: temp = selectionCriteria.split(':') buCode = temp[0] if temp[0] != '' else None finYearId = temp[1] if temp[1] != '' else None branchId = temp[2] if temp[2] != '' else None if (auth is None) or (auth == ''): processError(util.getErrorMessage('requiredAuthCredentials')) else: token = auth.split(' ')[-1] # get last word secret = cfg.get('jwt').get('secret') algorithm = cfg.get('jwt').get('algorithm') payload = jwt.decode(token, secret, algorithm) payload['buCode'] = buCode payload['finYearId'] = finYearId payload['branchId'] = branchId return payload except (Exception) as error: processError(util.getErrorMessage('authenticationFailue')) # gets dbName from TraceEntry database against clientId and entityName def setDbName(info, entityName): clientId = info.context['clientId'] sqlString = allSqls['get_dbName'] result = execSql(DB_NAME, sqlString, { 'clientId': clientId, 'entityName': entityName}, isMultipleRows=False) info.context['dbName'] = result['dbName'] @query.field("accounts") def resolve_accounts_query(parent, info): setDbName(info, 'accounts') return {} @mutation.field("accounts") def resolve_accounts_mutation(parent, info): setDbName(info, 'accounts') return {} @query.field("authentication") def resolve_authentication_query(parent, info): return {} @mutation.field("authentication") def resolve_authentication_mutation(parent, info): return {} query.set_field("sampleForms", sample_forms_artifacts.resolve_sample_forms) schema = make_executable_schema( type_defs, query, mutation, accounts_artifacts.accountsQuery, accounts_artifacts.accountsMutation, authentication_artifacts.authenticationQuery, authentication_artifacts.authenticationMutation) @app.route("/activation", methods=["GET"]) def user_activation(): code = request.args.get('code') uid = codecs.decode(code, 'rot13') # decrypted the uid sqlString = allSqls['activate_user'] ret = execSql(DB_NAME, sqlString, (uid,), False) out = f"{uid} activated" return infoMessages['activationSuccessful'], 200 @app.route("/forgotPwd", methods=["GET"]) def pwd_forgot(): code = request.args.get('code') userEmail = codecs.decode(code, 'rot13') # decrypted the email valueDict = {'userEmail': userEmail} sqlString = allSqls['forgot_pwd'] id = None if sqlString is not None: ret = execSql(DB_NAME, sqlString, valueDict) if type(ret) is list: if len(ret) > 0: id = ret[0].get('id') if id is not None: pwd = <PASSWORD>() settings = cfg['mailSettings'] line1 = settings['forgotPwdNewPwdBody']['line1'] line2 = settings['forgotPwdNewPwdBody']['line2'] line3 = settings['forgotPwdNewPwdBody']['line3'] htmlBody = f''' <div> <div> {line1} </div> <div> {line2} </div> <div>{pwd}</div> <div> {line3} </div> <div> sent on date and time: {datetime.now()} </div> </div>''' ret = util.sendMail( [userEmail], settings['forgotPwdNewPwdMessage'], htmlBody) if ret: # mail sent. Now update password hash into TraceUser table sqlString = allSqls['update_hash'] tHash = util.getPasswordHash(pwd) ret1 = execSql(DB_NAME, sqlString, { 'userEmail': userEmail, 'hash': tHash}) else: ret = False return infoMessages['newPwdCreationSuccessful'], 200 @app.route("/graphql", methods=["GET"]) def graphql_playgroud(): # On GET request serve GraphQL Playground # You don't need to provide Playground if you don't want to # but keep on mind this will not prohibit clients from # exploring your API using desktop GraphQL Playground app. return PLAYGROUND_HTML, 200 @app.route("/graphql", methods=["POST"]) def graphql_server(): # GraphQL queries are always sent as POST data = request.get_json() # data is graphql query sent from the client # Note: Passing the request to the context is optional. # In Flask, the current request is always accessible as flask.request success, result = graphql_sync( schema, data, context_value=contextValue(request), # debug=app.debug ) status_code = 200 if success else 400 # This is python ternary operator res = json.dumps(result, default=str) # return jsonify(result), status_code return res, status_code # return result, status_code @app.route("/test", methods=["GET"]) def testFunc(): return jsonify("Server ok"), 200 @app.route("/downloadFile", methods=["GET", "POST"]) def downloadFile(): payload = contextValue(request) return handleDownload(payload, request.json) @app.route('/') def index(): return render_template('index.html') # In cloudjiffy server in wsgi.conf 'Alias /index /var/www/webroot/ROOT/build/index.html'. The alias statement does not take blank hence redirect from Flask is done. return redirect('/index') @app.errorhandler(Exception) def handle_error(e): code = 500 if isinstance(e, HTTPException): code = e.code return (jsonify(error=str(e)), code) # Following lines are not executed in cloud. Because __name__ is not '__main__' if __name__ == '__main__': # app.run(debug=True, threaded=True) # socketio.run(app) pass # @app.route("/manifest.json") # def manifest(): # return send_from_directory('build', 'manifest.json') # @app.route('/favicon.ico') # def favicon(): # return send_from_directory('build', 'favicon.ico') # @app.route('/logo192.png') # def logo(): # return send_from_directory('build', 'logo192.png') # def enco(obj): return ( # obj.isoformat() # if isinstance(obj, datetime.datetime) # or isinstance(obj, datetime.date) # else None # ) # asyncio.run(connectToLinkServer(linkServerUrl, 'traceServer', token=cfg['linkServerKey']))
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preprocessor/base.py
shayanthrn/AGAIN-VC
3
9731
<reponame>shayanthrn/AGAIN-VC import os import logging import numpy as np from tqdm import tqdm from functools import partial from multiprocessing.pool import ThreadPool import pyworld as pw from util.dsp import Dsp logger = logging.getLogger(__name__) def preprocess_one(input_items, module, output_path=''): input_path, basename = input_items y = module.load_wav(input_path) if module.config.dtype == 'wav': ret = y elif module.config.dtype == 'melspectrogram': ret = module.wav2mel(y) elif module.config.dtype == 'f0': f0, sp, ap = pw.wav2world(y.astype(np.float64), module.config.sample_rate) ret = f0 if (f0 == 0).all(): logger.warn(f'f0 returns all zeros: {input_path}') elif module.config.dtype == 's3prl_spec': ret = module.wav2s3prl_spec(y) if ret is None: logger.warn(f'S3PRL spectrogram returns NoneType: {input_path}') elif module.config.dtype == 'resemblyzer': y = resemblyzer.preprocess_wav(input_path) ret = module.wav2resemblyzer(y) else: logger.warn(f'Not implement feature type {module.config.dtype}') if output_path == '': return ret else: if type(ret) is np.ndarray: np.save(os.path.join(output_path, f'{basename}.npy'), ret) else: logger.warn(f'Feature {module.config.dtype} is not saved: {input_path}.') return 1 class BasePreproceccor(): def __init__(self, config): self.dsp_modules = {} for feat in config.feat_to_preprocess: self.dsp_modules[feat] = Dsp(config.feat[feat]) def preprocess(self, input_path, output_path, feat, njobs): file_dict = self.gen_file_dict(input_path) logger.info(f'Starting to preprocess from {input_path}.') self.preprocess_from_file_dict(file_dict=file_dict, output_path=output_path, feat=feat, njobs=njobs) logger.info(f'Saving processed file to {output_path}.') return def preprocess_from_file_dict(self, file_dict, output_path, feat, njobs): os.makedirs(os.path.join(output_path, feat), exist_ok=True) module = self.dsp_modules[feat] task = partial(preprocess_one, module=module, output_path=os.path.join(output_path, feat)) with ThreadPool(njobs) as pool: _ = list(tqdm(pool.imap(task, file_dict.items()), total=len(file_dict), desc=f'Preprocessing ')) def gen_file_dict(self, input_path): raise NotImplementedError
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my_digesters_setup_sample.py
paul-hammant/imapdigester
25
113080
<filename>my_digesters_setup_sample.py from digesters.confluence.confluence_notification_digester import ConfluenceNotificationDigester from digesters.fidelity.fidelity_notification_digester import FidelityNotificationDigester from digesters.github.github_notification_digester import GithubNotificationDigester from digesters.hipchat.hipchat_notification_digester import HipchatNotificationDigester from digesters.jira.jira_notification_digester import JiraNotificationDigester from digesters.linkedin.linkedin_invitation_digester import LinkedinInvitationDigester from digesters.reddit.reddit_notification_digester import RedditNotificationDigester from digesters.stackexchange.stack_exchange_notification_digester import StackExchangeNotificationDigester from metastore import MetaStore from digesters.charges.charge_card_digester import ChargeCardDigester # This file - my_digesters_setup_sample.py - is activated despite the 'sample' name, UNLESS # 'my_digesters_setup.py' is present. You would have made that yourself by copying this source file # and replacing this notice with "copied from ...". Yes, it is an unconventional way of configuring # an application (Python versus XML, JSON, YAML, TOML, properties/ini files), but it is what it is. def add_digesters(digesters): # StackExchange site filtered notifications # Customize for your filter(s) ... digesters.append(StackExchangeNotificationDigester(MetaStore("stack_exchange_1"), "TBD")) # Charge (Credit) card spending alerts (and more) digesters.append(ChargeCardDigester(MetaStore("charge_cards")) .with_amex() .with_chase() .with_barclaycard() # non-operational presently .with_bofa() .with_jpmorgan() # non-operational presently .with_capitalone() # non-operational presently .with_citi()) # Regular Github.com notifications (you are watching a repo) digesters.append(GithubNotificationDigester(MetaStore("github_notifications"))) # Github Enterprise installation notifications (you are watching a repo) # Note the params for this need customization, or nothing will match ..... digesters.append(GithubNotificationDigester(MetaStore("ghe_notifications"), return_path_email="<EMAIL>", from_email="<EMAIL>", site="ghe.yourcompany.com", known_as="GHE")) # Atlassian HipChat (service) notifications digesters.append(HipchatNotificationDigester(MetaStore("hipchat_notifications"))) # Fidelity Investments (investments) Balance notifications digesters.append(FidelityNotificationDigester(MetaStore("fidelity_notifications"))) # Linkedin (service) Invitations digesters.append(LinkedinInvitationDigester(MetaStore("linkedin_invitations"))) # Atlassian Confluence Installation Notifications # Customize to the Confluence you're aiming at ..... digesters.append(ConfluenceNotificationDigester(MetaStore("apache_confluence_notifications"), "<EMAIL>", "Apache")) # Customize to the JIRA you're aiming at ..... # Atlassian's JIRA Installation Notifications digesters.append(JiraNotificationDigester(MetaStore("atlassian_jira_notifications"), "<EMAIL>", "Atlassian")) # Apache's JIRA Notifications digesters.append(JiraNotificationDigester(MetaStore("apache_jira_notifications"), "<EMAIL>", "Apache")) # Reddit notifictions digesters.append(RedditNotificationDigester(MetaStore("paul_h_reddit"), "paul_h")) # reddit account for paul_h digesters.append(RedditNotificationDigester(MetaStore("sdjhfsdfwewer_reddit"), "sdjhfsdfwewer")) # reddit account for sdjhfsdfwewer
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web/http_server_post_example.py
rolandovillca/python_introduction_basic
0
176465
<gh_stars>0 ''' ----------------------------------------------------------- BaseHTTPServer - base classes for implementing web servers: ----------------------------------------------------------- Purpose: BaseHTTPServer includes classes that can form the basis of a web server. Available In: 1.4 and later BaseHTTPServer uses classes from SocketServer to create base classes for making HTTP servers. HTTPServer can be used directly, but the BaseHTTPRequestHandler is intended to be extended to handle each protocol method (GET, POST, etc.). To add support for an HTTP method in your request handler class, implement the method do_METHOD(), replacing METHOD with the name of the HTTP method. For example, do_GET(), do_POST(), etc. For consistency, the method takes no arguments. All of the parameters for the request are parsed by BaseHTTPRequestHandler and stored as instance attributes of the request instance. https://pymotw.com/2/BaseHTTPServer/index.html#module-BaseHTTPServer ''' from BaseHTTPServer import BaseHTTPRequestHandler import cgi # EXAMPLE 1: HTTP POST: # ============================================================================== # Supporting POST requests is a little more work, because the base class does # not parse the form data for us. The cgi module provides the FieldStorage class # which knows how to parse the form, if it is given the correct inputs. class PostHandler(BaseHTTPRequestHandler): def do_POST(self): # Parse the form data posted: form = cgi.FieldStorage( fp=self.rfile, headers=self.headers, environ={'REQUEST_METHOD':'POST', 'CONTENT_TYPE':self.headers['Content-Type'], }) # Begin the response: self.send_response(200) self.end_headers() self.wfile.write('Client: %s\n' % str(self.client_address)) self.wfile.write('User-agent: %s\n' % str(self.headers['user-agent'])) self.wfile.write('Path: %s\n' % self.path) self.wfile.write('Form data:\n') # Echo back information about what was posted in the form: for field in form.keys(): field_item = form[field] if field_item.filename: # The field contains an uploaded file: file_data = field_item.file.read() file_len = len(file_data) del file_data self.wfile.write('\tUploaded %s as "%s" (%d bytes)\n' % \ (field, field_item.filename, file_len)) else: # Regular form value: self.wfile.write('\t%s=%s\n' % (field, form[field].value)) if __name__ == '__main__': from BaseHTTPServer import HTTPServer server = HTTPServer(('localhost', 8080), PostHandler) print 'Starting server, use <Strl-C> to stop.' server.serve_forever()
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sources/datasets/client_dataset_definitions/client_dataset.py
M4rukku/impact_of_non_iid_data_in_federated_learning
0
6234
import functools import gc from abc import ABC from sources.datasets.client_dataset_definitions.client_dataset_loaders.client_dataset_loader import ClientDatasetLoader, DatasetComponents from sources.datasets.client_dataset_definitions.client_dataset_processors.client_dataset_processor import ClientDatasetProcessor from sources.utils.exception_definitions import OutsideOfContextError def throw_error_outside_context(func): @functools.wraps(func) def wrapper_decorator(self, *args, **kwargs): if not self.within_context: raise OutsideOfContextError( """Error: Tried to access client Dataset outside of context manager. This might lead to data leaks and bad use of memory. Please wrap the usage of ClientDataset.dataset_x inside a "with statement". """) else: value = func(self, *args, **kwargs) return value return wrapper_decorator class ClientDataset(ABC): def __init__(self, client_identifier: str, client_dataset_loader: ClientDatasetLoader, client_dataset_processor: ClientDatasetProcessor, ): self.client_identifier = client_identifier self.client_dataset_loader = client_dataset_loader self.client_dataset_processor = client_dataset_processor self._train_data = None self._test_data = None self._validation_data = None self.within_context = False def process_x(self, raw_x_batch): """Pre-processes each batch of features before being fed to the model.""" return self.client_dataset_processor.process_x(raw_x_batch) def process_y(self, raw_y_batch): """Pre-processes each batch of labels before being fed to the model.""" return self.client_dataset_processor.process_y(raw_y_batch) def _lazy_initialise_data(self, data, dataset_component: DatasetComponents): if data is None: data = self.client_dataset_loader.load_dataset(self.client_identifier, dataset_component) return self.process_x(data["x"]), self.process_y(data["y"]) else: return data @property @throw_error_outside_context def training_data(self): """Returns the Training Data as pair of arrays containing the samples x, and classification y""" self._train_data = self._lazy_initialise_data(self._train_data, DatasetComponents.TRAIN) return self._train_data @property @throw_error_outside_context def training_data_x(self): """Returns the Training Data as an array of samples""" self._train_data = self._lazy_initialise_data(self._train_data, DatasetComponents.TRAIN) return self._train_data[0] @property @throw_error_outside_context def training_data_y(self): """Returns the Classifications for the Training Data as array""" self._train_data = self._lazy_initialise_data(self._train_data, DatasetComponents.TRAIN) return self._train_data[1] @property @throw_error_outside_context def test_data(self): """Returns the Training Data as pair of arrays containing the samples x, and classification y""" self._test_data = self._lazy_initialise_data(self._test_data, DatasetComponents.TEST) return self._test_data @property @throw_error_outside_context def test_data_x(self): """Returns the Test Data as an array of samples""" self._test_data = self._lazy_initialise_data(self._test_data, DatasetComponents.TEST) return self._test_data[0] @property @throw_error_outside_context def test_data_y(self): """Returns the Classifications for the Test Data as array""" self._test_data = self._lazy_initialise_data(self._test_data, DatasetComponents.TEST) return self._test_data[1] @property @throw_error_outside_context def validation_data(self): """Returns the Validation Data as pair of arrays containing the samples x, and classification y""" self._validation_data = self._lazy_initialise_data( self._validation_data, DatasetComponents.VALIDATION) return self._validation_data @property @throw_error_outside_context def validation_data_x(self): """Returns the Validation Data as an array of samples""" self._validation_data = self._lazy_initialise_data( self._validation_data, DatasetComponents.VALIDATION) return self._validation_data[0] @property @throw_error_outside_context def validation_data_y(self): """Returns the Classifications for the Validation Data as array""" self._validation_data = self._lazy_initialise_data( self._validation_data, DatasetComponents.VALIDATION) return self._validation_data[1] def __enter__(self): self.within_context = True def __exit__(self, exc_type, exc_value, exc_traceback): self.within_context = False self._train_data = None self._test_data = None self._validation_data = None gc.collect()
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export_util/normalize.py
amoghmatt/export-lib
3
36501
<gh_stars>1-10 import collections import schematics from export_util import ( template as tpl, value as val ) class Normalizer: """ Normalizer object formats data into ordered rows using provided template. Templates are building using `export_lib.template` functionality. First what should be passed into nomralizer constructor is the `export_lib.template.Object` instance. This is the way to say how to format each object at the provided data list. Each `export_lib.template.Object` takes `export_lib.template.Field` as an `field` argument, which allows to understand which fields and in which order they should be rendered. Also `Object` has other properties which are allows to specify how they should looks like in a table. Example: def create_duration(start_time, object_source): "" This function formats duration column depending on the object `cuesheet_start_time.$date` and `cuesheet_end_time.$date`. When we know the amount of seconds duration, we formatting it into the HH:MM:SS format. :param start_time: :param DataGetter object_source: :return: "" return time.strftime( '%H:%M:%S', time.gmtime( int((object_source.get('cuesheet_end_time.$date') - start_time) / 1000) ) ) def get_year_from_timestamp(milliseconds, object_source): "" This function returns year from the provided timestamp. :param milliseconds: :param DataGetter object_source: :return: "" return time.strftime('%Y', time.gmtime(int(milliseconds / 1000))) def verbose_boolean(boolean, object_source): "" This function returns "NO" if `boolean` is False, and returns "YES" if `boolean` is True. :param boolean: :param DataGetter object_source: :return: "" return 'YES' if boolean else 'NO' def verbose_list(list_objects, object_source): "" This function formats list of an objects into single string. :param list_objects: :param DataGetter object_source: :return: "" return ', '.join(map(lambda x: str(x), list_objects)) # This is how the data exporter should be initialized. ex = Exporter(normalizer=Normalizer( # This name - `OBJ_1`, will be used to describe hard logic. tpl.Object( # This is the number of column where the object should start render col=1, # If `titles` is True, then all headers of each field will be rendered before objects list. titles=True, # If you want to translate or rename some fields - use `translate` option. Note: # 1. Keys of this dict would be used as field name and values as a replacement. # 2. Keys are case-sensitive. So the field named `duration` will not be replaced in this example. # 3. Translation dict will be automatically converted to DataGetter object. So you can pass not only # field names, but path to replacement as of `DataGetter` specification too. translate={ 'Duration': 'Length', 'fields': { 'year': { 'title': 'Year } } }, # This is the object fields fields=[ # Required. First argument of the Field object is the column number. It's related to parent Object # col number, and always starts from 1. tpl.Field(1, 'Production ID', '_id.$oid'), # Required. The second argument is the field title / verbose_name. tpl.Field(2, 'Source', 'cuesheet_channel'), # Optional. The third argument is the where values is stored at the object. If you keep it empty - # title will be renderred instead. tpl.Field(3, 'Duration', 'cuesheet_start_time.$date', create_duration), # Optional. The fourth argument is the callable function which takes field value as the first arg # and the whole object `DataGetter` instance as the second argument. So you can compute absolutely # new value from field value and any amount of other objects values. tpl.Field(4, 'Year', 'updated_at.$date', get_year_from_timestamp), tpl.Field(5, 'Free Music', 'free_music', verbose_boolean), tpl.Field(6, 'Title', 'category.other_production.original_title'), tpl.Field(7, 'Gema AVR', 'cuesheet_progress'), tpl.Field(8, 'Country', 'production_country', verbose_list), # Then we rendering child objects list starting from the first column of table. Each nested object # is rendering under the parent object. So if you have more than one nested objects list to be # rendered, you need to fold them in a one table, see description below. # # This name - `OBJ_2`, will be used to describe hard logic. tpl.Object( # Nested objects table start column col=1, # How much rows should be kept empty before rendering this table. offset_top=5, # This is the Object verbose_name. It's required for all nested objects but should be empty # for the root. verbose_name='Cuesheets', # This is the path of the list of objects which are will be rendered. path='cuesheet.cues', # If `titles` is True, then all headers of each field will be rendered before objects list. titles=True, # This is the way to render multiple nested objects lists in a one table and not one under # other. If you kept it empty or set it to False, child `Objects` will be rendered as stairs. fold_nested=True, # Let's say, we want to keep one row empty before rendering next nested object, to get more # readable table. offset_item=1, # Object also has optional `preformat` parameter, which allows to pass callable object # preprocessor. For example if you want to change something in the object before rendering. # This is the nested object template fields. fields=[ tpl.Field(1, 'Start Time', 'start_time'), tpl.Field(2, 'Work ID', 'work_id'), tpl.Field(3, 'Length', 'length'), tpl.Field(4, 'Music Type', 'music_type'), tpl.Field(5, 'Use', 'use'), tpl.Field(6, 'Music Title', 'music_work.music_title'), tpl.Field(7, 'Origin', 'music_work.origin'), tpl.Field(8, 'Work ID', 'music_work.work_id.mpn_id'), tpl.Field(9, 'Work ID Type', 'music_work.work_id.iswc'), # This name - `OBJ_3`, will be used to describe hard logic. tpl.Object( col=10, verbose_name='Authors', path='music_work.author', # Inline option is the way to render nested object title in a one line with the # parent object. If you keep it empty, then parent object will have empty cells # before this table. inline=True, titles=False, fields=[ # Each field of this nested object, has numeration of columns starting from 1. # It's made to simplify templates building. Nested objects column numbers are # always relative to the parent object. # This column number will be calculated in a next way: # OBJ_1.column + OBJ_2.column + OBJ_3.column + self.column = 10 # It's hard to explain, just believe me it works as described. tpl.Field(1, 'Name', 'name'), tpl.Field(2, 'Rolle', 'rolle'), ] ), # In the previous object, we have two fields placed horizontally. This means that previous # object will take two columns of space. Then we need to give him a place and place next # nested object on a column `prev.column+2` tpl.Object( col=12, verbose_name='Publishers', path='music_work.publisher', inline=True, titles=False, fields=[ tpl.Field(1, 'Name', 'name'), tpl.Field(2, 'Rolle', 'rolle'), ] ), tpl.Object( col=14, verbose_name='Interpreters', path='music_work.interpreter', inline=True, titles=False, fields=[ tpl.Field(1, 'Name', 'name'), tpl.Field(2, 'Rolle', 'rolle'), ] ), ] ) ] )), output=XLSXBytesOutputWriter(cols_dimensions={ 'A': 28.06, 'B': 27.65, 'C': 10.0, 'D': 13.19, 'E': 11.25, 'F': 43.9, 'G': 13.89, 'H': 30.7, 'I': 14.72, 'J': 29.45, 'K': 8.67, 'L': 28.76, 'M': 8.67, 'N': 29.03, 'O': 8.67 }) ) *Preformat* argument is a function which takes two arguments. First - os the this column value, and the Second is the whole object `DataGetter`. So you can compute any difficult values which are depends on other object values. """ def __init__(self, template: tpl.Object, *args, **kwargs): """ Create normalizer instance. """ self.template = template def build_table(self, obj): """ Returns N rows which are representing this object due to provided. template. """ yield from self.template.render(obj) class SchematicsNormalizer(Normalizer): """ Creates object template from the schematics model. """ root_object_options = dict( col=1, titles=True, fold_nested=True, ) nested_object_options = dict( titles=True, inline=True, fold_nested=True, ) def __init__(self, model: schematics.Model, *args, **kwargs): """ Create objects template from schematics model. """ template = self._build_template(model, **kwargs) super(SchematicsNormalizer, self).__init__(template, *args, **kwargs) def _build_template(self, model: schematics.Model, **kwargs) -> tpl.Object: """ Creates object template from model. """ template_options = self.root_object_options template_options.update(kwargs) template = tpl.Object(**template_options) for field, preformat in self._get_model_renderable_fields(model): options = {} if preformat is not None: options['preformat'] = preformat template.add_field( field=self._create_field_template( field=field, parent=kwargs.get('parent'), previous=template.fields[-1] if template.fields else None, **options ) ) return template def _create_field_template(self, field: schematics.types.BaseType, parent=None, previous=None, **kwargs): if isinstance(field, schematics.types.ListType) and not kwargs.get('preformat'): return self._type_list_related_field(field, parent, previous, **kwargs) if isinstance(field, schematics.types.ModelType) and not kwargs.get('preformat'): return self._type_related_field(field, parent, previous, **kwargs) return self._type_base_field(field, parent, previous, **kwargs) def _type_base_field(self, field: schematics.types.BaseType, parent=None, previous=None, **kwargs): if 'col' in kwargs: column = kwargs.pop('col') else: column = self._get_next_column_number(previous) preformat = None if 'preformat' in kwargs: preformat = kwargs.pop('preformat') if preformat is None: preformat = val.any_to_string return tpl.Field( col=column, path=self._get_field_path(parent, field.name), preformat=preformat, verbose_name=self._get_field_verbose_name(field), **kwargs ) def _type_list_related_field(self, field: schematics.types.BaseType, parent=None, previous=None, **kwargs): if hasattr(field, 'model_class'): return self._type_related_field(field, parent, previous, **kwargs) if 'col' in kwargs: column = kwargs.pop('col') else: column = self._get_next_column_number(previous) preformat = None if 'preformat' in kwargs: preformat = kwargs.pop('preformat') if preformat is None: preformat = val.any_to_string return self._type_base_field( col=column, field=field, parent=parent, previous=previous, preformat=preformat, **kwargs ) def _type_related_field(self, field: schematics.types.BaseType, parent=None, previous=None, **kwargs): options = kwargs or self._get_model_template_options(field.model_class) if 'col' in options: column = options.pop('col') else: column = self._get_next_column_number(previous) return self._build_template( col=column, path=self._get_field_path(parent, field.name), model=field.model_class, parent=parent, verbose_name=self._get_field_verbose_name(field), **options ) def _get_model_template_options(self, model) -> dict: o = self.nested_object_options.copy() o.update({ k: v for k, v in model._options if k in tpl.Object.supported_options and v is not None }) return dict(filter(lambda x: x[1] is not None, o.items())) def _get_model_renderable_fields(self, model: schematics.models.Model): if 'fields' in dict(model._options) and model._options.fields is not None: for field_name in model._options.fields: # Get model field if field_name in model.fields: yield model.fields[field_name], self._get_model_preformat_field(model, field_name) continue # Get custom report field getter = f'get_{field_name}' if not hasattr(model, getter): raise NotImplementedError(f'{model.__name__}.{getter} is not implemented') getter = getattr(model, getter) getter.name = getter.serialized_name = field_name # Define preformatters and prepend getter preformatters = self._get_model_preformat_field(model, field_name) if not preformatters: preformatters = [getter] elif isinstance(preformatters, collections.Iterable): preformatters = [getter] + list(preformatters) elif callable(preformatters): preformatters = [getter, preformatters] yield getter, preformatters return yield from ( (v, self._get_model_preformat_field(model, k)) for k, v in model.fields.items() ) def _get_model_preformat_field(self, model: schematics.models.Model, field_name): if 'preformat' in dict(model._options) and model._options.preformat is not None: source_formatters = model._options.preformat.get(field_name) if callable(source_formatters) or not source_formatters: return source_formatters callable_formatters = [] if isinstance(source_formatters, collections.Iterable): for formatter in source_formatters: if isinstance(formatter, str): callable_formatters.append(getattr(model, formatter)) elif callable(formatter): callable_formatters.append(formatter) else: raise TypeError(f'{field_name} formatter must be callable or iterable of callable') return callable_formatters return None def _get_next_column_number(self, previous_field=None): if previous_field is None: return 1 return previous_field.column + previous_field.length def _get_field_verbose_name(self, field): return field.serialized_name or field.name def _get_field_path(self, parent, field_name): return '.'.join([parent or '', field_name]).strip('.') __all__ = [ 'Normalizer', 'SchematicsNormalizer', ]
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gridfs/grid_file.py
naomielst/mongo-python-driver
2
5500
# Copyright 2009-present MongoDB, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tools for representing files stored in GridFS.""" import datetime import io import math import os from bson.int64 import Int64 from bson.son import SON from bson.binary import Binary from bson.objectid import ObjectId from pymongo import ASCENDING from pymongo.collection import Collection from pymongo.cursor import Cursor from pymongo.errors import (ConfigurationError, CursorNotFound, DuplicateKeyError, InvalidOperation, OperationFailure) from pymongo.read_preferences import ReadPreference from gridfs.errors import CorruptGridFile, FileExists, NoFile try: _SEEK_SET = os.SEEK_SET _SEEK_CUR = os.SEEK_CUR _SEEK_END = os.SEEK_END # before 2.5 except AttributeError: _SEEK_SET = 0 _SEEK_CUR = 1 _SEEK_END = 2 EMPTY = b"" NEWLN = b"\n" """Default chunk size, in bytes.""" # Slightly under a power of 2, to work well with server's record allocations. DEFAULT_CHUNK_SIZE = 255 * 1024 _C_INDEX = SON([("files_id", ASCENDING), ("n", ASCENDING)]) _F_INDEX = SON([("filename", ASCENDING), ("uploadDate", ASCENDING)]) def _grid_in_property(field_name, docstring, read_only=False, closed_only=False): """Create a GridIn property.""" def getter(self): if closed_only and not self._closed: raise AttributeError("can only get %r on a closed file" % field_name) # Protect against PHP-237 if field_name == 'length': return self._file.get(field_name, 0) return self._file.get(field_name, None) def setter(self, value): if self._closed: self._coll.files.update_one({"_id": self._file["_id"]}, {"$set": {field_name: value}}) self._file[field_name] = value if read_only: docstring += "\n\nThis attribute is read-only." elif closed_only: docstring = "%s\n\n%s" % (docstring, "This attribute is read-only and " "can only be read after :meth:`close` " "has been called.") if not read_only and not closed_only: return property(getter, setter, doc=docstring) return property(getter, doc=docstring) def _grid_out_property(field_name, docstring): """Create a GridOut property.""" def getter(self): self._ensure_file() # Protect against PHP-237 if field_name == 'length': return self._file.get(field_name, 0) return self._file.get(field_name, None) docstring += "\n\nThis attribute is read-only." return property(getter, doc=docstring) def _clear_entity_type_registry(entity, **kwargs): """Clear the given database/collection object's type registry.""" codecopts = entity.codec_options.with_options(type_registry=None) return entity.with_options(codec_options=codecopts, **kwargs) def _disallow_transactions(session): if session and session.in_transaction: raise InvalidOperation( 'GridFS does not support multi-document transactions') class GridIn(object): """Class to write data to GridFS. """ def __init__(self, root_collection, session=None, **kwargs): """Write a file to GridFS Application developers should generally not need to instantiate this class directly - instead see the methods provided by :class:`~gridfs.GridFS`. Raises :class:`TypeError` if `root_collection` is not an instance of :class:`~pymongo.collection.Collection`. Any of the file level options specified in the `GridFS Spec <http://dochub.mongodb.org/core/gridfsspec>`_ may be passed as keyword arguments. Any additional keyword arguments will be set as additional fields on the file document. Valid keyword arguments include: - ``"_id"``: unique ID for this file (default: :class:`~bson.objectid.ObjectId`) - this ``"_id"`` must not have already been used for another file - ``"filename"``: human name for the file - ``"contentType"`` or ``"content_type"``: valid mime-type for the file - ``"chunkSize"`` or ``"chunk_size"``: size of each of the chunks, in bytes (default: 255 kb) - ``"encoding"``: encoding used for this file. Any :class:`str` that is written to the file will be converted to :class:`bytes`. :Parameters: - `root_collection`: root collection to write to - `session` (optional): a :class:`~pymongo.client_session.ClientSession` to use for all commands - `**kwargs` (optional): file level options (see above) .. versionchanged:: 4.0 Removed the `disable_md5` parameter. See :ref:`removed-gridfs-checksum` for details. .. versionchanged:: 3.7 Added the `disable_md5` parameter. .. versionchanged:: 3.6 Added ``session`` parameter. .. versionchanged:: 3.0 `root_collection` must use an acknowledged :attr:`~pymongo.collection.Collection.write_concern` """ if not isinstance(root_collection, Collection): raise TypeError("root_collection must be an " "instance of Collection") if not root_collection.write_concern.acknowledged: raise ConfigurationError('root_collection must use ' 'acknowledged write_concern') _disallow_transactions(session) # Handle alternative naming if "content_type" in kwargs: kwargs["contentType"] = kwargs.pop("content_type") if "chunk_size" in kwargs: kwargs["chunkSize"] = kwargs.pop("chunk_size") coll = _clear_entity_type_registry( root_collection, read_preference=ReadPreference.PRIMARY) # Defaults kwargs["_id"] = kwargs.get("_id", ObjectId()) kwargs["chunkSize"] = kwargs.get("chunkSize", DEFAULT_CHUNK_SIZE) object.__setattr__(self, "_session", session) object.__setattr__(self, "_coll", coll) object.__setattr__(self, "_chunks", coll.chunks) object.__setattr__(self, "_file", kwargs) object.__setattr__(self, "_buffer", io.BytesIO()) object.__setattr__(self, "_position", 0) object.__setattr__(self, "_chunk_number", 0) object.__setattr__(self, "_closed", False) object.__setattr__(self, "_ensured_index", False) def __create_index(self, collection, index_key, unique): doc = collection.find_one(projection={"_id": 1}, session=self._session) if doc is None: try: index_keys = [index_spec['key'] for index_spec in collection.list_indexes(session=self._session)] except OperationFailure: index_keys = [] if index_key not in index_keys: collection.create_index( index_key.items(), unique=unique, session=self._session) def __ensure_indexes(self): if not object.__getattribute__(self, "_ensured_index"): _disallow_transactions(self._session) self.__create_index(self._coll.files, _F_INDEX, False) self.__create_index(self._coll.chunks, _C_INDEX, True) object.__setattr__(self, "_ensured_index", True) def abort(self): """Remove all chunks/files that may have been uploaded and close. """ self._coll.chunks.delete_many( {"files_id": self._file['_id']}, session=self._session) self._coll.files.delete_one( {"_id": self._file['_id']}, session=self._session) object.__setattr__(self, "_closed", True) @property def closed(self): """Is this file closed? """ return self._closed _id = _grid_in_property("_id", "The ``'_id'`` value for this file.", read_only=True) filename = _grid_in_property("filename", "Name of this file.") name = _grid_in_property("filename", "Alias for `filename`.") content_type = _grid_in_property("contentType", "Mime-type for this file.") length = _grid_in_property("length", "Length (in bytes) of this file.", closed_only=True) chunk_size = _grid_in_property("chunkSize", "Chunk size for this file.", read_only=True) upload_date = _grid_in_property("uploadDate", "Date that this file was uploaded.", closed_only=True) md5 = _grid_in_property("md5", "MD5 of the contents of this file " "if an md5 sum was created.", closed_only=True) def __getattr__(self, name): if name in self._file: return self._file[name] raise AttributeError("GridIn object has no attribute '%s'" % name) def __setattr__(self, name, value): # For properties of this instance like _buffer, or descriptors set on # the class like filename, use regular __setattr__ if name in self.__dict__ or name in self.__class__.__dict__: object.__setattr__(self, name, value) else: # All other attributes are part of the document in db.fs.files. # Store them to be sent to server on close() or if closed, send # them now. self._file[name] = value if self._closed: self._coll.files.update_one({"_id": self._file["_id"]}, {"$set": {name: value}}) def __flush_data(self, data): """Flush `data` to a chunk. """ self.__ensure_indexes() if not data: return assert(len(data) <= self.chunk_size) chunk = {"files_id": self._file["_id"], "n": self._chunk_number, "data": Binary(data)} try: self._chunks.insert_one(chunk, session=self._session) except DuplicateKeyError: self._raise_file_exists(self._file['_id']) self._chunk_number += 1 self._position += len(data) def __flush_buffer(self): """Flush the buffer contents out to a chunk. """ self.__flush_data(self._buffer.getvalue()) self._buffer.close() self._buffer = io.BytesIO() def __flush(self): """Flush the file to the database. """ try: self.__flush_buffer() # The GridFS spec says length SHOULD be an Int64. self._file["length"] = Int64(self._position) self._file["uploadDate"] = datetime.datetime.utcnow() return self._coll.files.insert_one( self._file, session=self._session) except DuplicateKeyError: self._raise_file_exists(self._id) def _raise_file_exists(self, file_id): """Raise a FileExists exception for the given file_id.""" raise FileExists("file with _id %r already exists" % file_id) def close(self): """Flush the file and close it. A closed file cannot be written any more. Calling :meth:`close` more than once is allowed. """ if not self._closed: self.__flush() object.__setattr__(self, "_closed", True) def read(self, size=-1): raise io.UnsupportedOperation('read') def readable(self): return False def seekable(self): return False def write(self, data): """Write data to the file. There is no return value. `data` can be either a string of bytes or a file-like object (implementing :meth:`read`). If the file has an :attr:`encoding` attribute, `data` can also be a :class:`str` instance, which will be encoded as :attr:`encoding` before being written. Due to buffering, the data may not actually be written to the database until the :meth:`close` method is called. Raises :class:`ValueError` if this file is already closed. Raises :class:`TypeError` if `data` is not an instance of :class:`bytes`, a file-like object, or an instance of :class:`str`. Unicode data is only allowed if the file has an :attr:`encoding` attribute. :Parameters: - `data`: string of bytes or file-like object to be written to the file """ if self._closed: raise ValueError("cannot write to a closed file") try: # file-like read = data.read except AttributeError: # string if not isinstance(data, (str, bytes)): raise TypeError("can only write strings or file-like objects") if isinstance(data, str): try: data = data.encode(self.encoding) except AttributeError: raise TypeError("must specify an encoding for file in " "order to write str") read = io.BytesIO(data).read if self._buffer.tell() > 0: # Make sure to flush only when _buffer is complete space = self.chunk_size - self._buffer.tell() if space: try: to_write = read(space) except: self.abort() raise self._buffer.write(to_write) if len(to_write) < space: return # EOF or incomplete self.__flush_buffer() to_write = read(self.chunk_size) while to_write and len(to_write) == self.chunk_size: self.__flush_data(to_write) to_write = read(self.chunk_size) self._buffer.write(to_write) def writelines(self, sequence): """Write a sequence of strings to the file. Does not add seperators. """ for line in sequence: self.write(line) def writeable(self): return True def __enter__(self): """Support for the context manager protocol. """ return self def __exit__(self, exc_type, exc_val, exc_tb): """Support for the context manager protocol. Close the file and allow exceptions to propagate. """ self.close() # propagate exceptions return False class GridOut(io.IOBase): """Class to read data out of GridFS. """ def __init__(self, root_collection, file_id=None, file_document=None, session=None): """Read a file from GridFS Application developers should generally not need to instantiate this class directly - instead see the methods provided by :class:`~gridfs.GridFS`. Either `file_id` or `file_document` must be specified, `file_document` will be given priority if present. Raises :class:`TypeError` if `root_collection` is not an instance of :class:`~pymongo.collection.Collection`. :Parameters: - `root_collection`: root collection to read from - `file_id` (optional): value of ``"_id"`` for the file to read - `file_document` (optional): file document from `root_collection.files` - `session` (optional): a :class:`~pymongo.client_session.ClientSession` to use for all commands .. versionchanged:: 3.8 For better performance and to better follow the GridFS spec, :class:`GridOut` now uses a single cursor to read all the chunks in the file. .. versionchanged:: 3.6 Added ``session`` parameter. .. versionchanged:: 3.0 Creating a GridOut does not immediately retrieve the file metadata from the server. Metadata is fetched when first needed. """ if not isinstance(root_collection, Collection): raise TypeError("root_collection must be an " "instance of Collection") _disallow_transactions(session) root_collection = _clear_entity_type_registry(root_collection) super().__init__() self.__chunks = root_collection.chunks self.__files = root_collection.files self.__file_id = file_id self.__buffer = EMPTY self.__chunk_iter = None self.__position = 0 self._file = file_document self._session = session _id = _grid_out_property("_id", "The ``'_id'`` value for this file.") filename = _grid_out_property("filename", "Name of this file.") name = _grid_out_property("filename", "Alias for `filename`.") content_type = _grid_out_property("contentType", "Mime-type for this file.") length = _grid_out_property("length", "Length (in bytes) of this file.") chunk_size = _grid_out_property("chunkSize", "Chunk size for this file.") upload_date = _grid_out_property("uploadDate", "Date that this file was first uploaded.") aliases = _grid_out_property("aliases", "List of aliases for this file.") metadata = _grid_out_property("metadata", "Metadata attached to this file.") md5 = _grid_out_property("md5", "MD5 of the contents of this file " "if an md5 sum was created.") def _ensure_file(self): if not self._file: _disallow_transactions(self._session) self._file = self.__files.find_one({"_id": self.__file_id}, session=self._session) if not self._file: raise NoFile("no file in gridfs collection %r with _id %r" % (self.__files, self.__file_id)) def __getattr__(self, name): self._ensure_file() if name in self._file: return self._file[name] raise AttributeError("GridOut object has no attribute '%s'" % name) def readable(self): return True def readchunk(self): """Reads a chunk at a time. If the current position is within a chunk the remainder of the chunk is returned. """ received = len(self.__buffer) chunk_data = EMPTY chunk_size = int(self.chunk_size) if received > 0: chunk_data = self.__buffer elif self.__position < int(self.length): chunk_number = int((received + self.__position) / chunk_size) if self.__chunk_iter is None: self.__chunk_iter = _GridOutChunkIterator( self, self.__chunks, self._session, chunk_number) chunk = self.__chunk_iter.next() chunk_data = chunk["data"][self.__position % chunk_size:] if not chunk_data: raise CorruptGridFile("truncated chunk") self.__position += len(chunk_data) self.__buffer = EMPTY return chunk_data def read(self, size=-1): """Read at most `size` bytes from the file (less if there isn't enough data). The bytes are returned as an instance of :class:`str` (:class:`bytes` in python 3). If `size` is negative or omitted all data is read. :Parameters: - `size` (optional): the number of bytes to read .. versionchanged:: 3.8 This method now only checks for extra chunks after reading the entire file. Previously, this method would check for extra chunks on every call. """ self._ensure_file() remainder = int(self.length) - self.__position if size < 0 or size > remainder: size = remainder if size == 0: return EMPTY received = 0 data = io.BytesIO() while received < size: chunk_data = self.readchunk() received += len(chunk_data) data.write(chunk_data) # Detect extra chunks after reading the entire file. if size == remainder and self.__chunk_iter: try: self.__chunk_iter.next() except StopIteration: pass self.__position -= received - size # Return 'size' bytes and store the rest. data.seek(size) self.__buffer = data.read() data.seek(0) return data.read(size) def readline(self, size=-1): """Read one line or up to `size` bytes from the file. :Parameters: - `size` (optional): the maximum number of bytes to read """ remainder = int(self.length) - self.__position if size < 0 or size > remainder: size = remainder if size == 0: return EMPTY received = 0 data = io.BytesIO() while received < size: chunk_data = self.readchunk() pos = chunk_data.find(NEWLN, 0, size) if pos != -1: size = received + pos + 1 received += len(chunk_data) data.write(chunk_data) if pos != -1: break self.__position -= received - size # Return 'size' bytes and store the rest. data.seek(size) self.__buffer = data.read() data.seek(0) return data.read(size) def tell(self): """Return the current position of this file. """ return self.__position def seek(self, pos, whence=_SEEK_SET): """Set the current position of this file. :Parameters: - `pos`: the position (or offset if using relative positioning) to seek to - `whence` (optional): where to seek from. :attr:`os.SEEK_SET` (``0``) for absolute file positioning, :attr:`os.SEEK_CUR` (``1``) to seek relative to the current position, :attr:`os.SEEK_END` (``2``) to seek relative to the file's end. """ if whence == _SEEK_SET: new_pos = pos elif whence == _SEEK_CUR: new_pos = self.__position + pos elif whence == _SEEK_END: new_pos = int(self.length) + pos else: raise IOError(22, "Invalid value for `whence`") if new_pos < 0: raise IOError(22, "Invalid value for `pos` - must be positive") # Optimization, continue using the same buffer and chunk iterator. if new_pos == self.__position: return self.__position = new_pos self.__buffer = EMPTY if self.__chunk_iter: self.__chunk_iter.close() self.__chunk_iter = None def seekable(self): return True def __iter__(self): """Return an iterator over all of this file's data. The iterator will return lines (delimited by ``b'\\n'``) of :class:`bytes`. This can be useful when serving files using a webserver that handles such an iterator efficiently. .. versionchanged:: 3.8 The iterator now raises :class:`CorruptGridFile` when encountering any truncated, missing, or extra chunk in a file. The previous behavior was to only raise :class:`CorruptGridFile` on a missing chunk. .. versionchanged:: 4.0 The iterator now iterates over *lines* in the file, instead of chunks, to conform to the base class :py:class:`io.IOBase`. Use :meth:`GridOut.readchunk` to read chunk by chunk instead of line by line. """ return self def close(self): """Make GridOut more generically file-like.""" if self.__chunk_iter: self.__chunk_iter.close() self.__chunk_iter = None super().close() def write(self, value): raise io.UnsupportedOperation('write') def writelines(self, lines): raise io.UnsupportedOperation('writelines') def writable(self): return False def __enter__(self): """Makes it possible to use :class:`GridOut` files with the context manager protocol. """ return self def __exit__(self, exc_type, exc_val, exc_tb): """Makes it possible to use :class:`GridOut` files with the context manager protocol. """ self.close() return False def fileno(self): raise io.UnsupportedOperation('fileno') def flush(self): # GridOut is read-only, so flush does nothing. pass def isatty(self): return False def truncate(self, size=None): # See https://docs.python.org/3/library/io.html#io.IOBase.writable # for why truncate has to raise. raise io.UnsupportedOperation('truncate') # Override IOBase.__del__ otherwise it will lead to __getattr__ on # __IOBase_closed which calls _ensure_file and potentially performs I/O. # We cannot do I/O in __del__ since it can lead to a deadlock. def __del__(self): pass class _GridOutChunkIterator(object): """Iterates over a file's chunks using a single cursor. Raises CorruptGridFile when encountering any truncated, missing, or extra chunk in a file. """ def __init__(self, grid_out, chunks, session, next_chunk): self._id = grid_out._id self._chunk_size = int(grid_out.chunk_size) self._length = int(grid_out.length) self._chunks = chunks self._session = session self._next_chunk = next_chunk self._num_chunks = math.ceil(float(self._length) / self._chunk_size) self._cursor = None def expected_chunk_length(self, chunk_n): if chunk_n < self._num_chunks - 1: return self._chunk_size return self._length - (self._chunk_size * (self._num_chunks - 1)) def __iter__(self): return self def _create_cursor(self): filter = {"files_id": self._id} if self._next_chunk > 0: filter["n"] = {"$gte": self._next_chunk} _disallow_transactions(self._session) self._cursor = self._chunks.find(filter, sort=[("n", 1)], session=self._session) def _next_with_retry(self): """Return the next chunk and retry once on CursorNotFound. We retry on CursorNotFound to maintain backwards compatibility in cases where two calls to read occur more than 10 minutes apart (the server's default cursor timeout). """ if self._cursor is None: self._create_cursor() try: return self._cursor.next() except CursorNotFound: self._cursor.close() self._create_cursor() return self._cursor.next() def next(self): try: chunk = self._next_with_retry() except StopIteration: if self._next_chunk >= self._num_chunks: raise raise CorruptGridFile("no chunk #%d" % self._next_chunk) if chunk["n"] != self._next_chunk: self.close() raise CorruptGridFile( "Missing chunk: expected chunk #%d but found " "chunk with n=%d" % (self._next_chunk, chunk["n"])) if chunk["n"] >= self._num_chunks: # According to spec, ignore extra chunks if they are empty. if len(chunk["data"]): self.close() raise CorruptGridFile( "Extra chunk found: expected %d chunks but found " "chunk with n=%d" % (self._num_chunks, chunk["n"])) expected_length = self.expected_chunk_length(chunk["n"]) if len(chunk["data"]) != expected_length: self.close() raise CorruptGridFile( "truncated chunk #%d: expected chunk length to be %d but " "found chunk with length %d" % ( chunk["n"], expected_length, len(chunk["data"]))) self._next_chunk += 1 return chunk __next__ = next def close(self): if self._cursor: self._cursor.close() self._cursor = None class GridOutIterator(object): def __init__(self, grid_out, chunks, session): self.__chunk_iter = _GridOutChunkIterator(grid_out, chunks, session, 0) def __iter__(self): return self def next(self): chunk = self.__chunk_iter.next() return bytes(chunk["data"]) __next__ = next class GridOutCursor(Cursor): """A cursor / iterator for returning GridOut objects as the result of an arbitrary query against the GridFS files collection. """ def __init__(self, collection, filter=None, skip=0, limit=0, no_cursor_timeout=False, sort=None, batch_size=0, session=None): """Create a new cursor, similar to the normal :class:`~pymongo.cursor.Cursor`. Should not be called directly by application developers - see the :class:`~gridfs.GridFS` method :meth:`~gridfs.GridFS.find` instead. .. versionadded 2.7 .. seealso:: The MongoDB documentation on `cursors <https://dochub.mongodb.org/core/cursors>`_. """ _disallow_transactions(session) collection = _clear_entity_type_registry(collection) # Hold on to the base "fs" collection to create GridOut objects later. self.__root_collection = collection super(GridOutCursor, self).__init__( collection.files, filter, skip=skip, limit=limit, no_cursor_timeout=no_cursor_timeout, sort=sort, batch_size=batch_size, session=session) def next(self): """Get next GridOut object from cursor. """ _disallow_transactions(self.session) # Work around "super is not iterable" issue in Python 3.x next_file = super(GridOutCursor, self).next() return GridOut(self.__root_collection, file_document=next_file, session=self.session) __next__ = next def add_option(self, *args, **kwargs): raise NotImplementedError("Method does not exist for GridOutCursor") def remove_option(self, *args, **kwargs): raise NotImplementedError("Method does not exist for GridOutCursor") def _clone_base(self, session): """Creates an empty GridOutCursor for information to be copied into. """ return GridOutCursor(self.__root_collection, session=session)
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src/components/oneSpotApp/oneSpotApp.py
Naruita/tinyApps
1
108916
#!/usr/bin/python # -*- coding: utf-8 -*- # Front end window import tkinter as tk from tkinter import filedialog # System and File access import os """ oneSpotApp.py is a simple one click service that allows you to deploy all the applications that you wish to, through a one time setup process. """ # Owned __author__ = "<NAME>" __credits__ = ["<NAME>"] __license__ = "MIT" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" def apps_in_file(): if os.path.isfile("save.txt"): with open("save.txt", "r") as f: tempApps = f.read() tempApps = tempApps.split(",") apps = [x for x in tempApps if x.strip()] return apps class mainApp(tk.Tk): def __init__(self, *args, **kwargs): tk.Tk.__init__(self, *args, **kwargs) self.wm_title("One Spot App") # returns the app locations present in the file self.apps = apps_in_file() canvas = tk.Frame(self, height=700, width=700, bg="#DC143C") canvas.pack(side="top", fill="both", expand=True) frame = tk.Frame(self, bg="white") frame.place(relwidth=0.8, relheight=0.8, relx=0.1, rely=0.1) openFile = tk.Button( self, text="Open File", padx=10, pady=5, fg="white", bg="#DC143C", command=self.add_apps, ) openFile.pack() runApps = tk.Button( self, text="Run Apps", padx=10, pady=5, fg="white", bg="#DC143C", command=self.run_apps, ) runApps.pack() for app in self.apps: label = tk.Label(frame, text=app) label.pack() def add_apps(self): filename = filedialog.askopenfilename( initialdir="/", title="Select App to add", filetypes=((".exe files", "*.exe"), ("All files", "*.*")), ) self.apps.append(filename) def run_apps(self): for element in self.apps: os.startfile(element) def write_to_file(self): with open("save.txt", "a") as f: for element in self.apps: f.write(element + ",") if __name__ == "__main__": App = mainApp() App.mainloop() App.write_to_file()
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tests/test_predict_route.py
Build-Week-mdd-cabinet1/medcab1-api
0
98559
import unittest from mcapi.app import mc_api # UnitTest function, used to test whether or not the API will return suggested results. class TestApp(unittest.TestCase): def setUp(self): self.mc_api = mc_api.test_client() def tearDown(self): pass def test_predict_route(self): payload = { "id": 1, "race": "hybrid", "positive_effects": "", "negative_effects_avoid": "", "ailments": "Inflammation, Eye Pressure", "flavors": "", "additional_desired_effects": "I want to feel a sense of calm", "user_id": 10 } response = self.mc_api.post("/predict", json=payload) json_response = {'id': 1, 'strain_id': [ 962, 357, 604, 1365, 1332, 1293, 1815, 365, 343, 214 ], 'user_id': 10} print(response.json) self.assertEqual(response.status_code, 200) self.assertEqual(response.json, json_response) def test_predict_no_id(self): payload = { "race": "hybrid", "positive_effects": "", "negative_effects_avoid": "", "ailments": "Inflammation, Eye Pressure", "flavors": "", "additional_desired_effects": "I want to feel a sense of calm", "user_id": 10 } response = self.mc_api.post("/predict", json=payload) print(response.json) self.assertEqual(response.status_code, 200) if __name__ == "__main__": unittest.main()
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deepchem/data/tests/test_shape.py
deloragaskins/deepchem
3,782
24823
import deepchem as dc import numpy as np import os def test_numpy_dataset_get_shape(): """Test that get_shape works for numpy datasets.""" num_datapoints = 100 num_features = 10 num_tasks = 10 # Generate data X = np.random.rand(num_datapoints, num_features) y = np.random.randint(2, size=(num_datapoints, num_tasks)) w = np.random.randint(2, size=(num_datapoints, num_tasks)) ids = np.array(["id"] * num_datapoints) dataset = dc.data.NumpyDataset(X, y, w, ids) X_shape, y_shape, w_shape, ids_shape = dataset.get_shape() assert X_shape == X.shape assert y_shape == y.shape assert w_shape == w.shape assert ids_shape == ids.shape def test_disk_dataset_get_shape_single_shard(): """Test that get_shape works for disk dataset.""" num_datapoints = 100 num_features = 10 num_tasks = 10 # Generate data X = np.random.rand(num_datapoints, num_features) y = np.random.randint(2, size=(num_datapoints, num_tasks)) w = np.random.randint(2, size=(num_datapoints, num_tasks)) ids = np.array(["id"] * num_datapoints) dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids) X_shape, y_shape, w_shape, ids_shape = dataset.get_shape() assert X_shape == X.shape assert y_shape == y.shape assert w_shape == w.shape assert ids_shape == ids.shape def test_disk_dataset_get_shape_multishard(): """Test that get_shape works for multisharded disk dataset.""" num_datapoints = 100 num_features = 10 num_tasks = 10 # Generate data X = np.random.rand(num_datapoints, num_features) y = np.random.randint(2, size=(num_datapoints, num_tasks)) w = np.random.randint(2, size=(num_datapoints, num_tasks)) ids = np.array(["id"] * num_datapoints) dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids) # Should now have 10 shards dataset.reshard(shard_size=10) X_shape, y_shape, w_shape, ids_shape = dataset.get_shape() assert X_shape == X.shape assert y_shape == y.shape assert w_shape == w.shape assert ids_shape == ids.shape def test_disk_dataset_get_legacy_shape_single_shard(): """Test that get_shape works for legacy disk dataset.""" # This is the shape of legacy_data num_datapoints = 100 num_features = 10 num_tasks = 10 current_dir = os.path.dirname(os.path.abspath(__file__)) # legacy_dataset is a dataset in the legacy format kept around for testing # purposes. data_dir = os.path.join(current_dir, "legacy_dataset") dataset = dc.data.DiskDataset(data_dir) X_shape, y_shape, w_shape, ids_shape = dataset.get_shape() assert X_shape == (num_datapoints, num_features) assert y_shape == (num_datapoints, num_tasks) assert w_shape == (num_datapoints, num_tasks) assert ids_shape == (num_datapoints,) def test_disk_dataset_get_legacy_shape_multishard(): """Test that get_shape works for multisharded legacy disk dataset.""" # This is the shape of legacy_data_reshard num_datapoints = 100 num_features = 10 num_tasks = 10 # legacy_dataset_reshard is a sharded dataset in the legacy format kept # around for testing current_dir = os.path.dirname(os.path.abspath(__file__)) data_dir = os.path.join(current_dir, "legacy_dataset_reshard") dataset = dc.data.DiskDataset(data_dir) # Should now have 10 shards assert dataset.get_number_shards() == 10 X_shape, y_shape, w_shape, ids_shape = dataset.get_shape() assert X_shape == (num_datapoints, num_features) assert y_shape == (num_datapoints, num_tasks) assert w_shape == (num_datapoints, num_tasks) assert ids_shape == (num_datapoints,) def test_get_shard_size(): """ Test that using ids for getting the shard size does not break the method. The issue arises when attempting to load a dataset that does not have a labels column. The create_dataset method of the DataLoader class sets the y to None in this case, which causes the existing implementation of the get_shard_size() method to fail, as it relies on the dataset having a not None y column. This consequently breaks all methods depending on this, like the splitters for example. Note ---- DiskDatasets without labels cannot be resharded! """ current_dir = os.path.dirname(os.path.abspath(__file__)) file_path = os.path.join(current_dir, "reaction_smiles.csv") featurizer = dc.feat.DummyFeaturizer() loader = dc.data.CSVLoader( tasks=[], feature_field="reactions", featurizer=featurizer) dataset = loader.create_dataset(file_path) assert dataset.get_shard_size() == 4
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src/pyxplora_api/handler_gql.py
Ludy87/pyxplora_api
1
58324
<filename>src/pyxplora_api/handler_gql.py from __future__ import annotations import hashlib import math from time import time from datetime import datetime, timezone from typing import Any, Dict from .const import API_KEY, API_SECRET class HandlerGQL: def __init__( self, countryPhoneNumber: str, phoneNumber: str, password: str, userLang: str, timeZone: str, ) -> None: # init vars self.sessionId = None self.accessToken = None self.accessTokenExpire = 0 self.userLocale = userLang self.timeZone = timeZone self.countryPhoneNumber = countryPhoneNumber self.phoneNumber = phoneNumber self.passwordMD5 = hashlib.md5(password.encode()).hexdigest() self._API_KEY = API_KEY self._API_SECRET = API_SECRET self.issueDate = 0 self.expireDate = 0 self.userId = None self.variables = { "countryPhoneNumber": self.countryPhoneNumber, "phoneNumber": self.phoneNumber, "password": <PASSWORD>, "userLang": self.userLocale, "timeZone": self.timeZone, } self.issueToken: Dict[str, Any] def c(self) -> HandlerGQL: return self def getRequestHeaders(self, acceptedContentType: str) -> Dict[str, Any]: if acceptedContentType == "" or acceptedContentType is None: raise Exception("acceptedContentType MUST NOT be empty!") if self._API_KEY is None: raise Exception("Xplorao2o API_KEY MUST NOT be empty!") if self._API_SECRET is None: raise Exception("Xplorao2o API_SECRET MUST NOT be empty!") requestHeaders = {} if self.accessToken is None: # OPEN authorization authorizationHeader = f"Open {self._API_KEY}:{self._API_SECRET}" else: # BEARER authorization authorizationHeader = f"Bearer {self.accessToken}:{self._API_SECRET}" rfc1123DateString = datetime.now(timezone.utc).strftime("%a, %d %b %Y %H:%M:%S") + " GMT" requestHeaders["H-Date"] = rfc1123DateString requestHeaders["H-Authorization"] = authorizationHeader requestHeaders["H-BackDoor-Authorization"] = authorizationHeader requestHeaders["Accept"] = acceptedContentType requestHeaders["Content-Type"] = acceptedContentType requestHeaders["H-Tid"] = str(math.floor(time())) return requestHeaders
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norm/executable/schema/variable.py
reasoned-ai/norm
8
13361
<reponame>reasoned-ai/norm from norm.models.norm import Status, Lambda from norm.executable import NormExecutable from typing import Union, List import logging logger = logging.getLogger(__name__) class VariableName(NormExecutable): def __init__(self, scope, name): """ The variable and its scope :param scope: the scope of the variable :type scope: Union[VariableName, EvaluationExpr] :param name: the name of the variable :type name: str """ super().__init__() from norm.executable.expression.evaluation import EvaluationExpr self.scope: Union[VariableName, EvaluationExpr] = scope self.name: str = name self.output_projection: str = None @property def eval_lam(self): return self.lam def __str__(self): if self.scope is not None: return '{}{}{}'.format(self.scope.name, self.VARIABLE_SEPARATOR, self.name) else: return self.name def variable_type(self): return self.lam def compile(self, context): if self.name == context.THAT_VARIABLE_NAME: self.lam = context.that return self session = context.session if self.scope is None: name = self.name scope = context.get_scope(name) if scope is not None: return ColumnVariable(scope, name).compile(context) else: lam = self.try_retrieve_type(session, context.context_namespace, name) if lam is None: lam = self.try_retrieve_type(session, context.search_namespaces, name, status=Status.READY) self.lam = lam return self else: if isinstance(self.scope, ColumnVariable) and str(self) in self.scope.lam: # Already joined self.scope.name = str(self) return self.scope lam = self.scope.variable_type() if self.name in lam: if isinstance(self.scope, (ColumnVariable, JoinVariable)): # Join lam to the scope for the column return JoinVariable(self.scope, self.name, lam).compile(context) else: # A column of the scope variable return ColumnVariable(self.scope, self.name).compile(context) else: # An evaluation whose first argument is the scope lam = self.try_retrieve_type(session, context.context_namespace, self.name) if lam is None: lam = self.try_retrieve_type(session, context.search_namespaces, self.name, status=Status.READY) assert(lam is not None) self.lam = lam from norm.executable.expression.argument import ArgumentExpr arg = ArgumentExpr(expr=self.scope) self.scope = None from norm.executable.expression.evaluation import EvaluationExpr return EvaluationExpr([arg], self) class UnquoteVariable(VariableName): def __init__(self, name, unquoted_variables): """ The variable and its scope :param name: the name of the variable :type name: str :param unquoted_variables: a list of variables to unquote :type unquoted_variables: List[VariableName] """ super().__init__(None, name) self.unquoted_variables: List[VariableName] = unquoted_variables def __str__(self): return self.name def variable_type(self): raise NotImplementedError def compile(self, context): assert(len(self.unquoted_variables) > 0) assert(all([isinstance(v, ColumnVariable) for v in self.unquoted_variables])) lam = self.unquoted_variables[0].lam assert(all([v.lam is lam for v in self.unquoted_variables])) self.lam = lam return self def execute(self, context): # TODO: hacky assert(len(self.unquoted_variables) == 1) vname = self.unquoted_variables[0].name data = self.lam.data from pandas import DataFrame def _execute(x): try: result = context.execute(x[vname].values[0]) if isinstance(result, DataFrame): return result else: return None except: return None results = data.groupby(vname).apply(_execute).reset_index() if self.output_projection is not None: cols = dict((col, self.VARIABLE_SEPARATOR.join([self.output_projection, col])) for col in results.columns if col != vname and col != self.lam.VAR_OID) if self.lam.VAR_OID in results.columns: cols[self.lam.VAR_OID] = self.output_projection results = results.rename(columns=cols) return data.merge(results, on=vname) class ColumnVariable(VariableName): def __init__(self, scope, name): super().__init__(scope, name) def __str__(self): return self.name def variable_type(self): return self.lam.get_type(self.name) def compile(self, context): from norm.engine import QuantifiedLambda if self.scope is None: assert(context.scope is not None) self.lam = context.scope elif isinstance(self.scope, Lambda): self.lam = self.scope elif isinstance(self.scope, QuantifiedLambda): self.lam = self.scope else: self.lam = self.scope.lam return self def execute(self, context): result = self.lam.data[self.name] if self.output_projection is not None: return result.rename(self.output_projection) else: return result class JoinVariable(VariableName): def __init__(self, scope, name, joiner): super().__init__(scope, name) self.lam = joiner def variable_type(self): return self.lam.get_type(self.name) def compile(self, context): return self def execute(self, context): lam = self.scope.lam joiner = self.lam if str(self) not in lam.data.columns: to_join = joiner.data[[self.name]].rename(columns={self.name: str(self)}) lam.data = lam.data.join(to_join, on=str(self.scope)) return lam.data
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modal/modals.py
zhixuanli/SLN-Amodal
25
154586
import torch,math import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from roialign.roi_align.crop_and_resize import CropAndResizeFunction def log2(x): """Implementatin of Log2. Pytorch doesn't have a native implemenation.""" ln2 = Variable(torch.log(torch.FloatTensor([2.0])), requires_grad=False) if x.is_cuda: ln2 = ln2.cuda() return torch.log(x) / ln2 ############################################################ # ROIAlign Layer ############################################################ def pyramid_roi_align(inputs, pool_size, image_shape): """Implements ROI Pooling on multiple levels of the feature pyramid. Params: - pool_size: [height, width] of the output pooled regions. Usually [7, 7] - image_shape: [height, width, channels]. Shape of input image in pixels Inputs: - boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized coordinates. - Feature maps: List of feature maps from different levels of the pyramid. Each is [batch, channels, height, width] Output: Pooled regions in the shape: [num_boxes, height, width, channels]. The width and height are those specific in the pool_shape in the layer constructor. """ # Currently only supports batchsize 1 for i in range(len(inputs)): inputs[i] = inputs[i].squeeze(0) # Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords boxes = inputs[0] # Feature Maps. List of feature maps from different level of the # feature pyramid. Each is [batch, height, width, channels] feature_maps = inputs[1:] # Assign each ROI to a level in the pyramid based on the ROI area. boxes = boxes.view(-1,4) y1, x1, y2, x2 = boxes.chunk(4, dim=1) h = y2 - y1 w = x2 - x1 # Equation 1 in the Feature Pyramid Networks paper. Account for # the fact that our coordinates are normalized here. # e.g. a 224x224 ROI (in pixels) maps to P4 image_area = Variable(torch.FloatTensor([float(image_shape[0]*image_shape[1])]), requires_grad=False) if boxes.is_cuda: image_area = image_area.cuda() roi_level = 4 + log2(torch.sqrt(h*w)/(224.0/torch.sqrt(image_area))) roi_level = roi_level.round().int() roi_level = roi_level.clamp(2,5) # Loop through levels and apply ROI pooling to each. P2 to P5. pooled = [] box_to_level = [] for i, level in enumerate(range(2, 6)): ix = roi_level==level if not ix.any(): continue ix = torch.nonzero(ix)[:,0] level_boxes = boxes[ix.data, :] # Keep track of which box is mapped to which level box_to_level.append(ix.data) # Stop gradient propogation to ROI proposals level_boxes = level_boxes.detach() # Crop and Resize # From Mask R-CNN paper: "We sample four regular locations, so # that we can evaluate either max or average pooling. In fact, # interpolating only a single value at each bin center (without # pooling) is nearly as effective." # # Here we use the simplified approach of a single value per bin, # which is how it's done in tf.crop_and_resize() # Result: [batch * num_boxes, pool_height, pool_width, channels] ind = Variable(torch.zeros(level_boxes.size()[0]),requires_grad=False).int() if level_boxes.is_cuda: ind = ind.cuda() feature_maps[i] = feature_maps[i].unsqueeze(0) #CropAndResizeFunction needs batch dimension pooled_features = CropAndResizeFunction(pool_size, pool_size, 0)(feature_maps[i], level_boxes, ind) pooled.append(pooled_features) # Pack pooled features into one tensor pooled = torch.cat(pooled, dim=0) # Pack box_to_level mapping into one array and add another # column representing the order of pooled boxes box_to_level = torch.cat(box_to_level, dim=0) # Rearrange pooled features to match the order of the original boxes _, box_to_level = torch.sort(box_to_level) pooled = pooled[box_to_level, :, :] return pooled def pyramid_roi_align_image(inputs, pool_size, image_shape,istrain=False): """Implements ROI Pooling on multiple levels of the feature pyramid. Params: - pool_size: [height, width] of the output pooled regions. Usually [7, 7] - image_shape: [height, width, channels]. Shape of input image in pixels Inputs: - boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized coordinates. - Feature maps: List of feature maps from different levels of the pyramid. Each is [batch, channels, height, width] Output: Pooled regions in the shape: [num_boxes, height, width, channels]. The width and height are those specific in the pool_shape in the layer constructor. """ # Currently only supports batchsize 1 if istrain: start = 1 else: start = 0 for i in range(start,len(inputs)): inputs[i] = inputs[i].squeeze(0) # Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords boxes = inputs[0] # Feature Maps. List of feature maps from different level of the # feature pyramid. Each is [batch, height, width, channels] feature_maps = inputs[1:] # Loop through levels and apply ROI pooling to each. P2 to P5. pooled = [] ind = Variable(torch.zeros(boxes.size()[0]),requires_grad=False).int() if boxes.is_cuda: ind = ind.cuda() feature_maps[0] = feature_maps[0].unsqueeze(0) #CropAndResizeFunction needs batch dimension pooled_features = CropAndResizeFunction(pool_size, pool_size, 0)(feature_maps[0], boxes, ind) return pooled_features class SamePad2d(nn.Module): """Mimics tensorflow's 'SAME' padding. """ def __init__(self, kernel_size, stride): super(SamePad2d, self).__init__() self.kernel_size = torch.nn.modules.utils._pair(kernel_size) self.stride = torch.nn.modules.utils._pair(stride) def forward(self, input): in_width = input.size()[2] in_height = input.size()[3] out_width = math.ceil(float(in_width) / float(self.stride[0])) out_height = math.ceil(float(in_height) / float(self.stride[1])) pad_along_width = ((out_width - 1) * self.stride[0] + self.kernel_size[0] - in_width) pad_along_height = ((out_height - 1) * self.stride[1] + self.kernel_size[1] - in_height) pad_left = math.floor(pad_along_width / 2) pad_top = math.floor(pad_along_height / 2) pad_right = pad_along_width - pad_left pad_bottom = pad_along_height - pad_top return F.pad(input, (pad_left, pad_right, pad_top, pad_bottom), 'constant', 0) def __repr__(self): return self.__class__.__name__ ############################################################ # FPN Graph ############################################################ class TopDownLayer(nn.Module): def __init__(self, in_channels, out_channels): super(TopDownLayer, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1) self.padding2 = SamePad2d(kernel_size=3, stride=1) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1) def forward(self, x, y): y = F.upsample(y, scale_factor=2) x = self.conv1(x) return self.conv2(self.padding2(x+y)) class FPN(nn.Module): def __init__(self, C1, C2, C3, C4, C5, out_channels): super(FPN, self).__init__() self.out_channels = out_channels self.C1 = C1 self.C2 = C2 self.C3 = C3 self.C4 = C4 self.C5 = C5 self.P6 = nn.MaxPool2d(kernel_size=1, stride=2) self.P5_conv1 = nn.Conv2d(2048, self.out_channels, kernel_size=1, stride=1) self.P5_conv2 = nn.Sequential( SamePad2d(kernel_size=3, stride=1), nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1), ) self.P4_conv1 = nn.Conv2d(1024, self.out_channels, kernel_size=1, stride=1) self.P4_conv2 = nn.Sequential( SamePad2d(kernel_size=3, stride=1), nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1), ) self.P3_conv1 = nn.Conv2d(512, self.out_channels, kernel_size=1, stride=1) self.P3_conv2 = nn.Sequential( SamePad2d(kernel_size=3, stride=1), nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1), ) self.P2_conv1 = nn.Conv2d(256, self.out_channels, kernel_size=1, stride=1) self.P2_conv2 = nn.Sequential( SamePad2d(kernel_size=3, stride=1), nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1), ) def forward(self, x): x = self.C1(x) x = self.C2(x) c2_out = x x = self.C3(x) c3_out = x x = self.C4(x) c4_out = x x = self.C5(x) p5_out = self.P5_conv1(x) p4_out = self.P4_conv1(c4_out) + F.upsample(p5_out, scale_factor=2) p3_out = self.P3_conv1(c3_out) + F.upsample(p4_out, scale_factor=2) p2_out = self.P2_conv1(c2_out) + F.upsample(p3_out, scale_factor=2) p5_out = self.P5_conv2(p5_out) p4_out = self.P4_conv2(p4_out) p3_out = self.P3_conv2(p3_out) p2_out = self.P2_conv2(p2_out) # P6 is used for the 5th anchor scale in RPN. Generated by # subsampling from P5 with stride of 2. p6_out = self.P6(p5_out) return [p2_out, p3_out, p4_out, p5_out, p6_out] ############################################################ # Resnet Graph ############################################################ class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride) self.bn1 = nn.BatchNorm2d(planes, eps=0.001, momentum=0.01) self.padding2 = SamePad2d(kernel_size=3, stride=1) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3) self.bn2 = nn.BatchNorm2d(planes, eps=0.001, momentum=0.01) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1) self.bn3 = nn.BatchNorm2d(planes * 4, eps=0.001, momentum=0.01) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.padding2(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, architecture, stage5=False): super(ResNet, self).__init__() assert architecture in ["resnet50", "resnet101"] self.inplanes = 64 self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3] self.block = Bottleneck self.stage5 = stage5 self.C1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3), nn.BatchNorm2d(64, eps=0.001, momentum=0.01), nn.ReLU(inplace=True), SamePad2d(kernel_size=3, stride=2), nn.MaxPool2d(kernel_size=3, stride=2), ) self.C2 = self.make_layer(self.block, 64, self.layers[0]) self.C3 = self.make_layer(self.block, 128, self.layers[1], stride=2) self.C4 = self.make_layer(self.block, 256, self.layers[2], stride=2) if self.stage5: self.C5 = self.make_layer(self.block, 512, self.layers[3], stride=2) else: self.C5 = None def forward(self, x): x = self.C1(x) x = self.C2(x) x = self.C3(x) x = self.C4(x) x = self.C5(x) return x def stages(self): return [self.C1, self.C2, self.C3, self.C4, self.C5] def make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride), nn.BatchNorm2d(planes * block.expansion, eps=0.001, momentum=0.01), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) ############################################################ # Region Proposal Network ############################################################ class RPN(nn.Module): """Builds the model of Region Proposal Network. anchors_per_location: number of anchors per pixel in the feature map anchor_stride: Controls the density of anchors. Typically 1 (anchors for every pixel in the feature map), or 2 (every other pixel). Returns: rpn_logits: [batch, H, W, 2] Anchor classifier logits (before softmax) rpn_probs: [batch, W, W, 2] Anchor classifier probabilities. rpn_bbox: [batch, H, W, (dy, dx, log(dh), log(dw))] Deltas to be applied to anchors. """ def __init__(self, anchors_per_location, anchor_stride, depth): super(RPN, self).__init__() self.anchors_per_location = anchors_per_location self.anchor_stride = anchor_stride self.depth = depth self.padding = SamePad2d(kernel_size=3, stride=self.anchor_stride) self.conv_shared = nn.Conv2d(self.depth, 512, kernel_size=3, stride=self.anchor_stride) self.relu = nn.ReLU(inplace=True) self.conv_class = nn.Conv2d(512, 2 * anchors_per_location, kernel_size=1, stride=1) self.softmax = nn.Softmax(dim=2) self.conv_bbox = nn.Conv2d(512, 4 * anchors_per_location, kernel_size=1, stride=1) def forward(self, x): # Shared convolutional base of the RPN x = self.relu(self.conv_shared(self.padding(x))) # Anchor Score. [batch, anchors per location * 2, height, width]. rpn_class_logits = self.conv_class(x) # Reshape to [batch, 2, anchors] rpn_class_logits = rpn_class_logits.permute(0,2,3,1) rpn_class_logits = rpn_class_logits.contiguous() rpn_class_logits = rpn_class_logits.view(x.size()[0], -1, 2) # Softmax on last dimension of BG/FG. rpn_probs = self.softmax(rpn_class_logits) # Bounding box refinement. [batch, H, W, anchors per location, depth] # where depth is [x, y, log(w), log(h)] rpn_bbox = self.conv_bbox(x) # Reshape to [batch, 4, anchors] rpn_bbox = rpn_bbox.permute(0,2,3,1) rpn_bbox = rpn_bbox.contiguous() rpn_bbox = rpn_bbox.view(x.size()[0], -1, 4) return [rpn_class_logits, rpn_probs, rpn_bbox] ############################################################ # Feature Pyramid Network Heads ############################################################ class Classifier(nn.Module): def __init__(self, depth, pool_size, image_shape, num_classes): super(Classifier, self).__init__() self.depth = depth self.pool_size = pool_size self.image_shape = image_shape self.num_classes = num_classes self.conv1 = nn.Conv2d(self.depth, 1024, kernel_size=self.pool_size, stride=1) self.bn1 = nn.BatchNorm2d(1024, eps=0.001, momentum=0.01) self.conv2 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1) self.bn2 = nn.BatchNorm2d(1024, eps=0.001, momentum=0.01) self.relu = nn.ReLU(inplace=True) self.linear_class = nn.Linear(1024, num_classes) self.softmax = nn.Softmax(dim=1) self.linear_bbox = nn.Linear(1024, num_classes * 4) def forward(self, x, rois): x = pyramid_roi_align([rois]+x, self.pool_size, self.image_shape) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = x.view(-1,1024) mrcnn_class_logits = self.linear_class(x) mrcnn_probs = self.softmax(mrcnn_class_logits) mrcnn_bbox = self.linear_bbox(x) mrcnn_bbox = mrcnn_bbox.view(mrcnn_bbox.size()[0], -1, 4) return [mrcnn_class_logits, mrcnn_probs, mrcnn_bbox] class Mask(nn.Module): def __init__(self, depth, pool_size, image_shape, num_classes): super(Mask, self).__init__() self.depth = depth self.pool_size = pool_size self.image_shape = image_shape self.num_classes = num_classes self.padding = SamePad2d(kernel_size=3, stride=1) self.conv1 = nn.Conv2d(self.depth, 256, kernel_size=3, stride=1) self.bn1 = nn.BatchNorm2d(256, eps=0.001) self.conv2 = nn.Conv2d(256, 256, kernel_size=3, stride=1) self.bn2 = nn.BatchNorm2d(256, eps=0.001) self.conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1) self.bn3 = nn.BatchNorm2d(256, eps=0.001) self.conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1) self.bn4 = nn.BatchNorm2d(256, eps=0.001) self.deconv = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2) self.conv5 = nn.Conv2d(256, num_classes, kernel_size=1, stride=1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) def forward(self, x, rois,cls_feature): x = pyramid_roi_align([rois] + x, self.pool_size, self.image_shape) x = torch.cat((cls_feature,x),dim=1) x = self.conv1(self.padding(x)) x = self.bn1(x) x = self.relu(x) x = self.conv2(self.padding(x)) x = self.bn2(x) x = self.relu(x) x = self.conv3(self.padding(x)) x = self.bn3(x) x = self.relu(x) x = self.conv4(self.padding(x)) x = self.bn4(x) feat = self.relu(x) x = self.deconv(feat) x = self.relu(x) x = self.conv5(x) # x = self.sigmoid(x) return x,feat class Refine(nn.Module): def __init__(self, depth, num_classes): super(Refine, self).__init__() self.depth = depth self.padding = SamePad2d(kernel_size=3, stride=1) self.conv1 = nn.Conv2d(self.depth, 256, kernel_size=3, stride=1) self.bn1 = nn.BatchNorm2d(256, eps=0.001) self.conv2 = nn.Conv2d(256, 256, kernel_size=3, stride=1) self.bn2 = nn.BatchNorm2d(256, eps=0.001) self.conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1) self.bn3 = nn.BatchNorm2d(256, eps=0.001) self.conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1) self.bn4 = nn.BatchNorm2d(256, eps=0.001) self.deconv = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2) self.conv5 = nn.Conv2d(256, num_classes, kernel_size=1, stride=1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv1(self.padding(x)) x = self.bn1(x) x = self.relu(x) x = self.conv2(self.padding(x)) x = self.bn2(x) x = self.relu(x) x = self.conv3(self.padding(x)) x = self.bn3(x) x = self.relu(x) x = self.conv4(self.padding(x)) x = self.bn4(x) x = self.relu(x) x = self.conv5(x) # x = self.sigmoid(x) return x
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29916, 29897, 13, 4706, 921, 353, 1583, 29889, 2674, 29884, 29898, 29916, 29897, 13, 4706, 921, 353, 1583, 29889, 20580, 29941, 29898, 1311, 29889, 12791, 29898, 29916, 876, 13, 4706, 921, 353, 1583, 29889, 11197, 29941, 29898, 29916, 29897, 13, 4706, 921, 353, 1583, 29889, 2674, 29884, 29898, 29916, 29897, 13, 4706, 921, 353, 1583, 29889, 20580, 29946, 29898, 1311, 29889, 12791, 29898, 29916, 876, 13, 4706, 921, 353, 1583, 29889, 11197, 29946, 29898, 29916, 29897, 13, 4706, 921, 353, 1583, 29889, 2674, 29884, 29898, 29916, 29897, 13, 4706, 921, 353, 1583, 29889, 20580, 29945, 29898, 29916, 29897, 13, 4706, 396, 921, 353, 1583, 29889, 18816, 29885, 3398, 29898, 29916, 29897, 13, 13, 4706, 736, 921, 2 ]
client/canyons-of-mars/maze.py
GamesCreatorsClub/GCC-Rover
3
2679
<reponame>GamesCreatorsClub/GCC-Rover # # Copyright 2016-2019 Games Creators Club # # MIT License # import math import pyroslib import pyroslib.logging import time from pyroslib.logging import log, LOG_LEVEL_ALWAYS, LOG_LEVEL_INFO, LOG_LEVEL_DEBUG from rover import WheelOdos, WHEEL_NAMES from rover import normaiseAngle, angleDiference from challenge_utils import Action, PID SQRT2 = math.sqrt(2) PIhalf = math.pi / 2 class MazeAttitude: UNKNOWN = 0 LEFT_WALL = 1 RIGHT_WALL = 2 FRONT_WALL = 4 BACK_WALL = 8 NO_GAP = 0 FORWARD_GAP = 1 SIDE_GAP = 2 POINTS = [0, 45, 90, 135, 180, 225, 270, 315] WALLS = [90, 270, 0, 180] L0_45 = 0 L45_90 = 45 L90_135 = 90 L135_180 = 135 L180_225 = 180 L225_270 = 225 L270_315 = 270 L315_0 = 315 LINES = [L0_45, L45_90, L90_135, L135_180, L180_225, L225_270, L270_315, L315_0] ANGLE_TOLLERANCE = 1.075 @staticmethod def normAngle(a): if a > PIhalf: a = a - math.pi elif a <= -PIhalf: a = a + math.pi return a class Line: def __init__(self, line_index, long_point_index, short_point_index, factor, adjust): self.line_index = line_index self.short_point_index = short_point_index self.long_point_index = long_point_index self.factor = factor self.adjust = adjust self.angle = None def calcAngle(self, distances): long_distance = distances[self.long_point_index] short_distance = distances[self.short_point_index] if long_distance is not None and short_distance is not None: lsqrt2 = long_distance / SQRT2 self.angle = MazeAttitude.normAngle(math.atan2(lsqrt2, lsqrt2 - short_distance) * self.factor + self.adjust) else: self.angle = None class Wall: def __init__(self, distance_sensor_angle, distance_sensor_index, wall_point_kind, left_mid_point_index, left_point_index, mid_point_index, right_point_index): self.ds_angle = distance_sensor_angle self.ds_index = distance_sensor_index self.wall_point_kind = wall_point_kind self.left_mid_point_index = left_mid_point_index self.left_point_index = left_point_index self.mid_point_index = mid_point_index self.right_point_index = right_point_index self.is_front_or_back = self.ds_angle == 0 or self.ds_angle == 180 self.selected_line = None self.angle = None self.distance = None def setAngle(self, angle, distances): self.angle = angle distance = distances[self.mid_point_index] if distance < 1: self.distance = 0 else: if self.is_front_or_back: self.distance = abs(int(math.sin(angle) * distance)) else: self.distance = abs(int(math.cos(angle) * distance)) def setAngleAndDistance(self, angle, distance): self.angle = angle self.distance = distance def tryFindingWall(self, distances, lines, points): lmline = lines[self.left_mid_point_index] lline = lines[self.left_point_index] mline = lines[self.mid_point_index] rline = lines[self.right_point_index] dlong1 = distances[lline.long_point_index] dmid = distances[mline.short_point_index] dlong2 = distances[mline.long_point_index] plong1 = points[self.left_point_index] pmid = points[self.mid_point_index] plong2 = points[self.right_point_index] if dlong1 < dlong2 and plong1 != MazeAttitude.UNKNOWN and lmline.angle * MazeAttitude.ANGLE_TOLLERANCE >= lline.angle >= lmline.angle / MazeAttitude.ANGLE_TOLLERANCE: points[self.mid_point_index] = points[lline.long_point_index] angle = MazeAttitude.normAngle(mline.angle - PIhalf) distance = distances[self.right_point_index] * abs(math.sin(mline.angle) / SQRT2) self.setAngleAndDistance(angle, distance) elif dlong1 >= dlong2 and plong2 != MazeAttitude.UNKNOWN and mline.angle * MazeAttitude.ANGLE_TOLLERANCE >= rline.angle >= mline.angle / MazeAttitude.ANGLE_TOLLERANCE: points[self.mid_point_index] = points[rline.long_point_index] angle = MazeAttitude.normAngle(mline.angle + PIhalf) distance = distances[self.left_point_index] * abs(math.sin(mline.angle) / SQRT2) self.setAngleAndDistance(angle, distance) elif lline.angle is not None and mline.angle is not None: if lline.angle * MazeAttitude.ANGLE_TOLLERANCE >= mline.angle >= lline.angle / MazeAttitude.ANGLE_TOLLERANCE: if plong1 == MazeAttitude.UNKNOWN: points[self.left_point_index] = self.wall_point_kind if pmid == MazeAttitude.UNKNOWN: points[self.mid_point_index] = self.wall_point_kind if plong2 == MazeAttitude.UNKNOWN: points[self.right_point_index] = self.wall_point_kind self.setAngle(mline.angle, distances) else: if dlong1 < dlong2 and plong1 == MazeAttitude.UNKNOWN and pmid == MazeAttitude.UNKNOWN: points[self.left_point_index] = self.wall_point_kind points[self.mid_point_index] = self.wall_point_kind self.setAngle(lline.angle, distances) elif dlong1 >= dlong2 and plong2 == MazeAttitude.UNKNOWN and pmid == MazeAttitude.UNKNOWN: points[self.mid_point_index] = self.wall_point_kind points[self.right_point_index] = self.wall_point_kind self.setAngle(mline.angle, distances) elif plong1 == MazeAttitude.UNKNOWN and pmid == MazeAttitude.UNKNOWN and plong2 != MazeAttitude.UNKNOWN: points[self.left_point_index] = self.wall_point_kind points[self.mid_point_index] = self.wall_point_kind self.setAngle(lline.angle, distances) elif plong1 != MazeAttitude.UNKNOWN and pmid == MazeAttitude.UNKNOWN and plong2 == MazeAttitude.UNKNOWN: points[self.mid_point_index] = self.wall_point_kind points[self.right_point_index] = self.wall_point_kind self.setAngle(mline.angle, distances) elif lline.angle is not None and plong1 == MazeAttitude.UNKNOWN and pmid == MazeAttitude.UNKNOWN: points[self.left_point_index] = self.wall_point_kind points[self.mid_point_index] = self.wall_point_kind self.setAngle(lline.angle, distances) elif mline.angle is not None and pmid == MazeAttitude.UNKNOWN and plong2 == MazeAttitude.UNKNOWN: points[self.mid_point_index] = self.wall_point_kind points[self.right_point_index] = self.wall_point_kind self.setAngle(mline.angle, distances) def __init__(self): self.lines = {self.L315_0: self.Line(self.L315_0, 315, 0, -1, math.pi), self.L0_45: self.Line(self.L0_45, 45, 0, 1, -math.pi), self.L45_90: self.Line(self.L45_90, 45, 90, -1, PIhalf), self.L90_135: self.Line(self.L90_135, 135, 90, 1, -PIhalf), self.L135_180: self.Line(self.L135_180, 135, 180, -1, math.pi), self.L180_225: self.Line(self.L180_225, 225, 180, 1, -math.pi), self.L225_270: self.Line(self.L225_270, 225, 270, -1, PIhalf), self.L270_315: self.Line(self.L270_315, 315, 270, 1, -PIhalf)} self.right_wall = self.Wall(90, 2, self.RIGHT_WALL, 0, 45, 90, 135) self.left_wall = self.Wall(270, 6, self.LEFT_WALL, 180, 225, 270, 315) self.front_wall = self.Wall(0, 0, self.FRONT_WALL, 270, 315, 0, 45) self.back_wall = self.Wall(180, 4, self.BACK_WALL, 90, 135, 180, 225) self.left_gap = self.NO_GAP self.right_gap = self.NO_GAP self.walls = {self.right_wall.ds_angle: self.right_wall, self.left_wall.ds_angle: self.left_wall, self.front_wall.ds_angle: self.front_wall, self.back_wall.ds_angle: self.back_wall} self.points = {0: 0, 45: 0, 90: 0, 135: 0, 180: 0, 225: 0, 270: 0, 315: 0} self.distances = {0: 0, 45: 0, 90: 0, 135: 0, 180: 0, 225: 0, 270: 0, 315: 0} def calculate(self, state): def getPointDistance(state, angle): distance = state.radar.radar[angle] status = state.radar.status[angle] if status == 0: return distance last_distance = state.radar.last_radar[angle] if abs(distance - last_distance) < 100: return distance return None def updateUndefinedWall(wall, preferable_wall, wall_adjust, second_wall): if wall.angle is None and self.distances[wall.ds_angle] is not None: if preferable_wall.angle is not None: wall.setAngleAndDistance(self.normAngle(preferable_wall.angle + wall_adjust), self.distances[wall.mid_point_index]) else: wall.setAngleAndDistance(self.normAngle(second_wall.angle - wall_adjust), self.distances[wall.mid_point_index]) self.points[wall.ds_angle] = wall.wall_point_kind self.distances = {p: getPointDistance(state, p) for p in self.POINTS} for line in self.lines: self.lines[line].calcAngle(self.distances) wls = [self.walls[w_ds_angle] for w_ds_angle in self.WALLS if self.distances[w_ds_angle] is not None] wall_processing_order = sorted(wls, key=lambda wall: self.distances[wall.ds_angle]) for wall in wall_processing_order: wall.tryFindingWall(self.distances, self.lines, self.points) updateUndefinedWall(self.front_wall, self.right_wall, -PIhalf, self.left_wall) updateUndefinedWall(self.back_wall, self.right_wall, PIhalf, self.left_wall) updateUndefinedWall(self.right_wall, self.front_wall, PIhalf, self.back_wall) updateUndefinedWall(self.left_wall, self.front_wall, -PIhalf, self.back_wall) # TODO calc gaps class MoveForwardOnOdo(Action): def __init__(self, agent, stop_action=None): super(MoveForwardOnOdo, self).__init__(agent) self.stop_action = stop_action self.required_odo = {'fl': 0, 'fr': 0, 'bl': 0, 'br': 0} def setRequiredOdo(self, distance): for wheel_name in WHEEL_NAMES: self.required_odo[wheel_name] = distance def start(self): super(MoveForwardOnOdo, self).start() state = self.rover.getRoverState() for wheel in self.required_odo: self.required_odo[wheel] = WheelOdos.normaliseOdo(state.wheel_odos[wheel] + self.required_odo[wheel]) log(LOG_LEVEL_DEBUG, "Reset odo to " + str(self.required_odo) + "; starting...") self.rover.command(pyroslib.publish, 300, 120) # pyroslib.publish("move/steer", "300 120") def end(self): super(MoveForwardOnOdo, self).end() def next(self): state = self.rover.getRoverState() do_stop = False log(LOG_LEVEL_DEBUG, "Driving to " + str(self.required_odo)) for wheel_name in WHEEL_NAMES: if state.wheel_odos[wheel_name] >= self.required_odo[wheel_name]: do_stop = True if state.radar.radar[0] < 1.0 or state.radar.radar[315] < 1.0 or state.radar.radar[45] < 1.0: do_stop = True if do_stop: return self.stop_action else: return self def execute(self): pass def getActionName(self): return "Forward ODO" class MazeAction(Action): LEFT = -1 RIGHT = 1 def __init__(self, agent): super(MazeAction, self).__init__(agent) def check_next_action_conditions(self): return self class ChicaneAction(MazeAction): def __init__(self, agent, left_or_right, distance, speed, next_action=None): super(ChicaneAction, self).__init__(agent) self.left_or_right = left_or_right self.distance = distance self.speed = speed self.next_action = next_action if self.left_or_right == MazeAction.RIGHT: self.a1 = 45 self.a2 = 90 self.a3 = 135 else: self.a1 = 315 self.a2 = 270 self.a3 = 225 self.left_corner_action = MazeTurnAroundCornerAction(self, self.LEFT, self.distance, self.speed, self) self.right_corner_action = MazeTurnAroundCornerAction(self, self.RIGHT, self.distance, self.speed, DriverForwardForTimeAction(self, 10, self.speed, None)) def start(self): super(ChicaneAction, self).start() def end(self): super(ChicaneAction, self).end() def next(self): if self.left_or_right == self.LEFT: diagonal_distance = state.radar.radar[45] else: diagonal_distance = state.radar.radar[315] if self.left_or_right == self.LEFT and diagonal_distance > 800: log(LOG_LEVEL_INFO, "Found second part of chicane, rfd={: 4d}".format(int(diagonal_distance))) self.left_or_right = self.RIGHT elif self.left_or_right == self.RIGHT and diagonal_distance > 800: log(LOG_LEVEL_INFO, "Found end ofchicane - leaging, rfd={: 4d}".format(int(diagonal_distance))) return self.next_action return self def execute(self): state = self.rover.getRoverState() front_distance = state.radar.radar[0] gain = 60 offset = 150 # Values that worked speed=150, steer=5-7, dist=4 # self.speed = 150 # 150 speed = 50 # mm/second - TODO use odo to update to correct value! speed_steer_fudge_factor = 5 # 5-7 speed_distance_fudge_factor = 4 # 4 min_angle = 1 * math.pi / 180 steer_speed = speed * speed_steer_fudge_factor distance_speed = speed * speed_distance_fudge_factor if self.left_or_right == self.RIGHT: distance = -1000000000 distance_from_wall = state.radar.radar[90] distance_error = distance_from_wall - self.distance angle = 0 if abs(distance_error) < 10: angle = 0 elif distance_error > 0 and distance_error > distance_speed: angle = math.pi / 4 if front_distance < 450: angle += math.pi * (450 - front_distance) / 1800 # divide with 10 and by 180 -> 450/10 - 45deg elif distance_error < 0 and distance_error < -distance_speed: angle = -math.pi / 4 if front_distance < 450: angle -= math.pi * (450 - front_distance) / 1800 # divide with 10 and by 180 -> 450/10 - 45deg else: try: angle = math.asin(distance_error / distance_speed) except BaseException as ex: log(LOG_LEVEL_ALWAYS, "Domain error wa={: 3d} dw={: 4d} de={: 4d} d={: 4d} s={: 3d}".format(int(0), int(distance_from_wall), int(distance_error), int(distance), int(speed))) else: distance = 1000000000 distance_from_wall = state.radar.radar[270] distance_error = distance_from_wall - self.distance angle = 0 if abs(distance_error) < 10: angle = 0 elif distance_error > 0 and distance_error > distance_speed: angle = -math.pi / 4 if front_distance < 450: angle -= math.pi * (450 - front_distance) / 1800 # divide with 10 and by 180 -> 450/10 - 45deg elif distance_error < 0 and distance_error < -distance_speed: angle = math.pi / 4 if front_distance < 450: angle += math.pi * (450 - front_distance) / 1800 # divide with 10 and by 180 -> 450/10 - 45deg else: try: angle = -math.asin(distance_error / distance_speed) except BaseException as ex: log(LOG_LEVEL_ALWAYS, "Domain error wa={: 3d} dw={: 4d} de={: 4d} d={: 4d} s={: 3d}".format(int(0), int(distance_from_wall), int(distance_error), int(distance), int(speed))) distance = int(distance) angle = int(angle * 180 / math.pi) self.rover.command(pyroslib.publish, self.speed, angle, distance) # pyroslib.publish("move/steer", str(distance) + " " + str(self.speed) + " " + str(angle)) wheel_orientations = state.wheel_odos.odos log(LOG_LEVEL_INFO, "{:16.3f}: dist_f={: 4d} wa={: 3d} dist_w={: 4d} dist_err={: 3d} la={: 3d} ld={: 3d} ra={: 3d} rd={: 3d} s_spd={: 3d} dist_spd={: 3d} dist={: 4d} angle={: 3d} heading={: 3d} odo={:7.2f}".format( float(time.time()), int(front_distance), int(0 * 180 / math.pi), int(distance_from_wall), int(distance_error), int(0 * 180 / math.pi), int(0), int(0 * 180 / math.pi), int(0), int(steer_speed), int(distance_speed), int(distance), int(angle), int(state.heading.heading), float(state.wheel_orientations.orientations['fl']) )) def getActionName(self): return "Chicane " + ("L" if self.left_or_right == self.LEFT else "R") class MazeCorridorAction(MazeAction): def __init__(self, agent, left_or_right, distance, speed, next_action=None): super(MazeCorridorAction, self).__init__(agent) self.left_or_right = left_or_right self.distance = distance self.speed = speed self.next_action = next_action if self.left_or_right == MazeAction.RIGHT: self.a1 = 45 self.a2 = 90 self.a3 = 135 else: self.a1 = 315 self.a2 = 270 self.a3 = 225 self.left_corner_action = MazeTurnAroundCornerAction(self, self.LEFT, int(self.distance * 1), self.speed, self) self.right_corner_action = MazeTurnAroundCornerAction(self, self.RIGHT, int(self.distance * 1), self.speed, self) # self.right_corner_action = MazeTurnAroundCornerAction(self.odo, self.radar, self.heading, self.RIGHT, self.distance, self.speed, DriverForwardForTimeActoun(10, self.speed, None)) self.been_in_chicane = False def start(self): super(MazeCorridorAction, self).start() self.been_in_chicane = False def end(self): super(MazeCorridorAction, self).end() def next(self): left_diagonal_distance = state.radar.radar[315] front_distance = state.radar.radar[0] if state.radar.status[0] != 0 and abs(state.radar.radar_deltas[0]) > 100: log(LOG_LEVEL_INFO, "Front distance not correct: d={:4d} s={:2d} delta={:4d}".format(front_distance, state.radar.status[0], state.radar.radar_deltas[0])) else: if state.left_front_distance_of_wall > 100 and front_distance < 550: expected_diagonal_distance = 0 if state.left_wall_angle < 0: expected_diagonal_distance = front_distance * 2 * math.cos(math.pi / 4 + state.left_wall_angle) else: expected_diagonal_distance = front_distance * math.cos(state.left_wall_angle) * SQRT2 if False and not self.been_in_chicane and front_distance > 300 and left_diagonal_distance > expected_diagonal_distance * 1.2: log(LOG_LEVEL_INFO, "Found chicane... lfd={: 4d} fd={: 4d} dd={: 4d} ed={: 4d}".format(int(state.left_front_distance_of_wall), int(front_distance), int(left_diagonal_distance), int(expected_diagonal_distance))) self.been_in_chicane = True return ChicaneAction(self, self.LEFT, self.distance, self.speed, next_action=self) else: log(LOG_LEVEL_INFO, "Found corner - turning, lfd={: 4d} fd={: 4d} dd={: 4d} ed={: 4d}".format(int(state.left_front_distance_of_wall), int(front_distance), int(left_diagonal_distance), int(expected_diagonal_distance))) return self.left_corner_action if front_distance < 550 and state.radar.radar_deltas[0] < 0: left_distances = state.radar.radar[270] + state.radar.radar[315] right_distances = state.radar.radar[90] + state.radar.radar[45] if left_distances > right_distances: log(LOG_LEVEL_INFO, "Found corner 2 - turning left, fd={: 4d} ld={: 4d} rd={: 4d}".format(int(front_distance), int(left_distances), int(right_distances))) return self.left_corner_action else: log(LOG_LEVEL_INFO, "Found corner 2 - turning left, fd={: 4d} ld={: 4d} rd={: 4d}".format(int(front_distance), int(left_distances), int(right_distances))) return self.right_corner_action if state.right_front_distance_of_wall > 100 and state.left_front_distance_of_wall > 100 and front_distance < 700: log(LOG_LEVEL_INFO, "Found final corner - turning to finish, rfd={: 4d} fd={: 4d} ".format(int(state.right_front_distance_of_wall), int(front_distance))) return self.right_corner_action return self def execute(self): state = self.rover.getRoverState() left_diagonal_distance = state.radar.radar[315] front_distance = state.radar.radar[0] gain = 60 offset = 150 # Values that worked speed=150, steer=5-7, dist=4 # self.speed = 150 # 150 speed = 50 # mm/second - TODO use odo to update to correct value! speed_steer_fudge_factor = 5 # 5-7 speed_distance_fudge_factor = 4 # 4 min_angle = 1 * math.pi / 180 steer_speed = speed * speed_steer_fudge_factor distance_speed = speed * speed_distance_fudge_factor if self.left_or_right == self.RIGHT: wall_angle = state.right_wall_angle if -min_angle < state.right_wall_angle < min_angle: distance = 1000000000 else: distance = steer_speed / state.right_wall_angle if 0 <= distance < 150: distance = 150 elif -150 < distance < 0: distance = -150 distance = -distance distance_from_wall = state.right_wall_distance distance_error = distance_from_wall - self.distance angle = 0 if abs(distance_error) < 10: angle = 0 elif distance_error > 0 and distance_error > distance_speed: angle = math.pi / 4 elif distance_error < 0 and distance_error < -distance_speed: angle = -math.pi / 4 else: try: angle = math.asin(distance_error / distance_speed) except BaseException as ex: log(LOG_LEVEL_ALWAYS, "Domain error wa={: 3d} dw={: 4d} de={: 4d} d={: 4d} s={: 3d}".format(int(wall_angle), int(distance_from_wall), int(distance_error), int(distance), int(speed))) else: wall_angle = state.left_wall_angle if -min_angle < state.left_wall_angle < min_angle: distance = 1000000000 else: distance = steer_speed / state.left_wall_angle if 0 <= distance < 150: distance = 150 elif -150 < distance < 0: distance = -150 distance_from_wall = state.left_wall_distance distance_error = distance_from_wall - self.distance angle = 0 if abs(distance_error) < 10: angle = 0 elif distance_error > 0 and distance_error > distance_speed: angle = -math.pi / 4 elif distance_error < 0 and distance_error < -distance_speed: angle = math.pi / 4 else: try: angle = -math.asin(distance_error / distance_speed) except BaseException as ex: log(LOG_LEVEL_ALWAYS, "Domain error wa={: 3d} dw={: 4d} de={: 4d} d={: 4d} s={: 3d}".format(int(wall_angle), int(distance_from_wall), int(distance_error), int(distance), int(speed))) distance = int(distance) angle = int(angle * 180 / math.pi) self.rover.command(pyroslib.publish, self.speed, angle, distance) # pyroslib.publish("move/steer", str(distance) + " " + str(self.speed) + " " + str(angle)) wheel_orientations = state.wheel_odos.odos # log(LOG_LEVEL_INFO, "{:16.3f}: dist_f={: 4d} wa={: 3d} dist_w={: 4d} dist_err={: 3d} la={: 3d} ld={: 3d} ra={: 3d} rd={: 3d} s_spd={: 3d} dist_spd={: 3d} dist={: 4d} angle={: 3d} heading={: 3d} odo={:7.2f}".format( float(time.time()), int(front_distance), int(wall_angle * 180 / math.pi), int(distance_from_wall), int(distance_error), int(state.left_wall_angle * 180 / math.pi), int(state.left_front_distance_of_wall), int(state.right_wall_angle * 180 / math.pi), int(state.right_front_distance_of_wall), int(steer_speed), int(distance_speed), int(distance), int(angle), int(state.heading.heading), float(state.wheel_orientations.orientations['fl']) )) def getActionName(self): return "Corridor" class MazeTurnAroundCornerAction(MazeAction): def __init__(self, agent, left_or_right, distance, speed, next_action=None): super(MazeTurnAroundCornerAction, self).__init__(agent) self.left_or_right = left_or_right self.distance = distance * (1 if left_or_right == self.RIGHT else -1) self.speed = speed self.start_heading = 0 self.last_heading = 0 self.requested_heading = 0 self.pid = None self.next_action = next_action self.error = 0 def start(self): super(MazeTurnAroundCornerAction, self).start() state = self.rover.getRoverState() self.start_heading = state.heading.heading self.requested_heading = normaiseAngle(self.start_heading + 80 * -(1 if self.left_or_right == self.RIGHT else -1)) self.pid = PID(1, 0.0, 0.05, 1, 0, diff_method=angleDiference) self.pid.process(self.requested_heading, self.start_heading) log(LOG_LEVEL_INFO, "Starting to turn around corner at distance {:04d} at speed {:04d}, start heading {:07.3f}, requested heading {:07.3f}".format(self.distance, self.speed, self.start_heading, self.requested_heading)) self.rover.command(pyroslib.publish, self.speed, 0, self.distance) # pyroslib.publish("move/steer", str(self.distance) + " " + str(self.speed)) def end(self): super(MazeTurnAroundCornerAction, self).end() def next(self): heading = state.heading.heading self.error = self.pid.process(self.requested_heading, heading) if self.left_or_right == self.LEFT and self.error > 0: return self elif self.left_or_right == self.RIGHT and self.error < 0: return self else: if self.next_action is not None: log(LOG_LEVEL_INFO, "Finished turning around the corner - invoking next action " + self.next_action.getActionName()) else: log(LOG_LEVEL_INFO, "Finishing turning - no next action spectified.") return self.next_action def execute(self): state = self.rover.getRoverState() heading = state.heading.heading last_heading = self.last_heading self.last_heading = heading log(LOG_LEVEL_INFO, "Turning speed={:04d} h={:07.3f} lh={:07.3f} dh={:07.3f} rh={:07.3f} e={:07.3f}" .format(self.speed, heading, last_heading, angleDiference(heading, last_heading), self.requested_heading, self.error)) def getActionName(self): return "Turn-Around-Corner" class DriverForwardForTimeAction(Action): def __init__(self, agent, time, speed, next_action): super(DriverForwardForTimeAction, self).__init__(agent) self.time = time self.speed = speed self.next_action = next_action def start(self): self.rover.command(pyroslib.publish, self.speed, 0) # pyroslib.publish("move/drive", "0 " + str(self.speed)) log(LOG_LEVEL_INFO, "Going forward for " + str(self.time) + " ticks.") def end(self): pass def next(self): if self.time > 0: self.time -= 1 log(LOG_LEVEL_INFO, "Going forward for " + str(self.time) + " ticks.") return self return self.next_action if __name__ == "__main__": from rover import Radar, RoverState radar_values = {0: 10, 45: SQRT2 * 10, 90: 10, 135: SQRT2 * 10, 180: 10, 225: SQRT2 * 10, 270: 10, 315: SQRT2 * 10} radar_last_values = {0: 10, 45: SQRT2 * 10, 90: 10, 135: SQRT2 * 10, 180: 10, 225: SQRT2 * 10, 270: 10, 315: SQRT2 * 10} radar_status = {0: 0, 45: 0, 90: 0, 135: 0, 180: 0, 225: 0, 270: 0, 315: 0} attitude = MazeAttitude() radar = Radar(0, radar_values, radar_status, Radar(0, radar_last_values, radar_status)) state = RoverState(None, None, None, radar, None, None) def printWallLines(a): if attitude.lines[a].angle is None: print("{:3d} -> point too far - not calculated".format(a)) else: angle = int(attitude.lines[a].angle * 180 / math.pi) point = attitude.points[a] if point is None: print("{:3d} -> line at {:3d} angle".format(a, angle)) else: if point == MazeAttitude.LEFT_WALL: wall = "left wall" elif point == MazeAttitude.RIGHT_WALL: wall = "right wall" elif point == MazeAttitude.FRONT_WALL: wall = "front wall" elif point == MazeAttitude.BACK_WALL: wall = "back wall" else: wall = "no wall" print("{:3d} -> line at {:3d} angle belogs to {:s}".format(a, angle, wall)) def printWall(w): if w.angle is None: print("Wall {:3d} -> is too far - not calculated".format(w.ds_angle)) else: if w.distance is None: print("Wall {:3d} -> has angle {:3d} but is too far - distance not calculated".format(w.ds_angle, int(w.angle * 180 / math.pi))) else: print("Wall {:3d} -> has angle {:3d} and is at {:3d}".format(w.ds_angle, int(w.angle * 180 / math.pi), w.distance)) def printWalls(): for p in attitude.points: printWallLines(p) for w in attitude.walls: printWall(w) print("----------------------------------------------------------") # attitude.calculate(state) # printWalls() # # state.radar.radar[0] = 5 # state.radar.radar[45] = SQRT2 * 5 * 0.9 # state.radar.radar[315] = SQRT2 * 17 # state.radar.radar[270] = SQRT2 * 13 # state.radar.radar[225] = SQRT2 * 12 # attitude.calculate(state) # printWalls() state.radar.radar[180] = 50 state.radar.radar[315] = 30 attitude.calculate(state) printWalls()
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150-Challenges/Challenges 105 - 110/Challenge 107.py
DGrifferty/Python
0
111120
<gh_stars>0 # 107 # Open the Names.txt file and display the data in Python. with open('Names.txt', 'r') as f: names = f.readlines() for index, name in enumerate(names): # To remove /n names[index] = name[:-1] print(names)
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com/obs/models/lifecycle.py
xunmeibuyue/IntelligentPeephole
0
80230
<filename>com/obs/models/lifecycle.py #!/usr/bin/python # -*- coding:utf-8 -*- from com.obs.models.base_model import BaseModel,BASESTRING class Lifecycle(BaseModel): ''' classdocs ''' allowedAttr = {'rule': list} def __init__(self, rule=None): ''' Constructor ''' self.rule = rule
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python/Clique_Percolation_Method.py
JasonPap/graphproject
1
45444
__author__ = 'Jason' import itertools from Graph import * from GraphStatistics import * def get_cliques(graph, k, proc_pool): persons = [] for idx in graph.dictionary: persons.append(idx) cliques = [] combinations = itertools.combinations(persons, k) arg = [] for comb in combinations: arg.append((comb, graph)) results = proc_pool.map(test, arg) for result in results: if result[0] == True: cliques.append(result[1]) return cliques def test(arg): combination = arg[0] graph = arg[1] local_comb = list(combination) for idx in combination: others = list(local_comb) others.remove(idx) links = graph.dictionary[idx].links linked_ids = [] for edge in links: linked_ids.append(edge.edge_end) for neighbour in others: if neighbour not in linked_ids: return False, None local_comb.remove(idx) return True, combination def percolation_method(graph, k, proc_pool): l_cliques = get_cliques(graph, k, proc_pool) g_cliques = Graph() for i in range(len(l_cliques)): node = Node(i, [], []) g_cliques.insert_node(node) arg = [] for i in range(len(l_cliques)): arg.append((i, l_cliques, k)) results = proc_pool.map(get_hyper_edges, arg) for result in results: for hyper_edge in result: edge = Edge(hyper_edge[1], []) g_cliques.insert_edge(hyper_edge[0], edge) cc = get_connected_components(g_cliques) communities = [] counter = 1 for component in cc: persons = [] for clique_id in component: for p_id in l_cliques[clique_id]: if p_id not in persons: persons.append(p_id) persons.sort() communities.append((counter, persons)) counter += 1 return communities def get_hyper_edges(arg): clique_id = arg[0] l_cliques = arg[1] k = arg[2] result = [] main_clique = l_cliques[clique_id] for i in range(len(l_cliques)): counter = 0 if i != clique_id: for val in main_clique: if val in l_cliques[i]: counter += 1 if counter >= k - 1: result.append((clique_id, i)) return result
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Posts/migrations/0001_initial.py
rosekairu/Neighbourly
0
26316
# Generated by Django 3.0.8 on 2020-07-19 17:34 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import phone_field.models class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Neighborhood', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('location', models.CharField(max_length=100)), ('hood_name', models.CharField(max_length=100)), ('population', models.PositiveIntegerField(null=True)), ('user', models.ForeignKey(default=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('prof_pic', models.ImageField(blank=True, upload_to='images/')), ('bio', models.CharField(max_length=250, null=True)), ('email', models.EmailField(max_length=100)), ('date_joined', models.DateTimeField(auto_now_add=True)), ('neighborhood', models.ForeignKey(default=2, null=True, on_delete=django.db.models.deletion.CASCADE, to='Posts.Neighborhood')), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Police', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('station_name', models.CharField(max_length=100)), ('email', models.EmailField(max_length=100)), ('tel', phone_field.models.PhoneField(blank=True, help_text='Police Station Phone Number', max_length=31)), ('neighborhood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Posts.Neighborhood')), ], ), migrations.CreateModel( name='Notice', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('notice_title', models.CharField(max_length=100, null=True)), ('notice_pic', models.ImageField(blank=True, null=True, upload_to='images/')), ('notice_details', models.CharField(max_length=250, null=True)), ('post_date', models.DateField(auto_now_add=True, null=True)), ('neighborhood', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Posts.Neighborhood')), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='HealthCenter', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('hospital_name', models.CharField(max_length=100)), ('email', models.EmailField(max_length=100)), ('tel', phone_field.models.PhoneField(blank=True, help_text='Hospital Phone Number', max_length=31)), ('neighborhood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Posts.Neighborhood')), ], ), migrations.CreateModel( name='Business', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('bsns_name', models.CharField(max_length=250)), ('bsns_email', models.EmailField(max_length=100)), ('neighborhood', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Posts.Neighborhood')), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]
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theano_models/dA/ReLU_vs_GB_test_script.py
lzamparo/SdA_reduce
0
1615376
# Experiment script to compare ReLU dAs versus Gaussian-Bernoulli dAs # Train each with varying amounts of noise, import numpy import theano import theano.tensor as T from theano.tensor.shared_randomstreams import RandomStreams from AutoEncoder import AutoEncoder from AutoEncoder import ReluAutoEncoder from AutoEncoder import GaussianAutoEncoder from extract_datasets import extract_labeled_chunkrange from load_shared import load_data_labeled from tables import * import os import sys import time from datetime import datetime from optparse import OptionParser def drive_dA(learning_rate=0.00001, training_epochs=100, batch_size=32): """ This dA is driven with foci data :type learning_rate: float :param learning_rate: learning rate used for training the DeNosing AutoEncoder :type training_epochs: int :param training_epochs: number of epochs used for training :type batch_size: int :param batch_size: size of each minibatch """ parser = OptionParser() parser.add_option("-d", "--dir", dest="dir", help="test output directory") parser.add_option("-c", "--corruption", dest="corruption", help="use this amount of corruption for the denoising AE", type="float") parser.add_option("-i", "--inputfile", dest="inputfile", help="the hdf5 filename as an absolute pathname") (options, args) = parser.parse_args() #current_dir = os.getcwd() #os.chdir(options.dir) today = datetime.today() day = str(today.date()) hour = str(today.time()) corruptn = str(options.corruption) #output_filename = "gb_da." + "corruption_" + corruptn + "_" + day + "." + hour #output_file = open(output_filename,'w') #print >> output_file, "Run on " + str(datetime.now()) #os.chdir(current_dir) data_set_file = openFile(str(options.inputfile), mode = 'r') datafiles, labels = extract_labeled_chunkrange(data_set_file, num_files = 10) datasets = load_data_labeled(datafiles, labels) train_set_x, train_set_y = datasets[0] data_set_file.close() # compute number of minibatches for training, validation and testing n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size n_cols = train_set_x.get_value(borrow=True).shape[1] # allocate symbolic variables for the data index = T.lscalar() # index to a [mini]batch x = T.matrix('x') # the data matrix ################################## # Build the GaussianBernoulli dA # ################################## #rng = numpy.random.RandomState(2345) #theano_rng = RandomStreams(rng.randint(2 ** 30)) #da = GaussianAutoEncoder(numpy_rng=rng, theano_rng=theano_rng, input=x, #n_visible=n_cols, n_hidden=800) #cost, updates = da.get_cost_updates(corruption_level=options.corruption, #learning_rate=learning_rate) #train_da = theano.function([index], cost, updates=updates, #givens={x: train_set_x[index * batch_size: #(index + 1) * batch_size]}) #start_time = time.clock() ############# ## TRAINING # ############# ## go through training epochs #for epoch in xrange(training_epochs): ## go through training set #c = [] #for batch_index in xrange(n_train_batches): #c.append(train_da(batch_index)) #print >> output_file, 'Training epoch %d, cost ' % epoch, numpy.mean(c) #end_time = time.clock() #training_time = (end_time - start_time) #print >> output_file, ('The ' + str(options.corruption) + ' corruption code for file ' + #os.path.split(__file__)[1] + #' ran for %.2fm' % ((training_time) / 60.)) #output_file.close() ########## # Build the ReLU dA ########## output_filename = "relu_da." + "corruption_" + corruptn + "_" + day + "." + hour current_dir = os.getcwd() os.chdir(options.dir) output_file = open(output_filename,'w') os.chdir(current_dir) print >> output_file, "Run on " + str(datetime.now()) rng = numpy.random.RandomState(6789) theano_rng = RandomStreams(rng.randint(2 ** 30)) da = ReluAutoEncoder(numpy_rng=rng, theano_rng=theano_rng, input=x, n_visible=n_cols, n_hidden=800) cost, updates = da.get_cost_updates_safe(corruption_level=float(options.corruption), learning_rate=learning_rate,mb_size=batch_size) train_da = theano.function([index], cost, updates=updates, givens={x: train_set_x[index * batch_size: (index + 1) * batch_size]}) start_time = time.clock() ########## # Train the model ########## # go through training epochs for epoch in xrange(training_epochs): # go through trainng set c = [] for batch_index in xrange(n_train_batches): c.append(train_da(batch_index)) print >> output_file, 'Training epoch %d, cost ' % epoch, numpy.mean(c) end_time = time.clock() training_time = (end_time - start_time) print >> output_file, ('The ' + str(options.corruption) + ' corruption code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((training_time) / 60.)) output_file.close() if __name__ == '__main__': drive_dA()
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venv/lib/python3.8/site-packages/tomlkit/_compat.py
GiulianaPola/select_repeats
2
83209
<filename>venv/lib/python3.8/site-packages/tomlkit/_compat.py /home/runner/.cache/pip/pool/83/9a/18/620dc8665d157a95e8bd8529f1f10f3b4c237eccbe2e6418e048857edc
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misc-code/deprecated/sample_json_handler.py
anudeep22003/simple-langauge-pipeline
1
111499
<filename>misc-code/deprecated/sample_json_handler.py import json with open(file='src/knowledge_graph/sample.json') as f: converted = json.load(fp = f) for item in converted: print(type(item)) print(item.keys()) print(item['id']) pass
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shop/migrations/0001_initial.py
chidibede/Django-Ecommerce-Site
0
32461
# Generated by Django 2.2 on 2019-06-08 10:32 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Adult_Products', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.ImageField(upload_to='product_images')), ('name', models.CharField(max_length=200)), ('category', models.CharField(max_length=300)), ('slug', models.SlugField()), ('sales_price', models.IntegerField()), ('original_price', models.IntegerField()), ], ), migrations.CreateModel( name='Essential_Oils', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.ImageField(upload_to='product_images')), ('name', models.CharField(max_length=200)), ('category', models.CharField(max_length=300)), ('slug', models.SlugField()), ('sales_price', models.IntegerField()), ('original_price', models.IntegerField()), ], ), migrations.CreateModel( name='Smart_Watches', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.ImageField(upload_to='product_images')), ('name', models.CharField(max_length=200)), ('category', models.CharField(max_length=300)), ('slug', models.SlugField()), ('sales_price', models.IntegerField()), ('original_price', models.IntegerField()), ], ), ]
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ex44e.py
liggettla/python
0
20534
<reponame>liggettla/python #Rather than rely on inplicit inheritance from other classes, classes can just #call the functions from a class; termed composition class Other(object): def override(self): print "OTHER override()" def implicit(self): print "OTHER implicit()" def altered(self): print "OTHER altered()" class Child(object): def __init__(self): #Here the Child uses Other() to get its work done #Rather than just using implicit inheritance self.other = Other() def implicit(self): self.other.implicit() def override(self): print "CHILD override()" def altered(self): print "CHILD, BEFORE OTHER altered()" self.other.altered() print "CHILD, AFTER OTHER altered()" son = Child() son.implicit() son.override() son.altered()
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scrape.py
jayrav13/njit-course-scraper
3
90322
<reponame>jayrav13/njit-course-scraper<gh_stars>1-10 # imports from lxml import html import requests import mysql.connector import time # database setup my_host = "localhost" my_username = "root" my_password = "" my_database = "njit" # establish MySQL connection cnx = mysql.connector.connect(user=my_username, password=<PASSWORD>,host=my_host,database=my_database) cursor = cnx.cursor() # there are no inputs, output is an array of all departments for all available terms def refresh_departments(): # all possible semesters, variable to store all departments ALL_SEMESTERS = ["fall", "spring", "summer", "winter"] all_departments = [] # loop through all terms and extract data pertinent to each one for term in ALL_SEMESTERS: # request page, convert to tree, extract all anchor values page = requests.get("http://www.njit.edu/registrar/schedules/courses/" + term + "/index_list.html") tree = html.document_fromstring(page.text) #for each anchor value, grab pertinent data! for val in tree.xpath('//a'): all_departments.append([val.text, val.attrib['href'], term, val.attrib['href'][:4]]) # return all departments in a nested array return all_departments # takes the output of refresh_departments() as an input (array with 4 values in it), and inserts all courses in that # department into the localhost database def retrieve_courses(dept, url, term, year): # send a get request to the department page per input and convert to tree page = requests.get("http://www.njit.edu/registrar/schedules/courses/" + term + "/" + url) tree = html.document_fromstring(page.text) # check where the bold tag is - that's where we'll start for val in tree.xpath('//b'): # get ready to loop through all sections in the course in the table directly after the bold tag # starting with the bold tag for sec in val.xpath('following::table')[0].xpath('tr'): # but before looping through table data, grab the course number and name via the bold tag arr = [val.xpath('a')[0].text, val.xpath('u')[0].text.strip()] # loop through details of each section and append it to the array for det in sec.xpath('td'): arr.append(det.text_content().strip().encode('ascii','replace')) # add department info to array arr.append(dept) arr.append(url) arr.append(term) arr.append(year) # the first row is table headers, so there will be no table data to extract (uses th vs td), so ignore if there # are only 6 elements (course name and department data) if(len(arr) == 17): # when ready, create query string, insert and commit! query_string = "INSERT INTO courses (number, name, sect, cr, days, times, room, status, max, now, instructor, comments, credits, dept, url, term, year) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" cursor.execute(query_string, arr) cnx.commit() # main # returns a nested array of all departments departments = refresh_departments() # start a timer start = time.time() # run as an SQL transaction so that there is no down time in requests to the database cursor.execute("START TRANSACTION") cnx.commit() cursor.execute("TRUNCATE courses") cnx.commit() # for each department, retrieve courses for dept in departments: if dept[2] == "fall": retrieve_courses(dept[0], dept[1], dept[2], dept[3]) print dept[0] + ", " + dept[1] + ", " + dept[2] + ", " + dept[3] # complete commit cursor.execute("COMMIT") cnx.commit() # output run time print "Run time: " + str(time.time() - start) cursor.execute("SELECT * FROM requests") result = cursor.fetchall() print result #close connection cnx.close()
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metadata-ingestion/src/datahub/metadata/schema_classes.py
eileenjc/datahub
0
132296
<filename>metadata-ingestion/src/datahub/metadata/schema_classes.py # flake8: noqa # This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py # Do not modify manually! # fmt: off import json import os.path import decimal import datetime import six from avrogen.dict_wrapper import DictWrapper from avrogen import avrojson from avro.schema import RecordSchema, SchemaFromJSONData as make_avsc_object from avro import schema as avro_schema from typing import List, Dict, Union, Optional def __read_file(file_name): with open(file_name, "r") as f: return f.read() def __get_names_and_schema(json_str): names = avro_schema.Names() schema = make_avsc_object(json.loads(json_str), names) return names, schema SCHEMA_JSON_STR = __read_file(os.path.join(os.path.dirname(__file__), "schema.avsc")) __NAMES, SCHEMA = __get_names_and_schema(SCHEMA_JSON_STR) __SCHEMAS: Dict[str, RecordSchema] = {} def get_schema_type(fullname): return __SCHEMAS.get(fullname) __SCHEMAS = dict((n.fullname.lstrip("."), n) for n in six.itervalues(__NAMES.names)) class KafkaAuditHeaderClass(DictWrapper): """This header records information about the context of an event as it is emitted into kafka and is intended to be used by the kafka audit application. For more information see go/kafkaauditheader""" RECORD_SCHEMA = get_schema_type("com.linkedin.events.KafkaAuditHeader") def __init__(self, time: int, server: str, appName: str, messageId: bytes, instance: Union[None, str]=None, auditVersion: Union[None, int]=None, fabricUrn: Union[None, str]=None, clusterConnectionString: Union[None, str]=None, ): super().__init__() self.time = time self.server = server self.instance = instance self.appName = appName self.messageId = messageId self.auditVersion = auditVersion self.fabricUrn = fabricUrn self.clusterConnectionString = clusterConnectionString @classmethod def construct_with_defaults(cls) -> "KafkaAuditHeaderClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.time = int() self.server = str() self.instance = self.RECORD_SCHEMA.field_map["instance"].default self.appName = str() self.messageId = bytes() self.auditVersion = self.RECORD_SCHEMA.field_map["auditVersion"].default self.fabricUrn = self.RECORD_SCHEMA.field_map["fabricUrn"].default self.clusterConnectionString = self.RECORD_SCHEMA.field_map["clusterConnectionString"].default @property def time(self) -> int: """Getter: The time at which the event was emitted into kafka.""" return self._inner_dict.get('time') # type: ignore @time.setter def time(self, value: int) -> None: """Setter: The time at which the event was emitted into kafka.""" self._inner_dict['time'] = value @property def server(self) -> str: """Getter: The fully qualified name of the host from which the event is being emitted.""" return self._inner_dict.get('server') # type: ignore @server.setter def server(self, value: str) -> None: """Setter: The fully qualified name of the host from which the event is being emitted.""" self._inner_dict['server'] = value @property def instance(self) -> Union[None, str]: """Getter: The instance on the server from which the event is being emitted. e.g. i001""" return self._inner_dict.get('instance') # type: ignore @instance.setter def instance(self, value: Union[None, str]) -> None: """Setter: The instance on the server from which the event is being emitted. e.g. i001""" self._inner_dict['instance'] = value @property def appName(self) -> str: """Getter: The name of the application from which the event is being emitted. see go/appname""" return self._inner_dict.get('appName') # type: ignore @appName.setter def appName(self, value: str) -> None: """Setter: The name of the application from which the event is being emitted. see go/appname""" self._inner_dict['appName'] = value @property def messageId(self) -> bytes: """Getter: A unique identifier for the message""" return self._inner_dict.get('messageId') # type: ignore @messageId.setter def messageId(self, value: bytes) -> None: """Setter: A unique identifier for the message""" self._inner_dict['messageId'] = value @property def auditVersion(self) -> Union[None, int]: """Getter: The version that is being used for auditing. In version 0, the audit trail buckets events into 10 minute audit windows based on the EventHeader timestamp. In version 1, the audit trail buckets events as follows: if the schema has an outer KafkaAuditHeader, use the outer audit header timestamp for bucketing; else if the EventHeader has an inner KafkaAuditHeader use that inner audit header's timestamp for bucketing""" return self._inner_dict.get('auditVersion') # type: ignore @auditVersion.setter def auditVersion(self, value: Union[None, int]) -> None: """Setter: The version that is being used for auditing. In version 0, the audit trail buckets events into 10 minute audit windows based on the EventHeader timestamp. In version 1, the audit trail buckets events as follows: if the schema has an outer KafkaAuditHeader, use the outer audit header timestamp for bucketing; else if the EventHeader has an inner KafkaAuditHeader use that inner audit header's timestamp for bucketing""" self._inner_dict['auditVersion'] = value @property def fabricUrn(self) -> Union[None, str]: """Getter: The fabricUrn of the host from which the event is being emitted. Fabric Urn in the format of urn:li:fabric:{fabric_name}. See go/fabric.""" return self._inner_dict.get('fabricUrn') # type: ignore @fabricUrn.setter def fabricUrn(self, value: Union[None, str]) -> None: """Setter: The fabricUrn of the host from which the event is being emitted. Fabric Urn in the format of urn:li:fabric:{fabric_name}. See go/fabric.""" self._inner_dict['fabricUrn'] = value @property def clusterConnectionString(self) -> Union[None, str]: """Getter: This is a String that the client uses to establish some kind of connection with the Kafka cluster. The exact format of it depends on specific versions of clients and brokers. This information could potentially identify the fabric and cluster with which the client is producing to or consuming from.""" return self._inner_dict.get('clusterConnectionString') # type: ignore @clusterConnectionString.setter def clusterConnectionString(self, value: Union[None, str]) -> None: """Setter: This is a String that the client uses to establish some kind of connection with the Kafka cluster. The exact format of it depends on specific versions of clients and brokers. This information could potentially identify the fabric and cluster with which the client is producing to or consuming from.""" self._inner_dict['clusterConnectionString'] = value class ChartInfoClass(DictWrapper): """Information about a chart""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.chart.ChartInfo") def __init__(self, title: str, description: str, lastModified: "ChangeAuditStampsClass", customProperties: Optional[Dict[str, str]]=None, externalUrl: Union[None, str]=None, chartUrl: Union[None, str]=None, inputs: Union[None, List[str]]=None, type: Union[None, Union[str, "ChartTypeClass"]]=None, access: Union[None, Union[str, "AccessLevelClass"]]=None, lastRefreshed: Union[None, int]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.externalUrl = externalUrl self.title = title self.description = description self.lastModified = lastModified self.chartUrl = chartUrl self.inputs = inputs self.type = type self.access = access self.lastRefreshed = lastRefreshed @classmethod def construct_with_defaults(cls) -> "ChartInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.externalUrl = self.RECORD_SCHEMA.field_map["externalUrl"].default self.title = str() self.description = str() self.lastModified = ChangeAuditStampsClass.construct_with_defaults() self.chartUrl = self.RECORD_SCHEMA.field_map["chartUrl"].default self.inputs = self.RECORD_SCHEMA.field_map["inputs"].default self.type = self.RECORD_SCHEMA.field_map["type"].default self.access = self.RECORD_SCHEMA.field_map["access"].default self.lastRefreshed = self.RECORD_SCHEMA.field_map["lastRefreshed"].default @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def externalUrl(self) -> Union[None, str]: """Getter: URL where the reference exist""" return self._inner_dict.get('externalUrl') # type: ignore @externalUrl.setter def externalUrl(self, value: Union[None, str]) -> None: """Setter: URL where the reference exist""" self._inner_dict['externalUrl'] = value @property def title(self) -> str: """Getter: Title of the chart""" return self._inner_dict.get('title') # type: ignore @title.setter def title(self, value: str) -> None: """Setter: Title of the chart""" self._inner_dict['title'] = value @property def description(self) -> str: """Getter: Detailed description about the chart""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: str) -> None: """Setter: Detailed description about the chart""" self._inner_dict['description'] = value @property def lastModified(self) -> "ChangeAuditStampsClass": """Getter: Captures information about who created/last modified/deleted this chart and when""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "ChangeAuditStampsClass") -> None: """Setter: Captures information about who created/last modified/deleted this chart and when""" self._inner_dict['lastModified'] = value @property def chartUrl(self) -> Union[None, str]: """Getter: URL for the chart. This could be used as an external link on DataHub to allow users access/view the chart""" return self._inner_dict.get('chartUrl') # type: ignore @chartUrl.setter def chartUrl(self, value: Union[None, str]) -> None: """Setter: URL for the chart. This could be used as an external link on DataHub to allow users access/view the chart""" self._inner_dict['chartUrl'] = value @property def inputs(self) -> Union[None, List[str]]: """Getter: Data sources for the chart""" return self._inner_dict.get('inputs') # type: ignore @inputs.setter def inputs(self, value: Union[None, List[str]]) -> None: """Setter: Data sources for the chart""" self._inner_dict['inputs'] = value @property def type(self) -> Union[None, Union[str, "ChartTypeClass"]]: """Getter: Type of the chart""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[None, Union[str, "ChartTypeClass"]]) -> None: """Setter: Type of the chart""" self._inner_dict['type'] = value @property def access(self) -> Union[None, Union[str, "AccessLevelClass"]]: """Getter: Access level for the chart""" return self._inner_dict.get('access') # type: ignore @access.setter def access(self, value: Union[None, Union[str, "AccessLevelClass"]]) -> None: """Setter: Access level for the chart""" self._inner_dict['access'] = value @property def lastRefreshed(self) -> Union[None, int]: """Getter: The time when this chart last refreshed""" return self._inner_dict.get('lastRefreshed') # type: ignore @lastRefreshed.setter def lastRefreshed(self, value: Union[None, int]) -> None: """Setter: The time when this chart last refreshed""" self._inner_dict['lastRefreshed'] = value class ChartQueryClass(DictWrapper): """Information for chart query which is used for getting data of the chart""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.chart.ChartQuery") def __init__(self, rawQuery: str, type: Union[str, "ChartQueryTypeClass"], ): super().__init__() self.rawQuery = rawQuery self.type = type @classmethod def construct_with_defaults(cls) -> "ChartQueryClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.rawQuery = str() self.type = ChartQueryTypeClass.LOOKML @property def rawQuery(self) -> str: """Getter: Raw query to build a chart from input datasets""" return self._inner_dict.get('rawQuery') # type: ignore @rawQuery.setter def rawQuery(self, value: str) -> None: """Setter: Raw query to build a chart from input datasets""" self._inner_dict['rawQuery'] = value @property def type(self) -> Union[str, "ChartQueryTypeClass"]: """Getter: Chart query type""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[str, "ChartQueryTypeClass"]) -> None: """Setter: Chart query type""" self._inner_dict['type'] = value class ChartQueryTypeClass(object): # No docs available. """LookML queries""" LOOKML = "LOOKML" """SQL type queries""" SQL = "SQL" class ChartTypeClass(object): """The various types of charts""" """Chart showing a Bar chart""" BAR = "BAR" """Chart showing a Pie chart""" PIE = "PIE" """Chart showing a Scatter plot""" SCATTER = "SCATTER" """Chart showing a table""" TABLE = "TABLE" """Chart showing Markdown formatted text""" TEXT = "TEXT" LINE = "LINE" AREA = "AREA" HISTOGRAM = "HISTOGRAM" BOX_PLOT = "BOX_PLOT" class EditableChartPropertiesClass(DictWrapper): """Stores editable changes made to properties. This separates changes made from ingestion pipelines and edits in the UI to avoid accidental overwrites of user-provided data by ingestion pipelines""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.chart.EditableChartProperties") def __init__(self, created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, description: Union[None, str]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.description = description @classmethod def construct_with_defaults(cls) -> "EditableChartPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.description = self.RECORD_SCHEMA.field_map["description"].default @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def description(self) -> Union[None, str]: """Getter: Edited documentation of the chart """ return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Edited documentation of the chart """ self._inner_dict['description'] = value class AccessLevelClass(object): """The various access levels""" """Publicly available access level""" PUBLIC = "PUBLIC" """Private availability to certain set of users""" PRIVATE = "PRIVATE" class AuditStampClass(DictWrapper): """Data captured on a resource/association/sub-resource level giving insight into when that resource/association/sub-resource moved into a particular lifecycle stage, and who acted to move it into that specific lifecycle stage.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.AuditStamp") def __init__(self, time: int, actor: str, impersonator: Union[None, str]=None, ): super().__init__() self.time = time self.actor = actor self.impersonator = impersonator @classmethod def construct_with_defaults(cls) -> "AuditStampClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.time = int() self.actor = str() self.impersonator = self.RECORD_SCHEMA.field_map["impersonator"].default @property def time(self) -> int: """Getter: When did the resource/association/sub-resource move into the specific lifecycle stage represented by this AuditEvent.""" return self._inner_dict.get('time') # type: ignore @time.setter def time(self, value: int) -> None: """Setter: When did the resource/association/sub-resource move into the specific lifecycle stage represented by this AuditEvent.""" self._inner_dict['time'] = value @property def actor(self) -> str: """Getter: The entity (e.g. a member URN) which will be credited for moving the resource/association/sub-resource into the specific lifecycle stage. It is also the one used to authorize the change.""" return self._inner_dict.get('actor') # type: ignore @actor.setter def actor(self, value: str) -> None: """Setter: The entity (e.g. a member URN) which will be credited for moving the resource/association/sub-resource into the specific lifecycle stage. It is also the one used to authorize the change.""" self._inner_dict['actor'] = value @property def impersonator(self) -> Union[None, str]: """Getter: The entity (e.g. a service URN) which performs the change on behalf of the Actor and must be authorized to act as the Actor.""" return self._inner_dict.get('impersonator') # type: ignore @impersonator.setter def impersonator(self, value: Union[None, str]) -> None: """Setter: The entity (e.g. a service URN) which performs the change on behalf of the Actor and must be authorized to act as the Actor.""" self._inner_dict['impersonator'] = value class BrowsePathsClass(DictWrapper): """Shared aspect containing Browse Paths to be indexed for an entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.BrowsePaths") def __init__(self, paths: List[str], ): super().__init__() self.paths = paths @classmethod def construct_with_defaults(cls) -> "BrowsePathsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.paths = list() @property def paths(self) -> List[str]: """Getter: A list of valid browse paths for the entity. Browse paths are expected to be backslash-separated strings. For example: 'prod/snowflake/datasetName'""" return self._inner_dict.get('paths') # type: ignore @paths.setter def paths(self, value: List[str]) -> None: """Setter: A list of valid browse paths for the entity. Browse paths are expected to be backslash-separated strings. For example: 'prod/snowflake/datasetName'""" self._inner_dict['paths'] = value class ChangeAuditStampsClass(DictWrapper): """Data captured on a resource/association/sub-resource level giving insight into when that resource/association/sub-resource moved into various lifecycle stages, and who acted to move it into those lifecycle stages. The recommended best practice is to include this record in your record schema, and annotate its fields as @readOnly in your resource. See https://github.com/linkedin/rest.li/wiki/Validation-in-Rest.li#restli-validation-annotations""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.ChangeAuditStamps") def __init__(self, created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted @classmethod def construct_with_defaults(cls) -> "ChangeAuditStampsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value class CostClass(DictWrapper): # No docs available. RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.Cost") def __init__(self, costType: Union[str, "CostTypeClass"], cost: "CostCostClass", ): super().__init__() self.costType = costType self.cost = cost @classmethod def construct_with_defaults(cls) -> "CostClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.costType = CostTypeClass.ORG_COST_TYPE self.cost = CostCostClass.construct_with_defaults() @property def costType(self) -> Union[str, "CostTypeClass"]: # No docs available. return self._inner_dict.get('costType') # type: ignore @costType.setter def costType(self, value: Union[str, "CostTypeClass"]) -> None: # No docs available. self._inner_dict['costType'] = value @property def cost(self) -> "CostCostClass": # No docs available. return self._inner_dict.get('cost') # type: ignore @cost.setter def cost(self, value: "CostCostClass") -> None: # No docs available. self._inner_dict['cost'] = value class CostCostClass(DictWrapper): # No docs available. RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.CostCost") def __init__(self, fieldDiscriminator: Union[str, "CostCostDiscriminatorClass"], costId: Union[None, float]=None, costCode: Union[None, str]=None, ): super().__init__() self.costId = costId self.costCode = costCode self.fieldDiscriminator = fieldDiscriminator @classmethod def construct_with_defaults(cls) -> "CostCostClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.costId = self.RECORD_SCHEMA.field_map["costId"].default self.costCode = self.RECORD_SCHEMA.field_map["costCode"].default self.fieldDiscriminator = CostCostDiscriminatorClass.costId @property def costId(self) -> Union[None, float]: # No docs available. return self._inner_dict.get('costId') # type: ignore @costId.setter def costId(self, value: Union[None, float]) -> None: # No docs available. self._inner_dict['costId'] = value @property def costCode(self) -> Union[None, str]: # No docs available. return self._inner_dict.get('costCode') # type: ignore @costCode.setter def costCode(self, value: Union[None, str]) -> None: # No docs available. self._inner_dict['costCode'] = value @property def fieldDiscriminator(self) -> Union[str, "CostCostDiscriminatorClass"]: """Getter: Contains the name of the field that has its value set.""" return self._inner_dict.get('fieldDiscriminator') # type: ignore @fieldDiscriminator.setter def fieldDiscriminator(self, value: Union[str, "CostCostDiscriminatorClass"]) -> None: """Setter: Contains the name of the field that has its value set.""" self._inner_dict['fieldDiscriminator'] = value class CostCostDiscriminatorClass(object): # No docs available. costId = "costId" costCode = "costCode" class CostTypeClass(object): """Type of Cost Code""" """Org Cost Type to which the Cost of this entity should be attributed to""" ORG_COST_TYPE = "ORG_COST_TYPE" class DeprecationClass(DictWrapper): """Deprecation status of an entity""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.Deprecation") def __init__(self, deprecated: bool, note: str, actor: str, decommissionTime: Union[None, int]=None, ): super().__init__() self.deprecated = deprecated self.decommissionTime = decommissionTime self.note = note self.actor = actor @classmethod def construct_with_defaults(cls) -> "DeprecationClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.deprecated = bool() self.decommissionTime = self.RECORD_SCHEMA.field_map["decommissionTime"].default self.note = str() self.actor = str() @property def deprecated(self) -> bool: """Getter: Whether the entity is deprecated.""" return self._inner_dict.get('deprecated') # type: ignore @deprecated.setter def deprecated(self, value: bool) -> None: """Setter: Whether the entity is deprecated.""" self._inner_dict['deprecated'] = value @property def decommissionTime(self) -> Union[None, int]: """Getter: The time user plan to decommission this entity.""" return self._inner_dict.get('decommissionTime') # type: ignore @decommissionTime.setter def decommissionTime(self, value: Union[None, int]) -> None: """Setter: The time user plan to decommission this entity.""" self._inner_dict['decommissionTime'] = value @property def note(self) -> str: """Getter: Additional information about the entity deprecation plan, such as the wiki, doc, RB.""" return self._inner_dict.get('note') # type: ignore @note.setter def note(self, value: str) -> None: """Setter: Additional information about the entity deprecation plan, such as the wiki, doc, RB.""" self._inner_dict['note'] = value @property def actor(self) -> str: """Getter: The corpuser URN which will be credited for modifying this deprecation content.""" return self._inner_dict.get('actor') # type: ignore @actor.setter def actor(self, value: str) -> None: """Setter: The corpuser URN which will be credited for modifying this deprecation content.""" self._inner_dict['actor'] = value class FabricTypeClass(object): """Fabric group type""" """Designates development fabrics""" DEV = "DEV" """Designates early-integration (staging) fabrics""" EI = "EI" """Designates production fabrics""" PROD = "PROD" """Designates corporation fabrics""" CORP = "CORP" class GlobalTagsClass(DictWrapper): """Tag aspect used for applying tags to an entity""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.GlobalTags") def __init__(self, tags: List["TagAssociationClass"], ): super().__init__() self.tags = tags @classmethod def construct_with_defaults(cls) -> "GlobalTagsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.tags = list() @property def tags(self) -> List["TagAssociationClass"]: """Getter: Tags associated with a given entity""" return self._inner_dict.get('tags') # type: ignore @tags.setter def tags(self, value: List["TagAssociationClass"]) -> None: """Setter: Tags associated with a given entity""" self._inner_dict['tags'] = value class GlossaryTermAssociationClass(DictWrapper): """Properties of an applied glossary term.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.GlossaryTermAssociation") def __init__(self, urn: str, ): super().__init__() self.urn = urn @classmethod def construct_with_defaults(cls) -> "GlossaryTermAssociationClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() @property def urn(self) -> str: """Getter: Urn of the applied glossary term""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: Urn of the applied glossary term""" self._inner_dict['urn'] = value class GlossaryTermsClass(DictWrapper): """Related business terms information""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.GlossaryTerms") def __init__(self, terms: List["GlossaryTermAssociationClass"], auditStamp: "AuditStampClass", ): super().__init__() self.terms = terms self.auditStamp = auditStamp @classmethod def construct_with_defaults(cls) -> "GlossaryTermsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.terms = list() self.auditStamp = AuditStampClass.construct_with_defaults() @property def terms(self) -> List["GlossaryTermAssociationClass"]: """Getter: The related business terms""" return self._inner_dict.get('terms') # type: ignore @terms.setter def terms(self, value: List["GlossaryTermAssociationClass"]) -> None: """Setter: The related business terms""" self._inner_dict['terms'] = value @property def auditStamp(self) -> "AuditStampClass": """Getter: Audit stamp containing who reported the related business term""" return self._inner_dict.get('auditStamp') # type: ignore @auditStamp.setter def auditStamp(self, value: "AuditStampClass") -> None: """Setter: Audit stamp containing who reported the related business term""" self._inner_dict['auditStamp'] = value class InstitutionalMemoryClass(DictWrapper): """Institutional memory of an entity. This is a way to link to relevant documentation and provide description of the documentation. Institutional or tribal knowledge is very important for users to leverage the entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.InstitutionalMemory") def __init__(self, elements: List["InstitutionalMemoryMetadataClass"], ): super().__init__() self.elements = elements @classmethod def construct_with_defaults(cls) -> "InstitutionalMemoryClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.elements = list() @property def elements(self) -> List["InstitutionalMemoryMetadataClass"]: """Getter: List of records that represent institutional memory of an entity. Each record consists of a link, description, creator and timestamps associated with that record.""" return self._inner_dict.get('elements') # type: ignore @elements.setter def elements(self, value: List["InstitutionalMemoryMetadataClass"]) -> None: """Setter: List of records that represent institutional memory of an entity. Each record consists of a link, description, creator and timestamps associated with that record.""" self._inner_dict['elements'] = value class InstitutionalMemoryMetadataClass(DictWrapper): """Metadata corresponding to a record of institutional memory.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.InstitutionalMemoryMetadata") def __init__(self, url: str, description: str, createStamp: "AuditStampClass", ): super().__init__() self.url = url self.description = description self.createStamp = createStamp @classmethod def construct_with_defaults(cls) -> "InstitutionalMemoryMetadataClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.url = str() self.description = str() self.createStamp = AuditStampClass.construct_with_defaults() @property def url(self) -> str: """Getter: Link to an engineering design document or a wiki page.""" return self._inner_dict.get('url') # type: ignore @url.setter def url(self, value: str) -> None: """Setter: Link to an engineering design document or a wiki page.""" self._inner_dict['url'] = value @property def description(self) -> str: """Getter: Description of the link.""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: str) -> None: """Setter: Description of the link.""" self._inner_dict['description'] = value @property def createStamp(self) -> "AuditStampClass": """Getter: Audit stamp associated with creation of this record""" return self._inner_dict.get('createStamp') # type: ignore @createStamp.setter def createStamp(self, value: "AuditStampClass") -> None: """Setter: Audit stamp associated with creation of this record""" self._inner_dict['createStamp'] = value class MLFeatureDataTypeClass(object): """MLFeature Data Type""" """Useless data is unique, discrete data with no potential relationship with the outcome variable. A useless feature has high cardinality. An example would be bank account numbers that were generated randomly.""" USELESS = "USELESS" """Nominal data is made of discrete values with no numerical relationship between the different categories — mean and median are meaningless. Animal species is one example. For example, pig is not higher than bird and lower than fish.""" NOMINAL = "NOMINAL" """Ordinal data are discrete integers that can be ranked or sorted. For example, the distance between first and second may not be the same as the distance between second and third.""" ORDINAL = "ORDINAL" """Binary data is discrete data that can be in only one of two categories — either yes or no, 1 or 0, off or on, etc""" BINARY = "BINARY" """Count data is discrete whole number data — no negative numbers here. Count data often has many small values, such as zero and one.""" COUNT = "COUNT" """Time data is a cyclical, repeating continuous form of data. The relevant time features can be any period— daily, weekly, monthly, annual, etc.""" TIME = "TIME" """Interval data has equal spaces between the numbers and does not represent a temporal pattern. Examples include percentages, temperatures, and income.""" INTERVAL = "INTERVAL" """Image Data""" IMAGE = "IMAGE" """Video Data""" VIDEO = "VIDEO" """Audio Data""" AUDIO = "AUDIO" """Text Data""" TEXT = "TEXT" """Mapping Data Type ex: dict, map""" MAP = "MAP" """Sequence Data Type ex: list, tuple, range""" SEQUENCE = "SEQUENCE" """Set Data Type ex: set, frozenset""" SET = "SET" """Continuous data are made of uncountable values, often the result of a measurement such as height, weight, age etc.""" CONTINUOUS = "CONTINUOUS" """Bytes data are binary-encoded values that can represent complex objects.""" BYTE = "BYTE" """Unknown data are data that we don't know the type for.""" UNKNOWN = "UNKNOWN" class OwnerClass(DictWrapper): """Ownership information""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.Owner") def __init__(self, owner: str, type: Union[str, "OwnershipTypeClass"], source: Union[None, "OwnershipSourceClass"]=None, ): super().__init__() self.owner = owner self.type = type self.source = source @classmethod def construct_with_defaults(cls) -> "OwnerClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.owner = str() self.type = OwnershipTypeClass.DEVELOPER self.source = self.RECORD_SCHEMA.field_map["source"].default @property def owner(self) -> str: """Getter: Owner URN, e.g. urn:li:corpuser:ldap, urn:li:corpGroup:group_name, and urn:li:multiProduct:mp_name (Caveat: only corpuser is currently supported in the frontend.)""" return self._inner_dict.get('owner') # type: ignore @owner.setter def owner(self, value: str) -> None: """Setter: Owner URN, e.g. urn:li:corpuser:ldap, urn:li:corpGroup:group_name, and urn:li:multiProduct:mp_name (Caveat: only corpuser is currently supported in the frontend.)""" self._inner_dict['owner'] = value @property def type(self) -> Union[str, "OwnershipTypeClass"]: """Getter: The type of the ownership""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[str, "OwnershipTypeClass"]) -> None: """Setter: The type of the ownership""" self._inner_dict['type'] = value @property def source(self) -> Union[None, "OwnershipSourceClass"]: """Getter: Source information for the ownership""" return self._inner_dict.get('source') # type: ignore @source.setter def source(self, value: Union[None, "OwnershipSourceClass"]) -> None: """Setter: Source information for the ownership""" self._inner_dict['source'] = value class OwnershipClass(DictWrapper): """Ownership information of an entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.Ownership") def __init__(self, owners: List["OwnerClass"], lastModified: Optional["AuditStampClass"]=None, ): super().__init__() self.owners = owners if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified @classmethod def construct_with_defaults(cls) -> "OwnershipClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.owners = list() self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) @property def owners(self) -> List["OwnerClass"]: """Getter: List of owners of the entity.""" return self._inner_dict.get('owners') # type: ignore @owners.setter def owners(self, value: List["OwnerClass"]) -> None: """Setter: List of owners of the entity.""" self._inner_dict['owners'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: Audit stamp containing who last modified the record and when. A value of 0 in the time field indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: Audit stamp containing who last modified the record and when. A value of 0 in the time field indicates missing data.""" self._inner_dict['lastModified'] = value class OwnershipSourceClass(DictWrapper): """Source/provider of the ownership information""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.OwnershipSource") def __init__(self, type: Union[str, "OwnershipSourceTypeClass"], url: Union[None, str]=None, ): super().__init__() self.type = type self.url = url @classmethod def construct_with_defaults(cls) -> "OwnershipSourceClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.type = OwnershipSourceTypeClass.AUDIT self.url = self.RECORD_SCHEMA.field_map["url"].default @property def type(self) -> Union[str, "OwnershipSourceTypeClass"]: """Getter: The type of the source""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[str, "OwnershipSourceTypeClass"]) -> None: """Setter: The type of the source""" self._inner_dict['type'] = value @property def url(self) -> Union[None, str]: """Getter: A reference URL for the source""" return self._inner_dict.get('url') # type: ignore @url.setter def url(self, value: Union[None, str]) -> None: """Setter: A reference URL for the source""" self._inner_dict['url'] = value class OwnershipSourceTypeClass(object): # No docs available. """Auditing system or audit logs""" AUDIT = "AUDIT" """Database, e.g. GRANTS table""" DATABASE = "DATABASE" """File system, e.g. file/directory owner""" FILE_SYSTEM = "FILE_SYSTEM" """Issue tracking system, e.g. Jira""" ISSUE_TRACKING_SYSTEM = "ISSUE_TRACKING_SYSTEM" """Manually provided by a user""" MANUAL = "MANUAL" """Other ownership-like service, e.g. Nuage, ACL service etc""" SERVICE = "SERVICE" """SCM system, e.g. GIT, SVN""" SOURCE_CONTROL = "SOURCE_CONTROL" """Other sources""" OTHER = "OTHER" class OwnershipTypeClass(object): """Owner category or owner role""" """A person or group that is in charge of developing the code""" DEVELOPER = "DEVELOPER" """A person or group that is owning the data""" DATAOWNER = "DATAOWNER" """A person or a group that overseas the operation, e.g. a DBA or SRE.""" DELEGATE = "DELEGATE" """A person, group, or service that produces/generates the data""" PRODUCER = "PRODUCER" """A person, group, or service that consumes the data""" CONSUMER = "CONSUMER" """A person or a group that has direct business interest""" STAKEHOLDER = "STAKEHOLDER" class StatusClass(DictWrapper): """The status metadata of an entity, e.g. dataset, metric, feature, etc.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.Status") def __init__(self, removed: Optional[bool]=None, ): super().__init__() if removed is None: # default: False self.removed = self.RECORD_SCHEMA.field_map["removed"].default else: self.removed = removed @classmethod def construct_with_defaults(cls) -> "StatusClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.removed = self.RECORD_SCHEMA.field_map["removed"].default @property def removed(self) -> bool: """Getter: whether the entity is removed or not""" return self._inner_dict.get('removed') # type: ignore @removed.setter def removed(self, value: bool) -> None: """Setter: whether the entity is removed or not""" self._inner_dict['removed'] = value class TagAssociationClass(DictWrapper): """Properties of an applied tag. For now, just an Urn. In the future we can extend this with other properties, e.g. propagation parameters.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.TagAssociation") def __init__(self, tag: str, ): super().__init__() self.tag = tag @classmethod def construct_with_defaults(cls) -> "TagAssociationClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.tag = str() @property def tag(self) -> str: """Getter: Urn of the applied tag""" return self._inner_dict.get('tag') # type: ignore @tag.setter def tag(self, value: str) -> None: """Setter: Urn of the applied tag""" self._inner_dict['tag'] = value class VersionTagClass(DictWrapper): """A resource-defined string representing the resource state for the purpose of concurrency control""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.VersionTag") def __init__(self, versionTag: Union[None, str]=None, ): super().__init__() self.versionTag = versionTag @classmethod def construct_with_defaults(cls) -> "VersionTagClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.versionTag = self.RECORD_SCHEMA.field_map["versionTag"].default @property def versionTag(self) -> Union[None, str]: # No docs available. return self._inner_dict.get('versionTag') # type: ignore @versionTag.setter def versionTag(self, value: Union[None, str]) -> None: # No docs available. self._inner_dict['versionTag'] = value class WindowDurationClass(object): """Enum to define the length of a bucket when doing aggregations""" YEAR = "YEAR" MONTH = "MONTH" WEEK = "WEEK" DAY = "DAY" HOUR = "HOUR" class TransformationTypeClass(object): """Type of the transformation involved in generating destination fields from source fields.""" """Field transformation expressed as unknown black box function.""" BLACKBOX = "BLACKBOX" """Field transformation expressed as Identity function.""" IDENTITY = "IDENTITY" class UDFTransformerClass(DictWrapper): """Field transformation expressed in UDF""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.common.fieldtransformer.UDFTransformer") def __init__(self, udf: str, ): super().__init__() self.udf = udf @classmethod def construct_with_defaults(cls) -> "UDFTransformerClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.udf = str() @property def udf(self) -> str: """Getter: A UDF mentioning how the source fields got transformed to destination field. This is the FQCN(Fully Qualified Class Name) of the udf.""" return self._inner_dict.get('udf') # type: ignore @udf.setter def udf(self, value: str) -> None: """Setter: A UDF mentioning how the source fields got transformed to destination field. This is the FQCN(Fully Qualified Class Name) of the udf.""" self._inner_dict['udf'] = value class DashboardInfoClass(DictWrapper): """Information about a dashboard""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dashboard.DashboardInfo") def __init__(self, title: str, description: str, lastModified: "ChangeAuditStampsClass", customProperties: Optional[Dict[str, str]]=None, externalUrl: Union[None, str]=None, charts: Optional[List[str]]=None, dashboardUrl: Union[None, str]=None, access: Union[None, Union[str, "AccessLevelClass"]]=None, lastRefreshed: Union[None, int]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.externalUrl = externalUrl self.title = title self.description = description if charts is None: # default: [] self.charts = list() else: self.charts = charts self.lastModified = lastModified self.dashboardUrl = dashboardUrl self.access = access self.lastRefreshed = lastRefreshed @classmethod def construct_with_defaults(cls) -> "DashboardInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.externalUrl = self.RECORD_SCHEMA.field_map["externalUrl"].default self.title = str() self.description = str() self.charts = list() self.lastModified = ChangeAuditStampsClass.construct_with_defaults() self.dashboardUrl = self.RECORD_SCHEMA.field_map["dashboardUrl"].default self.access = self.RECORD_SCHEMA.field_map["access"].default self.lastRefreshed = self.RECORD_SCHEMA.field_map["lastRefreshed"].default @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def externalUrl(self) -> Union[None, str]: """Getter: URL where the reference exist""" return self._inner_dict.get('externalUrl') # type: ignore @externalUrl.setter def externalUrl(self, value: Union[None, str]) -> None: """Setter: URL where the reference exist""" self._inner_dict['externalUrl'] = value @property def title(self) -> str: """Getter: Title of the dashboard""" return self._inner_dict.get('title') # type: ignore @title.setter def title(self, value: str) -> None: """Setter: Title of the dashboard""" self._inner_dict['title'] = value @property def description(self) -> str: """Getter: Detailed description about the dashboard""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: str) -> None: """Setter: Detailed description about the dashboard""" self._inner_dict['description'] = value @property def charts(self) -> List[str]: """Getter: Charts in a dashboard""" return self._inner_dict.get('charts') # type: ignore @charts.setter def charts(self, value: List[str]) -> None: """Setter: Charts in a dashboard""" self._inner_dict['charts'] = value @property def lastModified(self) -> "ChangeAuditStampsClass": """Getter: Captures information about who created/last modified/deleted this dashboard and when""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "ChangeAuditStampsClass") -> None: """Setter: Captures information about who created/last modified/deleted this dashboard and when""" self._inner_dict['lastModified'] = value @property def dashboardUrl(self) -> Union[None, str]: """Getter: URL for the dashboard. This could be used as an external link on DataHub to allow users access/view the dashboard""" return self._inner_dict.get('dashboardUrl') # type: ignore @dashboardUrl.setter def dashboardUrl(self, value: Union[None, str]) -> None: """Setter: URL for the dashboard. This could be used as an external link on DataHub to allow users access/view the dashboard""" self._inner_dict['dashboardUrl'] = value @property def access(self) -> Union[None, Union[str, "AccessLevelClass"]]: """Getter: Access level for the dashboard""" return self._inner_dict.get('access') # type: ignore @access.setter def access(self, value: Union[None, Union[str, "AccessLevelClass"]]) -> None: """Setter: Access level for the dashboard""" self._inner_dict['access'] = value @property def lastRefreshed(self) -> Union[None, int]: """Getter: The time when this dashboard last refreshed""" return self._inner_dict.get('lastRefreshed') # type: ignore @lastRefreshed.setter def lastRefreshed(self, value: Union[None, int]) -> None: """Setter: The time when this dashboard last refreshed""" self._inner_dict['lastRefreshed'] = value class EditableDashboardPropertiesClass(DictWrapper): """Stores editable changes made to properties. This separates changes made from ingestion pipelines and edits in the UI to avoid accidental overwrites of user-provided data by ingestion pipelines""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dashboard.EditableDashboardProperties") def __init__(self, created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, description: Union[None, str]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.description = description @classmethod def construct_with_defaults(cls) -> "EditableDashboardPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.description = self.RECORD_SCHEMA.field_map["description"].default @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def description(self) -> Union[None, str]: """Getter: Edited documentation of the dashboard""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Edited documentation of the dashboard""" self._inner_dict['description'] = value class DataFlowInfoClass(DictWrapper): """Information about a Data processing flow""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.datajob.DataFlowInfo") def __init__(self, name: str, customProperties: Optional[Dict[str, str]]=None, externalUrl: Union[None, str]=None, description: Union[None, str]=None, project: Union[None, str]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.externalUrl = externalUrl self.name = name self.description = description self.project = project @classmethod def construct_with_defaults(cls) -> "DataFlowInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.externalUrl = self.RECORD_SCHEMA.field_map["externalUrl"].default self.name = str() self.description = self.RECORD_SCHEMA.field_map["description"].default self.project = self.RECORD_SCHEMA.field_map["project"].default @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def externalUrl(self) -> Union[None, str]: """Getter: URL where the reference exist""" return self._inner_dict.get('externalUrl') # type: ignore @externalUrl.setter def externalUrl(self, value: Union[None, str]) -> None: """Setter: URL where the reference exist""" self._inner_dict['externalUrl'] = value @property def name(self) -> str: """Getter: Flow name""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Flow name""" self._inner_dict['name'] = value @property def description(self) -> Union[None, str]: """Getter: Flow description""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Flow description""" self._inner_dict['description'] = value @property def project(self) -> Union[None, str]: """Getter: Optional project/namespace associated with the flow""" return self._inner_dict.get('project') # type: ignore @project.setter def project(self, value: Union[None, str]) -> None: """Setter: Optional project/namespace associated with the flow""" self._inner_dict['project'] = value class DataJobInfoClass(DictWrapper): """Information about a Data processing job""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.datajob.DataJobInfo") def __init__(self, name: str, type: Union[Union[str, "AzkabanJobTypeClass"], str], customProperties: Optional[Dict[str, str]]=None, externalUrl: Union[None, str]=None, description: Union[None, str]=None, flowUrn: Union[None, str]=None, status: Union[None, Union[str, "JobStatusClass"]]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.externalUrl = externalUrl self.name = name self.description = description self.type = type self.flowUrn = flowUrn self.status = status @classmethod def construct_with_defaults(cls) -> "DataJobInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.externalUrl = self.RECORD_SCHEMA.field_map["externalUrl"].default self.name = str() self.description = self.RECORD_SCHEMA.field_map["description"].default self.type = AzkabanJobTypeClass.COMMAND self.flowUrn = self.RECORD_SCHEMA.field_map["flowUrn"].default self.status = self.RECORD_SCHEMA.field_map["status"].default @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def externalUrl(self) -> Union[None, str]: """Getter: URL where the reference exist""" return self._inner_dict.get('externalUrl') # type: ignore @externalUrl.setter def externalUrl(self, value: Union[None, str]) -> None: """Setter: URL where the reference exist""" self._inner_dict['externalUrl'] = value @property def name(self) -> str: """Getter: Job name""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Job name""" self._inner_dict['name'] = value @property def description(self) -> Union[None, str]: """Getter: Job description""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Job description""" self._inner_dict['description'] = value @property def type(self) -> Union[Union[str, "AzkabanJobTypeClass"], str]: """Getter: Datajob type **NOTE**: AzkabanJobType is deprecated. Please use strings instead.""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[Union[str, "AzkabanJobTypeClass"], str]) -> None: """Setter: Datajob type **NOTE**: AzkabanJobType is deprecated. Please use strings instead.""" self._inner_dict['type'] = value @property def flowUrn(self) -> Union[None, str]: """Getter: DataFlow urn that this job is part of""" return self._inner_dict.get('flowUrn') # type: ignore @flowUrn.setter def flowUrn(self, value: Union[None, str]) -> None: """Setter: DataFlow urn that this job is part of""" self._inner_dict['flowUrn'] = value @property def status(self) -> Union[None, Union[str, "JobStatusClass"]]: """Getter: Status of the job""" return self._inner_dict.get('status') # type: ignore @status.setter def status(self, value: Union[None, Union[str, "JobStatusClass"]]) -> None: """Setter: Status of the job""" self._inner_dict['status'] = value class DataJobInputOutputClass(DictWrapper): """Information about the inputs and outputs of a Data processing job""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.datajob.DataJobInputOutput") def __init__(self, inputDatasets: List[str], outputDatasets: List[str], inputDatajobs: Union[None, List[str]]=None, ): super().__init__() self.inputDatasets = inputDatasets self.outputDatasets = outputDatasets self.inputDatajobs = inputDatajobs @classmethod def construct_with_defaults(cls) -> "DataJobInputOutputClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.inputDatasets = list() self.outputDatasets = list() self.inputDatajobs = self.RECORD_SCHEMA.field_map["inputDatajobs"].default @property def inputDatasets(self) -> List[str]: """Getter: Input datasets consumed by the data job during processing""" return self._inner_dict.get('inputDatasets') # type: ignore @inputDatasets.setter def inputDatasets(self, value: List[str]) -> None: """Setter: Input datasets consumed by the data job during processing""" self._inner_dict['inputDatasets'] = value @property def outputDatasets(self) -> List[str]: """Getter: Output datasets produced by the data job during processing""" return self._inner_dict.get('outputDatasets') # type: ignore @outputDatasets.setter def outputDatasets(self, value: List[str]) -> None: """Setter: Output datasets produced by the data job during processing""" self._inner_dict['outputDatasets'] = value @property def inputDatajobs(self) -> Union[None, List[str]]: """Getter: Input datajobs that this data job depends on""" return self._inner_dict.get('inputDatajobs') # type: ignore @inputDatajobs.setter def inputDatajobs(self, value: Union[None, List[str]]) -> None: """Setter: Input datajobs that this data job depends on""" self._inner_dict['inputDatajobs'] = value class EditableDataFlowPropertiesClass(DictWrapper): """Stores editable changes made to properties. This separates changes made from ingestion pipelines and edits in the UI to avoid accidental overwrites of user-provided data by ingestion pipelines""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.datajob.EditableDataFlowProperties") def __init__(self, created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, description: Union[None, str]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.description = description @classmethod def construct_with_defaults(cls) -> "EditableDataFlowPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.description = self.RECORD_SCHEMA.field_map["description"].default @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def description(self) -> Union[None, str]: """Getter: Edited documentation of the data flow""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Edited documentation of the data flow""" self._inner_dict['description'] = value class EditableDataJobPropertiesClass(DictWrapper): """Stores editable changes made to properties. This separates changes made from ingestion pipelines and edits in the UI to avoid accidental overwrites of user-provided data by ingestion pipelines""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.datajob.EditableDataJobProperties") def __init__(self, created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, description: Union[None, str]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.description = description @classmethod def construct_with_defaults(cls) -> "EditableDataJobPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.description = self.RECORD_SCHEMA.field_map["description"].default @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def description(self) -> Union[None, str]: """Getter: Edited documentation of the data job """ return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Edited documentation of the data job """ self._inner_dict['description'] = value class JobStatusClass(object): """Job statuses""" """Jobs being initialized.""" STARTING = "STARTING" """Jobs currently running.""" IN_PROGRESS = "IN_PROGRESS" """Jobs being stopped.""" STOPPING = "STOPPING" """Jobs that have stopped.""" STOPPED = "STOPPED" """Jobs with successful completion.""" COMPLETED = "COMPLETED" """Jobs that have failed.""" FAILED = "FAILED" """Jobs with unknown status (either unmappable or unavailable)""" UNKNOWN = "UNKNOWN" class AzkabanJobTypeClass(object): """The various types of support azkaban jobs""" """The command job type is one of the basic built-in types. It runs multiple UNIX commands using java processbuilder. Upon execution, Azkaban spawns off a process to run the command.""" COMMAND = "COMMAND" """Runs a java program with ability to access Hadoop cluster. https://azkaban.readthedocs.io/en/latest/jobTypes.html#java-job-type""" HADOOP_JAVA = "HADOOP_JAVA" """In large part, this is the same Command type. The difference is its ability to talk to a Hadoop cluster securely, via Hadoop tokens.""" HADOOP_SHELL = "HADOOP_SHELL" """Hive type is for running Hive jobs.""" HIVE = "HIVE" """Pig type is for running Pig jobs.""" PIG = "PIG" """SQL is for running Presto, mysql queries etc""" SQL = "SQL" """Glue type is for running AWS Glue job transforms.""" GLUE = "GLUE" class DataPlatformInfoClass(DictWrapper): """Information about a data platform""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataplatform.DataPlatformInfo") def __init__(self, name: str, type: Union[str, "PlatformTypeClass"], datasetNameDelimiter: str, displayName: Union[None, str]=None, logoUrl: Union[None, str]=None, ): super().__init__() self.name = name self.displayName = displayName self.type = type self.datasetNameDelimiter = datasetNameDelimiter self.logoUrl = logoUrl @classmethod def construct_with_defaults(cls) -> "DataPlatformInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() self.displayName = self.RECORD_SCHEMA.field_map["displayName"].default self.type = PlatformTypeClass.FILE_SYSTEM self.datasetNameDelimiter = str() self.logoUrl = self.RECORD_SCHEMA.field_map["logoUrl"].default @property def name(self) -> str: """Getter: Name of the data platform""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Name of the data platform""" self._inner_dict['name'] = value @property def displayName(self) -> Union[None, str]: """Getter: The name that will be used for displaying a platform type.""" return self._inner_dict.get('displayName') # type: ignore @displayName.setter def displayName(self, value: Union[None, str]) -> None: """Setter: The name that will be used for displaying a platform type.""" self._inner_dict['displayName'] = value @property def type(self) -> Union[str, "PlatformTypeClass"]: """Getter: Platform type this data platform describes""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[str, "PlatformTypeClass"]) -> None: """Setter: Platform type this data platform describes""" self._inner_dict['type'] = value @property def datasetNameDelimiter(self) -> str: """Getter: The delimiter in the dataset names on the data platform, e.g. '/' for HDFS and '.' for Oracle""" return self._inner_dict.get('datasetNameDelimiter') # type: ignore @datasetNameDelimiter.setter def datasetNameDelimiter(self, value: str) -> None: """Setter: The delimiter in the dataset names on the data platform, e.g. '/' for HDFS and '.' for Oracle""" self._inner_dict['datasetNameDelimiter'] = value @property def logoUrl(self) -> Union[None, str]: """Getter: The URL for a logo associated with the platform""" return self._inner_dict.get('logoUrl') # type: ignore @logoUrl.setter def logoUrl(self, value: Union[None, str]) -> None: """Setter: The URL for a logo associated with the platform""" self._inner_dict['logoUrl'] = value class PlatformTypeClass(object): """Platform types available at LinkedIn""" """Value for a file system, e.g. hdfs""" FILE_SYSTEM = "FILE_SYSTEM" """Value for a key value store, e.g. espresso, voldemort""" KEY_VALUE_STORE = "KEY_VALUE_STORE" """Value for a message broker, e.g. kafka""" MESSAGE_BROKER = "MESSAGE_BROKER" """Value for an object store, e.g. ambry""" OBJECT_STORE = "OBJECT_STORE" """Value for an OLAP datastore, e.g. pinot""" OLAP_DATASTORE = "OLAP_DATASTORE" """Value for other platforms, e.g salesforce, dovetail""" OTHERS = "OTHERS" """Value for a query engine, e.g. presto""" QUERY_ENGINE = "QUERY_ENGINE" """Value for a relational database, e.g. oracle, mysql""" RELATIONAL_DB = "RELATIONAL_DB" """Value for a search engine, e.g seas""" SEARCH_ENGINE = "SEARCH_ENGINE" class DataProcessInfoClass(DictWrapper): """The inputs and outputs of this data process""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataprocess.DataProcessInfo") def __init__(self, inputs: Union[None, List[str]]=None, outputs: Union[None, List[str]]=None, ): super().__init__() self.inputs = inputs self.outputs = outputs @classmethod def construct_with_defaults(cls) -> "DataProcessInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.inputs = self.RECORD_SCHEMA.field_map["inputs"].default self.outputs = self.RECORD_SCHEMA.field_map["outputs"].default @property def inputs(self) -> Union[None, List[str]]: """Getter: the inputs of the data process""" return self._inner_dict.get('inputs') # type: ignore @inputs.setter def inputs(self, value: Union[None, List[str]]) -> None: """Setter: the inputs of the data process""" self._inner_dict['inputs'] = value @property def outputs(self) -> Union[None, List[str]]: """Getter: the outputs of the data process""" return self._inner_dict.get('outputs') # type: ignore @outputs.setter def outputs(self, value: Union[None, List[str]]) -> None: """Setter: the outputs of the data process""" self._inner_dict['outputs'] = value class DatasetDeprecationClass(DictWrapper): """Dataset deprecation status""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.DatasetDeprecation") def __init__(self, deprecated: bool, note: str, decommissionTime: Union[None, int]=None, actor: Union[None, str]=None, ): super().__init__() self.deprecated = deprecated self.decommissionTime = decommissionTime self.note = note self.actor = actor @classmethod def construct_with_defaults(cls) -> "DatasetDeprecationClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.deprecated = bool() self.decommissionTime = self.RECORD_SCHEMA.field_map["decommissionTime"].default self.note = str() self.actor = self.RECORD_SCHEMA.field_map["actor"].default @property def deprecated(self) -> bool: """Getter: Whether the dataset is deprecated by owner.""" return self._inner_dict.get('deprecated') # type: ignore @deprecated.setter def deprecated(self, value: bool) -> None: """Setter: Whether the dataset is deprecated by owner.""" self._inner_dict['deprecated'] = value @property def decommissionTime(self) -> Union[None, int]: """Getter: The time user plan to decommission this dataset.""" return self._inner_dict.get('decommissionTime') # type: ignore @decommissionTime.setter def decommissionTime(self, value: Union[None, int]) -> None: """Setter: The time user plan to decommission this dataset.""" self._inner_dict['decommissionTime'] = value @property def note(self) -> str: """Getter: Additional information about the dataset deprecation plan, such as the wiki, doc, RB.""" return self._inner_dict.get('note') # type: ignore @note.setter def note(self, value: str) -> None: """Setter: Additional information about the dataset deprecation plan, such as the wiki, doc, RB.""" self._inner_dict['note'] = value @property def actor(self) -> Union[None, str]: """Getter: The corpuser URN which will be credited for modifying this deprecation content.""" return self._inner_dict.get('actor') # type: ignore @actor.setter def actor(self, value: Union[None, str]) -> None: """Setter: The corpuser URN which will be credited for modifying this deprecation content.""" self._inner_dict['actor'] = value class DatasetFieldMappingClass(DictWrapper): """Representation of mapping between fields in source dataset to the field in destination dataset""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.DatasetFieldMapping") def __init__(self, created: "AuditStampClass", transformation: Union[Union[str, "TransformationTypeClass"], "UDFTransformerClass"], sourceFields: List[str], destinationField: str, ): super().__init__() self.created = created self.transformation = transformation self.sourceFields = sourceFields self.destinationField = destinationField @classmethod def construct_with_defaults(cls) -> "DatasetFieldMappingClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = AuditStampClass.construct_with_defaults() self.transformation = TransformationTypeClass.BLACKBOX self.sourceFields = list() self.destinationField = str() @property def created(self) -> "AuditStampClass": """Getter: Audit stamp containing who reported the field mapping and when""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: Audit stamp containing who reported the field mapping and when""" self._inner_dict['created'] = value @property def transformation(self) -> Union[Union[str, "TransformationTypeClass"], "UDFTransformerClass"]: """Getter: Transfomration function between the fields involved""" return self._inner_dict.get('transformation') # type: ignore @transformation.setter def transformation(self, value: Union[Union[str, "TransformationTypeClass"], "UDFTransformerClass"]) -> None: """Setter: Transfomration function between the fields involved""" self._inner_dict['transformation'] = value @property def sourceFields(self) -> List[str]: """Getter: Source fields from which the fine grained lineage is derived""" return self._inner_dict.get('sourceFields') # type: ignore @sourceFields.setter def sourceFields(self, value: List[str]) -> None: """Setter: Source fields from which the fine grained lineage is derived""" self._inner_dict['sourceFields'] = value @property def destinationField(self) -> str: """Getter: Destination field which is derived from source fields""" return self._inner_dict.get('destinationField') # type: ignore @destinationField.setter def destinationField(self, value: str) -> None: """Setter: Destination field which is derived from source fields""" self._inner_dict['destinationField'] = value class DatasetLineageTypeClass(object): """The various types of supported dataset lineage""" """Direct copy without modification""" COPY = "COPY" """Transformed data with modification (format or content change)""" TRANSFORMED = "TRANSFORMED" """Represents a view defined on the sources e.g. Hive view defined on underlying hive tables or a Hive table pointing to a HDFS dataset or DALI view defined on multiple sources""" VIEW = "VIEW" class DatasetPropertiesClass(DictWrapper): """Properties associated with a Dataset""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.DatasetProperties") def __init__(self, customProperties: Optional[Dict[str, str]]=None, externalUrl: Union[None, str]=None, description: Union[None, str]=None, uri: Union[None, str]=None, tags: Optional[List[str]]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.externalUrl = externalUrl self.description = description self.uri = uri if tags is None: # default: [] self.tags = list() else: self.tags = tags @classmethod def construct_with_defaults(cls) -> "DatasetPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.externalUrl = self.RECORD_SCHEMA.field_map["externalUrl"].default self.description = self.RECORD_SCHEMA.field_map["description"].default self.uri = self.RECORD_SCHEMA.field_map["uri"].default self.tags = list() @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def externalUrl(self) -> Union[None, str]: """Getter: URL where the reference exist""" return self._inner_dict.get('externalUrl') # type: ignore @externalUrl.setter def externalUrl(self, value: Union[None, str]) -> None: """Setter: URL where the reference exist""" self._inner_dict['externalUrl'] = value @property def description(self) -> Union[None, str]: """Getter: Documentation of the dataset""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the dataset""" self._inner_dict['description'] = value @property def uri(self) -> Union[None, str]: """Getter: The abstracted URI such as hdfs:///data/tracking/PageViewEvent, file:///dir/file_name. Uri should not include any environment specific properties. Some datasets might not have a standardized uri, which makes this field optional (i.e. kafka topic).""" return self._inner_dict.get('uri') # type: ignore @uri.setter def uri(self, value: Union[None, str]) -> None: """Setter: The abstracted URI such as hdfs:///data/tracking/PageViewEvent, file:///dir/file_name. Uri should not include any environment specific properties. Some datasets might not have a standardized uri, which makes this field optional (i.e. kafka topic).""" self._inner_dict['uri'] = value @property def tags(self) -> List[str]: """Getter: [Legacy] Unstructured tags for the dataset. Structured tags can be applied via the `GlobalTags` aspect.""" return self._inner_dict.get('tags') # type: ignore @tags.setter def tags(self, value: List[str]) -> None: """Setter: [Legacy] Unstructured tags for the dataset. Structured tags can be applied via the `GlobalTags` aspect.""" self._inner_dict['tags'] = value class DatasetUpstreamLineageClass(DictWrapper): """Fine Grained upstream lineage for fields in a dataset""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.DatasetUpstreamLineage") def __init__(self, fieldMappings: List["DatasetFieldMappingClass"], ): super().__init__() self.fieldMappings = fieldMappings @classmethod def construct_with_defaults(cls) -> "DatasetUpstreamLineageClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.fieldMappings = list() @property def fieldMappings(self) -> List["DatasetFieldMappingClass"]: """Getter: Upstream to downstream field level lineage mappings""" return self._inner_dict.get('fieldMappings') # type: ignore @fieldMappings.setter def fieldMappings(self, value: List["DatasetFieldMappingClass"]) -> None: """Setter: Upstream to downstream field level lineage mappings""" self._inner_dict['fieldMappings'] = value class EditableDatasetPropertiesClass(DictWrapper): """EditableDatasetProperties stores editable changes made to dataset properties. This separates changes made from ingestion pipelines and edits in the UI to avoid accidental overwrites of user-provided data by ingestion pipelines""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.EditableDatasetProperties") def __init__(self, created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, description: Union[None, str]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.description = description @classmethod def construct_with_defaults(cls) -> "EditableDatasetPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.description = self.RECORD_SCHEMA.field_map["description"].default @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def description(self) -> Union[None, str]: """Getter: Documentation of the dataset""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the dataset""" self._inner_dict['description'] = value class UpstreamClass(DictWrapper): """Upstream lineage information about a dataset including the source reporting the lineage""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.Upstream") def __init__(self, dataset: str, type: Union[str, "DatasetLineageTypeClass"], auditStamp: Optional["AuditStampClass"]=None, ): super().__init__() if auditStamp is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.auditStamp = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["auditStamp"].default, writers_schema=self.RECORD_SCHEMA.field_map["auditStamp"].type) else: self.auditStamp = auditStamp self.dataset = dataset self.type = type @classmethod def construct_with_defaults(cls) -> "UpstreamClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.auditStamp = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["auditStamp"].default, writers_schema=self.RECORD_SCHEMA.field_map["auditStamp"].type) self.dataset = str() self.type = DatasetLineageTypeClass.COPY @property def auditStamp(self) -> "AuditStampClass": """Getter: Audit stamp containing who reported the lineage and when. WARNING: this field is deprecated and may be removed in a future release.""" return self._inner_dict.get('auditStamp') # type: ignore @auditStamp.setter def auditStamp(self, value: "AuditStampClass") -> None: """Setter: Audit stamp containing who reported the lineage and when. WARNING: this field is deprecated and may be removed in a future release.""" self._inner_dict['auditStamp'] = value @property def dataset(self) -> str: """Getter: The upstream dataset the lineage points to""" return self._inner_dict.get('dataset') # type: ignore @dataset.setter def dataset(self, value: str) -> None: """Setter: The upstream dataset the lineage points to""" self._inner_dict['dataset'] = value @property def type(self) -> Union[str, "DatasetLineageTypeClass"]: """Getter: The type of the lineage""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[str, "DatasetLineageTypeClass"]) -> None: """Setter: The type of the lineage""" self._inner_dict['type'] = value class UpstreamLineageClass(DictWrapper): """Upstream lineage of a dataset""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.dataset.UpstreamLineage") def __init__(self, upstreams: List["UpstreamClass"], ): super().__init__() self.upstreams = upstreams @classmethod def construct_with_defaults(cls) -> "UpstreamLineageClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.upstreams = list() @property def upstreams(self) -> List["UpstreamClass"]: """Getter: List of upstream dataset lineage information""" return self._inner_dict.get('upstreams') # type: ignore @upstreams.setter def upstreams(self, value: List["UpstreamClass"]) -> None: """Setter: List of upstream dataset lineage information""" self._inner_dict['upstreams'] = value class GlossaryNodeInfoClass(DictWrapper): """Properties associated with a GlossaryNode""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.glossary.GlossaryNodeInfo") def __init__(self, definition: str, parentNode: Union[None, str]=None, ): super().__init__() self.definition = definition self.parentNode = parentNode @classmethod def construct_with_defaults(cls) -> "GlossaryNodeInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.definition = str() self.parentNode = self.RECORD_SCHEMA.field_map["parentNode"].default @property def definition(self) -> str: """Getter: Definition of business node""" return self._inner_dict.get('definition') # type: ignore @definition.setter def definition(self, value: str) -> None: """Setter: Definition of business node""" self._inner_dict['definition'] = value @property def parentNode(self) -> Union[None, str]: """Getter: Parent node of the glossary term""" return self._inner_dict.get('parentNode') # type: ignore @parentNode.setter def parentNode(self, value: Union[None, str]) -> None: """Setter: Parent node of the glossary term""" self._inner_dict['parentNode'] = value class GlossaryTermInfoClass(DictWrapper): """Properties associated with a GlossaryTerm""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.glossary.GlossaryTermInfo") def __init__(self, definition: str, termSource: str, parentNode: Union[None, str]=None, sourceRef: Union[None, str]=None, sourceUrl: Union[None, str]=None, customProperties: Optional[Dict[str, str]]=None, ): super().__init__() self.definition = definition self.parentNode = parentNode self.termSource = termSource self.sourceRef = sourceRef self.sourceUrl = sourceUrl if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties @classmethod def construct_with_defaults(cls) -> "GlossaryTermInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.definition = str() self.parentNode = self.RECORD_SCHEMA.field_map["parentNode"].default self.termSource = str() self.sourceRef = self.RECORD_SCHEMA.field_map["sourceRef"].default self.sourceUrl = self.RECORD_SCHEMA.field_map["sourceUrl"].default self.customProperties = dict() @property def definition(self) -> str: """Getter: Definition of business term""" return self._inner_dict.get('definition') # type: ignore @definition.setter def definition(self, value: str) -> None: """Setter: Definition of business term""" self._inner_dict['definition'] = value @property def parentNode(self) -> Union[None, str]: """Getter: Parent node of the glossary term""" return self._inner_dict.get('parentNode') # type: ignore @parentNode.setter def parentNode(self, value: Union[None, str]) -> None: """Setter: Parent node of the glossary term""" self._inner_dict['parentNode'] = value @property def termSource(self) -> str: """Getter: Source of the Business Term (INTERNAL or EXTERNAL) with default value as INTERNAL""" return self._inner_dict.get('termSource') # type: ignore @termSource.setter def termSource(self, value: str) -> None: """Setter: Source of the Business Term (INTERNAL or EXTERNAL) with default value as INTERNAL""" self._inner_dict['termSource'] = value @property def sourceRef(self) -> Union[None, str]: """Getter: External Reference to the business-term""" return self._inner_dict.get('sourceRef') # type: ignore @sourceRef.setter def sourceRef(self, value: Union[None, str]) -> None: """Setter: External Reference to the business-term""" self._inner_dict['sourceRef'] = value @property def sourceUrl(self) -> Union[None, str]: """Getter: The abstracted URL such as https://spec.edmcouncil.org/fibo/ontology/FBC/FinancialInstruments/FinancialInstruments/CashInstrument.""" return self._inner_dict.get('sourceUrl') # type: ignore @sourceUrl.setter def sourceUrl(self, value: Union[None, str]) -> None: """Setter: The abstracted URL such as https://spec.edmcouncil.org/fibo/ontology/FBC/FinancialInstruments/FinancialInstruments/CashInstrument.""" self._inner_dict['sourceUrl'] = value @property def customProperties(self) -> Dict[str, str]: """Getter: A key-value map to capture any other non-standardized properties for the glossary term""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: A key-value map to capture any other non-standardized properties for the glossary term""" self._inner_dict['customProperties'] = value class CorpGroupInfoClass(DictWrapper): """group of corpUser, it may contains nested group""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.identity.CorpGroupInfo") def __init__(self, email: str, admins: List[str], members: List[str], groups: List[str], ): super().__init__() self.email = email self.admins = admins self.members = members self.groups = groups @classmethod def construct_with_defaults(cls) -> "CorpGroupInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.email = str() self.admins = list() self.members = list() self.groups = list() @property def email(self) -> str: """Getter: email of this group""" return self._inner_dict.get('email') # type: ignore @email.setter def email(self, value: str) -> None: """Setter: email of this group""" self._inner_dict['email'] = value @property def admins(self) -> List[str]: """Getter: owners of this group""" return self._inner_dict.get('admins') # type: ignore @admins.setter def admins(self, value: List[str]) -> None: """Setter: owners of this group""" self._inner_dict['admins'] = value @property def members(self) -> List[str]: """Getter: List of ldap urn in this group.""" return self._inner_dict.get('members') # type: ignore @members.setter def members(self, value: List[str]) -> None: """Setter: List of ldap urn in this group.""" self._inner_dict['members'] = value @property def groups(self) -> List[str]: """Getter: List of groups in this group.""" return self._inner_dict.get('groups') # type: ignore @groups.setter def groups(self, value: List[str]) -> None: """Setter: List of groups in this group.""" self._inner_dict['groups'] = value class CorpUserEditableInfoClass(DictWrapper): """Linkedin corp user information that can be edited from UI""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.identity.CorpUserEditableInfo") def __init__(self, aboutMe: Union[None, str]=None, teams: Optional[List[str]]=None, skills: Optional[List[str]]=None, pictureLink: Optional[str]=None, ): super().__init__() self.aboutMe = aboutMe if teams is None: # default: [] self.teams = list() else: self.teams = teams if skills is None: # default: [] self.skills = list() else: self.skills = skills if pictureLink is None: # default: 'https://raw.githubusercontent.com/linkedin/datahub/master/datahub-web/packages/data-portal/public/assets/images/default_avatar.png' self.pictureLink = self.RECORD_SCHEMA.field_map["pictureLink"].default else: self.pictureLink = pictureLink @classmethod def construct_with_defaults(cls) -> "CorpUserEditableInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.aboutMe = self.RECORD_SCHEMA.field_map["aboutMe"].default self.teams = list() self.skills = list() self.pictureLink = self.RECORD_SCHEMA.field_map["pictureLink"].default @property def aboutMe(self) -> Union[None, str]: """Getter: About me section of the user""" return self._inner_dict.get('aboutMe') # type: ignore @aboutMe.setter def aboutMe(self, value: Union[None, str]) -> None: """Setter: About me section of the user""" self._inner_dict['aboutMe'] = value @property def teams(self) -> List[str]: """Getter: Teams that the user belongs to e.g. Metadata""" return self._inner_dict.get('teams') # type: ignore @teams.setter def teams(self, value: List[str]) -> None: """Setter: Teams that the user belongs to e.g. Metadata""" self._inner_dict['teams'] = value @property def skills(self) -> List[str]: """Getter: Skills that the user possesses e.g. Machine Learning""" return self._inner_dict.get('skills') # type: ignore @skills.setter def skills(self, value: List[str]) -> None: """Setter: Skills that the user possesses e.g. Machine Learning""" self._inner_dict['skills'] = value @property def pictureLink(self) -> str: """Getter: A URL which points to a picture which user wants to set as a profile photo""" return self._inner_dict.get('pictureLink') # type: ignore @pictureLink.setter def pictureLink(self, value: str) -> None: """Setter: A URL which points to a picture which user wants to set as a profile photo""" self._inner_dict['pictureLink'] = value class CorpUserInfoClass(DictWrapper): """Linkedin corp user information""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.identity.CorpUserInfo") def __init__(self, active: bool, email: str, displayName: Union[None, str]=None, title: Union[None, str]=None, managerUrn: Union[None, str]=None, departmentId: Union[None, int]=None, departmentName: Union[None, str]=None, firstName: Union[None, str]=None, lastName: Union[None, str]=None, fullName: Union[None, str]=None, countryCode: Union[None, str]=None, ): super().__init__() self.active = active self.displayName = displayName self.email = email self.title = title self.managerUrn = managerUrn self.departmentId = departmentId self.departmentName = departmentName self.firstName = firstName self.lastName = lastName self.fullName = fullName self.countryCode = countryCode @classmethod def construct_with_defaults(cls) -> "CorpUserInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.active = bool() self.displayName = self.RECORD_SCHEMA.field_map["displayName"].default self.email = str() self.title = self.RECORD_SCHEMA.field_map["title"].default self.managerUrn = self.RECORD_SCHEMA.field_map["managerUrn"].default self.departmentId = self.RECORD_SCHEMA.field_map["departmentId"].default self.departmentName = self.RECORD_SCHEMA.field_map["departmentName"].default self.firstName = self.RECORD_SCHEMA.field_map["firstName"].default self.lastName = self.RECORD_SCHEMA.field_map["lastName"].default self.fullName = self.RECORD_SCHEMA.field_map["fullName"].default self.countryCode = self.RECORD_SCHEMA.field_map["countryCode"].default @property def active(self) -> bool: """Getter: Whether the corpUser is active, ref: https://iwww.corp.linkedin.com/wiki/cf/display/GTSD/Accessing+Active+Directory+via+LDAP+tools""" return self._inner_dict.get('active') # type: ignore @active.setter def active(self, value: bool) -> None: """Setter: Whether the corpUser is active, ref: https://iwww.corp.linkedin.com/wiki/cf/display/GTSD/Accessing+Active+Directory+via+LDAP+tools""" self._inner_dict['active'] = value @property def displayName(self) -> Union[None, str]: """Getter: displayName of this user , e.g. <NAME>(DataHQ)""" return self._inner_dict.get('displayName') # type: ignore @displayName.setter def displayName(self, value: Union[None, str]) -> None: """Setter: displayName of this user , e.g. <NAME>(DataHQ)""" self._inner_dict['displayName'] = value @property def email(self) -> str: """Getter: email address of this user""" return self._inner_dict.get('email') # type: ignore @email.setter def email(self, value: str) -> None: """Setter: email address of this user""" self._inner_dict['email'] = value @property def title(self) -> Union[None, str]: """Getter: title of this user""" return self._inner_dict.get('title') # type: ignore @title.setter def title(self, value: Union[None, str]) -> None: """Setter: title of this user""" self._inner_dict['title'] = value @property def managerUrn(self) -> Union[None, str]: """Getter: direct manager of this user""" return self._inner_dict.get('managerUrn') # type: ignore @managerUrn.setter def managerUrn(self, value: Union[None, str]) -> None: """Setter: direct manager of this user""" self._inner_dict['managerUrn'] = value @property def departmentId(self) -> Union[None, int]: """Getter: department id this user belong to""" return self._inner_dict.get('departmentId') # type: ignore @departmentId.setter def departmentId(self, value: Union[None, int]) -> None: """Setter: department id this user belong to""" self._inner_dict['departmentId'] = value @property def departmentName(self) -> Union[None, str]: """Getter: department name this user belong to""" return self._inner_dict.get('departmentName') # type: ignore @departmentName.setter def departmentName(self, value: Union[None, str]) -> None: """Setter: department name this user belong to""" self._inner_dict['departmentName'] = value @property def firstName(self) -> Union[None, str]: """Getter: first name of this user""" return self._inner_dict.get('firstName') # type: ignore @firstName.setter def firstName(self, value: Union[None, str]) -> None: """Setter: first name of this user""" self._inner_dict['firstName'] = value @property def lastName(self) -> Union[None, str]: """Getter: last name of this user""" return self._inner_dict.get('lastName') # type: ignore @lastName.setter def lastName(self, value: Union[None, str]) -> None: """Setter: last name of this user""" self._inner_dict['lastName'] = value @property def fullName(self) -> Union[None, str]: """Getter: Common name of this user, format is firstName + lastName (split by a whitespace)""" return self._inner_dict.get('fullName') # type: ignore @fullName.setter def fullName(self, value: Union[None, str]) -> None: """Setter: Common name of this user, format is firstName + lastName (split by a whitespace)""" self._inner_dict['fullName'] = value @property def countryCode(self) -> Union[None, str]: """Getter: two uppercase letters country code. e.g. US""" return self._inner_dict.get('countryCode') # type: ignore @countryCode.setter def countryCode(self, value: Union[None, str]) -> None: """Setter: two uppercase letters country code. e.g. US""" self._inner_dict['countryCode'] = value class ChartKeyClass(DictWrapper): """Key for a Chart""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.ChartKey") def __init__(self, dashboardTool: str, chartId: str, ): super().__init__() self.dashboardTool = dashboardTool self.chartId = chartId @classmethod def construct_with_defaults(cls) -> "ChartKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.dashboardTool = str() self.chartId = str() @property def dashboardTool(self) -> str: """Getter: The name of the dashboard tool such as looker, redash etc.""" return self._inner_dict.get('dashboardTool') # type: ignore @dashboardTool.setter def dashboardTool(self, value: str) -> None: """Setter: The name of the dashboard tool such as looker, redash etc.""" self._inner_dict['dashboardTool'] = value @property def chartId(self) -> str: """Getter: Unique id for the chart. This id should be globally unique for a dashboarding tool even when there are multiple deployments of it. As an example, chart URL could be used here for Looker such as 'looker.linkedin.com/looks/1234'""" return self._inner_dict.get('chartId') # type: ignore @chartId.setter def chartId(self, value: str) -> None: """Setter: Unique id for the chart. This id should be globally unique for a dashboarding tool even when there are multiple deployments of it. As an example, chart URL could be used here for Looker such as 'looker.linkedin.com/looks/1234'""" self._inner_dict['chartId'] = value class CorpGroupKeyClass(DictWrapper): """Key for a CorpGroup""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.CorpGroupKey") def __init__(self, name: str, ): super().__init__() self.name = name @classmethod def construct_with_defaults(cls) -> "CorpGroupKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() @property def name(self) -> str: """Getter: The name of the AD/LDAP group.""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: The name of the AD/LDAP group.""" self._inner_dict['name'] = value class CorpUserKeyClass(DictWrapper): """Key for a CorpUser""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.CorpUserKey") def __init__(self, username: str, ): super().__init__() self.username = username @classmethod def construct_with_defaults(cls) -> "CorpUserKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.username = str() @property def username(self) -> str: """Getter: The name of the AD/LDAP user.""" return self._inner_dict.get('username') # type: ignore @username.setter def username(self, value: str) -> None: """Setter: The name of the AD/LDAP user.""" self._inner_dict['username'] = value class DashboardKeyClass(DictWrapper): """Key for a Dashboard""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.DashboardKey") def __init__(self, dashboardTool: str, dashboardId: str, ): super().__init__() self.dashboardTool = dashboardTool self.dashboardId = dashboardId @classmethod def construct_with_defaults(cls) -> "DashboardKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.dashboardTool = str() self.dashboardId = str() @property def dashboardTool(self) -> str: """Getter: The name of the dashboard tool such as looker, redash etc.""" return self._inner_dict.get('dashboardTool') # type: ignore @dashboardTool.setter def dashboardTool(self, value: str) -> None: """Setter: The name of the dashboard tool such as looker, redash etc.""" self._inner_dict['dashboardTool'] = value @property def dashboardId(self) -> str: """Getter: Unique id for the dashboard. This id should be globally unique for a dashboarding tool even when there are multiple deployments of it. As an example, dashboard URL could be used here for Looker such as 'looker.linkedin.com/dashboards/1234'""" return self._inner_dict.get('dashboardId') # type: ignore @dashboardId.setter def dashboardId(self, value: str) -> None: """Setter: Unique id for the dashboard. This id should be globally unique for a dashboarding tool even when there are multiple deployments of it. As an example, dashboard URL could be used here for Looker such as 'looker.linkedin.com/dashboards/1234'""" self._inner_dict['dashboardId'] = value class DataFlowKeyClass(DictWrapper): """Key for a Data Flow""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.DataFlowKey") def __init__(self, orchestrator: str, flowId: str, cluster: str, ): super().__init__() self.orchestrator = orchestrator self.flowId = flowId self.cluster = cluster @classmethod def construct_with_defaults(cls) -> "DataFlowKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.orchestrator = str() self.flowId = str() self.cluster = str() @property def orchestrator(self) -> str: """Getter: Workflow manager like azkaban, airflow which orchestrates the flow""" return self._inner_dict.get('orchestrator') # type: ignore @orchestrator.setter def orchestrator(self, value: str) -> None: """Setter: Workflow manager like azkaban, airflow which orchestrates the flow""" self._inner_dict['orchestrator'] = value @property def flowId(self) -> str: """Getter: Unique Identifier of the data flow""" return self._inner_dict.get('flowId') # type: ignore @flowId.setter def flowId(self, value: str) -> None: """Setter: Unique Identifier of the data flow""" self._inner_dict['flowId'] = value @property def cluster(self) -> str: """Getter: Cluster where the flow is executed""" return self._inner_dict.get('cluster') # type: ignore @cluster.setter def cluster(self, value: str) -> None: """Setter: Cluster where the flow is executed""" self._inner_dict['cluster'] = value class DataJobKeyClass(DictWrapper): """Key for a Data Job""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.DataJobKey") def __init__(self, flow: str, jobId: str, ): super().__init__() self.flow = flow self.jobId = jobId @classmethod def construct_with_defaults(cls) -> "DataJobKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.flow = str() self.jobId = str() @property def flow(self) -> str: """Getter: Standardized data processing flow urn representing the flow for the job""" return self._inner_dict.get('flow') # type: ignore @flow.setter def flow(self, value: str) -> None: """Setter: Standardized data processing flow urn representing the flow for the job""" self._inner_dict['flow'] = value @property def jobId(self) -> str: """Getter: Unique Identifier of the data job""" return self._inner_dict.get('jobId') # type: ignore @jobId.setter def jobId(self, value: str) -> None: """Setter: Unique Identifier of the data job""" self._inner_dict['jobId'] = value class DataPlatformKeyClass(DictWrapper): """Key for a Data Platform""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.DataPlatformKey") def __init__(self, platformName: str, ): super().__init__() self.platformName = platformName @classmethod def construct_with_defaults(cls) -> "DataPlatformKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.platformName = str() @property def platformName(self) -> str: """Getter: Data platform name i.e. hdfs, oracle, espresso""" return self._inner_dict.get('platformName') # type: ignore @platformName.setter def platformName(self, value: str) -> None: """Setter: Data platform name i.e. hdfs, oracle, espresso""" self._inner_dict['platformName'] = value class DataProcessKeyClass(DictWrapper): """Key for a Data Process""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.DataProcessKey") def __init__(self, name: str, orchestrator: str, origin: Union[str, "FabricTypeClass"], ): super().__init__() self.name = name self.orchestrator = orchestrator self.origin = origin @classmethod def construct_with_defaults(cls) -> "DataProcessKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() self.orchestrator = str() self.origin = FabricTypeClass.DEV @property def name(self) -> str: """Getter: Process name i.e. an ETL job name""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Process name i.e. an ETL job name""" self._inner_dict['name'] = value @property def orchestrator(self) -> str: """Getter: Standardized Orchestrator where data process is defined. TODO: Migrate towards something that can be validated like DataPlatform urn""" return self._inner_dict.get('orchestrator') # type: ignore @orchestrator.setter def orchestrator(self, value: str) -> None: """Setter: Standardized Orchestrator where data process is defined. TODO: Migrate towards something that can be validated like DataPlatform urn""" self._inner_dict['orchestrator'] = value @property def origin(self) -> Union[str, "FabricTypeClass"]: """Getter: Fabric type where dataset belongs to or where it was generated.""" return self._inner_dict.get('origin') # type: ignore @origin.setter def origin(self, value: Union[str, "FabricTypeClass"]) -> None: """Setter: Fabric type where dataset belongs to or where it was generated.""" self._inner_dict['origin'] = value class DatasetKeyClass(DictWrapper): """Key for a Dataset""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.DatasetKey") def __init__(self, platform: str, name: str, origin: Union[str, "FabricTypeClass"], ): super().__init__() self.platform = platform self.name = name self.origin = origin @classmethod def construct_with_defaults(cls) -> "DatasetKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.platform = str() self.name = str() self.origin = FabricTypeClass.DEV @property def platform(self) -> str: """Getter: Data platform urn associated with the dataset""" return self._inner_dict.get('platform') # type: ignore @platform.setter def platform(self, value: str) -> None: """Setter: Data platform urn associated with the dataset""" self._inner_dict['platform'] = value @property def name(self) -> str: """Getter: Dataset native name e.g. <db>.<table>, /dir/subdir/<name>, or <name>""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Dataset native name e.g. <db>.<table>, /dir/subdir/<name>, or <name>""" self._inner_dict['name'] = value @property def origin(self) -> Union[str, "FabricTypeClass"]: """Getter: Fabric type where dataset belongs to or where it was generated.""" return self._inner_dict.get('origin') # type: ignore @origin.setter def origin(self, value: Union[str, "FabricTypeClass"]) -> None: """Setter: Fabric type where dataset belongs to or where it was generated.""" self._inner_dict['origin'] = value class GlossaryNodeKeyClass(DictWrapper): """Key for a GlossaryNode""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.GlossaryNodeKey") def __init__(self, name: str, ): super().__init__() self.name = name @classmethod def construct_with_defaults(cls) -> "GlossaryNodeKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() @property def name(self) -> str: # No docs available. return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: # No docs available. self._inner_dict['name'] = value class GlossaryTermKeyClass(DictWrapper): """Key for a GlossaryTerm""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.GlossaryTermKey") def __init__(self, name: str, ): super().__init__() self.name = name @classmethod def construct_with_defaults(cls) -> "GlossaryTermKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() @property def name(self) -> str: # No docs available. return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: # No docs available. self._inner_dict['name'] = value class MLFeatureKeyClass(DictWrapper): """Key for an MLFeature""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.MLFeatureKey") def __init__(self, featureNamespace: str, name: str, ): super().__init__() self.featureNamespace = featureNamespace self.name = name @classmethod def construct_with_defaults(cls) -> "MLFeatureKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.featureNamespace = str() self.name = str() @property def featureNamespace(self) -> str: """Getter: Namespace for the feature""" return self._inner_dict.get('featureNamespace') # type: ignore @featureNamespace.setter def featureNamespace(self, value: str) -> None: """Setter: Namespace for the feature""" self._inner_dict['featureNamespace'] = value @property def name(self) -> str: """Getter: Name of the feature""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Name of the feature""" self._inner_dict['name'] = value class MLFeatureTableKeyClass(DictWrapper): """Key for an MLFeatureTable""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.MLFeatureTableKey") def __init__(self, platform: str, name: str, ): super().__init__() self.platform = platform self.name = name @classmethod def construct_with_defaults(cls) -> "MLFeatureTableKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.platform = str() self.name = str() @property def platform(self) -> str: """Getter: Data platform urn associated with the feature table""" return self._inner_dict.get('platform') # type: ignore @platform.setter def platform(self, value: str) -> None: """Setter: Data platform urn associated with the feature table""" self._inner_dict['platform'] = value @property def name(self) -> str: """Getter: Name of the feature table""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Name of the feature table""" self._inner_dict['name'] = value class MLModelKeyClass(DictWrapper): """Key for an ML model""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.MLModelKey") def __init__(self, platform: str, name: str, origin: Union[str, "FabricTypeClass"], ): super().__init__() self.platform = platform self.name = name self.origin = origin @classmethod def construct_with_defaults(cls) -> "MLModelKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.platform = str() self.name = str() self.origin = FabricTypeClass.DEV @property def platform(self) -> str: """Getter: Standardized platform urn for the model""" return self._inner_dict.get('platform') # type: ignore @platform.setter def platform(self, value: str) -> None: """Setter: Standardized platform urn for the model""" self._inner_dict['platform'] = value @property def name(self) -> str: """Getter: Name of the MLModel""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Name of the MLModel""" self._inner_dict['name'] = value @property def origin(self) -> Union[str, "FabricTypeClass"]: """Getter: Fabric type where model belongs to or where it was generated""" return self._inner_dict.get('origin') # type: ignore @origin.setter def origin(self, value: Union[str, "FabricTypeClass"]) -> None: """Setter: Fabric type where model belongs to or where it was generated""" self._inner_dict['origin'] = value class MLPrimaryKeyKeyClass(DictWrapper): """Key for an MLPrimaryKey""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.MLPrimaryKeyKey") def __init__(self, featureNamespace: str, name: str, ): super().__init__() self.featureNamespace = featureNamespace self.name = name @classmethod def construct_with_defaults(cls) -> "MLPrimaryKeyKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.featureNamespace = str() self.name = str() @property def featureNamespace(self) -> str: """Getter: Namespace for the primary key""" return self._inner_dict.get('featureNamespace') # type: ignore @featureNamespace.setter def featureNamespace(self, value: str) -> None: """Setter: Namespace for the primary key""" self._inner_dict['featureNamespace'] = value @property def name(self) -> str: """Getter: Name of the primary key""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Name of the primary key""" self._inner_dict['name'] = value class TagKeyClass(DictWrapper): """Key for a Tag""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.key.TagKey") def __init__(self, name: str, ): super().__init__() self.name = name @classmethod def construct_with_defaults(cls) -> "TagKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() @property def name(self) -> str: """Getter: The unique tag name""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: The unique tag name""" self._inner_dict['name'] = value class ChartSnapshotClass(DictWrapper): """A metadata snapshot for a specific Chart entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.ChartSnapshot") def __init__(self, urn: str, aspects: List[Union["ChartKeyClass", "ChartInfoClass", "ChartQueryClass", "EditableChartPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "ChartSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["ChartKeyClass", "ChartInfoClass", "ChartQueryClass", "EditableChartPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the chart. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["ChartKeyClass", "ChartInfoClass", "ChartQueryClass", "EditableChartPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the chart. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class CorpGroupSnapshotClass(DictWrapper): """A metadata snapshot for a specific CorpGroup entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.CorpGroupSnapshot") def __init__(self, urn: str, aspects: List[Union["CorpGroupKeyClass", "CorpGroupInfoClass", "GlobalTagsClass", "StatusClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "CorpGroupSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["CorpGroupKeyClass", "CorpGroupInfoClass", "GlobalTagsClass", "StatusClass"]]: """Getter: The list of metadata aspects associated with the LdapUser. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["CorpGroupKeyClass", "CorpGroupInfoClass", "GlobalTagsClass", "StatusClass"]]) -> None: """Setter: The list of metadata aspects associated with the LdapUser. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class CorpUserSnapshotClass(DictWrapper): """A metadata snapshot for a specific CorpUser entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.CorpUserSnapshot") def __init__(self, urn: str, aspects: List[Union["CorpUserKeyClass", "CorpUserInfoClass", "CorpUserEditableInfoClass", "GlobalTagsClass", "StatusClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "CorpUserSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["CorpUserKeyClass", "CorpUserInfoClass", "CorpUserEditableInfoClass", "GlobalTagsClass", "StatusClass"]]: """Getter: The list of metadata aspects associated with the CorpUser. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["CorpUserKeyClass", "CorpUserInfoClass", "CorpUserEditableInfoClass", "GlobalTagsClass", "StatusClass"]]) -> None: """Setter: The list of metadata aspects associated with the CorpUser. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class DashboardSnapshotClass(DictWrapper): """A metadata snapshot for a specific Dashboard entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.DashboardSnapshot") def __init__(self, urn: str, aspects: List[Union["DashboardKeyClass", "DashboardInfoClass", "EditableDashboardPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "DashboardSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["DashboardKeyClass", "DashboardInfoClass", "EditableDashboardPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the dashboard. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["DashboardKeyClass", "DashboardInfoClass", "EditableDashboardPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the dashboard. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class DataFlowSnapshotClass(DictWrapper): """A metadata snapshot for a specific DataFlow entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.DataFlowSnapshot") def __init__(self, urn: str, aspects: List[Union["DataFlowKeyClass", "DataFlowInfoClass", "EditableDataFlowPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "DataFlowSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["DataFlowKeyClass", "DataFlowInfoClass", "EditableDataFlowPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the data flow. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["DataFlowKeyClass", "DataFlowInfoClass", "EditableDataFlowPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the data flow. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class DataJobSnapshotClass(DictWrapper): """A metadata snapshot for a specific DataJob entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.DataJobSnapshot") def __init__(self, urn: str, aspects: List[Union["DataJobKeyClass", "DataJobInfoClass", "DataJobInputOutputClass", "EditableDataJobPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "DataJobSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["DataJobKeyClass", "DataJobInfoClass", "DataJobInputOutputClass", "EditableDataJobPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the data job. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["DataJobKeyClass", "DataJobInfoClass", "DataJobInputOutputClass", "EditableDataJobPropertiesClass", "OwnershipClass", "StatusClass", "GlobalTagsClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the data job. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class DataPlatformSnapshotClass(DictWrapper): """A metadata snapshot for a specific dataplatform entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.DataPlatformSnapshot") def __init__(self, urn: str, aspects: List[Union["DataPlatformKeyClass", "DataPlatformInfoClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "DataPlatformSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["DataPlatformKeyClass", "DataPlatformInfoClass"]]: """Getter: The list of metadata aspects associated with the data platform. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["DataPlatformKeyClass", "DataPlatformInfoClass"]]) -> None: """Setter: The list of metadata aspects associated with the data platform. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class DataProcessSnapshotClass(DictWrapper): """A metadata snapshot for a specific Data process entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.DataProcessSnapshot") def __init__(self, urn: str, aspects: List[Union["DataProcessKeyClass", "OwnershipClass", "DataProcessInfoClass", "StatusClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "DataProcessSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["DataProcessKeyClass", "OwnershipClass", "DataProcessInfoClass", "StatusClass"]]: """Getter: The list of metadata aspects associated with the data process. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["DataProcessKeyClass", "OwnershipClass", "DataProcessInfoClass", "StatusClass"]]) -> None: """Setter: The list of metadata aspects associated with the data process. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class DatasetSnapshotClass(DictWrapper): # No docs available. RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot") def __init__(self, urn: str, aspects: List[Union["DatasetKeyClass", "DatasetPropertiesClass", "EditableDatasetPropertiesClass", "DatasetDeprecationClass", "DatasetUpstreamLineageClass", "UpstreamLineageClass", "InstitutionalMemoryClass", "OwnershipClass", "StatusClass", "SchemaMetadataClass", "EditableSchemaMetadataClass", "GlobalTagsClass", "GlossaryTermsClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "DatasetSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["DatasetKeyClass", "DatasetPropertiesClass", "EditableDatasetPropertiesClass", "DatasetDeprecationClass", "DatasetUpstreamLineageClass", "UpstreamLineageClass", "InstitutionalMemoryClass", "OwnershipClass", "StatusClass", "SchemaMetadataClass", "EditableSchemaMetadataClass", "GlobalTagsClass", "GlossaryTermsClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the dataset. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["DatasetKeyClass", "DatasetPropertiesClass", "EditableDatasetPropertiesClass", "DatasetDeprecationClass", "DatasetUpstreamLineageClass", "UpstreamLineageClass", "InstitutionalMemoryClass", "OwnershipClass", "StatusClass", "SchemaMetadataClass", "EditableSchemaMetadataClass", "GlobalTagsClass", "GlossaryTermsClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the dataset. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class GlossaryNodeSnapshotClass(DictWrapper): """A metadata snapshot for a specific GlossaryNode entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.GlossaryNodeSnapshot") def __init__(self, urn: str, aspects: List[Union["GlossaryNodeKeyClass", "GlossaryNodeInfoClass", "OwnershipClass", "StatusClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "GlossaryNodeSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["GlossaryNodeKeyClass", "GlossaryNodeInfoClass", "OwnershipClass", "StatusClass"]]: """Getter: The list of metadata aspects associated with the GlossaryNode. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["GlossaryNodeKeyClass", "GlossaryNodeInfoClass", "OwnershipClass", "StatusClass"]]) -> None: """Setter: The list of metadata aspects associated with the GlossaryNode. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class GlossaryTermSnapshotClass(DictWrapper): """A metadata snapshot for a specific GlossaryTerm entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.GlossaryTermSnapshot") def __init__(self, urn: str, aspects: List[Union["GlossaryTermKeyClass", "GlossaryTermInfoClass", "OwnershipClass", "StatusClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "GlossaryTermSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["GlossaryTermKeyClass", "GlossaryTermInfoClass", "OwnershipClass", "StatusClass"]]: """Getter: The list of metadata aspects associated with the GlossaryTerm. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["GlossaryTermKeyClass", "GlossaryTermInfoClass", "OwnershipClass", "StatusClass"]]) -> None: """Setter: The list of metadata aspects associated with the GlossaryTerm. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class MLFeatureSnapshotClass(DictWrapper): # No docs available. RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.MLFeatureSnapshot") def __init__(self, urn: str, aspects: List[Union["MLFeatureKeyClass", "MLFeaturePropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "MLFeatureSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["MLFeatureKeyClass", "MLFeaturePropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the MLFeature. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["MLFeatureKeyClass", "MLFeaturePropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the MLFeature. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class MLFeatureTableSnapshotClass(DictWrapper): # No docs available. RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.MLFeatureTableSnapshot") def __init__(self, urn: str, aspects: List[Union["MLFeatureTableKeyClass", "MLFeatureTablePropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "MLFeatureTableSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["MLFeatureTableKeyClass", "MLFeatureTablePropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the MLFeatureTable. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["MLFeatureTableKeyClass", "MLFeatureTablePropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the MLFeatureTable. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class MLModelSnapshotClass(DictWrapper): """MLModel Snapshot entity details.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.MLModelSnapshot") def __init__(self, urn: str, aspects: List[Union["MLModelKeyClass", "OwnershipClass", "MLModelPropertiesClass", "IntendedUseClass", "MLModelFactorPromptsClass", "MetricsClass", "EvaluationDataClass", "TrainingDataClass", "QuantitativeAnalysesClass", "EthicalConsiderationsClass", "CaveatsAndRecommendationsClass", "InstitutionalMemoryClass", "SourceCodeClass", "StatusClass", "CostClass", "DeprecationClass", "BrowsePathsClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "MLModelSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["MLModelKeyClass", "OwnershipClass", "MLModelPropertiesClass", "IntendedUseClass", "MLModelFactorPromptsClass", "MetricsClass", "EvaluationDataClass", "TrainingDataClass", "QuantitativeAnalysesClass", "EthicalConsiderationsClass", "CaveatsAndRecommendationsClass", "InstitutionalMemoryClass", "SourceCodeClass", "StatusClass", "CostClass", "DeprecationClass", "BrowsePathsClass"]]: """Getter: The list of metadata aspects associated with the MLModel. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["MLModelKeyClass", "OwnershipClass", "MLModelPropertiesClass", "IntendedUseClass", "MLModelFactorPromptsClass", "MetricsClass", "EvaluationDataClass", "TrainingDataClass", "QuantitativeAnalysesClass", "EthicalConsiderationsClass", "CaveatsAndRecommendationsClass", "InstitutionalMemoryClass", "SourceCodeClass", "StatusClass", "CostClass", "DeprecationClass", "BrowsePathsClass"]]) -> None: """Setter: The list of metadata aspects associated with the MLModel. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class MLPrimaryKeySnapshotClass(DictWrapper): # No docs available. RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.MLPrimaryKeySnapshot") def __init__(self, urn: str, aspects: List[Union["MLPrimaryKeyKeyClass", "MLPrimaryKeyPropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "MLPrimaryKeySnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["MLPrimaryKeyKeyClass", "MLPrimaryKeyPropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass"]]: """Getter: The list of metadata aspects associated with the MLPrimaryKey. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["MLPrimaryKeyKeyClass", "MLPrimaryKeyPropertiesClass", "OwnershipClass", "InstitutionalMemoryClass", "StatusClass", "DeprecationClass"]]) -> None: """Setter: The list of metadata aspects associated with the MLPrimaryKey. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class TagSnapshotClass(DictWrapper): """A metadata snapshot for a specific dataset entity.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.metadata.snapshot.TagSnapshot") def __init__(self, urn: str, aspects: List[Union["TagKeyClass", "OwnershipClass", "TagPropertiesClass", "StatusClass"]], ): super().__init__() self.urn = urn self.aspects = aspects @classmethod def construct_with_defaults(cls) -> "TagSnapshotClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.urn = str() self.aspects = list() @property def urn(self) -> str: """Getter: URN for the entity the metadata snapshot is associated with.""" return self._inner_dict.get('urn') # type: ignore @urn.setter def urn(self, value: str) -> None: """Setter: URN for the entity the metadata snapshot is associated with.""" self._inner_dict['urn'] = value @property def aspects(self) -> List[Union["TagKeyClass", "OwnershipClass", "TagPropertiesClass", "StatusClass"]]: """Getter: The list of metadata aspects associated with the dataset. Depending on the use case, this can either be all, or a selection, of supported aspects.""" return self._inner_dict.get('aspects') # type: ignore @aspects.setter def aspects(self, value: List[Union["TagKeyClass", "OwnershipClass", "TagPropertiesClass", "StatusClass"]]) -> None: """Setter: The list of metadata aspects associated with the dataset. Depending on the use case, this can either be all, or a selection, of supported aspects.""" self._inner_dict['aspects'] = value class BaseDataClass(DictWrapper): """BaseData record""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.BaseData") def __init__(self, dataset: str, motivation: Union[None, str]=None, preProcessing: Union[None, List[str]]=None, ): super().__init__() self.dataset = dataset self.motivation = motivation self.preProcessing = preProcessing @classmethod def construct_with_defaults(cls) -> "BaseDataClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.dataset = str() self.motivation = self.RECORD_SCHEMA.field_map["motivation"].default self.preProcessing = self.RECORD_SCHEMA.field_map["preProcessing"].default @property def dataset(self) -> str: """Getter: What dataset were used in the MLModel?""" return self._inner_dict.get('dataset') # type: ignore @dataset.setter def dataset(self, value: str) -> None: """Setter: What dataset were used in the MLModel?""" self._inner_dict['dataset'] = value @property def motivation(self) -> Union[None, str]: """Getter: Why was this dataset chosen?""" return self._inner_dict.get('motivation') # type: ignore @motivation.setter def motivation(self, value: Union[None, str]) -> None: """Setter: Why was this dataset chosen?""" self._inner_dict['motivation'] = value @property def preProcessing(self) -> Union[None, List[str]]: """Getter: How was the data preprocessed (e.g., tokenization of sentences, cropping of images, any filtering such as dropping images without faces)?""" return self._inner_dict.get('preProcessing') # type: ignore @preProcessing.setter def preProcessing(self, value: Union[None, List[str]]) -> None: """Setter: How was the data preprocessed (e.g., tokenization of sentences, cropping of images, any filtering such as dropping images without faces)?""" self._inner_dict['preProcessing'] = value class CaveatDetailsClass(DictWrapper): """This section should list additional concerns that were not covered in the previous sections. For example, did the results suggest any further testing? Were there any relevant groups that were not represented in the evaluation dataset? Are there additional recommendations for model use?""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.CaveatDetails") def __init__(self, needsFurtherTesting: Union[None, bool]=None, caveatDescription: Union[None, str]=None, groupsNotRepresented: Union[None, List[str]]=None, ): super().__init__() self.needsFurtherTesting = needsFurtherTesting self.caveatDescription = caveatDescription self.groupsNotRepresented = groupsNotRepresented @classmethod def construct_with_defaults(cls) -> "CaveatDetailsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.needsFurtherTesting = self.RECORD_SCHEMA.field_map["needsFurtherTesting"].default self.caveatDescription = self.RECORD_SCHEMA.field_map["caveatDescription"].default self.groupsNotRepresented = self.RECORD_SCHEMA.field_map["groupsNotRepresented"].default @property def needsFurtherTesting(self) -> Union[None, bool]: """Getter: Did the results suggest any further testing?""" return self._inner_dict.get('needsFurtherTesting') # type: ignore @needsFurtherTesting.setter def needsFurtherTesting(self, value: Union[None, bool]) -> None: """Setter: Did the results suggest any further testing?""" self._inner_dict['needsFurtherTesting'] = value @property def caveatDescription(self) -> Union[None, str]: """Getter: Caveat Description For ex: Given gender classes are binary (male/not male), which we include as male/female. Further work needed to evaluate across a spectrum of genders.""" return self._inner_dict.get('caveatDescription') # type: ignore @caveatDescription.setter def caveatDescription(self, value: Union[None, str]) -> None: """Setter: Caveat Description For ex: Given gender classes are binary (male/not male), which we include as male/female. Further work needed to evaluate across a spectrum of genders.""" self._inner_dict['caveatDescription'] = value @property def groupsNotRepresented(self) -> Union[None, List[str]]: """Getter: Relevant groups that were not represented in the evaluation dataset?""" return self._inner_dict.get('groupsNotRepresented') # type: ignore @groupsNotRepresented.setter def groupsNotRepresented(self, value: Union[None, List[str]]) -> None: """Setter: Relevant groups that were not represented in the evaluation dataset?""" self._inner_dict['groupsNotRepresented'] = value class CaveatsAndRecommendationsClass(DictWrapper): """This section should list additional concerns that were not covered in the previous sections. For example, did the results suggest any further testing? Were there any relevant groups that were not represented in the evaluation dataset? Are there additional recommendations for model use?""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.CaveatsAndRecommendations") def __init__(self, caveats: Union[None, "CaveatDetailsClass"]=None, recommendations: Union[None, str]=None, idealDatasetCharacteristics: Union[None, List[str]]=None, ): super().__init__() self.caveats = caveats self.recommendations = recommendations self.idealDatasetCharacteristics = idealDatasetCharacteristics @classmethod def construct_with_defaults(cls) -> "CaveatsAndRecommendationsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.caveats = self.RECORD_SCHEMA.field_map["caveats"].default self.recommendations = self.RECORD_SCHEMA.field_map["recommendations"].default self.idealDatasetCharacteristics = self.RECORD_SCHEMA.field_map["idealDatasetCharacteristics"].default @property def caveats(self) -> Union[None, "CaveatDetailsClass"]: """Getter: This section should list additional concerns that were not covered in the previous sections. For example, did the results suggest any further testing? Were there any relevant groups that were not represented in the evaluation dataset?""" return self._inner_dict.get('caveats') # type: ignore @caveats.setter def caveats(self, value: Union[None, "CaveatDetailsClass"]) -> None: """Setter: This section should list additional concerns that were not covered in the previous sections. For example, did the results suggest any further testing? Were there any relevant groups that were not represented in the evaluation dataset?""" self._inner_dict['caveats'] = value @property def recommendations(self) -> Union[None, str]: """Getter: Recommendations on where this MLModel should be used.""" return self._inner_dict.get('recommendations') # type: ignore @recommendations.setter def recommendations(self, value: Union[None, str]) -> None: """Setter: Recommendations on where this MLModel should be used.""" self._inner_dict['recommendations'] = value @property def idealDatasetCharacteristics(self) -> Union[None, List[str]]: """Getter: Ideal characteristics of an evaluation dataset for this MLModel""" return self._inner_dict.get('idealDatasetCharacteristics') # type: ignore @idealDatasetCharacteristics.setter def idealDatasetCharacteristics(self, value: Union[None, List[str]]) -> None: """Setter: Ideal characteristics of an evaluation dataset for this MLModel""" self._inner_dict['idealDatasetCharacteristics'] = value class EthicalConsiderationsClass(DictWrapper): """This section is intended to demonstrate the ethical considerations that went into MLModel development, surfacing ethical challenges and solutions to stakeholders.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.EthicalConsiderations") def __init__(self, data: Union[None, List[str]]=None, humanLife: Union[None, List[str]]=None, mitigations: Union[None, List[str]]=None, risksAndHarms: Union[None, List[str]]=None, useCases: Union[None, List[str]]=None, ): super().__init__() self.data = data self.humanLife = humanLife self.mitigations = mitigations self.risksAndHarms = risksAndHarms self.useCases = useCases @classmethod def construct_with_defaults(cls) -> "EthicalConsiderationsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.data = self.RECORD_SCHEMA.field_map["data"].default self.humanLife = self.RECORD_SCHEMA.field_map["humanLife"].default self.mitigations = self.RECORD_SCHEMA.field_map["mitigations"].default self.risksAndHarms = self.RECORD_SCHEMA.field_map["risksAndHarms"].default self.useCases = self.RECORD_SCHEMA.field_map["useCases"].default @property def data(self) -> Union[None, List[str]]: """Getter: Does the MLModel use any sensitive data (e.g., protected classes)?""" return self._inner_dict.get('data') # type: ignore @data.setter def data(self, value: Union[None, List[str]]) -> None: """Setter: Does the MLModel use any sensitive data (e.g., protected classes)?""" self._inner_dict['data'] = value @property def humanLife(self) -> Union[None, List[str]]: """Getter: Is the MLModel intended to inform decisions about matters central to human life or flourishing – e.g., health or safety? Or could it be used in such a way?""" return self._inner_dict.get('humanLife') # type: ignore @humanLife.setter def humanLife(self, value: Union[None, List[str]]) -> None: """Setter: Is the MLModel intended to inform decisions about matters central to human life or flourishing – e.g., health or safety? Or could it be used in such a way?""" self._inner_dict['humanLife'] = value @property def mitigations(self) -> Union[None, List[str]]: """Getter: What risk mitigation strategies were used during MLModel development?""" return self._inner_dict.get('mitigations') # type: ignore @mitigations.setter def mitigations(self, value: Union[None, List[str]]) -> None: """Setter: What risk mitigation strategies were used during MLModel development?""" self._inner_dict['mitigations'] = value @property def risksAndHarms(self) -> Union[None, List[str]]: """Getter: What risks may be present in MLModel usage? Try to identify the potential recipients, likelihood, and magnitude of harms. If these cannot be determined, note that they were considered but remain unknown.""" return self._inner_dict.get('risksAndHarms') # type: ignore @risksAndHarms.setter def risksAndHarms(self, value: Union[None, List[str]]) -> None: """Setter: What risks may be present in MLModel usage? Try to identify the potential recipients, likelihood, and magnitude of harms. If these cannot be determined, note that they were considered but remain unknown.""" self._inner_dict['risksAndHarms'] = value @property def useCases(self) -> Union[None, List[str]]: """Getter: Are there any known MLModel use cases that are especially fraught? This may connect directly to the intended use section""" return self._inner_dict.get('useCases') # type: ignore @useCases.setter def useCases(self, value: Union[None, List[str]]) -> None: """Setter: Are there any known MLModel use cases that are especially fraught? This may connect directly to the intended use section""" self._inner_dict['useCases'] = value class EvaluationDataClass(DictWrapper): """All referenced datasets would ideally point to any set of documents that provide visibility into the source and composition of the dataset.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.EvaluationData") def __init__(self, evaluationData: List["BaseDataClass"], ): super().__init__() self.evaluationData = evaluationData @classmethod def construct_with_defaults(cls) -> "EvaluationDataClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.evaluationData = list() @property def evaluationData(self) -> List["BaseDataClass"]: """Getter: Details on the dataset(s) used for the quantitative analyses in the MLModel""" return self._inner_dict.get('evaluationData') # type: ignore @evaluationData.setter def evaluationData(self, value: List["BaseDataClass"]) -> None: """Setter: Details on the dataset(s) used for the quantitative analyses in the MLModel""" self._inner_dict['evaluationData'] = value class IntendedUseClass(DictWrapper): """Intended Use for the ML Model""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.IntendedUse") def __init__(self, primaryUses: Union[None, List[str]]=None, primaryUsers: Union[None, List[Union[str, "IntendedUserTypeClass"]]]=None, outOfScopeUses: Union[None, List[str]]=None, ): super().__init__() self.primaryUses = primaryUses self.primaryUsers = primaryUsers self.outOfScopeUses = outOfScopeUses @classmethod def construct_with_defaults(cls) -> "IntendedUseClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.primaryUses = self.RECORD_SCHEMA.field_map["primaryUses"].default self.primaryUsers = self.RECORD_SCHEMA.field_map["primaryUsers"].default self.outOfScopeUses = self.RECORD_SCHEMA.field_map["outOfScopeUses"].default @property def primaryUses(self) -> Union[None, List[str]]: """Getter: Primary Use cases for the MLModel.""" return self._inner_dict.get('primaryUses') # type: ignore @primaryUses.setter def primaryUses(self, value: Union[None, List[str]]) -> None: """Setter: Primary Use cases for the MLModel.""" self._inner_dict['primaryUses'] = value @property def primaryUsers(self) -> Union[None, List[Union[str, "IntendedUserTypeClass"]]]: """Getter: Primary Intended Users - For example, was the MLModel developed for entertainment purposes, for hobbyists, or enterprise solutions?""" return self._inner_dict.get('primaryUsers') # type: ignore @primaryUsers.setter def primaryUsers(self, value: Union[None, List[Union[str, "IntendedUserTypeClass"]]]) -> None: """Setter: Primary Intended Users - For example, was the MLModel developed for entertainment purposes, for hobbyists, or enterprise solutions?""" self._inner_dict['primaryUsers'] = value @property def outOfScopeUses(self) -> Union[None, List[str]]: """Getter: Highlight technology that the MLModel might easily be confused with, or related contexts that users could try to apply the MLModel to.""" return self._inner_dict.get('outOfScopeUses') # type: ignore @outOfScopeUses.setter def outOfScopeUses(self, value: Union[None, List[str]]) -> None: """Setter: Highlight technology that the MLModel might easily be confused with, or related contexts that users could try to apply the MLModel to.""" self._inner_dict['outOfScopeUses'] = value class IntendedUserTypeClass(object): # No docs available. ENTERPRISE = "ENTERPRISE" HOBBY = "HOBBY" ENTERTAINMENT = "ENTERTAINMENT" class MLFeaturePropertiesClass(DictWrapper): """Properties associated with a MLFeature""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.MLFeatureProperties") def __init__(self, description: Union[None, str]=None, dataType: Union[None, Union[str, "MLFeatureDataTypeClass"]]=None, version: Union[None, "VersionTagClass"]=None, sources: Union[None, List[str]]=None, ): super().__init__() self.description = description self.dataType = dataType self.version = version self.sources = sources @classmethod def construct_with_defaults(cls) -> "MLFeaturePropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.description = self.RECORD_SCHEMA.field_map["description"].default self.dataType = self.RECORD_SCHEMA.field_map["dataType"].default self.version = self.RECORD_SCHEMA.field_map["version"].default self.sources = self.RECORD_SCHEMA.field_map["sources"].default @property def description(self) -> Union[None, str]: """Getter: Documentation of the MLFeature""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the MLFeature""" self._inner_dict['description'] = value @property def dataType(self) -> Union[None, Union[str, "MLFeatureDataTypeClass"]]: """Getter: Data Type of the MLFeature""" return self._inner_dict.get('dataType') # type: ignore @dataType.setter def dataType(self, value: Union[None, Union[str, "MLFeatureDataTypeClass"]]) -> None: """Setter: Data Type of the MLFeature""" self._inner_dict['dataType'] = value @property def version(self) -> Union[None, "VersionTagClass"]: """Getter: Version of the MLFeature""" return self._inner_dict.get('version') # type: ignore @version.setter def version(self, value: Union[None, "VersionTagClass"]) -> None: """Setter: Version of the MLFeature""" self._inner_dict['version'] = value @property def sources(self) -> Union[None, List[str]]: """Getter: Source of the MLFeature""" return self._inner_dict.get('sources') # type: ignore @sources.setter def sources(self, value: Union[None, List[str]]) -> None: """Setter: Source of the MLFeature""" self._inner_dict['sources'] = value class MLFeatureTablePropertiesClass(DictWrapper): """Properties associated with a MLFeatureTable""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.MLFeatureTableProperties") def __init__(self, customProperties: Optional[Dict[str, str]]=None, description: Union[None, str]=None, mlFeatures: Union[None, List[str]]=None, mlPrimaryKeys: Union[None, List[str]]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.description = description self.mlFeatures = mlFeatures self.mlPrimaryKeys = mlPrimaryKeys @classmethod def construct_with_defaults(cls) -> "MLFeatureTablePropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.description = self.RECORD_SCHEMA.field_map["description"].default self.mlFeatures = self.RECORD_SCHEMA.field_map["mlFeatures"].default self.mlPrimaryKeys = self.RECORD_SCHEMA.field_map["mlPrimaryKeys"].default @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def description(self) -> Union[None, str]: """Getter: Documentation of the MLFeatureTable""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the MLFeatureTable""" self._inner_dict['description'] = value @property def mlFeatures(self) -> Union[None, List[str]]: """Getter: List of features contained in the feature table""" return self._inner_dict.get('mlFeatures') # type: ignore @mlFeatures.setter def mlFeatures(self, value: Union[None, List[str]]) -> None: """Setter: List of features contained in the feature table""" self._inner_dict['mlFeatures'] = value @property def mlPrimaryKeys(self) -> Union[None, List[str]]: """Getter: List of primary keys in the feature table (if multiple, assumed to act as a composite key)""" return self._inner_dict.get('mlPrimaryKeys') # type: ignore @mlPrimaryKeys.setter def mlPrimaryKeys(self, value: Union[None, List[str]]) -> None: """Setter: List of primary keys in the feature table (if multiple, assumed to act as a composite key)""" self._inner_dict['mlPrimaryKeys'] = value class MLModelFactorPromptsClass(DictWrapper): """Prompts which affect the performance of the MLModel""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.MLModelFactorPrompts") def __init__(self, relevantFactors: Union[None, List["MLModelFactorsClass"]]=None, evaluationFactors: Union[None, List["MLModelFactorsClass"]]=None, ): super().__init__() self.relevantFactors = relevantFactors self.evaluationFactors = evaluationFactors @classmethod def construct_with_defaults(cls) -> "MLModelFactorPromptsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.relevantFactors = self.RECORD_SCHEMA.field_map["relevantFactors"].default self.evaluationFactors = self.RECORD_SCHEMA.field_map["evaluationFactors"].default @property def relevantFactors(self) -> Union[None, List["MLModelFactorsClass"]]: """Getter: What are foreseeable salient factors for which MLModel performance may vary, and how were these determined?""" return self._inner_dict.get('relevantFactors') # type: ignore @relevantFactors.setter def relevantFactors(self, value: Union[None, List["MLModelFactorsClass"]]) -> None: """Setter: What are foreseeable salient factors for which MLModel performance may vary, and how were these determined?""" self._inner_dict['relevantFactors'] = value @property def evaluationFactors(self) -> Union[None, List["MLModelFactorsClass"]]: """Getter: Which factors are being reported, and why were these chosen?""" return self._inner_dict.get('evaluationFactors') # type: ignore @evaluationFactors.setter def evaluationFactors(self, value: Union[None, List["MLModelFactorsClass"]]) -> None: """Setter: Which factors are being reported, and why were these chosen?""" self._inner_dict['evaluationFactors'] = value class MLModelFactorsClass(DictWrapper): """Factors affecting the performance of the MLModel.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.MLModelFactors") def __init__(self, groups: Union[None, List[str]]=None, instrumentation: Union[None, List[str]]=None, environment: Union[None, List[str]]=None, ): super().__init__() self.groups = groups self.instrumentation = instrumentation self.environment = environment @classmethod def construct_with_defaults(cls) -> "MLModelFactorsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.groups = self.RECORD_SCHEMA.field_map["groups"].default self.instrumentation = self.RECORD_SCHEMA.field_map["instrumentation"].default self.environment = self.RECORD_SCHEMA.field_map["environment"].default @property def groups(self) -> Union[None, List[str]]: """Getter: Groups refers to distinct categories with similar characteristics that are present in the evaluation data instances. For human-centric machine learning MLModels, groups are people who share one or multiple characteristics.""" return self._inner_dict.get('groups') # type: ignore @groups.setter def groups(self, value: Union[None, List[str]]) -> None: """Setter: Groups refers to distinct categories with similar characteristics that are present in the evaluation data instances. For human-centric machine learning MLModels, groups are people who share one or multiple characteristics.""" self._inner_dict['groups'] = value @property def instrumentation(self) -> Union[None, List[str]]: """Getter: The performance of a MLModel can vary depending on what instruments were used to capture the input to the MLModel. For example, a face detection model may perform differently depending on the camera’s hardware and software, including lens, image stabilization, high dynamic range techniques, and background blurring for portrait mode.""" return self._inner_dict.get('instrumentation') # type: ignore @instrumentation.setter def instrumentation(self, value: Union[None, List[str]]) -> None: """Setter: The performance of a MLModel can vary depending on what instruments were used to capture the input to the MLModel. For example, a face detection model may perform differently depending on the camera’s hardware and software, including lens, image stabilization, high dynamic range techniques, and background blurring for portrait mode.""" self._inner_dict['instrumentation'] = value @property def environment(self) -> Union[None, List[str]]: """Getter: A further factor affecting MLModel performance is the environment in which it is deployed.""" return self._inner_dict.get('environment') # type: ignore @environment.setter def environment(self, value: Union[None, List[str]]) -> None: """Setter: A further factor affecting MLModel performance is the environment in which it is deployed.""" self._inner_dict['environment'] = value class MLModelPropertiesClass(DictWrapper): """Properties associated with a ML Model""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.MLModelProperties") def __init__(self, customProperties: Optional[Dict[str, str]]=None, description: Union[None, str]=None, date: Union[None, int]=None, version: Union[None, "VersionTagClass"]=None, type: Union[None, str]=None, hyperParameters: Union[None, Dict[str, Union[str, int, float, float, bool]]]=None, mlFeatures: Union[None, List[str]]=None, tags: Optional[List[str]]=None, trainingJobs: Union[None, List[str]]=None, downstreamJobs: Union[None, List[str]]=None, ): super().__init__() if customProperties is None: # default: {} self.customProperties = dict() else: self.customProperties = customProperties self.description = description self.date = date self.version = version self.type = type self.hyperParameters = hyperParameters self.mlFeatures = mlFeatures if tags is None: # default: [] self.tags = list() else: self.tags = tags self.trainingJobs = trainingJobs self.downstreamJobs = downstreamJobs @classmethod def construct_with_defaults(cls) -> "MLModelPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.customProperties = dict() self.description = self.RECORD_SCHEMA.field_map["description"].default self.date = self.RECORD_SCHEMA.field_map["date"].default self.version = self.RECORD_SCHEMA.field_map["version"].default self.type = self.RECORD_SCHEMA.field_map["type"].default self.hyperParameters = self.RECORD_SCHEMA.field_map["hyperParameters"].default self.mlFeatures = self.RECORD_SCHEMA.field_map["mlFeatures"].default self.tags = list() self.trainingJobs = self.RECORD_SCHEMA.field_map["trainingJobs"].default self.downstreamJobs = self.RECORD_SCHEMA.field_map["downstreamJobs"].default @property def customProperties(self) -> Dict[str, str]: """Getter: Custom property bag.""" return self._inner_dict.get('customProperties') # type: ignore @customProperties.setter def customProperties(self, value: Dict[str, str]) -> None: """Setter: Custom property bag.""" self._inner_dict['customProperties'] = value @property def description(self) -> Union[None, str]: """Getter: Documentation of the MLModel""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the MLModel""" self._inner_dict['description'] = value @property def date(self) -> Union[None, int]: """Getter: Date when the MLModel was developed""" return self._inner_dict.get('date') # type: ignore @date.setter def date(self, value: Union[None, int]) -> None: """Setter: Date when the MLModel was developed""" self._inner_dict['date'] = value @property def version(self) -> Union[None, "VersionTagClass"]: """Getter: Version of the MLModel""" return self._inner_dict.get('version') # type: ignore @version.setter def version(self, value: Union[None, "VersionTagClass"]) -> None: """Setter: Version of the MLModel""" self._inner_dict['version'] = value @property def type(self) -> Union[None, str]: """Getter: Type of Algorithm or MLModel such as whether it is a Naive Bayes classifier, Convolutional Neural Network, etc""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[None, str]) -> None: """Setter: Type of Algorithm or MLModel such as whether it is a Naive Bayes classifier, Convolutional Neural Network, etc""" self._inner_dict['type'] = value @property def hyperParameters(self) -> Union[None, Dict[str, Union[str, int, float, float, bool]]]: """Getter: Hyper Parameters of the MLModel""" return self._inner_dict.get('hyperParameters') # type: ignore @hyperParameters.setter def hyperParameters(self, value: Union[None, Dict[str, Union[str, int, float, float, bool]]]) -> None: """Setter: Hyper Parameters of the MLModel""" self._inner_dict['hyperParameters'] = value @property def mlFeatures(self) -> Union[None, List[str]]: """Getter: List of features used for MLModel training""" return self._inner_dict.get('mlFeatures') # type: ignore @mlFeatures.setter def mlFeatures(self, value: Union[None, List[str]]) -> None: """Setter: List of features used for MLModel training""" self._inner_dict['mlFeatures'] = value @property def tags(self) -> List[str]: """Getter: Tags for the MLModel""" return self._inner_dict.get('tags') # type: ignore @tags.setter def tags(self, value: List[str]) -> None: """Setter: Tags for the MLModel""" self._inner_dict['tags'] = value @property def trainingJobs(self) -> Union[None, List[str]]: """Getter: List of jobs (if any) used to train the model""" return self._inner_dict.get('trainingJobs') # type: ignore @trainingJobs.setter def trainingJobs(self, value: Union[None, List[str]]) -> None: """Setter: List of jobs (if any) used to train the model""" self._inner_dict['trainingJobs'] = value @property def downstreamJobs(self) -> Union[None, List[str]]: """Getter: List of jobs (if any) that use the model""" return self._inner_dict.get('downstreamJobs') # type: ignore @downstreamJobs.setter def downstreamJobs(self, value: Union[None, List[str]]) -> None: """Setter: List of jobs (if any) that use the model""" self._inner_dict['downstreamJobs'] = value class MLPrimaryKeyPropertiesClass(DictWrapper): """Properties associated with a MLPrimaryKey""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.MLPrimaryKeyProperties") def __init__(self, sources: List[str], description: Union[None, str]=None, dataType: Union[None, Union[str, "MLFeatureDataTypeClass"]]=None, version: Union[None, "VersionTagClass"]=None, ): super().__init__() self.description = description self.dataType = dataType self.version = version self.sources = sources @classmethod def construct_with_defaults(cls) -> "MLPrimaryKeyPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.description = self.RECORD_SCHEMA.field_map["description"].default self.dataType = self.RECORD_SCHEMA.field_map["dataType"].default self.version = self.RECORD_SCHEMA.field_map["version"].default self.sources = list() @property def description(self) -> Union[None, str]: """Getter: Documentation of the MLPrimaryKey""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the MLPrimaryKey""" self._inner_dict['description'] = value @property def dataType(self) -> Union[None, Union[str, "MLFeatureDataTypeClass"]]: """Getter: Data Type of the MLPrimaryKey""" return self._inner_dict.get('dataType') # type: ignore @dataType.setter def dataType(self, value: Union[None, Union[str, "MLFeatureDataTypeClass"]]) -> None: """Setter: Data Type of the MLPrimaryKey""" self._inner_dict['dataType'] = value @property def version(self) -> Union[None, "VersionTagClass"]: """Getter: Version of the MLPrimaryKey""" return self._inner_dict.get('version') # type: ignore @version.setter def version(self, value: Union[None, "VersionTagClass"]) -> None: """Setter: Version of the MLPrimaryKey""" self._inner_dict['version'] = value @property def sources(self) -> List[str]: """Getter: Source of the MLPrimaryKey""" return self._inner_dict.get('sources') # type: ignore @sources.setter def sources(self, value: List[str]) -> None: """Setter: Source of the MLPrimaryKey""" self._inner_dict['sources'] = value class MetricsClass(DictWrapper): """Metrics to be featured for the MLModel.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.Metrics") def __init__(self, performanceMeasures: Union[None, List[str]]=None, decisionThreshold: Union[None, List[str]]=None, ): super().__init__() self.performanceMeasures = performanceMeasures self.decisionThreshold = decisionThreshold @classmethod def construct_with_defaults(cls) -> "MetricsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.performanceMeasures = self.RECORD_SCHEMA.field_map["performanceMeasures"].default self.decisionThreshold = self.RECORD_SCHEMA.field_map["decisionThreshold"].default @property def performanceMeasures(self) -> Union[None, List[str]]: """Getter: Measures of MLModel performance""" return self._inner_dict.get('performanceMeasures') # type: ignore @performanceMeasures.setter def performanceMeasures(self, value: Union[None, List[str]]) -> None: """Setter: Measures of MLModel performance""" self._inner_dict['performanceMeasures'] = value @property def decisionThreshold(self) -> Union[None, List[str]]: """Getter: Decision Thresholds used (if any)?""" return self._inner_dict.get('decisionThreshold') # type: ignore @decisionThreshold.setter def decisionThreshold(self, value: Union[None, List[str]]) -> None: """Setter: Decision Thresholds used (if any)?""" self._inner_dict['decisionThreshold'] = value class QuantitativeAnalysesClass(DictWrapper): """Quantitative analyses should be disaggregated, that is, broken down by the chosen factors. Quantitative analyses should provide the results of evaluating the MLModel according to the chosen metrics, providing confidence interval values when possible.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.QuantitativeAnalyses") def __init__(self, unitaryResults: Union[None, str]=None, intersectionalResults: Union[None, str]=None, ): super().__init__() self.unitaryResults = unitaryResults self.intersectionalResults = intersectionalResults @classmethod def construct_with_defaults(cls) -> "QuantitativeAnalysesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.unitaryResults = self.RECORD_SCHEMA.field_map["unitaryResults"].default self.intersectionalResults = self.RECORD_SCHEMA.field_map["intersectionalResults"].default @property def unitaryResults(self) -> Union[None, str]: """Getter: Link to a dashboard with results showing how the MLModel performed with respect to each factor""" return self._inner_dict.get('unitaryResults') # type: ignore @unitaryResults.setter def unitaryResults(self, value: Union[None, str]) -> None: """Setter: Link to a dashboard with results showing how the MLModel performed with respect to each factor""" self._inner_dict['unitaryResults'] = value @property def intersectionalResults(self) -> Union[None, str]: """Getter: Link to a dashboard with results showing how the MLModel performed with respect to the intersection of evaluated factors?""" return self._inner_dict.get('intersectionalResults') # type: ignore @intersectionalResults.setter def intersectionalResults(self, value: Union[None, str]) -> None: """Setter: Link to a dashboard with results showing how the MLModel performed with respect to the intersection of evaluated factors?""" self._inner_dict['intersectionalResults'] = value class SourceCodeClass(DictWrapper): """Source Code""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.SourceCode") def __init__(self, sourceCode: List["SourceCodeUrlClass"], ): super().__init__() self.sourceCode = sourceCode @classmethod def construct_with_defaults(cls) -> "SourceCodeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.sourceCode = list() @property def sourceCode(self) -> List["SourceCodeUrlClass"]: """Getter: Source Code along with types""" return self._inner_dict.get('sourceCode') # type: ignore @sourceCode.setter def sourceCode(self, value: List["SourceCodeUrlClass"]) -> None: """Setter: Source Code along with types""" self._inner_dict['sourceCode'] = value class SourceCodeUrlClass(DictWrapper): """Source Code Url Entity""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.SourceCodeUrl") def __init__(self, type: Union[str, "SourceCodeUrlTypeClass"], sourceCodeUrl: str, ): super().__init__() self.type = type self.sourceCodeUrl = sourceCodeUrl @classmethod def construct_with_defaults(cls) -> "SourceCodeUrlClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.type = SourceCodeUrlTypeClass.ML_MODEL_SOURCE_CODE self.sourceCodeUrl = str() @property def type(self) -> Union[str, "SourceCodeUrlTypeClass"]: """Getter: Source Code Url Types""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union[str, "SourceCodeUrlTypeClass"]) -> None: """Setter: Source Code Url Types""" self._inner_dict['type'] = value @property def sourceCodeUrl(self) -> str: """Getter: Source Code Url""" return self._inner_dict.get('sourceCodeUrl') # type: ignore @sourceCodeUrl.setter def sourceCodeUrl(self, value: str) -> None: """Setter: Source Code Url""" self._inner_dict['sourceCodeUrl'] = value class SourceCodeUrlTypeClass(object): # No docs available. ML_MODEL_SOURCE_CODE = "ML_MODEL_SOURCE_CODE" TRAINING_PIPELINE_SOURCE_CODE = "TRAINING_PIPELINE_SOURCE_CODE" EVALUATION_PIPELINE_SOURCE_CODE = "EVALUATION_PIPELINE_SOURCE_CODE" class TrainingDataClass(DictWrapper): """Ideally, the MLModel card would contain as much information about the training data as the evaluation data. However, there might be cases where it is not feasible to provide this level of detailed information about the training data. For example, the data may be proprietary, or require a non-disclosure agreement. In these cases, we advocate for basic details about the distributions over groups in the data, as well as any other details that could inform stakeholders on the kinds of biases the model may have encoded.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.ml.metadata.TrainingData") def __init__(self, trainingData: List["BaseDataClass"], ): super().__init__() self.trainingData = trainingData @classmethod def construct_with_defaults(cls) -> "TrainingDataClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.trainingData = list() @property def trainingData(self) -> List["BaseDataClass"]: """Getter: Details on the dataset(s) used for training the MLModel""" return self._inner_dict.get('trainingData') # type: ignore @trainingData.setter def trainingData(self, value: List["BaseDataClass"]) -> None: """Setter: Details on the dataset(s) used for training the MLModel""" self._inner_dict['trainingData'] = value class MetadataAuditEventClass(DictWrapper): """Kafka event for capturing update made to an entity's metadata.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.mxe.MetadataAuditEvent") def __init__(self, newSnapshot: Union["ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"], auditHeader: Union[None, "KafkaAuditHeaderClass"]=None, oldSnapshot: Union[None, "ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]=None, ): super().__init__() self.auditHeader = auditHeader self.oldSnapshot = oldSnapshot self.newSnapshot = newSnapshot @classmethod def construct_with_defaults(cls) -> "MetadataAuditEventClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.auditHeader = self.RECORD_SCHEMA.field_map["auditHeader"].default self.oldSnapshot = self.RECORD_SCHEMA.field_map["oldSnapshot"].default self.newSnapshot = ChartSnapshotClass.construct_with_defaults() @property def auditHeader(self) -> Union[None, "KafkaAuditHeaderClass"]: """Getter: Kafka audit header. See go/kafkaauditheader for more info.""" return self._inner_dict.get('auditHeader') # type: ignore @auditHeader.setter def auditHeader(self, value: Union[None, "KafkaAuditHeaderClass"]) -> None: """Setter: Kafka audit header. See go/kafkaauditheader for more info.""" self._inner_dict['auditHeader'] = value @property def oldSnapshot(self) -> Union[None, "ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]: """Getter: Snapshot of the metadata before the update. Set to null for newly created metadata. Only the metadata aspects affected by the update are included in the snapshot.""" return self._inner_dict.get('oldSnapshot') # type: ignore @oldSnapshot.setter def oldSnapshot(self, value: Union[None, "ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]) -> None: """Setter: Snapshot of the metadata before the update. Set to null for newly created metadata. Only the metadata aspects affected by the update are included in the snapshot.""" self._inner_dict['oldSnapshot'] = value @property def newSnapshot(self) -> Union["ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]: """Getter: Snapshot of the metadata after the update. Only the metadata aspects affected by the update are included in the snapshot.""" return self._inner_dict.get('newSnapshot') # type: ignore @newSnapshot.setter def newSnapshot(self, value: Union["ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]) -> None: """Setter: Snapshot of the metadata after the update. Only the metadata aspects affected by the update are included in the snapshot.""" self._inner_dict['newSnapshot'] = value class MetadataChangeEventClass(DictWrapper): """Kafka event for proposing a metadata change for an entity. A corresponding MetadataAuditEvent is emitted when the change is accepted and committed, otherwise a FailedMetadataChangeEvent will be emitted instead.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.mxe.MetadataChangeEvent") def __init__(self, proposedSnapshot: Union["ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"], auditHeader: Union[None, "KafkaAuditHeaderClass"]=None, proposedDelta: None=None, ): super().__init__() self.auditHeader = auditHeader self.proposedSnapshot = proposedSnapshot self.proposedDelta = proposedDelta @classmethod def construct_with_defaults(cls) -> "MetadataChangeEventClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.auditHeader = self.RECORD_SCHEMA.field_map["auditHeader"].default self.proposedSnapshot = ChartSnapshotClass.construct_with_defaults() self.proposedDelta = self.RECORD_SCHEMA.field_map["proposedDelta"].default @property def auditHeader(self) -> Union[None, "KafkaAuditHeaderClass"]: """Getter: Kafka audit header. See go/kafkaauditheader for more info.""" return self._inner_dict.get('auditHeader') # type: ignore @auditHeader.setter def auditHeader(self, value: Union[None, "KafkaAuditHeaderClass"]) -> None: """Setter: Kafka audit header. See go/kafkaauditheader for more info.""" self._inner_dict['auditHeader'] = value @property def proposedSnapshot(self) -> Union["ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]: """Getter: Snapshot of the proposed metadata change. Include only the aspects affected by the change in the snapshot.""" return self._inner_dict.get('proposedSnapshot') # type: ignore @proposedSnapshot.setter def proposedSnapshot(self, value: Union["ChartSnapshotClass", "CorpGroupSnapshotClass", "CorpUserSnapshotClass", "DashboardSnapshotClass", "DataFlowSnapshotClass", "DataJobSnapshotClass", "DatasetSnapshotClass", "DataProcessSnapshotClass", "DataPlatformSnapshotClass", "MLModelSnapshotClass", "MLPrimaryKeySnapshotClass", "MLFeatureSnapshotClass", "MLFeatureTableSnapshotClass", "TagSnapshotClass", "GlossaryTermSnapshotClass", "GlossaryNodeSnapshotClass"]) -> None: """Setter: Snapshot of the proposed metadata change. Include only the aspects affected by the change in the snapshot.""" self._inner_dict['proposedSnapshot'] = value @property def proposedDelta(self) -> None: """Getter: Delta of the proposed metadata partial update.""" return self._inner_dict.get('proposedDelta') # type: ignore @proposedDelta.setter def proposedDelta(self, value: None) -> None: """Setter: Delta of the proposed metadata partial update.""" self._inner_dict['proposedDelta'] = value class ArrayTypeClass(DictWrapper): """Array field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.ArrayType") def __init__(self, nestedType: Union[None, List[str]]=None, ): super().__init__() self.nestedType = nestedType @classmethod def construct_with_defaults(cls) -> "ArrayTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.nestedType = self.RECORD_SCHEMA.field_map["nestedType"].default @property def nestedType(self) -> Union[None, List[str]]: """Getter: List of types this array holds.""" return self._inner_dict.get('nestedType') # type: ignore @nestedType.setter def nestedType(self, value: Union[None, List[str]]) -> None: """Setter: List of types this array holds.""" self._inner_dict['nestedType'] = value class BinaryJsonSchemaClass(DictWrapper): """Schema text of binary JSON schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.BinaryJsonSchema") def __init__(self, schema: str, ): super().__init__() self.schema = schema @classmethod def construct_with_defaults(cls) -> "BinaryJsonSchemaClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.schema = str() @property def schema(self) -> str: """Getter: The native schema text for binary JSON file format.""" return self._inner_dict.get('schema') # type: ignore @schema.setter def schema(self, value: str) -> None: """Setter: The native schema text for binary JSON file format.""" self._inner_dict['schema'] = value class BooleanTypeClass(DictWrapper): """Boolean field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.BooleanType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "BooleanTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class BytesTypeClass(DictWrapper): """Bytes field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.BytesType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "BytesTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class DatasetFieldForeignKeyClass(DictWrapper): """For non-urn based foregin keys.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.DatasetFieldForeignKey") def __init__(self, parentDataset: str, currentFieldPaths: List[str], parentField: str, ): super().__init__() self.parentDataset = parentDataset self.currentFieldPaths = currentFieldPaths self.parentField = parentField @classmethod def construct_with_defaults(cls) -> "DatasetFieldForeignKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.parentDataset = str() self.currentFieldPaths = list() self.parentField = str() @property def parentDataset(self) -> str: """Getter: dataset that stores the resource.""" return self._inner_dict.get('parentDataset') # type: ignore @parentDataset.setter def parentDataset(self, value: str) -> None: """Setter: dataset that stores the resource.""" self._inner_dict['parentDataset'] = value @property def currentFieldPaths(self) -> List[str]: """Getter: List of fields in hosting(current) SchemaMetadata that conform a foreign key. List can contain a single entry or multiple entries if several entries in hosting schema conform a foreign key in a single parent dataset.""" return self._inner_dict.get('currentFieldPaths') # type: ignore @currentFieldPaths.setter def currentFieldPaths(self, value: List[str]) -> None: """Setter: List of fields in hosting(current) SchemaMetadata that conform a foreign key. List can contain a single entry or multiple entries if several entries in hosting schema conform a foreign key in a single parent dataset.""" self._inner_dict['currentFieldPaths'] = value @property def parentField(self) -> str: """Getter: SchemaField@fieldPath that uniquely identify field in parent dataset that this field references.""" return self._inner_dict.get('parentField') # type: ignore @parentField.setter def parentField(self, value: str) -> None: """Setter: SchemaField@fieldPath that uniquely identify field in parent dataset that this field references.""" self._inner_dict['parentField'] = value class DateTypeClass(DictWrapper): """Date field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.DateType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "DateTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class EditableSchemaFieldInfoClass(DictWrapper): """SchemaField to describe metadata related to dataset schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.EditableSchemaFieldInfo") def __init__(self, fieldPath: str, description: Union[None, str]=None, globalTags: Union[None, "GlobalTagsClass"]=None, ): super().__init__() self.fieldPath = fieldPath self.description = description self.globalTags = globalTags @classmethod def construct_with_defaults(cls) -> "EditableSchemaFieldInfoClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.fieldPath = str() self.description = self.RECORD_SCHEMA.field_map["description"].default self.globalTags = self.RECORD_SCHEMA.field_map["globalTags"].default @property def fieldPath(self) -> str: """Getter: FieldPath uniquely identifying the SchemaField this metadata is associated with""" return self._inner_dict.get('fieldPath') # type: ignore @fieldPath.setter def fieldPath(self, value: str) -> None: """Setter: FieldPath uniquely identifying the SchemaField this metadata is associated with""" self._inner_dict['fieldPath'] = value @property def description(self) -> Union[None, str]: """Getter: Description""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Description""" self._inner_dict['description'] = value @property def globalTags(self) -> Union[None, "GlobalTagsClass"]: """Getter: Tags associated with the field""" return self._inner_dict.get('globalTags') # type: ignore @globalTags.setter def globalTags(self, value: Union[None, "GlobalTagsClass"]) -> None: """Setter: Tags associated with the field""" self._inner_dict['globalTags'] = value class EditableSchemaMetadataClass(DictWrapper): """EditableSchemaMetadata stores editable changes made to schema metadata. This separates changes made from ingestion pipelines and edits in the UI to avoid accidental overwrites of user-provided data by ingestion pipelines.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.EditableSchemaMetadata") def __init__(self, editableSchemaFieldInfo: List["EditableSchemaFieldInfoClass"], created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, ): super().__init__() if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.editableSchemaFieldInfo = editableSchemaFieldInfo @classmethod def construct_with_defaults(cls) -> "EditableSchemaMetadataClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.editableSchemaFieldInfo = list() @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def editableSchemaFieldInfo(self) -> List["EditableSchemaFieldInfoClass"]: """Getter: Client provided a list of fields from document schema.""" return self._inner_dict.get('editableSchemaFieldInfo') # type: ignore @editableSchemaFieldInfo.setter def editableSchemaFieldInfo(self, value: List["EditableSchemaFieldInfoClass"]) -> None: """Setter: Client provided a list of fields from document schema.""" self._inner_dict['editableSchemaFieldInfo'] = value class EnumTypeClass(DictWrapper): """Enum field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.EnumType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "EnumTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class EspressoSchemaClass(DictWrapper): """Schema text of an espresso table schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.EspressoSchema") def __init__(self, documentSchema: str, tableSchema: str, ): super().__init__() self.documentSchema = documentSchema self.tableSchema = tableSchema @classmethod def construct_with_defaults(cls) -> "EspressoSchemaClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.documentSchema = str() self.tableSchema = str() @property def documentSchema(self) -> str: """Getter: The native espresso document schema.""" return self._inner_dict.get('documentSchema') # type: ignore @documentSchema.setter def documentSchema(self, value: str) -> None: """Setter: The native espresso document schema.""" self._inner_dict['documentSchema'] = value @property def tableSchema(self) -> str: """Getter: The espresso table schema definition.""" return self._inner_dict.get('tableSchema') # type: ignore @tableSchema.setter def tableSchema(self, value: str) -> None: """Setter: The espresso table schema definition.""" self._inner_dict['tableSchema'] = value class FixedTypeClass(DictWrapper): """Fixed field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.FixedType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "FixedTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class ForeignKeySpecClass(DictWrapper): """Description of a foreign key in a schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.ForeignKeySpec") def __init__(self, foreignKey: Union["DatasetFieldForeignKeyClass", "UrnForeignKeyClass"], ): super().__init__() self.foreignKey = foreignKey @classmethod def construct_with_defaults(cls) -> "ForeignKeySpecClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.foreignKey = DatasetFieldForeignKeyClass.construct_with_defaults() @property def foreignKey(self) -> Union["DatasetFieldForeignKeyClass", "UrnForeignKeyClass"]: """Getter: Foreign key definition in metadata schema.""" return self._inner_dict.get('foreignKey') # type: ignore @foreignKey.setter def foreignKey(self, value: Union["DatasetFieldForeignKeyClass", "UrnForeignKeyClass"]) -> None: """Setter: Foreign key definition in metadata schema.""" self._inner_dict['foreignKey'] = value class KafkaSchemaClass(DictWrapper): """Schema holder for kafka schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.KafkaSchema") def __init__(self, documentSchema: str, ): super().__init__() self.documentSchema = documentSchema @classmethod def construct_with_defaults(cls) -> "KafkaSchemaClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.documentSchema = str() @property def documentSchema(self) -> str: """Getter: The native kafka document schema. This is a human readable avro document schema.""" return self._inner_dict.get('documentSchema') # type: ignore @documentSchema.setter def documentSchema(self, value: str) -> None: """Setter: The native kafka document schema. This is a human readable avro document schema.""" self._inner_dict['documentSchema'] = value class KeyValueSchemaClass(DictWrapper): """Schema text of a key-value store schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.KeyValueSchema") def __init__(self, keySchema: str, valueSchema: str, ): super().__init__() self.keySchema = keySchema self.valueSchema = valueSchema @classmethod def construct_with_defaults(cls) -> "KeyValueSchemaClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.keySchema = str() self.valueSchema = str() @property def keySchema(self) -> str: """Getter: The raw schema for the key in the key-value store.""" return self._inner_dict.get('keySchema') # type: ignore @keySchema.setter def keySchema(self, value: str) -> None: """Setter: The raw schema for the key in the key-value store.""" self._inner_dict['keySchema'] = value @property def valueSchema(self) -> str: """Getter: The raw schema for the value in the key-value store.""" return self._inner_dict.get('valueSchema') # type: ignore @valueSchema.setter def valueSchema(self, value: str) -> None: """Setter: The raw schema for the value in the key-value store.""" self._inner_dict['valueSchema'] = value class MapTypeClass(DictWrapper): """Map field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.MapType") def __init__(self, keyType: Union[None, str]=None, valueType: Union[None, str]=None, ): super().__init__() self.keyType = keyType self.valueType = valueType @classmethod def construct_with_defaults(cls) -> "MapTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.keyType = self.RECORD_SCHEMA.field_map["keyType"].default self.valueType = self.RECORD_SCHEMA.field_map["valueType"].default @property def keyType(self) -> Union[None, str]: """Getter: Key type in a map""" return self._inner_dict.get('keyType') # type: ignore @keyType.setter def keyType(self, value: Union[None, str]) -> None: """Setter: Key type in a map""" self._inner_dict['keyType'] = value @property def valueType(self) -> Union[None, str]: """Getter: Type of the value in a map""" return self._inner_dict.get('valueType') # type: ignore @valueType.setter def valueType(self, value: Union[None, str]) -> None: """Setter: Type of the value in a map""" self._inner_dict['valueType'] = value class MySqlDDLClass(DictWrapper): """Schema holder for MySql data definition language that describes an MySql table.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.MySqlDDL") def __init__(self, tableSchema: str, ): super().__init__() self.tableSchema = tableSchema @classmethod def construct_with_defaults(cls) -> "MySqlDDLClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.tableSchema = str() @property def tableSchema(self) -> str: """Getter: The native schema in the dataset's platform. This is a human readable (json blob) table schema.""" return self._inner_dict.get('tableSchema') # type: ignore @tableSchema.setter def tableSchema(self, value: str) -> None: """Setter: The native schema in the dataset's platform. This is a human readable (json blob) table schema.""" self._inner_dict['tableSchema'] = value class NullTypeClass(DictWrapper): """Null field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.NullType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "NullTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class NumberTypeClass(DictWrapper): """Number data type: long, integer, short, etc..""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.NumberType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "NumberTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class OracleDDLClass(DictWrapper): """Schema holder for oracle data definition language that describes an oracle table.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.OracleDDL") def __init__(self, tableSchema: str, ): super().__init__() self.tableSchema = tableSchema @classmethod def construct_with_defaults(cls) -> "OracleDDLClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.tableSchema = str() @property def tableSchema(self) -> str: """Getter: The native schema in the dataset's platform. This is a human readable (json blob) table schema.""" return self._inner_dict.get('tableSchema') # type: ignore @tableSchema.setter def tableSchema(self, value: str) -> None: """Setter: The native schema in the dataset's platform. This is a human readable (json blob) table schema.""" self._inner_dict['tableSchema'] = value class OrcSchemaClass(DictWrapper): """Schema text of an ORC schema.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.OrcSchema") def __init__(self, schema: str, ): super().__init__() self.schema = schema @classmethod def construct_with_defaults(cls) -> "OrcSchemaClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.schema = str() @property def schema(self) -> str: """Getter: The native schema for ORC file format.""" return self._inner_dict.get('schema') # type: ignore @schema.setter def schema(self, value: str) -> None: """Setter: The native schema for ORC file format.""" self._inner_dict['schema'] = value class OtherSchemaClass(DictWrapper): """Schema holder for undefined schema types.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.OtherSchema") def __init__(self, rawSchema: str, ): super().__init__() self.rawSchema = rawSchema @classmethod def construct_with_defaults(cls) -> "OtherSchemaClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.rawSchema = str() @property def rawSchema(self) -> str: """Getter: The native schema in the dataset's platform.""" return self._inner_dict.get('rawSchema') # type: ignore @rawSchema.setter def rawSchema(self, value: str) -> None: """Setter: The native schema in the dataset's platform.""" self._inner_dict['rawSchema'] = value class PrestoDDLClass(DictWrapper): """Schema holder for presto data definition language that describes a presto view.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.PrestoDDL") def __init__(self, rawSchema: str, ): super().__init__() self.rawSchema = rawSchema @classmethod def construct_with_defaults(cls) -> "PrestoDDLClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.rawSchema = str() @property def rawSchema(self) -> str: """Getter: The raw schema in the dataset's platform. This includes the DDL and the columns extracted from DDL.""" return self._inner_dict.get('rawSchema') # type: ignore @rawSchema.setter def rawSchema(self, value: str) -> None: """Setter: The raw schema in the dataset's platform. This includes the DDL and the columns extracted from DDL.""" self._inner_dict['rawSchema'] = value class RecordTypeClass(DictWrapper): """Record field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.RecordType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "RecordTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class SchemaFieldClass(DictWrapper): """SchemaField to describe metadata related to dataset schema. Schema normalization rules: http://go/tms-schema""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.SchemaField") def __init__(self, fieldPath: str, type: "SchemaFieldDataTypeClass", nativeDataType: str, jsonPath: Union[None, str]=None, nullable: Optional[bool]=None, description: Union[None, str]=None, recursive: Optional[bool]=None, globalTags: Union[None, "GlobalTagsClass"]=None, glossaryTerms: Union[None, "GlossaryTermsClass"]=None, ): super().__init__() self.fieldPath = fieldPath self.jsonPath = jsonPath if nullable is None: # default: False self.nullable = self.RECORD_SCHEMA.field_map["nullable"].default else: self.nullable = nullable self.description = description self.type = type self.nativeDataType = nativeDataType if recursive is None: # default: False self.recursive = self.RECORD_SCHEMA.field_map["recursive"].default else: self.recursive = recursive self.globalTags = globalTags self.glossaryTerms = glossaryTerms @classmethod def construct_with_defaults(cls) -> "SchemaFieldClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.fieldPath = str() self.jsonPath = self.RECORD_SCHEMA.field_map["jsonPath"].default self.nullable = self.RECORD_SCHEMA.field_map["nullable"].default self.description = self.RECORD_SCHEMA.field_map["description"].default self.type = SchemaFieldDataTypeClass.construct_with_defaults() self.nativeDataType = str() self.recursive = self.RECORD_SCHEMA.field_map["recursive"].default self.globalTags = self.RECORD_SCHEMA.field_map["globalTags"].default self.glossaryTerms = self.RECORD_SCHEMA.field_map["glossaryTerms"].default @property def fieldPath(self) -> str: """Getter: Flattened name of the field. Field is computed from jsonPath field. For data translation rules refer to wiki page above.""" return self._inner_dict.get('fieldPath') # type: ignore @fieldPath.setter def fieldPath(self, value: str) -> None: """Setter: Flattened name of the field. Field is computed from jsonPath field. For data translation rules refer to wiki page above.""" self._inner_dict['fieldPath'] = value @property def jsonPath(self) -> Union[None, str]: """Getter: Flattened name of a field in JSON Path notation.""" return self._inner_dict.get('jsonPath') # type: ignore @jsonPath.setter def jsonPath(self, value: Union[None, str]) -> None: """Setter: Flattened name of a field in JSON Path notation.""" self._inner_dict['jsonPath'] = value @property def nullable(self) -> bool: """Getter: Indicates if this field is optional or nullable""" return self._inner_dict.get('nullable') # type: ignore @nullable.setter def nullable(self, value: bool) -> None: """Setter: Indicates if this field is optional or nullable""" self._inner_dict['nullable'] = value @property def description(self) -> Union[None, str]: """Getter: Description""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Description""" self._inner_dict['description'] = value @property def type(self) -> "SchemaFieldDataTypeClass": """Getter: Platform independent field type of the field.""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: "SchemaFieldDataTypeClass") -> None: """Setter: Platform independent field type of the field.""" self._inner_dict['type'] = value @property def nativeDataType(self) -> str: """Getter: The native type of the field in the dataset's platform as declared by platform schema.""" return self._inner_dict.get('nativeDataType') # type: ignore @nativeDataType.setter def nativeDataType(self, value: str) -> None: """Setter: The native type of the field in the dataset's platform as declared by platform schema.""" self._inner_dict['nativeDataType'] = value @property def recursive(self) -> bool: """Getter: There are use cases when a field in type B references type A. A field in A references field of type B. In such cases, we will mark the first field as recursive.""" return self._inner_dict.get('recursive') # type: ignore @recursive.setter def recursive(self, value: bool) -> None: """Setter: There are use cases when a field in type B references type A. A field in A references field of type B. In such cases, we will mark the first field as recursive.""" self._inner_dict['recursive'] = value @property def globalTags(self) -> Union[None, "GlobalTagsClass"]: """Getter: Tags associated with the field""" return self._inner_dict.get('globalTags') # type: ignore @globalTags.setter def globalTags(self, value: Union[None, "GlobalTagsClass"]) -> None: """Setter: Tags associated with the field""" self._inner_dict['globalTags'] = value @property def glossaryTerms(self) -> Union[None, "GlossaryTermsClass"]: """Getter: Glossary terms associated with the field""" return self._inner_dict.get('glossaryTerms') # type: ignore @glossaryTerms.setter def glossaryTerms(self, value: Union[None, "GlossaryTermsClass"]) -> None: """Setter: Glossary terms associated with the field""" self._inner_dict['glossaryTerms'] = value class SchemaFieldDataTypeClass(DictWrapper): """Schema field data types""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.SchemaFieldDataType") def __init__(self, type: Union["BooleanTypeClass", "FixedTypeClass", "StringTypeClass", "BytesTypeClass", "NumberTypeClass", "DateTypeClass", "TimeTypeClass", "EnumTypeClass", "NullTypeClass", "MapTypeClass", "ArrayTypeClass", "UnionTypeClass", "RecordTypeClass"], ): super().__init__() self.type = type @classmethod def construct_with_defaults(cls) -> "SchemaFieldDataTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.type = BooleanTypeClass.construct_with_defaults() @property def type(self) -> Union["BooleanTypeClass", "FixedTypeClass", "StringTypeClass", "BytesTypeClass", "NumberTypeClass", "DateTypeClass", "TimeTypeClass", "EnumTypeClass", "NullTypeClass", "MapTypeClass", "ArrayTypeClass", "UnionTypeClass", "RecordTypeClass"]: """Getter: Data platform specific types""" return self._inner_dict.get('type') # type: ignore @type.setter def type(self, value: Union["BooleanTypeClass", "FixedTypeClass", "StringTypeClass", "BytesTypeClass", "NumberTypeClass", "DateTypeClass", "TimeTypeClass", "EnumTypeClass", "NullTypeClass", "MapTypeClass", "ArrayTypeClass", "UnionTypeClass", "RecordTypeClass"]) -> None: """Setter: Data platform specific types""" self._inner_dict['type'] = value class SchemaMetadataClass(DictWrapper): """SchemaMetadata to describe metadata related to store schema""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.SchemaMetadata") def __init__(self, schemaName: str, platform: str, version: int, hash: str, platformSchema: Union["EspressoSchemaClass", "OracleDDLClass", "MySqlDDLClass", "PrestoDDLClass", "KafkaSchemaClass", "BinaryJsonSchemaClass", "OrcSchemaClass", "SchemalessClass", "KeyValueSchemaClass", "OtherSchemaClass"], fields: List["SchemaFieldClass"], created: Optional["AuditStampClass"]=None, lastModified: Optional["AuditStampClass"]=None, deleted: Union[None, "AuditStampClass"]=None, dataset: Union[None, str]=None, cluster: Union[None, str]=None, primaryKeys: Union[None, List[str]]=None, foreignKeysSpecs: Union[None, Dict[str, "ForeignKeySpecClass"]]=None, ): super().__init__() self.schemaName = schemaName self.platform = platform self.version = version if created is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) else: self.created = created if lastModified is None: # default: {'actor': 'urn:li:corpuser:unknown', 'impersonator': None, 'time': 0} self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) else: self.lastModified = lastModified self.deleted = deleted self.dataset = dataset self.cluster = cluster self.hash = hash self.platformSchema = platformSchema self.fields = fields self.primaryKeys = primaryKeys self.foreignKeysSpecs = foreignKeysSpecs @classmethod def construct_with_defaults(cls) -> "SchemaMetadataClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.schemaName = str() self.platform = str() self.version = int() self.created = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["created"].default, writers_schema=self.RECORD_SCHEMA.field_map["created"].type) self.lastModified = _json_converter.from_json_object(self.RECORD_SCHEMA.field_map["lastModified"].default, writers_schema=self.RECORD_SCHEMA.field_map["lastModified"].type) self.deleted = self.RECORD_SCHEMA.field_map["deleted"].default self.dataset = self.RECORD_SCHEMA.field_map["dataset"].default self.cluster = self.RECORD_SCHEMA.field_map["cluster"].default self.hash = str() self.platformSchema = EspressoSchemaClass.construct_with_defaults() self.fields = list() self.primaryKeys = self.RECORD_SCHEMA.field_map["primaryKeys"].default self.foreignKeysSpecs = self.RECORD_SCHEMA.field_map["foreignKeysSpecs"].default @property def schemaName(self) -> str: """Getter: Schema name e.g. PageViewEvent, identity.Profile, ams.account_management_tracking""" return self._inner_dict.get('schemaName') # type: ignore @schemaName.setter def schemaName(self, value: str) -> None: """Setter: Schema name e.g. PageViewEvent, identity.Profile, ams.account_management_tracking""" self._inner_dict['schemaName'] = value @property def platform(self) -> str: """Getter: Standardized platform urn where schema is defined. The data platform Urn (urn:li:platform:{platform_name})""" return self._inner_dict.get('platform') # type: ignore @platform.setter def platform(self, value: str) -> None: """Setter: Standardized platform urn where schema is defined. The data platform Urn (urn:li:platform:{platform_name})""" self._inner_dict['platform'] = value @property def version(self) -> int: """Getter: Every change to SchemaMetadata in the resource results in a new version. Version is server assigned. This version is differ from platform native schema version.""" return self._inner_dict.get('version') # type: ignore @version.setter def version(self, value: int) -> None: """Setter: Every change to SchemaMetadata in the resource results in a new version. Version is server assigned. This version is differ from platform native schema version.""" self._inner_dict['version'] = value @property def created(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" return self._inner_dict.get('created') # type: ignore @created.setter def created(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the creation of this resource/association/sub-resource. A value of 0 for time indicates missing data.""" self._inner_dict['created'] = value @property def lastModified(self) -> "AuditStampClass": """Getter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" return self._inner_dict.get('lastModified') # type: ignore @lastModified.setter def lastModified(self, value: "AuditStampClass") -> None: """Setter: An AuditStamp corresponding to the last modification of this resource/association/sub-resource. If no modification has happened since creation, lastModified should be the same as created. A value of 0 for time indicates missing data.""" self._inner_dict['lastModified'] = value @property def deleted(self) -> Union[None, "AuditStampClass"]: """Getter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" return self._inner_dict.get('deleted') # type: ignore @deleted.setter def deleted(self, value: Union[None, "AuditStampClass"]) -> None: """Setter: An AuditStamp corresponding to the deletion of this resource/association/sub-resource. Logically, deleted MUST have a later timestamp than creation. It may or may not have the same time as lastModified depending upon the resource/association/sub-resource semantics.""" self._inner_dict['deleted'] = value @property def dataset(self) -> Union[None, str]: """Getter: Dataset this schema metadata is associated with.""" return self._inner_dict.get('dataset') # type: ignore @dataset.setter def dataset(self, value: Union[None, str]) -> None: """Setter: Dataset this schema metadata is associated with.""" self._inner_dict['dataset'] = value @property def cluster(self) -> Union[None, str]: """Getter: The cluster this schema metadata resides from""" return self._inner_dict.get('cluster') # type: ignore @cluster.setter def cluster(self, value: Union[None, str]) -> None: """Setter: The cluster this schema metadata resides from""" self._inner_dict['cluster'] = value @property def hash(self) -> str: """Getter: the SHA1 hash of the schema content""" return self._inner_dict.get('hash') # type: ignore @hash.setter def hash(self, value: str) -> None: """Setter: the SHA1 hash of the schema content""" self._inner_dict['hash'] = value @property def platformSchema(self) -> Union["EspressoSchemaClass", "OracleDDLClass", "MySqlDDLClass", "PrestoDDLClass", "KafkaSchemaClass", "BinaryJsonSchemaClass", "OrcSchemaClass", "SchemalessClass", "KeyValueSchemaClass", "OtherSchemaClass"]: """Getter: The native schema in the dataset's platform.""" return self._inner_dict.get('platformSchema') # type: ignore @platformSchema.setter def platformSchema(self, value: Union["EspressoSchemaClass", "OracleDDLClass", "MySqlDDLClass", "PrestoDDLClass", "KafkaSchemaClass", "BinaryJsonSchemaClass", "OrcSchemaClass", "SchemalessClass", "KeyValueSchemaClass", "OtherSchemaClass"]) -> None: """Setter: The native schema in the dataset's platform.""" self._inner_dict['platformSchema'] = value @property def fields(self) -> List["SchemaFieldClass"]: """Getter: Client provided a list of fields from document schema.""" return self._inner_dict.get('fields') # type: ignore @fields.setter def fields(self, value: List["SchemaFieldClass"]) -> None: """Setter: Client provided a list of fields from document schema.""" self._inner_dict['fields'] = value @property def primaryKeys(self) -> Union[None, List[str]]: """Getter: Client provided list of fields that define primary keys to access record. Field order defines hierarchical espresso keys. Empty lists indicates absence of primary key access patter. Value is a SchemaField@fieldPath.""" return self._inner_dict.get('primaryKeys') # type: ignore @primaryKeys.setter def primaryKeys(self, value: Union[None, List[str]]) -> None: """Setter: Client provided list of fields that define primary keys to access record. Field order defines hierarchical espresso keys. Empty lists indicates absence of primary key access patter. Value is a SchemaField@fieldPath.""" self._inner_dict['primaryKeys'] = value @property def foreignKeysSpecs(self) -> Union[None, Dict[str, "ForeignKeySpecClass"]]: """Getter: Map captures all the references schema makes to external datasets. Map key is ForeignKeySpecName typeref.""" return self._inner_dict.get('foreignKeysSpecs') # type: ignore @foreignKeysSpecs.setter def foreignKeysSpecs(self, value: Union[None, Dict[str, "ForeignKeySpecClass"]]) -> None: """Setter: Map captures all the references schema makes to external datasets. Map key is ForeignKeySpecName typeref.""" self._inner_dict['foreignKeysSpecs'] = value class SchemalessClass(DictWrapper): """The dataset has no specific schema associated with it""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.Schemaless") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "SchemalessClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class StringTypeClass(DictWrapper): """String field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.StringType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "StringTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class TimeTypeClass(DictWrapper): """Time field type. This should also be used for datetimes.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.TimeType") def __init__(self, ): super().__init__() @classmethod def construct_with_defaults(cls) -> "TimeTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: pass class UnionTypeClass(DictWrapper): """Union field type.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.UnionType") def __init__(self, nestedTypes: Union[None, List[str]]=None, ): super().__init__() self.nestedTypes = nestedTypes @classmethod def construct_with_defaults(cls) -> "UnionTypeClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.nestedTypes = self.RECORD_SCHEMA.field_map["nestedTypes"].default @property def nestedTypes(self) -> Union[None, List[str]]: """Getter: List of types in union type.""" return self._inner_dict.get('nestedTypes') # type: ignore @nestedTypes.setter def nestedTypes(self, value: Union[None, List[str]]) -> None: """Setter: List of types in union type.""" self._inner_dict['nestedTypes'] = value class UrnForeignKeyClass(DictWrapper): """If SchemaMetadata fields make any external references and references are of type com.linkedin.pegasus2avro.common.Urn or any children, this models can be used to mark it.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.schema.UrnForeignKey") def __init__(self, currentFieldPath: str, ): super().__init__() self.currentFieldPath = currentFieldPath @classmethod def construct_with_defaults(cls) -> "UrnForeignKeyClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.currentFieldPath = str() @property def currentFieldPath(self) -> str: """Getter: Field in hosting(current) SchemaMetadata.""" return self._inner_dict.get('currentFieldPath') # type: ignore @currentFieldPath.setter def currentFieldPath(self, value: str) -> None: """Setter: Field in hosting(current) SchemaMetadata.""" self._inner_dict['currentFieldPath'] = value class TagPropertiesClass(DictWrapper): """Properties associated with a Tag""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.tag.TagProperties") def __init__(self, name: str, description: Union[None, str]=None, ): super().__init__() self.name = name self.description = description @classmethod def construct_with_defaults(cls) -> "TagPropertiesClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.name = str() self.description = self.RECORD_SCHEMA.field_map["description"].default @property def name(self) -> str: """Getter: Name of the tag""" return self._inner_dict.get('name') # type: ignore @name.setter def name(self, value: str) -> None: """Setter: Name of the tag""" self._inner_dict['name'] = value @property def description(self) -> Union[None, str]: """Getter: Documentation of the tag""" return self._inner_dict.get('description') # type: ignore @description.setter def description(self, value: Union[None, str]) -> None: """Setter: Documentation of the tag""" self._inner_dict['description'] = value class FieldUsageCountsClass(DictWrapper): """ Records field-level usage counts for a given resource """ RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.usage.FieldUsageCounts") def __init__(self, fieldName: str, count: int, ): super().__init__() self.fieldName = fieldName self.count = count @classmethod def construct_with_defaults(cls) -> "FieldUsageCountsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.fieldName = str() self.count = int() @property def fieldName(self) -> str: # No docs available. return self._inner_dict.get('fieldName') # type: ignore @fieldName.setter def fieldName(self, value: str) -> None: # No docs available. self._inner_dict['fieldName'] = value @property def count(self) -> int: # No docs available. return self._inner_dict.get('count') # type: ignore @count.setter def count(self, value: int) -> None: # No docs available. self._inner_dict['count'] = value class UsageAggregationClass(DictWrapper): """Usage data for a given resource, rolled up into a bucket.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.usage.UsageAggregation") def __init__(self, bucket: int, duration: Union[str, "WindowDurationClass"], resource: str, metrics: "UsageAggregationMetricsClass", ): super().__init__() self.bucket = bucket self.duration = duration self.resource = resource self.metrics = metrics @classmethod def construct_with_defaults(cls) -> "UsageAggregationClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.bucket = int() self.duration = WindowDurationClass.YEAR self.resource = str() self.metrics = UsageAggregationMetricsClass.construct_with_defaults() @property def bucket(self) -> int: """Getter: Bucket start time in milliseconds """ return self._inner_dict.get('bucket') # type: ignore @bucket.setter def bucket(self, value: int) -> None: """Setter: Bucket start time in milliseconds """ self._inner_dict['bucket'] = value @property def duration(self) -> Union[str, "WindowDurationClass"]: """Getter: Bucket duration """ return self._inner_dict.get('duration') # type: ignore @duration.setter def duration(self, value: Union[str, "WindowDurationClass"]) -> None: """Setter: Bucket duration """ self._inner_dict['duration'] = value @property def resource(self) -> str: """Getter: Resource associated with these usage stats """ return self._inner_dict.get('resource') # type: ignore @resource.setter def resource(self, value: str) -> None: """Setter: Resource associated with these usage stats """ self._inner_dict['resource'] = value @property def metrics(self) -> "UsageAggregationMetricsClass": """Getter: Metrics associated with this bucket """ return self._inner_dict.get('metrics') # type: ignore @metrics.setter def metrics(self, value: "UsageAggregationMetricsClass") -> None: """Setter: Metrics associated with this bucket """ self._inner_dict['metrics'] = value class UsageAggregationMetricsClass(DictWrapper): """Metrics for usage data for a given resource and bucket. Not all fields make sense for all buckets, so every field is optional.""" RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.usage.UsageAggregationMetrics") def __init__(self, uniqueUserCount: Union[None, int]=None, users: Union[None, List["UserUsageCountsClass"]]=None, totalSqlQueries: Union[None, int]=None, topSqlQueries: Union[None, List[str]]=None, fields: Union[None, List["FieldUsageCountsClass"]]=None, ): super().__init__() self.uniqueUserCount = uniqueUserCount self.users = users self.totalSqlQueries = totalSqlQueries self.topSqlQueries = topSqlQueries self.fields = fields @classmethod def construct_with_defaults(cls) -> "UsageAggregationMetricsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.uniqueUserCount = self.RECORD_SCHEMA.field_map["uniqueUserCount"].default self.users = self.RECORD_SCHEMA.field_map["users"].default self.totalSqlQueries = self.RECORD_SCHEMA.field_map["totalSqlQueries"].default self.topSqlQueries = self.RECORD_SCHEMA.field_map["topSqlQueries"].default self.fields = self.RECORD_SCHEMA.field_map["fields"].default @property def uniqueUserCount(self) -> Union[None, int]: """Getter: Unique user count """ return self._inner_dict.get('uniqueUserCount') # type: ignore @uniqueUserCount.setter def uniqueUserCount(self, value: Union[None, int]) -> None: """Setter: Unique user count """ self._inner_dict['uniqueUserCount'] = value @property def users(self) -> Union[None, List["UserUsageCountsClass"]]: """Getter: Users within this bucket, with frequency counts """ return self._inner_dict.get('users') # type: ignore @users.setter def users(self, value: Union[None, List["UserUsageCountsClass"]]) -> None: """Setter: Users within this bucket, with frequency counts """ self._inner_dict['users'] = value @property def totalSqlQueries(self) -> Union[None, int]: """Getter: Total SQL query count """ return self._inner_dict.get('totalSqlQueries') # type: ignore @totalSqlQueries.setter def totalSqlQueries(self, value: Union[None, int]) -> None: """Setter: Total SQL query count """ self._inner_dict['totalSqlQueries'] = value @property def topSqlQueries(self) -> Union[None, List[str]]: """Getter: Frequent SQL queries; mostly makes sense for datasets in SQL databases """ return self._inner_dict.get('topSqlQueries') # type: ignore @topSqlQueries.setter def topSqlQueries(self, value: Union[None, List[str]]) -> None: """Setter: Frequent SQL queries; mostly makes sense for datasets in SQL databases """ self._inner_dict['topSqlQueries'] = value @property def fields(self) -> Union[None, List["FieldUsageCountsClass"]]: """Getter: Field-level usage stats """ return self._inner_dict.get('fields') # type: ignore @fields.setter def fields(self, value: Union[None, List["FieldUsageCountsClass"]]) -> None: """Setter: Field-level usage stats """ self._inner_dict['fields'] = value class UserUsageCountsClass(DictWrapper): """ Records a single user's usage counts for a given resource """ RECORD_SCHEMA = get_schema_type("com.linkedin.pegasus2avro.usage.UserUsageCounts") def __init__(self, count: int, user: Union[None, str]=None, userEmail: Union[None, str]=None, ): super().__init__() self.user = user self.count = count self.userEmail = userEmail @classmethod def construct_with_defaults(cls) -> "UserUsageCountsClass": self = cls.construct({}) self._restore_defaults() return self def _restore_defaults(self) -> None: self.user = self.RECORD_SCHEMA.field_map["user"].default self.count = int() self.userEmail = self.RECORD_SCHEMA.field_map["userEmail"].default @property def user(self) -> Union[None, str]: # No docs available. return self._inner_dict.get('user') # type: ignore @user.setter def user(self, value: Union[None, str]) -> None: # No docs available. self._inner_dict['user'] = value @property def count(self) -> int: # No docs available. return self._inner_dict.get('count') # type: ignore @count.setter def count(self, value: int) -> None: # No docs available. self._inner_dict['count'] = value @property def userEmail(self) -> Union[None, str]: """Getter: If user_email is set, we attempt to resolve the user's urn upon ingest """ return self._inner_dict.get('userEmail') # type: ignore @userEmail.setter def userEmail(self, value: Union[None, str]) -> None: """Setter: If user_email is set, we attempt to resolve the user's urn upon ingest """ self._inner_dict['userEmail'] = value __SCHEMA_TYPES = { 'com.linkedin.events.KafkaAuditHeader': KafkaAuditHeaderClass, 'com.linkedin.pegasus2avro.chart.ChartInfo': ChartInfoClass, 'com.linkedin.pegasus2avro.chart.ChartQuery': ChartQueryClass, 'com.linkedin.pegasus2avro.chart.ChartQueryType': ChartQueryTypeClass, 'com.linkedin.pegasus2avro.chart.ChartType': ChartTypeClass, 'com.linkedin.pegasus2avro.chart.EditableChartProperties': EditableChartPropertiesClass, 'com.linkedin.pegasus2avro.common.AccessLevel': AccessLevelClass, 'com.linkedin.pegasus2avro.common.AuditStamp': AuditStampClass, 'com.linkedin.pegasus2avro.common.BrowsePaths': BrowsePathsClass, 'com.linkedin.pegasus2avro.common.ChangeAuditStamps': ChangeAuditStampsClass, 'com.linkedin.pegasus2avro.common.Cost': CostClass, 'com.linkedin.pegasus2avro.common.CostCost': CostCostClass, 'com.linkedin.pegasus2avro.common.CostCostDiscriminator': CostCostDiscriminatorClass, 'com.linkedin.pegasus2avro.common.CostType': CostTypeClass, 'com.linkedin.pegasus2avro.common.Deprecation': DeprecationClass, 'com.linkedin.pegasus2avro.common.FabricType': FabricTypeClass, 'com.linkedin.pegasus2avro.common.GlobalTags': GlobalTagsClass, 'com.linkedin.pegasus2avro.common.GlossaryTermAssociation': GlossaryTermAssociationClass, 'com.linkedin.pegasus2avro.common.GlossaryTerms': GlossaryTermsClass, 'com.linkedin.pegasus2avro.common.InstitutionalMemory': InstitutionalMemoryClass, 'com.linkedin.pegasus2avro.common.InstitutionalMemoryMetadata': InstitutionalMemoryMetadataClass, 'com.linkedin.pegasus2avro.common.MLFeatureDataType': MLFeatureDataTypeClass, 'com.linkedin.pegasus2avro.common.Owner': OwnerClass, 'com.linkedin.pegasus2avro.common.Ownership': OwnershipClass, 'com.linkedin.pegasus2avro.common.OwnershipSource': OwnershipSourceClass, 'com.linkedin.pegasus2avro.common.OwnershipSourceType': OwnershipSourceTypeClass, 'com.linkedin.pegasus2avro.common.OwnershipType': OwnershipTypeClass, 'com.linkedin.pegasus2avro.common.Status': StatusClass, 'com.linkedin.pegasus2avro.common.TagAssociation': TagAssociationClass, 'com.linkedin.pegasus2avro.common.VersionTag': VersionTagClass, 'com.linkedin.pegasus2avro.common.WindowDuration': WindowDurationClass, 'com.linkedin.pegasus2avro.common.fieldtransformer.TransformationType': TransformationTypeClass, 'com.linkedin.pegasus2avro.common.fieldtransformer.UDFTransformer': UDFTransformerClass, 'com.linkedin.pegasus2avro.dashboard.DashboardInfo': DashboardInfoClass, 'com.linkedin.pegasus2avro.dashboard.EditableDashboardProperties': EditableDashboardPropertiesClass, 'com.linkedin.pegasus2avro.datajob.DataFlowInfo': DataFlowInfoClass, 'com.linkedin.pegasus2avro.datajob.DataJobInfo': DataJobInfoClass, 'com.linkedin.pegasus2avro.datajob.DataJobInputOutput': DataJobInputOutputClass, 'com.linkedin.pegasus2avro.datajob.EditableDataFlowProperties': EditableDataFlowPropertiesClass, 'com.linkedin.pegasus2avro.datajob.EditableDataJobProperties': EditableDataJobPropertiesClass, 'com.linkedin.pegasus2avro.datajob.JobStatus': JobStatusClass, 'com.linkedin.pegasus2avro.datajob.azkaban.AzkabanJobType': AzkabanJobTypeClass, 'com.linkedin.pegasus2avro.dataplatform.DataPlatformInfo': DataPlatformInfoClass, 'com.linkedin.pegasus2avro.dataplatform.PlatformType': PlatformTypeClass, 'com.linkedin.pegasus2avro.dataprocess.DataProcessInfo': DataProcessInfoClass, 'com.linkedin.pegasus2avro.dataset.DatasetDeprecation': DatasetDeprecationClass, 'com.linkedin.pegasus2avro.dataset.DatasetFieldMapping': DatasetFieldMappingClass, 'com.linkedin.pegasus2avro.dataset.DatasetLineageType': DatasetLineageTypeClass, 'com.linkedin.pegasus2avro.dataset.DatasetProperties': DatasetPropertiesClass, 'com.linkedin.pegasus2avro.dataset.DatasetUpstreamLineage': DatasetUpstreamLineageClass, 'com.linkedin.pegasus2avro.dataset.EditableDatasetProperties': EditableDatasetPropertiesClass, 'com.linkedin.pegasus2avro.dataset.Upstream': UpstreamClass, 'com.linkedin.pegasus2avro.dataset.UpstreamLineage': UpstreamLineageClass, 'com.linkedin.pegasus2avro.glossary.GlossaryNodeInfo': GlossaryNodeInfoClass, 'com.linkedin.pegasus2avro.glossary.GlossaryTermInfo': GlossaryTermInfoClass, 'com.linkedin.pegasus2avro.identity.CorpGroupInfo': CorpGroupInfoClass, 'com.linkedin.pegasus2avro.identity.CorpUserEditableInfo': CorpUserEditableInfoClass, 'com.linkedin.pegasus2avro.identity.CorpUserInfo': CorpUserInfoClass, 'com.linkedin.pegasus2avro.metadata.key.ChartKey': ChartKeyClass, 'com.linkedin.pegasus2avro.metadata.key.CorpGroupKey': CorpGroupKeyClass, 'com.linkedin.pegasus2avro.metadata.key.CorpUserKey': CorpUserKeyClass, 'com.linkedin.pegasus2avro.metadata.key.DashboardKey': DashboardKeyClass, 'com.linkedin.pegasus2avro.metadata.key.DataFlowKey': DataFlowKeyClass, 'com.linkedin.pegasus2avro.metadata.key.DataJobKey': DataJobKeyClass, 'com.linkedin.pegasus2avro.metadata.key.DataPlatformKey': DataPlatformKeyClass, 'com.linkedin.pegasus2avro.metadata.key.DataProcessKey': DataProcessKeyClass, 'com.linkedin.pegasus2avro.metadata.key.DatasetKey': DatasetKeyClass, 'com.linkedin.pegasus2avro.metadata.key.GlossaryNodeKey': GlossaryNodeKeyClass, 'com.linkedin.pegasus2avro.metadata.key.GlossaryTermKey': GlossaryTermKeyClass, 'com.linkedin.pegasus2avro.metadata.key.MLFeatureKey': MLFeatureKeyClass, 'com.linkedin.pegasus2avro.metadata.key.MLFeatureTableKey': MLFeatureTableKeyClass, 'com.linkedin.pegasus2avro.metadata.key.MLModelKey': MLModelKeyClass, 'com.linkedin.pegasus2avro.metadata.key.MLPrimaryKeyKey': MLPrimaryKeyKeyClass, 'com.linkedin.pegasus2avro.metadata.key.TagKey': TagKeyClass, 'com.linkedin.pegasus2avro.metadata.snapshot.ChartSnapshot': ChartSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.CorpGroupSnapshot': CorpGroupSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.CorpUserSnapshot': CorpUserSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.DashboardSnapshot': DashboardSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.DataFlowSnapshot': DataFlowSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.DataJobSnapshot': DataJobSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.DataPlatformSnapshot': DataPlatformSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.DataProcessSnapshot': DataProcessSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot': DatasetSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.GlossaryNodeSnapshot': GlossaryNodeSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.GlossaryTermSnapshot': GlossaryTermSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.MLFeatureSnapshot': MLFeatureSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.MLFeatureTableSnapshot': MLFeatureTableSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.MLModelSnapshot': MLModelSnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.MLPrimaryKeySnapshot': MLPrimaryKeySnapshotClass, 'com.linkedin.pegasus2avro.metadata.snapshot.TagSnapshot': TagSnapshotClass, 'com.linkedin.pegasus2avro.ml.metadata.BaseData': BaseDataClass, 'com.linkedin.pegasus2avro.ml.metadata.CaveatDetails': CaveatDetailsClass, 'com.linkedin.pegasus2avro.ml.metadata.CaveatsAndRecommendations': CaveatsAndRecommendationsClass, 'com.linkedin.pegasus2avro.ml.metadata.EthicalConsiderations': EthicalConsiderationsClass, 'com.linkedin.pegasus2avro.ml.metadata.EvaluationData': EvaluationDataClass, 'com.linkedin.pegasus2avro.ml.metadata.IntendedUse': IntendedUseClass, 'com.linkedin.pegasus2avro.ml.metadata.IntendedUserType': IntendedUserTypeClass, 'com.linkedin.pegasus2avro.ml.metadata.MLFeatureProperties': MLFeaturePropertiesClass, 'com.linkedin.pegasus2avro.ml.metadata.MLFeatureTableProperties': MLFeatureTablePropertiesClass, 'com.linkedin.pegasus2avro.ml.metadata.MLModelFactorPrompts': MLModelFactorPromptsClass, 'com.linkedin.pegasus2avro.ml.metadata.MLModelFactors': MLModelFactorsClass, 'com.linkedin.pegasus2avro.ml.metadata.MLModelProperties': MLModelPropertiesClass, 'com.linkedin.pegasus2avro.ml.metadata.MLPrimaryKeyProperties': MLPrimaryKeyPropertiesClass, 'com.linkedin.pegasus2avro.ml.metadata.Metrics': MetricsClass, 'com.linkedin.pegasus2avro.ml.metadata.QuantitativeAnalyses': QuantitativeAnalysesClass, 'com.linkedin.pegasus2avro.ml.metadata.SourceCode': SourceCodeClass, 'com.linkedin.pegasus2avro.ml.metadata.SourceCodeUrl': SourceCodeUrlClass, 'com.linkedin.pegasus2avro.ml.metadata.SourceCodeUrlType': SourceCodeUrlTypeClass, 'com.linkedin.pegasus2avro.ml.metadata.TrainingData': TrainingDataClass, 'com.linkedin.pegasus2avro.mxe.MetadataAuditEvent': MetadataAuditEventClass, 'com.linkedin.pegasus2avro.mxe.MetadataChangeEvent': MetadataChangeEventClass, 'com.linkedin.pegasus2avro.schema.ArrayType': ArrayTypeClass, 'com.linkedin.pegasus2avro.schema.BinaryJsonSchema': BinaryJsonSchemaClass, 'com.linkedin.pegasus2avro.schema.BooleanType': BooleanTypeClass, 'com.linkedin.pegasus2avro.schema.BytesType': BytesTypeClass, 'com.linkedin.pegasus2avro.schema.DatasetFieldForeignKey': DatasetFieldForeignKeyClass, 'com.linkedin.pegasus2avro.schema.DateType': DateTypeClass, 'com.linkedin.pegasus2avro.schema.EditableSchemaFieldInfo': EditableSchemaFieldInfoClass, 'com.linkedin.pegasus2avro.schema.EditableSchemaMetadata': EditableSchemaMetadataClass, 'com.linkedin.pegasus2avro.schema.EnumType': EnumTypeClass, 'com.linkedin.pegasus2avro.schema.EspressoSchema': EspressoSchemaClass, 'com.linkedin.pegasus2avro.schema.FixedType': FixedTypeClass, 'com.linkedin.pegasus2avro.schema.ForeignKeySpec': ForeignKeySpecClass, 'com.linkedin.pegasus2avro.schema.KafkaSchema': KafkaSchemaClass, 'com.linkedin.pegasus2avro.schema.KeyValueSchema': KeyValueSchemaClass, 'com.linkedin.pegasus2avro.schema.MapType': MapTypeClass, 'com.linkedin.pegasus2avro.schema.MySqlDDL': MySqlDDLClass, 'com.linkedin.pegasus2avro.schema.NullType': NullTypeClass, 'com.linkedin.pegasus2avro.schema.NumberType': NumberTypeClass, 'com.linkedin.pegasus2avro.schema.OracleDDL': OracleDDLClass, 'com.linkedin.pegasus2avro.schema.OrcSchema': OrcSchemaClass, 'com.linkedin.pegasus2avro.schema.OtherSchema': OtherSchemaClass, 'com.linkedin.pegasus2avro.schema.PrestoDDL': PrestoDDLClass, 'com.linkedin.pegasus2avro.schema.RecordType': RecordTypeClass, 'com.linkedin.pegasus2avro.schema.SchemaField': SchemaFieldClass, 'com.linkedin.pegasus2avro.schema.SchemaFieldDataType': SchemaFieldDataTypeClass, 'com.linkedin.pegasus2avro.schema.SchemaMetadata': SchemaMetadataClass, 'com.linkedin.pegasus2avro.schema.Schemaless': SchemalessClass, 'com.linkedin.pegasus2avro.schema.StringType': StringTypeClass, 'com.linkedin.pegasus2avro.schema.TimeType': TimeTypeClass, 'com.linkedin.pegasus2avro.schema.UnionType': UnionTypeClass, 'com.linkedin.pegasus2avro.schema.UrnForeignKey': UrnForeignKeyClass, 'com.linkedin.pegasus2avro.tag.TagProperties': TagPropertiesClass, 'com.linkedin.pegasus2avro.usage.FieldUsageCounts': FieldUsageCountsClass, 'com.linkedin.pegasus2avro.usage.UsageAggregation': UsageAggregationClass, 'com.linkedin.pegasus2avro.usage.UsageAggregationMetrics': UsageAggregationMetricsClass, 'com.linkedin.pegasus2avro.usage.UserUsageCounts': UserUsageCountsClass, 'KafkaAuditHeader': KafkaAuditHeaderClass, 'ChartInfo': ChartInfoClass, 'ChartQuery': ChartQueryClass, 'ChartQueryType': ChartQueryTypeClass, 'ChartType': ChartTypeClass, 'EditableChartProperties': EditableChartPropertiesClass, 'AccessLevel': AccessLevelClass, 'AuditStamp': AuditStampClass, 'BrowsePaths': BrowsePathsClass, 'ChangeAuditStamps': ChangeAuditStampsClass, 'Cost': CostClass, 'CostCost': CostCostClass, 'CostCostDiscriminator': CostCostDiscriminatorClass, 'CostType': CostTypeClass, 'Deprecation': DeprecationClass, 'FabricType': FabricTypeClass, 'GlobalTags': GlobalTagsClass, 'GlossaryTermAssociation': GlossaryTermAssociationClass, 'GlossaryTerms': GlossaryTermsClass, 'InstitutionalMemory': InstitutionalMemoryClass, 'InstitutionalMemoryMetadata': InstitutionalMemoryMetadataClass, 'MLFeatureDataType': MLFeatureDataTypeClass, 'Owner': OwnerClass, 'Ownership': OwnershipClass, 'OwnershipSource': OwnershipSourceClass, 'OwnershipSourceType': OwnershipSourceTypeClass, 'OwnershipType': OwnershipTypeClass, 'Status': StatusClass, 'TagAssociation': TagAssociationClass, 'VersionTag': VersionTagClass, 'WindowDuration': WindowDurationClass, 'TransformationType': TransformationTypeClass, 'UDFTransformer': UDFTransformerClass, 'DashboardInfo': DashboardInfoClass, 'EditableDashboardProperties': EditableDashboardPropertiesClass, 'DataFlowInfo': DataFlowInfoClass, 'DataJobInfo': DataJobInfoClass, 'DataJobInputOutput': DataJobInputOutputClass, 'EditableDataFlowProperties': EditableDataFlowPropertiesClass, 'EditableDataJobProperties': EditableDataJobPropertiesClass, 'JobStatus': JobStatusClass, 'AzkabanJobType': AzkabanJobTypeClass, 'DataPlatformInfo': DataPlatformInfoClass, 'PlatformType': PlatformTypeClass, 'DataProcessInfo': DataProcessInfoClass, 'DatasetDeprecation': DatasetDeprecationClass, 'DatasetFieldMapping': DatasetFieldMappingClass, 'DatasetLineageType': DatasetLineageTypeClass, 'DatasetProperties': DatasetPropertiesClass, 'DatasetUpstreamLineage': DatasetUpstreamLineageClass, 'EditableDatasetProperties': EditableDatasetPropertiesClass, 'Upstream': UpstreamClass, 'UpstreamLineage': UpstreamLineageClass, 'GlossaryNodeInfo': GlossaryNodeInfoClass, 'GlossaryTermInfo': GlossaryTermInfoClass, 'CorpGroupInfo': CorpGroupInfoClass, 'CorpUserEditableInfo': CorpUserEditableInfoClass, 'CorpUserInfo': CorpUserInfoClass, 'ChartKey': ChartKeyClass, 'CorpGroupKey': CorpGroupKeyClass, 'CorpUserKey': CorpUserKeyClass, 'DashboardKey': DashboardKeyClass, 'DataFlowKey': DataFlowKeyClass, 'DataJobKey': DataJobKeyClass, 'DataPlatformKey': DataPlatformKeyClass, 'DataProcessKey': DataProcessKeyClass, 'DatasetKey': DatasetKeyClass, 'GlossaryNodeKey': GlossaryNodeKeyClass, 'GlossaryTermKey': GlossaryTermKeyClass, 'MLFeatureKey': MLFeatureKeyClass, 'MLFeatureTableKey': MLFeatureTableKeyClass, 'MLModelKey': MLModelKeyClass, 'MLPrimaryKeyKey': MLPrimaryKeyKeyClass, 'TagKey': TagKeyClass, 'ChartSnapshot': ChartSnapshotClass, 'CorpGroupSnapshot': CorpGroupSnapshotClass, 'CorpUserSnapshot': CorpUserSnapshotClass, 'DashboardSnapshot': DashboardSnapshotClass, 'DataFlowSnapshot': DataFlowSnapshotClass, 'DataJobSnapshot': DataJobSnapshotClass, 'DataPlatformSnapshot': DataPlatformSnapshotClass, 'DataProcessSnapshot': DataProcessSnapshotClass, 'DatasetSnapshot': DatasetSnapshotClass, 'GlossaryNodeSnapshot': GlossaryNodeSnapshotClass, 'GlossaryTermSnapshot': GlossaryTermSnapshotClass, 'MLFeatureSnapshot': MLFeatureSnapshotClass, 'MLFeatureTableSnapshot': MLFeatureTableSnapshotClass, 'MLModelSnapshot': MLModelSnapshotClass, 'MLPrimaryKeySnapshot': MLPrimaryKeySnapshotClass, 'TagSnapshot': TagSnapshotClass, 'BaseData': BaseDataClass, 'CaveatDetails': CaveatDetailsClass, 'CaveatsAndRecommendations': CaveatsAndRecommendationsClass, 'EthicalConsiderations': EthicalConsiderationsClass, 'EvaluationData': EvaluationDataClass, 'IntendedUse': IntendedUseClass, 'IntendedUserType': IntendedUserTypeClass, 'MLFeatureProperties': MLFeaturePropertiesClass, 'MLFeatureTableProperties': MLFeatureTablePropertiesClass, 'MLModelFactorPrompts': MLModelFactorPromptsClass, 'MLModelFactors': MLModelFactorsClass, 'MLModelProperties': MLModelPropertiesClass, 'MLPrimaryKeyProperties': MLPrimaryKeyPropertiesClass, 'Metrics': MetricsClass, 'QuantitativeAnalyses': QuantitativeAnalysesClass, 'SourceCode': SourceCodeClass, 'SourceCodeUrl': SourceCodeUrlClass, 'SourceCodeUrlType': SourceCodeUrlTypeClass, 'TrainingData': TrainingDataClass, 'MetadataAuditEvent': MetadataAuditEventClass, 'MetadataChangeEvent': MetadataChangeEventClass, 'ArrayType': ArrayTypeClass, 'BinaryJsonSchema': BinaryJsonSchemaClass, 'BooleanType': BooleanTypeClass, 'BytesType': BytesTypeClass, 'DatasetFieldForeignKey': DatasetFieldForeignKeyClass, 'DateType': DateTypeClass, 'EditableSchemaFieldInfo': EditableSchemaFieldInfoClass, 'EditableSchemaMetadata': EditableSchemaMetadataClass, 'EnumType': EnumTypeClass, 'EspressoSchema': EspressoSchemaClass, 'FixedType': FixedTypeClass, 'ForeignKeySpec': ForeignKeySpecClass, 'KafkaSchema': KafkaSchemaClass, 'KeyValueSchema': KeyValueSchemaClass, 'MapType': MapTypeClass, 'MySqlDDL': MySqlDDLClass, 'NullType': NullTypeClass, 'NumberType': NumberTypeClass, 'OracleDDL': OracleDDLClass, 'OrcSchema': OrcSchemaClass, 'OtherSchema': OtherSchemaClass, 'PrestoDDL': PrestoDDLClass, 'RecordType': RecordTypeClass, 'SchemaField': SchemaFieldClass, 'SchemaFieldDataType': SchemaFieldDataTypeClass, 'SchemaMetadata': SchemaMetadataClass, 'Schemaless': SchemalessClass, 'StringType': StringTypeClass, 'TimeType': TimeTypeClass, 'UnionType': UnionTypeClass, 'UrnForeignKey': UrnForeignKeyClass, 'TagProperties': TagPropertiesClass, 'FieldUsageCounts': FieldUsageCountsClass, 'UsageAggregation': UsageAggregationClass, 'UsageAggregationMetrics': UsageAggregationMetricsClass, 'UserUsageCounts': UserUsageCountsClass, } _json_converter = avrojson.AvroJsonConverter(use_logical_types=False, schema_types=__SCHEMA_TYPES) # fmt: on
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tests/test_class_children.py
FermiParadox/ipy_student_exercises
0
186873
<filename>tests/test_class_children.py from unittest import TestCase from arbitrary_pieces import class_children class Test_class_children(TestCase): def test_class_children(self): class A(object): pass class B1(A): pass class B2(A): pass class C(B1): pass class MultInhC(B1, B2): pass self.assertSetEqual({B1, B2, C, MultInhC}, class_children(A)) def test_parent_is_not_child(self): class A(object): pass class B1(A): pass self.assertNotIn(A, class_children(A))
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reader/fnoData/stkOptDailyDataWrapper.py
sifarone/gce_k8s_deployment
0
54894
<reponame>sifarone/gce_k8s_deployment<filename>reader/fnoData/stkOptDailyDataWrapper.py from . import fnoUtils as utils class StkOptDailyDataWrapper: def __init__(self, dailyData): self.date = utils.convertDateToString(dailyData['date']) self.stkOptOpenPrice = dailyData['stkOptOpenPrice'] self.stkOptHighPrice = dailyData['stkOptHighPrice'] self.stkOptLowPrice = dailyData['stkOptLowPrice'] self.stkOptClosePrice = dailyData['stkOptClosePrice'] self.stkOptSettlePrice = dailyData['stkOptSettlePrice'] self.stkOptContracts = dailyData['stkOptContracts'] self.stkOptValueInLakhs = dailyData['stkOptValueInLakhs'] self.stkOptOpenInterest = dailyData['stkOptOpenInterest'] self.stkOptChangeInOpenInterest = dailyData['stkOptChangeInOpenInterest'] def getDailyDatainListForm(self): returnList = [] returnList.append(self.date) returnList.append(self.stkOptOpenPrice) returnList.append(self.stkOptHighPrice) returnList.append(self.stkOptLowPrice) returnList.append(self.stkOptClosePrice) returnList.append(self.stkOptSettlePrice) returnList.append(self.stkOptContracts) returnList.append(self.stkOptValueInLakhs) returnList.append(self.stkOptOpenInterest) returnList.append(self.stkOptChangeInOpenInterest) return returnList def getDailyDataInfo(self): returnData = {} returnData.update({'date': self.date}) returnData.update({'stkOptOpenPrice': self.stkOptOpenPrice}) returnData.update({'stkOptHighPrice': self.stkOptHighPrice}) returnData.update({'stkOptLowPrice': self.stkOptLowPrice}) returnData.update({'stkOptClosePrice': self.stkOptClosePrice}) returnData.update({'stkOptSettlePrice': self.stkOptSettlePrice}) returnData.update({'stkOptContracts': self.stkOptContracts}) returnData.update({'stkOptValueInLakhs': self.stkOptValueInLakhs}) returnData.update({'stkOptOpenInterest': self.stkOptOpenInterest}) returnData.update({'stkOptChangeInOpenInterest': self.stkOptChangeInOpenInterest}) return returnData def getStkOptDate(self): return self.date def getStkOptOpenPrice(self): return self.stkOptOpenPrice def getStkOptHighPrice(self): return self.stkOptHighPrice def getStkOptLowPrice(self): return self.stkOptLowPrice def getStkOptClosePrice(self): return self.stkOptClosePrice def getStkOptSettlePrice(self): return self.stkOptSettlePrice def getStkOptContracts(self): return self.stkOptContracts def getStkOptValueInLakhs(self): return self.stkOptValueInLakhs def getStkOptOpenInterest(self): return self.stkOptOpenInterest def getStkOptChangeInOpenInterest(self): return self.stkOptChangeInOpenInterest def printTypesOfDailyDataFields(self): print('type(date) : ', type(self.date)) print('type(stkOptOpenPrice) : ', type(self.stkOptOpenPrice)) print('type(stkOptHighPrice) : ', type(self.stkOptHighPrice)) print('type(stkOptLowPrice) : ', type(self.stkOptLowPrice)) print('type(stkOptClosePrice) : ', type(self.stkOptClosePrice)) print('type(stkOptSettlePrice) : ', type(self.stkOptSettlePrice)) print('type(stkOptContracts) : ', type(self.stkOptContracts)) print('type(stkOptValueInLakhs) : ', type(self.stkOptValueInLakhs)) print('type(stkOptOpenInterest) : ', type(self.stkOptOpenInterest)) print('type(stkOptChangeInOpenInterest) : ', type(self.stkOptChangeInOpenInterest)) def printDailyData(self) : print('date : ', self.date) print('stkOptOpenPrice : ', self.stkOptOpenPrice) print('stkOptHighPrice : ', self.stkOptHighPrice) print('stkOptLowPrice : ', self.stkOptLowPrice) print('stkOptClosePrice : ', self.stkOptClosePrice) print('stkOptSettlePrice : ', self.stkOptSettlePrice) print('stkOptContracts : ', self.stkOptContracts) print('stkOptValueInLakhs : ', self.stkOptValueInLakhs) print('stkOptOpenInterest : ', self.stkOptOpenInterest) print('stkOptChangeInOpenInterest : ', self.stkOptChangeInOpenInterest)
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game/player.py
b1naryth1ef/mmo
7
9049
<reponame>b1naryth1ef/mmo from sprites import PlayerSprite import time class Player(object): def __init__(self, name, game): self.name = name self.pos = [50, 50] self.do_blit = False self.game = game self.surf = game.SCREEN self.lastMove = 99999999999 self.velo_def = [0, 0] self.velo_x = 0 self.velo_y = 0 self.sprite = PlayerSprite(self) self.moving = [False, False, False, False] def tick(self): if self.do_blit: self.game.reDraw = True self.sprite.display(self.surf.screen) #self.surface.screen.blit(self.image, self.pos) self.do_blit = False # print self.lastMove - time.time() if True in self.moving and abs(self.lastMove - time.time()) >= .08: self.lastMove = time.time() if self.moving[0]: self.move(x=-1) if self.moving[1]: self.move(x=1)#down if self.moving[2]: self.move(y=-1)#left if self.moving[3]: self.move(y=1)#right def move(self, x=0, y=0): self.pos[1]+=x*10 self.pos[0]+=y*10 self.do_blit = True if y < 0 and self.sprite.dir == 1: self.sprite.flip() elif y > 0 and self.sprite.dir == -1: self.sprite.flip()
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custom_components/ziggonext/const.py
BioGeekJoey/Home-Assistant-config
1
73223
"""Constants for the Ziggo Mediabox Next integration.""" ZIGGO_API = "ziggo_api" CONF_COUNTRY_CODE = "country_code"
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setup.py
Nikola-K/lpbot
10
65505
<reponame>Nikola-K/lpbot<filename>setup.py #!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import unicode_literals from distutils.core import setup import tempfile import os import shutil from lpbot import __version__ requires = ['feedparser', 'pytz', 'lxml', 'praw', 'enchant', 'pygeoip'] def do_setup(): try: # This special screwing is to make lpbot.py get installed to PATH as # lpbot, not lpbot.py. Don't remove it, or you'll break it. tmp_dir = tempfile.mkdtemp() tmp_main_script = os.path.join(tmp_dir, 'lpbot') shutil.copy('lpbot.py', tmp_main_script) setup( name='lpbot', version=__version__, description='IRC bot based on willie bot', author='<NAME>', author_email='<EMAIL>', url='https://github.com/Nikola-K/lpbot', long_description="""lpbot is an IRC bot based on open source willie bot by Edward Powell""", # Distutils is shit, and doesn't check if it's a list of basestring # but instead requires str. packages=[str('lpbot'), str('lpbot.modules')], scripts=[tmp_main_script], license='Eiffel Forum License, version 2', platforms='Linux x86, x86-64', requires=requires ) finally: try: shutil.rmtree(tmp_dir) except OSError as e: if e.errno != 2: # The directory is already gone, so ignore it raise if __name__ == "__main__": do_setup()
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nlp_architect/procedures/registry.py
unifiedcompliance/nlp-architect
0
151766
# ****************************************************************************** # Copyright 2017-2019 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** # Register procedures to be used by CLI from nlp_architect.cli.cmd_registry import CMD_REGISTRY from nlp_architect.procedures.procedure import Procedure def register_cmd(registry: dict, name: str, description: str): def register_cmd_fn(cls): if not issubclass(cls, Procedure): raise ValueError('Registered class must be subclassed from Procedure') if name in registry: raise ValueError('Cannot register duplicate model {}'.format(name)) run_fn = cls.run_procedure arg_adder_fn = cls.add_arguments new_cmd = {'name': name, 'description': description, 'fn': run_fn, 'arg_adder': arg_adder_fn} registry.append(new_cmd) return cls return register_cmd_fn def register_train_cmd(name: str, description: str): return register_cmd(CMD_REGISTRY['train'], name, description) def register_infer_cmd(name: str, description: str): return register_cmd(CMD_REGISTRY['infer'], name, description) def register_run_cmd(name: str, description: str): return register_cmd(CMD_REGISTRY['run'], name, description) def register_process_cmd(name: str, description: str): return register_cmd(CMD_REGISTRY['process'], name, description) def register_solution_cmd(name: str, description: str): return register_cmd(CMD_REGISTRY['solution'], name, description) def register_serve_cmd(name: str, description: str): return register_cmd(CMD_REGISTRY['serve'], name, description)
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consScore/amino_conservation_test.py
suliat16/consScore
3
78810
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Jul 1 16:58:25 2018 @author: suliat16 """ import os from consScore import aminoCons as am import biskit.test class test_amino_conservation(biskit.test.BiskitTest): """ Test suite testing the behaviour of the aminoCons module """ TAGS = [biskit.test.EXE, biskit.test.LONG] @classmethod def setUpClass(cls): cls.filepath = os.getcwd() + os.sep + 'example_data' def test_clean_alignment(self): """Tests to see that alignment file and the folder created are deleted after calling clean argument""" extra_aln = am.build_alignment(self.filepath +os.sep + 'multiFasta.fasta') self.assertTrue(os.path.exists(extra_aln)) am.clean_alignment(extra_aln, cache=False) self.assertFalse(os.path.exists(extra_aln)) def test_exe_mafft(self): """Tests that the Mafft executor module calls mafft""" extra_aln = am.MAFFT(self.filepath + os.sep + 'multiFasta.fasta') aln_path = extra_aln.run() self.assertTrue(os.path.exists(aln_path)) am.clean_alignment(aln_path, cache=False) self.assertFalse(os.path.exists(aln_path)) def test_get_alpha_g(self): """Tests that get alpha can retrieve the alpha parameter from the file""" digit = am.get_alpha(self.filepath + os.sep + 'multiFasta.res') self.assertEqual(digit, 2.83688) def test_get_alpha_nofile(self): """Test that an error is raised if a non-existent file is given to get_alpha""" with self.assertRaises(FileNotFoundError): am.get_alpha(self.filepath + os.sep + 'TheTree.txt') def test_getalpha_badfile(self): """Tests that an error is raised if get_alpha is called on an incompatible file""" with self.assertRaises(am.Rate4SiteError) as cm: am.get_alpha(self.filepath + os.sep + 'Fak2Human.fasta') err = cm.exception self.assertEqual(str(err), 'File format is not supported') def test_r2mat_g(self): """Tests that r2mat outputs the correct defaults for the dictionary""" test = am.Rate4Site(self.filepath + os.sep + 'multiFasta.fasta') output = test.rate2dict(self.filepath + os.sep + 'multiFasta.res') dictionary = {0:('A', 0.6979), 1:('A', 0.6979), 2:('C', 0.7026), 3:('C', 0.7026), 4:('G', 0.1769), 5:('G', 0.1769), 6:('T', -1.577), 7:('T', -1.577)} self.assertDictEqual(dictionary, output) def test_r2mat_multi(self): """Tests that the correct dictionary is output when some parameters are set""" test = am.Rate4Site(self.filepath + os.sep + 'multiFasta.fasta', score=True, qqint=True, gapped=True) output = test.rate2dict(self.filepath + os.sep + 'multiFasta.res') dictionary = {0:('A', 0.6979, (-1.946, 2.735), '3/3'), 1:('A', 0.6979, (-1.946, 2.735), '3/3'), 2:('C', 0.7026, (-1.946, 2.735), '3/3'), 3:('C', 0.7026, (-1.946, 2.735), '3/3'), 4:('G', 0.1769, (-2.332, 1.853), '3/3'), 5:('G', 0.1769, (-2.332, 1.853), '3/3'), 6:('T', -1.577, (-3.889, -0.7852), '3/3'), 7:('T', -1.577, (-3.889, -0.7852), '3/3')} self.assertDictEqual(output, dictionary) def test_r2mat_score(self): """Tests that correct dictionary is output when other parameters are set""" test = am.Rate4Site(self.filepath + os.sep + 'multiFasta.fasta', score=False, qqint=True, gapped=True, std=True) output = test.rate2dict(self.filepath + os.sep + 'multiFasta.res') dictionary = {0:('A', (-1.946, 2.735), 2.836, '3/3'), 1:('A', (-1.946, 2.735), 2.836, '3/3'), 2:('C', (-1.946, 2.735), 2.836, '3/3'), 3:('C', (-1.946, 2.735), 2.836, '3/3'), 4:('G', (-2.332, 1.853), 2.725, '3/3'), 5:('G', (-2.332, 1.853), 2.725, '3/3'), 6:('T', (-3.889, -0.7852), 2.309, '3/3'), 7:('T', (-3.889, -0.7852), 2.309, '3/3')} self.assertDictEqual(output, dictionary) def test_r2prof(self): """tests that rate2profile correctly creates profile collections with the correct output""" test = am.Rate4Site(self.filepath + os.sep + 'multiFasta.fasta', qqint=True, std=True, gapped=True) output = test.rate2profile(self.filepath + os.sep + 'multiFasta.res') self.assertEqual(5, len(output)) self.assertTrue('Amino Acid' in output) self.assertTrue('Conservation Score' in output) self.assertTrue('Standard Deviation' in output) self.assertTrue('QQ interval' in output) self.assertTrue('Gapped' in output) self.assertTrue(isinstance(output, biskit.ProfileCollection)) def test_r2prof_default(self): """Tests that the default rate2site instantiation produces the right profile""" test = am.Rate4Site(self.filepath + os.sep + 'multiFasta.fasta') output = test.rate2profile(self.filepath + os.sep + 'multiFasta.res') self.assertTrue('Amino Acid' in output) self.assertTrue('Conservation Score' in output) self.assertEqual(2, len(output)) self.assertTrue(isinstance(output, biskit.ProfileCollection)) def test_r4s_close(self): """Tests to see that close deletes the correct files""" r4sobject = am.Rate4Site(self.filepath + os.sep + 'multiFasta.aln') r4sobject.run() self.assertTrue(os.path.isfile(os.getcwd() + os.sep + 'multiFasta.res')) r4sobject.close() self.assertFalse(os.path.isfile(os.getcwd() + os.sep + 'multiFasta.res')) self.assertFalse(os.path.isdir(os.getcwd() + os.sep + 'multiFasta')) @classmethod def tearDownClass(cls): am.clean_alignment(os.getcwd() + os.sep + 'multiFasta.aln', cache=False) if __name__ == '__main__': biskit.test.localTest()
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pythonmisc/string_manipulation.py
davikawasaki/python-misc-module-library
0
20461
#! /usr/bin/env python # Version: 0.1.1 import re def convert_ws_header_vb_attributes(df): output_txt = "" for key in df.keys(): variant_array = "\'Dim " i = 0 for word in [w.capitalize().replace('\t', '') for w in str(key).lower().split("(")[0].split(" ")]: if i == 0: word = word.lower() if word != "" and word != "-": variant_array += word i += 1 variant_array = variant_array.rstrip() variant_array += "() As Variant\n" output_txt += variant_array return output_txt def remove_special_characters(text): _vec = ''.join(re.split(r'[^a-zA-Z]', text)).split() if len(_vec) == 1: return _vec[0] else: return text
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source/ports/scala_port/src/test/scala/scripts/s1.py
Duckxz/core
0
114469
def fn_in_s1(): return 'Hello from s1'
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URI 1017.py
Azefalo/Cluble-de-Programacao-UTFPR
1
23637
<filename>URI 1017.py # https://www.beecrowd.com.br/judge/en/problems/view/1017 car_efficiency = 12 # Km/L time = int(input()) average_speed = int(input()) liters = (time * average_speed) / car_efficiency print(f"{liters:.3f}")
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tests/schema/test_update_end_transaction_id.py
vhermecz/sqlalchemy-continuum
401
193601
<reponame>vhermecz/sqlalchemy-continuum from sqlalchemy_continuum import version_class from sqlalchemy_continuum.schema import update_end_tx_column from tests import TestCase class TestSchemaTools(TestCase): versioning_strategy = 'validity' def _insert(self, values): table = version_class(self.Article).__table__ stmt = table.insert().values(values) self.session.execute(stmt) def test_update_end_transaction_id(self): table = version_class(self.Article).__table__ self._insert( { 'id': 1, 'transaction_id': 1, 'name': u'Article 1', 'operation_type': 1, } ) self._insert( { 'id': 1, 'transaction_id': 2, 'name': u'Article 1 updated', 'operation_type': 2, } ) self._insert( { 'id': 2, 'transaction_id': 3, 'name': u'Article 2', 'operation_type': 1, } ) self._insert( { 'id': 1, 'transaction_id': 4, 'name': u'Article 1 updated (again)', 'operation_type': 2, } ) self._insert( { 'id': 2, 'transaction_id': 5, 'name': u'Article 2 updated', 'operation_type': 2, } ) update_end_tx_column(table, conn=self.session) rows = self.session.execute( 'SELECT * FROM article_version ORDER BY transaction_id' ).fetchall() assert rows[0].transaction_id == 1 assert rows[0].end_transaction_id == 2 assert rows[1].transaction_id == 2 assert rows[1].end_transaction_id == 4 assert rows[2].transaction_id == 3 assert rows[2].end_transaction_id == 5 assert rows[3].transaction_id == 4 assert rows[3].end_transaction_id is None assert rows[4].transaction_id == 5 assert rows[4].end_transaction_id is None
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process_R1.py
joannadiong/Kishimoto_et_al_2021_JAP
0
147867
<reponame>joannadiong/Kishimoto_et_al_2021_JAP import os, shutil import numpy as np import scipy.signal from scipy import interpolate import matplotlib.pyplot as plt import trials_key def find_trial_MVC_by_time(sub_info, sub_data): # get activation levels and trials key-value pairs sub_key = trials_key.gen(sub_info.sub) # make dict of trials sorted by activation level levels = ['01', '05', '10', '15', '25', '50', '75'] bysort_level = {} for level in levels: trial = sub_key[level] bysort_level.update({level: trial}) # make dict of trials sorted by test order over time bysort_time = {k: v for k, v in sorted(bysort_level.items(), key=lambda item: item[1])} levels_by_time = list(bysort_time.keys()) # get MVC torques sorted by tested order over time mvcs_by_time = [] levels_by_time_ = [] # get activation level only if MVC exists for level in levels_by_time: mvc_torque = _find_trial_MVC_normalize_torque_signals(sub_info, sub_data, level) if mvc_torque: mvcs_by_time.append(mvc_torque[0]) levels_by_time_.append(level) return mvcs_by_time, levels_by_time_ def find_mmax_amp(sub_info, sub_data): # index the last maximal stimulation idx = int(sub_data['max_curr'].sig['trig'].times[-1] * sub_info.freq) # calculate the peak to peak within a 50 ms window, 5 ms after the stimulus ptp_start, ptp_stop = 0.005, 0.055 # in sec idx1 = int(idx + ptp_start * sub_info.freq) idx2 = int(idx1 + ptp_stop * sub_info.freq) emg = sub_data['max_curr'].sig['emgSO'].raw time = sub_data['max_curr'].sig['emgSO'].times mmax_amp = np.ptp(emg[idx1: idx2]) buffer = int(0.040 * sub_info.freq) # in ms # plot window and check that it is correct fig = plt.figure(figsize=(11, 7)) plt.subplot(1, 3, 1) plt.grid() plt.plot((time[idx1: idx2] - time[idx1]) * 1000, emg[idx1: idx2], 'r') plt.xlabel('Time (ms)') plt.ylabel('EMG SO (mV)') plt.subplot(1, 3, (2, 3)) # set stim instance to zero, plot time in ms plt.plot(0, emg[idx] + max(emg), 'r|', markersize=6) # idx / sub_info.freq plt.plot((time[idx - buffer: idx2 + buffer] - time[idx]) * 1000, emg[idx - buffer: idx2 + buffer], 'k') plt.plot((time[idx1: idx2] - time[idx1] + ptp_start) * 1000, emg[idx1: idx2], 'r') plt.xlabel('Time (ms)') plt.ylabel('EMG SO (mV)') plt.tight_layout() plt.savefig('mmax.png', dpi=300) shutil.move('mmax.png', os.path.join('.', 'data', 'proc', sub_info.sub, 'mmax.png')) plt.close() return mmax_amp, idx1, idx2 def find_mmax_rms(sub_info, sub_data, idx1, idx2): # identify samples over which soleus M wave occurs; only soleus M waves were checked emg = sub_data['max_curr'].sig['emgSO'].raw time = sub_data['max_curr'].sig['emgSO'].times mmax = emg[idx1: idx2] # interpolate over the M wave xaxis = list(range(0, len(mmax))) f = interpolate.interp1d(xaxis, mmax) xaxis_new = np.arange(0, len(mmax) - 1, 0.1) mmax_new = f(xaxis_new) # identify the sample indexes where the first phase of the M wave crosses 0 volts # similarly to <NAME> (1997) Fatigue in human thenar muscles paralysed by spinal cord injury min_val = abs(min(mmax_new)) max_val = max(mmax_new) height = np.mean([min_val, max_val]) * .7 indexes, _ = scipy.signal.find_peaks(abs(mmax_new), height=height, distance=5) plt.plot(mmax_new,'.-') plt.plot(indexes, mmax_new[indexes], 'ro', label='min, max') peak_index = indexes[0] if mmax_new[peak_index] < 0: mmax_new *= -1 for i in range(peak_index, 0, -1): if mmax_new[i] > 0 and mmax_new[i - 1] < 0: idx_start_p1_mmax = i-1 break else: idx_start_p1_mmax = 0 for i in range(peak_index, len(mmax_new)): if mmax_new[i] > 0 and mmax_new[i + 1] < 0: idx_stop_p1_mmax = i + 1 break # print(idx_start_p1_mmax, idx_stop_p1_mmax) plt.plot([idx_start_p1_mmax, idx_stop_p1_mmax], [mmax_new[idx_start_p1_mmax], mmax_new[idx_stop_p1_mmax]], 'bo', label='cross 0V') plt.legend() plt.xlabel('Samples') plt.ylabel('EMG SO (mV)') plt.tight_layout() plt.savefig('mmax-p1.png', dpi=300) shutil.move('mmax-p1.png', os.path.join('.', 'data', 'proc', sub_info.sub, 'mmax-p1.png')) plt.close() # calculate the root-mean-square of the first phase of the M wave # from sklearn.metrics import mean_squared_error # i = np.zeros(len(mmax_new[idx_start_p1_mmax: idx_stop_p1_mmax])) # np.sqrt(mean_squared_error(i, mmax_new[idx_start_p1_mmax: idx_stop_p1_mmax])) # gets same answer mmax_p1_rms = np.sqrt(np.sum(mmax_new[idx_start_p1_mmax: idx_stop_p1_mmax] ** 2) / len(mmax_new[idx_start_p1_mmax: idx_stop_p1_mmax])) return mmax_p1_rms def find_mvc_emg_rms(sub_info, max_vals_and_indexes, signals_above_threshold): # use index of MVC torque to find MVC EMG mvc_torque_idx = max_vals_and_indexes.mvc_torque[1] mvc_torque = max_vals_and_indexes.mvc_torque[0] torque = signals_above_threshold.torques emgSO = signals_above_threshold.emgSO # rectified EMG, not enveloped plt.subplot(3,1,1) plt.plot(torque, 'k') plt.plot(mvc_torque_idx, mvc_torque, 'ro') plt.ylabel('Torque (Nm)') plt.subplot(3,1,2) plt.plot(emgSO, 'k') plt.plot(mvc_torque_idx, emgSO[mvc_torque_idx], 'ro') plt.ylabel('EMG SO (mV)') # get root mean square soleus EMG over 50 ms window over the MVC index half_win = int(sub_info.freq * 0.05 / 2) mvc_indexes = list(range(mvc_torque_idx - half_win, mvc_torque_idx + half_win)) mvc_emg = emgSO[mvc_torque_idx - half_win: mvc_torque_idx + half_win] mvc_emg_rms = np.sqrt(np.sum(mvc_emg ** 2) / len(mvc_emg)) # from sklearn.metrics import mean_squared_error # i = np.zeros(len(mvc_emg)) # np.sqrt(mean_squared_error(i, mvc_emg)) # gets same answer plt.subplot(3,1,3) plt.plot(list(range(mvc_torque_idx - int(sub_info.freq / 6), mvc_torque_idx + int(sub_info.freq / 6))), emgSO[mvc_torque_idx - int(sub_info.freq / 6): mvc_torque_idx + int(sub_info.freq / 6)], 'k') for i, j in zip(mvc_indexes, mvc_emg): plt.plot(i, j, 'go', markersize=2) plt.xlabel('Samples') plt.ylabel('EMG SO (mV)') plt.tight_layout() plt.savefig('mvc_torq_emg.png', dpi=300) shutil.move('mvc_torq_emg.png', os.path.join('.', 'data', 'proc', sub_info.sub, 'mvc_torq_emg.png')) plt.close() return mvc_emg_rms def find_trial_emg_rms(sub_info, sub_data): nsamples_before_trig = int(sub_info.freq * 0.05) # get EMG over 50 ms window emgs_rect = dict() i = 1 j = len(list(sub_data.keys())[4:]) def _determine_sit_rest_indexes(sub_info, sub_data, key): nsamples_before_trig = int(sub_info.freq * 0.5) idx1 = int(sub_data[key].sig['trig'].times[0] * sub_info.freq) idx2 = int(sub_data[key].sig['trig'].times[1] * sub_info.freq) if np.mean(sub_data[key].sig['torque'].proc[idx1 - nsamples_before_trig: idx1]) > \ np.mean(sub_data[key].sig['torque'].proc[idx2 - nsamples_before_trig: idx2]): index_sit = idx1 index_rest = idx2 else: index_sit = idx2 index_rest = idx1 return index_rest, index_sit for key in list(sub_data.keys())[4:]: index_rest, index_sit = _determine_sit_rest_indexes(sub_info, sub_data, key) # shift indexed EMG region away from filter artefact close to stimulus artefact filter_artefact_length = int(sub_info.freq * 0.05) index_start, index_stop = index_sit - (filter_artefact_length + nsamples_before_trig), index_sit - filter_artefact_length emgSO = sub_data[key].sig['emgSO'].rect[index_start: index_stop] emgMG = sub_data[key].sig['emgMG'].rect[index_start: index_stop] emgLG = sub_data[key].sig['emgLG'].rect[index_start: index_stop] emgs_rect_ = {key: {'emgSO': emgSO, 'emgMG': emgMG, 'emgLG': emgLG}} emgs_rect.update(emgs_rect_) plt.subplot(j, 1, i) plt.plot(emgSO, 'k', label='SO') plt.plot(emgMG, 'r', label='MG') plt.plot(emgLG, 'b', label='LG') plt.ylim(0, 0.5) plt.yticks(ticks=[], labels=[]) if i == 2: plt.legend() if i == 6: plt.ylabel('EMG (ylim 0-0.2 mV)') i += 1 plt.xlabel('Samples') plt.tight_layout() plt.savefig('emg_rect.png', dpi=300) shutil.move('emg_rect.png', os.path.join('.', 'data', 'proc', sub_info.sub, 'emg_rect.png')) plt.close() return emgs_rect def normalise_emg(sub_data, mvc_emg_rms, mmax_p1_rms, emgs_rect): emg_norm_mvc, emg_norm_mmax = dict(), dict() for key in list(sub_data.keys())[4:]: emg = emgs_rect[key]['emgSO'] emg_rms = np.sqrt(np.sum(emg ** 2) / len(emg)) # from sklearn.metrics import mean_squared_error # i = np.zeros(len(emg)) # np.sqrt(mean_squared_error(i, emg)) # gets same answer emg_mvc = emg_rms / mvc_emg_rms * 100 emg_norm_mvc_ = {key: {'norm_mvc': emg_mvc}} emg_norm_mvc.update(emg_norm_mvc_) emg_mmax = emg_rms / mmax_p1_rms * 100 emg_norm_mmax_ = {key: {'norm_mmax': emg_mmax}} emg_norm_mmax.update(emg_norm_mmax_) return emg_norm_mvc, emg_norm_mmax def _find_trial_MVC_normalize_torque_signals(sub_info, sub_data, level): # get calibrated max torque during the MVC for activation levels 1-75% trials torque = sub_data[level].sig['torque'].proc index_above_threshold = list(torque > 30) # set torque threshold at 30 Nm count = 0 indexes = [] # Extract indexes of torque data during the MVC preconditioning for i in range(0, len(index_above_threshold), 1): if index_above_threshold[i]: indexes.append(i) if not index_above_threshold[i-1]: count += 1 if count == 5: break if not indexes: # if MVC was not recorded pass else: mvc_torque = max(torque[indexes]) mvc_torque_index = np.argmax(torque[indexes]) mvc_torque = (mvc_torque, mvc_torque_index) # only torques above threshold are indexed fig = plt.figure(figsize=(11, 7)) plt.subplot(1, 3, 1) plt.grid() plt.plot(torque[indexes], 'k') plt.plot(mvc_torque[1], mvc_torque[0] + 2, 'ro') plt.ylabel('Torque (Nm)') plt.subplot(1, 3, (2, 3)) plt.plot(torque, 'k') plt.ylabel('Torque (Nm)') plt.tight_layout() plt.savefig('mvc_' + level + '.png', dpi=300) shutil.move('mvc_' + level + '.png', os.path.join('.', 'data', 'proc', sub_info.sub, 'mvc_' + level + '.png')) plt.close() return mvc_torque
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app/utils/count.py
Xerrors/Meco-Server
1
99980
from app.tables import Messages, LocalArticlesComment, PageViewTable, LocalDataCount from datetime import datetime, date from datetime import timedelta time_format = "%Y-%m-%d %H:%M:%S" date_format = "%Y-%m-%d" def get_days_ago(days, have_time=False): return date.today() - timedelta(days=days) def get_timezone_messages(start, end=None): if not end: end = date.today() return Messages.query.filter( Messages.date <= end ).filter( Messages.date > start ) def get_timezone_pv(start, end=None): if not end: end = date.today() return PageViewTable.query.filter(PageViewTable.user_agent!='google').filter( PageViewTable.date <= end ).filter( PageViewTable.date > start ) def get_all_count(): # TODO:如果访问量大的话,首先是第一次访问的时候计算并保存起来,之后访问的时候直接取出来就行了 day_ago = get_days_ago(1) week_ago = get_days_ago(7) month_ago = get_days_ago(30) month_message = get_timezone_messages(month_ago) week_message = month_message.filter(Messages.date > week_ago) day_message = week_message.filter(Messages.date > day_ago) month_pv = get_timezone_pv(month_ago).filter(PageViewTable.user_agent != 'google') week_pv = month_pv.filter(PageViewTable.date > week_ago) day_pv = week_pv.filter(PageViewTable.date > day_ago) day_pv_count = day_pv.count() week_pv_count = week_pv.count() month_pv_count = month_pv.count() day_like_count = day_message.filter(Messages.type=='like').count() week_like_count = week_message.filter(Messages.type=='like').count() month_like_count = month_message.filter(Messages.type=='like').count() day_comment_count = day_message.count() - day_like_count week_comment_count = week_message.count() - week_like_count month_comment_count = month_message.count() - month_like_count all_pv = PageViewTable.query.filter(PageViewTable.user_agent != 'google').count() all_comment = LocalArticlesComment.query.count() all_like = Messages.query.count() - all_comment data_count = { "pv": { "all": all_pv, "day": day_pv_count, "week": week_pv_count, "month": month_pv_count }, "like": { "all": all_like, "day": day_like_count, "week": week_like_count, "month": month_like_count }, "comment": { "all": all_comment, "day": day_comment_count, "week": week_comment_count, "month": month_comment_count }, "all": { "pv": all_pv, "like": all_like, "comment": all_comment }, "day": { "pv": day_pv_count, "like": day_like_count, "comment": day_comment_count, }, "week": { "pv": week_pv_count, "like": week_like_count, "comment": week_comment_count, }, "month": { "pv": month_pv_count, "like": month_like_count, "comment": month_comment_count, } } return data_count def store_day_value(day): # 默认存储昨天的数据 if not day: day = date.today() - timedelta(days=1) elif type(day) == str: day = datetime.strptime(day, date_format) next_day = day + timedelta(days=1) tempa = get_timezone_messages(day, next_day) day_like_count = tempa.filter(Messages.type=='like').count() day_comment_count = tempa.count() - day_like_count day_pv_count = get_timezone_pv(day, next_day).count() formatted_day = datetime.strftime(day, date_format) from app import db db.session.add(LocalDataCount( date=formatted_day, pv=day_pv_count, like=day_like_count, comment=day_comment_count )) db.session.commit() return [formatted_day, day_pv_count, day_like_count, day_comment_count] def get_day_count(day): """ 获取某一天的访问量数据(不一定用得到) """ if type(day) != str: day = datetime.strftime(day, date_format) item = LocalDataCount.query.filter(LocalDataCount.date==day).first() if item: return [day, item.pv, item.like, item.comment] else: return store_day_value(day) def get_last_days(n=90): start = date.today() data = [] for i in range(n): day = start - timedelta(days=i+1) data.append(get_day_count(day)) return data if __name__ == "__main__": # 备份昨日的数据 store_day_value()
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retailapp/webapp/app/products/products.py
kmsarabu/auroraglobaldb_eks
0
49397
<filename>retailapp/webapp/app/products/products.py<gh_stars>0 from flask import request, Flask , Blueprint , render_template, jsonify, request, abort ,redirect, url_for import requests from app.models import Product products_bp = Blueprint("products_bp", __name__, template_folder="templates/products") @products_bp.route("/<product>") def main(product): product_name = product product = Product(product) response = product.return_items() if response.status_code != 200: abort(401) product_items = response.json().get('product_items') page = int(request.args.get("page") or 1) if len(product_items) <= 8: page = 1 previous = page - 1 start = previous * 8 end = start + 8 if product_items is None: abort(404) else: product_items= [dict(p) for p in product_items] return render_template("list.html", products= product_items[start:end], title=product_name , length=len(product_items)) @products_bp.route("/<product>/<product_item>") def view_product(product, product_item): product = Product(product) response = product.return_items() if response.status_code != 200: abort(401) product_items = response.json().get('product_items') product_items = [dict(p) for p in product_items] product_name = [p for p in product_items if p['name'].lower() == product_item.lower()] if len(product_name) == 0: abort(404) else: return render_template("view.html", results={"item":product_name, "keyword":product_item}, title=product_item) @products_bp.route("/view") def view(): id = int(request.args.get("id")) product = Product() response = product.product.show_all_items() if response.status_code != 200: abort(401) product_items = response.json().get('product_items') product_items = [dict(p) for p in product_items if p['id'] == id] return render_template("view.html", results={"item":product_items}, title="Product View" ) @products_bp.route("/whereami") def whereami(): return Product().whereami
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python/right_couch_move.py
ktmock13/PiCouch
0
19811
<reponame>ktmock13/PiCouch<filename>python/right_couch_move.py import RPi.GPIO as GPIO from time import sleep import sys #setup GPIO.setmode(GPIO.BOARD) openRelay=11 closeRelay=13 GPIO.setup(openRelay, GPIO.OUT) GPIO.setup(closeRelay, GPIO.OUT) #get cmd args duration = float(sys.argv[1]) opening = sys.argv[2] in ['true', 'True', '1', 'TRUE'] relay = openRelay if opening else closeRelay #start GPIO.output(relay, GPIO.HIGH) print 'starting ' + ('open' if opening else 'close') + ' signal..' #wait print ' ' + str(duration) + 'secs' sleep(duration) #stop print ' ...ending signal' GPIO.output(relay, GPIO.LOW)
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setup.py
ztw33/code-sim
0
40590
<gh_stars>0 from setuptools import setup, find_packages VERSION = "0.1.0" setup(name="codesim", version=VERSION, description="A tool for measuring the similarity between two c++ code files.", author="<NAME>", author_email="<EMAIL>", license="MIT", packages=find_packages(), include_package_data=True, zip_safe=True, install_requires=[ "clang==6.0.0" ], entry_points={ "console_scripts": [ "codesim = codesim.codesim:main" ] }, )
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tests/peeringdb_mock/resource.py
peeringdb/django-peeringdb
22
196195
# mock resource classes from peeringdb-py client class Organization: pass class Facility: pass class Network: pass class InternetExchange: pass class InternetExchangeFacility: pass class InternetExchangeLan: pass class InternetExchangeLanPrefix: pass class NetworkFacility: pass class NetworkIXLan: pass class NetworkContact: pass
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new_rdsmysql.py
AdminTurnedDevOps/AWS_Solutions_Architect_Python
30
11161
import boto3 import sys import time import logging import getpass def new_rdsmysql(dbname, instanceID, storage, dbInstancetype, dbusername): masterPass = getpass.getpass('DBMasterPassword: ') if len(masterPass) < 10: logging.warning('Password is not at least 10 characters. Please try again') time.sleep(5) exit else: None try: rds_instance = boto3.client('rds') create_instance = rds_instance.create_db_instance( DBName = dbname, DBInstanceIdentifier = instanceID, AllocatedStorage = int(storage), DBInstanceClass = dbInstancetype, Engine = 'mysql', MasterUsername = dbusername, MasterUserPassword = str(<PASSWORD>), MultiAZ = True, EngineVersion = '5.7.23', AutoMinorVersionUpgrade = False, LicenseModel = 'general-public-license', PubliclyAccessible = False, Tags = [ { 'Key': 'Name', 'Value' : dbname } ] ) print(create_instance) except Exception as e: logging.warning('An error has occured') print(e) dbname = sys.argv[1] instanceID = sys.argv[2] storage = sys.argv[3] dbInstancetype = sys.argv[4] dbusername = sys.argv[5] new_rdsmysql(dbname, instanceID, storage, dbInstancetype, dbusername)
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test/python/test_biasadd.py
slyalin/openvino_tensorflow
0
165557
# ============================================================================== # Copyright (C) 2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 # ============================================================================== """Openvino Tensorflow BiasAdd operation test """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import pytest import numpy as np import tensorflow as tf tf.compat.v1.disable_eager_execution() from common import NgraphTest np.random.seed(8) class TestBiasAddOperations(NgraphTest): def test_BiasAdd1(self): input_data = (0, 1, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 4, 4, 5, 4) input_data = np.reshape(input_data, (2, 2, 2, 2)) input_var = tf.compat.v1.placeholder(tf.float32, shape=(2, 2, 2, 2)) bias_data = (100., -100.) bias_var = tf.compat.v1.placeholder(tf.float32, shape=(2)) out = tf.nn.bias_add(input_var, bias_var, 'NHWC') def run_test(sess): return sess.run( out, feed_dict={ input_var: input_data, bias_var: bias_data }) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_BiasAdd2(self): input_data = (0, 1, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 4, 4, 5, 4) input_data = np.reshape(input_data, (2, 2, 2, 2)) input_var = tf.compat.v1.placeholder(tf.float32, shape=(2, 2, 2, 2)) bias_data = (100., -100.) bias_var = tf.compat.v1.placeholder(tf.float32, shape=(2)) out = tf.nn.bias_add(input_var, bias_var, 'NCHW') def run_test(sess): return sess.run( out, feed_dict={ input_var: input_data, bias_var: bias_data }) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_BiasAdd3(self): input_data = (0, 1, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 4, 4, 5, 4, 3, 5, 1, 2, 0, 4, 0, 1) input_data = np.reshape(input_data, (2, 3, 2, 2)) input_var = tf.compat.v1.placeholder(tf.float32, shape=(2, 3, 2, 2)) bias_data = (100., -100., 50) # channels = 3 bias_var = tf.compat.v1.placeholder(tf.float32, shape=(3)) out = tf.nn.bias_add(input_var, bias_var, 'NCHW') def run_test(sess): return sess.run( out, feed_dict={ input_var: input_data, bias_var: bias_data }) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_BiasAdd4(self): input_data = (0, 1, 0, 1, 2, 1, 1, 0, 3, 1, 1, 0, 4, 4, 5, 4, 3, 5, 1, 2, 0, 4, 0, 1) input_data = np.reshape(input_data, (2, 2, 2, 3)) input_var = tf.compat.v1.placeholder(tf.float32, shape=(2, 2, 2, 3)) bias_data = (100., -100., 50) # channels = 3 bias_var = tf.compat.v1.placeholder(tf.float32, shape=(3)) out = tf.nn.bias_add(input_var, bias_var, 'NHWC') def run_test(sess): return sess.run( out, feed_dict={ input_var: input_data, bias_var: bias_data }) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all()
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apps/users/urls.py
Rigo-Villalta/django-alpha-bootstrap
0
181119
<reponame>Rigo-Villalta/django-alpha-bootstrap from django.contrib.auth.views import LoginView, LogoutView, PasswordResetDoneView from django.urls import path from django.utils.translation import gettext_lazy as _ from .views import ( ChangePassword, ResetPassword, LinkResetPassword, AddUserView, ) app_name = "users" urlpatterns = [ path(_("login/"), LoginView.as_view(), name="login"), path(_("logout/"), LogoutView.as_view(), name="logout"), # cambiar la contraseña de un usuario autenticado path(_("change_password/"), ChangePassword.as_view(), name="change_password"), # Formulario para ingresar correo solicitando recuperación de password path( _("form_reset_password/"), ResetPassword.as_view(), name="form_reset_password", ), # muestra de que la solicitud de restablecer contraseña ha sido hecha path( _("password_reset_aplication_done/"), PasswordResetDoneView.as_view(), name="password_reset_aplication_done"), # Formulario para ingresar la nueva contraseña path( "<uidb64>/<token>/", LinkResetPassword.as_view(), name="link_reset_password", ), path(_("add/"), AddUserView.as_view(), name="add"), ]
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fit_models.py
tyjo/clv
7
1602810
<gh_stars>1-10 import numpy as np import pickle as pkl from compositional_lotka_volterra import CompositionalLotkaVolterra from generalized_lotka_volterra import GeneralizedLotkaVolterra from linear_alr import LinearALR from linear_rel_abun import LinearRelAbun def fit_clv(observations, time_points, effects, held_out_observations, held_out_time_points, held_out_effects, using_rel_abun=False, ret_params=False, folds=None): # if observations are concentrations rel_abun = [] held_out_rel_abun = [] if folds is None: folds = len(observations) if not using_rel_abun: for obs in observations: rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) for obs in held_out_observations: held_out_rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) else: rel_abun = observations held_out_rel_abun = held_out_observations clv = CompositionalLotkaVolterra(rel_abun, time_points, effects) clv.train(folds=folds) predictions = [clv.predict(o[0],tpts,e) for (o,tpts,e) in zip(held_out_rel_abun, held_out_time_points, held_out_effects)] return predictions def fit_glv(observations, time_points, effects, held_out_observations, held_out_time_points, held_out_effects, use_rel_abun=False, folds=None): if folds is None: folds = len(observations) # fit on relative abundances instead of concentrations if use_rel_abun: rel_abun = [] for obs in observations: rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) observations = rel_abun held_out_rel_abun = [] for obs in held_out_observations: held_out_rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) held_out_observations = held_out_rel_abun glv = GeneralizedLotkaVolterra(observations, time_points, effects) glv.train(folds=folds) A, g, B = glv.get_params() predictions = [glv.predict(o[0],tpts,e) for (o,tpts,e) in zip(held_out_observations, held_out_time_points, held_out_effects)] return predictions def fit_linear_alr(observations, time_points, effects, held_out_observations, held_out_time_points, held_out_effects, using_rel_abun=False, folds=None): # if observations are concentrations rel_abun = [] held_out_rel_abun = [] if folds is None: folds = len(observations) if not using_rel_abun: for obs in observations: rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) for obs in held_out_observations: held_out_rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) else: rel_abun = observations held_out_rel_abun = held_out_observations alr = LinearALR(rel_abun, time_points, effects) alr.train(folds=folds) A, g, B = alr.get_params() predictions = [alr.predict(o[0],tpts,e) for (o,tpts,e) in zip(held_out_rel_abun, held_out_time_points, held_out_effects)] return predictions def fit_linear_rel_abun(observations, time_points, effects, held_out_observations, held_out_time_points, held_out_effects, using_rel_abun=False, folds=None): if folds is None: folds = len(observations) # if observations are concentrations rel_abun = [] held_out_rel_abun = [] if not using_rel_abun: for obs in observations: rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) for obs in held_out_observations: held_out_rel_abun.append(obs / obs.sum(axis=1,keepdims=True)) else: rel_abun = observations held_out_rel_abun = held_out_observations lra = LinearRelAbun(rel_abun, time_points, effects) lra.train(folds=folds) A, g, B = lra.get_params() predictions = [lra.predict(o[0],tpts,e) for (o,tpts,e) in zip(held_out_rel_abun, held_out_time_points, held_out_effects)] return predictions
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Machine learning Intermediate/Multiclass classification-75.py
vipmunot/Data-Analysis-using-Python
0
40237
## 1. Introduction to the data ## import pandas as pd cars = pd.read_csv("auto.csv") unique_regions = cars['origin'].unique() print(unique_regions) ## 2. Dummy variables ## dummy_cylinders = pd.get_dummies(cars["cylinders"], prefix="cyl") cars = pd.concat([cars, dummy_cylinders], axis=1) print(cars.head()) dummy_years = pd.get_dummies(cars["year"], prefix="year") cars = pd.concat([cars, dummy_years], axis=1) cars = cars.drop("year", axis=1) cars = cars.drop("cylinders", axis=1) print(cars.head()) ## 3. Multiclass classification ## shuffled_rows = np.random.permutation(cars.index) shuffled_cars = cars.iloc[shuffled_rows] highest_train_row = int(cars.shape[0] * .70) train = shuffled_cars.iloc[0:highest_train_row] test = shuffled_cars.iloc[highest_train_row:] ## 4. Training a multiclass logistic regression model ## from sklearn.linear_model import LogisticRegression import re unique_origins = cars["origin"].unique() unique_origins.sort() models = {} X = train[[x for x in train.columns if x.startswith("cyl") or x.startswith("year")]] print(X.shape) for origin in unique_origins: y = (train["origin"] == origin) lr = LogisticRegression() lr.fit(X, y) models[origin] = lr print(models) ## 5. Testing the models ## testing_probs = pd.DataFrame(columns=unique_origins) test = test[[x for x in test.columns if x.startswith("cyl") or x.startswith("year")]] print(test.shape) for origin in unique_origins: X_test = test[features] testing_probs[origin] = models[origin].predict_proba(X_test)[:,1] ## 6. Choose the origin ## predicted_origins = testing_probs.idxmax(axis = 1) print(predicted_origins)
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signal_fun/autocorr.py
awaldm/flowpost
0
75994
<gh_stars>0 import numpy as np def autocorr(x): result = np.correlate(x, x, mode='full') return result[result.size//2:] / (np.var(x) * len(x)) # the following three are from # https://stackoverflow.com/questions/16044491/statistical-scaling-of-autocorrelation-using-numpy-fft import numpy as np fft = np.fft def autocorrelation(x): """ Compute autocorrelation using FFT The idea comes from http://dsp.stackexchange.com/a/1923/4363 (Hilmar) """ x = np.asarray(x) N = len(x) x = x-x.mean() s = fft.fft(x, N*2-1) result = np.real(fft.ifft(s * np.conjugate(s), N*2-1)) result = result[:N] result /= result[0] return result def AutoCorrelation(x): x = np.asarray(x) y = x-x.mean() result = np.correlate(y, y, mode='full') result = result[len(result)//2:] result /= result[0] return result def autocorrelate(x): fftx = fft.fft(x) fftx_mean = np.mean(fftx) fftx_std = np.std(fftx) ffty = np.conjugate(fftx) ffty_mean = np.mean(ffty) ffty_std = np.std(ffty) result = fft.ifft((fftx - fftx_mean) * (ffty - ffty_mean)) result = fft.fftshift(result) return [i / (fftx_std * ffty_std) for i in result.real] # from https://stackoverflow.com/questions/14297012/estimate-autocorrelation-using-python def estimated_autocorrelation(x): n = len(x) variance = x.var() x = x-x.mean() r = np.correlate(x, x, mode = 'full')[-n:] #assert N.allclose(r, N.array([(x[:n-k]*x[-(n-k):]).sum() for k in range(n)])) result = r/(variance*(np.arange(n, 0, -1))) return result ''' returns index of first crossing and the integral length scale ''' def tscale(x, dt=None, threshold=0.2, verbose=False): ################################### # x must be an autocorrelation function # time scales ts_ind = np.argmax(x<=threshold) if dt is not None: ts_int = np.trapz(x[0:ts_ind], dx=dt) if verbose: print('lag of first threshold crossing for ' + str(threshold) + ' (in samples): ' + str(ts_ind) + ', corresponds to timescale ' + str(ts_int)) return ts_ind, ts_int else: if verbose: print('lag of first threshold crossing for ' + str(threshold) + ' (in samples): ' + str(ts_ind)) return ts_ind # print 'integral timescale: ' + str(ts_int) def t_int(ACF): ''' integrated autocorrelation time, according to Sokal (p.7) or Janke (p. 12) ''' mintime = 3 t = 1 increment = 1 ts_int = 0.5 while (t < len(ACF)): # compute normalized fluctuation correlation function at time t ts_int = ts_int + ACF[t] if (ACF[t] <= 0.2) and (t > mintime): break t += increment return ts_int
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LookProjeto/checkout/migrations/0005_auto_20200528_1826.py
jeffreyquirino/backup_looksistem
0
1608493
# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2020-05-28 21:26 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('checkout', '0004_auto_20200527_0124'), ] operations = [ migrations.AlterField( model_name='order', name='payment_option', field=models.CharField(choices=[('deposit', 'Depósito'), ('pagseguro', 'PagSeguro'), ('paypal', 'Paypal')], default='deposit', max_length=20, verbose_name='Opção de Pagamento'), ), ]
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api/basic/epsys.py
fras2560/mlsb-platform
1
94400
''' @author: <NAME> @date: 2015-08-25 @organization: MLSB API @summary: The basic espys API ''' from flask_restful import Resource, reqparse from flask import Response from json import dumps from api import DB from api.model import Espys from api.authentication import requires_admin from api.errors import EspysDoesNotExist from api.variables import PAGE_SIZE from api.routes import Routes from api.helper import pagination_response from flask import request from api.cached_items import handle_table_change from api.tables import Tables parser = reqparse.RequestParser() parser.add_argument('sponsor_id', type=int) parser.add_argument('team_id', type=int) parser.add_argument('description', type=str) parser.add_argument('points', type=str) parser.add_argument('receipt', type=str) parser.add_argument('date', type=str) parser.add_argument('time', type=str) post_parser = reqparse.RequestParser(bundle_errors=True) post_parser.add_argument('sponsor_id', type=int) post_parser.add_argument('team_id', type=int, required=True) post_parser.add_argument('description', type=str) post_parser.add_argument('points', type=str, required=True) post_parser.add_argument('receipt', type=str) post_parser.add_argument('date', type=str) post_parser.add_argument('time', type=str) class EspyAPI(Resource): def get(self, espy_id): """ GET request for Espys Object matching given espy_id Route: Routes['team']/<espy_id:int> Returns: if found status: 200 mimetype: application/json data: { 'date': date, 'team': string, 'sponsor': string, 'description': string, 'points': int, 'receipt': string } otherwise status: 404 mimetype: application/json data: None """ # expose a single team entry = Espys.query.get(espy_id) if entry is None: raise EspysDoesNotExist(payload={'details': espy_id}) response = Response(dumps(entry.json()), status=200, mimetype="application/json") return response @requires_admin def delete(self, espy_id): """ DELETE request for Espy Route: Routes['espy']/<espy_id:int> Returns: if found status: 200 mimetype: application/json data: None otherwise status: 404 mimetype: application/json data: None """ espy = Espys.query.get(espy_id) if espy is None: raise EspysDoesNotExist(payload={'details': espy_id}) # delete a single espy DB.session.delete(espy) DB.session.commit() response = Response(dumps(None), status=200, mimetype="application/json") handle_table_change(Tables.ESPYS, item=espy.json()) return response @requires_admin def put(self, espy_id): """ PUT request for espy Route: Routes['team']/<espy_id:int> Parameters : espy_id: The espy's id (int) description: The description of the transaction (string) sponsor_id: The sponsor's id (int) team_id: The team's id (int) points: The points awarded (int) receipt: the receipt number (string) Returns: if found and updated successfully status: 200 mimetype: application/json data: None otherwise possible errors are status: 404, IFSC, TDNESC, SDNESC mimetype: application/json data: None """ # update a single user espy = Espys.query.get(espy_id) args = parser.parse_args() description = None sponsor_id = None team_id = None points = None receipt = None date = None time = None if espy is None: raise EspysDoesNotExist(payload={'details': espy_id}) if args['description']: description = args['description'] if args['sponsor_id']: sponsor_id = args['sponsor_id'] if args['team_id']: team_id = args['team_id'] if args['points']: points = args['points'] if args['receipt']: receipt = args['receipt'] if args['date'] and args['time']: date = args['date'] time = args['time'] espy.update(sponsor_id=sponsor_id, team_id=team_id, description=description, points=points, receipt=receipt, date=date, time=time ) DB.session.commit() response = Response(dumps(None), status=200, mimetype="application/json") handle_table_change(Tables.ESPYS, item=espy.json()) return response def option(self): return {'Allow': 'PUT'}, 200, \ {'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Methods': 'PUT,GET'} class EspyListAPI(Resource): def get(self): """ GET request for Espys List Route: Routes['espy'] Parameters : Returns: status: 200 mimetype: application/json data: teams: { { 'date': date, 'team': string, 'sponsor': string, 'description': string, 'points': int, 'receipt': string } ,{...} ] """ # return a pagination of teams page = request.args.get('page', 1, type=int) pagination = Espys.query.paginate(page, PAGE_SIZE, False) result = pagination_response(pagination, Routes['espy']) resp = Response(dumps(result), status=200, mimetype="application/json") return resp @requires_admin def post(self): """ POST request for Espys List Route: Routes['team'] Parameters : league_id: the league's id (int) sponsor_id: the sponsor's id (int) color: the color of the team (string) year: the year the team is playing in (int) espys: the team espys points (int) Returns: if successful status: 200 mimetype: application/json data: the create team id (int) possible errors status: 400, IFSC, LDNESC, PDNESC or SDNESC mimetype: application/json data: the create team id (int) """ # create a new user args = post_parser.parse_args() description = None sponsor_id = None team_id = None points = None receipt = None date = None time = None if args['description']: description = args['description'] if args['sponsor_id']: sponsor_id = args['sponsor_id'] if args['team_id']: team_id = args['team_id'] if args['points']: points = args['points'] if args['receipt']: points = args['receipt'] if args['date'] and args['time']: date = args['date'] time = args['time'] espy = Espys(sponsor_id=sponsor_id, team_id=team_id, description=description, points=points, receipt=receipt, date=date, time=time) DB.session.add(espy) DB.session.commit() result = espy.id handle_table_change(Tables.ESPYS, item=espy.json()) return Response(dumps(result), status=201, mimetype="application/json") def option(self): return {'Allow': 'PUT'}, 200, \ {'Access-Control-Allow-Origin': '*', 'Access-Control-Allow-Methods': 'PUT,GET'}
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brewerslab-orig-commander/metroui/ajaxSubmitComments.py
allena29/brewerslabng
1
65802
#!/usr/bin/python import cgi import sys import json import re import mysql.connector from cloudNG import * y=sys.stdin.readline().split(",") x = sys.stdin.read() #con=mysql.connector.connect(user='brewerslab',password='<PASSWORD>',database="brewerslab") #sys.stdout.write("Content-Type:text/plain\n\n") sys.stdout.write("Content-Type:text/xml\n\n") form=cgi.FieldStorage() def xmlsafe(text): text=re.compile("[\n\r]").sub("</br>",text) safe=re.compile("<").sub("{:leftbracket:}", re.compile(">").sub("{:rightbracket:}", re.compile("&").sub("{:ampersand:}", re.compile("/").sub("{:forwardslash:}", text ) ) ) ) return text sys.stderr.write("brewlog: %s\n" %(y[0])) sys.stderr.write("act %s\n" %(y[1])) sys.stderr.write("step: %s\n" %(y[2])) saveComments=brewerslabCloudApi().saveComment("<EMAIL>", y[0],y[1],y[2],x ) sys.stdout.write("<xml>\n") sys.stdout.write("<stepNum>%s</stepNum>\n" %( y[2] )) sys.stdout.write("</xml>") sys.stdout.flush()
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reporting/tests/test_reports.py
flagshipenterprise/django-prickly-reports
1
53043
<gh_stars>1-10 from datetime import date from django.test import TestCase from django.forms import Form from reporting.base import Filter, Report from reporting.filters import CharFilter, DateFilter class ConcreteReportClass(Report): """ A sample report class, just for use within the tests module. It just filters a very simple set of hard-coded original data based on keywords and a date range. """ keywords = CharFilter() start_date = DateFilter() end_date = DateFilter() test_value = None original_data = [ {'date': date(year=2000, day=1, month=1), 'word': "hello"}, {'date': date(year=2001, day=1, month=1), 'word': "test"}, {'date': date(year=2002, day=1, month=1), 'word': "word"}, {'date': date(year=2003, day=1, month=1), 'word': "test"}, {'date': date(year=2004, day=1, month=1), 'word': "word"}, ] def generate(self): processed_data = [] for item in self.original_data: if self.start_date and item['date'] < self.start_date: continue if self.end_date and item['date'] > self.end_date: continue if self.keywords and item['word'] not in self.keywords: continue processed_data.append(item) return processed_data class BadConcreteReportClass(Report): """ Instantiation of this test class should fail since it does not implement the generate function. This is just used to ensure that the Report class was properly designed as an abstract base class. """ pass class TestReport(TestCase): def setUp(self): pass def test_get_attr(self): """ Some tests that ensure that the overridden __getattribute__ method on the Report abstract base class workds the way we want it to... it should return only the "value" attribute of one of it's own attributes is of a type which was derived from Filter. """ report = ConcreteReportClass({'keywords': "test"}) self.assertEquals(report.keywords, "test") def test_set_attr(self): """ Basically same test as above, but testing __setattr__ and __gettattribute__ together. Really test_get_attr, already tests for both of these since the initialization parameters internally call __setattr__, but this makes it explicit. """ report = ConcreteReportClass() report.keywords = "test" self.assertEquals(report.keywords, "test") def test_get_filters(self): """ Ensure that the get_filters function returns all/only the Filter based attributes on the Report object. """ report = ConcreteReportClass({ 'keywords': "word", 'start_date': date(year=2002, day=1, month=1)}) filters = [filt for filt in report.get_filters() if type(filt[1]) is CharFilter or type(filt[1]) is DateFilter] self.assertEquals(len(filters), 3) def test_generate(self): """ This essentially just tests the test report class... which isn't that usefull since it can only be used in the test. But this indirectly tests a use case. """ report = ConcreteReportClass({'keywords': "test"}) self.assertEquals(report.generate(), [ report.original_data[1], report.original_data[3], ]) report = ConcreteReportClass({ 'keywords': "word", 'start_date': date(year=2002, day=1, month=1)}) self.assertEquals(report.generate(), [ report.original_data[2], report.original_data[4], ]) report = ConcreteReportClass({ 'keywords': "word", 'end_date': date(year=2003, day=1, month=1)}) self.assertEquals(report.generate(), [ report.original_data[2], ]) def test_bad_report_class(self): """ Attempt to instantiate a concrete report class which has not implemented the generate() function. """ with self.assertRaises(Exception): report = self.BadConcreteReportClass() def test_get_form_class(self): """ Ensure that the get_form_class() function returns a valid django form with fields corresponding to the report's filters. """ form_class = ConcreteReportClass().get_form_class() self.assertTrue(issubclass(form_class, Form)) form = form_class() self.assertIn('keywords', form.fields) self.assertIn('start_date', form.fields) self.assertIn('end_date', form.fields) def test_get_filter_forms(self): forms = ConcreteReportClass().get_filter_forms() for form in forms: self.assertIn(form.fields.items()[0][0], ['keywords', 'start_date', 'end_date']) def test_get_form_class(self): """ Ensure that the get_form_class() function returns a valid django form with fields corresponding to the report's filters. """ """ form_class = ConcreteReportClass().get_form_class() self.assertTrue(issubclass(form_class, Form)) form = form_class() self.assertIn('keywords', form.fields) self.assertIn('start_date', form.fields) self.assertIn('end_date', form.fields) """ def test_get_form_class_filter_fields(self): pass # THIS FEATURE NO LONGER EXISTS... # ... but the test remains in case we want to implement it again """ Same as above, but with the filter_fields argument present to limit the filters which are converted into form fields. """ """ form_class = ConcreteReportClass().get_form_class( form_filters=['keywords', 'start_date']) form = form_class() self.assertIn('keywords', form.fields) self.assertIn('start_date', form.fields) self.assertNotIn('end_date', form.fields) """
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inmembrane/plugins/tatfind_web.py
pansapiens/inmembrane
4
192654
# -*- coding: utf-8 -*- citation = {'ref': u"<NAME>., <NAME>,. <NAME>, and M. " u"Pohlschröder. 2002. Adaptation of protein secretion " u"to extremely high salt concentrations by extensive use " u"of the twin arginine translocation pathway. Mol. Microbiol." u"5: 943-950 \n" u"<http://dx.doi.org/10.1046/j.1365-2958.2002.03090.x>", 'name': "TatFind 1.4" } __DEBUG__ = False import sys, os, time, StringIO import twill from twill.commands import find, formfile, follow, fv, go, show, \ showforms, showlinks, submit, agent from inmembrane.helpers import log_stderr, parse_fasta_header def parse_tatfind_output(output, proteins): """ Parses the TatFind HTML output (file-like object or a list of strings) an uses it to annotate and return an associated 'proteins' data structure. """ for l in output: if "Results for" in l: seqid = l.split("Results for ")[1].split(":")[:-1][0] # parse id string to bring it to our format seqid, unused = parse_fasta_header(seqid) # "TRUE" or "FALSE" tat_pred = l.split("Results for ")[1].split(":")[-1:][0].strip() if tat_pred == "TRUE": proteins[seqid]["is_tatfind"] = True else: proteins[seqid]["is_tatfind"] = False return proteins def annotate(params, proteins, \ url="http://signalfind.org/tatfind.html", force=False): """ Interfaces with the TatFind web service at (http://signalfind.org/tatfind.html) to predict if protein sequences contain Twin-Arginine Translocation (Tat) signal peptides. """ # set the user-agent so web services can block us if they want ... :/ python_version = sys.version.split()[0] agent("Python-urllib/%s (twill; inmembrane)" % python_version) outfn = 'tatfind.out' log_stderr("# TatFind(web) %s > %s" % (params['fasta'], outfn)) if not force and os.path.isfile(outfn): log_stderr("# -> skipped: %s already exists" % outfn) fh = open(outfn, 'r') proteins = parse_tatfind_output(fh, proteins) fh.close() return proteins # dump extraneous output into this blackhole so we don't see it if not __DEBUG__: twill.set_output(StringIO.StringIO()) go(url) if __DEBUG__: showforms() formfile("1", "seqFile", params["fasta"]) submit() if __DEBUG__: show() tatfind_output = show() if __DEBUG__: log_stderr(tatfind_output) # write raw TatFind output to a file fh = open(outfn, 'w') fh.write(tatfind_output) fh.close() proteins = parse_tatfind_output(tatfind_output.split("\n"), proteins) return proteins
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parseq/scripts/geoquery_geo880_basic_nar.py
saist1993/parseq
1
37151
<filename>parseq/scripts/geoquery_geo880_basic_nar.py<gh_stars>1-10 import os import re import sys from functools import partial from typing import * import torch import qelos as q from allennlp.modules.seq2seq_encoders import PytorchSeq2SeqWrapper from nltk import PorterStemmer from torch.utils.data import DataLoader # from funcparse.decoding import TransitionModel, TFActionSeqDecoder, LSTMCellTransition, BeamActionSeqDecoder, \ # GreedyActionSeqDecoder, TFTokenSeqDecoder # from funcparse.grammar import FuncGrammar, passtr_to_pas # from funcparse.states import FuncTreeState, FuncTreeStateBatch, BasicState, BasicStateBatch # from funcparse.vocab import VocabBuilder, SentenceEncoder, FuncQueryEncoder # from funcparse.nn import TokenEmb, PtrGenOutput, SumPtrGenOutput, BasicGenOutput from parseq.decoding import SeqDecoder, TFTransition, FreerunningTransition, merge_metric_dicts from parseq.eval import CELoss, SeqAccuracies, make_array_of_metrics, DerivedAccuracy from parseq.grammar import prolog_to_pas from parseq.nn import TokenEmb, BasicGenOutput from parseq.states import DecodableState, BasicDecoderState, State from parseq.tm import TransformerConfig, Transformer from parseq.transitions import TransitionModel, LSTMCellTransition from parseq.vocab import SequenceEncoder, Vocab def stem_id_words(pas, idparents, stem=False, strtok=None): if stem is True: assert(not isinstance(pas, tuple)) if not isinstance(pas, tuple): if stem is True: assert(isinstance(pas, str)) if re.match(r"'([^']+)'", pas): pas = re.match(r"'([^']+)'", pas).group(1) pas = strtok(pas) return [("_str", pas)] else: return [pas] else: return [pas] else: tostem = pas[0] in idparents children = [stem_id_words(k, idparents, stem=tostem, strtok=strtok) for k in pas[1]] children = [a for b in children for a in b] return [(pas[0], children)] def pas2toks(pas): if not isinstance(pas, tuple): return [pas] else: children = [pas2toks(k) for k in pas[1]] ret = [pas[0]] if pas[0] != "@NAMELESS@" else [] ret[0] += "(" for child in children: ret += child # ret.append(",") # ret.pop(-1) ret.append(")") return ret def basic_query_tokenizer(x:str, strtok=None): pas = prolog_to_pas(x) idpreds = set("_cityid _countryid _stateid _riverid _placeid".split(" ")) idpreds = set("cityid stateid countryid riverid placeid".split(" ")) pas = stem_id_words(pas, idpreds, strtok=strtok)[0] ret = pas2toks(pas) return ret def try_basic_query_tokenizer(): stemmer = PorterStemmer() x = "answer(cityid('new york', _))" y = basic_query_tokenizer(x, strtok=lambda x: [stemmer.stem(xe) for xe in x.split()]) # print(y) class GeoQueryDataset(object): def __init__(self, p="../../datasets/geoquery/", sentence_encoder:SequenceEncoder=None, min_freq:int=2, **kw): super(GeoQueryDataset, self).__init__(**kw) self.data = {} self.sentence_encoder = sentence_encoder questions = [x.strip() for x in open(os.path.join(p, "questions.txt"), "r").readlines()] queries = [x.strip() for x in open(os.path.join(p, "queries.funql"), "r").readlines()] trainidxs = set([int(x.strip()) for x in open(os.path.join(p, "train_indexes.txt"), "r").readlines()]) testidxs = set([int(x.strip()) for x in open(os.path.join(p, "test_indexes.txt"), "r").readlines()]) splits = [None]*len(questions) for trainidx in trainidxs: splits[trainidx] = "train" for testidx in testidxs: splits[testidx] = "test" if any([split == None for split in splits]): print(f"{len([split for split in splits if split == None])} examples not assigned to any split") self.query_encoder = SequenceEncoder(tokenizer=partial(basic_query_tokenizer, strtok=sentence_encoder.tokenizer), add_end_token=True) # build vocabularies for i, (question, query, split) in enumerate(zip(questions, queries, splits)): self.sentence_encoder.inc_build_vocab(question, seen=split=="train") self.query_encoder.inc_build_vocab(query, seen=split=="train") self.sentence_encoder.finalize_vocab(min_freq=min_freq) self.query_encoder.finalize_vocab(min_freq=min_freq) self.build_data(questions, queries, splits) def build_data(self, inputs:Iterable[str], outputs:Iterable[str], splits:Iterable[str]): for inp, out, split in zip(inputs, outputs, splits): state = BasicDecoderState([inp], [out], self.sentence_encoder, self.query_encoder) if split not in self.data: self.data[split] = [] self.data[split].append(state) def get_split(self, split:str): return DatasetSplitProxy(self.data[split]) @staticmethod def collate_fn(data:Iterable): goldmaxlen = 0 inpmaxlen = 0 data = [state.make_copy(detach=True, deep=True) for state in data] for state in data: goldmaxlen = max(goldmaxlen, state.gold_tensor.size(1)) inpmaxlen = max(inpmaxlen, state.inp_tensor.size(1)) for state in data: state.gold_tensor = torch.cat([ state.gold_tensor, state.gold_tensor.new_zeros(1, goldmaxlen - state.gold_tensor.size(1))], 1) state.inp_tensor = torch.cat([ state.inp_tensor, state.inp_tensor.new_zeros(1, inpmaxlen - state.inp_tensor.size(1))], 1) ret = data[0].merge(data) return ret def dataloader(self, split:str=None, batsize:int=5): if split is None: # return all splits ret = {} for split in self.data.keys(): ret[split] = self.dataloader(batsize=batsize, split=split) return ret else: assert(split in self.data.keys()) dl = DataLoader(self.get_split(split), batch_size=batsize, shuffle=split=="train", collate_fn=GeoQueryDataset.collate_fn) return dl def try_dataset(): tt = q.ticktock("dataset") tt.tick("building dataset") ds = GeoQueryDataset(sentence_encoder=SequenceEncoder(tokenizer=lambda x: x.split())) train_dl = ds.dataloader("train", batsize=19) test_dl = ds.dataloader("test", batsize=20) examples = set() examples_list = [] duplicates = [] for b in train_dl: print(len(b)) for i in range(len(b)): example = b.inp_strings[i] + " --> " + b.gold_strings[i] if example in examples: duplicates.append(example) examples.add(example) examples_list.append(example) # print(example) pass print(f"duplicates within train: {len(duplicates)} from {len(examples_list)} total") tt.tock("dataset built") class DatasetSplitProxy(object): def __init__(self, data, **kw): super(DatasetSplitProxy, self).__init__(**kw) self.data = data def __getitem__(self, item): return self.data[item].make_copy() def __len__(self): return len(self.data) class NARTMModel(TransitionModel): def __init__(self, tm, out, maxinplen=50, maxoutlen=50, numinpids:int=None, eval=tuple(), **kw): super(NARTMModel, self).__init__(**kw) self.tm = tm self.out = out self.maxinplen = maxinplen self.maxoutlen = maxoutlen self._metrics = eval self._numinpids = numinpids def forward(self, x:State): inpseq = x.inp_tensor position_ids = torch.arange(inpseq.size(1), dtype=torch.long, device=inpseq.device)[None, :].repeat(inpseq.size(0), 1) inpseq = torch.cat([inpseq, torch.arange(self.maxoutlen, dtype=inpseq.dtype, device=inpseq.device)[None, :].repeat(inpseq.size(0), 1)+self._numinpids], 1) position_ids_out = torch.arange(self.maxoutlen, dtype=torch.long, device=inpseq.device)[None, :].repeat(inpseq.size(0), 1) + self.maxinplen position_ids = torch.cat([position_ids, position_ids_out], 1) attention_mask = (inpseq != 0) y = self.tm(inpseq, attention_mask=attention_mask, position_ids=position_ids) outprobs = self.out(y[0]) outprobs = outprobs[:, self.maxinplen:] _, predactions = outprobs.max(-1) metrics = [metric(outprobs, predactions, x) for metric in self._metrics] metrics = merge_metric_dicts(*metrics) return metrics, x def create_model(hdim=128, dropout=0., numlayers:int=1, numheads:int=4, sentence_encoder:SequenceEncoder=None, query_encoder:SequenceEncoder=None, feedatt=False, maxtime=100): inpemb = torch.nn.Embedding(sentence_encoder.vocab.number_of_ids()+maxtime, hdim, padding_idx=0) inpemb = TokenEmb(inpemb, rare_token_ids=sentence_encoder.vocab.rare_ids, rare_id=1) tm_config = TransformerConfig(vocab_size=inpemb.emb.num_embeddings, num_attention_heads=numheads, num_hidden_layers=numlayers, hidden_size=hdim, intermediate_size=hdim*4, hidden_dropout_prob=dropout) tm = Transformer(tm_config) tm.embeddings.word_embeddings = inpemb decoder_out = BasicGenOutput(hdim, query_encoder.vocab) model = NARTMModel(tm, decoder_out, maxinplen=maxtime, maxoutlen=maxtime, numinpids=sentence_encoder.vocab.number_of_ids()) return model def do_rare_stats(ds:GeoQueryDataset): # how many examples contain rare words, in input and output, in both train and test def get_rare_portions(examples:List[State]): total = 0 rare_in_question = 0 rare_in_query = 0 rare_in_both = 0 rare_in_either = 0 for example in examples: total += 1 question_tokens = example.inp_tokens[0] query_tokens = example.gold_tokens[0] both = True either = False if len(set(question_tokens) & example.sentence_encoder.vocab.rare_tokens) > 0: rare_in_question += 1 either = True else: both = False if len(set(query_tokens) & example.query_encoder.vocab.rare_tokens) > 0: either = True rare_in_query += 1 else: both = False if both: rare_in_both += 1 if either: rare_in_either += 1 return rare_in_question / total, rare_in_query/total, rare_in_both/total, rare_in_either/total print("RARE STATS:::") print("training data:") ris, riq, rib, rie = get_rare_portions(ds.data["train"]) print(f"\t In question: {ris} \n\t In query: {riq} \n\t In both: {rib} \n\t In either: {rie}") print("test data:") ris, riq, rib, rie = get_rare_portions(ds.data["test"]) print(f"\t In question: {ris} \n\t In query: {riq} \n\t In both: {rib} \n\t In either: {rie}") return def tensor2tree(x, D:Vocab=None): # x: 1D int tensor x = list(x.detach().cpu().numpy()) x = [D(xe) for xe in x] x = [xe for xe in x if xe != D.padtoken] # find first @END@ and cut off parentheses_balance = 0 for i in range(len(x)): if x[i] ==D.endtoken: x = x[:i] break elif x[i] == "(" or x[i][-1] == "(": parentheses_balance += 1 elif x[i] == ")": parentheses_balance -= 1 else: pass # balance parentheses while parentheses_balance > 0: x.append(")") parentheses_balance -= 1 i = len(x) - 1 while parentheses_balance < 0 and i > 0: if x[i] == ")": x.pop(i) parentheses_balance += 1 i -= 1 # introduce comma's i = 1 while i < len(x): if x[i-1][-1] == "(": pass elif x[i] == ")": pass else: x.insert(i, ",") i += 1 i += 1 return " ".join(x) def run(lr=0.001, batsize=20, epochs=100, embdim=100, encdim=164, numlayers=4, numheads=4, dropout=.0, wreg=1e-10, cuda=False, gpu=0, minfreq=2, gradnorm=3000., cosine_restarts=1., ): print(locals()) tt = q.ticktock("script") device = torch.device("cpu") if not cuda else torch.device("cuda", gpu) tt.tick("loading data") stemmer = PorterStemmer() tokenizer = lambda x: [stemmer.stem(xe) for xe in x.split()] ds = GeoQueryDataset(sentence_encoder=SequenceEncoder(tokenizer=tokenizer), min_freq=minfreq) train_dl = ds.dataloader("train", batsize=batsize) test_dl = ds.dataloader("test", batsize=batsize) tt.tock("data loaded") do_rare_stats(ds) # batch = next(iter(train_dl)) # print(batch) # print("input graph") # print(batch.batched_states) model = create_model(hdim=encdim, dropout=dropout, numlayers=numlayers, numheads=numheads, sentence_encoder=ds.sentence_encoder, query_encoder=ds.query_encoder) model._metrics = [CELoss(ignore_index=0, mode="logprobs"), SeqAccuracies()] losses = make_array_of_metrics("loss", "elem_acc", "seq_acc") vlosses = make_array_of_metrics("loss", "seq_acc") # 4. define optim optim = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wreg) # optim = torch.optim.SGD(tfdecoder.parameters(), lr=lr, weight_decay=wreg) # lr schedule if cosine_restarts >= 0: # t_max = epochs * len(train_dl) t_max = epochs print(f"Total number of updates: {t_max} ({epochs} * {len(train_dl)})") lr_schedule = q.WarmupCosineWithHardRestartsSchedule(optim, 0, t_max, cycles=cosine_restarts) reduce_lr = [lambda: lr_schedule.step()] else: reduce_lr = [] # 6. define training function (using partial) clipgradnorm = lambda: torch.nn.utils.clip_grad_norm_(model.parameters(), gradnorm) trainbatch = partial(q.train_batch, on_before_optim_step=[clipgradnorm]) trainepoch = partial(q.train_epoch, model=model, dataloader=train_dl, optim=optim, losses=losses, _train_batch=trainbatch, device=device, on_end=reduce_lr) # 7. define validation function (using partial) validepoch = partial(q.test_epoch, model=model, dataloader=test_dl, losses=vlosses, device=device) # validepoch = partial(q.test_epoch, model=tfdecoder, dataloader=test_dl, losses=vlosses, device=device) # 7. run training tt.tick("training") q.run_training(run_train_epoch=trainepoch, run_valid_epoch=validepoch, max_epochs=epochs) tt.tock("done training") if __name__ == '__main__': try_basic_query_tokenizer() # try_build_grammar() # try_dataset() q.argprun(run)
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1712, 10812, 3838, 29892, 297, 1881, 322, 1962, 29892, 297, 1716, 7945, 322, 1243, 13, 1678, 822, 679, 29918, 25983, 29918, 637, 1080, 29898, 19057, 29901, 1293, 29961, 2792, 29962, 1125, 13, 4706, 3001, 353, 29871, 29900, 13, 4706, 10812, 29918, 262, 29918, 12470, 353, 29871, 29900, 13, 4706, 10812, 29918, 262, 29918, 1972, 353, 29871, 29900, 13, 4706, 10812, 29918, 262, 29918, 20313, 353, 29871, 29900, 13, 4706, 10812, 29918, 262, 29918, 29872, 2121, 353, 29871, 29900, 13, 4706, 363, 1342, 297, 6455, 29901, 13, 9651, 3001, 4619, 29871, 29896, 13, 9651, 1139, 29918, 517, 12360, 353, 1342, 29889, 262, 29886, 29918, 517, 12360, 29961, 29900, 29962, 13, 9651, 2346, 29918, 517, 12360, 353, 1342, 29889, 29887, 1025, 29918, 517, 12360, 29961, 29900, 29962, 13, 9651, 1716, 353, 5852, 13, 9651, 2845, 353, 7700, 13, 9651, 565, 7431, 29898, 842, 29898, 12470, 29918, 517, 12360, 29897, 669, 1342, 29889, 18616, 663, 29918, 3977, 6119, 29889, 29894, 542, 370, 29889, 25983, 29918, 517, 12360, 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1115, 13, 9651, 29494, 29918, 5521, 749, 22361, 29871, 29896, 13, 4706, 1683, 29901, 13, 9651, 1209, 13, 13, 1678, 396, 17346, 29494, 13, 1678, 1550, 29494, 29918, 5521, 749, 1405, 29871, 29900, 29901, 13, 4706, 921, 29889, 4397, 703, 25760, 13, 4706, 29494, 29918, 5521, 749, 22361, 29871, 29896, 13, 1678, 474, 353, 7431, 29898, 29916, 29897, 448, 29871, 29896, 13, 1678, 1550, 29494, 29918, 5521, 749, 529, 29871, 29900, 322, 474, 1405, 29871, 29900, 29901, 13, 4706, 565, 921, 29961, 29875, 29962, 1275, 16521, 1115, 13, 9651, 921, 29889, 7323, 29898, 29875, 29897, 13, 9651, 29494, 29918, 5521, 749, 4619, 29871, 29896, 13, 4706, 474, 22361, 29871, 29896, 13, 13, 1678, 396, 14944, 16694, 29915, 29879, 13, 1678, 474, 353, 29871, 29896, 13, 1678, 1550, 474, 529, 7431, 29898, 29916, 1125, 13, 4706, 565, 921, 29961, 29875, 29899, 29896, 3816, 29899, 29896, 29962, 1275, 376, 703, 29901, 13, 9651, 1209, 13, 4706, 25342, 921, 29961, 29875, 29962, 1275, 16521, 1115, 13, 9651, 1209, 13, 4706, 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2478, 580, 29962, 13, 13, 1678, 28495, 353, 1207, 29918, 2378, 29918, 974, 29918, 2527, 10817, 703, 6758, 613, 376, 20461, 29918, 5753, 613, 376, 11762, 29918, 5753, 1159, 13, 1678, 325, 6758, 267, 353, 1207, 29918, 2378, 29918, 974, 29918, 2527, 10817, 703, 6758, 613, 376, 11762, 29918, 5753, 1159, 13, 13, 1678, 396, 29871, 29946, 29889, 4529, 5994, 13, 1678, 5994, 353, 4842, 305, 29889, 20640, 29889, 3253, 314, 29898, 4299, 29889, 16744, 3285, 301, 29878, 29922, 29212, 29892, 7688, 29918, 7099, 388, 29922, 29893, 1727, 29897, 13, 1678, 396, 5994, 353, 4842, 305, 29889, 20640, 29889, 26016, 29928, 29898, 13264, 7099, 6119, 29889, 16744, 3285, 301, 29878, 29922, 29212, 29892, 7688, 29918, 7099, 388, 29922, 29893, 1727, 29897, 13, 13, 1678, 396, 301, 29878, 20410, 13, 1678, 565, 6776, 457, 29918, 5060, 5708, 6736, 29871, 29900, 29901, 13, 4706, 396, 260, 29918, 3317, 353, 21502, 12168, 334, 7431, 29898, 14968, 29918, 11671, 29897, 13, 4706, 260, 29918, 3317, 353, 21502, 12168, 13, 4706, 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study/demo.py
t-ntran/vscode
0
139754
def initials(name): parts = name.split(' ') letters = '' for part in parts: letters += part[0] return letters
[ 1, 29871, 13, 1753, 2847, 29879, 29898, 978, 1125, 13, 1678, 5633, 353, 1024, 29889, 5451, 877, 25710, 13, 1678, 8721, 353, 6629, 13, 1678, 363, 760, 297, 5633, 29901, 13, 4706, 8721, 4619, 760, 29961, 29900, 29962, 13, 1678, 736, 8721, 2 ]
discriminator.py
TobeyYang/S2S_Temp
12
121116
import torch import torch.autograd as autograd import torch.nn as nn import pdb from textcnn import TextCNN class Discriminator(nn.Module): def __init__(self, vocab_size, emb_dim, filter_num, filter_sizes, dropout=0.0): super(Discriminator, self).__init__() self.query_cnn = TextCNN(emb_dim, filter_num, filter_sizes) self.response_cnn = TextCNN(emb_dim, filter_num, filter_sizes) self.dropout = nn.Dropout(p=dropout) self.embeddings = nn.Embedding(vocab_size, emb_dim) # self.judger = nn.Sequential( nn.Linear(2 * filter_num * len(filter_sizes), 128), nn.ReLU(), self.dropout, nn.Linear(128, 2), nn.Softmax(dim=1) ) # self.query_h0 = nn.Parameter(torch.zeros(1, 1, 300)) # self.query_c0 = nn.Parameter(torch.zeros(1, 1, 300)) # self.response_h0 = nn.Parameter(torch.zeros(1, 1, 300)) # self.response_c0 = nn.Parameter(torch.zeros(1, 1, 300)) # # self.query_rnn = torch.nn.LSTM(emb_dim, 300, 1) # self.response_rnn = torch.nn.LSTM(emb_dim, 300, 1) # self.judger = nn.Sequential( # nn.Linear(2 * 300, 128), # nn.ReLU(), # self.dropout, # nn.Linear(128, 2), # nn.Softmax(dim=1) # # ) def forward(self, query, response): ''' Args: query: bsz x query_len response: bsz x response_len Returns: ''' bsz = query.size(0) query_emb = self.embeddings(query) # bsz x query_len x emb_size response_emb = self.embeddings(response) # bsz x response_len x emb_size query_features = self.query_cnn(query_emb) # [B, T, D] -> [B, all_features] response_features = self.response_cnn(response_emb) # h0 = self.query_h0.expand(1, bsz, 300).contiguous() # c0 = self.query_c0.expand(1, bsz, 300).contiguous() # _, (h_n, c_n) = self.query_rnn(query_emb.transpose(0, 1), (h0, c0)) # query_features = h_n.squeeze(0) # bsz x 300 # # h0 = self.response_h0.expand(1, bsz, 300).contiguous() # c0 = self.response_c0.expand(1, bsz, 300).contiguous() # _, (h_n, c_n) = self.response_rnn(response_emb.transpose(0, 1), (h0, c0)) # response_features = h_n.squeeze(0) # bsz x 300 inputs = torch.cat((query_features, response_features), 1) prob = self.judger(inputs)[:, 1] return prob def batchClassify(self, query, response): ''' Args: query: bsz x query_len response: bsz x response_len Returns: out: bsz ''' out = self.forward(query, response) return out def batchBCEloss(self, query, response, target): ''' Returns Binary Cross Entropy Loss for discriminator. Args: query: bsz x query_len response: bsz x response_len target: bsz (binary 0/1) Returns: ''' loss_fn = nn.BCELoss() out = self.forward(query, response) return loss_fn(out, target) def validate(self, valid_set): acc = 0 total = 0 for i in range(len(valid_set)): src, rep, target = valid_set[i] bsz, _ = src.size() out = self.batchClassify(src, rep) acc += torch.sum((out > 0.5) == (target > 0.5)).data.item() total += bsz return acc / total def save_model(self, save_path, **kwargs): kwargs['state_dict'] = self.state_dict() torch.save(kwargs, save_path) def load_model(self, save_path): saved_stuff = torch.load(save_path) # remove the 'module' prefix clean_state = {} saved_state = saved_stuff["state_dict"] for k, v in saved_state.items(): nk = k[7:] if k.startswith('module.') else k clean_state[nk] = v # self.load_state_dict(saved_stuff['state_dict']) self.load_state_dict(clean_state)
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Zad_FactoryMethod/BazaDanychOracle.py
Paarzivall/Wzorce-Projektowe
0
94286
from BazaDanych import BazaDanych class BazaDanychOracle(BazaDanych): def WykonajSelect(self, zapytanie): print(f"Oracle\> {zapytanie}\n\tWykonano pomyślnie")
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src/collage_renderer.py
yozw/metapixel
0
62980
"""Collage renderer.""" import itertools import logger import numpy from PIL import Image, ImageEnhance from distance_matrix import imageMSE ENABLE_POST_OPTIMIZATION = True def adjustImage(image, parameters): """Adjusts the brightness, contrast, and saturation of the given image.""" (brightness, contrast, saturation) = parameters newImage = ImageEnhance.Brightness(image).enhance(brightness) newImage = ImageEnhance.Contrast(newImage).enhance(contrast) newImage = ImageEnhance.Color(newImage).enhance(saturation) return newImage def postOptimize(image, goalImage): """Adjusts the brightness, contrast, and saturation of the given image in such a way that the MSE between the adjusted image and the goal image is minimized.""" if not ENABLE_POST_OPTIMIZATION: return (1, 1, 1) # Vary brightness, saturation, contrast to better match the goal image brightnessSet = numpy.arange(0.6, 1.3, 0.05) contrastSet = numpy.arange(0.9, 1.2, 0.05) saturationSet = numpy.arange(1.0, 1.3, 0.05) settings = itertools.product(brightnessSet, contrastSet, saturationSet) bestMSE = None for parameters in settings: newImage = adjustImage(image, parameters) MSE = imageMSE(newImage, goalImage) if not bestMSE or MSE < bestMSE: bestMSE = MSE bestParameters = parameters if not bestParameters: raise Exception("Post-optimization failed") return bestParameters def renderCollage(solution, grid, sampleGrid, imageLibrary, outputFile, cheatFactor=0): """Post-optimizes the solution and renders the output.""" logger.info("Post-optimizing ...") optimalParameters = {} for i in range(grid.imageCountX): logger.progress(i, grid.imageCountX) for j in range(grid.imageCountY): imageIndex = solution[i, j] image = imageLibrary.images[imageIndex] sampleImage = image.get(sampleGrid.imageWidth, sampleGrid.imageHeight).get() optimalParameters[i, j] = postOptimize(sampleImage, sampleGrid[i, j].get()) logger.info("Rendering collage ...") background = Image.new("RGB", grid.size, "white") collage = Image.new("RGB", grid.size, "white") for i in range(grid.imageCountX): logger.progress(i, grid.imageCountX) for j in range(grid.imageCountY): offset = (i * grid.imageWidth, j * grid.imageHeight) imageIndex = solution[i, j] image = imageLibrary.images[imageIndex] subImage = image.get(grid.imageWidth, grid.imageHeight).get() image = adjustImage(subImage, optimalParameters[i, j]) background.paste(grid[i, j].get(), offset) collage.paste(image, offset) logger.info("Saving ...") output = Image.blend(collage, background, cheatFactor) output.save(outputFile)
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gRPC/python/video_streaming/src/image_streaming_pb2.py
kaka-lin/Notes
0
151359
# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: image_streaming.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='image_streaming.proto', package='imagestreaming', syntax='proto3', serialized_options=None, create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x15image_streaming.proto\x12\x0eimagestreaming\"I\n\nImgRequest\x12\x0b\n\x03img\x18\x01 \x01(\x0c\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0e\n\x06height\x18\x03 \x01(\x05\x12\x0f\n\x07\x63hannel\x18\x04 \x01(\x05\"\x1d\n\x0bImgResponse\x12\x0e\n\x06\x63ounts\x18\x01 \x01(\x05\x32]\n\x0eImageStreaming\x12K\n\nVideoStart\x12\x1a.imagestreaming.ImgRequest\x1a\x1b.imagestreaming.ImgResponse\"\x00(\x01\x30\x01\x62\x06proto3' ) _IMGREQUEST = _descriptor.Descriptor( name='ImgRequest', full_name='imagestreaming.ImgRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='img', full_name='imagestreaming.ImgRequest.img', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='width', full_name='imagestreaming.ImgRequest.width', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='height', full_name='imagestreaming.ImgRequest.height', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='channel', full_name='imagestreaming.ImgRequest.channel', index=3, number=4, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=41, serialized_end=114, ) _IMGRESPONSE = _descriptor.Descriptor( name='ImgResponse', full_name='imagestreaming.ImgResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='counts', full_name='imagestreaming.ImgResponse.counts', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=116, serialized_end=145, ) DESCRIPTOR.message_types_by_name['ImgRequest'] = _IMGREQUEST DESCRIPTOR.message_types_by_name['ImgResponse'] = _IMGRESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) ImgRequest = _reflection.GeneratedProtocolMessageType('ImgRequest', (_message.Message,), { 'DESCRIPTOR' : _IMGREQUEST, '__module__' : 'image_streaming_pb2' # @@protoc_insertion_point(class_scope:imagestreaming.ImgRequest) }) _sym_db.RegisterMessage(ImgRequest) ImgResponse = _reflection.GeneratedProtocolMessageType('ImgResponse', (_message.Message,), { 'DESCRIPTOR' : _IMGRESPONSE, '__module__' : 'image_streaming_pb2' # @@protoc_insertion_point(class_scope:imagestreaming.ImgResponse) }) _sym_db.RegisterMessage(ImgResponse) _IMAGESTREAMING = _descriptor.ServiceDescriptor( name='ImageStreaming', full_name='imagestreaming.ImageStreaming', file=DESCRIPTOR, index=0, serialized_options=None, create_key=_descriptor._internal_create_key, serialized_start=147, serialized_end=240, methods=[ _descriptor.MethodDescriptor( name='VideoStart', full_name='imagestreaming.ImageStreaming.VideoStart', index=0, containing_service=None, input_type=_IMGREQUEST, output_type=_IMGRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), ]) _sym_db.RegisterServiceDescriptor(_IMAGESTREAMING) DESCRIPTOR.services_by_name['ImageStreaming'] = _IMAGESTREAMING # @@protoc_insertion_point(module_scope)
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29897, 13, 13, 25518, 3089, 353, 903, 999, 1464, 29889, 24565, 17830, 3728, 1542, 877, 25518, 3089, 742, 9423, 4906, 29889, 3728, 29892, 511, 426, 13, 29871, 525, 2287, 7187, 24290, 1955, 29915, 584, 903, 7833, 29954, 16244, 29892, 13, 29871, 525, 1649, 5453, 1649, 29915, 584, 525, 3027, 29918, 5461, 292, 29918, 24381, 29906, 29915, 13, 29871, 396, 732, 29992, 17529, 29883, 29918, 7851, 291, 29918, 3149, 29898, 1990, 29918, 6078, 29901, 326, 351, 342, 1633, 292, 29889, 25518, 3089, 29897, 13, 29871, 5615, 13, 29918, 11967, 29918, 2585, 29889, 15213, 3728, 29898, 25518, 3089, 29897, 13, 13, 25518, 5103, 353, 903, 999, 1464, 29889, 24565, 17830, 3728, 1542, 877, 25518, 5103, 742, 9423, 4906, 29889, 3728, 29892, 511, 426, 13, 29871, 525, 2287, 7187, 24290, 1955, 29915, 584, 903, 7833, 29954, 1525, 5550, 1164, 1660, 29892, 13, 29871, 525, 1649, 5453, 1649, 29915, 584, 525, 3027, 29918, 5461, 292, 29918, 24381, 29906, 29915, 13, 29871, 396, 732, 29992, 17529, 29883, 29918, 7851, 291, 29918, 3149, 29898, 1990, 29918, 6078, 29901, 326, 351, 342, 1633, 292, 29889, 25518, 5103, 29897, 13, 29871, 5615, 13, 29918, 11967, 29918, 2585, 29889, 15213, 3728, 29898, 25518, 5103, 29897, 13, 13, 13, 13, 29918, 2382, 1254, 1525, 5194, 4214, 353, 903, 2783, 11709, 29889, 3170, 19124, 29898, 13, 29871, 1024, 2433, 2940, 3835, 292, 742, 13, 29871, 2989, 29918, 978, 2433, 326, 351, 342, 1633, 292, 29889, 2940, 3835, 292, 742, 13, 29871, 934, 29922, 2287, 7187, 24290, 1955, 29892, 13, 29871, 2380, 29922, 29900, 29892, 13, 29871, 7797, 1891, 29918, 6768, 29922, 8516, 29892, 13, 29871, 1653, 29918, 1989, 29922, 29918, 2783, 11709, 3032, 7564, 29918, 3258, 29918, 1989, 29892, 13, 29871, 7797, 1891, 29918, 2962, 29922, 29896, 29946, 29955, 29892, 13, 29871, 7797, 1891, 29918, 355, 29922, 29906, 29946, 29900, 29892, 13, 29871, 3519, 11759, 13, 29871, 903, 2783, 11709, 29889, 4062, 19124, 29898, 13, 1678, 1024, 2433, 15167, 4763, 742, 13, 1678, 2989, 29918, 978, 2433, 326, 351, 342, 1633, 292, 29889, 2940, 3835, 292, 29889, 15167, 4763, 742, 13, 1678, 2380, 29922, 29900, 29892, 13, 1678, 6943, 29918, 5509, 29922, 8516, 29892, 13, 1678, 1881, 29918, 1853, 29922, 29918, 7833, 29954, 16244, 29892, 13, 1678, 1962, 29918, 1853, 29922, 29918, 7833, 29954, 1525, 5550, 1164, 1660, 29892, 13, 1678, 7797, 1891, 29918, 6768, 29922, 8516, 29892, 13, 1678, 1653, 29918, 1989, 29922, 29918, 2783, 11709, 3032, 7564, 29918, 3258, 29918, 1989, 29892, 13, 29871, 10353, 13, 2314, 13, 29918, 11967, 29918, 2585, 29889, 15213, 3170, 19124, 7373, 2382, 1254, 1525, 5194, 4214, 29897, 13, 13, 2287, 7187, 24290, 1955, 29889, 9916, 29918, 1609, 29918, 978, 1839, 2940, 3835, 292, 2033, 353, 903, 2382, 1254, 1525, 5194, 4214, 13, 13, 29937, 732, 29992, 17529, 29883, 29918, 7851, 291, 29918, 3149, 29898, 5453, 29918, 6078, 29897, 13, 2 ]
sdk/python/pulumi_azure_native/edgeorder/v20201201preview/outputs.py
pulumi-bot/pulumi-azure-native
31
24385
<filename>sdk/python/pulumi_azure_native/edgeorder/v20201201preview/outputs.py # coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs from ._enums import * __all__ = [ 'AdditionalErrorInfoResponse', 'AddressDetailsResponse', 'AddressPropertiesResponse', 'AvailabilityInformationResponseResult', 'BillingModelResponseResult', 'CloudErrorResponse', 'ConfigurationResponseResult', 'ContactDetailsResponse', 'CostInformationResponseResult', 'DescriptionResponseResult', 'DeviceDetailsResponse', 'FilterablePropertyResponseResult', 'HierarchyInformationResponse', 'ImageInformationResponseResult', 'LinkResponseResult', 'MeterDetailsResponseResult', 'NotificationPreferenceResponse', 'OrderDetailsResponse', 'OrderStatusDetailsResponse', 'PreferencesResponse', 'ProductDetailsResponse', 'ProductFamilyResponseResult', 'ProductLineResponseResult', 'ProductResponseResult', 'ShippingAddressResponse', 'ShippingDetailsResponse', 'SpecificationResponseResult', 'SystemDataResponse', 'TransportPreferencesResponse', ] @pulumi.output_type class AdditionalErrorInfoResponse(dict): def __init__(__self__, *, info: Optional[Any] = None, type: Optional[str] = None): if info is not None: pulumi.set(__self__, "info", info) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter def info(self) -> Optional[Any]: return pulumi.get(self, "info") @property @pulumi.getter def type(self) -> Optional[str]: return pulumi.get(self, "type") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class AddressDetailsResponse(dict): """ Address details for an order. """ def __init__(__self__, *, return_address: 'outputs.AddressPropertiesResponse', shipping_address: 'outputs.AddressPropertiesResponse'): """ Address details for an order. :param 'AddressPropertiesResponseArgs' return_address: Return shipping address :param 'AddressPropertiesResponseArgs' shipping_address: Customer address and contact details. It should be address resource """ pulumi.set(__self__, "return_address", return_address) pulumi.set(__self__, "shipping_address", shipping_address) @property @pulumi.getter(name="returnAddress") def return_address(self) -> 'outputs.AddressPropertiesResponse': """ Return shipping address """ return pulumi.get(self, "return_address") @property @pulumi.getter(name="shippingAddress") def shipping_address(self) -> 'outputs.AddressPropertiesResponse': """ Customer address and contact details. It should be address resource """ return pulumi.get(self, "shipping_address") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class AddressPropertiesResponse(dict): """ Address Properties """ def __init__(__self__, *, contact_details: 'outputs.ContactDetailsResponse', shipping_address: Optional['outputs.ShippingAddressResponse'] = None): """ Address Properties :param 'ContactDetailsResponseArgs' contact_details: Contact details for the address :param 'ShippingAddressResponseArgs' shipping_address: Shipping details for the address """ pulumi.set(__self__, "contact_details", contact_details) if shipping_address is not None: pulumi.set(__self__, "shipping_address", shipping_address) @property @pulumi.getter(name="contactDetails") def contact_details(self) -> 'outputs.ContactDetailsResponse': """ Contact details for the address """ return pulumi.get(self, "contact_details") @property @pulumi.getter(name="shippingAddress") def shipping_address(self) -> Optional['outputs.ShippingAddressResponse']: """ Shipping details for the address """ return pulumi.get(self, "shipping_address") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class AvailabilityInformationResponseResult(dict): """ Availability information of a product system. """ def __init__(__self__, *, availability_stage: str, disabled_reason: str, disabled_reason_message: str): """ Availability information of a product system. :param str availability_stage: Current availability stage of the product. Availability stage :param str disabled_reason: Reason why the product is disabled. :param str disabled_reason_message: Message for why the product is disabled. """ pulumi.set(__self__, "availability_stage", availability_stage) pulumi.set(__self__, "disabled_reason", disabled_reason) pulumi.set(__self__, "disabled_reason_message", disabled_reason_message) @property @pulumi.getter(name="availabilityStage") def availability_stage(self) -> str: """ Current availability stage of the product. Availability stage """ return pulumi.get(self, "availability_stage") @property @pulumi.getter(name="disabledReason") def disabled_reason(self) -> str: """ Reason why the product is disabled. """ return pulumi.get(self, "disabled_reason") @property @pulumi.getter(name="disabledReasonMessage") def disabled_reason_message(self) -> str: """ Message for why the product is disabled. """ return pulumi.get(self, "disabled_reason_message") @pulumi.output_type class BillingModelResponseResult(dict): """ Model to represent the billing cycle """ def __init__(__self__, *, model: str): """ Model to represent the billing cycle :param str model: String to represent the billing model """ pulumi.set(__self__, "model", model) @property @pulumi.getter def model(self) -> str: """ String to represent the billing model """ return pulumi.get(self, "model") @pulumi.output_type class CloudErrorResponse(dict): def __init__(__self__, *, additional_info: Sequence['outputs.AdditionalErrorInfoResponse'], details: Sequence['outputs.CloudErrorResponse'], code: Optional[str] = None, message: Optional[str] = None, target: Optional[str] = None): pulumi.set(__self__, "additional_info", additional_info) pulumi.set(__self__, "details", details) if code is not None: pulumi.set(__self__, "code", code) if message is not None: pulumi.set(__self__, "message", message) if target is not None: pulumi.set(__self__, "target", target) @property @pulumi.getter(name="additionalInfo") def additional_info(self) -> Sequence['outputs.AdditionalErrorInfoResponse']: return pulumi.get(self, "additional_info") @property @pulumi.getter def details(self) -> Sequence['outputs.CloudErrorResponse']: return pulumi.get(self, "details") @property @pulumi.getter def code(self) -> Optional[str]: return pulumi.get(self, "code") @property @pulumi.getter def message(self) -> Optional[str]: return pulumi.get(self, "message") @property @pulumi.getter def target(self) -> Optional[str]: return pulumi.get(self, "target") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ConfigurationResponseResult(dict): """ Configuration object. """ def __init__(__self__, *, availability_information: 'outputs.AvailabilityInformationResponseResult', cost_information: 'outputs.CostInformationResponseResult', description: 'outputs.DescriptionResponseResult', display_name: str, filterable_properties: Sequence['outputs.FilterablePropertyResponseResult'], hierarchy_information: 'outputs.HierarchyInformationResponse', image_information: Sequence['outputs.ImageInformationResponseResult'], specifications: Sequence['outputs.SpecificationResponseResult']): """ Configuration object. :param 'AvailabilityInformationResponseArgs' availability_information: Availability information of the product system. :param 'CostInformationResponseArgs' cost_information: Cost information for the product system. :param 'DescriptionResponseArgs' description: Description related to the product system. :param str display_name: Display Name for the product system. :param Sequence['FilterablePropertyResponseArgs'] filterable_properties: list of filters supported for a product :param 'HierarchyInformationResponseArgs' hierarchy_information: Hierarchy information of the product system. :param Sequence['ImageInformationResponseArgs'] image_information: Image information for the product system. :param Sequence['SpecificationResponseArgs'] specifications: Specifications of the configuration """ pulumi.set(__self__, "availability_information", availability_information) pulumi.set(__self__, "cost_information", cost_information) pulumi.set(__self__, "description", description) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "filterable_properties", filterable_properties) pulumi.set(__self__, "hierarchy_information", hierarchy_information) pulumi.set(__self__, "image_information", image_information) pulumi.set(__self__, "specifications", specifications) @property @pulumi.getter(name="availabilityInformation") def availability_information(self) -> 'outputs.AvailabilityInformationResponseResult': """ Availability information of the product system. """ return pulumi.get(self, "availability_information") @property @pulumi.getter(name="costInformation") def cost_information(self) -> 'outputs.CostInformationResponseResult': """ Cost information for the product system. """ return pulumi.get(self, "cost_information") @property @pulumi.getter def description(self) -> 'outputs.DescriptionResponseResult': """ Description related to the product system. """ return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ Display Name for the product system. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="filterableProperties") def filterable_properties(self) -> Sequence['outputs.FilterablePropertyResponseResult']: """ list of filters supported for a product """ return pulumi.get(self, "filterable_properties") @property @pulumi.getter(name="hierarchyInformation") def hierarchy_information(self) -> 'outputs.HierarchyInformationResponse': """ Hierarchy information of the product system. """ return pulumi.get(self, "hierarchy_information") @property @pulumi.getter(name="imageInformation") def image_information(self) -> Sequence['outputs.ImageInformationResponseResult']: """ Image information for the product system. """ return pulumi.get(self, "image_information") @property @pulumi.getter def specifications(self) -> Sequence['outputs.SpecificationResponseResult']: """ Specifications of the configuration """ return pulumi.get(self, "specifications") @pulumi.output_type class ContactDetailsResponse(dict): """ Contact Details. """ def __init__(__self__, *, contact_name: str, phone: str, mobile: Optional[str] = None, phone_extension: Optional[str] = None): """ Contact Details. :param str contact_name: Contact name of the person. :param str phone: Phone number of the contact person. :param str mobile: Mobile number of the contact person. :param str phone_extension: Phone extension number of the contact person. """ pulumi.set(__self__, "contact_name", contact_name) pulumi.set(__self__, "phone", phone) if mobile is not None: pulumi.set(__self__, "mobile", mobile) if phone_extension is not None: pulumi.set(__self__, "phone_extension", phone_extension) @property @pulumi.getter(name="contactName") def contact_name(self) -> str: """ Contact name of the person. """ return pulumi.get(self, "contact_name") @property @pulumi.getter def phone(self) -> str: """ Phone number of the contact person. """ return pulumi.get(self, "phone") @property @pulumi.getter def mobile(self) -> Optional[str]: """ Mobile number of the contact person. """ return pulumi.get(self, "mobile") @property @pulumi.getter(name="phoneExtension") def phone_extension(self) -> Optional[str]: """ Phone extension number of the contact person. """ return pulumi.get(self, "phone_extension") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class CostInformationResponseResult(dict): """ Cost information for the product system """ def __init__(__self__, *, meter_details: Sequence['outputs.MeterDetailsResponseResult'], primary_meter_type: str): """ Cost information for the product system :param Sequence['MeterDetailsResponseArgs'] meter_details: Details on the various billing aspects for the product system. :param str primary_meter_type: Primary meter i.e. basic billing type for the product system. """ pulumi.set(__self__, "meter_details", meter_details) pulumi.set(__self__, "primary_meter_type", primary_meter_type) @property @pulumi.getter(name="meterDetails") def meter_details(self) -> Sequence['outputs.MeterDetailsResponseResult']: """ Details on the various billing aspects for the product system. """ return pulumi.get(self, "meter_details") @property @pulumi.getter(name="primaryMeterType") def primary_meter_type(self) -> str: """ Primary meter i.e. basic billing type for the product system. """ return pulumi.get(self, "primary_meter_type") @pulumi.output_type class DescriptionResponseResult(dict): """ Description related properties of a product system. """ def __init__(__self__, *, attributes: Sequence[str], description_type: str, keywords: Sequence[str], links: Sequence['outputs.LinkResponseResult'], long_description: str, short_description: str): """ Description related properties of a product system. :param Sequence[str] attributes: Attributes for the product system. :param str description_type: Type of description. :param Sequence[str] keywords: Keywords for the product system. :param Sequence['LinkResponseArgs'] links: Links for the product system. :param str long_description: Long description of the product system. :param str short_description: Short description of the product system. """ pulumi.set(__self__, "attributes", attributes) pulumi.set(__self__, "description_type", description_type) pulumi.set(__self__, "keywords", keywords) pulumi.set(__self__, "links", links) pulumi.set(__self__, "long_description", long_description) pulumi.set(__self__, "short_description", short_description) @property @pulumi.getter def attributes(self) -> Sequence[str]: """ Attributes for the product system. """ return pulumi.get(self, "attributes") @property @pulumi.getter(name="descriptionType") def description_type(self) -> str: """ Type of description. """ return pulumi.get(self, "description_type") @property @pulumi.getter def keywords(self) -> Sequence[str]: """ Keywords for the product system. """ return pulumi.get(self, "keywords") @property @pulumi.getter def links(self) -> Sequence['outputs.LinkResponseResult']: """ Links for the product system. """ return pulumi.get(self, "links") @property @pulumi.getter(name="longDescription") def long_description(self) -> str: """ Long description of the product system. """ return pulumi.get(self, "long_description") @property @pulumi.getter(name="shortDescription") def short_description(self) -> str: """ Short description of the product system. """ return pulumi.get(self, "short_description") @pulumi.output_type class DeviceDetailsResponse(dict): """ Device details. """ def __init__(__self__, *, device_history: Sequence[str], serial_number: str): """ Device details. :param Sequence[str] device_history: Package Shipping details :param str serial_number: device serial number """ pulumi.set(__self__, "device_history", device_history) pulumi.set(__self__, "serial_number", serial_number) @property @pulumi.getter(name="deviceHistory") def device_history(self) -> Sequence[str]: """ Package Shipping details """ return pulumi.get(self, "device_history") @property @pulumi.getter(name="serialNumber") def serial_number(self) -> str: """ device serial number """ return pulumi.get(self, "serial_number") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class FilterablePropertyResponseResult(dict): """ Class defining the list of filter values on a filter type as part of configuration request. """ def __init__(__self__, *, type: str, supported_values: Optional[Sequence[str]] = None): """ Class defining the list of filter values on a filter type as part of configuration request. :param str type: Type of product filter. :param Sequence[str] supported_values: Values to be filtered. """ pulumi.set(__self__, "type", type) if supported_values is not None: pulumi.set(__self__, "supported_values", supported_values) @property @pulumi.getter def type(self) -> str: """ Type of product filter. """ return pulumi.get(self, "type") @property @pulumi.getter(name="supportedValues") def supported_values(self) -> Optional[Sequence[str]]: """ Values to be filtered. """ return pulumi.get(self, "supported_values") @pulumi.output_type class HierarchyInformationResponse(dict): """ Holds details about product hierarchy information """ def __init__(__self__, *, configuration_name: Optional[str] = None, product_family_name: Optional[str] = None, product_line_name: Optional[str] = None, product_name: Optional[str] = None): """ Holds details about product hierarchy information :param str configuration_name: Represents configuration name that uniquely identifies configuration :param str product_family_name: Represents product family name that uniquely identifies product family :param str product_line_name: Represents product line name that uniquely identifies product line :param str product_name: Represents product name that uniquely identifies product """ if configuration_name is not None: pulumi.set(__self__, "configuration_name", configuration_name) if product_family_name is not None: pulumi.set(__self__, "product_family_name", product_family_name) if product_line_name is not None: pulumi.set(__self__, "product_line_name", product_line_name) if product_name is not None: pulumi.set(__self__, "product_name", product_name) @property @pulumi.getter(name="configurationName") def configuration_name(self) -> Optional[str]: """ Represents configuration name that uniquely identifies configuration """ return pulumi.get(self, "configuration_name") @property @pulumi.getter(name="productFamilyName") def product_family_name(self) -> Optional[str]: """ Represents product family name that uniquely identifies product family """ return pulumi.get(self, "product_family_name") @property @pulumi.getter(name="productLineName") def product_line_name(self) -> Optional[str]: """ Represents product line name that uniquely identifies product line """ return pulumi.get(self, "product_line_name") @property @pulumi.getter(name="productName") def product_name(self) -> Optional[str]: """ Represents product name that uniquely identifies product """ return pulumi.get(self, "product_name") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ImageInformationResponseResult(dict): """ Image for the product """ def __init__(__self__, *, image_type: str, image_url: str): """ Image for the product :param str image_type: Type of the image :param str image_url: Url of the image """ pulumi.set(__self__, "image_type", image_type) pulumi.set(__self__, "image_url", image_url) @property @pulumi.getter(name="imageType") def image_type(self) -> str: """ Type of the image """ return pulumi.get(self, "image_type") @property @pulumi.getter(name="imageUrl") def image_url(self) -> str: """ Url of the image """ return pulumi.get(self, "image_url") @pulumi.output_type class LinkResponseResult(dict): """ Returns link related to the product """ def __init__(__self__, *, link_type: str, link_url: str): """ Returns link related to the product :param str link_type: Type of link :param str link_url: Url of the link """ pulumi.set(__self__, "link_type", link_type) pulumi.set(__self__, "link_url", link_url) @property @pulumi.getter(name="linkType") def link_type(self) -> str: """ Type of link """ return pulumi.get(self, "link_type") @property @pulumi.getter(name="linkUrl") def link_url(self) -> str: """ Url of the link """ return pulumi.get(self, "link_url") @pulumi.output_type class MeterDetailsResponseResult(dict): """ Billing details for each meter. """ def __init__(__self__, *, billing_model: 'outputs.BillingModelResponseResult', meter_id: str, meter_type: str): """ Billing details for each meter. :param 'BillingModelResponseArgs' billing_model: Billing model to represent billing cycle, i.e. Monthly, biweekly, daily, hourly etc. :param str meter_id: MeterId/ Billing Guid against which the product system will be charged :param str meter_type: Category of the billing meter. """ pulumi.set(__self__, "billing_model", billing_model) pulumi.set(__self__, "meter_id", meter_id) pulumi.set(__self__, "meter_type", meter_type) @property @pulumi.getter(name="billingModel") def billing_model(self) -> 'outputs.BillingModelResponseResult': """ Billing model to represent billing cycle, i.e. Monthly, biweekly, daily, hourly etc. """ return pulumi.get(self, "billing_model") @property @pulumi.getter(name="meterId") def meter_id(self) -> str: """ MeterId/ Billing Guid against which the product system will be charged """ return pulumi.get(self, "meter_id") @property @pulumi.getter(name="meterType") def meter_type(self) -> str: """ Category of the billing meter. """ return pulumi.get(self, "meter_type") @pulumi.output_type class NotificationPreferenceResponse(dict): """ Notification preference for a job stage. """ def __init__(__self__, *, send_notification: bool, stage_name: str): """ Notification preference for a job stage. :param bool send_notification: Notification is required or not. :param str stage_name: Name of the stage. """ pulumi.set(__self__, "send_notification", send_notification) pulumi.set(__self__, "stage_name", stage_name) @property @pulumi.getter(name="sendNotification") def send_notification(self) -> bool: """ Notification is required or not. """ return pulumi.get(self, "send_notification") @property @pulumi.getter(name="stageName") def stage_name(self) -> str: """ Name of the stage. """ return pulumi.get(self, "stage_name") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class OrderDetailsResponse(dict): """ Order details """ def __init__(__self__, *, cancellation_reason: str, cancellation_status: str, current_status: 'outputs.OrderStatusDetailsResponse', deletion_status: str, error: 'outputs.CloudErrorResponse', forward_shipping_details: 'outputs.ShippingDetailsResponse', management_rp_details: Any, order_status_history: Sequence['outputs.OrderStatusDetailsResponse'], order_type: str, product_details: 'outputs.ProductDetailsResponse', return_reason: str, return_status: str, reverse_shipping_details: 'outputs.ShippingDetailsResponse', notification_email_list: Optional[Sequence[str]] = None, preferences: Optional['outputs.PreferencesResponse'] = None): """ Order details :param str cancellation_reason: Cancellation reason. :param str cancellation_status: Describes whether the order is cancellable or not. :param 'OrderStatusDetailsResponseArgs' current_status: Current Order Status :param str deletion_status: Describes whether the order is deletable or not. :param 'CloudErrorResponseArgs' error: Top level error for the job. :param 'ShippingDetailsResponseArgs' forward_shipping_details: Forward Package Shipping details :param Any management_rp_details: parent RP details :param Sequence['OrderStatusDetailsResponseArgs'] order_status_history: Order history :param str order_type: Order type. :param 'ProductDetailsResponseArgs' product_details: Unique identifier for configuration. :param str return_reason: Return reason. :param str return_status: Describes whether the order is returnable or not. :param 'ShippingDetailsResponseArgs' reverse_shipping_details: Reverse Package Shipping details :param Sequence[str] notification_email_list: Package Shipping details :param 'PreferencesResponseArgs' preferences: Customer notification Preferences """ pulumi.set(__self__, "cancellation_reason", cancellation_reason) pulumi.set(__self__, "cancellation_status", cancellation_status) pulumi.set(__self__, "current_status", current_status) pulumi.set(__self__, "deletion_status", deletion_status) pulumi.set(__self__, "error", error) pulumi.set(__self__, "forward_shipping_details", forward_shipping_details) pulumi.set(__self__, "management_rp_details", management_rp_details) pulumi.set(__self__, "order_status_history", order_status_history) pulumi.set(__self__, "order_type", order_type) pulumi.set(__self__, "product_details", product_details) pulumi.set(__self__, "return_reason", return_reason) pulumi.set(__self__, "return_status", return_status) pulumi.set(__self__, "reverse_shipping_details", reverse_shipping_details) if notification_email_list is not None: pulumi.set(__self__, "notification_email_list", notification_email_list) if preferences is not None: pulumi.set(__self__, "preferences", preferences) @property @pulumi.getter(name="cancellationReason") def cancellation_reason(self) -> str: """ Cancellation reason. """ return pulumi.get(self, "cancellation_reason") @property @pulumi.getter(name="cancellationStatus") def cancellation_status(self) -> str: """ Describes whether the order is cancellable or not. """ return pulumi.get(self, "cancellation_status") @property @pulumi.getter(name="currentStatus") def current_status(self) -> 'outputs.OrderStatusDetailsResponse': """ Current Order Status """ return pulumi.get(self, "current_status") @property @pulumi.getter(name="deletionStatus") def deletion_status(self) -> str: """ Describes whether the order is deletable or not. """ return pulumi.get(self, "deletion_status") @property @pulumi.getter def error(self) -> 'outputs.CloudErrorResponse': """ Top level error for the job. """ return pulumi.get(self, "error") @property @pulumi.getter(name="forwardShippingDetails") def forward_shipping_details(self) -> 'outputs.ShippingDetailsResponse': """ Forward Package Shipping details """ return pulumi.get(self, "forward_shipping_details") @property @pulumi.getter(name="managementRpDetails") def management_rp_details(self) -> Any: """ parent RP details """ return pulumi.get(self, "management_rp_details") @property @pulumi.getter(name="orderStatusHistory") def order_status_history(self) -> Sequence['outputs.OrderStatusDetailsResponse']: """ Order history """ return pulumi.get(self, "order_status_history") @property @pulumi.getter(name="orderType") def order_type(self) -> str: """ Order type. """ return pulumi.get(self, "order_type") @property @pulumi.getter(name="productDetails") def product_details(self) -> 'outputs.ProductDetailsResponse': """ Unique identifier for configuration. """ return pulumi.get(self, "product_details") @property @pulumi.getter(name="returnReason") def return_reason(self) -> str: """ Return reason. """ return pulumi.get(self, "return_reason") @property @pulumi.getter(name="returnStatus") def return_status(self) -> str: """ Describes whether the order is returnable or not. """ return pulumi.get(self, "return_status") @property @pulumi.getter(name="reverseShippingDetails") def reverse_shipping_details(self) -> 'outputs.ShippingDetailsResponse': """ Reverse Package Shipping details """ return pulumi.get(self, "reverse_shipping_details") @property @pulumi.getter(name="notificationEmailList") def notification_email_list(self) -> Optional[Sequence[str]]: """ Package Shipping details """ return pulumi.get(self, "notification_email_list") @property @pulumi.getter def preferences(self) -> Optional['outputs.PreferencesResponse']: """ Customer notification Preferences """ return pulumi.get(self, "preferences") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class OrderStatusDetailsResponse(dict): """ Order status CurrentStatus """ def __init__(__self__, *, order_status: str, last_updated_time: Optional[str] = None): """ Order status CurrentStatus :param str order_status: Order status :param str last_updated_time: last time order was updated """ pulumi.set(__self__, "order_status", order_status) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) @property @pulumi.getter(name="orderStatus") def order_status(self) -> str: """ Order status """ return pulumi.get(self, "order_status") @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[str]: """ last time order was updated """ return pulumi.get(self, "last_updated_time") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class PreferencesResponse(dict): """ Preferences related to the order """ def __init__(__self__, *, notification_preferences: Optional[Sequence['outputs.NotificationPreferenceResponse']] = None, transport_preferences: Optional['outputs.TransportPreferencesResponse'] = None): """ Preferences related to the order :param Sequence['NotificationPreferenceResponseArgs'] notification_preferences: Notification preferences. :param 'TransportPreferencesResponseArgs' transport_preferences: Preferences related to the shipment logistics of the order. """ if notification_preferences is not None: pulumi.set(__self__, "notification_preferences", notification_preferences) if transport_preferences is not None: pulumi.set(__self__, "transport_preferences", transport_preferences) @property @pulumi.getter(name="notificationPreferences") def notification_preferences(self) -> Optional[Sequence['outputs.NotificationPreferenceResponse']]: """ Notification preferences. """ return pulumi.get(self, "notification_preferences") @property @pulumi.getter(name="transportPreferences") def transport_preferences(self) -> Optional['outputs.TransportPreferencesResponse']: """ Preferences related to the shipment logistics of the order. """ return pulumi.get(self, "transport_preferences") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ProductDetailsResponse(dict): """ Represents product details """ def __init__(__self__, *, device_details: Sequence['outputs.DeviceDetailsResponse'], hierarchy_information: 'outputs.HierarchyInformationResponse', count: Optional[int] = None): """ Represents product details :param Sequence['DeviceDetailsResponseArgs'] device_details: list of device details :param 'HierarchyInformationResponseArgs' hierarchy_information: Hierarchy of the product which uniquely identifies the product :param int count: Quantity of the product """ pulumi.set(__self__, "device_details", device_details) pulumi.set(__self__, "hierarchy_information", hierarchy_information) if count is not None: pulumi.set(__self__, "count", count) @property @pulumi.getter(name="deviceDetails") def device_details(self) -> Sequence['outputs.DeviceDetailsResponse']: """ list of device details """ return pulumi.get(self, "device_details") @property @pulumi.getter(name="hierarchyInformation") def hierarchy_information(self) -> 'outputs.HierarchyInformationResponse': """ Hierarchy of the product which uniquely identifies the product """ return pulumi.get(self, "hierarchy_information") @property @pulumi.getter def count(self) -> Optional[int]: """ Quantity of the product """ return pulumi.get(self, "count") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ProductFamilyResponseResult(dict): """ Product Family """ def __init__(__self__, *, availability_information: 'outputs.AvailabilityInformationResponseResult', cost_information: 'outputs.CostInformationResponseResult', description: 'outputs.DescriptionResponseResult', display_name: str, filterable_properties: Sequence['outputs.FilterablePropertyResponseResult'], hierarchy_information: 'outputs.HierarchyInformationResponse', image_information: Sequence['outputs.ImageInformationResponseResult'], product_lines: Sequence['outputs.ProductLineResponseResult']): """ Product Family :param 'AvailabilityInformationResponseArgs' availability_information: Availability information of the product system. :param 'CostInformationResponseArgs' cost_information: Cost information for the product system. :param 'DescriptionResponseArgs' description: Description related to the product system. :param str display_name: Display Name for the product system. :param Sequence['FilterablePropertyResponseArgs'] filterable_properties: list of filters supported for a product :param 'HierarchyInformationResponseArgs' hierarchy_information: Hierarchy information of the product system. :param Sequence['ImageInformationResponseArgs'] image_information: Image information for the product system. :param Sequence['ProductLineResponseArgs'] product_lines: List of product lines supported in the product family """ pulumi.set(__self__, "availability_information", availability_information) pulumi.set(__self__, "cost_information", cost_information) pulumi.set(__self__, "description", description) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "filterable_properties", filterable_properties) pulumi.set(__self__, "hierarchy_information", hierarchy_information) pulumi.set(__self__, "image_information", image_information) pulumi.set(__self__, "product_lines", product_lines) @property @pulumi.getter(name="availabilityInformation") def availability_information(self) -> 'outputs.AvailabilityInformationResponseResult': """ Availability information of the product system. """ return pulumi.get(self, "availability_information") @property @pulumi.getter(name="costInformation") def cost_information(self) -> 'outputs.CostInformationResponseResult': """ Cost information for the product system. """ return pulumi.get(self, "cost_information") @property @pulumi.getter def description(self) -> 'outputs.DescriptionResponseResult': """ Description related to the product system. """ return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ Display Name for the product system. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="filterableProperties") def filterable_properties(self) -> Sequence['outputs.FilterablePropertyResponseResult']: """ list of filters supported for a product """ return pulumi.get(self, "filterable_properties") @property @pulumi.getter(name="hierarchyInformation") def hierarchy_information(self) -> 'outputs.HierarchyInformationResponse': """ Hierarchy information of the product system. """ return pulumi.get(self, "hierarchy_information") @property @pulumi.getter(name="imageInformation") def image_information(self) -> Sequence['outputs.ImageInformationResponseResult']: """ Image information for the product system. """ return pulumi.get(self, "image_information") @property @pulumi.getter(name="productLines") def product_lines(self) -> Sequence['outputs.ProductLineResponseResult']: """ List of product lines supported in the product family """ return pulumi.get(self, "product_lines") @pulumi.output_type class ProductLineResponseResult(dict): """ Product line """ def __init__(__self__, *, availability_information: 'outputs.AvailabilityInformationResponseResult', cost_information: 'outputs.CostInformationResponseResult', description: 'outputs.DescriptionResponseResult', display_name: str, filterable_properties: Sequence['outputs.FilterablePropertyResponseResult'], hierarchy_information: 'outputs.HierarchyInformationResponse', image_information: Sequence['outputs.ImageInformationResponseResult'], products: Sequence['outputs.ProductResponseResult']): """ Product line :param 'AvailabilityInformationResponseArgs' availability_information: Availability information of the product system. :param 'CostInformationResponseArgs' cost_information: Cost information for the product system. :param 'DescriptionResponseArgs' description: Description related to the product system. :param str display_name: Display Name for the product system. :param Sequence['FilterablePropertyResponseArgs'] filterable_properties: list of filters supported for a product :param 'HierarchyInformationResponseArgs' hierarchy_information: Hierarchy information of the product system. :param Sequence['ImageInformationResponseArgs'] image_information: Image information for the product system. :param Sequence['ProductResponseArgs'] products: List of products in the product line """ pulumi.set(__self__, "availability_information", availability_information) pulumi.set(__self__, "cost_information", cost_information) pulumi.set(__self__, "description", description) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "filterable_properties", filterable_properties) pulumi.set(__self__, "hierarchy_information", hierarchy_information) pulumi.set(__self__, "image_information", image_information) pulumi.set(__self__, "products", products) @property @pulumi.getter(name="availabilityInformation") def availability_information(self) -> 'outputs.AvailabilityInformationResponseResult': """ Availability information of the product system. """ return pulumi.get(self, "availability_information") @property @pulumi.getter(name="costInformation") def cost_information(self) -> 'outputs.CostInformationResponseResult': """ Cost information for the product system. """ return pulumi.get(self, "cost_information") @property @pulumi.getter def description(self) -> 'outputs.DescriptionResponseResult': """ Description related to the product system. """ return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ Display Name for the product system. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="filterableProperties") def filterable_properties(self) -> Sequence['outputs.FilterablePropertyResponseResult']: """ list of filters supported for a product """ return pulumi.get(self, "filterable_properties") @property @pulumi.getter(name="hierarchyInformation") def hierarchy_information(self) -> 'outputs.HierarchyInformationResponse': """ Hierarchy information of the product system. """ return pulumi.get(self, "hierarchy_information") @property @pulumi.getter(name="imageInformation") def image_information(self) -> Sequence['outputs.ImageInformationResponseResult']: """ Image information for the product system. """ return pulumi.get(self, "image_information") @property @pulumi.getter def products(self) -> Sequence['outputs.ProductResponseResult']: """ List of products in the product line """ return pulumi.get(self, "products") @pulumi.output_type class ProductResponseResult(dict): """ List of Products """ def __init__(__self__, *, availability_information: 'outputs.AvailabilityInformationResponseResult', configurations: Sequence['outputs.ConfigurationResponseResult'], cost_information: 'outputs.CostInformationResponseResult', description: 'outputs.DescriptionResponseResult', display_name: str, filterable_properties: Sequence['outputs.FilterablePropertyResponseResult'], hierarchy_information: 'outputs.HierarchyInformationResponse', image_information: Sequence['outputs.ImageInformationResponseResult']): """ List of Products :param 'AvailabilityInformationResponseArgs' availability_information: Availability information of the product system. :param Sequence['ConfigurationResponseArgs'] configurations: List of configurations for the product :param 'CostInformationResponseArgs' cost_information: Cost information for the product system. :param 'DescriptionResponseArgs' description: Description related to the product system. :param str display_name: Display Name for the product system. :param Sequence['FilterablePropertyResponseArgs'] filterable_properties: list of filters supported for a product :param 'HierarchyInformationResponseArgs' hierarchy_information: Hierarchy information of the product system. :param Sequence['ImageInformationResponseArgs'] image_information: Image information for the product system. """ pulumi.set(__self__, "availability_information", availability_information) pulumi.set(__self__, "configurations", configurations) pulumi.set(__self__, "cost_information", cost_information) pulumi.set(__self__, "description", description) pulumi.set(__self__, "display_name", display_name) pulumi.set(__self__, "filterable_properties", filterable_properties) pulumi.set(__self__, "hierarchy_information", hierarchy_information) pulumi.set(__self__, "image_information", image_information) @property @pulumi.getter(name="availabilityInformation") def availability_information(self) -> 'outputs.AvailabilityInformationResponseResult': """ Availability information of the product system. """ return pulumi.get(self, "availability_information") @property @pulumi.getter def configurations(self) -> Sequence['outputs.ConfigurationResponseResult']: """ List of configurations for the product """ return pulumi.get(self, "configurations") @property @pulumi.getter(name="costInformation") def cost_information(self) -> 'outputs.CostInformationResponseResult': """ Cost information for the product system. """ return pulumi.get(self, "cost_information") @property @pulumi.getter def description(self) -> 'outputs.DescriptionResponseResult': """ Description related to the product system. """ return pulumi.get(self, "description") @property @pulumi.getter(name="displayName") def display_name(self) -> str: """ Display Name for the product system. """ return pulumi.get(self, "display_name") @property @pulumi.getter(name="filterableProperties") def filterable_properties(self) -> Sequence['outputs.FilterablePropertyResponseResult']: """ list of filters supported for a product """ return pulumi.get(self, "filterable_properties") @property @pulumi.getter(name="hierarchyInformation") def hierarchy_information(self) -> 'outputs.HierarchyInformationResponse': """ Hierarchy information of the product system. """ return pulumi.get(self, "hierarchy_information") @property @pulumi.getter(name="imageInformation") def image_information(self) -> Sequence['outputs.ImageInformationResponseResult']: """ Image information for the product system. """ return pulumi.get(self, "image_information") @pulumi.output_type class ShippingAddressResponse(dict): """ Shipping address where customer wishes to receive the device. """ def __init__(__self__, *, country: str, street_address1: str, address_type: Optional[str] = None, city: Optional[str] = None, company_name: Optional[str] = None, postal_code: Optional[str] = None, state_or_province: Optional[str] = None, street_address2: Optional[str] = None, street_address3: Optional[str] = None, zip_extended_code: Optional[str] = None): """ Shipping address where customer wishes to receive the device. :param str country: Name of the Country. :param str street_address1: Street Address line 1. :param str address_type: Type of address. :param str city: Name of the City. :param str company_name: Name of the company. :param str postal_code: Postal code. :param str state_or_province: Name of the State or Province. :param str street_address2: Street Address line 2. :param str street_address3: Street Address line 3. :param str zip_extended_code: Extended Zip Code. """ pulumi.set(__self__, "country", country) pulumi.set(__self__, "street_address1", street_address1) if address_type is not None: pulumi.set(__self__, "address_type", address_type) if city is not None: pulumi.set(__self__, "city", city) if company_name is not None: pulumi.set(__self__, "company_name", company_name) if postal_code is not None: pulumi.set(__self__, "postal_code", postal_code) if state_or_province is not None: pulumi.set(__self__, "state_or_province", state_or_province) if street_address2 is not None: pulumi.set(__self__, "street_address2", street_address2) if street_address3 is not None: pulumi.set(__self__, "street_address3", street_address3) if zip_extended_code is not None: pulumi.set(__self__, "zip_extended_code", zip_extended_code) @property @pulumi.getter def country(self) -> str: """ Name of the Country. """ return pulumi.get(self, "country") @property @pulumi.getter(name="streetAddress1") def street_address1(self) -> str: """ Street Address line 1. """ return pulumi.get(self, "street_address1") @property @pulumi.getter(name="addressType") def address_type(self) -> Optional[str]: """ Type of address. """ return pulumi.get(self, "address_type") @property @pulumi.getter def city(self) -> Optional[str]: """ Name of the City. """ return pulumi.get(self, "city") @property @pulumi.getter(name="companyName") def company_name(self) -> Optional[str]: """ Name of the company. """ return pulumi.get(self, "company_name") @property @pulumi.getter(name="postalCode") def postal_code(self) -> Optional[str]: """ Postal code. """ return pulumi.get(self, "postal_code") @property @pulumi.getter(name="stateOrProvince") def state_or_province(self) -> Optional[str]: """ Name of the State or Province. """ return pulumi.get(self, "state_or_province") @property @pulumi.getter(name="streetAddress2") def street_address2(self) -> Optional[str]: """ Street Address line 2. """ return pulumi.get(self, "street_address2") @property @pulumi.getter(name="streetAddress3") def street_address3(self) -> Optional[str]: """ Street Address line 3. """ return pulumi.get(self, "street_address3") @property @pulumi.getter(name="zipExtendedCode") def zip_extended_code(self) -> Optional[str]: """ Extended Zip Code. """ return pulumi.get(self, "zip_extended_code") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class ShippingDetailsResponse(dict): """ Package shipping details """ def __init__(__self__, *, carrier_display_name: str, carrier_name: str, tracking_id: str, tracking_url: str): """ Package shipping details :param str carrier_display_name: Carrier Name for display purpose. Not to be used for any processing. :param str carrier_name: Name of the carrier. :param str tracking_id: TrackingId of the package :param str tracking_url: TrackingUrl of the package. """ pulumi.set(__self__, "carrier_display_name", carrier_display_name) pulumi.set(__self__, "carrier_name", carrier_name) pulumi.set(__self__, "tracking_id", tracking_id) pulumi.set(__self__, "tracking_url", tracking_url) @property @pulumi.getter(name="carrierDisplayName") def carrier_display_name(self) -> str: """ Carrier Name for display purpose. Not to be used for any processing. """ return pulumi.get(self, "carrier_display_name") @property @pulumi.getter(name="carrierName") def carrier_name(self) -> str: """ Name of the carrier. """ return pulumi.get(self, "carrier_name") @property @pulumi.getter(name="trackingId") def tracking_id(self) -> str: """ TrackingId of the package """ return pulumi.get(self, "tracking_id") @property @pulumi.getter(name="trackingUrl") def tracking_url(self) -> str: """ TrackingUrl of the package. """ return pulumi.get(self, "tracking_url") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class SpecificationResponseResult(dict): """ Specifications of the configurations """ def __init__(__self__, *, name: str, value: str): """ Specifications of the configurations :param str name: Name of the specification :param str value: Value of the specification """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "value", value) @property @pulumi.getter def name(self) -> str: """ Name of the specification """ return pulumi.get(self, "name") @property @pulumi.getter def value(self) -> str: """ Value of the specification """ return pulumi.get(self, "value") @pulumi.output_type class SystemDataResponse(dict): """ Metadata pertaining to creation and last modification of the resource. """ def __init__(__self__, *, created_at: Optional[str] = None, created_by: Optional[str] = None, created_by_type: Optional[str] = None, last_modified_at: Optional[str] = None, last_modified_by: Optional[str] = None, last_modified_by_type: Optional[str] = None): """ Metadata pertaining to creation and last modification of the resource. :param str created_at: The timestamp of resource creation (UTC). :param str created_by: The identity that created the resource. :param str created_by_type: The type of identity that created the resource. :param str last_modified_at: The timestamp of resource last modification (UTC) :param str last_modified_by: The identity that last modified the resource. :param str last_modified_by_type: The type of identity that last modified the resource. """ if created_at is not None: pulumi.set(__self__, "created_at", created_at) if created_by is not None: pulumi.set(__self__, "created_by", created_by) if created_by_type is not None: pulumi.set(__self__, "created_by_type", created_by_type) if last_modified_at is not None: pulumi.set(__self__, "last_modified_at", last_modified_at) if last_modified_by is not None: pulumi.set(__self__, "last_modified_by", last_modified_by) if last_modified_by_type is not None: pulumi.set(__self__, "last_modified_by_type", last_modified_by_type) @property @pulumi.getter(name="createdAt") def created_at(self) -> Optional[str]: """ The timestamp of resource creation (UTC). """ return pulumi.get(self, "created_at") @property @pulumi.getter(name="createdBy") def created_by(self) -> Optional[str]: """ The identity that created the resource. """ return pulumi.get(self, "created_by") @property @pulumi.getter(name="createdByType") def created_by_type(self) -> Optional[str]: """ The type of identity that created the resource. """ return pulumi.get(self, "created_by_type") @property @pulumi.getter(name="lastModifiedAt") def last_modified_at(self) -> Optional[str]: """ The timestamp of resource last modification (UTC) """ return pulumi.get(self, "last_modified_at") @property @pulumi.getter(name="lastModifiedBy") def last_modified_by(self) -> Optional[str]: """ The identity that last modified the resource. """ return pulumi.get(self, "last_modified_by") @property @pulumi.getter(name="lastModifiedByType") def last_modified_by_type(self) -> Optional[str]: """ The type of identity that last modified the resource. """ return pulumi.get(self, "last_modified_by_type") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop @pulumi.output_type class TransportPreferencesResponse(dict): """ Preferences related to the shipment logistics of the sku """ def __init__(__self__, *, preferred_shipment_type: str): """ Preferences related to the shipment logistics of the sku :param str preferred_shipment_type: Indicates Shipment Logistics type that the customer preferred. """ pulumi.set(__self__, "preferred_shipment_type", preferred_shipment_type) @property @pulumi.getter(name="preferredShipmentType") def preferred_shipment_type(self) -> str: """ Indicates Shipment Logistics type that the customer preferred. """ return pulumi.get(self, "preferred_shipment_type") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
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day_1/day1.py
mickeelm/aoc2019
1
28189
def fuel_required_single_module(mass): fuel = int(mass / 3) - 2 return fuel if fuel > 0 else 0 def fuel_required_multiple_modules(masses): total_fuel = 0 for mass in masses: total_fuel += fuel_required_single_module(mass) return total_fuel def recursive_fuel_required_single_module(mass): total_fuel = 0 while mass := fuel_required_single_module(mass): total_fuel += mass return total_fuel def recursive_fuel_required_multiple_modules(masses): total_fuel = 0 for mass in masses: total_fuel += recursive_fuel_required_single_module(mass) return total_fuel
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ocelot/io/__init__.py
cmutel/Ocelot
21
23817
# -*- coding: utf-8 -*- __all__ = ( "extract_directory", "cleanup_data_directory", "dataset_schema", "validate_directory", "validate_directory_against_xsd", ) from .extract_ecospold2 import extract_ecospold2_directory from ..filesystem import check_cache_directory, get_from_cache, cache_data import os def extract_directory(data_path, use_cache=True, use_mp=True): """Extract ecospold2 files in directory ``dirpath``. Uses and writes to cache if ``use_cache`` is ``True``. Returns datasets in Ocelot internal format.""" data_path = os.path.abspath(data_path) if not use_cache: return extract_ecospold2_directory(data_path, use_mp) elif check_cache_directory(data_path): print("Using cached ecospold2 data") return get_from_cache(data_path) else: data = extract_ecospold2_directory(data_path, use_mp) cache_data(data, data_path) return data from .cleanup import cleanup_data_directory from .validate_ecospold2 import validate_directory_against_xsd, validate_directory from .validate_internal import dataset_schema
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thrift/compiler/test/fixtures/serialization_field_order/gen-py/module/ttypes.py
dgrnbrg-meta/fbthrift
0
61633
# # Autogenerated by Thrift # # DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING # @generated # from __future__ import absolute_import import sys from thrift.util.Recursive import fix_spec from thrift.Thrift import TType, TMessageType, TPriority, TRequestContext, TProcessorEventHandler, TServerInterface, TProcessor, TException, TApplicationException, UnimplementedTypedef from thrift.protocol.TProtocol import TProtocolException from json import loads import sys if sys.version_info[0] >= 3: long = int import thrift.annotation.thrift.ttypes import pprint import warnings from thrift import Thrift from thrift.transport import TTransport from thrift.protocol import TBinaryProtocol from thrift.protocol import TCompactProtocol from thrift.protocol import THeaderProtocol fastproto = None try: from thrift.protocol import fastproto except ImportError: pass all_structs = [] UTF8STRINGS = bool(0) or sys.version_info.major >= 3 __all__ = ['UTF8STRINGS', 'Foo', 'Foo2'] class Foo: """ Attributes: - field1 - field2 - field3 """ thrift_spec = None thrift_field_annotations = None thrift_struct_annotations = None __init__ = None @staticmethod def isUnion(): return False def read(self, iprot): if (isinstance(iprot, TBinaryProtocol.TBinaryProtocolAccelerated) or (isinstance(iprot, THeaderProtocol.THeaderProtocolAccelerate) and iprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_BINARY_PROTOCOL)) and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastproto is not None: fastproto.decode(self, iprot.trans, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=0) return if (isinstance(iprot, TCompactProtocol.TCompactProtocolAccelerated) or (isinstance(iprot, THeaderProtocol.THeaderProtocolAccelerate) and iprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_COMPACT_PROTOCOL)) and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastproto is not None: fastproto.decode(self, iprot.trans, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=2) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 3: if ftype == TType.I32: self.field1 = iprot.readI32() else: iprot.skip(ftype) elif fid == 1: if ftype == TType.I32: self.field2 = iprot.readI32() else: iprot.skip(ftype) elif fid == 2: if ftype == TType.I32: self.field3 = iprot.readI32() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if (isinstance(oprot, TBinaryProtocol.TBinaryProtocolAccelerated) or (isinstance(oprot, THeaderProtocol.THeaderProtocolAccelerate) and oprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_BINARY_PROTOCOL)) and self.thrift_spec is not None and fastproto is not None: oprot.trans.write(fastproto.encode(self, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=0)) return if (isinstance(oprot, TCompactProtocol.TCompactProtocolAccelerated) or (isinstance(oprot, THeaderProtocol.THeaderProtocolAccelerate) and oprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_COMPACT_PROTOCOL)) and self.thrift_spec is not None and fastproto is not None: oprot.trans.write(fastproto.encode(self, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=2)) return oprot.writeStructBegin('Foo') if self.field2 != None: oprot.writeFieldBegin('field2', TType.I32, 1) oprot.writeI32(self.field2) oprot.writeFieldEnd() if self.field3 != None: oprot.writeFieldBegin('field3', TType.I32, 2) oprot.writeI32(self.field3) oprot.writeFieldEnd() if self.field1 != None: oprot.writeFieldBegin('field1', TType.I32, 3) oprot.writeI32(self.field1) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def readFromJson(self, json, is_text=True, **kwargs): relax_enum_validation = bool(kwargs.pop('relax_enum_validation', False)) set_cls = kwargs.pop('custom_set_cls', set) dict_cls = kwargs.pop('custom_dict_cls', dict) if kwargs: extra_kwargs = ', '.join(kwargs.keys()) raise ValueError( 'Unexpected keyword arguments: ' + extra_kwargs ) json_obj = json if is_text: json_obj = loads(json) if 'field1' in json_obj and json_obj['field1'] is not None: self.field1 = json_obj['field1'] if self.field1 > 0x7fffffff or self.field1 < -0x80000000: raise TProtocolException(TProtocolException.INVALID_DATA, 'number exceeds limit in field') if 'field2' in json_obj and json_obj['field2'] is not None: self.field2 = json_obj['field2'] if self.field2 > 0x7fffffff or self.field2 < -0x80000000: raise TProtocolException(TProtocolException.INVALID_DATA, 'number exceeds limit in field') if 'field3' in json_obj and json_obj['field3'] is not None: self.field3 = json_obj['field3'] if self.field3 > 0x7fffffff or self.field3 < -0x80000000: raise TProtocolException(TProtocolException.INVALID_DATA, 'number exceeds limit in field') def __repr__(self): L = [] padding = ' ' * 4 if self.field1 is not None: value = pprint.pformat(self.field1, indent=0) value = padding.join(value.splitlines(True)) L.append(' field1=%s' % (value)) if self.field2 is not None: value = pprint.pformat(self.field2, indent=0) value = padding.join(value.splitlines(True)) L.append(' field2=%s' % (value)) if self.field3 is not None: value = pprint.pformat(self.field3, indent=0) value = padding.join(value.splitlines(True)) L.append(' field3=%s' % (value)) return "%s(%s)" % (self.__class__.__name__, "\n" + ",\n".join(L) if L else '') def __eq__(self, other): if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) # Override the __hash__ function for Python3 - t10434117 __hash__ = object.__hash__ class Foo2: """ Attributes: - field1 - field2 - field3 """ thrift_spec = None thrift_field_annotations = None thrift_struct_annotations = None __init__ = None @staticmethod def isUnion(): return False def read(self, iprot): if (isinstance(iprot, TBinaryProtocol.TBinaryProtocolAccelerated) or (isinstance(iprot, THeaderProtocol.THeaderProtocolAccelerate) and iprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_BINARY_PROTOCOL)) and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastproto is not None: fastproto.decode(self, iprot.trans, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=0) return if (isinstance(iprot, TCompactProtocol.TCompactProtocolAccelerated) or (isinstance(iprot, THeaderProtocol.THeaderProtocolAccelerate) and iprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_COMPACT_PROTOCOL)) and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastproto is not None: fastproto.decode(self, iprot.trans, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=2) return iprot.readStructBegin() while True: (fname, ftype, fid) = iprot.readFieldBegin() if ftype == TType.STOP: break if fid == 3: if ftype == TType.I32: self.field1 = iprot.readI32() else: iprot.skip(ftype) elif fid == 1: if ftype == TType.I32: self.field2 = iprot.readI32() else: iprot.skip(ftype) elif fid == 2: if ftype == TType.I32: self.field3 = iprot.readI32() else: iprot.skip(ftype) else: iprot.skip(ftype) iprot.readFieldEnd() iprot.readStructEnd() def write(self, oprot): if (isinstance(oprot, TBinaryProtocol.TBinaryProtocolAccelerated) or (isinstance(oprot, THeaderProtocol.THeaderProtocolAccelerate) and oprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_BINARY_PROTOCOL)) and self.thrift_spec is not None and fastproto is not None: oprot.trans.write(fastproto.encode(self, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=0)) return if (isinstance(oprot, TCompactProtocol.TCompactProtocolAccelerated) or (isinstance(oprot, THeaderProtocol.THeaderProtocolAccelerate) and oprot.get_protocol_id() == THeaderProtocol.THeaderProtocol.T_COMPACT_PROTOCOL)) and self.thrift_spec is not None and fastproto is not None: oprot.trans.write(fastproto.encode(self, [self.__class__, self.thrift_spec, False], utf8strings=UTF8STRINGS, protoid=2)) return oprot.writeStructBegin('Foo2') if self.field2 != None: oprot.writeFieldBegin('field2', TType.I32, 1) oprot.writeI32(self.field2) oprot.writeFieldEnd() if self.field3 != None: oprot.writeFieldBegin('field3', TType.I32, 2) oprot.writeI32(self.field3) oprot.writeFieldEnd() if self.field1 != None: oprot.writeFieldBegin('field1', TType.I32, 3) oprot.writeI32(self.field1) oprot.writeFieldEnd() oprot.writeFieldStop() oprot.writeStructEnd() def readFromJson(self, json, is_text=True, **kwargs): relax_enum_validation = bool(kwargs.pop('relax_enum_validation', False)) set_cls = kwargs.pop('custom_set_cls', set) dict_cls = kwargs.pop('custom_dict_cls', dict) if kwargs: extra_kwargs = ', '.join(kwargs.keys()) raise ValueError( 'Unexpected keyword arguments: ' + extra_kwargs ) json_obj = json if is_text: json_obj = loads(json) if 'field1' in json_obj and json_obj['field1'] is not None: self.field1 = json_obj['field1'] if self.field1 > 0x7fffffff or self.field1 < -0x80000000: raise TProtocolException(TProtocolException.INVALID_DATA, 'number exceeds limit in field') if 'field2' in json_obj and json_obj['field2'] is not None: self.field2 = json_obj['field2'] if self.field2 > 0x7fffffff or self.field2 < -0x80000000: raise TProtocolException(TProtocolException.INVALID_DATA, 'number exceeds limit in field') if 'field3' in json_obj and json_obj['field3'] is not None: self.field3 = json_obj['field3'] if self.field3 > 0x7fffffff or self.field3 < -0x80000000: raise TProtocolException(TProtocolException.INVALID_DATA, 'number exceeds limit in field') def __repr__(self): L = [] padding = ' ' * 4 if self.field1 is not None: value = pprint.pformat(self.field1, indent=0) value = padding.join(value.splitlines(True)) L.append(' field1=%s' % (value)) if self.field2 is not None: value = pprint.pformat(self.field2, indent=0) value = padding.join(value.splitlines(True)) L.append(' field2=%s' % (value)) if self.field3 is not None: value = pprint.pformat(self.field3, indent=0) value = padding.join(value.splitlines(True)) L.append(' field3=%s' % (value)) return "%s(%s)" % (self.__class__.__name__, "\n" + ",\n".join(L) if L else '') def __eq__(self, other): if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not (self == other) # Override the __hash__ function for Python3 - t10434117 __hash__ = object.__hash__ all_structs.append(Foo) Foo.thrift_spec = ( None, # 0 (1, TType.I32, 'field2', None, None, 2, ), # 1 (2, TType.I32, 'field3', None, None, 2, ), # 2 (3, TType.I32, 'field1', None, None, 2, ), # 3 ) Foo.thrift_struct_annotations = { } Foo.thrift_field_annotations = { } def Foo__init__(self, field1=None, field2=None, field3=None,): self.field1 = field1 self.field2 = field2 self.field3 = field3 Foo.__init__ = Foo__init__ def Foo__setstate__(self, state): state.setdefault('field1', None) state.setdefault('field2', None) state.setdefault('field3', None) self.__dict__ = state Foo.__getstate__ = lambda self: self.__dict__.copy() Foo.__setstate__ = Foo__setstate__ all_structs.append(Foo2) Foo2.thrift_spec = ( None, # 0 (1, TType.I32, 'field2', None, None, 2, ), # 1 (2, TType.I32, 'field3', None, None, 2, ), # 2 (3, TType.I32, 'field1', None, None, 2, ), # 3 ) Foo2.thrift_struct_annotations = { } Foo2.thrift_field_annotations = { } def Foo2__init__(self, field1=None, field2=None, field3=None,): self.field1 = field1 self.field2 = field2 self.field3 = field3 Foo2.__init__ = Foo2__init__ def Foo2__setstate__(self, state): state.setdefault('field1', None) state.setdefault('field2', None) state.setdefault('field3', None) self.__dict__ = state Foo2.__getstate__ = lambda self: self.__dict__.copy() Foo2.__setstate__ = Foo2__setstate__ fix_spec(all_structs) del all_structs
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mealpy/evolutionary_based/MA.py
Alhassan20/mealpy
1
7668
<filename>mealpy/evolutionary_based/MA.py<gh_stars>1-10 #!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "<NAME>" at 14:22, 11/04/2020 % # % # Email: <EMAIL> % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieu1995 % # ------------------------------------------------------------------------------------------------------% import time import numpy as np from mealpy.optimizer import Optimizer class BaseMA(Optimizer): """ The original version of: Memetic Algorithm (MA) (On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms) Link: Clever Algorithms: Nature-Inspired Programming Recipes - Memetic Algorithm (MA) http://www.cleveralgorithms.com/nature-inspired/physical/memetic_algorithm.html """ ID_POS = 0 ID_FIT = 1 ID_BIT = 2 def __init__(self, problem: dict, epoch=1000, pop_size=100, pc=0.98, pm=0.025, p_local=0.5, max_local_gens=10, bits_per_param=16): """ Args: problem (dict): a dictionary of your problem epoch (int): maximum number of iterations, default = 1000 pop_size (int): number of population size, default = 100 pc (float): cross-over probability, default = 0.95 pm (float): mutation probability, default = 0.025 p_local (): max_local_gens (): bits_per_param (): """ super().__init__(problem) self.epoch = epoch self.pop_size = pop_size self.pc = pc self.pm = pm self.p_local = p_local self.max_local_gens = max_local_gens self.bits_per_param = bits_per_param self.bits_total = self.problem_size * self.bits_per_param def create_solution(self): position = np.random.uniform(self.lb, self.ub) fitness = self.get_fitness_position(position=position) bitstring = ''.join(["1" if np.random.uniform() < 0.5 else "0" for _ in range(0, self.bits_total)]) return [position, fitness, bitstring] def _decode__(self, bitstring=None): """ Decode the random bitstring into real number Args: bitstring (str): "11000000100101000101010" - bits_per_param = 16, 32 bit for 2 variable. eg. x1 and x2 Returns: list of real number (vector) """ vector = np.ones(self.problem_size) for idx in range(0, self.problem_size): param = bitstring[idx * self.bits_per_param: (idx + 1) * self.bits_per_param] # Select 16 bit every time vector[idx] = self.lb[idx] + ((self.ub[idx] - self.lb[idx]) / ((2.0 ** self.bits_per_param) - 1)) * int(param, 2) return vector def _crossover__(self, dad=None, mom=None): if np.random.uniform() >= self.pc: temp = [dad].copy() return temp[0] else: child = "" for idx in range(0, self.bits_total): if np.random.uniform() < 0.5: child += dad[idx] else: child += mom[idx] return child def _point_mutation__(self, bitstring=None): child = "" for bit in bitstring: if np.random.uniform() < self.pc: child += "0" if bit == "1" else "1" else: child += bit return child def create_next_generation(self, pop: list): ## Binary tournament children = [self.get_solution_kway_tournament_selection(pop, k_way=2, output=1)[0] for _ in range(self.pop_size)] ## Reproduction for idx in range(0, self.pop_size): ancient = pop[idx + 1] if idx % 2 == 0 else pop[idx - 1] if idx == self.pop_size - 1: ancient = pop[0] bitstring_new = self._crossover__(pop[idx][self.ID_BIT], ancient[self.ID_BIT]) bitstring_new = self._point_mutation__(bitstring_new) pos_new = self._decode__(bitstring_new) fit_new = self.get_fitness_position(pos_new) children[idx] = [pos_new, fit_new, bitstring_new] return children def _bits_climber__(self, child=None): current = child.copy() for idx in range(0, self.max_local_gens): child = current.copy() bitstring_new = self._point_mutation__(child[self.ID_BIT]) pos_new = self._decode__(bitstring_new) fit_new = self.get_fitness_position(pos_new) current = self.get_better_solution(child, [pos_new, fit_new, bitstring_new]) return current def train(self): pop = [self.create_solution() for _ in range(self.pop_size)] pop, g_best = self.get_global_best_solution(pop) self.history_list_g_best = [g_best] self.history_list_c_best = self.history_list_g_best.copy() for epoch in range(0, self.epoch): time_start = time.time() # Create next generations pop = self.create_next_generation(pop) # Searching in local for i in range(0, self.pop_size): if np.random.uniform() < self.p_local: pop[i] = self._bits_climber__(pop[i]) # Sort the population and update the global best solution pop = self.update_global_best_solution(pop) ## Additional information for the framework time_start = time.time() - time_start self.history_list_epoch_time.append(time_start) self.print_epoch(epoch + 1, time_start) self.history_list_pop.append(pop.copy()) ## Additional information for the framework self.solution = self.history_list_g_best[-1] self.save_data() return self.solution[self.ID_POS], self.solution[self.ID_FIT][self.ID_TAR]
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train_frcnn.py
xuannianc/keras-frcnn
0
75739
from __future__ import division import random import pprint import sys import time import numpy as np from optparse import OptionParser import pickle from keras import backend as K from keras.optimizers import Adam, SGD, RMSprop from keras.layers import Input from keras.models import Model from frcnn import config, data_generators from frcnn import losses as losses import frcnn.roi_helpers as roi_helpers from log import logger from keras.utils import generic_utils import json from keras.utils import plot_model import os.path as osp parser = OptionParser() # DATASET_DIR = '/home/adam/.keras/datasets/VOCdevkit' parser.add_option("-d", "--dataset", dest="dataset_dir", help="Path to training dataset.") parser.add_option("-o", "--parser", dest="parser", help="Parser to use. One of 'simple' or 'pascal_voc'", default="pascal_voc") parser.add_option("-n", "--num_rois", type="int", dest="num_rois", help="Number of RoIs to process at once.", default=32) parser.add_option("--network", dest="network", help="Base network to use. Supports vgg and resnet50.", default='resnet50') parser.add_option("--hf", dest="horizontal_flips", help="Augment with horizontal flips in training.", action="store_true", default=False) parser.add_option("--vf", dest="vertical_flips", help="Augment with vertical flips in training.", action="store_true", default=False) parser.add_option("--rot", "--rotate", dest="rotate", help="Augment with 90,180,270 degree rotations in training.", action="store_true", default=False) parser.add_option("--image_min_size", type="int", dest="image_min_size", help="Min side of image to resize.", default=600) parser.add_option("--num_epochs", type="int", dest="num_epochs", help="Number of epochs.", default=1000) parser.add_option("--num_steps", type="int", dest="num_steps", help="Number of steps per epoch.", default=11540) parser.add_option("--config_output_path", dest="config_output_path", help="Location to store all the metadata related to the training (to be used when testing).", default="config.pickle") parser.add_option("--model_weight_path", dest="model_weight_path", help="Output path for model weights.") parser.add_option("--base_net_weight_path", dest="base_net_weight_path", help="Path for base network weights. If not specified, will try to load default weights provided by keras.") (options, args) = parser.parse_args() if not options.dataset_dir: # if dataset_dir is not specified parser.error('Path to training dataset must be specified. Pass -d or --dataset to command line') if options.parser == 'pascal_voc': from frcnn.pascal_voc_parser import get_annotation_data elif options.parser == 'simple': from frcnn.simple_parser import get_data else: parser.error("Option parser must be one of 'pascal_voc' or 'simple'") # pass the settings from the command line, and persist them in the config object C = config.Config() C.use_horizontal_flips = options.horizontal_flips C.use_vertical_flips = options.vertical_flips C.rotate = options.rotate C.num_rois = options.num_rois C.image_min_size = options.image_min_size if options.network == 'vgg': C.network = 'vgg' from frcnn import vgg as nn elif options.network == 'resnet50': from frcnn import resnet as nn C.network = 'resnet50' else: parser.error("Option network must be one of 'vgg' or 'resnet50'") # check if output weight path was passed via command line if options.model_weight_path: C.model_weight_path = options.model_weight_path else: C.model_weight_path = 'frcnn_{}_{{}}_{{:.4f}}_{{:.4f}}_{{:.4f}}_{{:.4f}}_{{:.4f}}.hdf5'.format(C.network) # check if base weight path was passed via command line if options.base_net_weight_path: C.base_net_weights_path = options.base_net_weight_path else: # set the path to weights based on backend and model C.base_net_weights_path = nn.get_weight_path() # all_annotation_data, classes_count, class_name_idx_mapping = get_annotation_data(DATASET_DIR) all_annotations = json.load(open('annotation_data.json')) classes_count = json.load(open('classes_count.json')) class_name_idx_mapping = json.load(open('class_name_idx_mapping.json')) if 'bg' not in classes_count: classes_count['bg'] = 0 class_name_idx_mapping['bg'] = len(class_name_idx_mapping) C.class_name_idx_mapping = class_name_idx_mapping logger.debug('class_count={}'.format(classes_count)) logger.info('Num of classes (including bg) = {}'.format(len(classes_count))) logger.debug('class_name_idx_mapping={}'.format(class_name_idx_mapping)) config_output_path = options.config_output_path with open(config_output_path, 'wb') as config_f: pickle.dump(C, config_f) logger.info('Config has been written to {}, and can be loaded when testing to ensure correct results'.format( config_output_path)) random.shuffle(all_annotations) train_annotations = [annotation for annotation in all_annotations if annotation['imageset'] == 'train'] val_annotations = [annotation for annotation in all_annotations if annotation['imageset'] == 'val'] logger.info('Num of samples {}'.format(len(all_annotations))) logger.info('Num of train samples {}'.format(len(train_annotations))) logger.info('Num of val samples {}'.format(len(val_annotations))) train_data_gen = data_generators.get_anchor_gt(train_annotations, classes_count, C, nn.get_feature_map_size, mode='train') val_data_gen = data_generators.get_anchor_gt(val_annotations, classes_count, C, nn.get_feature_map_size, mode='val') input_shape = (None, None, 3) image_input = Input(shape=input_shape) rois_input = Input(shape=(None, 4)) # define the base network (resnet here, can be VGG, Inception, etc) base_net_output = nn.base_net(image_input, trainable=True) # define the RPN, built on the base net num_anchors = len(C.anchor_scales) * len(C.anchor_ratios) rpn_output = nn.rpn(base_net_output, num_anchors) # [(batch_size=1, num_rois, num_classes),(batch_size=1, num_rois, 4 * (num_classes -1)) # 第二个元素是 regr 返回的值, 其中不包括 'bg' rcnn_output = nn.rcnn(base_net_output, rois_input, C.num_rois, num_classes=len(classes_count)) model_rpn = Model(image_input, rpn_output) model_rcnn = Model([image_input, rois_input], rcnn_output) # this is a model that holds both the RPN and the RCNN, used to load/save weights for the models model = Model([image_input, rois_input], rpn_output + rcnn_output) if not osp.exists('model.jpg'): plot_model(model, to_file='model.jpg') try: logger.info('loading weights from {}'.format(C.base_net_weights_path)) model_rpn.load_weights(C.base_net_weights_path, by_name=True) model_rcnn.load_weights(C.base_net_weights_path, by_name=True) except: logger.exception('Could not load pretrained model weights of base net. ' 'Weights can be found in https://github.com/fchollet/deep-learning-models/releases') optimizer = Adam(lr=1e-5) optimizer_classifier = Adam(lr=1e-5) model_rpn.compile(optimizer=optimizer, loss=[losses.rpn_class_loss(num_anchors), losses.rpn_regr_loss(num_anchors)]) model_rcnn.compile(optimizer=optimizer_classifier, loss=[losses.rcnn_class_loss, losses.rcnn_regr_loss(len(classes_count) - 1)], metrics={'rcnn_class': 'accuracy'}) model.compile(optimizer='sgd', loss='mae') num_epochs = int(options.num_epochs) # 每 1000 个 epoch 输出详细日志保存模型 num_steps = int(options.num_steps) step_idx = 0 # 每个 epoch 的 positive roi 的个数 num_pos_rois_per_epoch = [] losses = np.zeros((num_steps, 5)) start_time = time.time() best_loss = np.Inf logger.info('Training starts...') for epoch_idx in range(num_epochs): progbar = generic_utils.Progbar(num_steps) logger.info('Epoch {}/{}'.format(epoch_idx + 1, num_epochs)) while True: try: X1, Y1, augmented_annotation = next(train_data_gen) # loss_rpn = [loss,rpn_out_class_loss,rpn_out_regress_loss], 名字的组成有最后一层的 name + '_loss' # 这里还要注意 label 的 shape 可以和模型输出的 shape 不一样,这取决于 loss function rpn_loss = model_rpn.train_on_batch(X1, Y1) # [(1,m,n,9),(1,m,n,36)] rpn_prediction = model_rpn.predict_on_batch(X1) # rois 的 shape 为 (None,4) (x1,y1,x2,y2) rois = roi_helpers.rpn_to_roi(rpn_prediction[0], rpn_prediction[1], C, overlap_thresh=0.7, max_rois=300) # NOTE: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format # X2: x_roi Y21: y_class Y22: y_regr X2, Y21, Y22, IoUs = roi_helpers.calc_iou(rois, augmented_annotation, C, class_name_idx_mapping) if X2 is None: num_pos_rois_per_epoch.append(0) continue # 假设 Y21 为 np.array([[[0,0,0,1],[0,0,0,0]]]),np.where(Y21[0,:,-1]==1) 返回 (array([0]),) # Y21[0,:,-1] 表示的 class 为 'bg' 的值, 若为 1 表示 negative, 为 0 表示 positive neg_roi_idxs = np.where(Y21[0, :, -1] == 1)[0] pos_roi_idxs = np.where(Y21[0, :, -1] == 0)[0] num_pos_rois_per_epoch.append((len(pos_roi_idxs))) if C.num_rois > 1: # 如果正样本个数不足 num_rois//2,全部送入训练 if len(pos_roi_idxs) < C.num_rois // 2: selected_pos_idxs = pos_roi_idxs.tolist() # 如果正样本个数超过 num_rois//2, 随机抽取 num_rois//2 个 送入训练 else: # replace=False 表示不重复, without replacement selected_pos_idxs = np.random.choice(pos_roi_idxs, C.num_rois // 2, replace=False).tolist() try: selected_neg_idxs = np.random.choice(neg_roi_idxs, C.num_rois - len(selected_pos_idxs), replace=False).tolist() except: selected_neg_idxs = np.random.choice(neg_roi_idxs, C.num_rois - len(selected_pos_idxs), replace=True).tolist() selected_idxs = selected_pos_idxs + selected_neg_idxs else: # in the extreme case where num_rois = 1, we pick a random pos or neg sample selected_pos_idxs = pos_roi_idxs.tolist() selected_neg_idxs = neg_roi_idxs.tolist() if np.random.randint(0, 2): selected_idxs = random.choice(neg_roi_idxs) else: selected_idxs = random.choice(pos_roi_idxs) rcnn_loss = model_rcnn.train_on_batch([X1, X2[:, selected_idxs, :]], [Y21[:, selected_idxs, :], Y22[:, selected_idxs, :]]) losses[step_idx, 0] = rpn_loss[1] losses[step_idx, 1] = rpn_loss[2] losses[step_idx, 2] = rcnn_loss[1] losses[step_idx, 3] = rcnn_loss[2] # accuracy losses[step_idx, 4] = rcnn_loss[3] step_idx += 1 progbar.update(step_idx, [('rpn_class_loss', np.mean(losses[:step_idx, 0])), ('rpn_regr_loss', np.mean(losses[:step_idx, 1])), ('rcnn_class_loss', np.mean(losses[:step_idx, 2])), ('rcnn_regr_loss', np.mean(losses[:step_idx, 3]))]) if step_idx == num_steps: rpn_class_loss = np.mean(losses[:, 0]) rpn_regr_loss = np.mean(losses[:, 1]) rcnn_class_loss = np.mean(losses[:, 2]) rcnn_regr_loss = np.mean(losses[:, 3]) rcnn_class_acc = np.mean(losses[:, 4]) mean_num_pos_rois = float(sum(num_pos_rois_per_epoch)) / len(num_pos_rois_per_epoch) num_pos_rois_per_epoch = [] curr_loss = rpn_class_loss + rpn_regr_loss + rcnn_class_loss + rcnn_regr_loss if C.verbose: logger.debug('Mean number of positive rois: {}'.format( mean_num_pos_rois)) if mean_num_pos_rois == 0: logger.warning( 'RPN is not producing positive rois. Check settings or keep training.') logger.debug('RPN Classification Loss: {}'.format(rpn_class_loss)) logger.debug('RPN Regression Loss : {}'.format(rpn_regr_loss)) logger.debug('RCNN Classification Loss: {}'.format(rcnn_class_loss)) logger.debug('RCNN Regression Loss: {}'.format(rcnn_regr_loss)) logger.debug('Total Loss: {}'.format(curr_loss)) logger.debug('RCNN Classification Accuracy: {}'.format(rcnn_class_acc)) logger.debug('Elapsed time: {}'.format(time.time() - start_time)) step_idx = 0 start_time = time.time() if curr_loss < best_loss: if C.verbose: logger.debug('Total loss decreased from {} to {}, saving weights'.format(best_loss, curr_loss)) best_loss = curr_loss model.save_weights( C.model_weight_path.format(epoch_idx, rpn_class_loss, rpn_regr_loss, rcnn_class_loss, rcnn_regr_loss, rcnn_class_acc)) break except Exception as e: logger.exception('{}'.format(e)) continue logger.info('Training complete, exiting.')
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openrec/tf2/data/__init__.py
pbaiz/openrec
399
188544
<reponame>pbaiz/openrec from openrec.tf2.data.utils import _DataStore from openrec.tf2.data.utils import _ParallelDataset from openrec.tf2.data.dataset import Dataset
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mellophone/mellophone/settings_local.py
nchlswhttkr/mellophone
9
117903
# pylint: disable=unused-wildcard-import,wildcard-import """ Please try and avoid modifying this file where possible, doing so may cause different behaviours between local (development) and production environments. Instead consider modifying the base (default) config. Some features security features are disabled here for ease of development. """ from mellophone.settings_default import * DEBUG = True ALLOWED_HOSTS = ['localhost', '127.0.0.1'] SESSION_COOKIE_SECURE = False CSRF_COOKIE_SECURE = False SECURE_SSL_REDIRECT = False SECURE_HSTS_SECONDS = 0
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train.py
Rayaction/ECO-paddle
0
45715
<reponame>Rayaction/ECO-paddle<gh_stars>0 import argparse import ast import logging import os import numpy as np import paddle.fluid as fluid from config import parse_config, merge_configs, print_configs from model import ECO from reader import KineticsReader logging.root.handlers = [] FORMAT = '[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s' logging.basicConfig(filename='logger.log', level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) def parse_args(): parser = argparse.ArgumentParser("Paddle Video train script") parser.add_argument( '--model_name', type=str, default='tsn', help='name of model to train.') parser.add_argument( '--config', type=str, default='configs/tsn.txt', help='path to config file of model') parser.add_argument( '--batch_size', type=int, default=None, help='training batch size. None to use config file setting.') parser.add_argument( '--learning_rate', type=float, default=None, help='learning rate use for training. None to use config file setting.') parser.add_argument( '--pretrain', type=str, default=True, help='path to pretrain weights. None to use default weights path in ~/.paddle/weights.' ) parser.add_argument( '--use_gpu', type=ast.literal_eval, default=True, help='default use gpu.') parser.add_argument( '--epoch', type=int, default=100, help='epoch number, 0 for read from config file') parser.add_argument( '--save_dir', type=str, default='checkpoints_models', help='directory name to save train snapshoot') parser.add_argument( '--gpu_num', type=ast.literal_eval, default=1, help='default gpu num.') parser.add_argument( '--gd', type=int, default=50, help='clip gradient') parser.add_argument( '--selected_gpus', type=str, default='0,1', help='selected_gpus') parser.add_argument( '--num_saturate', type=int, default='5', help='num_saturate') parser.add_argument( '--eval_freq', type=int, default='1', help='eval_freq') parser.add_argument("--use_paddlecloud", type=bool, default=False) parser.add_argument("--cluster_node_ips", type=str, default="10.163.143.6") parser.add_argument("--node_ip", type=str, default="10.163.143.6", help='The?current?node?ip.') parser.add_argument( "--started_port", type=int, default=0, help="The?trainer's?started?port?on?a?single?node") parser.add_argument("--log_level", type=int, default=20, help='Logging?level,?default?is?logging.INFO') parser.add_argument("--log_dir", type=str, default="/home/w00445241/project_set/project_paddle/Eco/TSN/log_dir") parser.add_argument("--distributed", type=str, default=True) parser.add_argument("--dataset_base_path", type=str, default="/home/w00445241/project_set/project_paddle/UCF-101/") parser.add_argument("--output_base_path", type=str, default="/home/w00445241/project_set/project_paddle/Eco/TSN/checkpoints_models/ECO_distributed_1/") args = parser.parse_args() return args def train(args, distributed): #===================== GPU CONF =====================# if distributed: # if run on parallel mode place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) else: # if run on single GPU mode, and select gpu number. args.use_gpu = True place = fluid.CUDAPlace(args.gpu_num) if args.use_gpu else fluid.CPUPlace() # ===================== Dygraph Mode =====================# with fluid.dygraph.guard(place): # leverage from TSN training script config = parse_config(args.config) train_config = merge_configs(config, 'train', vars(args)) val_config = merge_configs(config, 'valid', vars(args)) print_configs(train_config, 'Train') # ===================== Init ECO =====================# train_model = ECO.ECO(num_classes=train_config['MODEL']['num_classes'], num_segments=train_config['MODEL']['seg_num']) if distributed: strategy = fluid.dygraph.parallel.prepare_context() train_model = fluid.dygraph.parallel.DataParallel(train_model, strategy) # trick 1: use clip gradient method to avoid gradient explosion if args.gd is not None: clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=args.gd) print('clip:', clip) # ===================== Init Optimizer =====================# # optimizer config: use momentum, nesterov, weight decay, lr decay learning_rate = 0.001 opt = fluid.optimizer.Momentum(learning_rate, 0.9, parameter_list=train_model.parameters(), use_nesterov=True, regularization=fluid.regularizer.L2Decay(regularization_coeff=5e-4), grad_clip=clip) # trick 2: Freezing BatchNorm2D except the first one. # trick 3: make all weight layer lr mult as 1, bias lr mult as 2. get_optim_policies(opt) print('get_optim_policies:--batch_norm_0.w_0', opt._parameter_list[2].optimize_attr,opt._parameter_list[2].stop_gradient) print('get_optim_policies:--batch_norm_0.b_0', opt._parameter_list[3].optimize_attr,opt._parameter_list[2].stop_gradient) # ===================== Use Pretrained Model =====================# # use pretrained model: ECO_Full_rgb_model_Kinetics.pth 2.tar(download from MZO git) # then transform it from torch to paddle weight except fc layer. if args.pretrain: model, _ = fluid.dygraph.load_dygraph(args.save_dir + '/ECO_FULL_RGB_seg16') # also tried using pretrained model on torch, 32F-92.9%,16F-91.8% precision trained on torch # model, _ = fluid.dygraph.load_dygraph(args.save_dir + '/eco_91.81_model_best') train_model.load_dict(model) # build model if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) # ===================== Init Data Reader =====================# # leverage from TSN training script train_config.TRAIN.batch_size = train_config.TRAIN.batch_size train_reader = KineticsReader('ECO', 'train', train_config).create_reader() print('train_reader', train_reader) val_reader = KineticsReader('ECO', 'valid', val_config).create_reader() if distributed: train_reader = fluid.contrib.reader.distributed_batch_reader(train_reader) # ===================== Init Trick Params =====================# epochs = args.epoch or train_model.epoch_num() loss_summ = 0 saturate_cnt = 0 exp_num = 0 best_prec1 = 0 for i in range(epochs): train_model.train() # trick 4: Saturate lr decay: different from lr piecewise decay or others # calculate prec every epoch, if prec1 does not rise for 5 times(named model saturated), then use decay lr. if saturate_cnt == args.num_saturate: exp_num = exp_num + 1 saturate_cnt = 0 decay = 0.1 ** (exp_num) learning_rate = learning_rate * decay opt = fluid.optimizer.Momentum(learning_rate, 0.9, parameter_list=train_model.parameters(), use_nesterov=True, regularization=fluid.regularizer.L2Decay(regularization_coeff=5e-4), grad_clip=clip) print('get_optim_policies:--batch_norm_0.w_0', opt._parameter_list[2].optimize_attr, opt._parameter_list[2].stop_gradient) print('get_optim_policies:--batch_norm_0.b_0', opt._parameter_list[3].optimize_attr, opt._parameter_list[2].stop_gradient) print("- Learning rate decreases by a factor of '{}'".format(10 ** (exp_num))) for batch_id, data in enumerate(train_reader()): lr = opt.current_step_lr() print('lr:', lr) # check lr every batch ids dy_x_data = np.array([x[0] for x in data]).astype('float32') y_data = np.array([[x[1]] for x in data]).astype('int64') img = fluid.dygraph.to_variable(dy_x_data) label = fluid.dygraph.to_variable(y_data) label.stop_gradient = True out, acc = train_model(img, label) loss = fluid.layers.cross_entropy(out, label) avg_loss = fluid.layers.mean(loss) loss_summ += avg_loss if distributed: avg_loss = train_model.scale_loss(avg_loss) avg_loss.backward() if distributed: train_model.apply_collective_grads() if (batch_id + 1) % 4 == 0: # trick 5: scale down gradients when iter size is functioning every 4 batches opt.minimize(loss_summ) opt.clear_gradients() loss_summ = 0 if batch_id % 1 == 0: logger.info( "Loss at epoch {} step {}: {}, acc: {}".format(i, batch_id, avg_loss.numpy(), acc.numpy())) print("Loss at epoch {} step {}: {}, acc: {}".format(i, batch_id, avg_loss.numpy(), acc.numpy())) if (i + 1) % args.eval_freq == 0 or i == args.epochs - 1: train_model.eval() acc_list = [] false_class = [] for batch_id, data in enumerate(val_reader()): dy_x_data = np.array([x[0] for x in data]).astype('float32') y_data = np.array([[x[1]] for x in data]).astype('int64') img = fluid.dygraph.to_variable(dy_x_data) label = fluid.dygraph.to_variable(y_data) label.stop_gradient = True out, acc = train_model(img, label) if acc.numpy()[0] != 1: false_class.append(label.numpy()[0][0]) acc_list.append(acc.numpy()[0]) print(batch_id, 'acc:', np.mean(acc_list)) if len(false_class) == 0: continue print("validate set acc:{}".format(np.mean(acc_list))) prec1 = np.mean(acc_list) # remember best prec@1 and save checkpoint is_best = prec1 > best_prec1 if is_best: saturate_cnt = 0 fluid.dygraph.save_dygraph(train_model.state_dict(), args.save_dir + '/ECO_FULL_1/' + str(i) + '_best_' + str(prec1)) else: saturate_cnt = saturate_cnt + 1 print("- Validation Prec@1 saturates for {} epochs.".format(saturate_cnt), best_prec1) best_prec1 = max(prec1, best_prec1) logger.info("Final loss: {}".format(avg_loss.numpy())) print("Final loss: {}".format(avg_loss.numpy())) def get_optim_policies(opt): # num = 0 for i, param in enumerate(opt._parameter_list): if 'batch_norm' in param.name: # bn not particepate in training process if 'batch_norm_0.w_0' in param.name: #except the first res 3d part param.optimize_attr['learning_rate'] = 1 # param.stop_gradient = False continue if 'batch_norm_0.b_0' in param.name: param.optimize_attr['learning_rate'] = 2 # param.stop_gradient = False continue else: if i >= 414 and i <= 484: continue else: param.stop_gradient = True continue if 'w' in param.name: param.optimize_attr['learning_rate'] = 1 if 'b' in param.name: param.optimize_attr['learning_rate'] = 2 print('freeze bn2d') if __name__ == "__main__": import paddle.distributed.launch as launch args = parse_args() distributed = False if distributed: import paddle.distributed.launch as launch args.training_script = "train.py" args.training_script_args = ["--distributed", "--dataset_base_path", "/home/w00445241/project_set/project_paddle/UCF-101/", "--output_base_path", "/home/w00445241/project_set/project_paddle/Eco/TSN/checkpoints_models/ECO_distributed_1/"] print(args.log_dir, args.training_script, args.training_script_args) launch.launch(args) logger.info(args) train(args, distributed)
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src/IceRayPy/core/material/instruction/label/color/const.py
dmilos/IceRay
2
171069
<reponame>dmilos/IceRay<filename>src/IceRayPy/core/material/instruction/label/color/const.py<gh_stars>1-10 _BEGIN = 0 BLACK=0 WHITE=1 GRAY=2 _END = 12
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lib/aquilon/worker/commands/show_building_preference_all.py
ned21/aquilon
7
1608855
# -*- cpy-indent-level: 4; indent-tabs-mode: nil -*- # ex: set expandtab softtabstop=4 shiftwidth=4: # # Copyright (C) 2012,2014,2015,2016,2017 Contributor # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contains the logic for `aq show building preference --all`. """ from collections import defaultdict from sqlalchemy.orm import aliased, contains_eager, joinedload, subqueryload from sqlalchemy.sql import null from aquilon.aqdb.model import BuildingPreference, Building, Archetype, Cluster from aquilon.worker.broker import BrokerCommand from aquilon.worker.formats.building_preference import BuildingClusterPreference class CommandShowBuildingPreferenceAll(BrokerCommand): def render(self, session, **_): # Pick up building-pair preferences AB = aliased(Building) BB = aliased(Building) q = session.query(BuildingPreference) q = q.join(AB, BuildingPreference.a) q = q.join(BB, BuildingPreference.b) q = q.join(Archetype) q = q.options(contains_eager('a', alias=AB), contains_eager('b', alias=BB), contains_eager('archetype')) bcprefs = defaultdict(list) for db_pref in q: key = db_pref.sorted_name + "," + db_pref.archetype.name bcprefs[key] = BuildingClusterPreference(db_pref.sorted_name, db_pref.archetype, [], db_pref.prefer) # Pick up clusters that have a preference override q = session.query(Cluster) q = q.filter(Cluster.preferred_location != null()) q = q.options(subqueryload('_hosts'), joinedload('_hosts.host'), joinedload('_hosts.host.hardware_entity'), joinedload('_hosts.host.hardware_entity.location')) for dbcluster in q: buildings = dbcluster.member_locations(location_class=Building) # TODO: do we really want to 'hide' things that have != 2 buildings? if len(buildings) != 2: continue sortname = ",".join(sorted(building.name for building in buildings)) key = sortname + "," + dbcluster.archetype.name if key not in bcprefs: bcprefs[key] = BuildingClusterPreference(sortname, dbcluster.archetype, [dbcluster]) else: bcprefs[key].clusters.append(dbcluster) return [bcprefs[key] for key in sorted(bcprefs.keys())]
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vim/base/YouCompleteMe/python/ycm/server/watchdog_plugin.py
fatih/subvim
97
185960
<reponame>fatih/subvim #!/usr/bin/env python # # Copyright (C) 2013 Google Inc. # # This file is part of YouCompleteMe. # # YouCompleteMe is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # YouCompleteMe is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with YouCompleteMe. If not, see <http://www.gnu.org/licenses/>. import time import os import copy from ycm import utils from threading import Thread, Lock # This class implements the Bottle plugin API: # http://bottlepy.org/docs/dev/plugindev.html # # The idea here is to decorate every route handler automatically so that on # every request, we log when the request was made. Then a watchdog thread checks # every check_interval_seconds whether the server has been idle for a time # greater that the passed-in idle_suicide_seconds. If it has, we kill the # server. # # We want to do this so that if something goes bonkers in Vim and the server # never gets killed by the client, we don't end up with lots of zombie servers. class WatchdogPlugin( object ): name = 'watchdog' api = 2 def __init__( self, idle_suicide_seconds, check_interval_seconds = 60 * 10 ): self._check_interval_seconds = check_interval_seconds self._idle_suicide_seconds = idle_suicide_seconds self._last_request_time = time.time() self._last_request_time_lock = Lock() if idle_suicide_seconds <= 0: return self._watchdog_thread = Thread( target = self._WatchdogMain ) self._watchdog_thread.daemon = True self._watchdog_thread.start() def _GetLastRequestTime( self ): with self._last_request_time_lock: return copy.deepcopy( self._last_request_time ) def _SetLastRequestTime( self, new_value ): with self._last_request_time_lock: self._last_request_time = new_value def _WatchdogMain( self ): while True: time.sleep( self._check_interval_seconds ) if time.time() - self._GetLastRequestTime() > self._idle_suicide_seconds: utils.TerminateProcess( os.getpid() ) def __call__( self, callback ): def wrapper( *args, **kwargs ): self._SetLastRequestTime( time.time() ) return callback( *args, **kwargs ) return wrapper
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hack/verify-publishing-bot.py
hmtai/kubernetes
39
174650
#!/usr/bin/env python # Copyright 2019 The Kubernetes Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import fnmatch import os import sys import json import yaml def get_godeps_dependencies(godeps): all_dependencies = {} for arg in godeps: with open(arg) as f: data = json.load(f) dependencies = [] for dep in data["Deps"]: if dep['Rev'].startswith('xxxx') and dep['ImportPath'].startswith("k8s.io"): package = "/".join(dep['ImportPath'].split('/')[0:2]) if package not in dependencies: dependencies.append(package.split('/')[1]) all_dependencies[(data["ImportPath"].split('/')[1])] = dependencies return all_dependencies def get_rules_dependencies(rules_file): with open(rules_file) as f: data = yaml.load(f) return data def main(): rootdir = os.path.dirname(__file__) + "/../" rootdir = os.path.abspath(rootdir) godeps = [] for root, dirnames, filenames in os.walk(rootdir + '/staging/'): for filename in fnmatch.filter(filenames, 'Godeps.json'): godeps.append(os.path.join(root, filename)) godep_dependencies = get_godeps_dependencies(godeps) rules_dependencies = get_rules_dependencies(rootdir + "/staging/publishing/rules.yaml") processed_repos = [] for rule in rules_dependencies["rules"]: branch = rule["branches"][0] if branch["name"] != "master": raise Exception("cannot find master branch for destination %s" % rule["destination"]) if branch["source"]["branch"] != "master": raise Exception("cannot find master source branch for destination %s" % rule["destination"]) print("processing : %s" % rule["destination"]) if rule["destination"] not in godep_dependencies: raise Exception("missing Godeps.json for %s" % rule["destination"]) processed_repos.append(rule["destination"]) for dep in set(godep_dependencies[rule["destination"]]): found = False if "dependencies" in branch: for dep2 in branch["dependencies"]: if dep2["branch"] != "master": raise Exception("Looking for master branch and found : %s for destination", dep2, rule["destination"]) if dep2["repository"] == dep: found = True else: raise Exception( "destination %s does not have any dependencies (looking for %s)" % (rule["destination"], dep)) if not found: raise Exception("destination %s does not have dependency %s" % (rule["destination"], dep)) else: print(" found dependency %s" % dep) items = set(godep_dependencies.keys()) - set(processed_repos) if len(items) > 0: raise Exception("missing rules for %s" % ','.join(str(s) for s in items)) print("Done.") if __name__ == "__main__": sys.exit(main())
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tests/test_parser.py
MacarielAerial/networkx-query
8
43257
<gh_stars>1-10 import pytest from networkx_query import ParserException from networkx_query.parser import compile_ast, explain, parse node_0 = {'application': 'test'} node_1 = {'application': 'test', 'weight': 3, 'group': 'my-group'} node_2 = {'_link': {'provider': "aws", 'resource_type': "test", 'other': {'weight': 2}}} def test_explain(): result = explain(parse({'not': {'has': ['group']}, 'has': 'application', 'eq': [('_link', 'other', 'weight'), 2]})) assert result == { 'and': [{'not': [{'has': ['group']}]}, {'has': ['application']}, {'eq': [('_link', 'other', 'weight'), 2]}] } def test_compile_expression_has(): func = compile_ast(parse({'has': 'application'})) assert func(node_0) assert func(node_1) def test_compile_expression_not_has(): func = compile_ast(parse({'not': {'has': 'group'}})) assert func(node_0) assert not func(node_1) def test_compile_expression_contains(): func = compile_ast(parse({'contains': ['group', 'my']})) assert not func(node_0) assert func(node_1) def test_compile_expression_has_dict(): func = compile_ast(parse({'has': '_link'})) assert not func(node_0) assert func(node_2) def test_compile_expression_has_path(): func = compile_ast(parse({'has': [('_link', 'other', 'weight')]})) assert not func(node_0) assert func(node_2) def test_compile_expression_eq_path(): func = compile_ast(parse({'eq': [('_link', 'other', 'weight'), 2]})) assert func(node_2) def test_compile_expression_neq_path(): func = compile_ast(parse({'neq': [('_link', 'other', 'weight'), 2]})) assert not func(node_2) func = compile_ast(parse({'neq': [('_link', 'other', 'weight'), 8]})) assert func(node_2) def test_compile_expression_gt_path(): func = compile_ast(parse({'gt': [('_link', 'other', 'weight'), 1]})) assert func(node_2) func = compile_ast(parse({'gt': [('_link', 'other', 'weight'), 2]})) assert not func(node_2) def test_compile_expression_gte_path(): func = compile_ast(parse({'gte': [('_link', 'other', 'weight'), 2]})) assert func(node_2) func = compile_ast(parse({'gte': [('_link', 'other', 'weight'), 1]})) assert func(node_2) def test_compile_expression_lte_path(): func = compile_ast(parse({'lte': [('_link', 'other', 'weight'), 2]})) assert func(node_2) func = compile_ast(parse({'lte': [('_link', 'other', 'weight'), 3]})) assert func(node_2) def test_compile_expression_lt_path(): func = compile_ast(parse({'lt': [('_link', 'other', 'weight'), 3]})) assert func(node_2) func = compile_ast(parse({'lt': [('_link', 'other', 'weight'), 2]})) assert not func(node_2) def test_compile_expression_in_path(): func = compile_ast(parse({'in': [('_link', 'other', 'weight'), [1, 2, 3]]})) assert func(node_2) def test_compile_expression_and(): func = compile_ast(parse({'and': [{'has': 'application'}, {'in': [('weight',), [1, 2, 3]]}]})) assert not func(node_0) assert func(node_1) assert not func(node_2) def test_compile_expression_or(): func = compile_ast(parse({'or': [{'has': 'application'}, {'in': [('weight',), [1, 2, 3]]}]})) assert func(node_0) assert func(node_1) assert not func(node_2) def test_compile_expression_xor(): func = compile_ast(parse({'xor': [{'has': 'application'}, {'in': [('weight',), [1, 2, 3]]}]})) assert func(node_0) assert not func(node_1) assert not func(node_2) def test_compile_expression_nxor(): func = compile_ast(parse({'nxor': [{'has': 'application'}, {'in': [('weight',), [1, 2, 3]]}]})) assert not func(node_0) assert func(node_1) assert func(node_2) def test_parse_exception(): with pytest.raises(ParserException): parse({'has': ['_link', 'other', 'weight']}) def test_parse_raise_unknown_operator(): with pytest.raises(ParserException): parse({'idontexistatall': ['_link', 'other', 'weight']})
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tools/visualize.py
MinesNicaicai/large-scale-pointcloud-matching
1
192508
<gh_stars>1-10 #!/usr/bin/env python3 import open3d as o3d import numpy as np print("Testing IO for point cloud ...") # pcd = o3d.io.read_point_cloud("../../TestData/pointcloud_1386.txt.npy.pcd") # np_array = np.load("/media/li/LENOVO/dataset/carla_data/scans_0704/scan_2.npy") np_array = np.load("/tmp/submap_0.npy") # Pass xyz to Open3D.o3d.geometry.PointCloud and visualize pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(np_array) pcd.paint_uniform_color([1, 0.706, 0]) o3d.visualization.draw_geometries([pcd]) # o3d.io.write_point_cloud("../../TestData/sync.ply", pcd) ####################### ## Open3d with Numpy ## ####################### # Load saved point cloud and visualize it # pcd_load = o3d.io.read_point_cloud("../../TestData/sync.ply") # convert Open3D.o3d.geometry.PointCloud to numpy array # xyz_load = np.asarray(pcd_load.points) # print('xyz_load') # print(xyz_load) # o3d.visualization.draw_geometries([pcd_load]) ####################### ## Visualization ## ####################### # print("Load a ply point cloud, print it, and render it") # pcd = o3d.io.read_point_cloud("../../TestData/fragment.ply") # o3d.visualization.draw_geometries([pcd], zoom=0.3412, # front=[0.4257, -0.2125, -0.8795], # lookat=[2.6172, 2.0475, 1.532], # up=[-0.0694, -0.9768, 0.2024]) import h5py import numpy as np import open3d as o3d f = h5py.File("my_file.h5", "r") x = np.array(f["MyGroup1/map/dset"]) print(x) # f = h5py.File("/media/admini/My_data/zhonghaun_06122/partial/partial_zhonghuan.bag_segmented_submaps.h5", "r") # f = h5py.File("/media/admini/My_data/0721/zhonghuan/Paul_Zhonghuan.bag_segmented_submaps.h5", "r") f = h5py.File("/media/li/LENOVO/Paul_Zhonghuan.bag_segmented_submaps.h5", "r") f.visit(print) submap_id = 2 pcds = [] for submap_id in range(100, 102): num_segments = np.array(f["submap_" + str(submap_id) + "/num_segments"])[0] for i in range(num_segments): segment = np.array(f["submap_" + str(submap_id) + "/segment_" + str(i)]) if segment.shape[0] > 5000: continue pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(segment) pcd = pcd.voxel_down_sample(voxel_size=0.1) pcd.paint_uniform_color([(0.17*i+submap_id*0.7) % 1, (0.31*i+submap_id*0.7) % 1, (0.53*i+submap_id*0.7)%1]) # pcd.paint_uniform_color( # [(submap_id * 0.7 + 0.2) % 1, (submap_id * 0.5 + 0.3) % 1, (submap_id * 0.3+0.5) % 1]) pcds.append(pcd) o3d.visualization.draw_geometries(pcds)
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horovod/tensorflow/compression.py
DelphianCalamity/horovod
0
122712
"""Gradient compression algorithms.""" from __future__ import division import tensorflow as tf from tensorflow.python.framework import load_library from tensorflow.python.platform import resource_loader tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) import random, math from horovod.tensorflow.mpi_ops import _allreduce from horovod.tensorflow.mpi_ops import rank import numpy as np import wandb class Compressor(object): """Interface for compressing and decompressing a given tensor.""" residuals = {} @staticmethod def compress(tensor, params): """Compresses a tensor and returns a list of compressed tensors with the context needed to decompress it.""" pass @staticmethod def decompress(tensors, ctx, params): """Decompress a list of compressed tensors with the given context.""" pass @classmethod def memory_compensate(cls, tensor, params): """Update the tensor with the residuals.""" use_memory = params['use_memory'] beta = params['beta'] gamma = params['gamma'] if use_memory: name = tensor.name cls.residuals[tensor.name] = tf.Variable(tf.zeros_like(tensor), trainable=False) tensor = beta * cls.residuals[name] + gamma * tensor return tensor @classmethod def memory_update(cls, tensor, tensor_compensate, tensor_compressed, ctx, params): """Update the residuals.""" use_memory = params['use_memory'] if use_memory: name = tensor.name params['mem_mode'] = 1 tensor_decompressed = cls.decompress(tensor_compressed, ctx, params) params['mem_mode'] = 0 delta = tensor_compensate - tensor_decompressed memory_update_op = cls.residuals[name].assign(delta) return [memory_update_op] if use_memory else [] @staticmethod def aggregate(tensors, params): """Aggregate a list of tensors.""" average = params['average'] agged_tensor = tf.math.add_n(tensors) horovod_size = tf.cast(params["horovod_size"], dtype=agged_tensor.dtype) agged_tensor = (agged_tensor / horovod_size) if average else agged_tensor return agged_tensor class NoneCompressor(Compressor): """Default no-op compression.""" @staticmethod def compress(tensor, params): """Returns the tensor unmodified.""" params['tensors_size_are_same'] = True return tensor, None @staticmethod def decompress(tensor, ctx, params): """Returns the tensor unmodified.""" return tensor class FP16Compressor(Compressor): """Compress all floating point gradients to 16-bit.""" @staticmethod def compress(tensor, params): """Downcasts the tensor to 16-bit.""" tensor_compressed = tensor if tensor.dtype.is_floating: # Only allow compression from other floating point types tensor_compressed = tf.cast(tensor, dtype=tf.float16) params['tensors_size_are_same'] = True return tensor_compressed, tensor.dtype @staticmethod def decompress(tensor, ctx, params): """Upcasts the tensor to the initialization dtype.""" tensor_decompressed = tensor dtype = ctx if dtype.is_floating: tensor_decompressed = tf.cast(tensor, dtype=dtype) return tensor_decompressed class SparsifierCompressor(Compressor): global_step = 0 @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) elemnum = tensor_flatten.get_shape().as_list()[0] compress_ratio = params["compress_ratio"] k = max(1, int(elemnum * compress_ratio)) params['K'] = k params['values_size'] = k ################################################################################ if params["compress_method"] == "topk": _, indices = tf.math.top_k(tf.math.abs(tensor_flatten), k, sorted=False) indices = tf.sort(indices, axis=0, direction='ASCENDING') values = tf.gather(tensor_flatten, indices) elif params["compress_method"] == "randomk": # all_indices = tf.range(elemnum, dtype=tf.int32) # indices = tf.random.shuffle(all_indices, seed=None)[:k] # indices = tf.sort(indices, axis=0, direction='ASCENDING') all_indices = tf.range(elemnum, dtype=tf.int32) h = hash(tensor.name) + RandomkCompressor.global_step tf.compat.v1.set_random_seed(1) indices = tf.random.shuffle(all_indices, seed=h)[:k] indices = tf.sort(indices, axis=0, direction='ASCENDING') RandomkCompressor.global_step += 1 values = tf.gather(tensor_flatten, indices) # elif params["compress_method"] == "threshold": # threshold_val = params["threshold_val"] # thr_mask = tf.math.greater_equal(tf.math.abs(tensor_flatten), threshold_val) # values = tf.boolean_mask(tensor_flatten, thr_mask) # indices = tf.reshape(tf.where(thr_mask), [-1]) # params['K'] = k # params['values_size'] = k ################################################################################ if params['encoding'] is not None: filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) if params['encoding'] == "integer_compression": integer_compressor = library.integer_compressor values = tf.bitcast(values, tf.int32) values_shape = tf.shape(values) indices = tf.bitcast(indices, tf.uint32) compressed_indices = integer_compressor(indices, tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank(), code=params['code']) compressed_indices = tf.bitcast(compressed_indices, tf.int32) elif params['encoding'] == "rle_compression": if params['code'] == "0_8": rle_compressor = library.rle_compressor_v0_code8 values = tf.bitcast(values, tf.uint8) values_shape = tf.shape(values) values = tf.reshape(values, [-1]) params['values_size'] = k*4 # elif params['code'] == "1_8": # rle_compressor = library.rle_compressor_v1_code8 # values = tf.bitcast(values, tf.uint8) # values_shape = tf.shape(values) # values = tf.reshape(values, [-1]) # params['values_size'] = k*4 # # elif params['code'] == "0_32": # rle_compressor = library.rle_compressor_v0_code32 # values = tf.bitcast(values, tf.int32) # params['values_size'] = k # # elif params['code'] == "1_32": # rle_compressor = library.rle_compressor_v1_code32 # values = tf.bitcast(values, tf.int32) # params['values_size'] = k compressed_indices = rle_compressor(indices, elemnum, tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank()) params['tensors_size_are_same'] = False else: compressed_indices = indices values = tf.bitcast(values, tf.int32) params['values_size'] = k values_shape = tf.shape(values) params['tensors_size_are_same'] = True tensor_compressed = tf.concat([values, compressed_indices], 0) ctx = [tensor_shape, values_shape] return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): """Decompress by filling empty slots with zeros and reshape back using the original shape""" compressed_tensor_size = tf.math.reduce_prod(tf.shape(tensor_compressed)) values, indices = tf.split(tensor_compressed, [params['values_size'], compressed_tensor_size-params['values_size']]) values = tf.reshape(values, ctx[1]) values = tf.bitcast(values, tf.float32) tensor_shape = ctx[0] tensor_size = tf.math.reduce_prod(tensor_shape) if params['encoding'] is not None: filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) if params['encoding'] == "integer_compression": integer_decompressor = library.integer_decompressor indices = tf.bitcast(indices, tf.uint32) decompressed_indices = integer_decompressor(indices, params['K'], tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], suffix=params['suffix'], rank=rank(), code=params['code']) decompressed_indices = tf.bitcast(decompressed_indices, tf.int32) elif params['encoding'] == "rle_compression": if params['code'] == "0_8": rle_decompressor = library.rle_decompressor_v0_code8 # elif params['code'] == "1_8": # rle_decompressor = library.rle_decompressor_v1_code8 # elif params['code'] == "0_32": # rle_decompressor = library.rle_decompressor_v0_code32 # elif params['code'] == "1_32": # rle_decompressor = library.rle_decompressor_v1_code32 decompressed_indices = rle_decompressor(indices, params['K'], tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], suffix=params['suffix'], rank=rank()) else: decompressed_indices = indices decompressed_indices = tf.expand_dims(decompressed_indices, 1) tensor_decompressed = tf.scatter_nd(decompressed_indices, values, [tensor_size]) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) # zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) # op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) # with tf.control_dependencies([op]): # tensor_decompressed = tf.scatter_update(zero_tensor, decompressed_indices, values) # tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class RandomkCompressor(Compressor): global_step = 0 @staticmethod def compress(tensor, params): """Use Python Random libraries RNG to compress by generating a list of indices to be transmitted.""" tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) elemnum = tensor_flatten.get_shape().as_list()[0] compress_ratio = params["compress_ratio"] all_indices = tf.range(elemnum, dtype=tf.int32) h = hash(tensor.name) + RandomkCompressor.global_step tf.compat.v1.set_random_seed(1) indices = tf.random.shuffle(all_indices, seed=h)[:max(1, int(elemnum * compress_ratio))] RandomkCompressor.global_step += 1 values = tf.gather(tensor_flatten, indices) ctx = indices, tensor_shape, tensor_flatten tensor_compressed = values params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): """Decompress by filling empty slots with zeros and reshape back using the original shape""" indices, tensor_shape, tensor_flatten = ctx values = tensor_compressed tensor_size = tf.math.reduce_prod(tensor_shape) zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): tensor_decompressed = tf.scatter_update(zero_tensor, indices, values) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class TopKCompressor(Compressor): @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) elemnum = tensor_flatten.get_shape().as_list()[0] compress_ratio = params["compress_ratio"] k = max(1, int(elemnum * compress_ratio)) params['topk_k'] = k params['values_size'] = k _, indices = tf.math.top_k(tf.math.abs(tensor_flatten), k, sorted=False) indices = tf.sort(indices, axis=0, direction='ASCENDING') values = tf.gather(tensor_flatten, indices) if params['encoding'] is not None: filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) if params['encoding'] == "integer_compression": integer_compressor = library.integer_compressor values = tf.bitcast(values, tf.int32) values_shape = tf.shape(values) indices = tf.bitcast(indices, tf.uint32) compressed_indices = integer_compressor(indices, tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank(), code=params['code']) compressed_indices = tf.bitcast(compressed_indices, tf.int32) elif params['encoding'] == "rle_compression": if params['code'] == "0_8": rle_compressor = library.rle_compressor_v0_code8 values = tf.bitcast(values, tf.uint8) values_shape = tf.shape(values) values = tf.reshape(values, [-1]) params['values_size'] = k*4 # elif params['code'] == "1_8": # rle_compressor = library.rle_compressor_v1_code8 # values = tf.bitcast(values, tf.uint8) # values_shape = tf.shape(values) # values = tf.reshape(values, [-1]) # params['values_size'] = k*4 # # elif params['code'] == "0_32": # rle_compressor = library.rle_compressor_v0_code32 # values = tf.bitcast(values, tf.int32) # params['values_size'] = k # # elif params['code'] == "1_32": # rle_compressor = library.rle_compressor_v1_code32 # values = tf.bitcast(values, tf.int32) # params['values_size'] = k compressed_indices = rle_compressor(indices, elemnum, tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank()) params['tensors_size_are_same'] = False else: compressed_indices = indices values = tf.bitcast(values, tf.int32) params['values_size'] = k values_shape = tf.shape(values) params['tensors_size_are_same'] = True tensor_compressed = tf.concat([values, compressed_indices], 0) ctx = [tensor_shape, values_shape] return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): """Decompress by filling empty slots with zeros and reshape back using the original shape""" compressed_tensor_size = tf.math.reduce_prod(tf.shape(tensor_compressed)) values, indices = tf.split(tensor_compressed, [params['values_size'], compressed_tensor_size-params['values_size']]) values = tf.reshape(values, ctx[1]) values = tf.bitcast(values, tf.float32) tensor_shape = ctx[0] tensor_size = tf.math.reduce_prod(tensor_shape) if params['encoding'] is not None: filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) if params['encoding'] == "integer_compression": integer_decompressor = library.integer_decompressor indices = tf.bitcast(indices, tf.uint32) decompressed_indices = integer_decompressor(indices, params['topk_k'], tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], suffix=params['suffix'], rank=rank(), code=params['code']) decompressed_indices = tf.bitcast(decompressed_indices, tf.int32) elif params['encoding'] == "rle_compression": if params['code'] == "0_8": rle_decompressor = library.rle_decompressor_v0_code8 elif params['code'] == "1_8": rle_decompressor = library.rle_decompressor_v1_code8 elif params['code'] == "0_32": rle_decompressor = library.rle_decompressor_v0_code32 elif params['code'] == "1_32": rle_decompressor = library.rle_decompressor_v1_code32 decompressed_indices = rle_decompressor(indices, params['topk_k'], tf.train.get_or_create_global_step(), logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], suffix=params['suffix'], rank=rank()) else: decompressed_indices = indices # decompressed_indices = tf.expand_dims(decompressed_indices, 1) # tensor_decompressed = tf.scatter_nd(decompressed_indices, values, [tensor_size]) # tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): tensor_decompressed = tf.scatter_update(zero_tensor, decompressed_indices, values) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class Bloom_Filter_Compressor(Compressor): """""" @staticmethod def bloom_configuration(k, fpr): # Given FPR compute M and H m = (k*abs(math.log(fpr))) / (math.pow(math.log(2), 2)) # Give bloom size in number of bytes ; bloom size must be a multiple of 8 m = int(m/8) rem = m % 8 if rem != 0 or m == 0: m += 1 h = (m*8 / k)*math.log(2) h = int(math.ceil(h)) return m, h @staticmethod def topk_indices(tensor, K): _, indices = tf.math.top_k(tf.math.abs(tensor), K, sorted=False) indices = tf.sort(indices, axis=0, direction='ASCENDING') return indices @staticmethod def randomk_indices(tensor_name, N, K): all_indices = tf.range(N, dtype=tf.int32) h = hash(tensor_name) + RandomkCompressor.global_step tf.compat.v1.set_random_seed(1) indices = tf.random.shuffle(all_indices, seed=h)[:K] indices = tf.sort(indices, axis=0, direction='ASCENDING') RandomkCompressor.global_step += 1 return indices @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) elemnum = tensor_flatten.get_shape().as_list()[0] compress_ratio = params["compress_ratio"] k = max(1, int(elemnum * compress_ratio)) # Configure bloom filter's m, k values assert params["bloom_fpr"] is not None, "False Positive Rate is None" params['m'], params['k'] = Bloom_Filter_Compressor.bloom_configuration(k, params["bloom_fpr"]) assert params['k'] < 256, "Number of hash functions too big" params["bloom_config"].add_data(k, params['m']*8, params['k'], params["bloom_fpr"]) params["throughput_info"].add_data(elemnum, elemnum/8, params['m']*8, (params['m']*8)/8, elemnum-params['m']*8, (elemnum-params['m']*8)/8) if params['bloom_on'] == "topk": # Topk Sparsification Method indices = Bloom_Filter_Compressor.topk_indices(tensor_flatten, k) elif params['bloom_on'] == "randomk": # Randomk Sparsification Method indices = Bloom_Filter_Compressor.randomk_indices(tensor.name, elemnum, k) values = tf.gather(tensor_flatten, indices) values = tf.bitcast(values, tf.int32) filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) bloom_compressor = library.bloom_compressor log_initial_tensor = tf.bitcast(tensor_flatten, tf.int32) compressed_tensor = bloom_compressor(values, indices, log_initial_tensor, tf.train.get_or_create_global_step(), false_positives_aware=params['bloom_false_positives_aware'], policy=params['bloom_policy'], hash_num=params['k'], bloom_size=params['m'], bloom_logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank()) ctx = tensor_shape params['tensors_size_are_same'] = True return compressed_tensor, ctx @staticmethod def decompress(compressed_tensor, ctx, params): tensor_shape = ctx tensor_size = tf.math.reduce_prod(tensor_shape) filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) bloom_decompressor = library.bloom_decompressor decompressed_tensor = bloom_decompressor(compressed_tensor, tensor_size, tf.train.get_or_create_global_step(), policy=params['bloom_policy'], mem_mode=params['mem_mode'], hash_num=params['k'], bloom_size=params['m'], bloom_logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], suffix=params['suffix'], rank=rank()) decompressed_tensor = tf.bitcast(decompressed_tensor, tf.float32) decompressed_tensor = tf.reshape(decompressed_tensor, tensor_shape) return decompressed_tensor class Values_Approximation_Helper(Compressor): @staticmethod def double_exponential_fit(X_, Y_, K): # S, SS initialization Ysum = Y_ + tf.roll(Y_, shift=-1, axis=0) Xsum = tf.roll(X_, shift=-1, axis=0) - X_ S = tf.tensor_scatter_nd_update(tf.roll(0.5 * Ysum * Xsum, shift=1, axis=0), [[0]], tf.zeros(1, tf.float64)) S = tf.math.cumsum(S) Ssum = S + tf.roll(S, shift=-1, axis=0) SS = tf.tensor_scatter_nd_update(tf.roll(0.5 * Ssum * Xsum, shift=1, axis=0), [[0]], tf.zeros(1, tf.float64)) SS = tf.math.cumsum(SS) sum_SSk_squared = tf.math.reduce_sum(tf.math.pow(SS, 2)) sum_SSk_Sk = tf.math.reduce_sum(S * SS) sum_SSk_xk = tf.math.reduce_sum(SS * X_) sum_SSk = tf.math.reduce_sum(SS) sum_Sk_squared = tf.math.reduce_sum(tf.math.pow(S, 2)) sum_Sk_xk = tf.math.reduce_sum(S * X_) sum_Sk = tf.math.reduce_sum(S) sum_data_x = tf.cast(K * (K + 1) / 2, tf.float64) sum_data_x_squared = tf.cast(K * (K + 1) * (2 * K + 1) / 6, tf.float64) K = tf.cast(K, tf.float64) # Form the first system values = tf.stack([sum_SSk_squared, sum_Sk_squared, sum_data_x_squared, K, sum_SSk_Sk, sum_SSk_xk, sum_SSk, sum_Sk_xk, sum_Sk, sum_data_x], axis=0) A_LS_1 = tf.scatter_nd([[0, 0], [1, 1], [2, 2], [3, 3], [0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]], values, [4, 4]) A_LS_1 = tf.tensor_scatter_nd_update(A_LS_1, [[0, 0], [1, 1], [2, 2], [3, 3], [1, 0], [2, 0], [3, 0], [2, 1], [3, 1], [3, 2]], values) a = tf.math.reduce_sum(tf.transpose(SS) * Y_) b = tf.math.reduce_sum(tf.transpose(S) * Y_) c = tf.math.reduce_sum(tf.transpose(X_) * Y_) d = tf.math.reduce_sum(Y_) b_vector_1 = tf.stack([a, b, c, d], axis=0) b_vector_1 = tf.reshape(b_vector_1, [4, 1]) # Solve the first system Coefficient_vector_1 = tf.linalg.solve(A_LS_1, b_vector_1) # Calculate p1 and q1 p1 = 0.5 * (Coefficient_vector_1[1] + tf.math.sqrt( tf.math.pow(Coefficient_vector_1[1], 2) + 4 * Coefficient_vector_1[0])) q1 = 0.5 * (Coefficient_vector_1[1] - tf.math.sqrt( tf.math.pow(Coefficient_vector_1[1], 2) + 4 * Coefficient_vector_1[0])) beta_k = tf.math.exp(p1 * X_) eta_k = tf.math.exp(q1 * X_) sum_betak_square = tf.math.reduce_sum(tf.math.pow(beta_k, 2)) sum_etak_square = tf.math.reduce_sum(tf.math.pow(eta_k, 2)) sum_betak_etak = tf.math.reduce_sum(beta_k * eta_k) # Form the second system A_LS_2 = tf.stack([sum_betak_square, sum_betak_etak, sum_betak_etak, sum_etak_square], axis=0) A_LS_2 = tf.reshape(A_LS_2, [2, 2]) a = tf.reshape(tf.math.reduce_sum(tf.transpose(beta_k) * Y_), [1, ]) b = tf.reshape(tf.math.reduce_sum(tf.transpose(eta_k) * Y_), [1, ]) b_vector_2 = tf.stack([a, b], axis=0) b_vector_2 = tf.reshape(b_vector_2, [2, 1]) # Solve the second system Coefficient_vector_2 = tf.linalg.solve(A_LS_2, b_vector_2) # print("Coefficient_vector_1: \n", Coefficient_vector_1) # print("p1:\n", p1) # print("Coefficient_vector_2:\n", Coefficient_vector_2) # print("q1:\n", q1) return Coefficient_vector_2[0], Coefficient_vector_2[1], p1, q1 @staticmethod def logit_basis(X, a, N): # log(p/(1-p)) return tf.cast(a * tf.math.log(X / ((N + 1) - X)), dtype=tf.float64) @staticmethod def exp_basis(X, b, c): return tf.cast(b * tf.math.exp(c * X), dtype=tf.float64) @staticmethod def polynomial_basis(X, a): return tf.cast(tf.pow(X, a), dtype=tf.float64) @staticmethod def GetInputMatrix_Polynomial(xcol, x): N = tf.size(x) Xtrans = tf.ones([1, N], tf.float64) for i in range(1, xcol): basis = Values_Approximation_Helper.polynomial_basis(x, i) Xtrans = tf.concat([Xtrans, basis], axis=0) return tf.transpose(Xtrans) @staticmethod def find_breaks(y_train, num_of_segments, N): b = tf.constant(0, shape=(1,), dtype=tf.int32) N = tf.constant(N, shape=(1,), dtype=tf.int32) y = y_train break_points = tf.zeros(num_of_segments + 1, tf.int32) for i in range(num_of_segments - 1): a = tf.math.argmax(tf.abs(tf.linspace(y[0], y[-1], tf.size(y)) - y)) b = b + tf.cast(a, tf.int32) break_points = tf.tensor_scatter_nd_update(break_points, [[i + 1]], b) y = tf.gather(y_train, tf.range(b[0], N[0])) break_points = tf.tensor_scatter_nd_update(break_points, [[num_of_segments]], N) sizes = [break_points[i + 1] - break_points[i] for i in range(num_of_segments)] return break_points, sizes @staticmethod def get_breaks(model, N): if model == "resnet20_v2": breaks = { 432 : [0, 353, 432], 2304 : [0, 1847, 2229, 2304], 4608 : [0, 4073, 4544, 4608], 9216 : [0, 8164, 9012, 9216], 18432 : [0, 16094, 18060, 18432], 36864 : [0, 33742, 36595, 36864]} # Alternative break points for when approximating TopK instead of whole curve # breaks = { # 432 : [0, 129], # 2304 : [0, 670, 691], # 4608 : [0, 1360, 1382], # 9216 : [0, 2650, 2764], # 18432 : [0, 5500, 5529], # 36864 : [0, 11039, 11059]} elif model == "vgg16": breaks = { 1728 : [0, 1443, 1663, 1728], 36864 : [0, 34097, 36467, 36815, 36864], 73728 : [0, 67595, 73032, 73630, 73728], 147456 : [0, 132193, 145286, 147125, 147456], 294912 : [0, 272485, 292623, 294580, 294844, 294912], 589824 : [0, 553577, 586620, 589431, 589764, 589824], 1179648 : [0, 1099105, 1172811, 1179005, 1179543, 1179648], 2359296 : [0, 2195844, 2343594, 2357633, 2359102, 2359296]} elif model == "resnet50": breaks = { 4096 : [0, 3656, 4018, 4096], 9408 : [0, 8476, 9165, 9408], 16384 : [0, 14406, 16145, 16327, 16384], 36864 : [0, 32238, 36292, 36726, 36864], 131072 : [0, 121069, 130381, 130989, 131072], 32768 : [0, 29429, 32320, 32692, 32768], 147456 : [0, 133258, 145944, 147255, 147456], 65536 : [0, 58690, 64507, 65371, 65536], 524288 : [0, 494762, 522078, 524067, 524238, 524288], 589824 : [0, 539407, 584654, 589214, 589738, 589824], 262144 : [0, 237433, 259437, 261782, 262062, 262144], 2097152 : [0, 1990620, 2088919, 2096322, 2097036, 2097152], 2359296 : [0, 2188168, 2341896, 2356580, 2358793, 2359296], 1048576 : [0, 981145, 1041707, 1047784, 1048461, 1048576], 2050048 : [0, 1980923, 2044274, 2049225, 2049929, 2050048]} return breaks[N] @staticmethod def GetInputMatrix(x, p0, N): Xtrans = tf.ones([1, N], tf.float64) # [np.ones(N)] #{1} for [a, b, c] in p0: basis = Values_Approximation_Logit_Compressor.logit_basis(x, a, N) Xtrans = tf.concat([Xtrans, basis], axis=0) basis = Values_Approximation_Logit_Compressor.exp_basis(x, b, c) Xtrans = tf.concat([Xtrans, basis], axis=0) return tf.transpose(Xtrans) @staticmethod def LeastSquares(X, y): # returns (X'X)^-1 X'y Xtrans = tf.transpose(X) tmp = tf.matmul(Xtrans, X) inverse = tf.linalg.inv(tmp) theta_estimates = tf.matmul(tf.matmul(inverse, Xtrans), y) return theta_estimates @staticmethod def is_convolutional(model, N): print(model) if model == "resnet20_v2": conv_sizes = [432, 2304, 4608, 9216, 18432, 36864] elif model == "vgg16": conv_sizes = [1728, 36864, 73728, 147456, 294912, 589824, 1179648, 2359296] elif model == "resnet50": conv_sizes = [4096, 9408, 16384, 36864, 131072, 32768, 147456, 65536, 524288, 589824, 262144, 2097152, 2359296, 1048576, 2050048] if N in conv_sizes: return True return False @staticmethod def get_num_of_segments(model, N): if model == "resnet20_v2": segments = {432:2, 2304:3, 4608:3, 9216:3, 18432:3, 36864:3} elif model == "vgg16": segments = {1728:3, 36864:4, 73728:4, 147456:4, 294912:5, 589824:5, 1179648:5, 2359296:5} elif model == "resnet50": segments = {4096:3, 9408:3, 16384:4, 36864:4, 131072:4, 32768:4, 147456:4, 65536:4, 524288:5, 589824:5, 262144:5, 2097152:5, 2359296:5, 1048576:5, 2050048:5} # return segments[N]-1 # Fewer segments for when approximating TopK values instead of whole curve return segments[N] # Double Exponential class Values_Approximation_Compressor(Compressor): @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) N = tensor_flatten.get_shape().as_list()[0] params['N'] = int(N) print("Tensor", tensor, "size:", params['N']) # Values Approximation if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): abs_values = tf.math.abs(tensor_flatten) mapping = tf.argsort(abs_values, axis=0, direction='ASCENDING') values = tf.gather(abs_values, mapping) # Indices have a negative sign if they correspond to negative values and positive otherwise negative_indices = tf.where(tf.less(tf.gather(tensor_flatten, mapping), 0)) X = tf.cast(tf.range(1, N+1), tf.float64) Nneg = tf.size(negative_indices) mask = tf.tensor_scatter_nd_update(tf.ones([N], dtype=tf.int32), negative_indices, -tf.ones(Nneg, dtype=tf.int32)) mapping = (mapping + 1) * mask # Fitting the curve coefficients = Values_Approximation_Helper.double_exponential_fit(X, tf.cast(values, tf.float64), N) num_of_coefficients = len(coefficients) # coefficients = tf.cast(coefficients, tf.float32) ##################### Logging ##################### filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) logger = library.logger logger = logger(tensor_flatten, coefficients, tf.train.get_or_create_global_step(), bloom_logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank()) ##################### / Logging ##################### compressed_indices = mapping # Possible indices compression with tf.control_dependencies([logger]): coefficients = tf.reshape(coefficients, [-1]) compressed_indices = tf.cast(compressed_indices, tf.float64) tensor_compressed = tf.concat([coefficients, compressed_indices], 0) params['message_size'] = num_of_coefficients params['X_train'] = X else: # No approximation tensor_compressed = tensor ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): message, indices = tf.split(tensor_compressed, [params['message_size'], params['N']]) decompressed_indices = tf.cast(indices, tf.int32) negative_indices = tf.where(tf.less(decompressed_indices, 0)) decompressed_indices = tf.math.abs(decompressed_indices) decompressed_indices = decompressed_indices - 1 y_estimates = message[0] * tf.math.exp(message[2] * params['X_train']) + \ message[1] * tf.math.exp(message[3] * params['X_train']) Nneg = tf.size(negative_indices) mask = tf.tensor_scatter_nd_update(tf.ones([params['N']], dtype=tf.int32), negative_indices, -tf.ones(Nneg, dtype=tf.int32)) y_estimates = y_estimates * tf.cast(mask, tf.float64) values = tf.reshape(y_estimates, [-1]) decompressed_indices = tf.expand_dims(decompressed_indices, 1) tensor_decompressed = tf.scatter_nd(decompressed_indices, tf.cast(values, tf.float32), [params['N']]) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) else: tensor_decompressed = tensor_compressed return tensor_decompressed class Polynomial_Segmented_Values_Approximation_Compressor(Compressor): @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) N = tensor_flatten.get_shape().as_list()[0] compress_ratio = params["compress_ratio"] params['N'] = params['K'] = int(N) print("Tensor", tensor, "size:", params['N']) if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): abs_values = tf.math.abs(tensor_flatten) if params['approximation_mode'] == "topk": K = max(1, int(N * compress_ratio)) params['K'] = K top_values, mapping = tf.math.top_k(abs_values, K, sorted=False) sorted_mapping = tf.argsort(top_values, axis=0, direction='ASCENDING') values = tf.gather(top_values, sorted_mapping) mapping = tf.gather(mapping, sorted_mapping) else: mapping = tf.argsort(abs_values, axis=0, direction='ASCENDING') values = tf.gather(abs_values, mapping) # Indices have a negative sign if they correspond to negative values and positive otherwise negative_indices = tf.where(tf.less(tf.gather(tensor_flatten, mapping), 0)) Nneg = tf.size(negative_indices) mask = tf.tensor_scatter_nd_update(tf.ones([params['K']], dtype=tf.int32), negative_indices, -tf.ones(Nneg, dtype=tf.int32)) mapping = (mapping + 1) * mask # Fitting the curve segments params['num_of_segments'] = Values_Approximation_Helper.get_num_of_segments(params['model_name'], params['N']) # break_points, sizes = Values_Approximation_Helper.find_breaks(values, params['num_of_segments'], params['K']) break_points = Values_Approximation_Helper.get_breaks(params['model_name'], N) sizes = [break_points[i+1]-break_points[i] for i in range(params['num_of_segments'])] params['X'] = {} coefficients = [] for i in range(params['num_of_segments']): x = tf.reshape(tf.cast(tf.range(0, sizes[i]), tf.float64), [1, sizes[i]]) X = Values_Approximation_Helper.GetInputMatrix_Polynomial(params['polynomial_degree'], x) y = tf.reshape(values[break_points[i]: break_points[i+1]], [sizes[i], 1]) coefficients += [Values_Approximation_Helper.LeastSquares(X, tf.cast(y, tf.float64))] params['X'][i] = X coefficients = tf.convert_to_tensor(coefficients) coefficients = tf.reshape(coefficients, [-1]) ##################### Logging ##################### filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) logger = library.logger logger = logger(tensor_flatten, coefficients, tf.train.get_or_create_global_step(), bloom_logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank()) ##################### / Logging ##################### compressed_indices = mapping # Possible indices compression with tf.control_dependencies([logger]): # sizes = tf.cast(sizes, tf.float64) compressed_indices = tf.cast(compressed_indices, tf.float64) # tensor_compressed = tf.concat([sizes, coefficients, compressed_indices], 0) tensor_compressed = tf.concat([coefficients, compressed_indices], 0) else: tensor_compressed = tensor ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): # sizes, coefficients, indices = tf.split(tensor_compressed, [params['num_of_segments'], # params['polynomial_degree'] * params[ # 'num_of_segments'], # params['K']]) coefficients, indices = tf.split(tensor_compressed, [params['polynomial_degree'] * params['num_of_segments'], params['K']]) coefficients = tf.reshape(coefficients, [params['num_of_segments'], params['polynomial_degree']]) decompressed_indices = tf.cast(indices, tf.int32) # sizes = tf.cast(sizes, tf.int32) negative_indices = tf.where(tf.less(decompressed_indices, 0)) decompressed_indices = tf.math.abs(decompressed_indices) decompressed_indices = decompressed_indices - 1 Nneg = tf.size(negative_indices) mask = tf.tensor_scatter_nd_update(tf.ones([params['K']], dtype=tf.int32), negative_indices, -tf.ones(Nneg, dtype=tf.int32)) y_segments = [] for i in range(params['num_of_segments']): # x = tf.reshape(tf.cast(tf.range(0, sizes[i]), tf.float64), [1, sizes[i]]) # X = Values_Approximation_Helper.GetInputMatrix_Polynomial(params['polynomial_degree'], x) X = params['X'][i] y_segments += [tf.matmul(X, tf.reshape(coefficients[i], [params['polynomial_degree'], 1]))] values = tf.reshape(tf.concat(y_segments, axis=0), [params['K']]) values = values * tf.cast(mask, tf.float64) decompressed_indices = tf.expand_dims(decompressed_indices, 1) tensor_decompressed = tf.scatter_nd(decompressed_indices, tf.cast(values, tf.float32), [params['N']]) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) else: tensor_decompressed = tensor_compressed return tensor_decompressed # class TopK_Values_Approximation_Compressor(Compressor): # # @staticmethod # def compress(tensor, params): # tensor_shape = tf.shape(tensor) # tensor_flatten = tf.reshape(tensor, [-1]) # N = tensor_flatten.get_shape().as_list()[0] # compress_ratio = params["compress_ratio"] # K = max(1, int(N*compress_ratio)) # If compress ratio is set to 1 then K=N # params['N'] = int(N) ; params['K'] = K # print("Tensor", tensor, "size:", params['N']) # # abs_values = tf.math.abs(tensor_flatten) # # # Values Approximation # if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): # top_values, mapping = tf.math.top_k(abs_values, K, sorted=False) # sorted_mapping = tf.argsort(top_values, axis=0, direction='ASCENDING') # values = tf.gather(top_values, sorted_mapping) # mapping = tf.gather(mapping, sorted_mapping) # # # Indices have a negative sign if they correspond to negative values and positive otherwise # negative_indices = tf.where(tf.less(tf.gather(tensor_flatten, mapping), 0)) # X = np.array(range(1, K+1), np.float64) # Kneg = tf.size(negative_indices) # mask = tf.tensor_scatter_nd_update(tf.ones([K], dtype=tf.int32), negative_indices, -tf.ones(Kneg, dtype=tf.int32)) # mapping = (mapping + 1) * mask # # # Fitting the curve # coefficients = Values_Approximation_Helper.double_exponential_fit(X, tf.cast(values, tf.float64), K) # num_of_coefficients = len(coefficients) # coefficients = tf.cast(coefficients, tf.float32) # # ##################### Logging ##################### # filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') # library = load_library.load_op_library(filename) # logger = library.logger # logger = logger(tensor_flatten, coefficients, tf.train.get_or_create_global_step(), # bloom_logs_path=params['bloom_logs_path'], # gradient_id=params['gradient_id'], # verbosity_frequency=params['bloom_verbosity_frequency'], # verbosity=params['bloom_verbosity'], # rank=rank()) # ##################### /Logging ##################### # # compressed_indices = mapping # Possible indices compression here # with tf.control_dependencies([logger]): # coefficients = tf.reshape(coefficients, [-1]) # compressed_indices = tf.cast(compressed_indices, tf.float64) # tensor_compressed = tf.concat([coefficients, compressed_indices], 0) # params['message_size'] = num_of_coefficients # params['X_train'] = X # # # No approximation # else: # _, mapping = tf.math.top_k(abs_values, K, sorted=False) # values = tf.gather(tensor_flatten, mapping) # compressed_indices = mapping # Possible indices compression here # compressed_indices = tf.cast(compressed_indices, tf.float32) # tensor_compressed = tf.concat([values, compressed_indices], 0) # params['message_size'] = K # # ctx = tensor_shape # params['tensors_size_are_same'] = True # return tensor_compressed, ctx # # @staticmethod # def decompress(tensor_compressed, ctx, params): # tensor_shape = ctx # tensor_compressed_size = tf.math.reduce_prod(tf.shape(tensor_compressed)) # message, indices = tf.split(tensor_compressed, [params['message_size'], tensor_compressed_size-params['message_size']]) # decompressed_indices = tf.cast(indices, tf.int32) # # if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): # negative_indices = tf.where(tf.less(decompressed_indices, 0)) # decompressed_indices = tf.math.abs(decompressed_indices) # decompressed_indices = decompressed_indices - 1 # # y_estimates = message[0] * tf.math.exp(message[2] * params['X_train']) + \ # message[1] * tf.math.exp(message[3] * params['X_train']) # # Kneg = tf.size(negative_indices) # mask = tf.tensor_scatter_nd_update(tf.ones([params['K']], dtype=tf.int32), negative_indices, -tf.ones(Kneg, dtype=tf.int32)) # y_estimates = y_estimates * tf.cast(mask, tf.float64) # values = tf.cast(tf.reshape(y_estimates, [-1]), tf.float32) # else: # values = message # # decompressed_indices = tf.expand_dims(decompressed_indices, 1) # tensor_decompressed = tf.scatter_nd(decompressed_indices, values, [params['N']]) # tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) # # return tensor_decompressed # # # class Two_TopK_Values_Approximation_Compressor(Compressor): # # @staticmethod # def compress(tensor, params): # tensor_shape = tf.shape(tensor) # tensor_flatten = tf.reshape(tensor, [-1]) # N = tensor_flatten.get_shape().as_list()[0] # compress_ratio = params["compress_ratio"] # K = max(1, int(N*compress_ratio)) # If compress ratio is set to 1 then K=N # params['N'] = int(N) ; params['K'] = K # print("Tensor", tensor, "size:", params['N']) # # abs_values = tf.math.abs(tensor_flatten) # # # Values Approximation # if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): # _, mapping = tf.math.top_k(abs_values, K, sorted=False) # top_values = tf.gather(tensor_flatten, mapping) # # sorted_mapping = tf.argsort(top_values, axis=0, direction='ASCENDING') # values = tf.gather(top_values, sorted_mapping) # mapping = tf.gather(mapping, sorted_mapping) # # # Indices have a negative sign if they correspond to negative values and positive otherwise # negative_indices = tf.where(tf.less(tf.gather(tensor_flatten, mapping), 0)) # Kneg = tf.size(negative_indices) ; Kpos = K-Kneg # mask = tf.tensor_scatter_nd_update(tf.ones([K], dtype=tf.int32), negative_indices, -tf.ones(Kneg, dtype=tf.int32)) # mapping = (mapping + 1) * mask # # # Fitting the curve of Negatives # Xneg = tf.cast(tf.range(1, Kneg + 1), tf.float64) # neg_coefficients = Values_Approximation_Helper.double_exponential_fit(Xneg, tf.cast(tf.gather(values, tf.range(0, Kneg)), tf.float64), Kneg) # neg_num_of_coefficients = len(neg_coefficients) # neg_coefficients = tf.reshape(neg_coefficients, [-1]) # # # Fitting the curve of Positives # Xpos = tf.cast(tf.range(1, Kpos + 1), tf.float64) # pos_coefficients = Values_Approximation_Helper.double_exponential_fit(Xpos, tf.cast(tf.gather(values, tf.range(K-Kpos, K)), tf.float64), Kpos) # pos_num_of_coefficients = len(pos_coefficients) # pos_coefficients = tf.reshape(pos_coefficients, [-1]) # # ##################### Logging ##################### # coefficients = tf.concat([neg_coefficients, pos_coefficients], 0) # filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') # library = load_library.load_op_library(filename) # logger = library.logger # logger = logger(tensor_flatten, coefficients, tf.train.get_or_create_global_step(), # bloom_logs_path=params['bloom_logs_path'], # gradient_id=params['gradient_id'], # verbosity_frequency=params['bloom_verbosity_frequency'], # verbosity=params['bloom_verbosity'], # rank=rank()) # ##################### /Logging ##################### # # compressed_indices = mapping # Possible indices compression here # with tf.control_dependencies([logger]): # compressed_indices = tf.cast(compressed_indices, tf.float64) # tensor_compressed = tf.concat([coefficients, compressed_indices], 0) # params['message_size'] = neg_num_of_coefficients + pos_num_of_coefficients # params['Xneg'] = Xneg # params['Xpos'] = Xpos # # # No approximation # else: # _, mapping = tf.math.top_k(abs_values, K, sorted=False) # values = tf.gather(tensor_flatten, mapping) # compressed_indices = mapping # Possible indices compression here # compressed_indices = tf.cast(compressed_indices, tf.float32) # tensor_compressed = tf.concat([values, compressed_indices], 0) # params['message_size'] = K # # ctx = tensor_shape # params['tensors_size_are_same'] = True # return tensor_compressed, ctx # # @staticmethod # def decompress(tensor_compressed, ctx, params): # tensor_shape = ctx # tensor_compressed_size = tf.math.reduce_prod(tf.shape(tensor_compressed)) # message, indices = tf.split(tensor_compressed, [params['message_size'], tensor_compressed_size-params['message_size']]) # decompressed_indices = tf.cast(indices, tf.int32) # # if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): # negative_indices = tf.where(tf.less(decompressed_indices, 0)) # decompressed_indices = tf.math.abs(decompressed_indices) # decompressed_indices = decompressed_indices - 1 # # message_neg, message_pos = tf.split(message, 2) # # y_estimates_neg = message_neg[0] * tf.math.exp(message_neg[2] * params['Xneg']) + \ # message_neg[1] * tf.math.exp(message_neg[3] * params['Xneg']) # # y_estimates_pos = message_pos[0] * tf.math.exp(message_pos[2] * params['Xpos']) + \ # message_pos[1] * tf.math.exp(message_pos[3] * params['Xpos']) # # y_estimates = tf.concat([y_estimates_neg, y_estimates_pos], 0) # # Kneg = tf.size(negative_indices) # mask = tf.tensor_scatter_nd_update(tf.ones([params['K']], dtype=tf.int32), negative_indices, -tf.ones(Kneg, dtype=tf.int32)) # y_estimates = y_estimates * tf.cast(mask, tf.float64) # values = tf.cast(tf.reshape(y_estimates, [-1]), tf.float32) # else: # values = message # # decompressed_indices = tf.expand_dims(decompressed_indices, 1) # tensor_decompressed = tf.scatter_nd(decompressed_indices, values, [params['N']]) # tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) # # return tensor_decompressed # class Values_Approximation_Logit_Compressor(Compressor): @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) N = tensor_flatten.get_shape().as_list()[0] params['N'] = int(N) print("Tensor", tensor, "size:", params['N']) # params["layers"].add_data(tensor, params['N']) if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): # p0 = [[0.004, -0.01, -0.04]] p0 = [[0.001, 0.1, 0.00001]] num_of_coefficients = len(p0[0]) sorted_indices = tf.argsort(tensor_flatten, axis=0, direction='ASCENDING') values_sorted = tf.gather(tensor_flatten, sorted_indices) values_sorted = tf.reshape(values_sorted, [N, 1]) X = np.array(range(1, N + 1), np.float64).reshape([1, N]) X_train = Values_Approximation_Helper.GetInputMatrix(X, p0, N) coefficients = Values_Approximation_Helper.LeastSquares(X_train, tf.cast(values_sorted, tf.float64)) ##################### Logging ##################### filename = resource_loader.get_path_to_datafile('mpi_lib.cpython-36m-x86_64-linux-gnu.so') library = load_library.load_op_library(filename) logger = library.logger logger = logger(tensor_flatten, coefficients, tf.train.get_or_create_global_step(), bloom_logs_path=params['bloom_logs_path'], gradient_id=params['gradient_id'], verbosity_frequency=params['bloom_verbosity_frequency'], verbosity=params['bloom_verbosity'], rank=rank()) ##################### / Logging ##################### compressed_indices = sorted_indices with tf.control_dependencies([logger]): coefficients = tf.reshape(coefficients, [-1]) compressed_indices = tf.cast(compressed_indices, tf.float64) tensor_compressed = tf.concat([coefficients, compressed_indices], 0) params['message_size'] = num_of_coefficients params['X_train'] = X_train else: tensor_compressed = tensor ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx if Values_Approximation_Helper.is_convolutional(params['model_name'], params['N']): message, indices = tf.split(tensor_compressed, [params['message_size'], params['N']]) decompressed_indices = tf.cast(indices, tf.int32) message = tf.expand_dims(message, 1) y_estimates = tf.matmul(params['X_train'], message) y_estimates = tf.reshape(y_estimates, [-1]) values = tf.reshape(y_estimates, [-1]) decompressed_indices = tf.expand_dims(decompressed_indices, 1) tensor_decompressed = tf.scatter_nd(decompressed_indices, tf.cast(values, tf.float32), [params['N']]) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) else: tensor_decompressed = tensor_compressed return tensor_decompressed ########################################################################################## class ThresholdCompressor(Compressor): """""" @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) threshold_val = params["threshold_val"] thr_mask = tf.math.greater_equal(tf.math.abs(tensor_flatten), threshold_val) values = tf.boolean_mask(tensor_flatten, thr_mask) indices = tf.reshape(tf.where(thr_mask), [-1]) ctx = tensor_shape values = tf.bitcast(values, tf.int32) indices = tf.cast(indices, dtype=tf.int32) tensor_compressed = tf.concat([values, indices], 0) params['tensors_size_are_same'] = False return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): values, indices = tf.split(tensor_compressed, 2) values = tf.bitcast(values, tf.float32) tensor_shape = ctx tensor_size = tf.math.reduce_prod(tensor_shape) zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): tensor_decompressed = tf.scatter_update(zero_tensor, indices, values) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class SignSGDCompressor(Compressor): """""" @staticmethod def aggregate(tensors, params): """Aggregate a list of tensors.""" agged_tensor = tf.math.add_n(tensors) agged_tensor = tf.cast(tf.math.greater_equal(agged_tensor, 0), dtype=tf.float32) agged_tensor = agged_tensor * 2.0 - 1.0 return agged_tensor @staticmethod def compress(tensor, params): """Encoding and compressing the signs """ tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) tensor_compressed = tf.math.greater_equal(tensor_flatten, 0) ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(sign_encode, ctx, params): """Decoding the signs to float format """ tensor_shape = ctx sign_decode = tf.cast(sign_encode, dtype=tf.float32) * 2.0 - 1.0 tensor_decompressed = tf.reshape(sign_decode, tensor_shape) return tensor_decompressed class EFSignSGDCompressor(Compressor): """""" residuals = {} @classmethod def memory_compensate(cls, tensor, params): """Update the tensor with the residuals.""" name = tensor.name lr = params["learning_rate"] cls.residuals[tensor.name] = tf.Variable(tf.zeros_like(tensor), trainable=False) tensor = cls.residuals[name] + lr * tensor return tensor @classmethod def memory_update(cls, tensor, tensor_compensate, tensor_compressed, ctx, params): """Update the residuals.""" name = tensor.name tensor_decompressed = cls.decompress(tensor_compressed, ctx, params) delta = tensor_compensate - tensor_decompressed memory_update_op = cls.residuals[name].assign(delta) return [memory_update_op] @staticmethod def aggregate(tensors, params): """Aggregate a list of tensors.""" lr = params["learning_rate"] agged_tensor = tf.math.add_n(tensors) agged_tensor = agged_tensor / lr return agged_tensor @staticmethod def compress(tensor, params): """Encoding and compressing the signs """ tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) sign_encode = tf.math.greater_equal(tensor_flatten, 0) mean = tf.math.reduce_mean(tf.abs(tensor_flatten)) ctx = tensor_shape tensor_compressed = mean, sign_encode params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): """Decoding the signs to float format """ mean, sign_encode = tensor_compressed tensor_shape = ctx sign_decode = tf.cast(sign_encode, dtype=tf.float32) * 2.0 - 1.0 sign_decode = mean * sign_decode tensor_decompressed = tf.reshape(sign_decode, tensor_shape) return tensor_decompressed class SignumCompressor(Compressor): """""" momentum = {} @staticmethod def aggregate(tensors, params): """Aggregate a list of tensors.""" agged_tensor = tf.math.add_n(tensors) agged_tensor = tf.cast(tf.math.greater_equal(agged_tensor, 0), dtype=tf.float32) agged_tensor = agged_tensor * 2.0 - 1.0 return agged_tensor @staticmethod def compress(tensor, params): """Encoding and compressing the signs """ # update tensor by momentum momentum = params["momentum"] name = tensor.name SignumCompressor.momentum[name] = tf.Variable(tf.zeros_like(tensor), trainable=False) tensor = (1.0 - momentum) * tensor + momentum * SignumCompressor.momentum[name] temp = SignumCompressor.momentum[name].assign(tensor) tensor = tensor + temp - temp tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) tensor_compressed = tf.math.greater_equal(tensor_flatten, 0) ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(sign_encode, ctx, params): """Decoding the signs to float format """ tensor_shape = ctx sign_decode = tf.cast(sign_encode, dtype=tf.float32) * 2.0 - 1.0 tensor_decompressed = tf.reshape(sign_decode, tensor_shape) return tensor_decompressed class QsgdCompressor(Compressor): """""" @staticmethod def compress(tensor, params): def encode2bool(tensor, quantiles): tensor = tf.cast(tensor, dtype=tf.int32) bits = tf.cast(math.log(quantiles, 2) + 1, dtype=tf.int32) def cond(step, input_tensor, output): return step < bits def encode(step, input_tensor, output): base = tf.constant(2, tf.int32) temp = tf.floormod(input_tensor, base) output = output.write(step, temp) input_tensor = tf.floordiv(input_tensor, base) return step + 1, input_tensor, output step = tf.constant(0) output = tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True) _, _, final_output = tf.while_loop(cond, encode, loop_vars=[step, tensor, output]) encode_output = tf.cast(final_output.stack(), dtype=tf.bool) return encode_output quantum_num = params["quantum_num"] tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) norm = tf.reshape(tf.norm(tensor_flatten), [-1]) abs_gradient = tf.abs(tensor_flatten) qnum = tf.cast(quantum_num, dtype=tf.float32) level_float = qnum / norm * abs_gradient previous_level = tf.math.floor(level_float) prob = tf.random.uniform(tf.shape(tensor_flatten)) is_next_level = tf.cast(tf.math.less(prob, (level_float - previous_level)), tf.float32) new_level = tf.cast(previous_level + is_next_level, tf.float32) #new_level = tf.cast(previous_level + is_next_level, tf.int32) #encode_output = encode2bool(new_level, quantum_num) #sign = tf.reshape(tf.greater_equal(tensor, 0), [1, -1]) #encode_output = tf.concat([sign, encode_output], 0) sign = tf.sign(tensor_flatten) tensor_compressed = new_level * sign tensor_compressed = tf.cast(tensor_compressed, dtype=tf.int8 if quantum_num < 128 else tf.int16) tensor_compressed = tensor_compressed, norm ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): def decode4bool(tensor, quantiles): tensor = tf.cast(tensor, dtype=tf.int32) bits = tf.cast(math.log(quantiles, 2) + 1, dtype=tf.int32) def cond(step, input_tensor, output): return step < bits def decode(step, input_tensor, output): base = tf.constant(2, tf.int32) temp = input_tensor[step, :] output = output + temp * tf.math.pow(base, step) return step + 1, input_tensor, output output = tf.zeros([tf.shape(tensor)[1]], dtype=tf.int32) step = tf.constant(0) _, _, decode_output = tf.while_loop(cond, decode, loop_vars=[step, tensor, output]) return decode_output quantum_num = params["quantum_num"] qnum = tf.cast(quantum_num, dtype=tf.float32) tensor_shape = ctx tensor_compressed, norm = tensor_compressed #encode_output = tf.cast(encode_output, dtype=tf.int32) #sign = encode_output[0, :] * 2 - 1 #input_tensor = encode_output[1:, :] #decode_output = decode4bool(input_tensor, quantum_num) #decode_output = sign * decode_output #decode_output = tf.cast(decode_output, dtype=tf.float32) decode_output = tf.cast(tensor_compressed, dtype=tf.float32) tensor_decompressed = norm / qnum * decode_output tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class OnebitCompressor(Compressor): """""" @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) mask0 = tf.math.less(tensor_flatten, 0) sum0 = tf.math.reduce_sum(tf.boolean_mask(tensor_flatten, mask0)) num0 = tf.math.reduce_sum(tf.cast(mask0, dtype=tf.float32)) num0 = tf.where(tf.math.greater(num0, 0), num0, 1.0) mean0 = sum0 / num0 mask1 = tf.math.logical_not(mask0) sum1 = tf.math.reduce_sum(tf.boolean_mask(tensor_flatten, mask1)) num1 = tf.math.reduce_sum(tf.cast(mask1, dtype=tf.float32)) num1 = tf.where(tf.math.greater(num1, 0), num1, 1.0) mean1 = sum1 / num1 mean0 = tf.reshape(mean0, [-1]) mean1 = tf.reshape(mean1, [-1]) mean = tf.concat([mean0, mean1], 0) ctx = tensor_shape tensor_compressed = mask0, mean params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx mask0, mean = tensor_compressed mean0, mean1 = tf.split(mean, 2) mask0 = tf.cast(mask0, dtype=tf.float32) tensor_decompressed = mask0 * mean0 + (1-mask0) * mean1 tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class TerngradCompressor(Compressor): """""" @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) std = tf.math.square(tensor_flatten - tf.math.reduce_mean(tensor_flatten)) std = tf.math.sqrt(tf.math.reduce_mean(std)) c = 2.5 gradient = tf.clip_by_value(tensor_flatten, -c * std, c * std) scaler = tf.math.reduce_max(tf.math.abs(gradient)) zeros = tf.zeros(tf.shape(tensor_flatten)) abs_gradient = tf.abs(gradient) sign_gradient = tf.sign(gradient) rnd_sample = tf.random_uniform(tf.shape(tensor_flatten), 0, scaler) where_cond = tf.less(rnd_sample, abs_gradient) binarized_gradient = tf.where(where_cond, sign_gradient * scaler, zeros) new_sign = tf.sign(binarized_gradient) # -1, 0, 1 """ a = tf.add(new_sign, 1) # shift -1,0,1 to 0,1,2 (2'b00,2'b01,2'b10) a = tf.reshape(a, [-1]) pad_size = 4 - tf.mod(tf.size(a), 4) pad = tf.range(0.0, pad_size) a = tf.concat([a, pad], 0) a_split1, a_split2, a_split3, a_split4 = tf.split(a, 4) # assume the size is dividable by 4 # encode 4 grads into 1 Byte sum_1 = tf.add(a_split1, a_split2 * 4) sum_2 = tf.add(a_split3 * 16, a_split4 * 64) sum_all = tf.add(sum_1, sum_2) tensor_compressed = tf.cast(sum_all, tf.uint8) """ scaler = tf.reshape(scaler, [-1]) ctx = tensor_shape tensor_compressed = tf.cast(new_sign, tf.int8), scaler params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx tensor_compressed, scaler = tensor_compressed """ a = tf.cast(tensor_compressed, tf.int32) a_split1 = tf.mod(a, 4) a_split2 = tf.to_int32(tf.mod(a / 4, 4)) a_split3 = tf.to_int32(tf.mod(a / 16, 4)) a_split4 = tf.to_int32(tf.mod(a / 64, 4)) a = tf.concat([a_split1, a_split2, a_split3, a_split4], 0) real_size = tf.reduce_prod(tensor_shape) a = tf.to_float(a) a = tf.gather(a, tf.range(0, real_size)) a = tf.reshape(a, tensor_shape) sign = tf.subtract(a, 1) """ sign = tf.cast(tensor_compressed, dtype=tf.float32) tensor_decompressed = sign * scaler tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class DgcCompressor(Compressor): """""" residuals = {} gradients = {} @classmethod def memory_compensate(cls, tensor, params): """Update the tensor with the residuals.""" name = tensor.name horovod_size = params["horovod_size"] momentum = params["momentum"] gradient_clipping = params["gradient_clipping"] if gradient_clipping: tensor_squ_sum = tf.math.reduce_sum(tf.math.square(tensor)) # if params['debug']: # tensor_squ_sum = tf.Print(tensor_squ_sum, [tf.size(tensor_squ_sum)], # message=f"==Debug== tensor 0/1 on rank {rank()} {tensor_squ_sum.dtype} size:") thr_global = tf.math.sqrt(_allreduce(tensor_squ_sum)) clipping_val = thr_global / tf.math.sqrt(float(horovod_size)) tensor = tf.clip_by_value(tensor, -clipping_val, clipping_val) cls.residuals[name] = tf.Variable(tf.zeros_like(tensor), trainable=False) cls.gradients[name] = tf.Variable(tf.zeros_like(tensor), trainable=False) u = cls.residuals[name].assign(momentum * cls.residuals[name] + tensor) tensor_compensate = cls.gradients[name].assign(cls.gradients[name] + u) + tf.zeros_like(tensor) return tensor_compensate @classmethod def memory_update(cls, tensor, tensor_compensate, tensor_compressed, ctx, params): """Update the residuals.""" name = tensor.name _, mask = ctx not_mask = tf.cast(tf.math.logical_not(mask), tf.float32) #not_mask = tf.Print(not_mask, ['not_mask2', tf.math.reduce_sum(not_mask)]) not_mask = tf.reshape(not_mask, tf.shape(tensor)) op1 = cls.residuals[name].assign(cls.residuals[name] * not_mask) op2 = cls.gradients[name].assign(cls.gradients[name] * not_mask) return [op1, op2] @staticmethod def compress(tensor, params): compress_ratio = params["compress_ratio"] tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) elemnum = tensor_flatten.get_shape().as_list()[0] sample_shape = tf.reshape(tf.convert_to_tensor(max(1, int(elemnum * 0.01)), dtype=tf.int32), [-1]) sample_index = tf.random.uniform(sample_shape, minval=0, maxval=elemnum, dtype=tf.int32) sample_tensor = tf.gather(tensor_flatten, sample_index) k = max(1, int(elemnum * compress_ratio * 0.01)) vals, indices = tf.math.top_k(tf.math.abs(sample_tensor), k) thr = tf.math.reduce_min(vals) mask = tf.math.greater(tf.math.abs(tensor_flatten), thr) selected = tf.math.reduce_sum(tf.cast(mask, dtype=tf.float32)) #selected = tf.Print(selected, ['selected:', selected]) def body(thr, mask, selected): thr = tf.cond(selected > 1.25 * max(1, int(elemnum * compress_ratio)), lambda: 1.25 * thr, lambda: 0.9 * thr) mask = tf.math.greater(tf.math.abs(tensor_flatten), thr) selected = tf.math.reduce_sum(tf.cast(mask, dtype=tf.float32)) return thr, mask, selected def condition(thr, mask, selected): cond_a = selected > 1.25 * max(1, int(elemnum * compress_ratio)) cond_b = selected < 0.8 * max(1, int(elemnum * compress_ratio)) return tf.math.logical_or(cond_a, cond_b) thr, mask, new_selected = tf.while_loop(condition, body, (thr, mask, selected), maximum_iterations=20) thr = tf.cond(new_selected < 1, lambda: 0.8 * thr, lambda: thr) # mask = tf.math.greater_equal(tf.math.abs(tensor_flatten), thr) mask = tf.math.greater(tf.math.abs(tensor_flatten), thr) # fix the dgc NCF data volume issue indices = tf.reshape(tf.where(mask), [-1]) #indices = tf.Print(indices, ["selected:", selected, new_selected, 'size changes:', tf.size(indices), tf.size(tensor), "ratio:", compress_ratio]) values = tf.gather(tensor_flatten, indices) values = tf.bitcast(values, tf.int32) indices = tf.cast(indices, dtype=tf.int32) tensor_compressed = tf.concat([values, indices], 0) ctx = tensor_shape, mask params['tensors_size_are_same'] = False return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape,_ = ctx values, indices = tf.split(tensor_compressed, 2) values = tf.bitcast(values, tf.float32) tensor_size = tf.math.reduce_prod(tensor_shape) zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): tensor_decompressed = tf.scatter_update(zero_tensor, indices, values) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class AdaqCompressor(Compressor): """""" @staticmethod def compress(tensor, params): compress_ratio = params["compress_ratio"] def quan(tensor, tensor_mask, compress_ratio): # tensor_mask = tf.math.greater_equal(tf.math.abs(tensor),0) # for testing and debuging tensor_value = tf.boolean_mask(tensor, tensor_mask) mask_size = tf.reduce_sum(tf.cast(tensor_mask, dtype=tf.int32)) sample_size = tf.cast(tf.reshape((tf.math.ceil(tf.cast(mask_size, dtype=tf.float32) * 0.01)), [-1]), dtype=tf.int32) sample_index = tf.random.uniform(sample_size, minval=0, maxval=mask_size, dtype=tf.int32) sample_tensor = tf.gather(tensor_value, sample_index) k = tf.cast((tf.math.ceil(tf.cast(mask_size, dtype=tf.float32) * 0.01 * compress_ratio)), dtype=tf.int32) vals, indices = tf.math.top_k(tf.math.abs(sample_tensor), k) thr = tf.math.reduce_min(vals) tensor_masked = tf.cast(tensor_mask, dtype=tf.float32) * tensor mask = tf.math.greater(tf.math.abs(tensor_masked), thr) # fix the issue of sampling in topk selected = tf.math.reduce_sum(tf.cast(mask, dtype=tf.float32)) elemnum = tf.cast(mask_size, dtype=tf.float32) def body(thr, mask, selected): thr = tf.cond(selected > 1.25 * tf.ceil(elemnum * compress_ratio), lambda: 1.25 * thr, lambda: 0.9 * thr) mask = tf.math.greater_equal(tf.math.abs(tensor_masked), thr) selected = tf.math.reduce_sum(tf.cast(mask, dtype=tf.float32)) return thr, mask, selected def condition(thr, mask, selected): cond_a = selected > 1.25 * tf.ceil(elemnum * compress_ratio) cond_b = selected < 0.8 * tf.ceil(elemnum * compress_ratio) return tf.math.logical_or(cond_a, cond_b) thr2, mask2, selected2 = tf.while_loop(condition, body, (thr, mask, selected), maximum_iterations=20) thr2 = tf.cond(selected2 < 1, lambda: 0.8 * thr2, lambda: thr2) mask2 = tf.math.greater(tf.math.abs(tensor_masked), thr2) indices = tf.reshape(tf.where(mask2), [-1]) #indices = tf.Print(indices, ["selected:", selected2, 'size changes:', tf.size(indices), elemnum, "ratio:", compress_ratio]) tensor_value2 = tf.boolean_mask(tensor_masked, mask2) mean = tf.reshape(tf.math.reduce_mean(tensor_value2), [-1]) return indices, mean, mask2 tensor_shape = tf.shape(tensor) tensor_size = tf.size(tensor) tensor_flatten = tf.reshape(tensor, [-1]) plus_mask = tf.math.greater(tensor_flatten, 0) minus_mask = tf.math.less(tensor_flatten, 0) plus_indices, plus_mean, plus_mask = quan(tensor_flatten, plus_mask, compress_ratio) minus_indices, minus_mean, minus_mask = quan(tensor_flatten, minus_mask, compress_ratio) plus_mean = tf.bitcast(plus_mean, tf.int32) plus_indices = tf.reshape(tf.cast(plus_indices, dtype=tf.int32), [-1]) minus_mean = tf.bitcast(minus_mean, tf.int32) minus_indices = tf.reshape(tf.cast(minus_indices, dtype=tf.int32), [-1]) plus_indices_size = tf.reshape(tf.size(plus_indices), [-1]) # minus_indices_size = tf.reshape(tf.size(minus_indices), [-1]) tensor_compressed = tf.concat([plus_mean, minus_mean, plus_indices_size, plus_indices, minus_indices], 0) ctx = tensor_shape, tensor_size params['tensors_size_are_same'] = False return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): plus_mean = tensor_compressed[0] minus_mean = tensor_compressed[1] plus_indices_size = tensor_compressed[2] plus_indices = tensor_compressed[3:3 + plus_indices_size] minus_indices = tensor_compressed[3 + plus_indices_size:] plus_mean = tf.bitcast(plus_mean, tf.float32) minus_mean = tf.bitcast(minus_mean, tf.float32) tensor_shape, tensor_size = ctx zero_tensor = tf.Variable(tf.zeros([tensor_size]), trainable=False) # solve the error 'Tensor' object has no attribute '_lazy_read' plus_mean = tf.ones(tf.shape(plus_indices), dtype=tf.float32) * plus_mean minus_mean = tf.ones(tf.shape(minus_indices), dtype=tf.float32) * minus_mean op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): temp1 = tf.scatter_update(zero_tensor, plus_indices, plus_mean) tensor_decompressed = tf.scatter_update(temp1, minus_indices, minus_mean) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class AdapSparseCompressor(Compressor): """""" @staticmethod def compress(tensor, params): k = params["compress_ratio"] tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) tensor_size = tf.cast(tf.size(tensor), dtype=tensor.dtype) prob = k * tensor_size * tf.abs(tensor_flatten) / tf.math.reduce_sum(tf.abs(tensor_flatten)) prob = tf.minimum(prob, 1.0) c = tf.constant(2.0) def body(c, prob): mask = tf.less(prob, 1.0) size_indices = tf.cast(tf.size(tf.where(mask)), dtype=tf.float32) sum_prob = tf.math.reduce_sum(tf.boolean_mask(prob, mask)) c = ((k - 1.0) * tensor_size + size_indices) / sum_prob prob = tf.minimum(c * prob, 1.0) return c, prob def condition(c, prob): return tf.greater(c, 1.0) res = tf.while_loop(condition, body, (c, prob)) prob = res[1] rnd_sample = tf.random_uniform(tf.shape(tensor_flatten)) mask = tf.less(rnd_sample, prob) indices = tf.reshape(tf.where(mask), [-1]) values = tf.gather(tensor_flatten / prob, indices) values = tf.bitcast(values, tf.int32) indices = tf.cast(indices, dtype=tf.int32) tensor_compressed = tf.concat([values, indices], 0) ctx = tensor_shape params['tensors_size_are_same'] = False return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): """Decompress by filling empty slots with zeros and reshape back using the original shape""" values, indices = tf.split(tensor_compressed, 2) values = tf.bitcast(values, tf.float32) tensor_shape = ctx tensor_size = tf.math.reduce_prod(tensor_shape) zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): tensor_decompressed = tf.scatter_update(zero_tensor, indices, values) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class PowerSGDCompressor(Compressor): """""" momentum = {} q_memory = {} @classmethod def memory_compensate(cls, tensor, params): """Update the tensor with the residuals.""" compress_rank = params["compress_rank"] tensor_name = params["tensor_name"] tensor_dims = params['tensor_dims'] cls.momentum[tensor_name] = tf.Variable(tf.zeros_like(tensor), trainable=False) if tensor_dims == 1: return tensor cls.residuals[tensor_name] = tf.Variable(tf.zeros_like(tensor), trainable=False) tensor = cls.residuals[tensor_name] + tensor n = tensor.get_shape().as_list()[0] m = int(1) for dim in tensor.get_shape().as_list()[1:]: m = m * dim r = int(min([m, n, compress_rank])) cls.q_memory[tensor_name] = tf.Variable(tf.random.normal([m, r]), trainable=False) return tensor @classmethod def memory_update(cls, tensor, tensor_compensate, tensor_compressed, ctx, params): """Update the residuals.""" tensor_name = params["tensor_name"] tensor_dims = params['tensor_dims'] if tensor_dims == 1: return [] new_tensor = cls.decompress(tensor, ctx, params) op = cls.residuals[tensor_name].assign(tensor_compensate - new_tensor) return [op] @classmethod def compress(cls, tensor, params): tensor_dims = params['tensor_dims'] if tensor_dims == 1: return tensor, None horovod_size = params["horovod_size"] tensor_name = params["tensor_name"] tensor_shape = tf.shape(tensor) matrix = tf.reshape(tensor, [tensor_shape[0], -1]) q = cls.q_memory[tensor_name] q, _ = tf.linalg.qr(q) p = tf.linalg.matmul(matrix, q) # if params['debug']: # p = tf.Print(p, [tf.size(p)], # message=f"==Debug== tensor 0/1 on rank {rank()} {p.dtype} size:") p = _allreduce(p) / horovod_size p, _ = tf.linalg.qr(p) q = tf.linalg.matmul(matrix, p, transpose_a=True) # if params['debug']: # q = tf.Print(q, [tf.size(q)], # message=f"==Debug== tensor 0/1 on rank {rank()} {q.dtype} size:") q = _allreduce(q) / horovod_size new_q = cls.q_memory[tensor_name].assign(q) ctx = p, new_q, tensor_shape return None, ctx @classmethod def decompress(cls, tensor, ctx, params): tensor_dims = params['tensor_dims'] if tensor_dims == 1: return tensor p, q, tensor_shape = ctx new_tensor = tf.linalg.matmul(p, q, transpose_b=True) new_tensor = tf.reshape(new_tensor, tensor_shape) return new_tensor class U8bitCompressor(Compressor): """""" @staticmethod def compress(tensor, params): dict128 = tf.constant( [ 1.5000001e-06, 2.7500000e-06, 7.2499997e-06, 1.8750001e-05, 3.6250000e-05, 5.8749996e-05, 8.6249995e-05, 1.4375000e-04, 2.3125000e-04, 3.1875001e-04, 4.0625001e-04, 5.1874999e-04, 6.5624999e-04, 7.9374999e-04, 9.3124999e-04, 1.2187500e-03, 1.6562500e-03, 2.0937501e-03, 2.5312500e-03, 2.9687500e-03, 3.4062499e-03, 3.8437501e-03, 4.2812498e-03, 4.8437500e-03, 5.5312500e-03, 6.2187500e-03, 6.9062500e-03, 7.5937500e-03, 8.2812496e-03, 8.9687500e-03, 9.6562495e-03, 1.1093750e-02, 1.3281250e-02, 1.5468750e-02, 1.7656250e-02, 1.9843750e-02, 2.2031249e-02, 2.4218749e-02, 2.6406251e-02, 2.8593751e-02, 3.0781250e-02, 3.2968748e-02, 3.5156250e-02, 3.7343752e-02, 3.9531250e-02, 4.1718751e-02, 4.3906249e-02, 4.6718750e-02, 5.0156251e-02, 5.3593751e-02, 5.7031251e-02, 6.0468748e-02, 6.3906237e-02, 6.7343749e-02, 7.0781253e-02, 7.4218743e-02, 7.7656247e-02, 8.1093743e-02, 8.4531240e-02, 8.7968737e-02, 9.1406241e-02, 9.4843738e-02, 9.8281242e-02, 1.0546875e-01, 1.1640625e-01, 1.2734374e-01, 1.3828126e-01, 1.4921875e-01, 1.6015625e-01, 1.7109375e-01, 1.8203124e-01, 1.9296876e-01, 2.0390625e-01, 2.1484375e-01, 2.2578125e-01, 2.3671874e-01, 2.4765626e-01, 2.5859374e-01, 2.6953125e-01, 2.8046876e-01, 2.9140624e-01, 3.0234376e-01, 3.1328124e-01, 3.2421875e-01, 3.3515626e-01, 3.4609374e-01, 3.5703126e-01, 3.6796874e-01, 3.7890625e-01, 3.8984376e-01, 4.0078124e-01, 4.1171876e-01, 4.2265624e-01, 4.3359375e-01, 4.4453126e-01, 4.5859376e-01, 4.7578123e-01, 4.9296874e-01, 5.1015621e-01, 5.2734375e-01, 5.4453123e-01, 5.6171870e-01, 5.7890624e-01, 5.9609371e-01, 6.1328125e-01, 6.3046873e-01, 6.4765620e-01, 6.6484374e-01, 6.8203121e-01, 6.9921869e-01, 7.1640623e-01, 7.3359370e-01, 7.5078118e-01, 7.6796871e-01, 7.8515619e-01, 8.0234367e-01, 8.1953120e-01, 8.3671868e-01, 8.5390615e-01, 8.7109369e-01, 8.8828117e-01, 9.0546864e-01, 9.2265618e-01, 9.3984365e-01, 9.5703113e-01, 9.7421867e-01, 9.9140614e-01, 9.9570298e-01, ], dtype=tf.float32 ) tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) scaler = tf.math.reduce_max(tf.abs(tensor_flatten)) new_tensor = tensor_flatten / scaler sign = tf.sign(tensor_flatten) new_tensor = tf.abs(new_tensor) """ pivot = 64 * tf.ones([tensor_size], dtype=tf.int32) left = tf.zeros([tensor_size], dtype=tf.int32) right = 127 * tf.ones([tensor_size], dtype=tf.int32) step = 5 def cond(step, input_tensor, pivot, left, right): return step > -1 def body(step, input_tensor, pivot, left, right): base = tf.constant(2, tf.int32) vals_pivot = tf.gather(dict128, pivot) mask = tf.math.greater(input_tensor, vals_pivot) left = tf.where(mask, pivot, left) right = tf.where(mask, right, pivot) sign_mask = tf.cast(mask, dtype=tf.int32) * 2 - 1 pivot = pivot + sign_mask * tf.math.pow(base, step) return step - 1, input_tensor, pivot, left, right step, _, pivot, left, right = tf.while_loop(cond, body, loop_vars=[step, new_tensor, pivot, left, right]) tensor_compressed = tf.cast(pivot, dtype=tf.int8) * tf.cast(sign, dtype=tf.int8) """ import tensorflow_probability as tfp edges = dict128 bins = tf.cast(tfp.stats.find_bins(new_tensor, edges), dtype=tf.int8) scaler = tf.reshape(scaler, [-1]) tensor_compressed = bins * tf.cast(sign, dtype=tf.int8), scaler ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx tensor, scaler = tensor_compressed dict128 = tf.constant( [ 1.5000001e-06, 2.7500000e-06, 7.2499997e-06, 1.8750001e-05, 3.6250000e-05, 5.8749996e-05, 8.6249995e-05, 1.4375000e-04, 2.3125000e-04, 3.1875001e-04, 4.0625001e-04, 5.1874999e-04, 6.5624999e-04, 7.9374999e-04, 9.3124999e-04, 1.2187500e-03, 1.6562500e-03, 2.0937501e-03, 2.5312500e-03, 2.9687500e-03, 3.4062499e-03, 3.8437501e-03, 4.2812498e-03, 4.8437500e-03, 5.5312500e-03, 6.2187500e-03, 6.9062500e-03, 7.5937500e-03, 8.2812496e-03, 8.9687500e-03, 9.6562495e-03, 1.1093750e-02, 1.3281250e-02, 1.5468750e-02, 1.7656250e-02, 1.9843750e-02, 2.2031249e-02, 2.4218749e-02, 2.6406251e-02, 2.8593751e-02, 3.0781250e-02, 3.2968748e-02, 3.5156250e-02, 3.7343752e-02, 3.9531250e-02, 4.1718751e-02, 4.3906249e-02, 4.6718750e-02, 5.0156251e-02, 5.3593751e-02, 5.7031251e-02, 6.0468748e-02, 6.3906237e-02, 6.7343749e-02, 7.0781253e-02, 7.4218743e-02, 7.7656247e-02, 8.1093743e-02, 8.4531240e-02, 8.7968737e-02, 9.1406241e-02, 9.4843738e-02, 9.8281242e-02, 1.0546875e-01, 1.1640625e-01, 1.2734374e-01, 1.3828126e-01, 1.4921875e-01, 1.6015625e-01, 1.7109375e-01, 1.8203124e-01, 1.9296876e-01, 2.0390625e-01, 2.1484375e-01, 2.2578125e-01, 2.3671874e-01, 2.4765626e-01, 2.5859374e-01, 2.6953125e-01, 2.8046876e-01, 2.9140624e-01, 3.0234376e-01, 3.1328124e-01, 3.2421875e-01, 3.3515626e-01, 3.4609374e-01, 3.5703126e-01, 3.6796874e-01, 3.7890625e-01, 3.8984376e-01, 4.0078124e-01, 4.1171876e-01, 4.2265624e-01, 4.3359375e-01, 4.4453126e-01, 4.5859376e-01, 4.7578123e-01, 4.9296874e-01, 5.1015621e-01, 5.2734375e-01, 5.4453123e-01, 5.6171870e-01, 5.7890624e-01, 5.9609371e-01, 6.1328125e-01, 6.3046873e-01, 6.4765620e-01, 6.6484374e-01, 6.8203121e-01, 6.9921869e-01, 7.1640623e-01, 7.3359370e-01, 7.5078118e-01, 7.6796871e-01, 7.8515619e-01, 8.0234367e-01, 8.1953120e-01, 8.3671868e-01, 8.5390615e-01, 8.7109369e-01, 8.8828117e-01, 9.0546864e-01, 9.2265618e-01, 9.3984365e-01, 9.5703113e-01, 9.7421867e-01, 9.9140614e-01, 9.9570298e-01, ], dtype=tf.float32 ) # tensor is int8 tensor = tf.cast(tensor, dtype=tf.int32) sign = tf.cast(tf.sign(tensor), dtype=tf.float32) index = tf.cast(tf.abs(tensor), dtype=tf.int32) tensor_decompressed = tf.gather(dict128, index) * scaler * sign tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class NaturalCompressor_old(Compressor): """""" @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) sign = tf.cast(tf.sign(tensor_flatten), dtype=tf.int8) zeros = tf.zeros_like(tensor_flatten) exponent = tf.math.log(tf.abs(tensor_flatten)) / tf.math.log(2.0) h1 = tf.math.floor(tf.where(tf.math.abs(tensor_flatten) != 0, exponent, zeros)) h2 = tf.where(tf.math.abs(tensor_flatten) != 0, tf.math.pow(2.0, h1), zeros) # extract probability p = tf.where(h2 != 0, tf.abs(tensor_flatten) / h2 - 1, h2) u = tf.floor(tf.random_uniform(tf.shape(p)) + p) tensor_compressed = h1 + u tensor_compressed = tf.cast(tensor_compressed, dtype=tf.int8), sign ctx = tensor_shape params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): tensor_shape = ctx tensor, sign = tensor_compressed tensor = tf.cast(tensor, dtype=tf.float32) sign = tf.cast(sign, dtype=tf.float32) tensor_decompressed = sign * tf.math.pow(2.0, tensor) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class NaturalCompressor(Compressor): """""" @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) tensor_cast = tf.bitcast(tensor_flatten, tf.int32) sign = tf.bitwise.bitwise_and(tensor_cast, 0b10000000000000000000000000000000) exp = tf.bitwise.bitwise_and(tensor_cast, 0b01111111100000000000000000000000) mantissa = tf.bitwise.bitwise_and(tensor_cast, 0b00000000011111111111111111111111) exp_add_one = mantissa > tf.random.uniform(tf.shape(tensor_flatten), minval=0, maxval=0x007ffff, dtype=tf.int32) # exp_add_one = mantissa > 0x00400000 # deterministic exponent = tf.where(exp_add_one, exp + 0b00000000100000000000000000000000, exp) # original exponent range: -128 ~ 127, clip to -110, 17 # view as uint8_t: 0 ~ 255 18 145 exp_shift = tf.clip_by_value(exponent, 0b00001001000000000000000000000000, 0b01001000100000000000000000000000) exps = tf.bitwise.right_shift(exp_shift, 23) # shift 18 so that 0 maps to -110 and 127 maps to 145 # set MSB if negative exps = tf.bitwise.bitwise_or(tf.bitwise.right_shift(sign, 24), exps - 18) tensor_compressed = tf.cast(exps, tf.uint8) params['tensors_size_are_same'] = True return tensor_compressed, tensor_shape @staticmethod def decompress(tensor_compressed, tensor_shape, params=None): sign = tensor_compressed > 127 exps = tf.bitwise.bitwise_and(tensor_compressed, 0b01111111) exps_shift = tf.bitwise.left_shift(tf.cast(exps + 18, tf.int32), 23) floats = tf.bitcast(exps_shift, tf.float32) tensor_decompressed = tf.where(sign, -floats, floats) tensor_decompressed = tf.multiply(tf.cast(exps >= 1, tensor_decompressed.dtype), tensor_decompressed) return tf.reshape(tensor_decompressed, tensor_shape) class SketchCompressor(Compressor): """""" @staticmethod def compress(tensor, params): """ def encode2bool(tensor, quantiles): tensor = tf.cast(tensor, dtype=tf.int32) bits = tf.cast(math.log(quantiles, 2) + 1, dtype=tf.int32) def cond(step, input_tensor, output): return step < bits def encode(step, input_tensor, output): base = tf.constant(2, tf.int32) temp = tf.floormod(input_tensor, base) output = output.write(step, temp) input_tensor = tf.floordiv(input_tensor, base) return step + 1, input_tensor, output step = tf.constant(0) output = tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True) _, _, final_output = tf.while_loop(cond, encode, loop_vars=[step, tensor, output]) encode_output = tf.cast(final_output.stack(), dtype=tf.bool) return encode_output """ import tensorflow_probability as tfp tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) x = tensor_flatten quantiles = params["quantum_num"] edges = tfp.stats.quantiles(x, num_quantiles=quantiles, interpolation='linear') bins = tf.cast(tfp.stats.find_bins(x, edges), dtype=tf.int32) means = tf.unsorted_segment_mean(x, bins, num_segments=quantiles) tensor_compressed = tf.cast(bins, dtype=tf.uint8 if quantiles < 256 else tf.uint16) #tensor_compressed = encode2bool(tensor_compressed, quantiles) means = tf.reshape(means, [-1]) ctx = tensor_shape tensor_compressed = tensor_compressed, means params['tensors_size_are_same'] = True return tensor_compressed, ctx @staticmethod def decompress(tensor_compressed, ctx, params): """ def decode4bool(tensor, quantiles): tensor = tf.cast(tensor, dtype=tf.int32) bits = tf.cast(math.log(quantiles, 2) + 1, dtype=tf.int32) def cond(step, input_tensor, output): return step < bits def decode(step, input_tensor, output): base = tf.constant(2, tf.int32) temp = input_tensor[step, :] output = output + temp * tf.math.pow(base, step) return step + 1, input_tensor, output output = tf.zeros([tf.shape(tensor)[1]], dtype=tf.int32) step = tf.constant(0) _, _, decode_output = tf.while_loop(cond, decode, loop_vars=[step, tensor, output]) return decode_output """ # tensor_compressed = decode4bool(tensor_compressed, params["quantum_num"]) tensor_shape = ctx tensor_compressed, means = tensor_compressed bins = tf.cast(tensor_compressed, dtype=tf.int32) tensor_decompressed = tf.gather(means, bins) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class INCEPTIONNCompressor(Compressor): """""" @staticmethod def compress(tensor, params): tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) tensor_cast = tf.bitcast(tensor_flatten, tf.uint32) sign = tf.bitwise.bitwise_and(tensor_cast, 0b10000000000000000000000000000000) exp = tf.bitwise.bitwise_and(tensor_cast, 0b01111111100000000000000000000000) mantissa = tf.bitwise.bitwise_and(tensor_cast, 0b00000000011111111111111111111111) exp = tf.bitwise.right_shift(exp, 23) error_bound_val = params["error_bound"] error_bound = 127 + int(math.log(error_bound_val / 2, 10)) # error_bound exponent: 117 for 2e-10 radius = math.ceil((127 - error_bound) / 2) mid = error_bound + radius mask_32bit = exp >= 127 mask_16bit = (exp >= mid) & (exp < 127) mask_8bit = (exp >= error_bound) & (exp < mid) indices_32bit = tf.reshape(tf.where(mask_32bit), [-1]) indices_16bit = tf.reshape(tf.where(mask_16bit), [-1]) indices_8bit = tf.reshape(tf.where(mask_8bit), [-1]) # no compress v_32bit = tf.gather(tensor_flatten, indices_32bit) # 16bit compress s_16bit = tf.gather(sign, indices_16bit) e_16bit = tf.gather(exp, indices_16bit) m_16bit = tf.gather(mantissa, indices_16bit) n_shift = 127 - tf.cast(e_16bit, dtype=tf.int32) n_shift = tf.cast(n_shift, tf.uint32) shifted_s = tf.bitwise.right_shift(s_16bit, 8) marker = 0b00000000010000000000000000000000 m_16bit_concat = tf.bitwise.bitwise_or(tf.bitwise.right_shift(m_16bit, 1), marker) shifted_m = tf.bitwise.right_shift(m_16bit_concat, n_shift) temp = tf.bitwise.bitwise_or(shifted_s, shifted_m) v_16bit = tf.cast(tf.bitwise.right_shift(temp, 8), dtype=tf.uint16) # 8bit compress s_8bit = tf.gather(sign, indices_8bit) e_8bit = tf.gather(exp, indices_8bit) m_8bit = tf.gather(mantissa, indices_8bit) n_shift = 127 - tf.cast(e_8bit, dtype=tf.int32) n_shift = tf.cast(n_shift, tf.uint32) shifted_s = tf.bitwise.right_shift(s_8bit, 8) marker = 0b00000000010000000000000000000000 m_8bit_concat = tf.bitwise.bitwise_or(tf.bitwise.right_shift(m_8bit, 1), marker) shifted_m = tf.bitwise.right_shift(m_8bit_concat, n_shift) temp = tf.bitwise.bitwise_or(shifted_s, shifted_m) v_8bit = tf.cast(tf.bitwise.right_shift(temp, 16), dtype=tf.uint8) # concat indices # indices_all = tf.concat([indices_32bit, indices_16bit, indices_8bit], 0) # indices_all = tf.cast(indices_all, dtype=tf.int32) def encode_byte(a): # input: int32 type tensor with values in range 0,1,2,3 (2'b00,2'b01,2'b10,3'b11) # output: encoded uint8 type tensor a = tf.reshape(a, [-1]) pad_size = 4 - tf.mod(tf.size(a), 4) pad = tf.range(0, pad_size) a = tf.concat([a, pad], 0) a_split1, a_split2, a_split3, a_split4 = tf.split(a, 4) # encode 4 grads into 1 Byte sum_1 = tf.add(a_split1, a_split2 * 4) sum_2 = tf.add(a_split3 * 16, a_split4 * 64) sum_all = tf.add(sum_1, sum_2) return tf.cast(sum_all, tf.uint8) # encode indices mask_encode = 0 for mask, code in zip([mask_8bit, mask_16bit, mask_32bit], [1, 2, 3]): mask_encode += tf.cast(mask, tf.int32) * code mask_encode = encode_byte(mask_encode) tensor_compressed = v_32bit, v_16bit, v_8bit, mask_encode ctx = tensor_shape return tensor_compressed, ctx # decompress @staticmethod def decompress(tensor_compressed, ctx, params): def decode_byte(encoded, real_size): # input: encoded uint8 type tensor # output: int32 type tensor with values in range 0,1,2,3 (2'b00,2'b01,2'b10,3'b11) a = tf.cast(encoded, tf.int32) a_split1 = tf.mod(a, 4) a_split2 = tf.cast(tf.mod(a / 4, 4), tf.int32) a_split3 = tf.cast(tf.mod(a / 16, 4), tf.int32) a_split4 = tf.cast(tf.mod(a / 64, 4), tf.int32) a = tf.concat([a_split1, a_split2, a_split3, a_split4], 0) a = a[:real_size] return a v_32bit, v_16bit, v_8bit, mask_encode = tensor_compressed tensor_shape = ctx tensor_size = tf.math.reduce_prod(tensor_shape) # decode mask and gather indices mask_decode = decode_byte(mask_encode, tensor_size) mask_32bit = tf.equal(mask_decode, 3) mask_16bit = tf.equal(mask_decode, 2) mask_8bit = tf.equal(mask_decode, 1) indices_32bit = tf.reshape(tf.where(mask_32bit), [-1]) indices_16bit = tf.reshape(tf.where(mask_16bit), [-1]) indices_8bit = tf.reshape(tf.where(mask_8bit), [-1]) edges_16bit = [0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536] edges_8bit = [0, 2, 4, 8, 16, 32, 64, 128, 256] import tensorflow_probability as tfp # 16bit decompress # get the sign bit s_16bit and remove MSB from v_16bit s_16bit = tf.bitwise.bitwise_and(v_16bit, 0b1000000000000000) s_16bit = tf.cast(s_16bit, dtype=tf.int32) s_16bit = tf.bitwise.left_shift(s_16bit, 16) v_16bit = tf.bitwise.left_shift(v_16bit, 1) # 8bit decompress # get the sign bit s_8bit and remove MSB from v_8bit s_8bit = tf.bitwise.bitwise_and(v_8bit, 0b10000000) s_8bit = tf.cast(s_8bit, dtype=tf.int32) s_8bit = tf.bitwise.left_shift(s_8bit, 24) v_8bit = tf.bitwise.left_shift(v_8bit, 1) # find the marker bit in v_16bit and get the exponent zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.int32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.int32)) with tf.control_dependencies([op]): temp = tf.scatter_update(zero_tensor, indices_16bit, tf.cast(v_16bit, tf.int32)) temp = tf.scatter_update(temp, indices_8bit, tf.cast(v_8bit, tf.int32)) n_shift_all = tfp.stats.find_bins(tf.cast(temp, dtype=tf.int32), edges_16bit) n_shift = 16 - tf.gather(n_shift_all, indices_16bit) e_16bit = 127 - (n_shift - 1) e_16bit = tf.bitwise.left_shift(e_16bit, 23) # restore the mantissa n_shift = tf.cast(n_shift, dtype=tf.uint16) v_16bit = tf.bitwise.left_shift(v_16bit, n_shift) v_16bit = tf.cast(v_16bit, dtype=tf.int32) m_16bit = tf.bitwise.left_shift(v_16bit, 7) # concat all temp = tf.bitwise.bitwise_or(s_16bit, e_16bit) v_16bit = tf.bitwise.bitwise_or(temp, m_16bit) v_16bit = tf.bitcast(v_16bit, tf.float32) # find the marker bit in v_8bit and get the exponent # zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=v_8bit.dtype), trainable=False) # op = zero_tensor.assign(tf.zeros([tensor_size], dtype=v_8bit.dtype)) # with tf.control_dependencies([op]): # temp = tf.scatter_update(zero_tensor, indices_8bit, v_8bit) # n_shift = 8 - tfp.stats.find_bins(tf.cast(temp, dtype=tf.int32), edges_8bit) n_shift = 8 - tf.gather(n_shift_all, indices_8bit) e_8bit = 127 - (n_shift - 1) e_8bit = tf.bitwise.left_shift(e_8bit, 23) # restore the mantissa n_shift = tf.cast(n_shift, dtype=tf.uint8) v_8bit = tf.bitwise.left_shift(v_8bit, n_shift) v_8bit = tf.cast(v_8bit, dtype=tf.int32) m_8bit = tf.bitwise.left_shift(v_8bit, 15) # concat all temp = tf.bitwise.bitwise_or(s_8bit, e_8bit) v_8bit = tf.bitwise.bitwise_or(temp, m_8bit) v_8bit = tf.bitcast(v_8bit, tf.float32) zero_tensor = tf.Variable(tf.zeros([tensor_size], dtype=tf.float32), trainable=False) op = zero_tensor.assign(tf.zeros([tensor_size], dtype=tf.float32)) with tf.control_dependencies([op]): temp = tf.scatter_update(zero_tensor, indices_32bit, v_32bit) temp = tf.scatter_update(temp, indices_16bit, v_16bit) temp = tf.scatter_update(temp, indices_8bit, v_8bit) tensor_decompressed = tf.reshape(temp, tensor_shape) return tensor_decompressed class FakeCompressor(Compressor): """Default no-op compression.""" @classmethod def memory_compensate(cls, tensor, params): """Update the tensor with the residuals.""" return tensor @classmethod def memory_update(cls, tensor, tensor_compensate, tensor_compressed, ctx, params): """Update the residuals.""" return [] @classmethod def compress(cls, tensor, params): """Returns the tensor unmodified.""" tensor_shape = tf.shape(tensor) tensor_flatten = tf.reshape(tensor, [-1]) elemnum = tensor_flatten.get_shape().as_list()[0] compress_ratio = params["compress_ratio"] if params['compress_method'] in ['randomk']: tensor_compressed = tensor_flatten[:max(1, int(elemnum * compress_ratio))] params['tensors_size_are_same'] = True elif params['compress_method'] in ['topk']: tensor_compressed = tensor_flatten[: 2 * max(1, int(elemnum * compress_ratio))] params['tensors_size_are_same'] = True elif params['compress_method'] in ['dgc', 'adas']: tensor_compressed = tensor_flatten[: 2 * max(1, int(elemnum * compress_ratio))] params['tensors_size_are_same'] = False elif params['compress_method'] in ['adaq']: tensor_compressed = tensor_flatten[: min(elemnum, 3 + int(elemnum * compress_ratio))] params['tensors_size_are_same'] = False elif params['compress_method'] in ['signsgd', 'signum', 'natural']: tensor_compressed = tf.cast(tensor_flatten, tf.int8) params['tensors_size_are_same'] = True elif params['compress_method'] in ['efsignsgd', 'terngrad', '8bit']: tensor_compressed = tensor_flatten[0], tf.cast(tensor_flatten, tf.int8) params['tensors_size_are_same'] = True elif params['compress_method'] in ['qsgd']: quantum_num = params["quantum_num"] if quantum_num < 128: tensor_compressed = tensor_flatten[0], tf.cast(tensor_flatten, tf.int8) else: tensor_compressed = tensor_flatten[0], tf.cast(tensor_flatten, tf.int16) params['tensors_size_are_same'] = True elif params['compress_method'] in ['sketch']: quantum_num = params["quantum_num"] mean_cache = tensor_flatten[0] * tf.ones([quantum_num], dtype=tf.float32) if quantum_num < 256: tensor_compressed = mean_cache, tf.cast(tensor_flatten, tf.int8) else: tensor_compressed = mean_cache, tf.cast(tensor_flatten, tf.int16) params['tensors_size_are_same'] = True elif params['compress_method'] in ['onebit']: tensor_compressed = tensor_flatten[:2], tf.cast(tensor_flatten, tf.int8) params['tensors_size_are_same'] = True elif params['compress_method'] in ['threshold']: #tensor_double = tf.concat([tensor_flatten, tensor_flatten], 0) if params['model_name'] == 'resnet20_v2': if params["threshold_val"] == 0.01 and params["use_memory"]: tensor_compressed = tensor_flatten[:max(1, int(0.004551207 * elemnum))] elif params['model_name'] == 'densenet40_k12': if params["threshold_val"] == 0.01 and params["use_memory"]: tensor_compressed = tensor_flatten[:max(1, int(0.016955392 * elemnum))] elif params['model_name'] == 'resnet50': if params["threshold_val"] == 0.01 and params["use_memory"]: tensor_compressed = tensor_flatten[:max(1, int(0.116419225 * elemnum))] elif params['model_name'] == 'ncf': if params["threshold_val"] == 0.0001 and params["use_memory"]: tensor_compressed = tensor_flatten[:max(1, int(0.001318898 * elemnum))] elif params['model_name'] == 'ptb': if params["threshold_val"] == 0.01 and params["use_memory"]: tensor_compressed = tensor_flatten[:max(1, int(0.0182253132 * elemnum))] elif params['model_name'] == 'segmentation': if params["threshold_val"] == 0.01 and params["use_memory"]: tensor_compressed = tensor_flatten[:max(1, int(0.0145350 * elemnum))] elif params['model_name'] == 'vgg19': tensor_compressed = tensor_flatten[:max(1, int(0.1 * elemnum))] params['tensors_size_are_same'] = True elif params['compress_method'] in ['powersgd']: if params['model_name'] == 'resnet20_v2': temp1 = tensor_flatten[:max(1, int(0.691594033 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.691594033 * 0.5 * elemnum)):] elif params['model_name'] == 'densenet40_k12': temp1 = tensor_flatten[:max(1, int(0.5 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.5 * 0.5 * elemnum)):] elif params['model_name'] == 'resnet50': temp1 = tensor_flatten[:max(1, int(0.53 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.53 * 0.5 * elemnum)):] elif params['model_name'] == 'ncf': temp1 = tensor_flatten[:max(1, int(0.006557669 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.006557669 * 0.5 * elemnum)):] elif params['model_name'] == 'ptb': temp1 = tensor_flatten[:max(1, int(0.003924611 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.003924611 * 0.5 * elemnum)):] elif params['model_name'] == 'segmentation': temp1 = tensor_flatten[:max(1, int(0.69452657 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.69452657 * 0.5 * elemnum)):] elif params['model_name'] == 'vgg19': temp1 = tensor_flatten[:max(1, int(0.5 * 0.5 * elemnum))] temp2 = tensor_flatten[-max(1, int(0.5 * 0.5 * elemnum)):] tensor_compressed = temp1, temp2 params['tensors_size_are_same'] = True ctx = elemnum, tensor_shape return tensor_compressed, ctx @classmethod def decompress(cls, tensor_compressed, ctx, params): """Returns the tensor unmodified.""" elemnum, tensor_shape = ctx if params['compress_method'] in ['randomk', 'topk', 'dgc', 'adas','adaq', 'threshold']: temp = tf.reshape(tensor_compressed[0], [-1]) tensor_decompressed = tf.concat([temp, tf.ones([elemnum-1], dtype=tf.float32)], 0) elif params['compress_method'] in ['signsgd', 'signum', 'natural']: tensor_decompressed = tf.cast(tensor_compressed, tf.float32) elif params['compress_method'] in ['efsignsgd', 'terngrad', '8bit', 'qsgd']: temp = tf.reshape(tensor_compressed[0], [-1]) tensor_decompressed = tf.concat([temp, tf.cast(tensor_compressed[1], tf.float32)[1:]], 0) elif params['compress_method'] in ['sketch', 'onebit']: temp = tf.reshape(tensor_compressed[0][0], [-1]) tensor_decompressed = tf.concat([temp, tf.cast(tensor_compressed[1], tf.float32)[1:]], 0) elif params['compress_method'] in ['powersgd']: temp1, temp2 = tensor_compressed temp = tf.reshape((temp1+temp2)[0], [-1]) tensor_decompressed = tf.concat([temp, tf.ones([elemnum - 1], dtype=tf.float32)], 0) tensor_decompressed = tf.reshape(tensor_decompressed, tensor_shape) return tensor_decompressed class Compression(object): """Optional gradient compression algorithm used during allreduce.""" """Do not compress the gradients. This is the default.""" none = NoneCompressor """Compress all floating point gradients to 16-bit.""" fp16 = FP16Compressor randomk = RandomkCompressor topk = TopKCompressor threshold = ThresholdCompressor terngrad = TerngradCompressor qsgd = QsgdCompressor dgc = DgcCompressor adaq = AdaqCompressor signsgd = SignSGDCompressor efsignsgd = EFSignSGDCompressor signum = SignumCompressor adas = AdapSparseCompressor onebit = OnebitCompressor powersgd = PowerSGDCompressor u8bit = U8bitCompressor natural = NaturalCompressor sketch = SketchCompressor inceptionn = INCEPTIONNCompressor fake = FakeCompressor sparsifier = SparsifierCompressor bloom = Bloom_Filter_Compressor values_approximation = Values_Approximation_Compressor values_approximation_logit = Values_Approximation_Logit_Compressor topk_values_approximation = TopK_Values_Approximation_Compressor two_topk_values_approximation = Two_TopK_Values_Approximation_Compressor polynomial_segmented_values_approximation = Polynomial_Segmented_Values_Approximation_Compressor
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gp/GPhelpers.py
ericlee0803/surrogate-GCP
0
145873
import GPy import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm # ~~~~WARNING: ONLY SUPPORT FOR 1D RIGHT NOW~~~~ # # TODO def batch(): pass # Calculates GP from input and output vectors X and Y respectively def calcGP(X, Y, kernel='rbf', variance=1., lengthscale=1.): # Reshape in 1D Case to proper column vector form if(len(X.shape) == 1): X = np.reshape(X, (len(X),1)) if(len(Y.shape) == 1): Y = np.reshape(Y, (len(Y),1)) if(kernel=='rbf'): kernel = GPy.kern.RBF(input_dim=1, variance=variance, lengthscale=lengthscale) m = GPy.models.GPRegression(X,Y,kernel) return m else: print('Kernel is not supported, please use one that is supported or use the default RBF Kernel') return None # Updates GP with a set of new function evaluations Y at points X def updateGP(model, kernel, Xnew, Ynew): # Reshape in 1D Case if(len(Xnew.shape) == 1): Xnew = np.reshape(X, (len(Xnew),1)) if(len(Ynew.shape) == 1): Ynew = np.reshape(Y, (len(Ynew),1)) X = np.append(model.X, Xnew, 0) Y = np.append(model.Y, Ynew, 0) m = GPy.models.GPRegression(X,Y,kernel) return m # Using Expected Improvement, send out a number of further evaluations # -batchsize = number of new evals # -fidelity = number of points used to estimate EI # -bounds = determines how new evals points are spaced def batchNewEvals_EI(model, bounds=1, batchsize=50, fidelity=100): P, ei = compute_ei(model, fidelity) idx = np.argmax(ei) xnew = P[idx] X = np.linspace(xnew-bounds, xnew+bounds, num=batchsize) return X # Calculates EI given means mu and variances sigma2 def compute_ei_inner(ybest, mu, sigma2): sigma = np.sqrt(sigma2) u = (ybest - mu) / sigma ucdf = norm.cdf(u) updf = norm.pdf(u) ei = sigma * (updf + u * ucdf) return ei # Takes in GP model from GPy and computes EI at points P # We are assuming minimization, and thus ybest represents the smallest point we have so far def compute_ei(model, numsamples): P = np.linspace(model.X[0], model.X[-1], num=numsamples) ybest = np.amax(model.Y) P = np.reshape(P, [len(P), 1]) mu, sigma2 = model.predict(P) return P, compute_ei_inner(ybest, mu, sigma2) def plotGP(model): fig = model.plot() regfig = GPy.plotting.show(fig) regfig.savefig('GPmodel.png')
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ring/eran/gurobi900/linux64/examples/python/gc_pwl.py
Practical-Formal-Methods/clam-racetrack
4
130198
#!/usr/bin/env python3.7 # Copyright 2019, Gurobi Optimization, LLC # This example formulates and solves the following simple model # with PWL constraints: # # maximize # sum c[j] * x[j] # subject to # sum A[i,j] * x[j] <= 0, for i = 0, ..., m-1 # sum y[j] <= 3 # y[j] = pwl(x[j]), for j = 0, ..., n-1 # x[j] free, y[j] >= 0, for j = 0, ..., n-1 # where pwl(x) = 0, if x = 0 # = 1+|x|, if x != 0 # # Note # 1. sum pwl(x[j]) <= b is to bound x vector and also to favor sparse x vector. # Here b = 3 means that at most two x[j] can be nonzero and if two, then # sum x[j] <= 1 # 2. pwl(x) jumps from 1 to 0 and from 0 to 1, if x moves from negative 0 to 0, # then to positive 0, so we need three points at x = 0. x has infinite bounds # on both sides, the piece defined with two points (-1, 2) and (0, 1) can # extend x to -infinite. Overall we can use five points (-1, 2), (0, 1), # (0, 0), (0, 1) and (1, 2) to define y = pwl(x) # import gurobipy as gp from gurobipy import GRB try: n = 5 m = 5 c = [0.5, 0.8, 0.5, 0.1, -1] A = [[0, 0, 0, 1, -1], [0, 0, 1, 1, -1], [1, 1, 0, 0, -1], [1, 0, 1, 0, -1], [1, 0, 0, 1, -1]] # Create a new model model = gp.Model("gc_pwl") # Create variables x = model.addVars(n, lb=-GRB.INFINITY, name="x") y = model.addVars(n, name="y") # Set objective model.setObjective(gp.quicksum(c[j]*x[j] for j in range(n)), GRB.MAXIMIZE) # Add Constraints for i in range(m): model.addConstr(gp.quicksum(A[i][j]*x[j] for j in range(n)) <= 0) model.addConstr(y.sum() <= 3) for j in range(n): model.addGenConstrPWL(x[j], y[j], [-1, 0, 0, 0, 1], [2, 1, 0, 1, 2]) # Optimize model model.optimize() for j in range(n): print('%s = %g' % (x[j].varName, x[j].x)) print('Obj: %g' % model.objVal) except gp.GurobiError as e: print('Error code ' + str(e.errno) + ": " + str(e)) except AttributeError: print('Encountered an attribute error')
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题源分类/LeetCode/LeetCode日刷/python/47.全排列-ii.py
ZhengyangXu/Algorithm-Daily-Practice
0
16227
<reponame>ZhengyangXu/Algorithm-Daily-Practice # # @lc app=leetcode.cn id=47 lang=python3 # # [47] 全排列 II # # https://leetcode-cn.com/problems/permutations-ii/description/ # # algorithms # Medium (59.58%) # Likes: 371 # Dislikes: 0 # Total Accepted: 78.7K # Total Submissions: 132.1K # Testcase Example: '[1,1,2]' # # 给定一个可包含重复数字的序列,返回所有不重复的全排列。 # # 示例: # # 输入: [1,1,2] # 输出: # [ # ⁠ [1,1,2], # ⁠ [1,2,1], # ⁠ [2,1,1] # ] # # # @lc code=start class Solution: def permuteUnique(self, nums: List[int]) -> List[List[int]]: def backtrack(nums,track,visited): if len(nums) == len(track): track = track[:] res.append(track) for i in range(len(nums)): if visited[i]: continue if i > 0 and nums[i] == nums[i-1] and visited[i-1]: break track.append(nums[i]) visited[i] = True backtrack(nums,track,visited) track.pop() visited[i] = False nums.sort() visited = [False]*len(nums) res = [] track = [] backtrack(nums,track,visited) return res # @lc code=end # def permuteUnique(self, nums: List[int]) -> List[List[int]]: # def helper(nums,res,path): # if not nums and path not in res: # res.append(path) # for i in range(len(nums)): # helper(nums[:i]+nums[i+1:],res,path+[nums[i]]) # res = [] # helper(nums,res,[]) # return res
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p7t.py
Frosty-nee/aoc2020
0
1601667
#! python import p7 testinput = { 'pale green bag': {'dark purple bag': 2, 'bright green bag': 1}, 'dark purple bag': {'dark yellow bag': 6, 'bright orange bag': 3}, 'bright green bag': {}, 'bright orange bag':{'bright green bag':1}, 'dark yellow bag':{}, } p7.rule_list = testinput print(p7.count_contained_bags('dark purple bag')) assert p7.count_contained_bags('dark purple bag')-1 == 12
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pattern/vector/__init__.py
aesmin/pattern
1
131308
#### PATTERN | VECTOR ############################################################################## # -*- coding: utf-8 -*- # Copyright (c) 2010 University of Antwerp, Belgium # Author: <NAME> <<EMAIL>> # License: BSD (see LICENSE.txt for details). # http://www.clips.ua.ac.be/pages/pattern #################################################################################################### # Vector space search, based on cosine similarity using tf-idf. # Term frequency – inverse document frequency is a statistical measure used to evaluate # how important a word is to a document in a collection or corpus. # The importance increases proportionally to the number of times a word appears in the document # but is offset by the frequency of the word in the corpus. # Variations of the tf–idf weighting scheme are often used by search engines # as a central tool in scoring and ranking a document's relevance given a user query. import sys import os import glob import heapq import codecs import cPickle; BINARY=1 import stemmer; _stemmer=stemmer from math import log, sqrt from time import time from random import random, choice from itertools import izip, chain from bisect import insort from operator import itemgetter from StringIO import StringIO from codecs import open try: MODULE = os.path.dirname(__file__) except: MODULE = "" try: from pattern.en.inflect import singularize, conjugate except: try: import sys; sys.path.insert(0, os.path.join(MODULE, "..")) from en.inflect import singularize, conjugate except: singularize = lambda w: w conjugate = lambda w,t: w #--- STRING FUNCTIONS ------------------------------------------------------------------------------ def decode_utf8(string): """ Returns the given string as a unicode string (if possible). """ if isinstance(string, str): for encoding in (("utf-8",), ("windows-1252",), ("utf-8", "ignore")): try: return string.decode(*encoding) except: pass return string return unicode(string) def encode_utf8(string): """ Returns the given string as a Python byte string (if possible). """ if isinstance(string, unicode): try: return string.encode("utf-8") except: return string return str(string) def lreplace(a, b, string): """ Replaces the head of the string. """ if string.startswith(a): return b + string[len(a):] return string def rreplace(a, b, string): """ Replaces the tail of the string. """ if string.endswith(a): return string[:len(string)-len(a)] + b return string def filename(path, map={"_":" "}): """ Returns the basename of the file at the given path, without the extension. For example: /users/tom/desktop/corpus/aesthetics.txt => aesthetics. """ f = os.path.splitext(os.path.basename(path))[0] for k in map: f = f.replace(k, map[k]) return f def shi(i, base="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"): """ Returns a short string hash for a given int. """ s = [] while i > 0: i, r = divmod(i, len(base)) s.append(base[r]) return "".join(reversed(s)) #--- LIST FUNCTIONS -------------------------------------------------------------------------------- def shuffled(list): """ Yields a copy of the given list with the items in random order. """ return sorted(list, key=lambda x: random()) def chunk(list, n): """ Yields n successive equal-sized chunks from the given list. """ i = 0 for m in xrange(n): j = i + len(list[m::n]) yield list[i:j] i=j def pimap(function, iterable, *args, **kwargs): """ Returns an iterator of function(x, *args, **kwargs) for the iterable (x1, x2, x3, ...). The function is applied in parallel over available CPU cores. """ from multiprocessing import Pool global worker def worker(x): return function(x, *args, **kwargs) return Pool(processes=None).imap(worker, iterable) #--- READ-ONLY DICTIONARY -------------------------------------------------------------------------- class ReadOnlyError(Exception): pass # Read-only dictionary, used for Document.terms and Document.vector. # These can't be updated because it invalidates the cache. class readonlydict(dict): @classmethod def fromkeys(cls, k, default=None): d=readonlydict((k, default) for k in k); return d def __init__(self, *args, **kwargs): dict.__init__(self, *args, **kwargs) def __setitem__(self, k, v): raise ReadOnlyError def __delitem__(self, k): raise ReadOnlyError def pop(self, k, default=None): raise ReadOnlyError def popitem(self, kv): raise ReadOnlyError def clear(self): raise ReadOnlyError def update(self, kv): raise ReadOnlyError def setdefault(self, k, default=None): if k in self: return self[k] raise ReadOnlyError # Read-only list, used for Corpus.documents. class readonlylist(list): def __init__(self, *args, **kwargs): list.__init__(self, *args, **kwargs) def __setitem__(self, i, v): raise ReadOnlyError def __delitem__(self, i): raise ReadOnlyError def append(self, v): raise ReadOnlyError def insert(self, i, v): raise ReadOnlyError def extend(self, v): raise ReadOnlyError def remove(self, v): raise ReadOnlyError def pop(self, i): raise ReadOnlyError #### DOCUMENT ###################################################################################### #--- STOP WORDS ------------------------------------------------------------------------------------ stopwords = _stopwords = dict.fromkeys( open(os.path.join(MODULE, "stopwords.txt")).read().split(", "), True) # The following words could also be meaningful nouns: #for w in ["mine", "us", "will", "can", "may", "might"]: # stopwords.pop(w) #--- WORD COUNT ------------------------------------------------------------------------------------ PUNCTUATION = "*#[]():;,.!?\n\r\t\f- " def words(string, filter=lambda w: w.isalpha() and len(w)>1, punctuation=PUNCTUATION, **kwargs): """ Returns a list of words from the given string. Common punctuation marks are stripped from words. """ if isinstance(string, unicode): string = string.replace(u"’", u"'") words = string.replace("\n", "\n ") words = (rreplace("'s", "", w.strip(punctuation)) for w in words.split(" ")) words = [w for w in words if filter is None or filter(w) is not False] return words PORTER, LEMMA = "porter", "lemma" def stem(word, stemmer=PORTER, **kwargs): """ Returns the base form of the word when counting words in count(). With stemmer=PORTER, the Porter2 stemming algorithm is used. With stemmer=LEMMA, either uses Word.lemma or inflect.singularize(). """ if isinstance(word, basestring): word = decode_utf8(word.lower()) if stemmer is None: return word if stemmer == PORTER: return _stemmer.stem(word, **kwargs) if stemmer == LEMMA: if word.__class__.__name__ == "Word": if word.lemma is not None: return word.lemma if word.pos == "NNS": return singularize(word.string.lower()) if word.pos.startswith("VB"): return conjugate(word.string.lower(), "infinitive") or word return singularize(word) if type(stemmer).__name__ == "function": return decode_utf8(stemmer(word)) return word def count(words=[], top=None, threshold=0, stemmer=None, exclude=[], stopwords=False, **kwargs): """ Returns a dictionary of (word, count)-items, in lowercase. Words in the exclude list and stop words are not counted. Words whose count falls below (or equals) the given threshold are excluded. Words that are not in the given top most counted are excluded. """ # An optional dict-parameter can be used to specify a subclass of dict, # e.g., count(words, dict=readonlydict) as used in Document. count = kwargs.get("dict", dict)() for w in words: if w.__class__.__name__ == "Word": w = w.string.lower() if isinstance(w, basestring): w = w.lower() if (stopwords or not w in _stopwords) and not w in exclude: if stemmer is not None: w = stem(w, stemmer, **kwargs) dict.__setitem__(count, w, (w in count) and count[w]+1 or 1) for k in count.keys(): if count[k] <= threshold: dict.__delitem__(count, k) if top is not None: count = count.__class__(heapq.nsmallest(top, count.iteritems(), key=lambda (k,v): (-v,k))) return count #--- DOCUMENT -------------------------------------------------------------------------------------- # Document is a bag of words in which each word is a feature. # Document is represented as a vector of weighted (TF-IDF) features. __UID = 0 __SESSION = shi(int(time()*1000)) # Avoids collision with pickled documents. def _uid(): """ Returns a string id, for example: "NPIJYaS-1", "NPIJYaS-2", ... The string part is based on the current time, the number suffix is auto-incremental. """ global __UID; __UID+=1; return __SESSION+"-"+str(__UID) # Term relevancy weight: TF, TFIDF, TF_IDF = "tf", "tf-idf", "tf-idf" class Document(object): # Document(string="", filter, punctuation, top, threshold, stemmer, exclude, stopwords, name, type) def __init__(self, string="", **kwargs): """ A dictionary of (word, count)-items parsed from the string. Punctuation marks are stripped from the words. Stop words in the exclude list are excluded from the document. Only words whose count exceeds the threshold and who are in the top are included in the document. """ kwargs.setdefault("filter", lambda w: w.isalpha() and len(w)>1) kwargs.setdefault("threshold", 0) kwargs.setdefault("dict", readonlydict) # A string of words, map to read-only dict of (word, count)-items. if isinstance(string, basestring): w = words(string, **kwargs) w = count(w, **kwargs) v = None # A list of words, map to read-only dict of (word, count)-items. elif isinstance(string, (list, tuple)) and not string.__class__.__name__ == "Text": w = string w = count(w, **kwargs) v = None # A Vector of (word, TF weight)-items, copy as document vector. elif isinstance(string, Vector) and string.weight == TF: w = string w = kwargs["dict"](w) v = Vector(w) # A Vector of (word, TF-IDF weight)-items, copy as document vector. elif isinstance(string, Vector) and string.weight == TF_IDF: w = string w = kwargs["dict"](w) # XXX term count is lost. v = Vector(w) # A dict of (word, count)-items, make read-only. elif isinstance(string, dict): w = string w = kwargs["dict"](w) v = None # pattern.en.Sentence with Word objects, can use stemmer=LEMMA. elif string.__class__.__name__ == "Sentence": w = string.words w = [w for w in w if kwargs["filter"](w.string)] w = count(w, **kwargs) v = None # pattern.en.Text with Sentence objects, can use stemmer=LEMMA. elif string.__class__.__name__ == "Text": w = []; [w.extend(sentence.words) for sentence in string] w = [w for w in w if kwargs["filter"](w.string)] w = count(w, **kwargs) v = None else: raise TypeError, "document string is not str, unicode, list, Vector, Sentence or Text." self._id = _uid() # Document ID, used when comparing objects. self._name = kwargs.get("name") # Name that describes the document content. self._type = kwargs.get("type") # Type that describes the category or class of the document. self._terms = w # Dictionary of (word, count)-items. self._vector = v # Cached tf-idf vector. self._count = None # Total number of words (minus stop words). self._corpus = None # Corpus this document belongs to. @classmethod def open(cls, path, *args, **kwargs): """ Creates and returns a new document from the given text file path. """ s = codecs.open(path, encoding=kwargs.get("encoding", "utf-8")).read() return cls(s, *args, **kwargs) @classmethod def load(cls, path): """ Returns a new Document from the given text file path. The given text file must be generated with Document.save(). """ # Open unicode file. s = open(path, "rb").read() s = s.lstrip(codecs.BOM_UTF8) s = decode_utf8(s) a = {} v = {} # Parse document name and type. # Parse document terms and frequency. for s in s.splitlines(): if s.startswith("#"): # comment pass elif s.startswith("@name:"): a["name"] = s[len("@name:")+1:].replace("\\n", "\n") elif s.startswith("@type:"): a["type"] = s[len("@type:")+1:].replace("\\n", "\n") else: s = s.split(" ") w, f = " ".join(s[:-1]), s[-1] if f.isdigit(): v[w] = int(f) else: v[w] = float(f) return cls(v, name=a.get("name"), type=a.get("type")) def save(self, path): """ Saves the terms in the document as a text file at the given path. The advantage is that terms no longer need to be filtered or stemmed in Document.load(). """ s = [] # Parse document name and type. for k, v in (("@name:", self.name), ("@type:", self.type)): if v is not None: s.append("%s %s" % (k, v.replace("\n", "\\n"))) # Parse document terms and frequency. for w, f in sorted(self.terms.items()): if isinstance(f, int): s.append("%s %i" % (w, f)) if isinstance(f, float): s.append("%s %.3f" % (w, f)) s = "\n".join(s) s = encode_utf8(s) # Save unicode file. f = open(path, "wb") f.write(codecs.BOM_UTF8) f.write(s) f.close() def _get_corpus(self): return self._corpus def _set_corpus(self, corpus): self._vector = None self._corpus and self._corpus._update() self._corpus = corpus self._corpus and self._corpus._update() corpus = property(_get_corpus, _set_corpus) @property def id(self): return self._id @property def name(self): return self._name @property def type(self): return self._type @property def terms(self): return self._terms words = terms @property def features(self): return self._terms.keys() @property def count(self): # Yields the number of words (excluding stop words) in the document. # Cache the word count so we can reuse it when calculating tf. if not self._count: self._count = sum(self.terms.values()) return self._count def __len__(self): return len(self.terms) def __iter__(self): return iter(self.terms) def __contains__(self, word): return word in self.terms def __getitem__(self, word): return self.terms.__getitem__(word) def get(self, word, default=None): return self.terms.get(word, default) def term_frequency(self, word): """ Returns the term frequency of a word in the document. tf = number of occurences of the word / number of words in document. The more occurences of the word, the higher its tf weight. """ return float(self.terms.get(word, 0)) / (self.count or 1) tf = term_frequency def term_frequency_inverse_document_frequency(self, word, weight=TFIDF): """ Returns the word relevancy as tf*idf. The relevancy is a measure of how frequent the word occurs in the document, compared to its frequency in other documents in the corpus. If the document is not part of a corpus, returns tf weight. """ w = self.tf(word) if weight == TFIDF: # Use tf if no corpus, or idf==None (happens when the word is not in the corpus). w *= self.corpus and self.corpus.idf(word) or 1 return w tf_idf = tfidf = term_frequency_inverse_document_frequency @property def vector(self): """ Yields a dictionary of (word, relevancy)-items from the document, based on tf-idf. """ if not self._vector: # See the Vector class below = a dict with extra functionality (copy, norm). # Corpus.weight (TFIDF or TF) determines how the weights will be calculated. # When a document is added/deleted from a corpus, the cached vector is deleted. if getattr(self.corpus, "weight", TF) == TFIDF: w, f = TFIDF, self.tf_idf else: w, f = TF, self.tf self._vector = Vector(((w, f(w)) for w in self.terms), weight=w) return self._vector def keywords(self, top=10, normalized=True): """ Returns a sorted list of (relevancy, word)-tuples that are top keywords in the document. With normalized=True, weights are normalized between 0.0 and 1.0 (their sum will be 1.0). """ n = normalized and sum(self.vector.itervalues()) or 1.0 v = ((f/n, w) for w, f in self.vector.iteritems()) v = heapq.nsmallest(top, v, key=lambda v: (-v[0], v[1])) return v def cosine_similarity(self, document): """ Returns the similarity between the two documents as a number between 0.0-1.0. If both documents are in the same corpus the calculations are cached for reuse. """ if self.corpus: return self.corpus.cosine_similarity(self, document) if document.corpus: return document.corpus.cosine_similarity(self, document) # Merge terms to ensure that the values are in the same order: W = set(self.terms.keys()) | set(document.terms.keys()) v1 = [self.terms.get(w, 0) for w in W] v2 = [document.terms.get(w, 0) for w in W] return cosine_similarity(v1, v2) similarity = cosine_similarity def copy(self): d = Document(name=self.name, type=self.type); dict.update(d.terms, self.terms); return d def __eq__(self, document): return isinstance(document, Document) and self.id == document.id def __ne__(self, document): return not self.__eq__(document) def __repr__(self): return "Document(id=%s%s)" % ( repr(self._id), self.name and ", name=%s" % repr(self.name) or "") #--- VECTOR ---------------------------------------------------------------------------------------- # Document vector, using a sparse representation (i.e., dictionary with only features > 0). # Sparse representation is fast, usually even faster than LSA, # since LSA creates a dense vector with non-zero values. # Average feature length: # sum(len(d.vector) for d in corpus.documents) / float(len(corpus)) class Vector(readonlydict): id = 1 def __init__(self, *args, **kwargs): """ Vector is a dictionary of (word, weight)-items based on the terms in a Document. """ self.id = Vector.id; Vector.id+=1 self.weight = kwargs.pop("weight", TFIDF) # Vector weights based on tf or tf-idf? self._norm = None readonlydict.__init__(self, *args, **kwargs) @property def features(self): return self.keys() @property def l2_norm(self): """ Yields the Frobenius matrix norm. n = the square root of the sum of the absolute squares of the values. The matrix norm is used to normalize (0.0-1.0) cosine similarity between documents. """ if not self._norm: self._norm = l2_norm(self.itervalues()) return self._norm norm = frobenius_norm = l2_norm def copy(self): return Vector(self, weight=self.weight) def __call__(self, vector={}): if isinstance(vector, (Document, Corpus)): vector = vector.vector # Return a copy of the vector, updated with values from the other vector. # Only keys that appear in this vector will be updated (i.e. no new keys are added). V = self.copy(); dict.update(V, ((k, v) for k, v in vector.iteritems() if k in V)); return V # These functions are useful if you work with a bare matrix instead of Document and Corpus. # Given vectors must be lists of values (not iterators). def tf_idf(vectors): idf = [] for i in range(len(vectors[0])): idf.append(log(len(vectors) / (sum(1.0 for v in vectors if v[i]) or 1))) return [[v[i] * idf[i] for i in range(len(v))] for v in vectors] def cosine_similarity(vector1, vector2): return sum(a*b for a, b in izip(vector1, vector2)) / (l2_norm(vector1) * l2_norm(vector2) or 1) def l2_norm(vector): return sum(x**2 for x in vector) ** 0.5 def entropy(vector): s = float(sum(vector)) or 1 return -sum(x/s * log(x/s, 2) for x in vector if x != 0) #### CORPUS ######################################################################################## #--- CORPUS ---------------------------------------------------------------------------------------- # Export formats: ORANGE, WEKA = "orange", "weka" # LSA reduction methods: NORM, L1, L2, TOP300 = "norm", "L1", "L2", "top300" # Feature selection methods: IG = INFOGAIN = "infogain" KLD = "kullback-leibler" # Clustering methods: KMEANS, HIERARCHICAL, ALL = "k-means", "hierarchical", "all" class Corpus(object): def __init__(self, documents=[], weight=TFIDF): """ A corpus is a collection of documents, where each document is a bag of (word, count)-items. Documents can be compared for similarity. """ self.description = "" # Description of the dataset, author e-mail, etc. self._documents = readonlylist() # List of documents (read-only). self._index = {} # Document.name => Document self._df = {} # Cache of document frequency per word. self._similarity = {} # Cache of ((D1.id,D2.id), weight)-items (cosine similarity). self._divergence = {} # Cache of Kullback-leibler divergence per (word1, word2). self._ig = {} # Cache of (word, information gain)-items. self._vector = None # Cache of corpus vector with all the words in the corpus. self._lsa = None # LSA matrix with reduced dimensionality. self._weight = weight # Weight used in Document.vector (TF-IDF or TF). self._update() self.extend(documents) @property def documents(self): return self._documents @property def terms(self): return self.vector.keys() features = words = terms @property def classes(self): return list(set(d.type for d in self.documents)) def _get_lsa(self): return self._lsa def _set_lsa(self, v=None): self._lsa = v self._update() lsa = property(_get_lsa, _set_lsa) def _get_weight(self): return self._weight def _set_weight(self, w): self._weight = w self._update() # Clear the cache. weight = property(_get_weight, _set_weight) @classmethod def build(cls, path, *args, **kwargs): """ Builds the corpus from a folder of text documents (e.g. path="folder/*.txt"). Each file is split into words and the words are counted. """ name = kwargs.pop("name", lambda path: None) documents = [] for f in glob.glob(path): documents.append(Document.open(f, *args, **kwargs)) documents[-1]._name = name(f) return cls(documents) @classmethod def load(cls, path): """ Loads the corpus from a pickle file created with Corpus.save(). """ return cPickle.load(open(path)) def save(self, path, update=False): """ Saves the corpus as a pickle file at the given path. It can be loaded with Corpus.load(). This is faster because the words in the documents do not need to be stemmed again, and cached vectors and similarities are stored """ if update: for d1 in self.documents: for d2 in self.documents: self.cosine_similarity(d1, d2) # Update the entire cache before saving. m = dict.fromkeys((d.id for d in self.documents), True) for id1, id2 in self._similarity.keys(): if id1 not in m \ or id2 not in m: self._similarity.pop((id1, id2)) # Remove Corpus.search() query cache. cPickle.dump(self, open(path, "w"), BINARY) def export(self, path, format=ORANGE, **kwargs): """ Exports the corpus as a file for other machine learning applications, e.g., Orange or Weka which both have a GUI and are faster. """ # Note: the Document.vector space is exported without cache or LSA concept space. keys = sorted(self.vector.keys()) s = [] # Orange tab format: if format == ORANGE: s.append("\t".join(keys + ["m#name", "c#type"])) for document in self.documents: v = document.vector v = [v.get(k, 0) for k in keys] v = "\t".join(x==0 and "0" or "%.4f" % x for x in v) v = "%s\t%s\t%s" % (v, document.name or "", document.type or "") s.append(v) # Weka ARFF format: if format == WEKA: s.append("@RELATION %s" % kwargs.get("name", hash(self))) s.append("\n".join("@ATTRIBUTE %s NUMERIC" % k for k in keys)) s.append("@ATTRIBUTE class {%s}" % ",".join(set(d.type or "" for d in self.documents))) s.append("@DATA") for document in self.documents: v = document.vector v = [v.get(k, 0) for k in keys] v = ",".join(x==0 and "0" or "%.4f" % x for x in v) v = "%s,%s" % (v, document.type or "") s.append(v) s = "\n".join(s) f = open(path, "w", encoding="utf-8") f.write(decode_utf8(s)) f.close() def _update(self): # Ensures that all document relevancy vectors are recalculated # when a document is added or deleted in the corpus (= new words or less words). self._df = {} self._similarity = {} self._divergence = {} self._ig = {} self._vector = None self._lsa = None for document in self.documents: document._vector = None def __len__(self): return len(self.documents) def __iter__(self): return iter(self.documents) def __getitem__(self, i): return self.documents.__getitem__(i) def __delitem__(self, i): d = list.pop(self.documents, i) d._corpus = None self._index.pop(d.name, None) self._update() def clear(self): self._documents = readonlylist() self._update() def append(self, document): """ Appends the given Document to the corpus, setting the corpus as its parent. The corpus is updated, meaning that the cache of vectors and similarities is cleared (relevancy and similarity weights will be different now that there is a new document). """ if not isinstance(document, Document): raise TypeError, "Corpus.append() expects a Document." document._corpus = self if document.name is not None: self._index[document.name] = document list.append(self.documents, document) self._update() def extend(self, documents): """ Extends the corpus with the given list of documents. Clears the cache of vectors and similarities. """ for document in documents: document._corpus = self if document.name is not None: self._index[document.name] = document list.extend(self.documents, documents) self._update() def remove(self, document): """ Removes the given Document from the corpus (sets Document.corpus=None). """ self.__delitem__(self.documents.index(document)) def document(self, name): """ Returns the Document with the given name. """ # This assumes document names are unique. if name in self._index: return self._index[name] def document_frequency(self, word): """ Returns the document frequency of a word. Returns 0 if there are no documents in the corpus (e.g. no word frequency). df = number of documents containing the word / number of documents. The more occurences of the word across the corpus, the higher its df weight. """ if len(self.documents) == 0: return 0 if len(self._df) == 0: # Caching document frequency for each word gives a 300x performance boost # (calculate all of them once). Drawback: if you need TF-IDF for just one document. for d in self.documents: for w in d.terms: self._df[w] = (w in self._df) and self._df[w]+1 or 1 for w in self._df: self._df[w] /= float(len(self.documents)) return self._df.get(word, 0.0) df = document_frequency def inverse_document_frequency(self, word): """ Returns the inverse document frequency of a word. Returns None if the word is not in the corpus, or if there are no documents in the corpus. Using the natural logarithm: idf = log(1/df) The more occurences of the word, the lower its idf weight (log() makes it grow slowly). """ df = self.df(word) return df != 0 and log(1.0/df) or None idf = inverse_document_frequency @property def vector(self): """ Returns a dictionary of (word, 0)-items from the corpus. It includes all words from all documents (i.e. it is the dimension of the vector space). If a document is given, sets the document word relevancy values in the vector. """ # Note: # - Corpus.vector is the dictionary of all (word, 0)-items. # - Corpus.vector(document) returns a copy with the document's word relevancy values in it. # - This is the full document vector, opposed to the sparse Document.vector. # Words in a document that are not in the corpus vector are ignored # (e.g. the document was not in the corpus, this can be the case in Corpus.search() for example). # See Vector.__call__() why this is possible. if not self._vector: self._vector = Vector((w, 0) for w in chain(*(d.terms for d in self.documents))) return self._vector @property def vectors(self): """ Yields a list of all document vectors. """ return [d.vector for d in self.documents] @property def density(self): """ Yields the overall word coverage as a number between 0.0-1.0. """ return float(sum(len(d.vector) for d in self.documents)) / len(self.vector)**2 # Following methods rely on Document.vector: # frequent sets, cosine similarity, nearest neighbors, search, clustering, # latent semantic analysis, divergence. def frequent_concept_sets(self, threshold=0.5): """ Returns a dictionary of (set(words), frequency) of word combinations with a frequency above the given threshold. """ return apriori([d.terms for d in self.documents], support=threshold) sets = frequent = frequent_concept_sets def cosine_similarity(self, document1, document2): """ Returns the similarity between two documents in the corpus as a number between 0.0-1.0. The weight is based on the document relevancy vectors (i.e. tf-idf of words in the text). cos = dot(v1,v2) / (norm(v1) * norm(v2)) """ # If we already calculated the similarity between the given documents, # it is available in cache for reuse. id1 = document1.id id2 = document2.id if (id1, id2) in self._similarity: return self._similarity[(id1, id2)] if (id2, id1) in self._similarity: return self._similarity[(id2, id1)] # Calculate the matrix multiplication of the document vectors. #v1 = self.vector(document1) #v2 = self.vector(document2) #s = cosine_similarity(v1.itervalues(), v2.itervalues()) / (v1.norm * v2.norm or 1) if not getattr(self, "lsa", None): # This is exponentially faster for sparse vectors: v1 = document1.vector v2 = document2.vector s = sum(v1.get(w, 0) * f for w, f in v2.iteritems()) / (v1.norm * v2.norm or 1) else: # Using LSA concept space: v1 = id1 in self.lsa and self.lsa[id1] or self._lsa.transform(document1) v2 = id2 in self.lsa and self.lsa[id2] or self._lsa.transform(document2) s = sum(a*b for a, b in izip(v1.itervalues(), v2.itervalues())) / (v1.norm * v2.norm or 1) # Cache the similarity weight for reuse. self._similarity[(id1, id2)] = s return s similarity = cosine_similarity def nearest_neighbors(self, document, top=10): """ Returns a list of (weight, document)-tuples in the corpus, sorted by similarity to the given document. """ v = ((self.cosine_similarity(document, d), d) for d in self.documents) # Filter the input document from the matches. # Filter documents that scored 0 and return the top. v = [(w, d) for w, d in v if w > 0 and d.id != document.id] v = heapq.nsmallest(top, v, key=lambda v: (-v[0],v[1])) return v similar = related = neighbors = nn = nearest_neighbors def vector_space_search(self, words=[], **kwargs): """ Returns related documents from the corpus, as a list of (weight, document)-tuples. The given words can be a string (one word), a list or tuple of words, or a Document. """ top = kwargs.pop("top", 10) if not isinstance(words, (list, tuple, Document)): words = [words] if not isinstance(words, Document): kwargs.setdefault("threshold", 0) # Same stemmer as other documents should be given. words = Document(" ".join(words), **kwargs) words._corpus = self # So we can calculate tf-idf. # Documents that are not in the corpus consisting only of words that are not in the corpus # have no related documents in the corpus. if len([True for w in words if w in self.vector]) == 0: return [] return self.nearest_neighbors(words, top) search = vector_space_search def distance(self, document1, document2, *args, **kwargs): """ Returns the distance (COSINE, EUCLIDEAN, ...) between two document vectors (0.0-1.0). """ return distance(document1.vector, document2.vector, *args, **kwargs) def cluster(self, documents=ALL, method=KMEANS, **kwargs): """ Clustering is an unsupervised machine learning method for grouping similar documents. For k-means clustering, returns a list of k clusters (each is a list of documents). For hierarchical clustering, returns a list of documents and Cluster objects. A Cluster is a list of documents and other clusters, with a Cluster.flatten() method. """ if documents == ALL: documents = self.documents if not getattr(self, "lsa", None): # Using document vectors: vectors, keys = [d.vector for d in documents], self.vector.keys() else: # Using LSA concept space: vectors, keys = [self.lsa[d.id] for d in documents], range(len(self.lsa)) # Create a dictionary of vector.id => Document. # We'll need it to map the clustered vectors back to the actual documents. map = dict((v.id, documents[i]) for i, v in enumerate(vectors)) kw = kwargs.pop if method in (KMEANS, "kmeans"): # def cluster(self, method=KMEANS, k=10, iterations=10) clusters = k_means(vectors, kw("k", 10), kw("iterations", 10), keys=keys, **kwargs) clusters = [[map[v.id] for v in cluster] for cluster in clusters] if method == HIERARCHICAL: # def cluster(self, method=HIERARCHICAL, k=1, iterations=1000) clusters = hierarchical(vectors, kw("k", 1), kw("iterations", 1000), keys=keys, **kwargs) clusters.traverse(visit=lambda cluster: \ [cluster.__setitem__(i, map[v.id]) for i, v in enumerate(cluster) if not isinstance(v, Cluster)]) return clusters def latent_semantic_analysis(self, dimensions=NORM): """ Creates LSA concept vectors by reducing dimensions. The concept vectors are then used in Corpus.cosine_similarity() and Corpus.cluster(). The reduction can be undone by setting Corpus.lsa=False. """ self._lsa = LSA(self, k=dimensions) self._similarity = {} reduce = latent_semantic_analysis def information_gain(self, word): """ Returns the information gain for the given feature, by looking at how much it contributes to each document type (class). High IG means low entropy (predictability), e.g., interesting for feature selection. """ if not self._ig: # Based on <NAME>, http://www.clips.ua.ac.be/~vincent/scripts/textgain.py # For classes {xi...xn} and features {yi...yn}: # IG(X,Y) = H(X) - H(X|Y) # H(X) = -sum(p(x) * log2(x) for x in X) # H(X|Y) = sum(p(y) * H(X|Y=y) for y in Y) # H(X|Y=y) = -sum(p(x) * log2(x) for x in X if y in x) # H is the entropy for a list of probabilities. # Lower entropy indicates predictability, i.e., some values are more probable. # H([0.50,0.50]) = 1.00 # H([0.75,0.25]) = 0.81 H = entropy # X = document type (class) distribution. # "How many documents have class xi?" X = dict.fromkeys(self.classes, 0) for d in self.documents: X[d.type] += 1 # Y = document feature distribution. # "How many documents have feature yi?" Y = dict.fromkeys(self.features, 0) for d in self.documents: for y, v in d.vector.items(): if v > 0: Y[y] += 1 # Discrete: feature is present (1) or not (0). Y = dict((y, Y[y] / float(len(self.documents))) for y in Y) # XY = features by class distribution. # "How many documents of class xi have feature yi?" XY = dict.fromkeys(self.features, {}) for d in self.documents: for y, v in d.vector.items(): if v != 0: XY[y][d.type] = XY[y].get(d.type, 0) + 1 # IG. for y in self.features: self._ig[y] = H(X.values()) - Y[y] * H(XY[y].values()) return self._ig[word] IG = ig = infogain = gain = information_gain def kullback_leibler_divergence(self, word1, word2, cached=True, _vectors=[], _map={}): """ Returns the difference between two given features (i.e. words from Corpus.terms), on average over all document vectors, using symmetric Kullback-Leibler divergence. Higher values represent more distinct features. """ if not (word1, word2) in self._divergence: kl1 = 0 kl2 = 0 # It is not log() that is "slow", but the document.vector getter and dict.__contains__(). # If you use KLD in a loop, collect the vectors once and pass them to _vectors (2x faster), # or pass a (word, vectors-that-contain-word) dictionary to _map (7x faster). for v in _map.get(word1) or _vectors or (d.vector for d in self.documents): if word1 in v: kl1 += v[word1] * (log(v[word1], 2) - log(v.get(word2, 0.000001), 2)) for v in _map.get(word2) or _vectors or (d.vector for d in self.documents): if word2 in v: kl2 += v[word2] * (log(v[word2], 2) - log(v.get(word1, 0.000001), 2)) # Cache the calculations for reuse. # This is not always possible, e.g., 10,000 features = 5GB cache. # The measurement is symmetric, so we also know KL(word2, word1). if cached is True: self._divergence[(word1, word2)] = \ self._divergence[(word2, word1)] = (kl1 + kl2) / 2 return self._divergence[(word1, word2)] relative_entropy = kl = kld = kullback_leibler_divergence def feature_selection(self, top=100, method=INFOGAIN, verbose=False): """ Returns the top unpredictable ("original") features (terms), using information gain. This is a subset of Corpus.terms that can be used to build a Classifier that is faster (less features = less matrix columns) but quite efficient. """ if method == IG: subset = ((self.information_gain(w), w) for w in self.terms) subset = sorted(subset, key=itemgetter(1)) subset = sorted(subset, key=itemgetter(0), reverse=True) subset = [w for ig, w in subset[:top]] return subset if method == KLD: v = [d.vector for d in self.documents] m = dict((w, filter(lambda v: w in v, v)) for w in self.terms) D = {} for i, w1 in enumerate(self.terms): for j, w2 in enumerate(self.terms[i+1:]): d = self.kullback_leibler_divergence(w1, w2, _vectors=v, _map=m) D[w1] = w1 in D and D[w1]+d or d D[w2] = w2 in D and D[w2]+d or d if verbose: # kullback-leibler 80.0% (550/700) print method + " " + ("%.1f"%(float(i) / len(self.terms) * 100)).rjust(4) + "% " \ + "(%s/%s)" % (i+1, len(self.terms)) subset = sorted(((d, w) for w, d in D.iteritems()), reverse=True) subset = [w for d, w in subset[:top]] return subset def filter(self, features=[]): """ Returns a new Corpus with documents only containing the given list of words, for example a subset returned from Corpus.feature_selection(). """ features = dict.fromkeys(features, True) corpus = Corpus(weight=self.weight) corpus.extend([ Document(dict((w, f) for w, f in d.terms.iteritems() if w in features), name = d.name, type = d.type) for d in self.documents]) return corpus #### FREQUENT CONCEPT SETS ######################################################################### # <NAME>. & <NAME>. (1994), Fast algorithms for mining association rules in large databases. # Based on: https://gist.github.com/1423287 class Apriori: def __init__(self): self._candidates = [] self._support = {} def C1(self, sets): """ Returns the unique words from all sets as a list of (hashable) frozensets. """ return [frozenset([v]) for v in set(chain(*sets))] def Ck(self, sets): """ For the given sets of length k, returns combined candidate sets of length k+1. """ Ck = [] for i, s1 in enumerate(sets): for j, s2 in enumerate(sets[i+1:]): if set(list(s1)[:-1]) == set(list(s2)[:-1]): Ck.append(s1 | s2) return Ck def Lk(self, sets, candidates, support=0.0): """ Prunes candidate sets whose frequency < support threshold. Returns a dictionary of (candidate set, frequency)-items. """ Lk, x = {}, 1.0 / (len(sets) or 1) # relative count for s1 in candidates: for s2 in sets: if s1.issubset(s2): Lk[s1] = s1 in Lk and Lk[s1]+x or x return dict((s, f) for s, f in Lk.items() if f >= support) def __call__(self, sets, support=0.5): """ Returns a dictionary of (set(features), frequency)-items. The given support (0.0-1.0) is the relative amount of documents in which a combination of word must appear. """ C1 = self.C1(sets) L1 = self.Lk(sets, C1, support) self._candidates = [L1.keys()] self._support = L1 while True: # Terminate when no further extensions are found. if len(self._candidates[-1]) == 0: break # Extend frequent subsets one item at a time. Ck = self.Ck(self._candidates[-1]) Lk = self.Lk(sets, Ck, support) self._candidates.append(Lk.keys()) self._support.update(Lk) return self._support apriori = Apriori() #### LATENT SEMANTIC ANALYSIS ###################################################################### # Based on: # http://en.wikipedia.org/wiki/Latent_semantic_analysis # http://blog.josephwilk.net/projects/latent-semantic-analysis-in-python.html class LSA: def __init__(self, corpus, k=NORM): """ Latent Semantic Analysis is a statistical machine learning method based on singular value decomposition (SVD). Related terms in the corpus are grouped into "concepts". Documents then get a concept vector that is an approximation of the original vector, but with reduced dimensionality so that cosine similarity and clustering run faster. """ import numpy matrix = [corpus.vector(d).values() for d in corpus.documents] matrix = numpy.array(matrix) # Singular value decomposition, where u * sigma * vt = svd(matrix). # Sigma is the diagonal matrix of singular values, # u has document rows and concept columns, vt has concept rows and term columns. u, sigma, vt = numpy.linalg.svd(matrix, full_matrices=False) # Delete the smallest coefficients in the diagonal matrix (i.e., at the end of the list). # The difficulty and weakness of LSA is knowing how many dimensions to reduce # (generally L2-norm is used). if k == L1: k = int(round(numpy.linalg.norm(sigma, 1))) if k == L2 or k == NORM: k = int(round(numpy.linalg.norm(sigma, 2))) if k == TOP300: k = max(0, len(sigma) - 300) if isinstance(k, int): k = max(0, len(sigma) - k) if type(k).__name__ == "function": k = max(0, int(k(sigma))) #print numpy.dot(u, numpy.dot(numpy.diag(sigma), vt)) # Apply dimension reduction. # The maximum length of a concept vector = the number of documents. assert k < len(corpus.documents), \ "can't create more dimensions than there are documents" tail = lambda list, i: range(len(list)-i, len(list)) u, sigma, vt = ( numpy.delete(u, tail(u[0], k), axis=1), numpy.delete(sigma, tail(sigma, k), axis=0), numpy.delete(vt, tail(vt, k), axis=0) ) # Store as Python dict and lists so we can cPickle it. self.corpus = corpus self._terms = dict(enumerate(corpus.vector().keys())) # Vt-index => word. self.u, self.sigma, self.vt = ( dict((d.id, Vector((i, float(x)) for i, x in enumerate(v))) for d, v in izip(corpus, u)), list(sigma), [[float(x) for x in v] for v in vt] ) @property def terms(self): # Yields a list of all words, identical to LSA.corpus.vector.keys() return self._terms.values() features = terms @property def concepts(self): """ Yields a list of all concepts, each a dictionary of (word, weight)-items. """ # Round the weight so 9.0649330400000009e-17 becomes a more meaningful 0.0. return [dict((self._terms[i], round(w, 15)) for i, w in enumerate(concept)) for concept in self.vt] @property def vectors(self): """ Yields a dictionary of (Document.id, concepts), where each concept is a dictionary of (concept_index, weight)-items. """ return self.u def __getitem__(self, id): return self.u[id] def __contains__(self, id): return id in self.u def __iter__(self): return iter(self.u) def __len__(self): return len(self.u) def transform(self, document): """ Given a document not in the corpus, returns a vector in LSA concept space. """ if document.id in self.u: return self.u[document.id] if document.id in _lsa_transform_cache: return _lsa_transform_cache[document.id] import numpy v = self.corpus.vector(document) v = [v[self._terms[i]] for i in range(len(v))] v = numpy.dot(numpy.dot(numpy.linalg.inv(numpy.diag(self.sigma)), self.vt), v) v = _lsa_transform_cache[document.id] = Vector(enumerate(v)) return v # LSA cache for Corpus.search() shouldn't be stored with Corpus.save(), # so it is a global variable instead of a property of the LSA class. _lsa_transform_cache = {} #def iter2array(iterator, typecode): # a = numpy.array([iterator.next()], typecode) # shape0 = a.shape[1:] # for (i, item) in enumerate(iterator): # a.resize((i+2,) + shape0) # a[i+1] = item # return a #def filter(matrix, min=0): # columns = numpy.max(matrix, axis=0) # columns = [i for i, v in enumerate(columns) if v <= min] # Indices of removed columns. # matrix = numpy.delete(matrix, columns, axis=1) # return matrix, columns #### CLUSTERING #################################################################################### # Clustering assigns vectors to subsets based on a distance measure, # which determines how "similar" two vectors are. # For example, for (x,y) coordinates in 2D we could use Euclidean distance ("as the crow flies"); # for document vectors we could use cosine similarity. def features(vectors): """ Returns a set of unique keys from all the given Vectors. """ return set(k for k in chain(*vectors)) def mean(iterator, length): """ Returns the arithmetic mean of the values in the given iterator. """ return sum(iterator) / float(length or 1) def centroid(vectors, keys=[]): """ Returns the center of the list of vectors (e.g., the geometric center of a list of (x,y)-coordinates forming a polygon). Since vectors are sparse, the list of all keys (=Corpus.vector) must be given. """ c = [] for v in vectors: if isinstance(v, Cluster): c.extend(v.flatten()) elif isinstance(v, Document): c.append(v.vector) else: c.append(v) if not keys: keys = features(c) c = [(k, mean((v.get(k, 0) for v in c), len(c))) for k in keys] c = Vector((k, v) for k, v in c if v != 0) return c COSINE, EUCLIDEAN, MANHATTAN, HAMMING = \ "cosine", "euclidean", "manhattan", "hamming" def distance(v1, v2, method=COSINE): """ Returns the distance between two vectors. """ if method == COSINE: return 1 - sum(v1.get(w,0) * f for w, f in v2.iteritems()) / (v1.norm * v2.norm or 1.0) if method == EUCLIDEAN: # Squared distance is 1.5x faster. return sum((v1.get(w,0) - v2.get(w,0))**2 for w in set(chain(v1, v2))) if method == MANHATTAN: return sum(abs(v1.get(w,0) - v2.get(w,0)) for w in set(chain(v1, v2))) if method == HAMMING: d = sum(not (w in v1 and w in v2 and v1[w] == v2[w]) for w in set(chain(v1, v2))) d = d / float(max(len(v1), len(v2)) or 1) return d if isinstance(method, type(distance)): # Given method is a function of the form: distance(v1, v2) => float. return method(v1, v2) _distance = distance class DistanceMap: def __init__(self, method=COSINE): """ A lazy map of cached distances between vectors. """ self.method = method self._cache = {} def distance(self, v1, v2): """ Returns the cached distance between two vectors. """ try: d = self._cache[(v1.id, v2.id)] except KeyError: d = self._cache[(v1.id, v2.id)] = distance(v1, v2, method=self.method) return d #--- K-MEANS --------------------------------------------------------------------------------------- # Fast, no guarantee of convergence or optimal solution (random starting clusters). # 3000 vectors with 100 features (LSA, density 1.0): 1 minute with k=100 (20 iterations). # 3000 vectors with 200 features (LSA, density 1.0): 3 minutes with k=100 (20 iterations). # Initialization methods: RANDOM, KMPP = "random", "kmeans++" def k_means(vectors, k=None, iterations=10, distance=COSINE, **kwargs): """ Returns a list of k clusters, where each cluster is a list of similar vectors (Lloyd's algorithm). There is no guarantee of convergence or optimal solution. """ init = kwargs.get("seed", kwargs.get("initialization", RANDOM)) keys = kwargs.get("keys") or list(features(vectors)) if k is None: k = sqrt(len(vectors) / 2) if k < 2: return [[v for v in vectors]] if init == KMPP: clusters = kmpp(vectors, k, distance) else: clusters = [[] for i in xrange(int(k))] for i, v in enumerate(sorted(vectors, key=lambda x: random())): # Randomly partition the vectors across k clusters. clusters[i % int(k)].append(v) # Cache the distance calculations between vectors (4x faster). map = DistanceMap(method=distance); distance = map.distance converged = False while not converged and iterations > 0 and k > 0: # Calculate the center of each cluster. centroids = [centroid(cluster, keys) for cluster in clusters] # Triangle inequality: one side is shorter than the sum of the two other sides. # We can exploit this to avoid costly distance() calls (up to 3x faster). p = 0.5 * kwargs.get("p", 0.8) # "Relaxed" triangle inequality (cosine distance is a semimetric) 0.25-0.5. D = {} for i in range(len(centroids)): for j in range(i, len(centroids)): # center1–center2 < center1–vector + vector–center2 ? D[(i,j)] = D[(j,i)] = p * distance(centroids[i], centroids[j]) # For every vector in every cluster, # check if it is nearer to the center of another cluster (if so, assign it). # When visualized, this produces a Voronoi diagram. converged = True for i in xrange(len(clusters)): for v in clusters[i]: nearest, d1 = i, distance(v, centroids[i]) for j in xrange(len(clusters)): if D[(i,j)] < d1: # Triangle inequality (Elkan, 2003). d2 = distance(v, centroids[j]) if d2 < d1: nearest = j if nearest != i: # Other cluster is nearer. clusters[nearest].append(clusters[i].pop(clusters[i].index(v))) converged = False iterations -= 1; #print iterations return clusters kmeans = k_means def kmpp(vectors, k, distance=COSINE): """ The k-means++ initialization algorithm, with the advantage that: - it generates better clusterings than standard k-means (RANDOM) on virtually all data sets, - it runs faster than standard k-means on average, - it has a theoretical approximation guarantee. """ # Cache the distance calculations between vectors (4x faster). map = DistanceMap(method=distance); distance = map.distance # <NAME>, 2006, http://theory.stanford.edu/~sergei/slides/BATS-Means.pdf # Based on: # http://www.stanford.edu/~darthur/kmpp.zip # http://yongsun.me/2008/10/k-means-and-k-means-with-python # Choose one center at random. # Calculate the distance between each vector and the nearest center. centroids = [choice(vectors)] d = [distance(v, centroids[0]) for v in vectors] s = sum(d) for _ in range(int(k) - 1): # Choose a random number y between 0 and d1 + d2 + ... + dn. # Find vector i so that: d1 + d2 + ... + di >= y > d1 + d2 + ... + dj. # Perform a number of local tries so that y yields a small distance sum. i = 0 for _ in range(int(2 + log(k))): y = random() * s for i1, v1 in enumerate(vectors): if y <= d[i1]: break y -= d[i1] s1 = sum(min(d[j], distance(v1, v2)) for j, v2 in enumerate(vectors)) if s1 < s: s, i = s1, i1 # Add vector i as a new center. # Repeat until we have chosen k centers. centroids.append(vectors[i]) d = [min(d[i], distance(v, centroids[-1])) for i, v in enumerate(vectors)] s = sum(d) # Assign points to the nearest center. clusters = [[] for i in xrange(int(k))] for v1 in vectors: d = [distance(v1, v2) for v2 in centroids] clusters[d.index(min(d))].append(v1) return clusters #--- HIERARCHICAL ---------------------------------------------------------------------------------- # Slow, optimal solution guaranteed in O(len(vectors)^3). # 100 vectors with 6 features (density 1.0): 0.1 seconds. # 1000 vectors with 6 features (density 1.0): 1 minute. # 3000 vectors with 6 features (density 1.0): 15 minutes. class Cluster(list): def __init__(self, *args, **kwargs): list.__init__(self, *args, **kwargs) @property def depth(self): """ Yields the maximum depth of nested clusters. Cluster((1, Cluster((2, Cluster((3, 4)))))).depth => 2. """ return max([0] + [1+n.depth for n in self if isinstance(n, Cluster)]) def flatten(self, depth=1000): """ Flattens nested clusters to a list, down to the given depth. Cluster((1, Cluster((2, Cluster((3, 4)))))).flatten(1) => [1, 2, Cluster(3, 4)]. """ a = [] for item in self: if isinstance(item, Cluster) and depth > 0: a.extend(item.flatten(depth-1)) else: a.append(item) return a def traverse(self, visit=lambda cluster: None): """ Calls the visit() function on this and each nested cluster. """ visit(self) for item in self: if isinstance(item, Cluster): item.traverse(visit) def __repr__(self): return "Cluster(%s)" % list.__repr__(self)[1:-1] def hierarchical(vectors, k=1, iterations=1000, distance=COSINE, **kwargs): """ Returns a Cluster containing k items (vectors or clusters with nested items). With k=1, the top-level cluster contains a single cluster. """ keys = kwargs.get("keys", list(features(vectors))) clusters = Cluster((v for v in sorted(vectors, key=lambda x: random()))) centroids = [(v.id, v) for v in clusters] map = {} for _ in range(iterations): if len(clusters) <= max(k, 1): break nearest, d0 = None, None for i, (id1, v1) in enumerate(centroids): for j, (id2, v2) in enumerate(centroids[i+1:]): # Cache the distance calculations between vectors. # Code is identical to DistanceMap.distance(), # but it is faster in the inner loop to use it directly. try: d = map[(id1, id2)] except KeyError: d = map[(id1, id2)] = _distance(v1, v2, method=distance) if d0 is None or d < d0: nearest, d0 = (i, j+i+1), d # Pairs of nearest clusters are merged as we move up the hierarchy: i, j = nearest merged = Cluster((clusters[i], clusters[j])) clusters.pop(j) clusters.pop(i) clusters.append(merged) # Cache the center of the new cluster. v = centroid(merged.flatten(), keys) centroids.pop(j) centroids.pop(i) centroids.append((v.id, v)) return clusters #v1 = Vector(wings=0, beak=0, claws=1, paws=1, fur=1) # cat #v2 = Vector(wings=0, beak=0, claws=0, paws=1, fur=1) # dog #v3 = Vector(wings=1, beak=1, claws=1, paws=0, fur=0) # bird #print hierarchical([v1, v2, v3]) #### CLASSIFIER #################################################################################### #--- CLASSIFIER BASE CLASS ------------------------------------------------------------------------- # The baseline (default predicted class) is set to the most frequent class: FREQUENCY = "frequency" class Classifier: def __init__(self, train=[], baseline=FREQUENCY): self._classes = {} self._baseline = baseline # Train on the list of Document objects or (document, type)-tuples: for d in (isinstance(d, Document) and (d, d.type) or d for d in train): self.train(*d) # In Pattern 2.5-, Classifier.test() is a classmethod. # In Pattern 2.6+, it is replaced with Classifier._test() once instantiated. self.test = self._test @property def features(self): """ Yields a list of trained features. """ # Must be implemented in a subclass. return [] @property def classes(self): """ Yields a list of trained classes. """ return self._classes.keys() terms, types = features, classes @property def binary(self): """ Yields True if the classifier predicts either True (0) or False (1). """ return sorted(self.classes) in ([False, True], [0, 1]) @property def distribution(self): """ Yields a dictionary of trained (class, frequency)-items. """ return self._classes.copy() @property def majority(self): """ Yields the majority class (= most frequent class). """ d = sorted((v, k) for k, v in self._classes.iteritems()) return d and d[-1][1] or None @property def minority(self): """ Yields the minority class (= least frequent class). """ d = sorted((v, k) for k, v in self._classes.iteritems()) return d and d[0][1] or None @property def baseline(self): """ Yields the most frequent class in the training data, or a user-defined class if Classifier(baseline != FREQUENCY). """ if self._baseline != FREQUENCY: return self._baseline return ([(0, None)] + sorted([(v, k) for k, v in self._classes.iteritems()]))[-1][1] @property def skewness(self): """ Yields 0.0 if the classes are evenly distributed. Yields > +1.0 or < -1.0 if the training data is highly skewed. """ def moment(a, m, k=1): return sum([(x-m)**k for x in a]) / (len(a) or 1) # List each training instance by an int that represents its class: a = list(chain(*([i] * v for i, (k, v) in enumerate(self._classes.iteritems())))) m = float(sum(a)) / len(a) # mean return moment(a, m, 3) / (moment(a, m, 2) ** 1.5 or 1) def train(self, document, type=None): """ Trains the classifier with the given document of the given type (i.e., class). A document can be a Document object, list or dictionary. If no type is given, Document.type will be used instead. """ # Must be implemented in a subclass. if type is None and isinstance(document, Document): type = document.type if type not in self._classes: self._classes[type] = 0 self._classes[type] += 1 def classify(self, document): """ Returns the type with the highest probability for the given document. """ # Must be implemented in a subclass. return self.baseline def _vector(self, document, type=None): """ Returns a (type, Vector)-tuple for the given document. If the document is part of a LSA-reduced corpus, returns the LSA concept vector. If the given type is None, returns document.type (if a Document is given). """ if isinstance(document, Document): if type is None: type = document.type if document.corpus and document.corpus.lsa: return type, document.corpus.lsa[document.id] # LSA concept vector. return type, document.vector if isinstance(document, dict): return type, Vector(document) if isinstance(document, (list, tuple)): return type, Document(document, filter=None, stopwords=True).vector if isinstance(document, basestring): return type, Document(document, filter=None, stopwords=True).vector @classmethod def k_fold_cross_validation(cls, corpus=[], k=10, **kwargs): # Backwards compatibility. return K_fold_cross_validation(cls, documents=corpus, folds=k, **kwargs) crossvalidate = cross_validate = cv = k_fold_cross_validation @classmethod def test(cls, corpus=[], d=0.65, folds=1, **kwargs): # Backwards compatibility. # In Pattern 2.5-, Classifier.test() is a classmethod. # In Pattern 2.6+, it is replaced with Classifier._test() once instantiated. if folds > 1: return K_fold_cross_validation(cls, documents=corpus, folds=folds, **kwargs) i = int(round(max(0.0, min(1.0, d)) * len(corpus))) d = shuffled(corpus) return cls(train=d[:i]).test(d[i:]) def _test(self, documents=[], **kwargs): """ Returns an (accuracy, precision, recall, F-score)-tuple for the given documents, with values between 0.0 (0%) and 1.0 (100%). For non-binary classifiers, precision, recall and F-score are None. """ documents = [isinstance(d, Document) and (d, d.type) or d for d in documents] TP = 0 # True positives. TN = 0 # True negatives. FP = 0 # False positives (type I error). FN = 0 # False negatives (type II error). if not self.binary: # We can only measure accuracy for multi-label classifiers # (i.e., classifiers that predict classes other than True-False). TP = len([1 for d, type in documents if self.classify(d) == type]) else: # Calculate the confusion matrix to measure precision and recall. for d, b1 in documents: b2 = self.classify(d) if b1 and b2: TP += 1 elif not b1 and not b2: TN += 1 elif not b1 and b2: FP += 1 elif b1 and not b2: FN += 1 #print "%s\t%s\t%s\t%s\t%s\t%s" % (b1, b2, TP, TN, FP, FN) # Calculate accuracy, precision, recall and F1-score. b = self.binary A = float(TP + TN) / ((TP + TN + FP + FN) or 1) P = float(TP) / ((TP + FP) or 1) if b else None R = float(TP) / ((TP + FN) or 1) if b else None F = 2.0 * P * R / ((P + R) or 1) if b else None return A, P, R, F def save(self, path): self.test = None # Can't pickle instancemethods. cPickle.dump(self, open(path, "w"), BINARY) @classmethod def load(cls, path): self = cPickle.load(open(path)) self.test = self._test return self def K_fold_cross_validation(Classifier, documents=[], folds=10, **kwargs): """ For 10-fold cross-validations, performs 10 separate tests of the classifier, each with a different 9/10 training and 1/10 testing documents. The given classifier is a class (Bayes, KNN, SVM) which is initialized with the given optional parameters. """ K = kwargs.pop("K", folds) d = [isinstance(d, Document) and (d, d.type) or d for d in documents] d = shuffled(d) # Avoid a list sorted by type (because we take successive folds). m = [0.0, 0.0, 0.0, 0.0] # mean accuracy | precision | recall | F1-score. for i in range(K): n = len(d) / float(K) # Test fold size. x = int(round(i * n)) # Test fold start index. y = int(round(i * n + n)) # Test fold stop index. classifier = Classifier(train=d[:x]+d[y:], **kwargs) A, P, R, F = classifier.test(d[x:y]) if not classifier.binary: m[0] += A else: m[0] += A m[1] += P m[2] += R m[3] += F if not classifier.binary: return m[0] / (K or 1), None, None, None return tuple([v / (K or 1) for v in m]) K_fold_cv = k_fold_cv = k_fold_cross_validation = K_fold_cross_validation #--- NAIVE BAYES CLASSIFIER ------------------------------------------------------------------------ # Based on: <NAME>, http://hetland.org/coding/python/nbayes.py # We can't include these in the NaiveBayes class description, # because you can't pickle functions: # NBid1: store word index, used with aligned=True # NBid1: ignore word index, used with aligned=False. NBid1 = lambda type, v, i: (type, v, i) NBid2 = lambda type, v, i: (type, v, 1) class NaiveBayes(Classifier): def __init__(self, aligned=False, train=[], baseline=FREQUENCY): """ Naive Bayes is a simple supervised learning method for text classification. For example: if we have a set of documents of movie reviews (training data), and we know the star rating of each document, we can predict the star rating for other movie review documents. With aligned=True, the word index is taken into account when training on lists of words. """ self._aligned = aligned self._classes = {} # Frequency of each class (or type). self._features = {} # Frequency of each feature, as (type, feature, value)-tuples. self._count = 0 # Number of training instances. Classifier.__init__(self, train, baseline) @property def classes(self): return self._classes.keys() @property def features(self): return list(set(k[1] for k in self._features.iterkeys())) def train(self, document, type=None): """ Trains the classifier with the given document of the given type (i.e., class). A document can be a Document object, list or dictionary. If no type is given, Document.type will be used instead. """ id = self._aligned and NBid1 or NBid2 type, vector = self._vector(document, type=type) self._classes[type] = self._classes.get(type, 0) + 1 for i, (w, f) in enumerate(vector.iteritems()): self._features[id(type, w, i)] = self._features.get(id(type, w, i), 0) + f self._count += 1 def classify(self, document): """ Returns the type with the highest probability for the given document. If the training documents come from a LSA-reduced corpus, the given document must be Corpus.lsa.transform(document). """ id = self._aligned and NBid1 or NBid2 def g(document, type): # Bayesian discriminant, proportional to posterior probability. g = 1.0 * self._classes[type] / self._count for i, (w, f) in enumerate(self._vector(document)[1].iteritems()): g /= self._classes[type] g *= self._features.get(id(type, w, i), 0) g *= f return g try: return max((g(document, type), type) for type in self._classes)[1] except ValueError: # max() arg is an empty sequence pass return self.baseline Bayes = NaiveBayes #--- SUPPORT VECTOR MACHINE ------------------------------------------------------------------------ # pattern.vector comes bundled with LIBSVM 3.11. # http://www.csie.ntu.edu.tw/~cjlin/libsvm/ # # Precompiled binaries for 32-bit Windows and Mac OS X, and 64-bit Mac OS X and Ubuntu are included. # - If these don't work, you need to download and compile LIBSVM from source. # - Mac OS X may complain, if so, rename "-soname" to "-install_name" in libsvm/Makefile. # - Put the shared library (i.e., "libsvm.dll", "libsvm.so") in pattern/vector/svm/. # - If the shared library is named "libsvm.so.2", strip the ".2". # SVM type. SVC = CLASSIFICATION = 0 SVR = REGRESSION = 3 SVO = DETECTION = 2 # One-class SVM: X belongs to the class or not? # SVM kernel functions. # The simplest way to divide two clusters is a straight line. # If the clusters are separated by a curved line, # separation may be easier in higher dimensions (using a kernel). LINEAR = 0 # Straight line => u' * v POLYNOMIAL = 1 # Curved line => (gamma * u' * v + coef0) ** degree RADIAL = RBF = 2 # Curved path => exp(-gamma * |u-v| ** 2) class SVM(Classifier): def __init__(self, *args, **kwargs): """ Support Vector Machine is a supervised learning method, where training documents are represented as points in an n-dimensional space. The SVM constructs a number of "hyperplanes" that subdivide the space. Optional parameters include: type=CLASSIFICATION, kernel=LINEAR, degree=3, gamma=1/len(SVM.features), coeff0=0, cost=1, epsilon=0.01, cache=100, probability=False, debug=False """ import svm self._libsvm = svm self._vectors = [] self._model = None if len(args) > 0: kwargs.setdefault("type", args[0]) if len(args) > 1: kwargs.setdefault("kernel", args[1]) for k, v in ( ( "type", CLASSIFICATION), ( "kernel", LINEAR), ( "degree", 3), ( "gamma", 0), ( "coeff0", 0), ( "cost", 1), ( "epsilon", 0.1), ( "nu", 0.5), ( "cache", 100), ("probability", False), ( "debug", False)): setattr(self, k, kwargs.get(k, v)) Classifier.__init__(self, train=kwargs.get("train", []), baseline=FREQUENCY) @property def features(self): return list(features(v for type, v in self._vectors)) @property def support_vectors(self): if self._model is None: self._libsvm_train() return self._model[0].get_SV() sv = support_vectors def _libsvm_train(self): """ Calls libsvm.svm_train() to create a model. Vector classes and features (i.e., words) are mapped to integers. """ M = [v for type, v in self._vectors] # List of vectors. H1 = dict((w, i) for i, w in enumerate(self.features)) # Feature => integer hash. H2 = dict((w, i) for i, w in enumerate(self.classes)) # Class => integer hash. H3 = dict((i, w) for i, w in enumerate(self.classes)) # Class reversed hash. x = [dict((H1[k], v) for k, v in v.items()) for v in M] # Hashed vectors. y = [H2[type] for type, v in self._vectors] # Hashed classes. o = "-s %s -t %s -d %s -g %s -r %s -c %s -p %s -n %s -m %s -b %s %s" % ( self.type, self.kernel, self.degree, self.gamma, self.coeff0, self.cost, self.epsilon, self.nu, self.cache, self.probability is True and 1 or 0, self.debug is False and "-q" or "" ) # Cache the model and the feature hash. # SVM.train() will remove the cached model (since it needs to be retrained). self._model = (self._libsvm.svm_train(y, x, o), H1, H2, H3) def _libsvm_predict(self, document): """ Calls libsvm.svm_predict() with the cached model. For CLASSIFICATION, returns a predicted class. For CLASSIFICATION with probability=True, returns a list of (weight, class)-tuples. For REGRESSION, returns a float. """ if self._model is None: return None if self.debug is False: # Redirect stdout to a file stream. so, sys.stdout = sys.stdout, StringIO() M = self._model[0] H1 = self._model[1] H2 = self._model[2] H3 = self._model[3] v = self._vector(document)[1] v = dict((H1.get(k, len(H1)+i), v) for i, (k,v) in enumerate(v.items())) p = self._libsvm.svm_predict([0], [v], M, "-b %s" % int(self.probability)) t = M.get_svm_type() if self.debug is False: sys.stdout = so if t == CLASSIFICATION and self.probability is True: return [(H3[i], w) for i, w in enumerate(p[2][0])] if t == CLASSIFICATION: return H3.get(int(p[0][0])) if t == REGRESSION: return p[0][0] if t == DETECTION: return p[0][0] > 0 # -1 = outlier => return False return p[0][0] def train(self, document, type=None): """ Trains the classifier with the given document of the given type (i.e., class). A document can be a Document object, list or dictionary. If no type is given, Document.type will be used instead. """ Classifier.train(self, document, type) self._model = None self._vectors.append(self._vector(document, type=type)) def classify(self, document): """ Returns the type with the highest probability for the given document. If the training documents come from a LSA-reduced corpus, the given document must be Corpus.lsa.transform(document). """ if self._model is None: self._libsvm_train() return self._libsvm_predict(document) def save(self, path): svm, model = self._libsvm, self._model if model is not None: svm.svm_save_model(path, model[0]) self._libsvm = None self._model = ((open(path).read(),) + model[1:]) if model else None Classifier.save(self, path) self._libsvm = svm self._model = model @classmethod def load(cls, path): import svm classifier = Classifier.load(path) classifier._libsvm = svm if classifier._model is not None: f = open(path + ".tmp", "w") f.write(classifier._model[0]) f.close() classifier._model = (svm.svm_load_model(path + ".tmp"),) + classifier._model[1:] os.remove(f.name) return classifier #--- K-NEAREST NEIGHBOR CLASSIFIER ----------------------------------------------------------------- class NearestNeighbor(Classifier): def __init__(self, k=10, distance=COSINE, default=None, train=[], baseline=FREQUENCY): """ k-nearest neighbor (kNN) is a simple supervised learning method for text classification. Documents are classified by a majority vote of nearest neighbors (cosine distance) in the training corpus. """ self.k = k # Number of nearest neighbors to observe. self.distance = distance # COSINE, EUCLIDEAN, ... self._vectors = [] # Training instances. self._kdtree = None Classifier.__init__(self, train, baseline) @property def features(self): return list(features(v for type, v in self._vectors)) def train(self, document, type=None): """ Trains the classifier with the given document of the given type (i.e., class). A document can be a Document object, list or dictionary. If no type is given, Document.type will be used instead. """ Classifier.train(self, document, type) self._vectors.append(self._vector(document, type=type)) def classify(self, document): """ Returns the type with the highest probability for the given document. If the training documents come from a LSA-reduced corpus, the given document must be Corpus.lsa.transform(document). """ # Basic majority voting. # Distance is calculated between the document vector and all training instances. # This will make NearestNeighbor.test() slow in higher dimensions. classes = {} v1 = self._vector(document)[1] # k-d trees are slower than brute-force for vectors with high dimensionality: #if self._kdtree is None: # self._kdtree = kdtree((v for type, v in self._vectors)) # self._kdtree.map = dict((v.id, type) for type, v in self._vectors) #D = self._kdtree.nearest_neighbors(v1, self.k, self.distance) D = ((distance(v1, v2, method=self.distance), type) for type, v2 in self._vectors) D = ((d, type) for d, type in D if d < 1) # Nothing in common if distance=1.0. D = heapq.nsmallest(self.k, D) # k-least distant. for d, type in D: classes.setdefault(type, 0) classes[type] += 1 / (d or 0.0000000001) try: # Pick random winner if several candidates have equal highest score. return choice([k for k, v in classes.iteritems() if v == max(classes.values()) > 0]) except IndexError: pass return self.baseline kNN = KNN = NearestNeighbor #d1 = Document("cats have stripes, purr and drink milk", type="cat", threshold=0, stemmer=None) #d2 = Document("cows are black and white, they moo and give milk", type="cow", threshold=0, stemmer=None) #d3 = Document("birds have wings and can fly", type="bird", threshold=0, stemmer=None) #knn = kNN() #for d in (d1,d2,d3): # knn.train(d) #print knn.binary #print knn.classes #print knn.classify(Document("something that can fly", threshold=0, stemmer=None)) #print NearestNeighbor.test((d1,d2,d3), folds=2) #### K-D TREE ###################################################################################### class KDTree: _v1 = Vector({0:0}) _v2 = Vector({0:0}) def __init__(self, vectors, map={}): """ A partitioned vector space that is (sometimes) faster for nearest neighbor search. A k-d tree is an extension of a binary tree for k-dimensional data, where every vector generates a hyperplane that splits the space into two subspaces. The given list can contain Document or Vector objects.stu """ class Node: def __init__(self, vector, left, right, axis): self.vector, self.left, self.right, self.axis = vector, left, right, axis def balance(vectors, depth=0, keys=None): # Based on: http://en.wikipedia.org/wiki/Kd-tree if not vectors: return None if not keys: keys = sorted(features(vectors)) a = keys[depth % len(keys)] # splitting axis v = sorted(vectors, key=lambda v: v.get(a, 0)) m = len(v) // 2 # median pivot return Node( vector = v[m], left = balance(v[:m], depth+1, keys), right = balance(v[m+1:], depth+1, keys), axis = a) self.map = map self.root = balance([self._vector(v) for v in vectors]) def _vector(self, v): """ Returns a Vector for the given document or vector. """ if isinstance(v, Document): self.map.setdefault(v.vector.id, v); return v.vector return v def nearest_neighbors(self, vector, k=10, distance=COSINE): """ Returns a list of (distance, vector)-tuples from the search space, sorted nearest-first to the given vector. """ class NN(list): def update(self, v1, v2): d = _distance(v1, v2, method=distance) if len(self) < k or self[-1][0] > d: # Add nearer vectors to the sorted list. insort(self, (d, v1)) def search(self, vector, k, best=NN()): # Based on: http://code.google.com/p/python-kdtree/ if self is None: return best if self.left is self.right is None: # leaf best.update(self.vector, vector) return best # Compare points in current dimension to select near and far subtree. # We may need to search the far subtree too (see below). if vector.get(self.axis) < self.vector.get(self.axis): near, far = self.left, self.right else: near, far = self.right, self.left # Recursively search near subtree down to leaf. best = search(near, vector, k, best) best.update(self.vector, vector) # It's faster to reuse two Vectors than to create them: dict.__setitem__(KDTree._v1, 0, self.vector.get(self.axis, 0)) dict.__setitem__(KDTree._v2, 0, vector.get(self.axis, 0)) KDTree._v1._norm = None # clear norm cache KDTree._v2._norm = None # If the hypersphere crosses the plane, # there could be nearer points on the far side of the plane. if _distance(KDTree._v1, KDTree._v2, method=distance) <= best[-1][0]: best = search(far, vector, k, best) return best n = search(self.root, self._vector(vector), k+1) n = [(d, self.map.get(v.id, v)) for d, v in n] n = [(d, v) for d, v in n if v != vector][:k] return n nn = nearest_neighbors kdtree = KDTree #### GENETIC ALGORITHM ############################################################################# class GeneticAlgorithm: def __init__(self, candidates=[], **kwargs): """ A genetic algorithm is a stochastic search method based on natural selection. Each generation, the fittest candidates are selected and recombined into a new generation. With each new generation the system converges towards an optimal fitness. """ self.population = candidates self.generation = 0 self._avg = None # Average fitness for this generation. # GeneticAlgorithm.fitness(), crossover(), mutate() can be given as functions: for f in ("fitness", "crossover", "mutate"): if f in kwargs: setattr(self, f, kwargs[f]) def fitness(self, candidate): # Must be implemented in a subclass, returns 0.0-1.0. return 1.0 def crossover(self, candidate1, candidate2, d=0.5): # Must be implemented in a subclass. return None def mutate(self, candidate, d=0.1): # Must be implemented in a subclass. return None or candidate def update(self, top=0.7, crossover=0.5, mutation=0.1, d=0.9): """ Updates the population by selecting the top fittest candidates, and recombining them into a new generation. """ # Selection. p = sorted((self.fitness(x), x) for x in self.population) # Weakest-first. a = self._avg = float(sum(f for f, x in p)) / len(p) x = min(f for f, x in p) y = max(f for f, x in p) i = 0 while len(p) > len(self.population) * top: # Weaker candidates have a higher chance of being removed, # chance being equal to (1-fitness), starting with the weakest. if x + (y-x) * random() >= p[i][0]: p.pop(i) else: i = (i+1) % len(p) # Reproduction. g = [] while len(g) < len(self.population): # Choose randomly between recombination of parents or mutation. # Mutation avoids local optima by maintaining genetic diversity. if random() < d: i = int(round(random() * (len(p)-1))) j = choice(range(0,i) + range(i+1, len(p))) g.append(self.crossover(p[i][1], p[j][1], d=crossover)) else: g.append(self.mutate(choice(p)[1], d=mutation)) self.population = g self.generation += 1 @property def avg(self): # Average fitness is supposed to increase each generation. if not self._avg: self._avg = float(sum(map(self.fitness, self.population))) / len(self.population) return self._avg average_fitness = avg GA = GeneticAlgorithm # GA for floats between 0.0-1.0 that prefers higher numbers: #class HighFloatGA(GeneticAlgorithm): # def fitness(self, x): # return x # def crossover(self, x, y, d=0.5): # return (x+y) / 2 # def mutate(self, x, d=0.1): # return min(1, max(0, x + random()*0.2-0.1)) # #ga = HighFloatGA([random() for i in range(100)]) #for i in range(100): # ga.update() # print ga.average_fitness #print ga.population
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Rio_olympics/flags/temp.py
Data-Analytics/data-analytics.github.io
12
118736
import urllib image = urllib.URLopener() for k in xrange(300,400): try: image.retrieve("http://olympicshub.stats.com/flags/48x48/"+str(k)+".png",str(k)+".png") except: print k
[ 1, 1053, 3142, 1982, 13, 3027, 353, 3142, 1982, 29889, 4219, 459, 759, 580, 13, 1454, 413, 297, 921, 3881, 29898, 29941, 29900, 29900, 29892, 29946, 29900, 29900, 1125, 13, 29871, 1018, 29901, 13, 12, 3027, 29889, 276, 509, 2418, 703, 1124, 597, 324, 962, 16447, 845, 431, 29889, 16202, 29889, 510, 29914, 15764, 29914, 29946, 29947, 29916, 29946, 29947, 12975, 29974, 710, 29898, 29895, 7240, 1642, 2732, 613, 710, 29898, 29895, 7240, 1642, 2732, 1159, 13, 29871, 5174, 29901, 13, 259, 12, 2158, 413, 29871, 2 ]
src/apis/text/text/sentiment-analyses/distilbert-base-uncased/distilbert-base-uncased.py
jqueguiner/ai-api-marketplace-
0
111100
from happytransformer import HappyTextClassification import json def predict(text): happy_tc = HappyTextClassification("DISTILBERT", "distilbert-base-uncased", num_labels=2) result = happy_tc.classify_text(text) #print(result) # TextClassificationResult(label='LABEL_0', score=0.9998761415481567) return json.dumps({"label": "POSITIVE" if result.label == "LABEL_0" else "NEGATIVE", "score": result.score})
[ 1, 515, 9796, 9067, 261, 1053, 28569, 1626, 2385, 2450, 13, 5215, 4390, 13, 13, 13, 1753, 8500, 29898, 726, 1125, 13, 1678, 9796, 29918, 14246, 353, 28569, 1626, 2385, 2450, 703, 4571, 1254, 6227, 13635, 29911, 613, 376, 5721, 309, 2151, 29899, 3188, 29899, 4661, 1463, 613, 954, 29918, 21134, 29922, 29906, 29897, 268, 13, 1678, 1121, 353, 9796, 29918, 14246, 29889, 1990, 1598, 29918, 726, 29898, 726, 29897, 13, 1678, 396, 2158, 29898, 2914, 29897, 29871, 396, 3992, 2385, 2450, 3591, 29898, 1643, 2433, 24461, 6670, 29918, 29900, 742, 8158, 29922, 29900, 29889, 29929, 29929, 29929, 29947, 29955, 29953, 29896, 29946, 29896, 29945, 29946, 29947, 29896, 29945, 29953, 29955, 29897, 13, 1678, 736, 4390, 29889, 29881, 17204, 3319, 29908, 1643, 1115, 376, 24815, 1806, 18474, 29908, 565, 1121, 29889, 1643, 1275, 376, 24461, 6670, 29918, 29900, 29908, 29871, 1683, 376, 8186, 29954, 1299, 18474, 613, 376, 13628, 1115, 1121, 29889, 13628, 1800, 13, 2 ]
python/nagcat/unittests/test_merlin.py
marineam/nagcat
0
199643
<reponame>marineam/nagcat<filename>python/nagcat/unittests/test_merlin.py<gh_stars>0 # Copyright 2011 Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from twisted.trial import unittest from nagcat import simple, merlin, scheduler from coil.struct import Struct import os import warnings try: import MySQLdb except ImportError: skip = "MySQLdb is not available" class TestNagcatMerlinCase(unittest.TestCase): _merlin_db_info = { 'merlin_db_user': os.environ.get('MYSQL_USER', None), 'merlin_db_host': os.environ.get('MYSQL_HOST', None), 'merlin_db_pass': os.environ.get('MYSQL_PASS', None), 'merlin_db_name': os.environ.get('MYSQL_NAME', None), 'merlin_db_port': os.environ.get('MYSQL_PORT', 3306), } if not all((_merlin_db_info['merlin_db_user'], _merlin_db_info['merlin_db_host'], _merlin_db_info['merlin_db_name'])): skip = "Missing MySQL credentials" def setUp(self): db = MySQLdb.connect( user=self._merlin_db_info['merlin_db_user'], host=self._merlin_db_info['merlin_db_host'], passwd=self._merlin_db_info['merlin_db_pass'], db=self._merlin_db_info['merlin_db_name']) cursor = db.cursor() # drop table raises a warning if the table doesn't exist, so shutup! warnings.filterwarnings('ignore', 'Unknown table.*') cursor.execute("""drop table if exists merlin_peers;""") cursor.execute("""create table merlin_peers( name varchar(70) NOT NULL PRIMARY KEY, id int(22), sock int(22), type int(1) NOT NULL, state int(22) NOT NULL, peer_id int(22) NOT NULL);""") cursor.execute("""insert into merlin_peers values( 'localhost', '1', '1', '1', '3', '0');""") cursor.execute("""insert into merlin_peers values( 'otherhost', '1', '1', '1', '3', '1');""") def testNagcatMerlin(self): nagcatMerlin = NagcatMerlinDummy( merlin_db_info=self._merlin_db_info) self.assertEquals(nagcatMerlin.get_peer_id_num_peers(), (0,2)) def tearDown(self): db = MySQLdb.connect( user = self._merlin_db_info['merlin_db_user'], host = self._merlin_db_info['merlin_db_host'], passwd = self._merlin_db_info['merlin_db_pass'], db = self._merlin_db_info['merlin_db_name']) curs = db.cursor() curs.execute("""DROP table merlin_peers;""") class NagcatMerlinDummy(merlin.NagcatMerlin): """For testing purposes.""" def __init__(self, merlin_db_info={}): self._merlin_db_info = merlin_db_info scheduler.Scheduler.__init__(self) self._peer_id = None self._num_rows = None self._peer_id_timestamp = None def build_tests(self, config): return [] def nagios_status(self): return simple.ObjectDummy()
[ 1, 529, 276, 1112, 420, 29958, 24556, 314, 29914, 29876, 351, 4117, 29966, 9507, 29958, 4691, 29914, 29876, 351, 4117, 29914, 348, 986, 9197, 29914, 1688, 29918, 1050, 1915, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29896, 5087, 29892, 9266, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 13, 29937, 2823, 278, 19245, 363, 278, 2702, 4086, 14765, 1076, 11239, 322, 13, 29937, 27028, 1090, 278, 19245, 29889, 13, 13, 3166, 3252, 12652, 29889, 3626, 284, 1053, 443, 27958, 13, 3166, 17995, 4117, 1053, 2560, 29892, 2778, 1915, 29892, 1364, 14952, 13, 3166, 1302, 309, 29889, 4984, 1053, 28771, 13, 5215, 2897, 13, 5215, 18116, 13, 13, 2202, 29901, 13, 1678, 1053, 9254, 2585, 13, 19499, 16032, 2392, 29901, 13, 1678, 14383, 353, 376, 3421, 4176, 2585, 338, 451, 3625, 29908, 13, 13, 1990, 4321, 29940, 351, 4117, 15836, 1915, 8259, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 903, 1050, 1915, 29918, 2585, 29918, 3888, 353, 426, 13, 4706, 525, 1050, 1915, 29918, 2585, 29918, 1792, 2396, 2897, 29889, 21813, 29889, 657, 877, 17870, 4176, 29918, 11889, 742, 6213, 511, 13, 4706, 525, 1050, 1915, 29918, 2585, 29918, 3069, 2396, 2897, 29889, 21813, 29889, 657, 877, 17870, 4176, 29918, 20832, 742, 6213, 511, 13, 4706, 525, 1050, 1915, 29918, 2585, 29918, 3364, 2396, 2897, 29889, 21813, 29889, 657, 877, 17870, 4176, 29918, 25711, 742, 6213, 511, 13, 4706, 525, 1050, 1915, 29918, 2585, 29918, 978, 2396, 2897, 29889, 21813, 29889, 657, 877, 17870, 4176, 29918, 5813, 742, 6213, 511, 13, 4706, 525, 1050, 1915, 29918, 2585, 29918, 637, 2396, 2897, 29889, 21813, 29889, 657, 877, 17870, 4176, 29918, 15082, 742, 29871, 29941, 29941, 29900, 29953, 511, 13, 1678, 500, 13, 13, 1678, 565, 451, 599, 3552, 29918, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 1792, 7464, 13, 18884, 903, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 3069, 7464, 13, 18884, 903, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 978, 2033, 22164, 13, 4706, 14383, 353, 376, 18552, 292, 9254, 16140, 29908, 13, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 4833, 353, 9254, 2585, 29889, 6915, 29898, 13, 9651, 1404, 29922, 1311, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 1792, 7464, 13, 9651, 3495, 29922, 1311, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 3069, 7464, 13, 9651, 1209, 9970, 29922, 1311, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 3364, 7464, 13, 9651, 4833, 29922, 1311, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 978, 11287, 13, 13, 4706, 10677, 353, 4833, 29889, 18127, 580, 13, 4706, 396, 5768, 1591, 1153, 4637, 263, 9177, 565, 278, 1591, 1838, 29915, 29873, 1863, 29892, 577, 12522, 786, 29991, 13, 4706, 18116, 29889, 4572, 25442, 886, 877, 17281, 742, 525, 14148, 1591, 5575, 1495, 13, 4706, 10677, 29889, 7978, 703, 15945, 8865, 1591, 565, 4864, 2778, 1915, 29918, 412, 414, 15458, 29908, 1159, 13, 4706, 10677, 29889, 7978, 703, 15945, 3258, 1591, 2778, 1915, 29918, 412, 414, 29898, 13, 18884, 1024, 1678, 15236, 29898, 29955, 29900, 29897, 6058, 4265, 29778, 14636, 29892, 13, 18884, 1178, 418, 938, 29898, 29906, 29906, 511, 13, 18884, 577, 384, 1678, 938, 29898, 29906, 29906, 511, 13, 18884, 1134, 1678, 938, 29898, 29896, 29897, 6058, 4265, 29892, 13, 18884, 2106, 259, 938, 29898, 29906, 29906, 29897, 6058, 4265, 29892, 13, 18884, 23533, 29918, 333, 938, 29898, 29906, 29906, 29897, 6058, 4265, 416, 15945, 1159, 13, 4706, 10677, 29889, 7978, 703, 15945, 7851, 964, 2778, 1915, 29918, 412, 414, 1819, 29898, 13, 18884, 525, 7640, 742, 13, 18884, 525, 29896, 742, 13, 18884, 525, 29896, 742, 13, 18884, 525, 29896, 742, 13, 18884, 525, 29941, 742, 13, 18884, 525, 29900, 2157, 15945, 1159, 13, 4706, 10677, 29889, 7978, 703, 15945, 7851, 964, 2778, 1915, 29918, 412, 414, 1819, 29898, 13, 18884, 525, 1228, 3069, 742, 13, 18884, 525, 29896, 742, 13, 18884, 525, 29896, 742, 13, 18884, 525, 29896, 742, 13, 18884, 525, 29941, 742, 13, 18884, 525, 29896, 2157, 15945, 1159, 13, 13, 1678, 822, 1243, 29940, 351, 4117, 15836, 1915, 29898, 1311, 1125, 13, 4706, 17995, 4117, 15836, 1915, 353, 14461, 4117, 15836, 1915, 29928, 11770, 29898, 13, 9651, 2778, 1915, 29918, 2585, 29918, 3888, 29922, 1311, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 29897, 13, 4706, 1583, 29889, 9294, 14776, 29898, 29876, 351, 4117, 15836, 1915, 29889, 657, 29918, 412, 261, 29918, 333, 29918, 1949, 29918, 412, 414, 3285, 13, 9651, 313, 29900, 29892, 29906, 876, 13, 13, 1678, 822, 734, 279, 6767, 29898, 1311, 1125, 13, 4706, 4833, 353, 9254, 2585, 29889, 6915, 29898, 13, 9651, 1404, 353, 1583, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 1792, 7464, 13, 9651, 3495, 353, 1583, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 3069, 7464, 13, 9651, 1209, 9970, 353, 1583, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 3364, 7464, 13, 9651, 4833, 353, 1583, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 1839, 1050, 1915, 29918, 2585, 29918, 978, 11287, 13, 4706, 18580, 353, 4833, 29889, 18127, 580, 13, 4706, 18580, 29889, 7978, 703, 15945, 29928, 29366, 1591, 2778, 1915, 29918, 412, 414, 15458, 29908, 1159, 13, 13, 1990, 14461, 4117, 15836, 1915, 29928, 11770, 29898, 1050, 1915, 29889, 29940, 351, 4117, 15836, 1915, 1125, 13, 1678, 9995, 2831, 6724, 11976, 1213, 15945, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2778, 1915, 29918, 2585, 29918, 3888, 3790, 29913, 1125, 13, 4706, 1583, 3032, 1050, 1915, 29918, 2585, 29918, 3888, 353, 2778, 1915, 29918, 2585, 29918, 3888, 13, 4706, 1364, 14952, 29889, 4504, 14952, 17255, 2344, 12035, 1311, 29897, 13, 4706, 1583, 3032, 412, 261, 29918, 333, 353, 6213, 13, 4706, 1583, 3032, 1949, 29918, 5727, 353, 6213, 13, 4706, 1583, 3032, 412, 261, 29918, 333, 29918, 16394, 353, 6213, 13, 13, 1678, 822, 2048, 29918, 21150, 29898, 1311, 29892, 2295, 1125, 13, 4706, 736, 5159, 13, 13, 1678, 822, 17995, 2363, 29918, 4882, 29898, 1311, 1125, 13, 4706, 736, 2560, 29889, 2061, 29928, 11770, 580, 13, 2 ]
server/cauth/views.py
mashaka/TravelHelper
0
1798
<reponame>mashaka/TravelHelper from django.shortcuts import render from django.contrib.auth.decorators import login_required from django.contrib.auth.forms import AdminPasswordChangeForm, PasswordChangeForm, UserCreationForm from django.contrib.auth import update_session_auth_hash, login, authenticate from django.contrib import messages from django.shortcuts import render, redirect from social_django.models import UserSocialAuth from django.http import HttpResponse from django.shortcuts import get_object_or_404, redirect from rest_framework.authtoken.models import Token from app.methods import prepare_user def get_token(request): if request.user: user = request.user prepare_user(user) token,_ = Token.objects.get_or_create(user=user) url = "travel://?token=" + token.key + '&id=' + str(user.id) else: url = "travel://error" response = HttpResponse(url, status=302) response['Location'] = url return response @login_required def get_facebook_token(request): q = get_object_or_404(UserSocialAuth, user=request.user, provider='facebook') return HttpResponse(str(q.extra_data)) def signup(request): return render(request, 'signup.html') @login_required def home(request): return render(request, 'home.html') @login_required def settings(request): user = request.user try: github_login = user.social_auth.get(provider='github') except UserSocialAuth.DoesNotExist: github_login = None try: twitter_login = user.social_auth.get(provider='twitter') except UserSocialAuth.DoesNotExist: twitter_login = None try: facebook_login = user.social_auth.get(provider='facebook') except UserSocialAuth.DoesNotExist: facebook_login = None can_disconnect = (user.social_auth.count() > 1 or user.has_usable_password()) return render(request, 'settings.html', { 'facebook_login': facebook_login, 'can_disconnect': can_disconnect }) @login_required def password(request): if request.user.has_usable_password(): PasswordForm = PasswordChangeForm else: PasswordForm = AdminPasswordChangeForm if request.method == 'POST': form = PasswordForm(request.user, request.POST) if form.is_valid(): form.save() update_session_auth_hash(request, form.user) messages.success(request, 'Your password was successfully updated!') return redirect('password') else: messages.error(request, 'Please correct the error below.') else: form = PasswordForm(request.user) return render(request, 'password.html', {'form': form})
[ 1, 529, 276, 1112, 420, 29958, 29885, 1161, 8245, 29914, 5323, 955, 10739, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 4050, 13, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 19557, 4097, 1053, 6464, 29918, 12403, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 9514, 1053, 10229, 10048, 7277, 2500, 29892, 25280, 7277, 2500, 29892, 4911, 9832, 362, 2500, 13, 3166, 9557, 29889, 21570, 29889, 5150, 1053, 2767, 29918, 7924, 29918, 5150, 29918, 8568, 29892, 6464, 29892, 15585, 403, 13, 3166, 9557, 29889, 21570, 1053, 7191, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 4050, 29892, 6684, 13, 13, 3166, 5264, 29918, 14095, 29889, 9794, 1053, 4911, 6295, 1455, 6444, 13, 3166, 9557, 29889, 1124, 1053, 9056, 5103, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 679, 29918, 3318, 29918, 272, 29918, 29946, 29900, 29946, 29892, 6684, 13, 3166, 1791, 29918, 4468, 29889, 1300, 400, 4476, 29889, 9794, 1053, 25159, 13, 13, 3166, 623, 29889, 23515, 1053, 19012, 29918, 1792, 13, 13, 13, 1753, 679, 29918, 6979, 29898, 3827, 1125, 13, 1678, 565, 2009, 29889, 1792, 29901, 13, 4706, 1404, 353, 2009, 29889, 1792, 13, 4706, 19012, 29918, 1792, 29898, 1792, 29897, 13, 4706, 5993, 29892, 29918, 353, 25159, 29889, 12650, 29889, 657, 29918, 272, 29918, 3258, 29898, 1792, 29922, 1792, 29897, 13, 4706, 3142, 353, 376, 3018, 955, 597, 29973, 6979, 543, 718, 5993, 29889, 1989, 718, 525, 29987, 333, 2433, 718, 851, 29898, 1792, 29889, 333, 29897, 13, 1678, 1683, 29901, 13, 4706, 3142, 353, 376, 3018, 955, 597, 2704, 29908, 13, 1678, 2933, 353, 9056, 5103, 29898, 2271, 29892, 4660, 29922, 29941, 29900, 29906, 29897, 13, 1678, 2933, 1839, 6508, 2033, 353, 3142, 13, 1678, 736, 2933, 13, 13, 29992, 7507, 29918, 12403, 13, 1753, 679, 29918, 15445, 29918, 6979, 29898, 3827, 1125, 13, 1678, 3855, 353, 679, 29918, 3318, 29918, 272, 29918, 29946, 29900, 29946, 29898, 2659, 6295, 1455, 6444, 29892, 1404, 29922, 3827, 29889, 1792, 29892, 13113, 2433, 15445, 1495, 13, 1678, 736, 9056, 5103, 29898, 710, 29898, 29939, 29889, 17833, 29918, 1272, 876, 13, 13, 1753, 1804, 786, 29898, 3827, 1125, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 4530, 786, 29889, 1420, 1495, 13, 13, 29992, 7507, 29918, 12403, 13, 1753, 3271, 29898, 3827, 1125, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 5184, 29889, 1420, 1495, 13, 13, 29992, 7507, 29918, 12403, 13, 1753, 6055, 29898, 3827, 1125, 13, 1678, 1404, 353, 2009, 29889, 1792, 13, 13, 1678, 1018, 29901, 13, 4706, 18546, 29918, 7507, 353, 1404, 29889, 24911, 29918, 5150, 29889, 657, 29898, 18121, 2433, 3292, 1495, 13, 1678, 5174, 4911, 6295, 1455, 6444, 29889, 25125, 3664, 1252, 391, 29901, 13, 4706, 18546, 29918, 7507, 353, 6213, 13, 1678, 1018, 29901, 13, 4706, 23394, 29918, 7507, 353, 1404, 29889, 24911, 29918, 5150, 29889, 657, 29898, 18121, 2433, 24946, 1495, 13, 1678, 5174, 4911, 6295, 1455, 6444, 29889, 25125, 3664, 1252, 391, 29901, 13, 4706, 23394, 29918, 7507, 353, 6213, 13, 1678, 1018, 29901, 13, 4706, 18335, 29918, 7507, 353, 1404, 29889, 24911, 29918, 5150, 29889, 657, 29898, 18121, 2433, 15445, 1495, 13, 1678, 5174, 4911, 6295, 1455, 6444, 29889, 25125, 3664, 1252, 391, 29901, 13, 4706, 18335, 29918, 7507, 353, 6213, 13, 13, 1678, 508, 29918, 2218, 6915, 353, 313, 1792, 29889, 24911, 29918, 5150, 29889, 2798, 580, 1405, 29871, 29896, 470, 1404, 29889, 5349, 29918, 27979, 29918, 5630, 3101, 13, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 11027, 29889, 1420, 742, 426, 13, 4706, 525, 15445, 29918, 7507, 2396, 18335, 29918, 7507, 29892, 13, 4706, 525, 3068, 29918, 2218, 6915, 2396, 508, 29918, 2218, 6915, 13, 1678, 5615, 13, 13, 29992, 7507, 29918, 12403, 13, 1753, 4800, 29898, 3827, 1125, 13, 1678, 565, 2009, 29889, 1792, 29889, 5349, 29918, 27979, 29918, 5630, 7295, 13, 4706, 25280, 2500, 353, 25280, 7277, 2500, 13, 1678, 1683, 29901, 13, 4706, 25280, 2500, 353, 10229, 10048, 7277, 2500, 13, 13, 1678, 565, 2009, 29889, 5696, 1275, 525, 5438, 2396, 13, 4706, 883, 353, 25280, 2500, 29898, 3827, 29889, 1792, 29892, 2009, 29889, 5438, 29897, 13, 4706, 565, 883, 29889, 275, 29918, 3084, 7295, 13, 9651, 883, 29889, 7620, 580, 13, 9651, 2767, 29918, 7924, 29918, 5150, 29918, 8568, 29898, 3827, 29892, 883, 29889, 1792, 29897, 13, 9651, 7191, 29889, 8698, 29898, 3827, 29892, 525, 10858, 4800, 471, 8472, 4784, 29991, 1495, 13, 9651, 736, 6684, 877, 5630, 1495, 13, 4706, 1683, 29901, 13, 9651, 7191, 29889, 2704, 29898, 3827, 29892, 525, 12148, 1959, 278, 1059, 2400, 29889, 1495, 13, 1678, 1683, 29901, 13, 4706, 883, 353, 25280, 2500, 29898, 3827, 29889, 1792, 29897, 13, 1678, 736, 4050, 29898, 3827, 29892, 525, 5630, 29889, 1420, 742, 11117, 689, 2396, 883, 1800, 13, 2 ]
src/level1/두개_빼서_더하기.py
iml1111/programmers_coding_study
1
82776
# -*- coding: utf-8 -*- ''' 월간 코딩 챌린지 시즌 2 - 두개 뺴서 더하기 ''' def solution(numbers): answer = set() num_len = len(numbers) for i in range(num_len): for j in range(num_len): if i != j: answer.add(numbers[i] + numbers[j]) answer = list(answer) answer.sort() return answer if __name__ == '__main__': numbers = [2,1,3,4,1] result = solution(numbers) print(result)
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Beginner/Fibonacci.py
man21/IOSD-UIETKUK-HacktoberFest-Meetup-2019
22
199715
def fib(n): ''' uses generater to return fibonacci sequence up to given # n dynamically ''' a,b = 1,1 for _ in range(0,n): yield a a,b = b,a+b return a
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src/logger.py
rinchieeeee/cnn-pipline-for-data-competition
0
197112
import math import time from contextlib import contextmanager class AverageMeter(object): """ objectは別に書かなくてもOK. ただし, recommendされているらしいぞ... Computes and stores the average and current value. """ def __init__(self): self.reset() # 最初に呼び出されるごとに初期化 def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n = 1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def Second2Minutes(second): """ return example ... if m = 3, s = 34 then, return 3m, 34s. """ m = math.floor(second / 60) second -= m * 60 return "%dm %ds" % (m, second) def timeSince(since, percent): now = time.time() s = now - since es = s/(percent) # elapsed time rs = es - s # remain time return "%s (remain %s)" % (Second2Minutes(second = s), Second2Minutes(second = rs)) # 時間管理 @contextmanager def timer(name): t0 = time.time() LOGGER.info(f'[{name}] start') yield LOGGER.info(f'[{name}] done in {time.time() - t0:.0f} s.') def init_logger(config): """ config is equal to config_general or config["general"] """ log_file = config["output_dir"] + "/" + "train.log" from logging import getLogger, INFO, FileHandler, Formatter, StreamHandler logger = getLogger(__name__) logger.setLevel(INFO) handler1 = StreamHandler() handler1.setFormatter(Formatter("%(message)s")) handler2 = FileHandler(filename = log_file) handler2.setFormatter(Formatter("%(message)s")) logger.addHandler(handler1) logger.addHandler(handler2) return logger
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setup.py
heathcliff233/pldpr
0
72034
<filename>setup.py #!/usr/bin/env python from setuptools import setup, find_packages setup( name='DPR_pl', version='0.0.1', description='Amino acid sequences retieval', author='<NAME>', author_email='<EMAIL>', # REPLACE WITH YOUR OWN GITHUB PROJECT LINK url='https://github.com/heathcliff233/pldpr', install_requires=['pytorch-lightning'], packages=find_packages(), )
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sso/oauth2/migrations/0004_application_allow_access_by_email_suffix_squashed_0005_auto_20180205_2004.py
uktrade/staff-sso
7
96375
# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2018-02-05 20:05 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): replaces = [ ("oauth2", "0004_application_allow_access_by_email_suffix"), ("oauth2", "0005_auto_20180205_2004"), ] dependencies = [ ("oauth2", "0003_application_provide_immutable_email"), ] operations = [ migrations.AddField( model_name="application", name="allow_access_by_email_suffix", field=models.CharField( blank=True, help_text='A comma separated list of email domains, e.g. "mobile.ukti.gov.uk, trade.gsi.gov.uk, fco.gov.uk" User\'s with an email in this list will be given access. NOTE: all user emails are checked - including aliases.', max_length=255, null=True, verbose_name="allow access by email", ), ), ]
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tefingerprint/util/__init__.py
timothymillar/TEFingerprint
0
40157
from . import numpy
[ 1, 515, 869, 1053, 12655, 13, 2 ]
driving_force.py
bcgreen24/ten-lines-or-less
44
82853
<filename>driving_force.py b1 >> fuzz([0, 2, 3, 5], dur=1/2, amp=0.8, lpf=linvar([100, 1000], 12), lpr=0.4, oct=3).every(16, "shuffle").every(8, "bubble") d1 >> play("x o [xx] oxx o [xx] {oO} ", room=0.4).every(16, "shuffle") d2 >> play("[--]", amp=[1.3, 0.5, 0.5, 0.5], hpf=linvar([6000, 10000], 8), hpr=0.4, spin=4) p1 >> pasha([0, 2, 3, 5], dur=4, oct=4, amp=0.65, pan=[-0.5, 0.5], striate=16).every(9, "bubble") m1 >> karp([0, 2, 3, 7, 9], dur=[1/2, 1, 1/2, 1, 1, 1/2]).every(12, "shuffle").every(7, "bubble") p1.every(4, "stutter", 4) b1.every(8, "rotate")
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libreverse/app_factory.py
TheAssassin/3d.models
1
58647
<filename>libreverse/app_factory.py<gh_stars>1-10 import os from flask import Flask, safe_join def create_app(config: dict = None) -> Flask: """ Idiomatic Flask way allowing to create different applications. This allows for creating more app objects, e.g., for unit testing or some special deployments. :return: configured Flask app """ app = Flask(__name__) app.config.setdefault("FREEZER_DESTINATION", os.path.join(os.getcwd(), "frozen")) # load configuration from current working directory, if any app.config.from_pyfile(os.path.join(os.getcwd(), "config.py")) if config is not None: app.config.update(config) try: if not os.path.isdir(app.config["MODELS_DIR"]): raise IOError("Could not find models dir %s" % app.config["MODELS_DIR"]) except KeyError: raise ValueError("MODELS_DIR not configured") # register views from .views import bp app.register_blueprint(bp) # make some Python functions available in Jinja2 app.jinja_env.globals.update( safe_join=safe_join, sorted=sorted, ) return app
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src/sorl/thumbnail/parsers.py
daniel-werner/stelagifts
1
132251
<reponame>daniel-werner/stelagifts #coding=utf-8 import re from sorl.thumbnail.helpers import ThumbnailError, toint bgpos_pat = re.compile(r'^(?P<value>\d+)(?P<unit>%|px)$') geometry_pat = re.compile(r'^(?P<x>\d+)?(?:x(?P<y>\d+))?$') class ThumbnailParseError(ThumbnailError): pass def parse_geometry(geometry, ratio=None): """ Parses a geometry string syntax and returns a (width, height) tuple """ m = geometry_pat.match(geometry) def syntax_error(): return ThumbnailParseError('Geometry does not have the correct ' 'syntax: %s' % geometry) if not m: raise syntax_error() x = m.group('x') y = m.group('y') if x is None and y is None: raise syntax_error() if x is not None: x = int(x) if y is not None: y = int(y) # calculate x or y proportionally if not set but we need the image ratio # for this if ratio is not None: ratio = float(ratio) if x is None: x = toint(y * ratio) elif y is None: y = toint(x / ratio) return x, y def parse_crop(crop, xy_image, xy_window): """ Returns x, y offsets for cropping. The window area should fit inside image but it works out anyway """ def syntax_error(): raise ThumbnailParseError('Unrecognized crop option: %s' % crop) x_alias_percent = { 'left': '0%', 'center': '50%', 'right': '100%', } y_alias_percent = { 'top': '0%', 'center': '50%', 'bottom': '100%', } xy_crop = crop.split(' ') if len(xy_crop) == 1: if crop in x_alias_percent: x_crop = x_alias_percent[crop] y_crop = '50%' elif crop in y_alias_percent: y_crop = y_alias_percent[crop] x_crop = '50%' else: x_crop, y_crop = crop, crop elif len(xy_crop) == 2: x_crop, y_crop = xy_crop x_crop = x_alias_percent.get(x_crop, x_crop) y_crop = y_alias_percent.get(y_crop, y_crop) else: syntax_error() def get_offset(crop, epsilon): m = bgpos_pat.match(crop) if not m: syntax_error() value = int(m.group('value')) # we only take ints in the regexp unit = m.group('unit') if unit == '%': value = epsilon * value / 100.0 # return ∈ [0, epsilon] return int(max(0, min(value, epsilon))) offset_x = get_offset(x_crop, xy_image[0] - xy_window[0]) offset_y = get_offset(y_crop, xy_image[1] - xy_window[1]) return offset_x, offset_y
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motivationalolof.py
Leek727/olofcoom
0
180900
<reponame>Leek727/olofcoom from PyQt5.QtWidgets import * from PyQt5.QtGui import QPixmap import sys from PyQt5 import QtGui, QtCore, uic from PyQt5 import QtWidgets from PyQt5.QtWidgets import QMainWindow, QApplication class Window(QMainWindow): def __init__(self): super().__init__() self.setWindowFlags( QtCore.Qt.WindowStaysOnTopHint | QtCore.Qt.FramelessWindowHint | QtCore.Qt.X11BypassWindowManagerHint ) self.setGeometry( QtWidgets.QStyle.alignedRect( QtCore.Qt.LeftToRight, QtCore.Qt.AlignCenter, QtCore.QSize(220, 32), QtWidgets.qApp.desktop().availableGeometry() )) self.acceptDrops() # set the title self.setWindowTitle("I JUST SAY OLOFMEISTER I LOVE YOU") # setting the geometry of window self.setGeometry(1500, 666, 400, 417) self.setWindowOpacity(1) # creating label self.label = QLabel(self) # loading image self.pixmap = QPixmap('resources/olof.png', format="png") self.setAttribute(QtCore.Qt.WA_TranslucentBackground) # adding image to label self.label.setPixmap(self.pixmap) # Optional, resize label to image size self.label.resize(self.pixmap.width(), self.pixmap.height()) # show all the widgets self.show() def mousePressEvent(self, event): QtWidgets.qApp.quit() # create pyqt5 app App = QApplication(sys.argv) # create the instance of our Window window = Window() # start the app sys.exit(App.exec())
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aphelper/enums.py
jfcarter2358/aphelper
0
183765
from enum import Enum, unique class BaseEnum(Enum): @classmethod def values(cls) -> list: """Returns a list of raw values for the class""" values = [member.value for role, member in cls.__members__.items()] return values @unique class Keys(BaseEnum): """Represents categories of objects in cli definition files""" META = "meta" SUBPARSERS = "subparsers" SUBCOMMANDS = "subcommands" @unique class LogicalOperators(BaseEnum): """Represents logical operators that can be used in determining required arguments""" NOT = "NOT" OR = "OR" AND = "AND" XOR = "XOR" NAND = "NAND" NOR = "NOR" @unique class SubparserMeta(BaseEnum): """Represents fields that are used to define subparser metadata""" PARSER_DESCRIPTION = "parser_description" PARSER_HELP = "parser_help" SUBPARSER_TITLE = "subparser_title" SUBPARSER_DESCRIPTION = "subparser_description" SUBPARSER_DEST = "subparser_dest" @unique class SubcommandMeta(BaseEnum): """Represents fields that are used to define subcommand metadata""" SUBCOMMAND_DESCRIPTION = "description" SUBCOMMAND_HELP = "help" SUBCOMMAND_FUNCTION = "function" SUBCOMMAND_REQUIRES = "requires" class Argument(BaseEnum): ARGUMENT_SHORT = "short" ARGUMENT_LONG = "long"
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