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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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2396,
1583,
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13,
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4706,
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736,
3633,
29918,
1272,
13,
2
] |
selfsupervised3d/dataset/__init__.py | jcreinhold/selfsupervised3d | 5 | 63847 | <filename>selfsupervised3d/dataset/__init__.py
from .blendowski import *
from .context import *
from .doersch import *
| [
1,
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9507,
29958,
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9136,
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29941,
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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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4928,
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5847,
2176,
705,
556,
29918,
1127,
27423,
4968,
13,
462,
462,
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376,
4928,
29926,
29882,
5847,
2176,
705,
556,
5783,
396,
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3633,
363,
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19776,
29882,
5847,
2176,
705,
556,
13,
2
] |
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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13,
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5552,
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22484,
29952,
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304,
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13,
965,
304,
278,
1857,
2602,
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5552,
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359,
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22048,
29968,
29918,
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29952,
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29906,
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304,
13,
965,
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6198,
304,
278,
934,
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13,
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13,
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663,
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13,
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3283,
718,
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13,
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663,
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29901,
13,
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29918,
1066,
353,
938,
29898,
1311,
29889,
2848,
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718,
926,
13,
4706,
1683,
29901,
13,
9651,
12020,
10663,
2392,
29898,
29906,
29906,
29892,
376,
13919,
995,
363,
421,
1332,
663,
29952,
1159,
13,
13,
4706,
565,
716,
29918,
1066,
529,
29871,
29900,
29901,
13,
9651,
12020,
10663,
2392,
29898,
29906,
29906,
29892,
376,
13919,
995,
363,
421,
1066,
29952,
448,
1818,
367,
6374,
1159,
13,
13,
4706,
396,
20693,
326,
2133,
29892,
6773,
773,
278,
1021,
6835,
322,
19875,
20380,
29889,
13,
4706,
565,
716,
29918,
1066,
1275,
1583,
17255,
3283,
29901,
13,
9651,
736,
13,
13,
4706,
1583,
17255,
3283,
353,
716,
29918,
1066,
13,
4706,
1583,
17255,
9040,
353,
382,
3580,
15631,
13,
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565,
1583,
17255,
29812,
29918,
1524,
29901,
13,
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1583,
17255,
29812,
29918,
1524,
29889,
5358,
580,
13,
9651,
1583,
17255,
29812,
29918,
1524,
353,
6213,
13,
13,
1678,
822,
16508,
519,
29898,
1311,
1125,
13,
4706,
736,
5852,
13,
13,
1678,
822,
4770,
1524,
12035,
1311,
1125,
13,
4706,
9995,
11609,
385,
20380,
975,
599,
310,
445,
934,
29915,
29879,
848,
29889,
13,
13,
4706,
450,
20380,
674,
736,
3454,
313,
6144,
326,
1573,
491,
4954,
29890,
29915,
1966,
29876,
11120,
6348,
310,
13,
4706,
584,
1990,
18078,
13193,
1412,
910,
508,
367,
5407,
746,
16330,
2066,
13,
4706,
773,
263,
1856,
2974,
393,
17766,
1316,
385,
20380,
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13,
13,
4706,
6317,
1873,
15033,
1057,
29871,
29941,
29889,
29947,
13,
965,
450,
20380,
1286,
1153,
4637,
584,
1990,
18078,
12521,
6685,
5756,
2283,
29952,
746,
11735,
292,
13,
965,
738,
21022,
630,
29892,
4567,
29892,
470,
4805,
19875,
297,
263,
934,
29889,
450,
3517,
13,
965,
6030,
471,
304,
871,
12020,
584,
1990,
18078,
12521,
6685,
5756,
2283,
29952,
373,
263,
4567,
13,
965,
19875,
29889,
13,
13,
4706,
6317,
1873,
15033,
1057,
29871,
29946,
29889,
29900,
13,
965,
450,
20380,
1286,
4256,
1078,
975,
334,
9012,
29930,
297,
278,
934,
29892,
2012,
13,
965,
310,
521,
18801,
29892,
304,
14670,
304,
278,
2967,
770,
584,
2272,
29901,
1990,
18078,
601,
29889,
5971,
5160,
1412,
13,
965,
4803,
584,
29885,
621,
18078,
5756,
3744,
29889,
949,
29812,
29952,
304,
1303,
19875,
491,
19875,
2012,
13,
965,
310,
1196,
491,
1196,
29889,
13,
4706,
9995,
13,
4706,
736,
1583,
13,
13,
1678,
822,
3802,
29898,
1311,
1125,
13,
4706,
9995,
9984,
11657,
3744,
901,
1176,
1711,
934,
29899,
4561,
1213,
15945,
13,
4706,
565,
1583,
17255,
29812,
29918,
1524,
29901,
13,
9651,
1583,
17255,
29812,
29918,
1524,
29889,
5358,
580,
13,
9651,
1583,
17255,
29812,
29918,
1524,
353,
6213,
13,
4706,
2428,
2141,
5358,
580,
13,
13,
1678,
822,
2436,
29898,
1311,
29892,
995,
1125,
13,
4706,
12020,
12013,
29889,
25807,
29884,
3016,
287,
10925,
877,
3539,
1495,
13,
13,
1678,
822,
2044,
24210,
29898,
1311,
29892,
3454,
1125,
13,
4706,
12020,
12013,
29889,
25807,
29884,
3016,
287,
10925,
877,
8231,
24210,
1495,
13,
13,
1678,
822,
2044,
519,
29898,
1311,
1125,
13,
4706,
736,
7700,
13,
13,
1678,
822,
4770,
5893,
12035,
1311,
1125,
13,
4706,
9995,
29924,
6926,
372,
1950,
304,
671,
584,
1990,
18078,
5756,
3744,
29952,
2066,
13,
4706,
411,
278,
3030,
8455,
9608,
29889,
13,
4706,
9995,
13,
4706,
736,
1583,
13,
13,
1678,
822,
4770,
13322,
12035,
1311,
29892,
5566,
29918,
1853,
29892,
5566,
29918,
791,
29892,
5566,
29918,
22625,
1125,
13,
4706,
9995,
29924,
6926,
372,
1950,
304,
671,
584,
1990,
18078,
5756,
3744,
29952,
2066,
13,
4706,
411,
278,
3030,
8455,
9608,
29889,
13,
4706,
9995,
13,
4706,
1583,
29889,
5358,
580,
13,
4706,
736,
7700,
13,
13,
1678,
822,
977,
8154,
29898,
1311,
1125,
13,
4706,
12020,
12013,
29889,
25807,
29884,
3016,
287,
10925,
877,
1777,
8154,
1495,
13,
13,
1678,
822,
28371,
29898,
1311,
1125,
13,
4706,
396,
11657,
3744,
338,
1303,
29899,
6194,
29892,
577,
28371,
947,
3078,
29889,
13,
4706,
1209,
13,
13,
1678,
822,
338,
271,
1017,
29898,
1311,
1125,
13,
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736,
7700,
13,
13,
1678,
822,
21022,
403,
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1311,
29892,
2159,
29922,
8516,
1125,
13,
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396,
2823,
2045,
597,
2640,
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4691,
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990,
29914,
29941,
29914,
5258,
29914,
601,
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1420,
29937,
601,
29889,
5971,
5160,
29889,
8231,
519,
13,
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396,
363,
2020,
21022,
403,
756,
304,
12020,
29889,
13,
4706,
12020,
12013,
29889,
25807,
29884,
3016,
287,
10925,
877,
509,
4661,
403,
1495,
13,
13,
1678,
396,
6811,
2426,
10663,
5160,
17255,
6144,
1649,
6467,
372,
674,
3275,
304,
4770,
657,
5552,
1649,
373,
13,
1678,
396,
4770,
5971,
5160,
29918,
15603,
607,
5717,
903,
7469,
29918,
1445,
322,
19998,
23233,
306,
29914,
29949,
29889,
13,
1678,
396,
1334,
2609,
437,
306,
29914,
29949,
297,
4770,
6144,
1649,
1951,
372,
508,
3275,
304,
263,
7123,
908,
29889,
13,
1678,
822,
4770,
6144,
12035,
1311,
1125,
13,
4706,
1209,
13,
13,
13,
1990,
903,
5756,
3744,
1451,
2960,
20277,
29898,
3318,
1125,
13,
1678,
9995,
13463,
1078,
975,
263,
934,
29915,
29879,
521,
18801,
773,
263,
2323,
10677,
29889,
13,
13,
1678,
390,
1759,
267,
2994,
6685,
5756,
2283,
746,
11735,
292,
738,
21022,
630,
29892,
4567,
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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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278,
9027,
310,
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14213,
1711,
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313,
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13,
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259,
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1990,
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13,
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1990,
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1990,
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1990,
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13,
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1990,
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1188,
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771,
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364,
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13,
13,
13,
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13383,
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13,
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5169,
1535,
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2572,
333,
8527,
940,
7925,
13,
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13383,
13383,
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13,
13,
1990,
4134,
3709,
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7355,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
10809,
29892,
11565,
29918,
2311,
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1967,
29918,
12181,
29892,
954,
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13203,
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2428,
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2385,
3709,
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10109,
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2311,
353,
11565,
29918,
2311,
13,
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3027,
29918,
12181,
353,
1967,
29918,
12181,
13,
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1949,
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353,
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13,
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19399,
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2674,
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1123,
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262,
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29922,
5574,
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13,
13,
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1583,
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10660,
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1990,
353,
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12697,
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29900,
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334,
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696,
275,
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2572,
333,
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307,
29875,
29918,
2520,
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307,
275,
10062,
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10109,
29918,
2311,
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29889,
3027,
29918,
12181,
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] |
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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268,
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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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315,
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21533,
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13,
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2391,
3366,
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13,
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13,
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13,
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1990,
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13,
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3386,
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25932,
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13,
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11433,
3319,
1800,
13,
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3032,
5060,
487,
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308,
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268,
13,
1990,
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5983,
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21533,
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13,
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9995,
4435,
5920,
501,
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13,
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2752,
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268,
13,
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732,
1990,
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13,
1678,
822,
3386,
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2541,
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25932,
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3319,
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308,
13,
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13,
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903,
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29901,
13,
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1853,
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7562,
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1542,
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29931,
29918,
27839,
4741,
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16524,
13,
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13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
1134,
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1311,
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7761,
29961,
710,
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4435,
3399,
5983,
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3108,
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13,
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9995,
2577,
357,
29901,
7562,
5920,
501,
2096,
28025,
15945,
29908,
13,
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3032,
3993,
29918,
8977,
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657,
877,
1853,
1495,
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396,
1134,
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13,
1678,
732,
1853,
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842,
357,
13,
1678,
822,
1134,
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1311,
29892,
995,
29901,
7761,
29961,
710,
29892,
376,
4435,
3399,
5983,
1542,
2385,
20068,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
7562,
5920,
501,
2096,
28025,
15945,
29908,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
1853,
2033,
353,
995,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
2752,
3399,
5983,
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1311,
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1599,
851,
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13,
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2577,
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29901,
7562,
5920,
501,
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13,
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736,
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3032,
3993,
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8977,
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657,
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268,
13,
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5983,
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2752,
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7562,
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3993,
29918,
8977,
1839,
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5983,
2033,
353,
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13,
268,
13,
268,
13,
1990,
7562,
3399,
5983,
1542,
2385,
29898,
3318,
1125,
13,
1678,
396,
1939,
10561,
3625,
29889,
13,
268,
13,
1678,
23158,
29918,
20387,
29931,
29918,
27839,
4741,
29918,
16524,
353,
376,
1988,
29918,
20387,
29931,
29918,
27839,
4741,
29918,
16524,
29908,
13,
1678,
323,
4717,
1177,
4214,
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2227,
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18521,
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27839,
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29918,
16524,
353,
376,
29911,
4717,
1177,
4214,
29918,
2227,
4162,
18521,
29918,
27839,
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29918,
16524,
29908,
13,
1678,
382,
8932,
29965,
8098,
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2227,
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27839,
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16524,
353,
376,
29923,
8932,
29965,
8098,
29918,
2227,
4162,
18521,
29918,
27839,
4741,
29918,
16524,
29908,
13,
268,
13,
268,
13,
1990,
26101,
1469,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
29902,
311,
635,
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278,
23158,
3195,
5881,
723,
1712,
408,
1568,
2472,
1048,
278,
6694,
848,
408,
278,
17983,
848,
29889,
2398,
29892,
727,
1795,
367,
4251,
988,
372,
338,
451,
28326,
1821,
304,
3867,
445,
3233,
310,
13173,
2472,
1048,
278,
6694,
848,
29889,
1152,
1342,
29892,
278,
848,
1122,
367,
24440,
653,
29892,
470,
1996,
263,
1661,
29899,
2218,
25071,
17327,
29889,
512,
1438,
4251,
29892,
591,
22545,
403,
363,
6996,
4902,
1048,
278,
18822,
975,
6471,
297,
278,
848,
29892,
408,
1532,
408,
738,
916,
4902,
393,
1033,
1871,
380,
1296,
8948,
414,
373,
278,
17690,
310,
4768,
2129,
278,
1904,
1122,
505,
18511,
1213,
15945,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
828,
29889,
19635,
29889,
5323,
2827,
1469,
1159,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
4706,
6694,
1469,
29901,
2391,
3366,
5160,
1469,
2385,
12436,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
308,
13,
4706,
1583,
29889,
26495,
1469,
353,
6694,
1469,
13,
268,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
3386,
29918,
2541,
29918,
4381,
29879,
29898,
25932,
29897,
1599,
376,
5323,
2827,
1469,
2385,
1115,
13,
4706,
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353,
1067,
29879,
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11433,
3319,
1800,
13,
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3032,
5060,
487,
29918,
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580,
13,
308,
13,
4706,
736,
1583,
13,
268,
13,
1678,
822,
903,
5060,
487,
29918,
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1311,
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1599,
6213,
29901,
13,
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26495,
1469,
353,
1051,
580,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
6694,
1469,
29898,
1311,
29897,
1599,
2391,
3366,
5160,
1469,
2385,
3108,
29901,
13,
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357,
29901,
25577,
373,
278,
8783,
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1304,
363,
6694,
278,
23158,
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15945,
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13,
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736,
1583,
3032,
3993,
29918,
8977,
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657,
877,
26495,
1469,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
26495,
1469,
29889,
842,
357,
13,
1678,
822,
6694,
1469,
29898,
1311,
29892,
995,
29901,
2391,
3366,
5160,
1469,
2385,
20068,
1599,
6213,
29901,
13,
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29901,
25577,
373,
278,
8783,
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13,
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3032,
3993,
29918,
8977,
1839,
26495,
1469,
2033,
353,
995,
13,
268,
13,
268,
13,
1990,
4737,
7221,
29909,
566,
277,
2624,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
29968,
20817,
1741,
363,
4332,
3864,
2767,
1754,
304,
385,
7855,
29915,
29879,
15562,
1213,
15945,
13,
268,
13,
1678,
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29907,
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29903,
3210,
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679,
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29918,
1853,
703,
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2324,
287,
262,
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294,
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29906,
485,
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29889,
16838,
29872,
29889,
18417,
29909,
566,
277,
2624,
1159,
13,
1678,
822,
4770,
2344,
12035,
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16390,
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376,
1469,
7032,
21913,
2385,
613,
376,
1469,
21889,
21913,
2385,
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1988,
3195,
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1988,
26666,
2558,
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1988,
19132,
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1988,
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3562,
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8176,
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29954,
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29954,
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13,
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7850,
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29968,
20817,
29909,
566,
277,
7850,
2385,
3108,
29922,
8516,
29892,
13,
4706,
2030,
21913,
29901,
7761,
29961,
8516,
29892,
376,
14732,
21913,
2385,
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8977,
1839,
1079,
647,
2558,
2033,
353,
995,
13,
268,
13,
268,
13,
1990,
476,
20817,
12763,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
12763,
19464,
363,
413,
20817,
10938,
1213,
15945,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
11010,
29889,
29968,
20817,
12763,
1159,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
4706,
1842,
12763,
29901,
851,
29892,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
308,
13,
4706,
1583,
29889,
3225,
12763,
353,
1842,
12763,
13,
268,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
3386,
29918,
2541,
29918,
4381,
29879,
29898,
25932,
29897,
1599,
376,
29968,
20817,
12763,
2385,
1115,
13,
4706,
1583,
353,
1067,
29879,
29889,
11433,
3319,
1800,
13,
4706,
1583,
3032,
5060,
487,
29918,
4381,
29879,
580,
13,
308,
13,
4706,
736,
1583,
13,
268,
13,
1678,
822,
903,
5060,
487,
29918,
4381,
29879,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
1583,
29889,
3225,
12763,
353,
851,
580,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
1842,
12763,
29898,
1311,
29897,
1599,
851,
29901,
13,
4706,
9995,
2577,
357,
29901,
450,
7531,
413,
20817,
1842,
10938,
29889,
910,
338,
263,
5199,
19909,
1029,
307,
1842,
10938,
1213,
15945,
13,
4706,
736,
1583,
3032,
3993,
29918,
8977,
29889,
657,
877,
3225,
12763,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
3225,
12763,
29889,
842,
357,
13,
1678,
822,
1842,
12763,
29898,
1311,
29892,
995,
29901,
851,
29897,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
450,
7531,
413,
20817,
1842,
10938,
29889,
910,
338,
263,
5199,
19909,
1029,
307,
1842,
10938,
1213,
15945,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
3225,
12763,
2033,
353,
995,
13,
268,
13,
268,
13,
1990,
7670,
1917,
12763,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
12763,
1426,
310,
263,
1820,
29899,
1767,
3787,
10938,
1213,
15945,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
11010,
29889,
2558,
1917,
12763,
1159,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
4706,
1820,
12763,
29901,
851,
29892,
13,
4706,
995,
12763,
29901,
851,
29892,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
308,
13,
4706,
1583,
29889,
1989,
12763,
353,
1820,
12763,
13,
4706,
1583,
29889,
1767,
12763,
353,
995,
12763,
13,
268,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
3386,
29918,
2541,
29918,
4381,
29879,
29898,
25932,
29897,
1599,
376,
2558,
1917,
12763,
2385,
1115,
13,
4706,
1583,
353,
1067,
29879,
29889,
11433,
3319,
1800,
13,
4706,
1583,
3032,
5060,
487,
29918,
4381,
29879,
580,
13,
308,
13,
4706,
736,
1583,
13,
268,
13,
1678,
822,
903,
5060,
487,
29918,
4381,
29879,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
1583,
29889,
1989,
12763,
353,
851,
580,
13,
4706,
1583,
29889,
1767,
12763,
353,
851,
580,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
1820,
12763,
29898,
1311,
29897,
1599,
851,
29901,
13,
4706,
9995,
2577,
357,
29901,
450,
10650,
10938,
363,
278,
1820,
297,
278,
1820,
29899,
1767,
3787,
1213,
15945,
13,
4706,
736,
1583,
3032,
3993,
29918,
8977,
29889,
657,
877,
1989,
12763,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
1989,
12763,
29889,
842,
357,
13,
1678,
822,
1820,
12763,
29898,
1311,
29892,
995,
29901,
851,
29897,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
450,
10650,
10938,
363,
278,
1820,
297,
278,
1820,
29899,
1767,
3787,
1213,
15945,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
1989,
12763,
2033,
353,
995,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
995,
12763,
29898,
1311,
29897,
1599,
851,
29901,
13,
4706,
9995,
2577,
357,
29901,
450,
10650,
10938,
363,
278,
995,
297,
278,
1820,
29899,
1767,
3787,
1213,
15945,
13,
4706,
736,
1583,
3032,
3993,
29918,
8977,
29889,
657,
877,
1767,
12763,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
1767,
12763,
29889,
842,
357,
13,
1678,
822,
995,
12763,
29898,
1311,
29892,
995,
29901,
851,
29897,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
450,
10650,
10938,
363,
278,
995,
297,
278,
1820,
29899,
1767,
3787,
1213,
15945,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
1767,
12763,
2033,
353,
995,
13,
268,
13,
268,
13,
1990,
7315,
1542,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
3388,
1746,
1134,
1213,
15945,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
11010,
29889,
3388,
1542,
1159,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
4706,
1820,
1542,
29901,
7761,
29961,
8516,
29892,
851,
13192,
8516,
29892,
13,
4706,
995,
1542,
29901,
7761,
29961,
8516,
29892,
851,
13192,
8516,
29892,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
308,
13,
4706,
1583,
29889,
1989,
1542,
353,
1820,
1542,
13,
4706,
1583,
29889,
1767,
1542,
353,
995,
1542,
13,
268,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
3386,
29918,
2541,
29918,
4381,
29879,
29898,
25932,
29897,
1599,
376,
3388,
1542,
2385,
1115,
13,
4706,
1583,
353,
1067,
29879,
29889,
11433,
3319,
1800,
13,
4706,
1583,
3032,
5060,
487,
29918,
4381,
29879,
580,
13,
308,
13,
4706,
736,
1583,
13,
268,
13,
1678,
822,
903,
5060,
487,
29918,
4381,
29879,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
1583,
29889,
1989,
1542,
353,
1583,
29889,
1525,
29907,
25593,
29918,
29903,
3210,
26862,
29889,
2671,
29918,
1958,
3366,
1989,
1542,
16862,
4381,
13,
4706,
1583,
29889,
1767,
1542,
353,
1583,
29889,
1525,
29907,
25593,
29918,
29903,
3210,
26862,
29889,
2671,
29918,
1958,
3366,
1767,
1542,
16862,
4381,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
1820,
1542,
29898,
1311,
29897,
1599,
7761,
29961,
8516,
29892,
851,
5387,
13,
4706,
9995,
2577,
357,
29901,
7670,
1134,
297,
263,
2910,
15945,
29908,
13,
4706,
736,
1583,
3032,
3993,
29918,
8977,
29889,
657,
877,
1989,
1542,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
1989,
1542,
29889,
842,
357,
13,
1678,
822,
1820,
1542,
29898,
1311,
29892,
995,
29901,
7761,
29961,
8516,
29892,
851,
2314,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
7670,
1134,
297,
263,
2910,
15945,
29908,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
1989,
1542,
2033,
353,
995,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
995,
1542,
29898,
1311,
29897,
1599,
7761,
29961,
8516,
29892,
851,
5387,
13,
4706,
9995,
2577,
357,
29901,
5167,
310,
278,
995,
297,
263,
2910,
15945,
29908,
13,
4706,
736,
1583,
3032,
3993,
29918,
8977,
29889,
657,
877,
1767,
1542,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
1767,
1542,
29889,
842,
357,
13,
1678,
822,
995,
1542,
29898,
1311,
29892,
995,
29901,
7761,
29961,
8516,
29892,
851,
2314,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
5167,
310,
278,
995,
297,
263,
2910,
15945,
29908,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
1767,
1542,
2033,
353,
995,
13,
268,
13,
268,
13,
1990,
1619,
10520,
7858,
29931,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
12763,
19464,
363,
1619,
10520,
848,
5023,
4086,
393,
16612,
385,
1619,
10520,
1591,
1213,
15945,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
11010,
29889,
3421,
10520,
7858,
29931,
1159,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
4706,
1591,
12763,
29901,
851,
29892,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
308,
13,
4706,
1583,
29889,
2371,
12763,
353,
1591,
12763,
13,
268,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
3386,
29918,
2541,
29918,
4381,
29879,
29898,
25932,
29897,
1599,
376,
3421,
10520,
7858,
29931,
2385,
1115,
13,
4706,
1583,
353,
1067,
29879,
29889,
11433,
3319,
1800,
13,
4706,
1583,
3032,
5060,
487,
29918,
4381,
29879,
580,
13,
308,
13,
4706,
736,
1583,
13,
268,
13,
1678,
822,
903,
5060,
487,
29918,
4381,
29879,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
1583,
29889,
2371,
12763,
353,
851,
580,
13,
268,
13,
268,
13,
1678,
732,
6799,
13,
1678,
822,
1591,
12763,
29898,
1311,
29897,
1599,
851,
29901,
13,
4706,
9995,
2577,
357,
29901,
450,
7531,
10938,
297,
278,
8783,
29915,
29879,
7481,
29889,
910,
338,
263,
5199,
19909,
313,
3126,
23755,
29897,
1591,
10938,
1213,
15945,
13,
4706,
736,
1583,
3032,
3993,
29918,
8977,
29889,
657,
877,
2371,
12763,
1495,
29871,
396,
1134,
29901,
11455,
13,
268,
13,
1678,
732,
2371,
12763,
29889,
842,
357,
13,
1678,
822,
1591,
12763,
29898,
1311,
29892,
995,
29901,
851,
29897,
1599,
6213,
29901,
13,
4706,
9995,
22304,
29901,
450,
7531,
10938,
297,
278,
8783,
29915,
29879,
7481,
29889,
910,
338,
263,
5199,
19909,
313,
3126,
23755,
29897,
1591,
10938,
1213,
15945,
13,
4706,
1583,
3032,
3993,
29918,
8977,
1839,
2371,
12763,
2033,
353,
995,
13,
268,
13,
268,
13,
1990,
19014,
1542,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
7327,
1746,
1134,
1213,
15945,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
11010,
29889,
7327,
1542,
1159,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
308,
13,
268,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
3386,
29918,
2541,
29918,
4381,
29879,
29898,
25932,
29897,
1599,
376,
7327,
1542,
2385,
1115,
13,
4706,
1583,
353,
1067,
29879,
29889,
11433,
3319,
1800,
13,
4706,
1583,
3032,
5060,
487,
29918,
4381,
29879,
580,
13,
308,
13,
4706,
736,
1583,
13,
268,
13,
1678,
822,
903,
5060,
487,
29918,
4381,
29879,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
1209,
13,
268,
13,
268,
13,
1990,
9681,
1542,
2385,
29898,
21533,
15646,
1125,
13,
1678,
9995,
4557,
848,
1134,
29901,
1472,
29892,
6043,
29892,
3273,
29892,
2992,
636,
15945,
29908,
13,
268,
13,
1678,
5195,
29907,
25593,
29918,
29903,
3210,
26862,
353,
679,
29918,
11010,
29918,
1853,
703,
510,
29889,
2324,
287,
262,
29889,
29886,
387,
294,
375,
29906,
485,
307,
29889,
11010,
29889,
4557,
1542,
1159,
13,
1678,
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13,
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13,
308,
13,
4706,
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353,
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13,
268,
13,
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13,
1678,
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263,
544,
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360,
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322,
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3993,
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8977,
1839,
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2033,
353,
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13,
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13,
268,
13,
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21533,
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1746,
1134,
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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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13,
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1426,
13,
2
] |
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() | [
1,
515,
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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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study/demo.py | t-ntran/vscode | 0 | 139754 |
def initials(name):
parts = name.split(' ')
letters = ''
for part in parts:
letters += part[0]
return letters | [
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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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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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2
] |
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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9995,
25584,
993,
24221,
14009,
1213,
15945,
13,
13,
3166,
4770,
29888,
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1053,
8542,
13,
5215,
26110,
408,
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29889,
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13,
13,
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422,
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272,
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3318,
1125,
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363,
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292,
322,
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292,
263,
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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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353,
29871,
29906,
396,
3118,
29899,
1990,
317,
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29901,
1060,
14393,
304,
278,
770,
470,
451,
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13,
13,
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317,
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491,
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1122,
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313,
4746,
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13,
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3579,
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13,
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317,
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3709,
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268,
13,
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4770,
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12035,
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408,
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297,
385,
302,
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450,
317,
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3386,
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310,
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24947,
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393,
1014,
4563,
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8824,
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8824,
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1053,
3731,
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1982,
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565,
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401,
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510,
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13,
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338,
6213,
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13,
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302,
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13,
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302,
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1583,
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657,
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333,
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325,
876,
363,
270,
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325,
297,
302,
29962,
13,
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302,
353,
17288,
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325,
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363,
270,
29892,
325,
297,
302,
565,
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13,
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13,
308,
13,
1678,
302,
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29918,
484,
1141,
29890,
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13,
13,
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13,
13,
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402,
1430,
2544,
2965,
14445,
29954,
1955,
13054,
29924,
835,
13383,
13383,
13383,
13383,
7346,
2277,
13,
13,
1990,
5739,
7492,
22461,
4540,
29901,
13,
268,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
21669,
11759,
1402,
3579,
19290,
1125,
13,
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9995,
319,
2531,
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5687,
338,
263,
380,
28225,
2740,
1158,
29871,
2729,
373,
5613,
9262,
29889,
13,
9651,
7806,
12623,
29892,
278,
285,
27958,
21669,
526,
4629,
322,
337,
17743,
1312,
964,
263,
716,
12623,
29889,
29871,
13,
9651,
2973,
1269,
716,
12623,
278,
1788,
24144,
7113,
385,
14413,
6216,
2264,
29889,
13,
4706,
9995,
13,
4706,
1583,
29889,
7323,
2785,
353,
21669,
13,
4706,
1583,
29889,
4738,
362,
353,
29871,
29900,
13,
4706,
1583,
3032,
485,
29887,
353,
6213,
396,
319,
19698,
6216,
2264,
363,
445,
12623,
29889,
13,
4706,
396,
5739,
7492,
22461,
4540,
29889,
9202,
2264,
3285,
274,
1883,
578,
369,
3285,
5478,
403,
580,
508,
367,
2183,
408,
3168,
29901,
13,
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363,
285,
297,
4852,
9202,
2264,
613,
376,
29883,
1883,
578,
369,
613,
376,
6149,
403,
29908,
1125,
13,
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565,
285,
297,
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5085,
29901,
29871,
13,
18884,
731,
5552,
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1311,
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285,
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29961,
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2314,
13,
268,
13,
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6216,
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8762,
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263,
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29900,
29899,
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29900,
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29900,
13,
268,
13,
1678,
822,
274,
1883,
578,
369,
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1311,
29892,
14020,
29896,
29892,
14020,
29906,
29892,
270,
29922,
29900,
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29945,
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13,
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396,
19928,
367,
8762,
297,
263,
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29889,
13,
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736,
6213,
13,
308,
13,
1678,
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403,
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14020,
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270,
29922,
29900,
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13,
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19928,
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297,
263,
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6213,
470,
14020,
13,
308,
13,
1678,
822,
2767,
29898,
1311,
29892,
2246,
29922,
29900,
29889,
29955,
29892,
274,
1883,
578,
369,
29922,
29900,
29889,
29945,
29892,
5478,
362,
29922,
29900,
29889,
29896,
29892,
270,
29922,
29900,
29889,
29929,
1125,
13,
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9995,
5020,
15190,
278,
4665,
491,
18851,
278,
2246,
285,
27958,
21669,
29892,
13,
9651,
322,
27878,
2109,
292,
963,
964,
263,
716,
12623,
29889,
13,
4706,
9995,
13,
4706,
396,
27930,
29889,
13,
4706,
282,
353,
12705,
3552,
1311,
29889,
9202,
2264,
29898,
29916,
511,
921,
29897,
363,
921,
297,
1583,
29889,
7323,
2785,
29897,
396,
1334,
557,
342,
29899,
4102,
29889,
13,
4706,
263,
353,
1583,
3032,
485,
29887,
353,
5785,
29898,
2083,
29898,
29888,
363,
285,
29892,
921,
297,
282,
876,
847,
7431,
29898,
29886,
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13,
4706,
921,
353,
1375,
29898,
29888,
363,
285,
29892,
921,
297,
282,
29897,
13,
4706,
343,
353,
4236,
29898,
29888,
363,
285,
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921,
297,
282,
29897,
13,
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474,
353,
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13,
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1550,
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1405,
7431,
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1311,
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2785,
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334,
2246,
29901,
13,
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396,
1334,
5790,
21669,
505,
263,
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8825,
310,
1641,
6206,
29892,
13,
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396,
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1641,
5186,
304,
313,
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29899,
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2264,
511,
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411,
278,
8062,
342,
29889,
13,
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565,
921,
718,
313,
29891,
29899,
29916,
29897,
334,
4036,
580,
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282,
29961,
29875,
3816,
29900,
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13,
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282,
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29875,
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13,
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29901,
13,
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474,
353,
313,
29875,
29974,
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1273,
7431,
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29897,
13,
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396,
830,
24601,
29889,
13,
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330,
353,
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13,
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1550,
7431,
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529,
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1311,
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7323,
2785,
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13,
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396,
14542,
852,
20459,
1546,
27878,
2109,
362,
310,
11825,
470,
5478,
362,
29889,
13,
9651,
396,
20749,
362,
4772,
29879,
1887,
3523,
2946,
491,
7344,
292,
2531,
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6894,
537,
29889,
13,
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565,
4036,
580,
529,
270,
29901,
13,
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474,
353,
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14486,
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580,
334,
313,
2435,
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6817,
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4961,
13,
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432,
353,
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3881,
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29900,
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29875,
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718,
3464,
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29875,
29974,
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7431,
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4961,
13,
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330,
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1311,
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578,
369,
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29961,
29875,
3816,
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1402,
282,
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29926,
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270,
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369,
876,
13,
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13,
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330,
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403,
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270,
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362,
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13,
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1583,
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2785,
353,
330,
13,
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1583,
29889,
4738,
362,
4619,
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13,
308,
13,
1678,
732,
6799,
13,
1678,
822,
1029,
29887,
29898,
1311,
1125,
13,
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396,
319,
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6216,
2264,
338,
7424,
304,
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1269,
12623,
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13,
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565,
451,
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3032,
485,
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353,
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2785,
4961,
847,
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2785,
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13,
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736,
1583,
3032,
485,
29887,
13,
268,
13,
1678,
6588,
29918,
9202,
2264,
353,
1029,
29887,
13,
13,
12739,
353,
5739,
7492,
22461,
4540,
13,
13,
29937,
402,
29909,
363,
5685,
1446,
1546,
29871,
29900,
29889,
29900,
29899,
29896,
29889,
29900,
393,
758,
25534,
6133,
3694,
29901,
13,
29937,
1990,
5057,
11031,
12739,
29898,
15462,
7492,
22461,
4540,
1125,
13,
29937,
1678,
822,
6216,
2264,
29898,
1311,
29892,
921,
1125,
13,
29937,
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736,
921,
13,
29937,
1678,
822,
274,
1883,
578,
369,
29898,
1311,
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921,
29892,
343,
29892,
270,
29922,
29900,
29889,
29945,
1125,
13,
29937,
4706,
736,
313,
29916,
29974,
29891,
29897,
847,
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29906,
13,
29937,
1678,
822,
5478,
403,
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1311,
29892,
921,
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270,
29922,
29900,
29889,
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1125,
13,
29937,
4706,
736,
1375,
29898,
29896,
29892,
4236,
29898,
29900,
29892,
921,
718,
4036,
580,
29930,
29900,
29889,
29906,
29899,
29900,
29889,
29896,
876,
13,
29937,
13,
29937,
3249,
353,
5057,
11031,
12739,
4197,
8172,
580,
363,
474,
297,
3464,
29898,
29896,
29900,
29900,
29897,
2314,
13,
29937,
1454,
474,
297,
3464,
29898,
29896,
29900,
29900,
1125,
13,
29937,
1678,
10364,
29889,
5504,
580,
13,
29937,
1678,
1596,
10364,
29889,
12483,
482,
29918,
9202,
2264,
13,
29937,
2158,
10364,
29889,
7323,
2785,
13,
2
] |
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,
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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()
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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})
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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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] |
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