text stringlengths 0 1.05M | meta dict |
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__author__ = 'Gennady Kovalev <gik@bigur.ru>'
__copyright__ = '(c) 2016-2019 Development management business group'
__licence__ = 'For license information see LICENSE'
import collections
class Table(object):
def __init__(self):
self.__numbered = []
self.__named = collections.OrderedDict()
de... | {
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__author__ = 'Gennady Kovalev <gik@bigur.ru>'
__copyright__ = '(c) 2016-2019 Development management business group'
__licence__ = 'For license information see LICENSE'
import esl.table
import esl.interpreter
def next_(obj, key=None):
if isinstance(obj, list):
keys = range(0, len(obj))
elif isinstance... | {
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"path": "esl/extensions/basic.py",
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__author__ = 'Gennady Kovalev <gik@bigur.ru>'
__copyright__ = '(c) 2016-2019 Development management business group'
__licence__ = 'For license information see LICENSE'
import logging
import ply.lex
import collections
logger = logging.getLogger(__name__)
class LexError(Exception):
pass
class Lexer(object):
... | {
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__author__ = 'Gennady Kovalev <gik@bigur.ru>'
__copyright__ = '(c) 2016-2019 Development management business group'
__licence__ = 'For license information see LICENSE'
import logging
import ply.yacc
import esl.lex
import esl.interpreter
logger = logging.getLogger(__name__)
class ParseError(Exception):
pass
c... | {
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__author__ = 'Gennady Kovalev <gik@bigur.ru>'
__copyright__ = '(c) 2016-2019 Development management business group'
__licence__ = 'For license information see LICENSE'
import sys
import inspect
import logging
import traceback
import esl.parse
import esl.namespace
import esl.table
import esl.function
import esl.extens... | {
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__author__ = 'geoffc'
from base_service import BaseService
from bson.objectid import ObjectId
class PrivateMessagingService(BaseService):
def __init__(self):
super(PrivateMessagingService, self).__init__()
self.collection = self.db.private_messages
def add_private_message(self, from_user_id, ... | {
"repo_name": "GeoffColburn/hackathon",
"path": "api/services/private_messaging.py",
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__author__ = 'geoffc'
from pymongo import MongoClient
from bson.objectid import ObjectId
class UserService(object):
def __init__(self):
self.client = MongoClient('localhost', 27017)
self.db = self.client["hack"]
self.collection = self.db.users
self.default_avatar = '/images/anon.jp... | {
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"path": "api/services/user_service.py",
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__author__ = 'geoffc'
import cherrypy
import os
from api.resources.Todo import Todo
from api.resources.User import User
from api.resources.Project import Project
from api.resources.PrivateMessage import PrivateMessage
from api.tools.jsonify import jsonify
from api.services.user_service import UserService
... | {
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"path": "server.py",
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"""
AbsoluteImport
AbsoluteImport ensures module and package imports always work, from relative or absolute path.
Cyclical imports are fine.
"""
# AbsoluteImport:
#
# - Get absolute path from the relative path give (or take absolute path)
# - Load module, store in absolute path key
# - Reload() module is availab... | {
"repo_name": "ghowland/AbsoluteImport",
"path": "AbsoluteImport/__init__.py",
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__author__ = 'Geoff'
import cherrypy
from BaseResource import BaseResource
from ..services.private_messaging import PrivateMessagingService
@cherrypy.popargs('id')
class PrivateMessage(BaseResource):
exposed = True
def __init__(self):
super(PrivateMessage, self).__init__()
self.service = Priv... | {
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"path": "api/resources/PrivateMessage.py",
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__author__ = 'Geoff'
import cherrypy
from BaseResource import BaseResource
from ..services.project_service import ProjectService
@cherrypy.popargs('id')
class Project(BaseResource):
exposed = True
def __init__(self):
super(Project, self).__init__()
self.service = ProjectService()
def GET... | {
"repo_name": "GeoffColburn/hackathon",
"path": "api/resources/Project.py",
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__author__ = 'Geoffrey Cheung'
# coding: utf-8
from os import listdir, remove
from os.path import isfile, join, split
from hashlib import md5, sha1
from fileobj import fileObject
fileList = []
sortedList = []
debug = False
path = ''
debug_path = "C:\Users\Geoffrey&Gillian\Desktop\Test Folder"
def isSameFile(pth1, pt... | {
"repo_name": "kahogeoff/Repeated-Remover",
"path": "src/core.py",
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"size": "1285",
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"has_no_key... |
from flask import Flask, jsonify, render_template
import utorrentclient
from datetime import timedelta
from Maraschino import app
from maraschino.tools import *
@app.route('/xhr/utorrent/')
@requires_auth
def xhr_utorrent():
# initialize empty list, which will be later populated with listing
# of active torr... | {
"repo_name": "Sir-Henry-Curtis/Ironworks",
"path": "builtinPlugins/utorrent.py",
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"line_max": 183,
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"autogenerated": false,
"ratio": 3.71,
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... |
from flask import Flask, jsonify, render_template
import utorrentclient
from datetime import timedelta
from Maraschino import app
from maraschino.tools import *
def app_link():
utorrent_ip = get_setting_value('utorrent_ip')
utorrent_port = get_setting_value('utorrent_port')
return 'http://%s:%s/gui/... | {
"repo_name": "mrkipling/maraschino",
"path": "modules/utorrent.py",
"copies": "7",
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"line_max": 183,
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"autogenerated": false,
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from flask import Flask, jsonify, render_template
import transmissionrpc
from datetime import timedelta
from maraschino.tools import *
from maraschino import app, logger
def log_exception(e):
logger.log('Transmission :: EXCEPTION -- %s' % e, 'DEBUG')
@app.route('/xhr/transmission')
@app.route('/xhr/transmissio... | {
"repo_name": "Sir-Henry-Curtis/Ironworks",
"path": "builtinPlugins/transmission.py",
"copies": "3",
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"config... |
from flask import render_template
import transmissionrpc
from datetime import timedelta
from maraschino.tools import *
from maraschino import app, logger
def log_exception(e):
logger.log('Transmission :: EXCEPTION -- %s' % e, 'DEBUG')
@app.route('/xhr/transmission/')
@requires_auth
def xhr_transmission():
... | {
"repo_name": "gugahoi/maraschino",
"path": "modules/transmission.py",
"copies": "2",
"size": "2760",
"license": "mit",
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"autogenerated": false,
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__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Program"
__license__ = "MIT"
import sys
import gzip
def get_distance(dataPointX, centroidX):
# Calculate Euclidean distance.
return abs(centroidX - dataPointX)
# ---------------------------------------------------------------... | {
"repo_name": "georgeha/k-means-map-reduce",
"path": "mapper.py",
"copies": "1",
"size": "3667",
"license": "mit",
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"h... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Program"
__license__ = "MIT"
import sys
import os
import radical.pilot as rp
import time
import gzip
""" DESCRIPTION: mpk-means
For every task A_n (mapper) is started
"""
# READ: The RADICAL-Pilot documentation:
# http://radi... | {
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__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Program"
__license__ = "MIT"
import os, sys, math
from random import randint
def get_distance(dataPointX, dataPointY, centroidX, centroidY):
# Calculate Euclidean distance.
return math.sqrt(math.pow((centroidY - ... | {
"repo_name": "georgeha/k-means-version_2",
"path": "clustering_the_elements.py",
"copies": "1",
"size": "3073",
"license": "mit",
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"autogenerated": false,
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"config_te... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Project"
__license__ = "MIT"
""" A Mandelbrot Fractal Generator Using Pilot Job
This is an example of mandelbrot Fracatl Generator
using the capabilities of Pilot Job API.
It requires the Python Image Library (PIL... | {
"repo_name": "georgeha/mandelbrot",
"path": "mandel_with_txt_files/mandelbrot_pilot_cores.py",
"copies": "1",
"size": "8079",
"license": "mit",
"hash": -7257832465514424000,
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"autogenerated": false,
"ratio": 3.71961325966850... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Project"
__license__ = "MIT"
""" A Mandelbrot Fractal Generator Using Pilot Job
This is an example of mandelbrot Fracatl Generator
using the capabilities of Pilot Job API.
It requires the Python Image Library (PIL) which c... | {
"repo_name": "georgeha/mandelbrot",
"path": "mandelbrot_core/mandelbrot_pilot_cores.py",
"copies": "1",
"size": "7097",
"license": "mit",
"hash": -8474553532810824000,
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"line_max": 125,
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"autogenerated": false,
"ratio": 3.1997294860234446,
"c... |
_author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Project"
__license__ = "MIT"
import os, sys, radical.pilot, math # , multiprocessing
from random import randint
import time
"""
This is a simple impementation of k-means algorithm
using the Radical-Pilot API.
"""
#... | {
"repo_name": "georgeha/k-means-version_2",
"path": "k-means.py",
"copies": "1",
"size": "14529",
"license": "mit",
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"autogenerated": false,
"ratio": 3.926756756756757,
"config_test": false,
... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2012-2013, The Pilot Project"
__license__ = "MIT"
"""
This is an implementation of mandelbrot using the Pilot
Job API.
It requires the Python Image Library (PIL) which can be easily
installed with 'easy_install PIL'.
Also, it requires ... | {
"repo_name": "georgeha/mandelbrot",
"path": "mandelbrot_CUs/mandelbrot_pilot.py",
"copies": "1",
"size": "7077",
"license": "mit",
"hash": 3818461902787658000,
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"autogenerated": false,
"ratio": 3.183535762483131,
"config_t... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2013-2014, http://radical.rutgers.edu"
__license__ = "MIT"
import sys
import numpy as np
def get_distance(dataPoint, centroid):
# Calculate Euclidean distance.
return np.sqrt(sum((dataPoint - centroid) ** 2))
# -----------------------------------... | {
"repo_name": "georgeha/k-means",
"path": "mapper.py",
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"config_test": false,
"has_no_keywo... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2013-2014, http://radical.rutgers.edu"
__license__ = "MIT"
import sys
import os
import radical.pilot as rp
import time
import copy
import numpy as np
SHARED_INPUT_FILE = 'dataset.in'
MY_STAGING_AREA = 'staging:///'
""" DESCRIPTION: k-means
For every ta... | {
"repo_name": "georgeha/k-means",
"path": "k-means.py",
"copies": "1",
"size": "11326",
"license": "mit",
"hash": 1484047892875371300,
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"has_no_ke... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2014, The RADICAL Group"
__license__ = "MIT"
import os
import sys
import radical.pilot as rp
import math
import time
"""
This is a simple implementation of k-means algorithm using the RADICAl-Pilot API.
"""
#-----------------------------------... | {
"repo_name": "JensTimmerman/radical.pilot",
"path": "examples/kmeans/k-means.py",
"copies": "1",
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"license": "mit",
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"ratio": 3.9404866608032836,
"config_... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2014, The RADICAL Group"
__license__ = "MIT"
import os, sys, math
def get_distance(dataPointX, dataPointY, centroidX, centroidY):
# Calculate Euclidean distance.
return math.sqrt(math.pow((centroidY - dataPointY), 2) + math.pow((centroidX -... | {
"repo_name": "JensTimmerman/radical.pilot",
"path": "examples/kmeans/finding_the_new_centroids.py",
"copies": "1",
"size": "2480",
"license": "mit",
"hash": -8944830847040610000,
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"autogenerated": false,
"ratio": 3.663220088... |
__author__ = "George Chantzialexiou"
__copyright__ = "Copyright 2014, The RADICAL Group"
__license__ = "MIT"
import sys
import random
#------------------------------------------------------------------------------
if __name__ == "__main__":
args = sys.argv[1:]
if len(args) < 1:
print "Usage: py... | {
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"""
Module for parsing URLs in chat or on demand
"""
import re
import requests
from googl import Googl
from bs4 import BeautifulSoup
from logsetup import strip_colors
from basemodule import BaseModule, BaseCommandContext
regex = re.compile("""
^( # Starts with
htt... | {
"repo_name": "nickraptis/fidibot",
"path": "src/modules/urlparser.py",
"copies": "1",
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"license": "bsd-2-clause",
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"ratio": 3.918348623853211,
"config_test... |
import argparse, sys
from fluff.commands.heatmap import heatmap
from fluff.commands.bandplot import bandplot
from fluff.commands.profile import profile
from fluff.color import DEFAULT_COLORS
from fluff.config import *
from fluff.fluffio import *
def parse_cmds():
description = """
fluff v{0}
""".format(F... | {
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"config_test": false,
"has_no_ke... |
__author__ = 'george'
import os
import sys
import pysam
### External imports ###
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import numpy as np
from scipy.stats import scoreatpercentile
### My imports ###
from fluff.color import parse_colors
from fluff.fluffio import load_read_... | {
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"path": "fluff/commands/bandplot.py",
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"autogenerated": false,
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... |
__author__ = 'george'
import os
import sys
import pysam
### My imports ###
from fluff.color import parse_colors
from fluff.plot import profile_screenshot
from fluff.util import process_groups
def profile(args):
interval = args.interval
datafiles = [x.strip() for x in args.datafiles]
annotation = args.ann... | {
"repo_name": "simonvh/fluff",
"path": "fluff/commands/profile.py",
"copies": "1",
"size": "2024",
"license": "mit",
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"alpha_frac": 0.5662055336,
"autogenerated": false,
"ratio": 4.225469728601253,
"config_test": false,
... |
__author__ = 'George'
SMART_WATER_BUTTON_MAC = '74:75:48:2e:2b:4c'
HEFTY_BUTTON_MAC = '74:c2:46:4f:56:d8'
GILETTE_BUTTON_MAC = '74:c2:46:84:ab:8e'
from scapy.all import *
from actions import *
from database import *
buttonAddresses = [button.macAddress for button in Button.select()]
def arp_display(pkt):
if pkt[A... | {
"repo_name": "FireEater64/DashBroker",
"path": "dashbroker.py",
"copies": "1",
"size": "1168",
"license": "mit",
"hash": 9083564017544630000,
"line_mean": 33.3529411765,
"line_max": 108,
"alpha_frac": 0.6900684932,
"autogenerated": false,
"ratio": 2.9346733668341707,
"config_test": false,
"h... |
__author__ = 'George Oblapenko'
__license__ = "GPLv3"
"""
Everything is serialized and stored in string columns
"""
import sqlite3
from json import loads, dumps
def create_db(db_data: dict):
conn = sqlite3.connect(db_data['db_path'])
c = conn.cursor()
sql_string = 'CREATE TABLE ' + db_data['table_name'] ... | {
"repo_name": "Kunstmord/krakenous",
"path": "krakenous/backend_sqlite.py",
"copies": "1",
"size": "5035",
"license": "mit",
"hash": -2984214910022787000,
"line_mean": 31.4838709677,
"line_max": 100,
"alpha_frac": 0.5851042701,
"autogenerated": false,
"ratio": 3.334437086092715,
"config_test": ... |
__author__ = 'George Oblapenko'
__license__ = "GPLv3"
"""
The structure is {id: {'feature1': feature1, ...}, ...}
"""
import shelve
def open_db(db_data: dict, writeback: bool=False) -> dict:
"""
open the db for reading, return everything in a dict
"""
return {'db': shelve.open(db_data['db_path'], wri... | {
"repo_name": "Kunstmord/krakenous",
"path": "krakenous/backend_shelve.py",
"copies": "1",
"size": "3780",
"license": "mit",
"hash": -139085527151305460,
"line_mean": 27.6363636364,
"line_max": 97,
"alpha_frac": 0.5775132275,
"autogenerated": false,
"ratio": 3.2755632582322356,
"config_test": f... |
__author__ = 'georgeoblapenko'
from rest_framework import serializers
from abandoned.models import Author, Reason, Tag, Project, Language
class BaseProjectSerializer(serializers.ModelSerializer):
class Meta:
model = Project
fields = ('id', 'name', 'link', 'upvotes')
class BaseAuthorSerializer(s... | {
"repo_name": "Kunstmord/abandoned",
"path": "abandoned/serializers.py",
"copies": "1",
"size": "2072",
"license": "mit",
"hash": -8900844864139869000,
"line_mean": 25.9220779221,
"line_max": 119,
"alpha_frac": 0.6703667954,
"autogenerated": false,
"ratio": 4.17741935483871,
"config_test": fals... |
__author__ = 'George Oblapenko, Viktor Evstratov'
__license__ = "GPLv3"
from krakenous.errors import *
import os.path
from json import dumps
class DataSet(object):
def __init__(self, **kwargs):
"""
:param kwargs: Parameters need to connect to the database. The ``backend`` parameter specifies the
... | {
"repo_name": "Kunstmord/krakenous",
"path": "krakenous/dataset.py",
"copies": "1",
"size": "18871",
"license": "mit",
"hash": 7062138514705600000,
"line_mean": 48.6631578947,
"line_max": 121,
"alpha_frac": 0.5653648455,
"autogenerated": false,
"ratio": 4.046097770154374,
"config_test": false,
... |
__author__ = 'George Oblapenko, Viktor Evstratov'
__license__ = "GPLv3"
"""
Various pre-rolled functions to ease common operations - extracting data from csv files or files in a folder,
sync/dump/copy datasets
"""
from csv import reader
from os.path import join, isfile
import numpy as np
from os import walk
from kraken... | {
"repo_name": "Kunstmord/krakenous",
"path": "krakenous/prerolled.py",
"copies": "1",
"size": "8099",
"license": "mit",
"hash": -755131192073956200,
"line_mean": 46.9289940828,
"line_max": 120,
"alpha_frac": 0.624891962,
"autogenerated": false,
"ratio": 3.8005631159080244,
"config_test": false,... |
__author__ = "Georges Goetz"
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
from cfsite.apps.crawlers.gdcrawl import GdocsCrawler
from cfsite.apps.events.models import Event, Category
from cfsite.apps.crawlers.deduplication import SimpleDeduplicator
class GdocsCrawlerController:
""" GdocsCrawlerContr... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/crawlers/controllers.py",
"copies": "1",
"size": "6706",
"license": "mit",
"hash": 8605568204761102000,
"line_mean": 40.91875,
"line_max": 79,
"alpha_frac": 0.6200417537,
"autogenerated": false,
"ratio": 4.079075425790754,
"config_... |
__author__ = "Georges Goetz"
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
from django.contrib import admin
from django.contrib.admin import SimpleListFilter
from cfsite.apps.events.models import Event, Location, Category
class LocationAdmin(admin.ModelAdmin):
""" LocationAdmin
----------
Ba... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/events/admin.py",
"copies": "1",
"size": "2912",
"license": "mit",
"hash": 6821206817712581000,
"line_mean": 29.6526315789,
"line_max": 80,
"alpha_frac": 0.6301510989,
"autogenerated": false,
"ratio": 4.301329394387001,
"config_tes... |
__author__ = "Georges Goetz"
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
import datetime, math
from django.shortcuts import render
from django.http import HttpResponseRedirect
from cfsite.apps.events.models import Location, Category, Event, CF_CATEGORIES
from cfsite.apps.events.forms import SearchForm
f... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/events/views.py",
"copies": "1",
"size": "21924",
"license": "mit",
"hash": -8419293062083762000,
"line_mean": 34.4184168013,
"line_max": 91,
"alpha_frac": 0.5857051633,
"autogenerated": false,
"ratio": 3.692152239811384,
"config_t... |
__author__ = 'Georges Goetz'
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
import datetime
from cfsite.apps.events.models import Event
class SimpleDeduplicator:
""" SimpleDeduplicator
----------
SimpleDeduplicator does simple de-duplication by looking for exact
matches for events in the e... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/crawlers/deduplication.py",
"copies": "1",
"size": "3443",
"license": "mit",
"hash": -566158356776291800,
"line_mean": 34.8645833333,
"line_max": 96,
"alpha_frac": 0.6212605286,
"autogenerated": false,
"ratio": 4.031615925058548,
"... |
__author__ = "Georges Goetz"
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
import datetime
from django.db import models
from django.core.exceptions import ValidationError
# the crazyfish categories
ART = 'arts & culture'
CLASS = 'classes & workshop'
CONF = 'conference'
FAM = 'family'
SPORT = 'sport'
MUSI... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/events/models.py",
"copies": "1",
"size": "12704",
"license": "mit",
"hash": -8751016268075820000,
"line_mean": 36.0379008746,
"line_max": 80,
"alpha_frac": 0.6298016373,
"autogenerated": false,
"ratio": 4.355159410353102,
"config_... |
__author__ = "Georges Goetz"
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
import gspread
from cfsite.apps.events.models import Event, Location, Category
from django.core.exceptions import ValidationError
# TODO: figure out how to securely connect to the spreadsheet
# Username and password in the code is... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/crawlers/gdcrawl.py",
"copies": "1",
"size": "17165",
"license": "mit",
"hash": 3106076356666432000,
"line_mean": 36.3965141612,
"line_max": 101,
"alpha_frac": 0.5834547043,
"autogenerated": false,
"ratio": 4.130173243503369,
"conf... |
__author__ = "Georges Goetz"
__email__ = "ggoetz@stanford.edu"
__status__ = "Prototype"
import pytz
from datetime import datetime
from django import forms
from cfsite.apps.events.models import Category, Location
class SearchForm(forms.Form):
""" SearchForm
----------
A class which handles user search dat... | {
"repo_name": "susanctu/Crazyfish-Public",
"path": "cfsite/apps/events/forms.py",
"copies": "1",
"size": "5420",
"license": "mit",
"hash": 1630426087755676400,
"line_mean": 32.0487804878,
"line_max": 109,
"alpha_frac": 0.6175276753,
"autogenerated": false,
"ratio": 4.471947194719472,
"config_te... |
__author__ = 'georgevanburgh'
from databaseAccess import *
import redisBroker
import spotipy
from redisBroker import RedisBroker
from twilioBroker import TwilioBroker
from playlistUtils import PlaylistUtils
class SmsBroker():
def __init__(self):
self.mySpotipy = spotipy.Spotify()
self.twilioBroke... | {
"repo_name": "prakharbahuguna/PyPlyServer",
"path": "src/smsBroker.py",
"copies": "1",
"size": "6106",
"license": "mit",
"hash": -8633836039673070000,
"line_mean": 39.1710526316,
"line_max": 138,
"alpha_frac": 0.6459220439,
"autogenerated": false,
"ratio": 4.10073875083949,
"config_test": fals... |
__author__ = 'georgevanburgh'
import json
from redis import Redis
from databaseAccess import *
class RedisBroker:
topicName = "party"
def __init__(self):
self.redisClient = Redis(host="redis92559-pyply.j.layershift.co.uk", password="O3KcaI9RRj")
def partyTogglePause(self, partyId):
me... | {
"repo_name": "prakharbahuguna/PyPlyServer",
"path": "src/redisBroker.py",
"copies": "1",
"size": "1101",
"license": "mit",
"hash": 3757454497032783400,
"line_mean": 28.7837837838,
"line_max": 109,
"alpha_frac": 0.6675749319,
"autogenerated": false,
"ratio": 3.540192926045016,
"config_test": fa... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from collections import defaultdict
from reveal_user_annotation.text.clean_text import clean_document
from reveal_user_annotation.text.text_util import reduce_list_of_bags_of_words
def clean_twitter_list(twitter_list,
sent_tokenize, _treebank... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "reveal_user_annotation/twitter/clean_twitter_list.py",
"copies": "1",
"size": "6415",
"license": "apache-2.0",
"hash": 1770363774246986000,
"line_mean": 55.2719298246,
"line_max": 143,
"alpha_frac": 0.551208106,
"autogenerated": false,
"r... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from collections import deque
import numpy as np
from reveal_graph_embedding.eps_randomwalk.push import pagerank_limit_push
from reveal_graph_embedding.eps_randomwalk.push import pagerank_lazy_push
from reveal_graph_embedding.eps_randomwalk.push import cumulative_pag... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/eps_randomwalk/similarity.py",
"copies": "1",
"size": "9150",
"license": "apache-2.0",
"hash": 246414637663764670,
"line_mean": 40.2162162162,
"line_max": 120,
"alpha_frac": 0.4643715847,
"autogenerated": false,
"ra... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from dateutil import parser as duparser
import calendar
# import datetime
########################################################################################################################
# Reddit author features.
#############################################... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/features/author.py",
"copies": "1",
"size": "10270",
"license": "apache-2.0",
"hash": -6597080007978971000,
"line_mean": 31.5,
"line_max": 120,
"alpha_frac": 0.69... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from io import StringIO
import xml.etree.cElementTree as etree
def document_generator(source_file_path_list):
for file_path in source_file_path_list:
with open(file_path, "r", encoding="iso-8859-1") as f:
# Remove .html entities.
... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/discussion/slashdot.py",
"copies": "1",
"size": "1892",
"license": "apache-2.0",
"hash": -790954836902392300,
"line_mean": 24.5675675676,
"line_max": 81,
"alpha_frac": 0.6390063425,
"autogenerated": false,
"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from news_popularity_prediction.features.common import update_feature_value
from news_popularity_prediction.features.basic import update_max_depth, update_ave_depth,\
update_max_width, update_ave_width, update_max_depth_max_width_ratio, update_depth_width_ratio_av... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/features/basic_wrappers.py",
"copies": "1",
"size": "2728",
"license": "apache-2.0",
"hash": 809705538265929700,
"line_mean": 52.4901960784,
"line_max": 112,
"alpha_frac": 0.6418621701,
"autogenerated": false,... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from news_popularity_prediction.features.common import update_feature_value
from news_popularity_prediction.features.branching import update_hirsch_index, update_wiener_index, update_randic_index
def update_branching_hirsch_index(feature_array, i, j, intermediate_di... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/features/branching_wrappers.py",
"copies": "1",
"size": "1421",
"license": "apache-2.0",
"hash": -8401985070075916000,
"line_mean": 49.75,
"line_max": 119,
"alpha_frac": 0.663617171,
"autogenerated": false,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from news_popularity_prediction.features.common import update_feature_value
from news_popularity_prediction.features.temporal import update_first_half_time_difference_mean,\
update_last_half_time_difference_mean, update_time_difference_std, update_timestamp_range
... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/features/temporal_wrappers.py",
"copies": "1",
"size": "1423",
"license": "apache-2.0",
"hash": 7263172238257490000,
"line_mean": 53.7307692308,
"line_max": 118,
"alpha_frac": 0.7505270555,
"autogenerated": fa... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from news_popularity_prediction.features.common import update_feature_value
from news_popularity_prediction.features.user_graph import update_user_count_eponymous,\
update_user_count_estimated, update_user_hirsch_eponymous,\
update_graph_outdegree_entropy, upd... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/features/user_graph_wrappers.py",
"copies": "1",
"size": "4220",
"license": "apache-2.0",
"hash": -7367903151866513000,
"line_mean": 64.9375,
"line_max": 136,
"alpha_frac": 0.6528436019,
"autogenerated": false... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from news_popularity_prediction.learning.single_experiment import DiscussionModellingExperiment
def reddit_news_experiments(data_folder):
EXPERIMENT_CONSTRUCTION_TYPE = dict()
EXPERIMENT_CONSTRUCTION_TYPE["add_branching_features"] = False
EXPERIMENT_CONS... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/entry_points/snow_2016_workshop/experiment_configurations.py",
"copies": "1",
"size": "39038",
"license": "apache-2.0",
"hash": 4907162746229361000,
"line_mean": 49.5019404916,
"line_max": 105,
"alpha_frac": 0.5... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from reveal_popularity_prediction.common.config_package import get_threads_number
from reveal_popularity_prediction.reveal.utility import make_time_window_filter, safe_establish_mongo_connection,\
process_tweets_and_extract_urls, collect_social_context, form_graph... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/reveal/integration.py",
"copies": "1",
"size": "6660",
"license": "apache-2.0",
"hash": -6090459146896161000,
"line_mean": 57.9380530973,
"line_max": 120,
"alpha_... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from reveal_popularity_prediction.features import comment_tree
from reveal_popularity_prediction.features import user_graph
from reveal_popularity_prediction.features import temporal
from reveal_popularity_prediction.features import author
def wrapper_comment_count(... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/features/wrappers.py",
"copies": "1",
"size": "9591",
"license": "apache-2.0",
"hash": 7883678094850239000,
"line_mean": 38.3073770492,
"line_max": 158,
"alpha_fr... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from reveal_user_annotation.common.config_package import get_package_path
from reveal_user_annotation.common.datarw import get_file_row_generator
def get_topic_set(file_path):
"""
Opens one of the topic set resource files and returns a set of topics.
- ... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "reveal_user_annotation/twitter/manage_resources.py",
"copies": "1",
"size": "3845",
"license": "apache-2.0",
"hash": 6783256295000493000,
"line_mean": 30.5163934426,
"line_max": 122,
"alpha_frac": 0.6564369311,
"autogenerated": false,
"ra... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from reveal_user_annotation.common.config_package import get_threads_number
from reveal_user_annotation.rabbitmq.rabbitmq_util import establish_rabbitmq_connection, simple_notification,\
rabbitmq_server_service
from reveal_user_classification.reveal.utility impor... | {
"repo_name": "MKLab-ITI/reveal-user-classification",
"path": "reveal_user_classification/reveal/integration.py",
"copies": "1",
"size": "13827",
"license": "apache-2.0",
"hash": 1458931532717646000,
"line_mean": 51.5741444867,
"line_max": 131,
"alpha_frac": 0.4201923772,
"autogenerated": false,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from reveal_user_classification.preprocess.insight.insight_curation_util import make_folder_paths,\
get_number_of_nodes, preprocess_graph_data, preprocess_feature_data, make_implicit_graphs, make_labelling
def preprocess_insight_curation_dataset(insight_curation... | {
"repo_name": "MKLab-ITI/reveal-user-classification",
"path": "reveal_user_classification/preprocess/insight/preprocess_curation_datasets.py",
"copies": "2",
"size": "2951",
"license": "apache-2.0",
"hash": -7766280402403424000,
"line_mean": 46.5967741935,
"line_max": 120,
"alpha_frac": 0.4947475432,... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from reveal_user_classification.preprocess.insight.insight_multiview_util import make_folder_paths,\
get_number_of_nodes, preprocess_graph_data, preprocess_feature_data, make_implicit_graphs, make_labelling
def preprocess_insight_multiview_dataset(insight_curati... | {
"repo_name": "VinACE/reveal-user-classification",
"path": "reveal_user_classification/preprocess/insight/preprocess_multiview_datasets.py",
"copies": "2",
"size": "2925",
"license": "apache-2.0",
"hash": 4241139143204977000,
"line_mean": 45.4285714286,
"line_max": 120,
"alpha_frac": 0.4823931624,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from setuptools import setup
# from setuptools.extension import Extension
# try:
# from Cython.Distutils import build_ext
# except ImportError:
# USE_CYTHON = False
# else:
# USE_CYTHON = True
# import numpy
# C_OPT_FLAG = "-O3"
def readme():
with op... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "setup.py",
"copies": "1",
"size": "6333",
"license": "apache-2.0",
"hash": 3265258471354980400,
"line_mean": 55.0442477876,
"line_max": 119,
"alpha_frac": 0.5791883783,
"autogenerated": false,
"ratio": 3.863941427699817,
"config_test": ... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from setuptools import setup
def readme():
with open("README.md") as f:
return f.read()
setup(
name='reveal-user-classification',
version='0.1.17',
author='Georgios Rizos',
author_email='georgerizos@iti.gr',
packages=['reveal_user_c... | {
"repo_name": "VinACE/reveal-user-classification",
"path": "setup.py",
"copies": "1",
"size": "2211",
"license": "apache-2.0",
"hash": 6340050837410684000,
"line_mean": 44.1224489796,
"line_max": 159,
"alpha_frac": 0.6558118498,
"autogenerated": false,
"ratio": 4.243761996161228,
"config_test":... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from setuptools import setup
def readme():
with open("README.md") as f:
return f.read()
setup(
name='news-popularity-prediction',
version='0.1.2',
author='Georgios Rizos',
author_email='georgerizos@iti.gr',
packages=['news_popularity... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "setup.py",
"copies": "1",
"size": "1783",
"license": "apache-2.0",
"hash": 5932918811597418000,
"line_mean": 40.4651162791,
"line_max": 132,
"alpha_frac": 0.6438586652,
"autogenerated": false,
"ratio": 4.215130023640662,
"config_tes... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
from setuptools import setup
def readme():
with open("README.md") as f:
return f.read()
setup(
name='reveal-user-annotation',
version='0.2.2',
author='Georgios Rizos',
author_email='georgerizos@iti.gr',
packages=['reveal_user_annotat... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "setup.py",
"copies": "1",
"size": "2168",
"license": "apache-2.0",
"hash": -1376004872240288300,
"line_mean": 42.36,
"line_max": 132,
"alpha_frac": 0.6365313653,
"autogenerated": false,
"ratio": 4,
"config_test": false,
"has_no_keywor... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import argparse
import os
from multiprocessing import Pool
from functools import partial
import json
from reveal_user_annotation.common.config_package import get_threads_number
from reveal_user_annotation.common.datarw import load_pickle
from reveal_user_annotation.t... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "reveal_user_annotation/entry_points/extract_twitter_list_keywords.py",
"copies": "1",
"size": "3577",
"license": "apache-2.0",
"hash": -543575825798104400,
"line_mean": 42.6219512195,
"line_max": 135,
"alpha_frac": 0.6116857702,
"autogenera... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import argparse
from reveal_user_classification.preprocess.snow.snow_2014_graph_dataset_util import process_tweet_collection,\
make_directory_tree, weakly_connected_graph, make_implicit_graphs, make_annotation
from reveal_user_annotation.mongo.store_snow_data imp... | {
"repo_name": "VinACE/reveal-user-classification",
"path": "reveal_user_classification/preprocess/snow/make_snow_2014_graph_dataset.py",
"copies": "2",
"size": "4762",
"license": "apache-2.0",
"hash": 1006696371892292100,
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"line_max": 125,
"alpha_frac": 0.6593868123,
"aut... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import argparse
import scipy.sparse as spsp
from reveal_graph_embedding.common import get_threads_number
from reveal_graph_embedding.datautil.datarw import read_adjacency_matrix, write_features
from reveal_graph_embedding.embedding.arcte.arcte import arcte
def mai... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/entry_points/arcte.py",
"copies": "1",
"size": "3882",
"license": "apache-2.0",
"hash": 5518647278580490000,
"line_mean": 45.2142857143,
"line_max": 120,
"alpha_frac": 0.5350334879,
"autogenerated": false,
"ratio": ... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import collections
import numpy as np
import nltk
def combine_word_list(word_list):
"""
Combine word list into a bag-of-words.
Input: - word_list: This is a python list of strings.
Output: - bag_of_words: This is the corresponding multi-set or ba... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "reveal_user_annotation/text/text_util.py",
"copies": "1",
"size": "5084",
"license": "apache-2.0",
"hash": -1312210842806488300,
"line_mean": 35.8333333333,
"line_max": 164,
"alpha_frac": 0.644698013,
"autogenerated": false,
"ratio": 3.66... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import collections
import numpy as np
import scipy.sparse as spsp
from news_popularity_prediction.features.common import update_feature_value, replicate_feature_value
from news_popularity_prediction.features.intermediate import update_branching_randic_graph,\
up... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/discussion/features.py",
"copies": "1",
"size": "27771",
"license": "apache-2.0",
"hash": -8045712025739214000,
"line_mean": 53.6673228346,
"line_max": 125,
"alpha_frac": 0.541176047,
"autogenerated": false,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import copy
import networkx as nx
import community
import numpy as np
import scipy.sparse as sparse
import scipy.sparse.linalg as spla
from reveal_graph_embedding.embedding.laplacian import get_normalized_laplacian
def mroc(adjacency_matrix, alpha):
"""
Ext... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/competing_methods.py",
"copies": "1",
"size": "13396",
"license": "apache-2.0",
"hash": -95830354708380180,
"line_mean": 36.9490084986,
"line_max": 124,
"alpha_frac": 0.6105553897,
"autogenerated": false,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import datetime
from dateutil import parser as duparser
from urllib.parse import urlparse
import numpy as np
from reveal_popularity_prediction.builder.targets import ci_lower_bound
def extract_author_metadata(document):
author_metadata = document["author_metad... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/builder/collect/youtube/extract.py",
"copies": "1",
"size": "6821",
"license": "apache-2.0",
"hash": -498243017749565760,
"line_mean": 37.3202247191,
"line_max": 12... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import datetime
from praw.helpers import flatten_tree
def fetch_discussion(reddit_handler, url):
"""
Fetches the full discussion under a submission and stores it as a .json file.
"""
###################################################################... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/builder/collect/reddit/discussion_collector.py",
"copies": "1",
"size": "1122",
"license": "apache-2.0",
"hash": 8879757409125543000,
"line_mean": 36.4,
"line_max":... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import datetime
import time
from oauth2client.tools import argparser
from googleapiclient.errors import HttpError
from youtube_discussion_collector.auth_new import get_authenticated_service
from youtube_discussion_collector.collect import get_video_metadata, get_all... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/youtube-discussion-collector/youtube_discussion_collector/entry_points/collect_youtube_discussion.py",
"copies": "1",
"size": "2814",
"license": "apache-2.0",
"hash": 7608043736570145000,
"line_mean": 38.0833333333,
"line_... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import gc
import numpy as np
from scipy import sparse as spsp
from reveal_graph_embedding.common import get_file_row_generator
from reveal_graph_embedding.datautil.insight_datautil.insight_read_data import scipy_sparse_to_csv,\
read_adjacency_matrix
from reveal_g... | {
"repo_name": "MKLab-ITI/reveal-user-classification",
"path": "reveal_user_classification/preprocess/insight/insight_curation_util.py",
"copies": "2",
"size": "10289",
"license": "apache-2.0",
"hash": -962308171004014300,
"line_mean": 42.050209205,
"line_max": 125,
"alpha_frac": 0.6094858587,
"auto... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import gc
import os
import json
from multiprocessing import Pool
from functools import partial
import numpy as np
import scipy.sparse as spsp
import networkx as nx
from reveal_user_annotation.common.config_package import get_threads_number
from reveal_user_annotation... | {
"repo_name": "MKLab-ITI/reveal-user-classification",
"path": "reveal_user_classification/preprocess/snow/snow_2014_graph_dataset_util.py",
"copies": "2",
"size": "26162",
"license": "apache-2.0",
"hash": 5906065123967049000,
"line_mean": 53.617954071,
"line_max": 254,
"alpha_frac": 0.5831740693,
"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import heapq
import collections
import numpy as np
import scipy.sparse as spsp
from reveal_popularity_prediction.builder.collect.youtube import extract as youtube_extract
from reveal_popularity_prediction.builder.collect.reddit import extract as reddit_extract
def... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/builder/build_graphs.py",
"copies": "1",
"size": "22165",
"license": "apache-2.0",
"hash": 3775587093076856000,
"line_mean": 42.5461689587,
"line_max": 120,
"alph... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import itertools
from itertools import islice, zip_longest
import numpy as np
def grouper(iterable, n, pad_value=None):
"""
Returns a generator of n-length chunks of an input iterable, with appropriate padding at the end.
Example: grouper(3, 'abcdefg',... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "reveal_user_annotation/text/map_data.py",
"copies": "1",
"size": "1687",
"license": "apache-2.0",
"hash": -606543571179536600,
"line_mean": 28.0862068966,
"line_max": 101,
"alpha_frac": 0.6271487848,
"autogenerated": false,
"ratio": 3.428... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import json
import datetime
import numpy as np
import scipy.sparse as spsp
from scipy.stats import rankdata
from news_popularity_prediction.discussion.reddit import document_generator, get_post_url, get_post_title,\
calculate_targets, comment_generator, extract_... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/visualization/make_vizualization_json.py",
"copies": "1",
"size": "29002",
"license": "apache-2.0",
"hash": -1510998095263392000,
"line_mean": 48.6609589041,
"line_max": 248,
"alpha_frac": 0.598820771,
"autoge... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import json
import resource
import sys
resource.setrlimit(resource.RLIMIT_STACK, (2**29, -1))
sys.setrecursionlimit(10**6)
import praw
from reveal_popularity_prediction.builder.collect.reddit.reddit_util import login
from reveal_popularity_prediction.builder.collect... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/builder/collect/reddit/social_context.py",
"copies": "1",
"size": "1877",
"license": "apache-2.0",
"hash": 7625203952909292000,
"line_mean": 30.2833333333,
"line_ma... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import json
import time
import resource
import sys
resource.setrlimit(resource.RLIMIT_STACK, (2**29, -1))
sys.setrecursionlimit(10**6)
from oauth2client.tools import argparser
from googleapiclient.errors import HttpError
from youtube_discussion_collector.auth_new im... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/youtube-discussion-collector/youtube_discussion_collector/entry_points/get_social_context_json_string.py",
"copies": "1",
"size": "3561",
"license": "apache-2.0",
"hash": 4206937247753720000,
"line_mean": 41.9036144578,
"l... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import json
import numpy as np
from news_popularity_prediction.discussion.targets import ci_lower_bound
def document_generator(source_file_path_list):
file_counter = 0
document_counter = 0
for file_path in source_file_path_list:
file_end = Fal... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/discussion/reddit.py",
"copies": "1",
"size": "5173",
"license": "apache-2.0",
"hash": -7532877170292594000,
"line_mean": 29.4294117647,
"line_max": 126,
"alpha_frac": 0.5151749468,
"autogenerated": false,
"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import json
def document_generator(source_file_path_list):
document_counter = 0
for file_path in source_file_path_list:
with open(file_path, "r") as batch_file:
for file_row in batch_file:
clean_file_row = file_row.strip(... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/discussion/anonymized.py",
"copies": "1",
"size": "1715",
"license": "apache-2.0",
"hash": 3126807402999354400,
"line_mean": 22.1756756757,
"line_max": 88,
"alpha_frac": 0.6233236152,
"autogenerated": false,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import multiprocessing as mp
import itertools
import numpy as np
import scipy.sparse as sparse
from reveal_graph_embedding.common import get_threads_number
from reveal_graph_embedding.eps_randomwalk.transition import get_natural_random_walk_matrix
from reveal_graph_e... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/arcte/arcte.py",
"copies": "1",
"size": "31089",
"license": "apache-2.0",
"hash": -4349601350105960000,
"line_mean": 44.1875,
"line_max": 126,
"alpha_frac": 0.5750265367,
"autogenerated": false,
"ratio": 4... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import multiprocessing as mp
import itertools
import time
import numpy as np
import scipy.sparse as spsp
import networkx as nx
import networkx.algorithms.components as nxalgcom
from pymongo import ASCENDING
from reveal_user_annotation.text.clean_text import clean_do... | {
"repo_name": "MKLab-ITI/reveal-user-annotation",
"path": "reveal_user_annotation/mongo/preprocess_data.py",
"copies": "1",
"size": "38688",
"license": "apache-2.0",
"hash": 8982759260109491000,
"line_mean": 46.8811881188,
"line_max": 164,
"alpha_frac": 0.5525227461,
"autogenerated": false,
"rati... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
from scipy.sparse import issparse
from sklearn.preprocessing import normalize
from sklearn.utils.validation import check_array
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.preprocessing import LabelBinarizer
def chi2_contingency_... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/community_weighting.py",
"copies": "1",
"size": "4862",
"license": "apache-2.0",
"hash": 5107299913212408000,
"line_mean": 34.75,
"line_max": 135,
"alpha_frac": 0.6110654052,
"autogenerated": false,
"ratio... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
from scipy.sparse import issparse
from reveal_graph_embedding.common import load_pickle, store_pickle
def read_features(method_name, path):
sparse_feature_method_set = set()
sparse_feature_method_set.update(["lapple",
... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/datautil/feature_rw_util.py",
"copies": "1",
"size": "1887",
"license": "apache-2.0",
"hash": 465582679657471550,
"line_mean": 25.5774647887,
"line_max": 67,
"alpha_frac": 0.5092739799,
"autogenerated": false,
"rati... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
from sklearn.kernel_approximation import AdditiveChi2Sampler
from sklearn.preprocessing import normalize, scale
def normalize_community_features(features):
"""
This performs TF-IDF-like normalization of community embedding features.
I... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/common.py",
"copies": "1",
"size": "2711",
"license": "apache-2.0",
"hash": -2455812329798093000,
"line_mean": 32.4691358025,
"line_max": 120,
"alpha_frac": 0.6761342678,
"autogenerated": false,
"ratio": 3... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import numpy.linalg as npla
import scipy as sp
import scipy.sparse as spsp
import scipy.sparse.linalg as spla
import networkx as nx
from networkx.algorithms.link_analysis import pagerank_scipy
from reveal_graph_embedding.eps_randomwalk.transition i... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/implicit.py",
"copies": "1",
"size": "18204",
"license": "apache-2.0",
"hash": 139986600674726180,
"line_mean": 48.2,
"line_max": 202,
"alpha_frac": 0.6465062624,
"autogenerated": false,
"ratio": 4.0435362... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as sparse
import ctypes as c
import multiprocessing as mp
def get_label_based_random_walk_matrix(adjacency_matrix, labelled_nodes, label_absorption_probability):
"""
Returns the label-absorbing random walk transition pr... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/eps_randomwalk/transition.py",
"copies": "1",
"size": "4482",
"license": "apache-2.0",
"hash": -6583358189759707000,
"line_mean": 44.2727272727,
"line_max": 159,
"alpha_frac": 0.6773761714,
"autogenerated": false,
"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as sparse
from reveal_graph_embedding.common import get_file_row_generator
def read_adjacency_matrix(file_path, separator):
"""
Reads an edge list in csv format and returns the adjacency matrix in SciPy Sparse COOrdina... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/datautil/asu_datautil/asu_read_data.py",
"copies": "1",
"size": "3506",
"license": "apache-2.0",
"hash": 7739011533472357000,
"line_mean": 33.0388349515,
"line_max": 118,
"alpha_frac": 0.6597261837,
"autogenerated": f... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as spsp
from collections import defaultdict
from reveal_graph_embedding.common import get_file_row_generator
def read_adjacency_matrix(file_path, separator, numbering="matlab"):
"""
Reads an edge list in csv format and... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/datautil/snow_datautil/snow_read_data.py",
"copies": "1",
"size": "6993",
"license": "apache-2.0",
"hash": 9121931460106166000,
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"line_max": 134,
"alpha_frac": 0.5923065923,
"autogenerated": false,... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as spsp
from sklearn.decomposition import TruncatedSVD
from annoy import AnnoyIndex
def make_text_graph(user_lemma_matrix, dimensionality, metric, number_of_estimators, number_of_neighbors):
user_lemma_matrix_tfidf = augmen... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/text_graph.py",
"copies": "1",
"size": "3432",
"license": "apache-2.0",
"hash": 6628167248275306000,
"line_mean": 38.8953488372,
"line_max": 164,
"alpha_frac": 0.6470416788,
"autogenerated": false,
"ratio"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as spsp
from reveal_graph_embedding.common import get_file_row_generator
def read_adjacency_matrix(file_path, separator, numbering="matlab"):
"""
Reads an edge list in csv format and returns the adjacency matrix in Sci... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/datautil/insight_datautil/insight_read_data.py",
"copies": "1",
"size": "5679",
"license": "apache-2.0",
"hash": -3107044730087050000,
"line_mean": 31.6379310345,
"line_max": 118,
"alpha_frac": 0.5942947702,
"autogene... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as spsp
from reveal_graph_embedding.embedding.implicit import get_implicit_combinatorial_adjacency_matrix,\
get_implicit_directed_adjacency_matrix
def get_unnormalized_laplacian(adjacency_matrix):
# Calculate diagonal ... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/embedding/laplacian.py",
"copies": "1",
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"hash": -4674284847818926000,
"line_mean": 33.7066666667,
"line_max": 116,
"alpha_frac": 0.7355880092,
"autogenerated": false,
"ratio"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as spsp
from reveal_user_annotation.common.datarw import get_file_row_generator
def read_oslom_features(oslom_folder, number_of_nodes):
oslom_path = oslom_folder + "/tp"
number_of_levels = 0
while True:
... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/datautil/read_exotic_features.py",
"copies": "1",
"size": "5345",
"license": "apache-2.0",
"hash": -5791575413045564000,
"line_mean": 28.8603351955,
"line_max": 93,
"alpha_frac": 0.5861552853,
"autogenerated": false,
... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
import scipy.sparse as spsp
def get_binary_graph(graph):
graph = spsp.coo_matrix(graph)
binary_graph = spsp.coo_matrix((np.ones_like(graph.data,
dtype=np.float64),
... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "reveal_fp7_module/reveal-popularity-prediction/reveal_popularity_prediction/features/common.py",
"copies": "1",
"size": "1894",
"license": "apache-2.0",
"hash": 6208719307469095000,
"line_mean": 32.2280701754,
"line_max": 89,
"alpha_frac"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
from news_popularity_prediction.datautil.feature_rw import h5load_from, get_kth_row
def fill_X_handcrafted_k_actual(dataset_k,
h5_store_files,
h5_keys,
... | {
"repo_name": "MKLab-ITI/news-popularity-prediction",
"path": "news_popularity_prediction/learning/concatenate_features.py",
"copies": "1",
"size": "3738",
"license": "apache-2.0",
"hash": -4484682567731281000,
"line_mean": 41.9655172414,
"line_max": 126,
"alpha_frac": 0.4432851792,
"autogenerated"... |
__author__ = 'Georgios Rizos (georgerizos@iti.gr)'
import numpy as np
from reveal_graph_embedding.common import get_threads_number
from reveal_graph_embedding.experiments.utility import run_experiment
####################################################################################################################... | {
"repo_name": "MKLab-ITI/reveal-graph-embedding",
"path": "reveal_graph_embedding/experiments/demo.py",
"copies": "1",
"size": "3853",
"license": "apache-2.0",
"hash": -3191426867047894000,
"line_mean": 44.3294117647,
"line_max": 120,
"alpha_frac": 0.6005709836,
"autogenerated": false,
"ratio": 4... |
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