repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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tweet-analysis-2020 | tweet-analysis-2020-main/app/twitter_service.py |
import os
from pprint import pprint
#from app.timelines.status_parser import parse_urls, parse_full_text, parse_timeline_status
from dotenv import load_dotenv
from tweepy import OAuthHandler, API, Cursor
from tweepy.error import TweepError
load_dotenv()
CONSUMER_KEY = os.getenv("TWITTER_API_KEY", default="OO... | 6,032 | 34.280702 | 169 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/file_storage.py |
import os
from pprint import pprint
from dotenv import load_dotenv
from app import DATA_DIR, seek_confirmation
from app.decorators.datetime_decorators import logstamp
from app.gcs_service import GoogleCloudStorageService
load_dotenv()
DIRPATH = os.getenv("DIRPATH", default="example/file_storage")
WIFI = (os.getenv... | 2,158 | 31.712121 | 130 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/__init__.py |
import os
import time
from dotenv import load_dotenv
load_dotenv()
APP_ENV = os.getenv("APP_ENV", "development")
SERVER_NAME = os.getenv("SERVER_NAME", "mjr-local") # the name of your Heroku app (e.g. "impeachment-tweet-analysis-9")
SERVER_DASHBOARD_URL = f"https://dashboard.heroku.com/apps/{SERVER_NAME}"
DATA_DI... | 745 | 26.62963 | 119 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/job.py |
import time
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
class Job():
def __init__(self):
self.start_at = None
self.end_at = None
self.duration_seconds = None
self.counter = None # represents the number of items process... | 962 | 23.692308 | 97 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_follower_graphs/pg_grapher.py | #from memory_profiler import profile
#
#from networkx import DiGraph
#
#from app import seek_confirmation
#from app.decorators.datetime_decorators import logstamp
#from app.decorators.number_decorators import fmt_n
#from app.pg_pipeline.pg_service import PgService
#from app.retweet_graphs_v2.graph_storage import GraphS... | 2,225 | 29.493151 | 106 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_follower_graphs/bq_grapher.py | from memory_profiler import profile
from networkx import DiGraph
from app import seek_confirmation
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryService
from app.retweet_graphs_v2.graph_storage import GraphStorage
from app... | 1,924 | 28.166667 | 102 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_follower_graphs/bq_prep.py |
from app.bq_service import BigQueryService
if __name__ == "__main__":
bq_service = BigQueryService(verbose=True)
print("FLATTENING USER FRIENDS TABLE...")
bq_service.destructively_migrate_user_friends_flat()
print("BOTS ABOVE 80...")
bq_service.destructively_migrate_bots_table()
print("BOT... | 404 | 24.3125 | 58 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_analysis/top_status_tags.py |
import os
from collections import Counter
from pandas import DataFrame, read_csv
import re
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
from app.job import Job
from app.decorators.number_decorators import fmt_n
LIMIT = os.getenv("LIMIT") # 1000 # None # os.getenv("LIMIT")
BAT... | 4,422 | 34.384 | 114 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_analysis/download_tweet_details_v6.py |
import os
from pandas import read_csv, DataFrame
from app.file_storage import FileStorage
from app.bq_service import BigQueryService
from app.job import Job
from app.decorators.number_decorators import fmt_n
LIMIT = os.getenv("LIMIT") # for development purposes
BATCH_SIZE = int(os.getenv("BATCH_SIZE", default="10_0... | 1,331 | 30.714286 | 130 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_analysis/user_details_vq.py |
import os
from pprint import pprint
import json
from pandas import DataFrame, read_csv
from scipy.stats import ks_2samp #, ttest_ind
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
from app.job import Job
from app.decorators.number_decorators import fmt_n
from app.decorators.datet... | 4,852 | 37.515873 | 137 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/tweets.py | import os
from dotenv import load_dotenv
load_dotenv()
START_AT = os.getenv("START_AT", default="2019-12-10")
END_AT = os.getenv("END_AT", default="2020-02-10")
from app.pg_pipeline import Pipeline
if __name__ == "__main__":
pipeline = Pipeline()
print("START_AT:", START_AT)
print("END AT:", END_AT)
... | 383 | 19.210526 | 62 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/bot_followers.py |
import os
from app.pg_pipeline import Pipeline
BOT_MIN = float(os.getenv("BOT_MIN", default="0.8"))
if __name__ == "__main__":
pipeline = Pipeline()
pipeline.download_bot_followers(bot_min=BOT_MIN)
| 212 | 15.384615 | 52 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/pg_service.py |
from psycopg2 import connect
from psycopg2.extras import DictCursor
from app.decorators.number_decorators import fmt_n
from app.decorators.datetime_decorators import logstamp
from app.pg_pipeline.models import DATABASE_URL, USER_FRIENDS_TABLE_NAME
class PgService:
def __init__(self, database_url=DATABASE_URL):
... | 3,349 | 34.638298 | 164 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/community_predictions.py | #import os
#from dotenv import load_dotenv
#load_dotenv()
#START_AT = os.getenv("START_AT", default="2019-12-12")
#END_AT = os.getenv("END_AT", default="2020-02-10")
from app.pg_pipeline import Pipeline
if __name__ == "__main__":
pipeline = Pipeline()
#print("START_AT:", START_AT)
#print("END AT:", EN... | 451 | 22.789474 | 78 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/user_friends.py |
from app.pg_pipeline import Pipeline
if __name__ == "__main__":
pipeline = Pipeline()
pipeline.download_user_friends()
| 132 | 12.3 | 36 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/models.py |
import os
from dotenv import load_dotenv
from sqlalchemy import create_engine, Column, Integer, BigInteger, String, Boolean, DateTime, ARRAY, TIMESTAMP
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm.exc import NoResultFound
load_dotenv()
DATABASE_U... | 6,147 | 37.425 | 197 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/__init__.py |
import time
import os
from dotenv import load_dotenv
from app import APP_ENV
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryService
from app.pg_pipeline.models import BoundSession, db, Tweet, UserFriend, UserDetail, Retweet... | 10,402 | 38.555133 | 186 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/user_details.py |
from app.pg_pipeline import Pipeline
if __name__ == "__main__":
pipeline = Pipeline()
pipeline.download_user_details() # takes about 50 minutes for 3.6M users in batches of 2500
| 191 | 18.2 | 95 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/retweeter_details.py |
from app.pg_pipeline import Pipeline
if __name__ == "__main__":
pipeline = Pipeline()
pipeline.download_retweeter_details() # takes about 16 minutes for 2.7M users in batches of 2500
| 196 | 18.7 | 100 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/pg_pipeline/investigate.py |
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.pg_pipeline.pg_service import PgService
def fetch_in_batches():
pg_service = PgService()
pg_service.cursor.execute("SELECT user_id, screen_name, friend_count FROM user_friends LIMIT 1000;")
... | 1,658 | 26.65 | 165 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_impact_v4/daily_active_edge_downloader.py |
import os
from pandas import read_csv, DataFrame
from app.retweet_graphs_v2.k_days.generator import DateRangeGenerator
from app.file_storage import FileStorage
from app.bq_service import BigQueryService
from app.job import Job
from app.decorators.number_decorators import fmt_n
LIMIT = os.getenv("LIMIT") # for deve... | 2,704 | 38.779412 | 142 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_impact_v4/daily_active_edge_friend_grapher_v2.py |
import os
from pandas import DataFrame, read_csv
from networkx import DiGraph, write_gpickle, read_gpickle
from memory_profiler import profile
from app.decorators.number_decorators import fmt_n
from app.job import Job
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
DATE = os.get... | 3,954 | 32.803419 | 130 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_impact_v4/active_edge_v6_downloader.py |
import os
from pandas import read_csv, DataFrame
from app.file_storage import FileStorage
from app.bq_service import BigQueryService
from app.job import Job
from app.decorators.number_decorators import fmt_n
LIMIT = os.getenv("LIMIT") # for development purposes
BATCH_SIZE = int(os.getenv("BATCH_SIZE", default="10_0... | 1,296 | 31.425 | 130 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_impact_v4/daily_active_edge_friend_grapher.py |
import os
from pandas import DataFrame, read_csv
from networkx import DiGraph, write_gpickle, read_gpickle
from memory_profiler import profile
from app.decorators.number_decorators import fmt_n
from app.job import Job
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
DATE = os.get... | 6,941 | 29.716814 | 125 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_impact_v4/active_edge_v7_downloader.py |
import os
from pandas import read_csv, DataFrame
from app.file_storage import FileStorage
from app.bq_service import BigQueryService
from app.job import Job
from app.decorators.number_decorators import fmt_n
LIMIT = os.getenv("LIMIT") # for development purposes
BATCH_SIZE = int(os.getenv("BATCH_SIZE", default="10_0... | 2,028 | 33.982759 | 130 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_impact_v4/daily_active_user_friend_grapher.py |
import os
from pandas import DataFrame, read_csv
from networkx import DiGraph, write_gpickle, read_gpickle
from app.decorators.number_decorators import fmt_n
from app.job import Job
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
DATE = os.getenv("DATE", default="2020-01-23")
TWE... | 4,479 | 31.941176 | 111 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp_v2/model_promotion.py | import os
from app.nlp.model_storage import ModelStorage
SOURCE = os.getenv("SOURCE", default="nlp_v2/models/dev/multinomial_nb")
DESTINATION = os.getenv("DESTINATION", default="nlp_v2/models/best/multinomial_nb")
if __name__ == "__main__":
storage = ModelStorage(dirpath=SOURCE)
storage.promote_model(desti... | 340 | 25.230769 | 83 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp_v2/bulk_predict.py | import os
from pandas import DataFrame, read_csv
from app import seek_confirmation, DATA_DIR
from app.job import Job
from app.bq_service import BigQueryService
from app.nlp.model_storage import ModelStorage
LIMIT = os.getenv("LIMIT")
BATCH_SIZE = int(os.getenv("BATCH_SIZE", default="100000"))
CSV_FILEPATH = os.path... | 2,580 | 32.960526 | 140 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp_v2/model_training.py |
import os
from datetime import datetime
from pprint import pprint
from pandas import DataFrame, read_csv
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report # accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import... | 7,434 | 34.574163 | 236 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp_v2/client.py | import os
from app.nlp.model_storage import ModelStorage
MODEL_DIRPATH = os.getenv("MODEL", default="nlp_v2/models/best/multinomial_nb")
if __name__ == "__main__":
storage = ModelStorage(dirpath=MODEL_DIRPATH)
tv = storage.load_vectorizer()
print(type(tv))
print("FEATURES / TOKENS:", len(tv.get_fea... | 693 | 22.133333 | 79 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp_v2/bulk_predict_old.py | # Don't use this method. Just fetch tweets once otherwise its very expensive
#import os
#
#from app import seek_confirmation
#from app.job import Job
#from app.bq_service import BigQueryService
#from app.nlp.model_storage import ModelStorage
#
#LIMIT = os.getenv("LIMIT")
#BATCH_SIZE = int(os.getenv("BATCH_SIZE", defau... | 2,078 | 33.65 | 112 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp_v2/bert_score_uploader.py |
import os
from pandas import read_csv
from app import DATA_DIR, seek_confirmation
from app.job import Job
from app.bq_service import BigQueryService
from app.retweet_graphs_v2.k_days.generator import DateRangeGenerator
BATCH_SIZE = int(os.getenv("BATCH_SIZE", default=25000)) # the max number of processed users to s... | 1,628 | 37.785714 | 202 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/retweet_analyzer.py |
import os
from functools import lru_cache
from pandas import DataFrame
import matplotlib.pyplot as plt
import plotly.express as px
import squarify
from app import APP_ENV, seek_confirmation
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.bot_communi... | 7,116 | 41.873494 | 174 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/bot_similarity_grapher.py |
import os
from networkx import write_gpickle, read_gpickle, jaccard_coefficient, Graph
from app import seek_confirmation
from app.bot_communities.bot_retweet_grapher import BotRetweetGrapher
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
class BotSimilarit... | 5,089 | 40.382114 | 141 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/token_analyzer.py | from collections import Counter
from pandas import DataFrame
from gensim.corpora import Dictionary
from gensim.models.ldamulticore import LdaMulticore
#from gensim.models import TfidfModel
def summarize_token_frequencies(token_sets):
"""
Param token_sets : a list of tokens for each document in a collection
... | 2,787 | 40 | 210 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/bot_profile_analyzer_v2.py | import os
from app import DATA_DIR
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
from app.bot_communities.tokenizers import Tokenizer #, SpacyTokenizer
from app.bot_communities.token_analyzer import summarize_token_frequencies
from pandas import DataFrame
if __name__ == "__main... | 3,836 | 48.192308 | 163 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/bot_tweet_analyzer_v2.py | import os
from app import DATA_DIR, seek_confirmation
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
from app.bot_communities.tokenizers import Tokenizer
from app.bot_communit... | 4,750 | 45.126214 | 147 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/bot_retweet_grapher.py |
import os
from networkx import DiGraph, read_gpickle
from app.retweet_graphs_v2.retweet_grapher import RetweetGrapher
BOT_MIN = float(os.getenv("BOT_MIN", default="0.8"))
class BotRetweetGrapher(RetweetGrapher):
def __init__(self, bot_min=BOT_MIN):
self.bot_min = float(bot_min)
super().__init__... | 1,232 | 27.022727 | 102 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/tokenizers.py | import os
from collections import Counter
from functools import lru_cache
from pprint import pprint
import re
from dotenv import load_dotenv
from pandas import DataFrame
from nltk.corpus import stopwords as NLTK_STOPWORDS
from gensim.parsing.preprocessing import STOPWORDS as GENSIM_STOPWORDS
from spacy.lang.en.stop_wo... | 5,396 | 38.108696 | 137 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/tweet_analyzer.py |
import os
from pandas import DataFrame, read_csv
from app.decorators.datetime_decorators import dt_to_s, logstamp
from app.decorators.number_decorators import fmt_n
from app.bot_communities.spectral_clustermaker import SpectralClustermaker
BATCH_SIZE = 50_000 # we are talking about downloading 1-2M tweets
class Co... | 2,690 | 36.901408 | 159 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/daily_retweet_analyzer.py |
import os
from functools import lru_cache
import time
from concurrent.futures import as_completed, ProcessPoolExecutor, ThreadPoolExecutor
from threading import current_thread #, #Thread, Lock, BoundedSemaphore
from dotenv import load_dotenv
from pandas import to_datetime
from app import APP_ENV, seek_confirmation
f... | 4,807 | 41.175439 | 157 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/spectral_clustermaker.py | import os
from functools import lru_cache
from dotenv import load_dotenv
from pandas import DataFrame
from networkx import adjacency_matrix, Graph
from sklearn.cluster import SpectralClustering
import matplotlib.pyplot as plt
from app import seek_confirmation, APP_ENV
from app.bot_communities.bot_similarity_grapher i... | 4,582 | 38.852174 | 181 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/csv_storage.py |
import os
from pandas import DataFrame, read_csv
from app.decorators.datetime_decorators import dt_to_s, logstamp
from app.decorators.number_decorators import fmt_n
from app.bot_communities.spectral_clustermaker import SpectralClustermaker
BATCH_SIZE = 50_000 # we are talking about downloading millions of records
... | 3,037 | 39.506667 | 163 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/bot_communities/bot_profile_analyzer_v1.py | import os
from app import DATA_DIR
from app.bq_service import BigQueryService
from app.bot_communities.tokenizers import Tokenizer, SpacyTokenizer
from app.bot_communities.token_analyzer import summarize_token_frequencies
from pandas import DataFrame
if __name__ == "__main__":
local_dirpath = os.path.join(DATA_... | 4,124 | 40.25 | 130 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/bq_topic_grapher.py |
import os
from networkx import DiGraph
from memory_profiler import profile
from dotenv import load_dotenv
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.bq_grapher import BigQueryGrapher
load_dotenv()
USERS_LIMIT = int(os.getenv("US... | 3,146 | 36.464286 | 196 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/base_grapher.py |
import time
import os
from datetime import datetime as dt
import pickle
import json
from dotenv import load_dotenv
from networkx import DiGraph, write_gpickle
from pandas import DataFrame
from app import APP_ENV, DATA_DIR
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators i... | 7,716 | 40.047872 | 162 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/psycopg_batch_grapher.py |
from networkx import DiGraph
from memory_profiler import profile
from app.friend_graphs.psycopg_grapher import PsycopgGrapher
class Grapher(PsycopgGrapher):
@profile
def perform(self):
self.start()
self.graph = DiGraph()
self.cursor.execute(self.sql)
while True:
r... | 1,011 | 28.764706 | 85 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/psycopg_grapher.py |
import psycopg2
from networkx import DiGraph
from memory_profiler import profile
from app import APP_ENV
from app.pg_pipeline.models import DATABASE_URL, USER_FRIENDS_TABLE_NAME
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.base_graph... | 2,660 | 34.959459 | 155 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/bq_list_grapher.py |
import pickle
from networkx import DiGraph
from memory_profiler import profile
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.bq_grapher import BigQueryGrapher
class BigQueryListGrapher(BigQueryGrapher):
@profile
def perform... | 2,112 | 30.073529 | 94 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/psycopg_list_grapher.py |
from networkx import DiGraph
from memory_profiler import profile
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.psycopg_grapher import PsycopgGrapher
class Grapher(PsycopgGrapher):
@profile
def perform(self):
self.ed... | 1,545 | 26.607143 | 98 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/bq_grapher.py |
from networkx import DiGraph
from memory_profiler import profile
from app.bq_service import BigQueryService
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.base_grapher import BaseGrapher
class BigQueryGrapher(BaseGrapher):
def __... | 1,768 | 30.035088 | 128 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/psycopg_set_grapher.py |
from networkx import DiGraph
from memory_profiler import profile
from app.friend_graphs.psycopg_grapher import PsycopgGrapher
class Grapher(PsycopgGrapher):
@profile
def perform(self):
self.edges = set() # set prevents duplicates
self.running_results = []
self.cursor.execute(self.sq... | 1,400 | 29.456522 | 101 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_graphs/graph_analyzer.py |
import os
import time
from functools import lru_cache
from networkx import read_gpickle, DiGraph
from dotenv import load_dotenv
from memory_profiler import profile
from app import DATA_DIR
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.base_grapher import BaseGrapher
load_dotenv()
JOB_ID... | 2,266 | 30.486111 | 106 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/model_promotion.py | import os
from app.nlp.model_storage import ModelStorage
MODEL_DIRPATH = os.getenv("MODEL_DIRPATH", default="tweet_classifier/models/logistic_regression/2020-09-08-1229")
if __name__ == "__main__":
storage = ModelStorage(dirpath=MODEL_DIRPATH)
storage.promote_model()
| 281 | 22.5 | 113 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/model_storage.py | import os
import pickle
import json
from pprint import pprint
from shutil import copytree
from memory_profiler import profile
from app import DATA_DIR, seek_confirmation
from app.file_storage import FileStorage
from app.decorators.datetime_decorators import logstamp
MODELS_DIRPATH = "tweet_classifier/models" # os.pa... | 5,211 | 32.197452 | 120 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/bulk_predict.py | import os
from app import seek_confirmation
from app.job import Job
from app.bq_service import BigQueryService
from app.nlp.model_storage import ModelStorage, MODELS_DIRPATH
MODEL_NAME = os.getenv("MODEL_NAME", default="current_best")
LIMIT = os.getenv("LIMIT")
BATCH_SIZE = int(os.getenv("BATCH_SIZE", default="10000... | 1,743 | 29.596491 | 108 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/model_training.py |
import os
from datetime import datetime
from pprint import pprint
from pandas import DataFrame
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report # accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticR... | 4,403 | 35.098361 | 138 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/client.py | import os
from app.nlp.model_storage import ModelStorage, BEST_MODEL_DIRPATH
if __name__ == "__main__":
storage = ModelStorage(dirpath=BEST_MODEL_DIRPATH)
tv = storage.load_vectorizer()
print(type(tv))
print("FEATURES / TOKENS:", len(tv.get_feature_names())) #> 3842
clf = storage.load_model()
... | 649 | 22.214286 | 68 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/basilica/embedder.py | import os
from app.decorators.datetime_decorators import logstamp
from app.job import Job
from app.bq_service import BigQueryService
from app.nlp.basilica.service import BasilicaService
MIN_VAL = float(os.getenv("MIN_VAL", default="0.0"))
MAX_VAL = float(os.getenv("MAX_VAL", default="1.0"))
LIMIT = os.getenv("LIMIT")... | 2,164 | 30.376812 | 157 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/basilica/embedder_parallel.py | import os
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import current_thread #, #Thread, Lock, BoundedSemaphore
from app.job import Job
from app.decorators.datetime_decorators import logstamp
from app.bq_service import BigQueryService, split_into_batches
from app.nlp.basilica.service ... | 2,381 | 31.630137 | 123 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/nlp/basilica/service.py |
import basilica
import os
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv("BASILICA_API_KEY")
class BasilicaService:
def __init__(self):
self.client = basilica.Connection(API_KEY)
print("-------------------------")
print("BASILICA SERVICE...")
print(" CLIENT:"... | 1,272 | 26.673913 | 110 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_collection/twitter_scraper.py |
import os
from pprint import pprint
from http.cookiejar import CookieJar
import urllib
from dotenv import load_dotenv
from bs4 import BeautifulSoup
load_dotenv()
SCREEN_NAME = os.getenv("SCREEN_NAME", default="s2t2")
MAX_FRIENDS = int(os.getenv("MAX_FRIENDS", default=2000)) # the max number of friends to fetch per ... | 4,069 | 35.666667 | 137 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_collection/investigate_threadpool_executor.py | # super h/t: https://www.youtube.com/watch?v=IEEhzQoKtQU
import os
import time
import random
from dotenv import load_dotenv
from concurrent.futures import ThreadPoolExecutor, as_completed # see: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor
from threading import Threa... | 2,319 | 37.032787 | 166 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_collection/twint_scraper.py |
# import os
# from pprint import pprint
#
# import twint
# from dotenv import load_dotenv
#
# load_dotenv()
#
# SCREEN_NAME = os.getenv("TWITTER_SCREEN_NAME", default="elonmusk") # just one to use for testing purposes
# VERBOSE = (os.getenv("VERBOSE_SCRAPER", default="false") == "true") # set like... VERBOSE_SCRAPER=... | 2,169 | 35.166667 | 108 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_collection/twitter_service.py |
# import os
# from pprint import pprint
# from dotenv import load_dotenv
# import tweepy
#
# load_dotenv()
#
# CONSUMER_KEY = os.getenv("TWITTER_CONSUMER_KEY", default="OOPS")
# CONSUMER_SECRET = os.getenv("TWITTER_CONSUMER_SECRET", default="OOPS")
# ACCESS_KEY = os.getenv("TWITTER_ACCESS_TOKEN", default="OOPS")
# ACC... | 3,855 | 36.076923 | 121 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_collection/batch_per_thread.py |
import time
from threading import current_thread, BoundedSemaphore
from concurrent.futures import ThreadPoolExecutor #, as_completed
from app import SERVER_NAME, SERVER_DASHBOARD_URL
from app.email_service import send_email
from app.friend_collection import (MAX_THREADS, BATCH_SIZE, LIMIT, MIN_ID, MAX_ID,
user_wi... | 2,328 | 35.390625 | 106 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/friend_collection/__init__.py |
import os
from datetime import datetime
from dotenv import load_dotenv
from threading import current_thread, BoundedSemaphore
from concurrent.futures import ThreadPoolExecutor, as_completed # see: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor
from app import APP_ENV
f... | 4,005 | 42.075269 | 166 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode/mpi_cluster_manager.py |
#import os
#from pprint import pprint
#
#from mpi4py import MPI
#import numpy as np
#from networkx import DiGraph
#
#class ClusterManager:
# def __init__(self):
# self.node_name = MPI.Get_processor_name()
# self.intracomm = MPI.COMM_WORLD
#
# def inspect(self):
# print("-------------------... | 1,583 | 28.886792 | 123 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode/classifier.py |
import os
from pandas import DataFrame
from app.friend_graphs.graph_analyzer import GraphAnalyzer
from app.decorators.number_decorators import fmt_n, fmt_pct
from app.botcode.investigation import classify_bot_probabilities
if __name__ == "__main__":
manager = GraphAnalyzer()
retweet_graph = manager.graph... | 1,469 | 34 | 89 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode/network_classifier_helper.py |
#
# AN ADAPTATION OF THE ORIGINAL BOTCODE (SEE THE "START" DIR)
#
import os
from dotenv import load_dotenv
import numpy as np
from networkx import DiGraph, minimum_cut
# load_dotenv()
# HYPERPARAMETERS
MU = float(os.getenv("MU", default="1")) # called "gamma" in the paper
ALPHA_1 = float(os.getenv("ALPHA_1", def... | 9,650 | 32.27931 | 229 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode/investigation.py |
import os
from pprint import pprint
import numpy as np
from networkx import DiGraph
from app.botcode.network_classifier_helper import parse_bidirectional_links, compute_link_energy, compile_energy_graph
from app.decorators.number_decorators import fmt_n
from conftest import compile_mock_rt_graph
def classify_bot_... | 4,946 | 40.225 | 129 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/tweet_recollection/collector.py |
import os
from pprint import pprint
from functools import lru_cache
from dotenv import load_dotenv
from app import seek_confirmation, server_sleep
from app.bq_service import BigQueryService, split_into_batches, generate_timestamp
from app.twitter_service import TwitterService
from app.tweet_recollection.parser impor... | 5,169 | 42.445378 | 248 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/tweet_recollection/parser.py |
def parse_full_text(status):
"""Param status (tweepy.models.Status)"""
return clean_text(status.full_text)
def clean_text(my_str):
"""Removes line-breaks for cleaner CSV storage. Handles string or null value.
Returns string or null value
Param my_str (str)
"""
try:
my_str... | 489 | 23.5 | 81 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/impeachment_topic_pair_analyzers.py |
import os
from pprint import pprint
from itertools import combinations
from pandas import read_csv
from app.ks_test.topic_pair_analyzer import TopicPairAnalyzer, RESULTS_CSV_FILEPATH
from app.ks_test.impeachment_topics import IMPEACHMENT_TOPICS # todo: allow customization of topics list via CSV file
if __name__ == ... | 1,187 | 33.941176 | 117 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/interpreter.py |
import os
from dotenv import load_dotenv
load_dotenv()
PVAL_MAX = float(os.getenv("PVAL_MAX", default="0.01")) # the maximum pvalue under which to reject the ks test null hypothesis
def interpret(ks_test_result, pval_max=PVAL_MAX):
"""
Interprets the results of a KS test, indicates whether or not to reject... | 1,008 | 33.793103 | 126 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/topic_analyzer.py |
import os
from functools import lru_cache
from pprint import pprint
from dotenv import load_dotenv
import numpy as np
from scipy.stats import ks_2samp
from pandas import DataFrame, read_csv, concat
from app import DATA_DIR
from app.decorators.datetime_decorators import to_ts, fmt_date
from app.bq_service import BigQ... | 4,138 | 30.838462 | 118 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/impeachment_topic_analyzers.py |
import os
from pprint import pprint
from pandas import read_csv
from app.ks_test.topic_analyzer import TopicAnalyzer, RESULTS_CSV_FILEPATH
from app.ks_test.impeachment_topics import IMPEACHMENT_TOPICS # todo: allow customization of topics list via CSV file
if __name__ == "__main__":
if os.path.isfile(RESULTS_C... | 1,055 | 34.2 | 117 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/topic_pair_analyzer.py |
import os
from pprint import pprint
from functools import lru_cache
from dotenv import load_dotenv
from app import DATA_DIR
from app.decorators.datetime_decorators import to_ts
from app.ks_test.topic_analyzer import TopicAnalyzer
load_dotenv()
X_TOPIC = os.getenv("X_TOPIC", default="#MAGA")
Y_TOPIC = os.getenv("Y_... | 2,565 | 35.140845 | 148 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/investigation.py |
import numpy as np
from scipy.stats import kstest, ks_2samp
np.random.seed(2020)
if __name__ == "__main__":
# np.random.normal(center, spread, size)
x = np.random.normal(0, 1, 10)
y = np.random.normal(0, 1, 10)
z = np.random.normal(1.1, 0.9, 10) # one of these things is not like the other
print... | 1,560 | 46.30303 | 152 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/ks_test/impeachment_topics.py | # todo: allow customization of topics list via CSV file
IMPEACHMENT_TOPICS = [
# TAGS
"#MAGA",
# "#IGHearing", -- very small sample size
"#ImpeachAndConvict",
"#TrumpImpeachment",
##"#IGReport",
##"#SenateHearing",
##"#FactsMatter",
##"#ImpeachmentRally",
##"#ImpeachmentEve",
... | 1,357 | 20.903226 | 65 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/decorators/number_decorators.py |
def fmt_n(large_number):
"""
Formats a large number with thousands separator, for printing and logging.
Param large_number (int) like 1_000_000_000
Returns (str) like '1,000,000,000'
"""
return f"{large_number:,.0f}"
def fmt_pct(decimal_number):
"""
Formats a large number with th... | 502 | 19.12 | 78 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/decorators/datetime_decorators.py |
from datetime import datetime, timezone
def logstamp():
"""
Formats current timestamp, for printing and logging.
"""
return dt_to_s(datetime.now())
#
# DATETIME DECORATORS
#
def dt_to_s(dt):
"""
Converts datetime object to date string like "2020-01-01 00:00:00"
Params: dt (datetime) lik... | 2,136 | 23.848837 | 144 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/tweet_collection_v2/bq_migrations.py |
from pprint import pprint
from app import seek_confirmation
from app.decorators.datetime_decorators import dt_to_s
from app.bq_service import BigQueryService
from app.tweet_collection_v2.csv_storage import LocalStorageService
if __name__ == "__main__":
bq_service = BigQueryService()
# TOPICS
bq_servi... | 881 | 25.727273 | 136 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/tweet_collection_v2/tweet_parser.py |
from app.decorators.datetime_decorators import dt_to_s
def parse_status(status):
"""
Param status (tweepy.models.Status)
Converts a nested status structure into a flat row of non-normalized status and user attributes.
"""
if hasattr(status, "retweeted_status") and status.retweeted_status:
... | 2,316 | 31.180556 | 107 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/tweet_collection_v2/stream_listener.py |
import os
from pprint import pprint
from time import sleep
from dotenv import load_dotenv
from tweepy.streaming import StreamListener
from tweepy import Stream
from urllib3.exceptions import ProtocolError
from app import seek_confirmation
from app.twitter_service import TwitterService
from app.bq_service import BigQ... | 5,598 | 32.526946 | 128 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/tweet_collection_v2/csv_storage.py |
import os
from dotenv import load_dotenv
from pandas import DataFrame, read_csv, concat
from app import DATA_DIR, seek_confirmation
load_dotenv()
EVENT_NAME = os.getenv("EVENT_NAME", default="impeachment")
class LocalStorageService:
"""
Must have same methods and params as the remote version - see append... | 2,379 | 34.522388 | 120 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode_v2/classifier.py |
import os
import datetime
import time
from functools import lru_cache
from dotenv import load_dotenv
import numpy as np
from pandas import DataFrame
import matplotlib.pyplot as plt
from conftest import compile_mock_rt_graph
from app import APP_ENV
from app.decorators.number_decorators import fmt_n, fmt_pct
from app.... | 8,235 | 35.767857 | 185 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode_v2/network_classifier_helper.py |
#
# A NEAR REPLICA OF BOTCODE VERSION 2 (SEE THE "START" DIR)
#
import math
from collections import defaultdict
from operator import itemgetter
import time
from datetime import datetime
import numpy as np
import networkx as nx
##########################################################################
##############... | 8,570 | 33.011905 | 121 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botcode_v2/investigation.py |
import os
import datetime
import time
from dotenv import load_dotenv
import numpy as np
from pandas import DataFrame
import matplotlib.pyplot as plt
#from sklearn import metrics
#from scipy.sparse import csc_matrix
from conftest import compile_mock_rt_graph
from app.decorators.number_decorators import fmt_n, fmt_pct... | 4,726 | 29.496774 | 178 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/checkpoint_scorer.py |
import os
from functools import lru_cache
#from pprint import pprint
import gc
from dotenv import load_dotenv
from app import server_sleep, seek_confirmation
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryService, generate_timestamp, split_into_batches
from app.toxicity.model... | 4,743 | 31.272109 | 197 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/model_manager.py |
#
# adapted from: https://github.com/unitaryai/detoxify/blob/master/detoxify/detoxify.py
#
# using the pre-trained toxicity models provided via Detoxify checkpoints, but...
# 1) let's try different / lighter torch requirement approaches (to enable installation on heroku) - see requirements.txt file
# 2) let's also try... | 6,115 | 39.773333 | 155 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/scorer_async.py |
import os
from threading import current_thread, BoundedSemaphore
from concurrent.futures import ThreadPoolExecutor, as_completed # see: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor
from dotenv import load_dotenv
from app import server_sleep
from app.decorators.numbe... | 2,081 | 32.580645 | 166 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/investigate_models.py |
from pprint import pprint
from detoxify import Detoxify
from pandas import DataFrame
if __name__ == '__main__':
texts = [
"RT @realDonaldTrump: I was very surprised & disappointed that Senator Joe Manchin of West Virginia voted against me on the Democrat’s total…",
"RT @realDonaldTrump: Craz... | 1,583 | 38.6 | 155 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/scorer.py |
import os
from functools import lru_cache
#from pprint import pprint
from dotenv import load_dotenv
from detoxify import Detoxify
from pandas import DataFrame
from app import server_sleep
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryService, generate_timestamp, split_into_bat... | 5,428 | 35.436242 | 171 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/investigate_benchmarks.py |
from time import perf_counter # see: https://stackoverflow.com/questions/25785243/understanding-time-perf-counter-and-time-process-time
from functools import wraps
from detoxify import Detoxify
def performance_timer(func):
"""
Wrap your function in this decorator to see how long it takes.
Returns the dur... | 1,494 | 25.696429 | 135 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/toxicity/checkpoint_scorer_async.py |
import os
from threading import current_thread #, BoundedSemaphore
from concurrent.futures import ThreadPoolExecutor, as_completed # see: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor
import gc
from dotenv import load_dotenv
from app import server_sleep
from app.deco... | 2,172 | 31.924242 | 166 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/graph_storage.py |
import os
import json
import pickle
from sys import getsizeof
from memory_profiler import profile #, memory_usage
from pprint import pprint
from pandas import DataFrame
from networkx import write_gpickle, read_gpickle
from dotenv import load_dotenv
from conftest import compile_mock_rt_graph
from app import DATA_DIR,... | 8,149 | 31.213439 | 154 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/retweet_grapher.py |
import os
from datetime import datetime
import time
from memory_profiler import profile
from dotenv import load_dotenv
from networkx import DiGraph
from conftest import compile_mock_rt_graph
from app import APP_ENV, DATA_DIR, SERVER_NAME, SERVER_DASHBOARD_URL, seek_confirmation
from app.decorators.number_decorators ... | 4,571 | 35.285714 | 322 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/job.py |
import time
from app.decorators.number_decorators import fmt_n
class Job():
def __init__(self):
self.start_at = None
self.end_at = None
self.duration_seconds = None
self.counter = 0 # represents the number of items processed
def start(self):
print("-----------------")... | 935 | 26.529412 | 111 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/prep/lookup_user_ids.py |
import os
import json
from tweepy.error import TweepError
from pandas import DataFrame
from dotenv import load_dotenv
from app import DATA_DIR, seek_confirmation
from app.decorators.datetime_decorators import logstamp
from app.bq_service import BigQueryService
from app.twitter_service import TwitterService
load_dot... | 2,114 | 32.046875 | 187 | py |
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