tf_comments.py

   1 #!/usr/bin/env python3
   2 import pyarrow as pa
   3 import pyarrow.dataset as ds
   4 import pyarrow.parquet as pq
   5 from itertools import groupby, islice, chain
   6 import fire
   7 from collections import Counter
   8 import pandas as pd
   9 import os
  10 import datetime
  11 import re
  12 from nltk import wordpunct_tokenize, MWETokenizer, sent_tokenize
  13 from nltk.corpus import stopwords
  14 from nltk.util import ngrams
  15 import string
  16 from random import random
  17
  18 # remove urls
  19 # taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
  20 urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
  21
  22 # compute term frequencies for comments in each subreddit by week
  23 def weekly_tf(partition, mwe_pass = 'first'):
  24     dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
  25
  26     if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
  27         os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
  28
  29     if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
  30         os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
  31
  32     ngram_output = partition.replace("parquet","txt")
  33
  34     if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"):
  35         os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}")
  36
  37     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
  38
  39
  40     schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
  41                         pa.field('term', pa.string(), nullable=False),
  42                         pa.field('week', pa.date32(), nullable=False),
  43                         pa.field('tf', pa.int64(), nullable=False)]
  44     )
  45
  46     author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
  47                                pa.field('author', pa.string(), nullable=False),
  48                                pa.field('week', pa.date32(), nullable=False),
  49                                pa.field('tf', pa.int64(), nullable=False)]
  50     )
  51
  52     dfs = (b.to_pandas() for b in batches)
  53
  54     def add_week(df):
  55         df['week'] = (df.CreatedAt - pd.to_timedelta(df.CreatedAt.dt.dayofweek, unit='d')).dt.date
  56         return(df)
  57
  58     dfs = (add_week(df) for df in dfs)
  59
  60     def iterate_rows(dfs):
  61         for df in dfs:
  62             for row in df.itertuples():
  63                 yield row
  64
  65     rows = iterate_rows(dfs)
  66
  67     subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
  68
  69     if mwe_pass != 'first':
  70         mwe_dataset = pd.read_feather(f'/gscratch/comdata/users/nathante/reddit_multiword_expressions.feather')
  71         mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
  72         mwe_phrases = list(mwe_dataset.phrase)
  73         mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
  74         mwe_tokenizer = MWETokenizer(mwe_phrases)
  75         mwe_tokenize = mwe_tokenizer.tokenize
  76
  77     else:
  78         mwe_tokenize = MWETokenizer().tokenize
  79
  80     def remove_punct(sentence):
  81         new_sentence = []
  82         for token in sentence:
  83             new_token = ''
  84             for c in token:
  85                 if c not in string.punctuation:
  86                     new_token += c
  87             if len(new_token) > 0:
  88                 new_sentence.append(new_token)
  89         return new_sentence
  90
  91
  92     stopWords = set(stopwords.words('english'))
  93
  94     # we follow the approach described in datta, phelan, adar 2017
  95     def my_tokenizer(text):
  96         # remove stopwords, punctuation, urls, lower case
  97         # lowercase
  98         text = text.lower()
  99
 100         # remove urls
 101         text = urlregex.sub("", text)
 102
 103         # sentence tokenize
 104         sentences = sent_tokenize(text)
 105
 106         # wordpunct_tokenize
 107         sentences = map(wordpunct_tokenize, sentences)
 108
 109         # remove punctuation
 110
 111         sentences = map(remove_punct, sentences)
 112
 113         # remove sentences with less than 2 words
 114         sentences = filter(lambda sentence: len(sentence) > 2, sentences)
 115
 116         # datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase.
 117         # they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
 118         # here we take a 10 percent sample of sentences
 119         if mwe_pass == 'first':
 120             sentences = list(sentences)
 121             for sentence in sentences:
 122                 if random() <= 0.1:
 123                     grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
 124                     with open(f'/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
 125                         for ng in grams:
 126                             gram_file.write(' '.join(ng) + '\n')
 127                 for token in sentence:
 128                     if token not in stopWords:
 129                         yield token
 130
 131         else:
 132             # remove stopWords
 133             sentences = map(mwe_tokenize, sentences)
 134             sentences = map(lambda s: filter(lambda token: token not in stopWords, s), sentences)
 135             for sentence in sentences:
 136                 for token in sentence:
 137                     yield token
 138
 139     def tf_comments(subreddit_weeks):
 140         for key, posts in subreddit_weeks:
 141             subreddit, week = key
 142             tfs = Counter([])
 143             authors = Counter([])
 144             for post in posts:
 145                 tokens = my_tokenizer(post.body)
 146                 tfs.update(tokens)
 147                 authors.update([post.author])
 148
 149             for term, tf in tfs.items():
 150                 yield [True, subreddit, term, week, tf]
 151
 152             for author, tf in authors.items():
 153                 yield [False, subreddit, author, week, tf]
 154
 155     outrows = tf_comments(subreddit_weeks)
 156
 157     outchunksize = 10000
 158
 159     with pq.ParquetWriter(f"/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter(f"/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
 160
 161         while True:
 162
 163             chunk = islice(outrows,outchunksize)
 164             chunk = (c for c in chunk if c.subreddit is not None)
 165             pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names)
 166
 167             author_pddf = pddf.loc[pddf.is_token == False, schema.names]
 168             pddf = pddf.loc[pddf.is_token == True, schema.names]
 169             author_pddf = author_pddf.rename({'term':'author'}, axis='columns')
 170             author_pddf = author_pddf.loc[:,author_schema.names]
 171
 172             table = pa.Table.from_pandas(pddf,schema=schema)
 173             author_table = pa.Table.from_pandas(author_pddf,schema=author_schema)
 174             if table.shape[0] == 0:
 175                 break
 176             writer.write_table(table)
 177             author_writer.write_table(author_table)
 178
 179         writer.close()
 180         author_writer.close()
 181
 182
 183 def gen_task_list(mwe_pass='first'):
 184     files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
 185     with open("tf_task_list",'w') as outfile:
 186         for f in files:
 187             if f.endswith(".parquet"):
 188                 outfile.write(f"python3 tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
 189
 190 if __name__ == "__main__":
 191     fire.Fire({"gen_task_list":gen_task_list,
 192                "weekly_tf":weekly_tf})