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[cdsc_reddit.git] / tf_comments.py
1 import pyarrow as pa
2 import pyarrow.dataset as ds
3 import pyarrow.parquet as pq
4 from itertools import groupby, islice, chain
5 import fire
6 from collections import Counter
7 import pandas as pd
8 import os
9 import datetime
10 import re
11 from nltk import wordpunct_tokenize, MWETokenizer, sent_tokenize
12 from nltk.corpus import stopwords
13 from nltk.util import ngrams
14 import string
15 from random import random
16
17 # remove urls
18 # taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
19 urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
20
21 # compute term frequencies for comments in each subreddit by week
22 def weekly_tf(partition, mwe_pass = 'first'):
23     dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
24
25     if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
26         os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
27
28     if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
29         os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
30
31     ngram_output = partition.replace("parquet","txt")
32
33     if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"):
34         os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}")
35     
36     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
37
38     schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
39                         pa.field('term', pa.string(), nullable=False),
40                         pa.field('week', pa.date32(), nullable=False),
41                         pa.field('tf', pa.int64(), nullable=False)]
42     )
43
44     author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
45                                pa.field('author', pa.string(), nullable=False),
46                                pa.field('week', pa.date32(), nullable=False),
47                                pa.field('tf', pa.int64(), nullable=False)]
48     )
49
50     dfs = (b.to_pandas() for b in batches)
51
52     def add_week(df):
53         df['week'] = (df.CreatedAt - pd.to_timedelta(df.CreatedAt.dt.dayofweek, unit='d')).dt.date
54         return(df)
55
56     dfs = (add_week(df) for df in dfs)
57
58     def iterate_rows(dfs):
59         for df in dfs:
60             for row in df.itertuples():
61                 yield row
62
63     rows = iterate_rows(dfs)
64
65     subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
66
67     if mwe_pass != 'first':
68         mwe_dataset = ds.dataset(f'/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet',format='parquet')
69         mwe_dataset = mwe_dataset.to_pandas(columns=['phrase','phraseCount','phrasePWMI'])
70         mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
71         mwe_phrases = list(mwe_dataset.phrase[0:1000])
72         
73         
74         mwe_tokenize = MWETokenizer(mwe_phrases).tokenize
75         
76
77     def remove_punct(sentence):
78         new_sentence = []
79         for token in sentence:
80             new_token = ''
81             for c in token:
82                 if c not in string.punctuation:
83                     new_token += c
84             if len(new_token) > 0:
85                 new_sentence.append(new_token)
86         return new_sentence
87
88
89     stopWords = set(stopwords.words('english'))
90
91     # we follow the approach described in datta, phelan, adar 2017
92     def my_tokenizer(text):
93         # remove stopwords, punctuation, urls, lower case
94         # lowercase        
95         text = text.lower()
96
97         # remove urls
98         text = urlregex.sub("", text)
99
100         # sentence tokenize
101         sentences = sent_tokenize(text)
102
103         # wordpunct_tokenize
104         sentences = map(wordpunct_tokenize, sentences)
105
106         # remove punctuation
107                         
108         sentences = map(remove_punct, sentences)
109
110         # remove sentences with less than 2 words
111         sentences = filter(lambda sentence: len(sentence) > 2, sentences)
112
113         # 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.
114         # 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
115         # here we take a 10 percent sample of sentences 
116         if mwe_pass == 'first':
117             sentences = list(sentences)
118             for sentence in sentences:
119                 if random() <= 0.1:
120                     grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
121                     with open(f'/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
122                         for ng in grams:
123                             gram_file.write(' '.join(ng) + '\n')
124                 for token in sentence:
125                     if token not in stopWords:
126                         yield token
127
128         else:
129             # remove stopWords
130             sentences = map(mwe_tokenize, sentences)
131             sentences = map(lambda s: filter(lambda token: token not in stopWords, s), sentences)
132             return chain(* sentences)
133
134     def tf_comments(subreddit_weeks):
135         for key, posts in subreddit_weeks:
136             subreddit, week = key
137             tfs = Counter([])
138             authors = Counter([])
139             for post in posts:
140                 tokens = my_tokenizer(post.body)
141                 tfs.update(tokens)
142                 authors.update([post.author])
143
144             for term, tf in tfs.items():
145                 yield [True, subreddit, term, week, tf]
146
147             for author, tf in authors.items():
148                 yield [False, subreddit, author, week, tf]
149
150     outrows = tf_comments(subreddit_weeks)
151
152     outchunksize = 10000
153
154     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:
155         while True:
156             chunk = islice(outrows,outchunksize)
157             pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names)
158
159             author_pddf = pddf.loc[pddf.is_token == False, schema.names]
160             pddf = pddf.loc[pddf.is_token == True, schema.names]
161
162             author_pddf = author_pddf.rename({'term':'author'}, axis='columns')
163             author_pddf = author_pddf.loc[:,author_schema.names]
164
165             table = pa.Table.from_pandas(pddf,schema=schema)
166             author_table = pa.Table.from_pandas(author_pddf,schema=author_schema)
167             if table.shape[0] == 0:
168                 break
169             writer.write_table(table)
170             author_writer.write_table(author_table)
171             
172         writer.close()
173         author_writer.close()
174
175
176 def gen_task_list():
177     files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
178     with open("tf_task_list",'w') as outfile:
179         for f in files:
180             if f.endswith(".parquet"):
181                 outfile.write(f"python3 tf_comments.py weekly_tf {f}\n")
182
183 if __name__ == "__main__":
184     fire.Fire({"gen_task_list":gen_task_list,
185                "weekly_tf":weekly_tf})

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