179 lines
6.9 KiB
Python
179 lines
6.9 KiB
Python
import pyarrow as pa
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import pyarrow.dataset as ds
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import pyarrow.parquet as pq
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from itertools import groupby, islice, chain
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import fire
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from collections import Counter
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import pandas as pd
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import os
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import datetime
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import re
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from nltk import wordpunct_tokenize, MWETokenizer, sent_tokenize
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from nltk.corpus import stopwords
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from nltk.util import ngrams
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import string
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from random import random
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# remove urls
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# taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
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urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
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# compute term frequencies for comments in each subreddit by week
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def weekly_tf(partition, mwe_pass = 'first'):
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dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
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if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
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os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
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if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
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os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
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ngram_output = partition.replace("parquet","txt")
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if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"):
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os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}")
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batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
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schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
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pa.field('term', pa.string(), nullable=False),
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pa.field('week', pa.date32(), nullable=False),
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pa.field('tf', pa.int64(), nullable=False)]
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)
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author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
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pa.field('author', pa.string(), nullable=False),
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pa.field('week', pa.date32(), nullable=False),
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pa.field('tf', pa.int64(), nullable=False)]
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)
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dfs = (b.to_pandas() for b in batches)
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def add_week(df):
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df['week'] = (df.CreatedAt - pd.to_timedelta(df.CreatedAt.dt.dayofweek, unit='d')).dt.date
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return(df)
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dfs = (add_week(df) for df in dfs)
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def iterate_rows(dfs):
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for df in dfs:
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for row in df.itertuples():
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yield row
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rows = iterate_rows(dfs)
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subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
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mwe_tokenize = MWETokenizer().tokenize
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def remove_punct(sentence):
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new_sentence = []
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for token in sentence:
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new_token = ''
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for c in token:
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if c not in string.punctuation:
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new_token += c
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if len(new_token) > 0:
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new_sentence.append(new_token)
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return new_sentence
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stopWords = set(stopwords.words('english'))
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# we follow the approach described in datta, phelan, adar 2017
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def my_tokenizer(text):
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# remove stopwords, punctuation, urls, lower case
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# lowercase
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text = text.lower()
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# remove urls
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text = urlregex.sub("", text)
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# sentence tokenize
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sentences = sent_tokenize(text)
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# wordpunct_tokenize
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sentences = map(wordpunct_tokenize, sentences)
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# remove punctuation
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sentences = map(remove_punct, sentences)
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# remove sentences with less than 2 words
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sentences = filter(lambda sentence: len(sentence) > 2, sentences)
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# 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.
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# 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
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# here we take a 10 percent sample of sentences
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if mwe_pass == 'first':
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sentences = list(sentences)
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for sentence in sentences:
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if random() <= 0.1:
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grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
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with open(f'/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
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for ng in grams:
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gram_file.write(' '.join(ng) + '\n')
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for token in sentence:
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if token not in stopWords:
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yield token
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else:
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# remove stopWords
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sentences = map(lambda s: filter(lambda token: token not in stopWords, s), sentences)
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return chain(* sentences)
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def tf_comments(subreddit_weeks):
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for key, posts in subreddit_weeks:
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subreddit, week = key
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tfs = Counter([])
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authors = Counter([])
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for post in posts:
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tokens = my_tokenizer(post.body)
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tfs.update(tokens)
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authors.update([post.author])
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for term, tf in tfs.items():
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yield [True, subreddit, term, week, tf]
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for author, tf in authors.items():
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yield [False, subreddit, author, week, tf]
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outrows = tf_comments(subreddit_weeks)
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outchunksize = 10000
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with pq.ParquetWriter("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
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while True:
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chunk = islice(outrows,outchunksize)
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pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names)
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print(pddf)
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author_pddf = pddf.loc[pddf.is_token == False]
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author_pddf = author_pddf.rename({'term':'author'}, axis='columns')
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author_pddf = author_pddf.loc[:,author_schema.names]
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pddf = pddf.loc[pddf.is_token == True, schema.names]
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print(pddf)
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print(author_pddf)
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table = pa.Table.from_pandas(pddf,schema=schema)
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author_table = pa.Table.from_pandas(author_pddf,schema=author_schema)
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if table.shape[0] == 0:
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break
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writer.write_table(table)
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author_writer.write_table(author_table)
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writer.close()
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author_writer.close()
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def gen_task_list():
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files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
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with open("tf_task_list",'w') as outfile:
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for f in files:
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if f.endswith(".parquet"):
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outfile.write(f"python3 tf_reddit_comments.py weekly_tf {f}\n")
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if __name__ == "__main__":
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fire.Fire({"gen_task_list":gen_task_list,
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"weekly_tf":weekly_tf})
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