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