192 lines
7.6 KiB
Python
Executable File
192 lines
7.6 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
import pandas as pd
|
|
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 os
|
|
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
|
|
from redditcleaner import clean
|
|
|
|
# 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 mwe_pass == 'first':
|
|
if os.path.exists(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}"):
|
|
os.remove(f"/gscratch/comdata/output/reddit_ngrams/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))
|
|
|
|
if mwe_pass != 'first':
|
|
mwe_dataset = pd.read_feather(f'/gscratch/comdata/output/reddit_ngrams/multiword_expressions.feather')
|
|
mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
|
|
mwe_phrases = list(mwe_dataset.phrase)
|
|
mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
|
|
mwe_tokenizer = MWETokenizer(mwe_phrases)
|
|
mwe_tokenize = mwe_tokenizer.tokenize
|
|
|
|
else:
|
|
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()
|
|
|
|
# redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings)
|
|
text = clean(text)
|
|
|
|
# sentence tokenize
|
|
sentences = sent_tokenize(text)
|
|
|
|
# wordpunct_tokenize
|
|
sentences = map(wordpunct_tokenize, sentences)
|
|
|
|
# remove punctuation
|
|
|
|
sentences = map(remove_punct, 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':
|
|
|
|
# remove sentences with less than 2 words
|
|
sentences = filter(lambda sentence: len(sentence) > 2, sentences)
|
|
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/output/reddit_ngrams/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(mwe_tokenize, sentences)
|
|
sentences = map(lambda s: filter(lambda token: token not in stopWords, s), sentences)
|
|
for sentence in sentences:
|
|
for token in sentence:
|
|
yield token
|
|
|
|
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(f"/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
|
|
|
|
while True:
|
|
|
|
chunk = islice(outrows,outchunksize)
|
|
chunk = (c for c in chunk if c[1] is not None)
|
|
pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names)
|
|
author_pddf = pddf.loc[pddf.is_token == False, schema.names]
|
|
pddf = pddf.loc[pddf.is_token == True, schema.names]
|
|
author_pddf = author_pddf.rename({'term':'author'}, axis='columns')
|
|
author_pddf = author_pddf.loc[:,author_schema.names]
|
|
table = pa.Table.from_pandas(pddf,schema=schema)
|
|
author_table = pa.Table.from_pandas(author_pddf,schema=author_schema)
|
|
do_break = True
|
|
|
|
if table.shape[0] != 0:
|
|
writer.write_table(table)
|
|
do_break = False
|
|
if author_table.shape[0] != 0:
|
|
author_writer.write_table(author_table)
|
|
do_break = False
|
|
|
|
if do_break:
|
|
break
|
|
|
|
writer.close()
|
|
author_writer.close()
|
|
|
|
|
|
def gen_task_list(mwe_pass='first'):
|
|
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"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
|
|
|
|
if __name__ == "__main__":
|
|
fire.Fire({"gen_task_list":gen_task_list,
|
|
"weekly_tf":weekly_tf})
|