similarities/weekly_cosine_similarities.py

   1 from pyspark.sql import functions as f
   2 from pyspark.sql import SparkSession
   3 from pyspark.sql import Window
   4 import numpy as np
   5 import pyarrow
   6 import pandas as pd
   7 import fire
   8 from itertools import islice
   9 from pathlib import Path
  10 from similarities_helper import *
  11 from multiprocessing import pool
  12
  13 def _week_similarities(tempdir, term_colname, week):
  14         print(f"loading matrix: {week}")
  15         mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
  16         print('computing similarities')
  17         sims = column_similarities(mat)
  18         del mat
  19
  20         names = subreddit_names.loc[subreddit_names.week == week]
  21         sims = pd.DataFrame(sims.todense())
  22
  23         sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
  24         sims['_subreddit'] = names.subreddit.values
  25
  26         write_weekly_similarities(outfile, sims, week, names)
  27
  28 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
  29 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
  30     spark = SparkSession.builder.getOrCreate()
  31     conf = spark.sparkContext.getConf()
  32     print(outfile)
  33     tfidf = spark.read.parquet(tfidf_path)
  34
  35     if included_subreddits is None:
  36         included_subreddits = select_topN_subreddits(topN)
  37     else:
  38         included_subreddits = set(open(included_subreddits))
  39
  40     print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
  41
  42     print("creating temporary parquet with matrix indicies")
  43     tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
  44
  45     tfidf = spark.read.parquet(tempdir.name)
  46
  47     # the ids can change each week.
  48     subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
  49     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  50     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  51     spark.stop()
  52
  53     weeks = sorted(list(subreddit_names.week.drop_duplicates()))
  54     # do this step in parallel if we have the memory for it.
  55     # should be doable with pool.map
  56
  57     def week_similarities_helper(week):
  58         _week_similarities(tempdir, term_colname, week)
  59
  60     with Pool(40) as pool: # maybe it can be done with 40 cores on the huge machine?
  61         list(pool.map(weeks,week_similarities_helper))
  62
  63 def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
  64     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_100k.parquet',
  65                                       outfile,
  66                                       'author',
  67                                       min_df,
  68                                       included_subreddits,
  69                                       topN)
  70
  71 def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
  72     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_100k.parquet',
  73                                       outfile,
  74                                       'term',
  75                                       min_df,
  76                                       included_subreddits,
  77                                       topN)
  78
  79 if __name__ == "__main__":
  80     fire.Fire({'authors':author_cosine_similarities_weekly,
  81                'terms':term_cosine_similarities_weekly})