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
  12
  13 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
  14 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
  15     spark = SparkSession.builder.getOrCreate()
  16     conf = spark.sparkContext.getConf()
  17     print(outfile)
  18     tfidf = spark.read.parquet(tfidf_path)
  19
  20     if included_subreddits is None:
  21         included_subreddits = select_topN_subreddits(topN)
  22     else:
  23         included_subreddits = set(open(included_subreddits))
  24
  25     print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
  26
  27     print("creating temporary parquet with matrix indicies")
  28     tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
  29
  30     tfidf = spark.read.parquet(tempdir.name)
  31
  32     # the ids can change each week.
  33     subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
  34     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  35     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  36     spark.stop()
  37
  38 d    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
  39     for week in weeks:
  40         print(f"loading matrix: {week}")
  41         mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
  42         print('computing similarities')
  43         sims = column_similarities(mat)
  44         del mat
  45
  46         names = subreddit_names.loc[subreddit_names.week == week]
  47         sims = pd.DataFrame(sims.todense())
  48
  49         sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
  50         sims['subreddit'] = names.subreddit.values
  51
  52         write_weekly_similarities(outfile, sims, week, names)
  53
  54
  55 def author_cosine_similarities_weekly(outfile, min_df=None , included_subreddits=None, topN=500):
  56     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
  57                                       outfile,
  58                                       'author',
  59                                       min_df,
  60                                       included_subreddits,
  61                                       topN)
  62
  63 def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
  64     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
  65                                       outfile,
  66                                       'term',
  67                                       min_df,
  68                                       included_subreddits,
  69                                       topN)
  70
  71 if __name__ == "__main__":
  72     fire.Fire({'author':author_cosine_similarities_weekly,
  73                'term':term_cosine_similarities_weekly})