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 pyarrow.dataset as ds
   7 import pandas as pd
   8 import fire
   9 from itertools import islice, chain
  10 from pathlib import Path
  11 from similarities_helper import *
  12 from multiprocessing import Pool, cpu_count
  13 from functools import partial
  14
  15
  16 def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
  17     term = term_colname
  18     term_id = term + '_id'
  19     term_id_new = term + '_id_new'
  20     print(f"loading matrix: {week}")
  21     entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
  22                                              term_colname=term_colname,
  23                                              min_df=min_df,
  24                                              max_df=max_df,
  25                                              included_subreddits=included_subreddits,
  26                                              topN=topN,
  27                                              week=week)
  28     mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
  29     print('computing similarities')
  30     sims = column_similarities(mat)
  31     del mat
  32     sims = pd.DataFrame(sims.todense())
  33     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
  34     sims['_subreddit'] = names.subreddit.values
  35     outfile = str(Path(outdir) / str(week))
  36     write_weekly_similarities(outfile, sims, week, names)
  37
  38 def pull_weeks(batch):
  39     return set(batch.to_pandas()['week'])
  40
  41 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
  42 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
  43     print(outfile)
  44     tfidf_ds = ds.dataset(tfidf_path)
  45     tfidf_ds = tfidf_ds.to_table(columns=["week"])
  46     batches = tfidf_ds.to_batches()
  47
  48     with Pool(cpu_count()) as pool:
  49         weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
  50
  51     weeks = sorted(weeks)
  52     # do this step in parallel if we have the memory for it.
  53     # should be doable with pool.map
  54
  55     print(f"computing weekly similarities")
  56     week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
  57
  58     with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
  59         list(pool.map(week_similarities_helper,weeks))
  60
  61 def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
  62     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
  63                                       outfile,
  64                                       'author',
  65                                       min_df,
  66                                       max_df,
  67                                       included_subreddits,
  68                                       topN)
  69
  70 def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
  71         return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
  72                                           outfile,
  73                                           'term',
  74                                           min_df,
  75                                           max_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})