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 pull_tfidf, column_similarities, write_weekly_similarities
  12 from scipy.sparse import csr_matrix
  13 from multiprocessing import Pool, cpu_count
  14 from functools import partial
  15
  16 # infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
  17 # tfidf_path = infile
  18 # min_df=None
  19 # max_df = None
  20 # topN=100
  21 # term_colname='author'
  22 # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
  23 # included_subreddits=None
  24
  25 def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
  26     term = term_colname
  27     term_id = term + '_id'
  28     term_id_new = term + '_id_new'
  29     print(f"loading matrix: {week}")
  30
  31     entries = pull_tfidf(infile = tfidf_path,
  32                          term_colname=term_colname,
  33                          min_df=min_df,
  34                          max_df=max_df,
  35                          included_subreddits=included_subreddits,
  36                          topN=topN,
  37                          week=week.isoformat(),
  38                          rescale_idf=False)
  39
  40     tfidf_colname='tf_idf'
  41     # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
  42     mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
  43
  44     print('computing similarities')
  45     sims = simfunc(mat.T)
  46     del mat
  47     sims = pd.DataFrame(sims)
  48     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
  49     sims['_subreddit'] = subreddit_names.subreddit.values
  50     outfile = str(Path(outdir) / str(week))
  51     write_weekly_similarities(outfile, sims, week, subreddit_names)
  52
  53 def pull_weeks(batch):
  54     return set(batch.to_pandas()['week'])
  55
  56 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
  57 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
  58     print(outfile)
  59     # do this step in parallel if we have the memory for it.
  60     # should be doable with pool.map
  61
  62     spark = SparkSession.builder.getOrCreate()
  63     df = spark.read.parquet(tfidf_path)
  64     subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
  65     subreddit_names = subreddit_names.sort_values("subreddit_id")
  66     nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
  67     weeks = df.select(f.col("week")).distinct().toPandas().week.values
  68     spark.stop()
  69
  70     print(f"computing weekly similarities")
  71     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, subreddit_names=subreddit_names,nterms=nterms)
  72
  73     pool = Pool(cpu_count())
  74
  75     list(pool.imap(week_similarities_helper,weeks))
  76     pool.close()
  77     #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
  78
  79
  80 def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
  81     return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
  82                                       outfile,
  83                                       'author',
  84                                       min_df,
  85                                       max_df,
  86                                       included_subreddits,
  87                                       topN)
  88
  89 def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
  90         return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
  91                                           outfile,
  92                                           'term',
  93                                           min_df,
  94                                           max_df,
  95                                           included_subreddits,
  96                                           topN)
  97
  98 if __name__ == "__main__":
  99     fire.Fire({'authors':author_cosine_similarities_weekly,
 100                'terms':term_cosine_similarities_weekly})