old/author_cosine_similarity.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 #tfidf = spark.read.parquet('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/subreddit_terms.parquet')
  13 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
  14     spark = SparkSession.builder.getOrCreate()
  15     conf = spark.sparkContext.getConf()
  16     print(outfile)
  17     tfidf = spark.read.parquet(tfidf_path)
  18
  19     if included_subreddits is None:
  20         included_subreddits = select_topN_subreddits(topN)
  21
  22     else:
  23         included_subreddits = set(open(included_subreddits))
  24
  25     print("creating temporary parquet with matrix indicies")
  26     tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
  27
  28     tfidf = spark.read.parquet(tempdir.name)
  29
  30     # the ids can change each week.
  31     subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
  32     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  33     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  34     spark.stop()
  35
  36     weeks = list(subreddit_names.week.drop_duplicates())
  37     for week in weeks:
  38         print("loading matrix")
  39         mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
  40         print('computing similarities')
  41         sims = column_similarities(mat)
  42         del mat
  43
  44         names = subreddit_names.loc[subreddit_names.week==week]
  45
  46         sims = sims.rename({i:sr for i, sr in enumerate(names.subreddit.values)},axis=1)
  47         sims['subreddit'] = names.subreddit.values
  48         write_weekly_similarities(outfile, sims, week)
  49
  50
  51
  52 def cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500):
  53     '''
  54     Compute similarities between subreddits based on tfi-idf vectors of author comments
  55
  56     included_subreddits : string
  57         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
  58
  59     min_df : int (default = 0.1 * (number of included_subreddits)
  60          exclude terms that appear in fewer than this number of documents.
  61
  62     outfile: string
  63          where to output csv and feather outputs
  64 '''
  65
  66     spark = SparkSession.builder.getOrCreate()
  67     conf = spark.sparkContext.getConf()
  68     print(outfile)
  69
  70     tfidf = spark.read.parquet('/gscratch/comdata/output/reddit_similarity/tfidf/subreddit_comment_authors.parquet')
  71
  72     if included_subreddits is None:
  73         included_subreddits = select_topN_subreddits(topN)
  74
  75     else:
  76         included_subreddits = set(open(included_subreddits))
  77
  78     print("creating temporary parquet with matrix indicies")
  79     tempdir = prep_tfidf_entries(tfidf, 'author', min_df, included_subreddits)
  80     tfidf = spark.read.parquet(tempdir.name)
  81     subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
  82     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  83     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  84     spark.stop()
  85
  86     print("loading matrix")
  87     mat = read_tfidf_matrix(tempdir.name,'author')
  88     print('computing similarities')
  89     sims = column_similarities(mat)
  90     del mat
  91
  92     sims = pd.DataFrame(sims.todense())
  93     sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)},axis=1)
  94     sims['subreddit'] = subreddit_names.subreddit.values
  95
  96     p = Path(outfile)
  97
  98     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  99     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
 100     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
 101
 102     sims.to_feather(outfile)
 103     tempdir.cleanup()
 104
 105 if __name__ == '__main__':
 106     fire.Fire(author_cosine_similarities)