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 cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities
  11
  12 spark = SparkSession.builder.getOrCreate()
  13 conf = spark.sparkContext.getConf()
  14
  15 # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
  16 def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500):
  17     '''
  18     Compute similarities between subreddits based on tfi-idf vectors of author comments
  19
  20     included_subreddits : string
  21         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
  22
  23     similarity_threshold : double (default = 0)
  24         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
  25 https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
  26
  27     min_df : int (default = 0.1 * (number of included_subreddits)
  28          exclude terms that appear in fewer than this number of documents.
  29
  30     outfile: string
  31          where to output csv and feather outputs
  32 '''
  33
  34     spark = SparkSession.builder.getOrCreate()
  35     conf = spark.sparkContext.getConf()
  36     print(outfile)
  37
  38     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')
  39
  40     if included_subreddits is None:
  41         rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
  42         included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
  43
  44     else:
  45         included_subreddits = set(open(included_subreddits))
  46
  47     print("creating temporary parquet with matrix indicies")
  48     tempdir = prep_tfidf_entries(tfidf, 'author', min_df, included_subreddits)
  49     tfidf = spark.read.parquet(tempdir.name)
  50     subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
  51     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  52     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  53     spark.stop()
  54
  55     print("loading matrix")
  56     mat = read_tfidf_matrix(tempdir.name,'author')
  57     print('computing similarities')
  58     sims = column_similarities(mat)
  59     del mat
  60
  61     sims = pd.DataFrame(sims.todense())
  62     sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)},axis=1)
  63     sims['subreddit'] = subreddit_names.subreddit.values
  64
  65     p = Path(outfile)
  66
  67     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  68     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
  69     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
  70
  71     sims.to_feather(outfile)
  72     tempdir.cleanup()
  73
  74     # print(outfile)
  75
  76     # tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')
  77
  78     # if included_subreddits is None:
  79     #     included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
  80     #     included_subreddits = {s.strip('\n') for s in included_subreddits}
  81
  82     # else:
  83     #     included_subreddits = set(open(included_subreddits))
  84
  85     # sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)
  86
  87     # p = Path(outfile)
  88
  89     # output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  90     # output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
  91     # output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
  92     # sim_dist = sim_dist.entries.toDF()
  93
  94     # sim_dist = sim_dist.repartition(1)
  95     # sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
  96
  97
  98
  99     # #instead of toLocalMatrix() why not read as entries and put strait into numpy
 100     # sim_entries = pd.read_parquet(output_parquet)
 101
 102     # df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
 103
 104     # spark.stop()
 105     # df['subreddit_id_new'] = df['subreddit_id_new'] - 1
 106     # df = df.sort_values('subreddit_id_new').reset_index(drop=True)
 107     # df = df.set_index('subreddit_id_new')
 108
 109     # similarities = sim_entries.join(df, on='i')
 110     # similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
 111     # similarities = similarities.join(df, on='j')
 112     # similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
 113
 114     # similarities.to_feather(output_feather)
 115     # similarities.to_csv(output_csv)
 116     # return similarities
 117
 118 if __name__ == '__main__':
 119     fire.Fire(author_cosine_similarities)