term_cosine_similarity.py

   1 from pyspark.sql import functions as f
   2 from pyspark.sql import SparkSession
   3 from pyspark.sql import Window
   4 from pyspark.mllib.linalg.distributed import RowMatrix, CoordinateMatrix
   5 import numpy as np
   6 import pyarrow
   7 import pandas as pd
   8 import fire
   9 from itertools import islice
  10 from pathlib import Path
  11 from similarities_helper import cosine_similarities
  12
  13 spark = SparkSession.builder.getOrCreate()
  14 conf = spark.sparkContext.getConf()
  15
  16 # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
  17 def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
  18     '''
  19     Compute similarities between subreddits based on tfi-idf vectors of comment texts
  20
  21     included_subreddits : string
  22         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
  23
  24     similarity_threshold : double (default = 0)
  25         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
  26 https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
  27
  28     min_df : int (default = 0.1 * (number of included_subreddits)
  29          exclude terms that appear in fewer than this number of documents.
  30
  31     outfile: string
  32          where to output csv and feather outputs
  33 '''
  34
  35     print(outfile)
  36     print(exclude_phrases)
  37
  38     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
  39
  40     if included_subreddits is None:
  41         included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
  42         included_subreddits = {s.strip('\n') for s in included_subreddits}
  43
  44     else:
  45         included_subreddits = set(open(included_subreddits))
  46
  47     if exclude_phrases == True:
  48         tfidf = tfidf.filter(~f.col(term).contains("_"))
  49
  50     sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
  51
  52     p = Path(outfile)
  53
  54     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  55     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
  56     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
  57
  58     sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
  59
  60     #instead of toLocalMatrix() why not read as entries and put strait into numpy
  61     sim_entries = pd.read_parquet(output_parquet)
  62
  63     df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
  64     spark.stop()
  65     df['subreddit_id_new'] = df['subreddit_id_new'] - 1
  66     df = df.sort_values('subreddit_id_new').reset_index(drop=True)
  67     df = df.set_index('subreddit_id_new')
  68
  69     similarities = sim_entries.join(df, on='i')
  70     similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
  71     similarities = similarities.join(df, on='j')
  72     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
  73
  74     similarities.write_feather(output_feather)
  75     similarities.write_csv(output_csv)
  76     return similarities
  77
  78 if __name__ == '__main__':
  79     fire.Fire(term_cosine_similarities)