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
  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, exclude_phrases=True):
  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     print(outfile)
  35     print(exclude_phrases)
  36
  37     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet_test1/part-00000-107cee94-92d8-4265-b804-40f1e7f1aaf2-c000.snappy.parquet')
  38
  39     if included_subreddits is None:
  40         included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
  41         included_subreddits = {s.strip('\n') for s in included_subreddits}
  42
  43     else:
  44         included_subreddits = set(open(included_subreddits))
  45
  46     sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)
  47
  48     p = Path(outfile)
  49
  50     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  51     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
  52     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
  53     sim_dist = sim_dist.entries.toDF()
  54
  55     sim_dist = sim_dist.repartition(1)
  56     sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
  57
  58     spark.stop()
  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     df['subreddit_id_new'] = df['subreddit_id_new'] - 1
  65     df = df.sort_values('subreddit_id_new').reset_index(drop=True)
  66     df = df.set_index('subreddit_id_new')
  67
  68     similarities = sim_entries.join(df, on='i')
  69     similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
  70     similarities = similarities.join(df, on='j')
  71     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
  72
  73     similarities.write_feather(output_feather)
  74     similarities.write_csv(output_csv)
  75     return similarities
  76
  77 if __name__ == '__main__':
  78     fire.Fire(term_cosine_similarities)