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, prep_tfidf_entries, read_tfidf_matrix, column_similarities
  12 import scipy
  13 # outfile='test_similarities_500.feather';
  14 # min_df = None;
  15 # included_subreddits=None; topN=100; exclude_phrases=True;
  16
  17 def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500, exclude_phrases=False):
  18     spark = SparkSession.builder.getOrCreate()
  19     conf = spark.sparkContext.getConf()
  20     print(outfile)
  21     print(exclude_phrases)
  22
  23     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
  24
  25     if included_subreddits is None:
  26         rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
  27         included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
  28
  29     else:
  30         included_subreddits = set(open(included_subreddits))
  31
  32     if exclude_phrases == True:
  33         tfidf = tfidf.filter(~f.col(term).contains("_"))
  34
  35     print("creating temporary parquet with matrix indicies")
  36     tempdir = prep_tfidf_entries(tfidf, 'term', min_df, included_subreddits)
  37     tfidf = spark.read.parquet(tempdir.name)
  38     subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
  39     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
  40     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
  41     spark.stop()
  42
  43     print("loading matrix")
  44     mat = read_tfidf_matrix(tempdir.name,'term')
  45     print('computing similarities')
  46     sims = column_similarities(mat)
  47     del mat
  48
  49     sims = pd.DataFrame(sims.todense())
  50     sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)},axis=1)
  51     sims['subreddit'] = subreddit_names.subreddit.values
  52
  53     p = Path(outfile)
  54
  55     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  56     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
  57     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
  58
  59     sims.to_feather(outfile)
  60     tempdir.cleanup()
  61     path = "term_tfidf_entriesaukjy5gv.parquet"
  62
  63
  64 # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
  65 # def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
  66 #     '''
  67 #     Compute similarities between subreddits based on tfi-idf vectors of comment texts
  68
  69 #     included_subreddits : string
  70 #         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
  71
  72 #     similarity_threshold : double (default = 0)
  73 #         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
  74 # https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
  75
  76 #     min_df : int (default = 0.1 * (number of included_subreddits)
  77 #          exclude terms that appear in fewer than this number of documents.
  78
  79 #     outfile: string
  80 #          where to output csv and feather outputs
  81 # '''
  82
  83 #     print(outfile)
  84 #     print(exclude_phrases)
  85
  86 #     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
  87
  88 #     if included_subreddits is None:
  89 #         included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
  90 #         included_subreddits = {s.strip('\n') for s in included_subreddits}
  91
  92 #     else:
  93 #         included_subreddits = set(open(included_subreddits))
  94
  95 #     if exclude_phrases == True:
  96 #         tfidf = tfidf.filter(~f.col(term).contains("_"))
  97
  98 #     sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
  99
 100 #     p = Path(outfile)
 101
 102 #     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
 103 #     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
 104 #     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
 105
 106 #     sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
 107
 108 #     #instead of toLocalMatrix() why not read as entries and put strait into numpy
 109 #     sim_entries = pd.read_parquet(output_parquet)
 110
 111 #     df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
 112 #     spark.stop()
 113 #     df['subreddit_id_new'] = df['subreddit_id_new'] - 1
 114 #     df = df.sort_values('subreddit_id_new').reset_index(drop=True)
 115 #     df = df.set_index('subreddit_id_new')
 116
 117 #     similarities = sim_entries.join(df, on='i')
 118 #     similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
 119 #     similarities = similarities.join(df, on='j')
 120 #     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
 121
 122 #     similarities.to_feather(output_feather)
 123 #     similarities.to_csv(output_csv)
 124 #     return similarities
 125
 126 if __name__ == '__main__':
 127     fire.Fire(term_cosine_similarities)