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
  12 min_df = 1000
  13
  14 spark = SparkSession.builder.getOrCreate()
  15 conf = spark.sparkContext.getConf()
  16
  17 # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
  18 def spark_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0):
  19     '''
  20     Compute similarities between subreddits based on tfi-idf vectors of comment texts
  21
  22     included_subreddits : string
  23         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
  24
  25     similarity_threshold : double (default = 0)
  26         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
  27 https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
  28
  29     min_df : int (default = 0.1 * (number of included_subreddits)
  30          exclude terms that appear in fewer than this number of documents.
  31
  32     outfile: string
  33          where to output csv and feather outputs
  34 '''
  35
  36     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
  37
  38     if included_subreddits is None:
  39         included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),500))
  40         included_subreddits = [s.strip('\n') for s in included_subreddits]
  41
  42     else:
  43         included_subreddits = set(open(included_subreddits))
  44
  45     if min_df is None:
  46         min_df = 0.1 * len(included_subreddits)
  47
  48     tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
  49
  50     # reset the subreddit ids
  51     sub_ids = tfidf.select('subreddit_id').distinct()
  52     sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
  53     tfidf = tfidf.join(sub_ids,'subreddit_id')
  54
  55     # only use terms in at least min_df included subreddits
  56     new_count = tfidf.groupBy('term_id').agg(f.count('term_id').alias('new_count'))
  57     term_ids = term_ids.join(new_count,'term_id')
  58     term_ids = term_ids.filter(new_count >= min_df)
  59
  60     # reset the term ids
  61     term_ids = tfidf.select('term_id').distinct()
  62     term_ids = term_ids.withColumn("term_id_new",f.row_number().over(Window.orderBy("term_id")))
  63     tfidf = tfidf.join(term_ids,'term_id')
  64
  65     # step 1 make an rdd of entires
  66     # sorted by (dense) spark subreddit id
  67     entries = tfidf.select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
  68
  69     # step 2 make it into a distributed.RowMatrix
  70     coordMat = CoordinateMatrix(entries)
  71
  72     # this needs to be an IndexedRowMatrix()
  73     mat = coordMat.toRowMatrix()
  74
  75     #goal: build a matrix of subreddit columns and tf-idfs rows
  76     sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
  77
  78     print(sim_dist.numRows(), sim_dist.numCols())
  79
  80     #instead of toLocalMatrix() why not read as entries and put strait into numpy
  81     sim_entries = sim_dist.entries.collect()
  82
  83     sim_entries = pd.DataFrame([{'i':me.i,'j':me.j,'value':me.value} for me in sim_entries])
  84
  85     df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
  86
  87     df = df.sort_values('subreddit_id_new').reset_index(drop=True)
  88
  89     df = df.set_index('subreddit_id_new')
  90
  91     similarities = sim_entries.join(df, on='i')
  92     similarities = sim_entries.rename(columns={'subreddit':"subreddit_i"})
  93     similarities = sim_entries.join(df, on='j')
  94     similarities = sim_entries.rename(columns={'subreddit':"subreddit_j"})
  95
  96     p = Path(outfile)
  97     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
  98     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
  99
 100     pyarrow.write_feather(similarities,output_feather)
 101     pyarrow.write_csv(similarities,output_csv)
 102     return similarities
 103
 104 if __name__ == '__main__':
 105     fire.Fire(spark_similarities)