similarities/weekly_cosine_similarities.py

   1 #!/usr/bin/env python3
   2 from pyspark.sql import functions as f
   3 from pyspark.sql import SparkSession
   4 from pyspark.sql import Window
   5 import numpy as np
   6 import pyarrow
   7 import pyarrow.dataset as ds
   8 import pandas as pd
   9 import fire
  10 from itertools import islice, chain
  11 from pathlib import Path
  12 from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
  13 from scipy.sparse import csr_matrix
  14 from multiprocessing import Pool, cpu_count
  15 from functools import partial
  16 import pickle
  17
  18 # tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
  19 # #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
  20 # min_df=2
  21 # included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
  22 # max_df = None
  23 # topN=100
  24 # term_colname='author'
  25 # # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
  26 # # included_subreddits=None
  27 outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors.parquet"; infile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_authors_tfidf.parquet"; included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"; lsi_model="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/2000_authors_LSIMOD.pkl"; n_components=1500; algorithm="randomized"; term_colname='author'; tfidf_path=infile; random_state=1968;
  28
  29 # static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
  30 # dftest = spark.read.parquet(static_tfidf)
  31
  32 def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
  33     term = term_colname
  34     term_id = term + '_id'
  35     term_id_new = term + '_id_new'
  36     print(f"loading matrix: {week}")
  37
  38     entries = pull_tfidf(infile = tfidf_path,
  39                          term_colname=term_colname,
  40                          included_subreddits=included_subreddits,
  41                          topN=topN,
  42                          week=week.isoformat(),
  43                          rescale_idf=False)
  44
  45     tfidf_colname='tf_idf'
  46     # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
  47     mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
  48     print('computing similarities')
  49     print(simfunc)
  50     sims = simfunc(mat)
  51     del mat
  52     sims = next(sims)[0]
  53     sims = pd.DataFrame(sims)
  54     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
  55     sims['_subreddit'] = subreddit_names.subreddit.values
  56     outfile = str(Path(outdir) / str(week))
  57     write_weekly_similarities(outfile, sims, week, subreddit_names)
  58
  59 def pull_weeks(batch):
  60     return set(batch.to_pandas()['week'])
  61
  62 # This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
  63 def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
  64     print(args)
  65     print(kwargs)
  66     term_colname= kwargs.get('term_colname')
  67     # lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
  68
  69     lsi_model = pickle.load(open(lsi_model,'rb'))
  70     #simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
  71     simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
  72
  73     return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
  74
  75 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
  76 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
  77     print(outfile)
  78     # do this step in parallel if we have the memory for it.
  79     # should be doable with pool.map
  80
  81     spark = SparkSession.builder.getOrCreate()
  82     df = spark.read.parquet(tfidf_path)
  83
  84     # load subreddits + topN
  85
  86     subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
  87     subreddit_names = subreddit_names.sort_values("subreddit_id")
  88     nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
  89     weeks = df.select(f.col("week")).distinct().toPandas().week.values
  90     spark.stop()
  91
  92     print(f"computing weekly similarities")
  93     week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms)
  94
  95     for week in weeks:
  96         week_similarities_helper(week)
  97     # pool = Pool(cpu_count())
  98
  99     # list(pool.imap(week_similarities_helper, weeks))
 100     # pool.close()
 101     #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
 102
 103
 104 def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
 105     return cosine_similarities_weekly(infile,
 106                                       outfile,
 107                                       'author',
 108                                       max_df,
 109                                       included_subreddits,
 110                                       topN,
 111                                       min_df=2
 112 )
 113
 114 def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
 115         return cosine_similarities_weekly(infile,
 116                                           outfile,
 117                                           'term',
 118                                           min_df,
 119                                           max_df,
 120                                           included_subreddits,
 121                                           topN)
 122
 123
 124 def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', included_subreddits=None, n_components=100,lsi_model=None):
 125     return cosine_similarities_weekly_lsi(infile,
 126                                           outfile,
 127                                           'author',
 128                                           included_subreddits=included_subreddits,
 129                                           n_components=n_components,
 130                                           lsi_model=lsi_model
 131                                           )
 132
 133
 134 def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
 135         return cosine_similarities_weekly_lsi(infile,
 136                                               outfile,
 137                                               'term',
 138                                               included_subreddits=included_subreddits,
 139                                               n_components=n_components,
 140                                               lsi_model=lsi_model,
 141                                               )
 142
 143 if __name__ == "__main__":
 144     fire.Fire({'authors':author_cosine_similarities_weekly,
 145                'terms':term_cosine_similarities_weekly,
 146                'authors-lsi':author_cosine_similarities_weekly_lsi,
 147                'terms-lsi':term_cosine_similarities_weekly_lsi
 148                })
 149