changes for archiving.

[cdsc_reddit.git] / similarities / weekly_cosine_similarities.py

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
deleted file mode 100644 (file)
index 7cafcb9..0000000
--- a/similarities/weekly_cosine_similarities.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-from pyspark.sql import Window
-import numpy as np
-import pyarrow
-import pyarrow.dataset as ds
-import pandas as pd
-import fire
-from itertools import islice, chain
-from pathlib import Path
-from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
-from scipy.sparse import csr_matrix
-from multiprocessing import Pool, cpu_count
-from functools import partial
-
-# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
-# tfidf_path = infile 
-# min_df=None
-# max_df = None
-# topN=100
-# term_colname='author'
-# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
-# included_subreddits=None
-
-def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-    print(f"loading matrix: {week}")
-
-    entries = pull_tfidf(infile = tfidf_path,
-                         term_colname=term_colname,
-                         min_df=min_df,
-                         max_df=max_df,
-                         included_subreddits=included_subreddits,
-                         topN=topN,
-                         week=week.isoformat(),
-                         rescale_idf=False)
-    
-    tfidf_colname='tf_idf'
-    # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
-
-    print('computing similarities')
-    sims = simfunc(mat.T)
-    del mat
-    sims = pd.DataFrame(sims)
-    sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['_subreddit'] = subreddit_names.subreddit.values
-    outfile = str(Path(outdir) / str(week))
-    write_weekly_similarities(outfile, sims, week, subreddit_names)
-
-def pull_weeks(batch):
-    return set(batch.to_pandas()['week'])
-
-#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
-    print(outfile)
-    # do this step in parallel if we have the memory for it.
-    # should be doable with pool.map
-
-    spark = SparkSession.builder.getOrCreate()
-    df = spark.read.parquet(tfidf_path)
-    subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id")
-    nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
-    weeks = df.select(f.col("week")).distinct().toPandas().week.values
-    spark.stop()
-
-    print(f"computing weekly similarities")
-    week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
-
-    pool = Pool(cpu_count())
-    
-    list(pool.imap(week_similarities_helper,weeks))
-    pool.close()
-    #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
-
-
-def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
-                                      outfile,
-                                      'author',
-                                      min_df,
-                                      max_df,
-                                      included_subreddits,
-                                      topN)
-
-def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
-        return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
-                                          outfile,
-                                          'term',
-                                          min_df,
-                                          max_df,
-                                          included_subreddits,
-                                          topN)
-
-if __name__ == "__main__":
-    fire.Fire({'authors':author_cosine_similarities_weekly,
-               'terms':term_cosine_similarities_weekly})