X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/4e20dce18834f7276776a1ab824ff95e8c44ef99..07b0dff9bc0dae2ab6f7fb7334007a5269a512ad:/similarities/weekly_cosine_similarities.py

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
deleted file mode 100644
index 4d496f0..0000000
--- a/similarities/weekly_cosine_similarities.py
+++ /dev/null
@@ -1,73 +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 pandas as pd
-import fire
-from itertools import islice
-from pathlib import Path
-from similarities_helper import *
-
-
-#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(outfile)
-    tfidf = spark.read.parquet(tfidf_path)
-    
-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
-
-    tfidf = spark.read.parquet(tempdir.name)
-
-    # the ids can change each week.
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    spark.stop()
-
-    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
-    for week in weeks:
-        print(f"loading matrix: {week}")
-        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
-        print('computing similarities')
-        sims = column_similarities(mat)
-        del mat
-
-        names = subreddit_names.loc[subreddit_names.week == week]
-        sims = pd.DataFrame(sims.todense())
-
-        sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
-        sims['subreddit'] = names.subreddit.values
-
-        write_weekly_similarities(outfile, sims, week, names)
-
-
-def author_cosine_similarities_weekly(outfile, min_df=None , included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
-                                      outfile,
-                                      'author',
-                                      min_df,
-                                      included_subreddits,
-                                      topN)
-
-def term_cosine_similarities_weekly(outfile, min_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,
-                                      included_subreddits,
-                                      topN)
-
-if __name__ == "__main__":
-    fire.Fire({'author':author_cosine_similarities_weekly,
-               'term':term_cosine_similarities_weekly})