Some improvements to run affinity clustering on larger dataset and

[cdsc_reddit.git] / similarities / #cosine_similarities.py#

diff --git a/similarities/#cosine_similarities.py# b/similarities/#cosine_similarities.py#
deleted file mode 100644 (file)
index ae080d5..0000000
--- a/similarities/#cosine_similarities.py#
+++ /dev/null
@@ -1,73 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-import pandas as pd
-import fire
-from pathlib import Path
-from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits
-
-
-def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(outfile)
-    print(exclude_phrases)
-
-    tfidf = spark.read.parquet(infile)
-
-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    if exclude_phrases == True:
-        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits)
-    tfidf = spark.read.parquet(tempdir.name)
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    spark.stop()
-
-    print("loading matrix")
-    mat = read_tfidf_matrix(tempdir.name, term_colname)
-    print('computing similarities')
-    sims = column_similarities(mat)
-    del mat
-    
-    sims = pd.DataFrame(sims.todense())
-    sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['subreddit'] = subreddit_names.subreddit.values
-
-    p = Path(outfile)
-
-    output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
-    output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
-    output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
-
-    sims.to_feather(outfile)
-    tempdir.cleanup()
-
-def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
-                               'term',
-                               outfile,
-                               min_df,
-                               included_subreddits,
-                               topN,
-                               exclude_phrases)
-
-def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
-                               'author',
-                               outfile,
-                               min_df,
-                               included_subreddits,
-                               topN,
-                               exclude_phrases=False)
-
-if __name__ == "__main__":
-    fire.Fire({'term':term_cosine_similarities,
-               'author':author_cosine_similarities})
-