+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})
+