+# else:
+# included_subreddits = set(open(included_subreddits))
+
+# if exclude_phrases == True:
+# tfidf = tfidf.filter(~f.col(term).contains("_"))
+
+# sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)
+
+# 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"))
+
+# sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
+
+# #instead of toLocalMatrix() why not read as entries and put strait into numpy
+# sim_entries = pd.read_parquet(output_parquet)
+
+# df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
+# spark.stop()
+# df['subreddit_id_new'] = df['subreddit_id_new'] - 1
+# df = df.sort_values('subreddit_id_new').reset_index(drop=True)
+# df = df.set_index('subreddit_id_new')
+
+# similarities = sim_entries.join(df, on='i')
+# similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
+# similarities = similarities.join(df, on='j')
+# similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
+
+# similarities.to_feather(output_feather)
+# similarities.to_csv(output_csv)
+# return similarities