- 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.write_feather(output_feather)
- similarities.write_csv(output_csv)
- return similarities
+# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
+# def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
+# '''
+# Compute similarities between subreddits based on tfi-idf vectors of comment texts
+
+# included_subreddits : string
+# Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
+
+# similarity_threshold : double (default = 0)
+# set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
+# https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
+
+# min_df : int (default = 0.1 * (number of included_subreddits)
+# exclude terms that appear in fewer than this number of documents.
+
+# outfile: string
+# where to output csv and feather outputs
+# '''
+
+# print(outfile)
+# print(exclude_phrases)
+
+# tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
+
+# if included_subreddits is None:
+# included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
+# included_subreddits = {s.strip('\n') for s in included_subreddits}
+
+# 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