-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
+from similarities_helper import similarities
+def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False,from_date=None, to_date=None):
+ return similiarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date)
-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):
+def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
'term',
outfile,
min_df,
included_subreddits,
topN,
- exclude_phrases)
+ exclude_phrasesby.)
-def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
+def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000, from_date=None, to_date=None):
return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
'author',
outfile,