X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/a60747292e91a47d122158659182f82bfd2e922a..e6294b5b90135a5163441c8dc62252dd6a188412:/similarities/weekly_cosine_similarities.py?ds=sidebyside

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
new file mode 100644
index 0000000..2b3c90b
--- /dev/null
+++ b/similarities/weekly_cosine_similarities.py
@@ -0,0 +1,73 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+import numpy as np
+import pyarrow
+import pandas as pd
+import fire
+from itertools import islice
+from pathlib import Path
+from similarities_helper import *
+
+
+#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(outfile)
+    tfidf = spark.read.parquet(tfidf_path)
+    
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+    else:
+        included_subreddits = set(open(included_subreddits))
+
+    print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
+
+    tfidf = spark.read.parquet(tempdir.name)
+
+    # the ids can change each week.
+    subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
+    spark.stop()
+
+    weeks = list(subreddit_names.week.drop_duplicates())
+    for week in weeks:
+        print(f"loading matrix: {week}")
+        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
+        print('computing similarities')
+        sims = column_similarities(mat)
+        del mat
+
+        names = subreddit_names.loc[subreddit_names.week == week]
+        sims = pd.DataFrame(sims.todense())
+
+        sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
+        sims['subreddit'] = names.subreddit.values
+
+        write_weekly_similarities(outfile, sims, week, names)
+
+
+def author_cosine_similarities_weekly(outfile, min_df=None , included_subreddits=None, topN=500):
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+                                      outfile,
+                                      'author',
+                                      min_df,
+                                      included_subreddits,
+                                      topN)
+
+def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+                                      outfile,
+                                      'term',
+                                      min_df,
+                                      included_subreddits,
+                                      topN)
+
+if __name__ == "__main__":
+    fire.Fire({'author':author_cosine_similarities_weekly,
+               'term':term_cosine_similarities_weekly})