X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/a60747292e91a47d122158659182f82bfd2e922a..e6294b5b90135a5163441c8dc62252dd6a188412:/old/term_cosine_similarity.py

diff --git a/old/term_cosine_similarity.py b/old/term_cosine_similarity.py
new file mode 100644
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+++ b/old/term_cosine_similarity.py
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+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+from pyspark.mllib.linalg.distributed import RowMatrix, CoordinateMatrix
+import numpy as np
+import pyarrow
+import pandas as pd
+import fire
+from itertools import islice
+from pathlib import Path
+from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, column_similarities, select_topN
+import scipy
+
+# outfile='test_similarities_500.feather';
+# min_df = None;
+# included_subreddits=None; topN=100; exclude_phrases=True;
+def term_cosine_similarities(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('/gscratch/comdata/output/reddit_similarity/tfidf/subreddit_terms.parquet')
+
+    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).contains("_"))
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, 'term', 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')
+    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()
+    
+if __name__ == '__main__':
+    fire.Fire(term_cosine_similarities)