]> code.communitydata.science - cdsc_reddit.git/blobdiff - term_cosine_similarity.py
bugfix in completing tfidf similarity matrices.
[cdsc_reddit.git] / term_cosine_similarity.py
index 44af4e606082185611b077100b7ab24cd7397e1a..48132a83649c8271cade025913c0bbcc2bac72e7 100644 (file)
@@ -8,7 +8,7 @@ import pandas as pd
 import fire
 from itertools import islice
 from pathlib import Path
-from similarities_helper import build_cosine_similarities
+from similarities_helper import cosine_similarities
 
 spark = SparkSession.builder.getOrCreate()
 conf = spark.sparkContext.getConf()
@@ -57,12 +57,11 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
 
     sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
     
-    spark.stop()
-
     #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')
@@ -72,8 +71,8 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
     similarities = similarities.join(df, on='j')
     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
 
-    similarities.write_feather(output_feather)
-    similarities.write_csv(output_csv)
+    similarities.to_feather(output_feather)
+    similarities.to_csv(output_csv)
     return similarities
     
 if __name__ == '__main__':

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