Add code for running tf-idf at the weekly level.

[cdsc_reddit.git] / author_cosine_similarity.py

diff --git a/author_cosine_similarity.py b/author_cosine_similarity.py
index 7b2a766d24461269061a244f0350725ef8a15936..7ae708b72289bb8cb36eb332cbc11b54e22f6dfa 100644 (file)
--- a/author_cosine_similarity.py
+++ b/author_cosine_similarity.py
@@ -7,13 +7,13 @@ import pandas as pd
 import fire
 from itertools import islice
 from pathlib import Path
-from similarities_helper import cosine_similarities
+from similarities_helper import cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities
 
 spark = SparkSession.builder.getOrCreate()
 conf = spark.sparkContext.getConf()
 
 # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
-def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
+def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500):
     '''
     Compute similarities between subreddits based on tfi-idf vectors of author comments
     
@@ -31,48 +31,89 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
          where to output csv and feather outputs
 '''
 
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
     print(outfile)
-    print(exclude_phrases)
 
-    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet_test1/part-00000-107cee94-92d8-4265-b804-40f1e7f1aaf2-c000.snappy.parquet')
+    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.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}
+        rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
+        included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
 
     else:
         included_subreddits = set(open(included_subreddits))
 
-    sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, 'author', 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,'author')
+    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"))
-    sim_dist = sim_dist.entries.toDF()
 
-    sim_dist = sim_dist.repartition(1)
-    sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
+    sims.to_feather(outfile)
+    tempdir.cleanup()
+
+    # print(outfile)
+
+    # tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.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))
+
+    # sim_dist, tfidf = cosine_similarities(tfidf, 'author', 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 = sim_dist.entries.toDF()
+
+    # sim_dist = sim_dist.repartition(1)
+    # sim_dist.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()
-    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')
+    # #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 = 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
+    # similarities.to_feather(output_feather)
+    # similarities.to_csv(output_csv)
+    # return similarities
     
 if __name__ == '__main__':
-    fire.Fire(term_cosine_similarities)
+    fire.Fire(author_cosine_similarities)