Merge remote-tracking branch 'refs/remotes/origin/master' into master

[cdsc_reddit.git] / term_cosine_similarity.py

diff --git a/term_cosine_similarity.py b/term_cosine_similarity.py
index ba6d2c93ae87aeeb53f3c6422f1082424cd80989..dd92b2c616932d92bacb387912c20a708a9591f9 100644 (file)
--- a/term_cosine_similarity.py
+++ b/term_cosine_similarity.py
@@ -8,98 +8,120 @@ import pandas as pd
 import fire
 from itertools import islice
 from pathlib import Path
 import fire
 from itertools import islice
 from pathlib import Path

-
-min_df = 1000
-
-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 spark_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0):
-    '''
-    Compute similarities between subreddits based on tfi-idf vectors of comment texts 
-    
-    included_subreddits : string
-        Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
-
-    similarity_threshold : double (default = 0)
-        set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
-https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
-
-    min_df : int (default = 0.1 * (number of included_subreddits)
-         exclude terms that appear in fewer than this number of documents.
-
-    outfile: string
-         where to output csv and feather outputs
-'''
+from similarities_helper import cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities
+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/users/nathante/subreddit_tfidf.parquet')
 
     if included_subreddits is None:
 
     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
 
     if included_subreddits is None:

-        included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),500))
-        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))
 
 
     else:
         included_subreddits = set(open(included_subreddits))
 

-    if min_df is None:
-        min_df = 0.1 * len(included_subreddits)
-
-    tfidf = tfidf.filter(f.col("subreddit").isin(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

 
 

-    # reset the subreddit ids
-    sub_ids = tfidf.select('subreddit_id').distinct()
-    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
-    tfidf = tfidf.join(sub_ids,'subreddit_id')
+    p = Path(outfile)

 
 

-    # only use terms in at least min_df included subreddits
-    new_count = tfidf.groupBy('term_id').agg(f.count('term_id').alias('new_count'))
-    term_ids = term_ids.join(new_count,'term_id')
-    term_ids = term_ids.filter(new_count >= min_df)
+    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"))

 
 

-    # reset the term ids
-    term_ids = tfidf.select('term_id').distinct()
-    term_ids = term_ids.withColumn("term_id_new",f.row_number().over(Window.orderBy("term_id")))
-    tfidf = tfidf.join(term_ids,'term_id')
+    sims.to_feather(outfile)
+    tempdir.cleanup()
+    path = "term_tfidf_entriesaukjy5gv.parquet"
+    

 
 

-    # step 1 make an rdd of entires
-    # sorted by (dense) spark subreddit id
-    entries = tfidf.select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
+# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
+# def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True):
+#     '''
+#     Compute similarities between subreddits based on tfi-idf vectors of comment texts 
+    
+#     included_subreddits : string
+#         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits

 
 

-    # step 2 make it into a distributed.RowMatrix
-    coordMat = CoordinateMatrix(entries)
+#     similarity_threshold : double (default = 0)
+#         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
+# https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.

 
 

-    # this needs to be an IndexedRowMatrix()
-    mat = coordMat.toRowMatrix()
+#     min_df : int (default = 0.1 * (number of included_subreddits)
+#          exclude terms that appear in fewer than this number of documents.

 
 

-    #goal: build a matrix of subreddit columns and tf-idfs rows
-    sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
+#     outfile: string
+#          where to output csv and feather outputs
+# '''

 
 

-    print(sim_dist.numRows(), sim_dist.numCols())
+#     print(outfile)
+#     print(exclude_phrases)

 
 

-    #instead of toLocalMatrix() why not read as entries and put strait into numpy
-    sim_entries = sim_dist.entries.collect()
+#     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')

 
 

-    sim_entries = pd.DataFrame([{'i':me.i,'j':me.j,'value':me.value} for me in sim_entries])
+#     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}

 
 

-    df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
+#     else:
+#         included_subreddits = set(open(included_subreddits))

 
 

-    df = df.sort_values('subreddit_id_new').reset_index(drop=True)
+#     if exclude_phrases == True:
+#         tfidf = tfidf.filter(~f.col(term).contains("_"))

 
 

-    df = df.set_index('subreddit_id_new')
+#     sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold)

 
 

-    similarities = sim_entries.join(df, on='i')
-    similarities = sim_entries.rename(columns={'subreddit':"subreddit_i"})
-    similarities = sim_entries.join(df, on='j')
-    similarities = sim_entries.rename(columns={'subreddit':"subreddit_j"})
+#     p = Path(outfile)

 
 

-    p = Path(outfile)
-    output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
-    output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
+#     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"))

 
 

-    pyarrow.write_feather(similarities,output_feather)
-    pyarrow.write_csv(similarities,output_csv)
-    return similarities
+#     sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
+    
+#     #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.to_feather(output_feather)
+#     similarities.to_csv(output_csv)
+#     return similarities

     
 if __name__ == '__main__':
     
 if __name__ == '__main__':

-    fire.Fire(spark_similarities)
+    fire.Fire(term_cosine_similarities)