Updates to similarities code for smap project.

[cdsc_reddit.git] / similarities / weekly_cosine_similarities.py

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
index aeafe743ca1f0ecfe832741d2081fcbee5ae3a14..7cafcb9387628185953c565a62e5cbb63891b72c 100644 (file)
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -3,78 +3,97 @@ from pyspark.sql import SparkSession
 from pyspark.sql import Window
 import numpy as np
 import pyarrow
 from pyspark.sql import Window
 import numpy as np
 import pyarrow

+import pyarrow.dataset as ds

 import pandas as pd
 import fire
 import pandas as pd
 import fire

-from itertools import islice
+from itertools import islice, chain

 from pathlib import Path
 from pathlib import Path

-from similarities_helper import *
-from multiprocessing import pool
+from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
+from scipy.sparse import csr_matrix
+from multiprocessing import Pool, cpu_count
+from functools import partial

 
 

-def _week_similarities(tempdir, term_colname, week):
-        print(f"loading matrix: {week}")
-        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
-        print('computing similarities')
-        sims = column_similarities(mat)
-        del mat
+# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
+# tfidf_path = infile 
+# min_df=None
+# max_df = None
+# topN=100
+# term_colname='author'
+# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
+# included_subreddits=None

 
 

-        names = subreddit_names.loc[subreddit_names.week == week]
-        sims = pd.DataFrame(sims.todense())
+def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+    print(f"loading matrix: {week}")

 
 

-        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)
-
-#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)
+    entries = pull_tfidf(infile = tfidf_path,
+                         term_colname=term_colname,
+                         min_df=min_df,
+                         max_df=max_df,
+                         included_subreddits=included_subreddits,
+                         topN=topN,
+                         week=week.isoformat(),
+                         rescale_idf=False)

     
     

-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
+    tfidf_colname='tf_idf'
+    # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))

 
 

-    print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
+    print('computing similarities')
+    sims = simfunc(mat.T)
+    del mat
+    sims = pd.DataFrame(sims)
+    sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
+    sims['_subreddit'] = subreddit_names.subreddit.values
+    outfile = str(Path(outdir) / str(week))
+    write_weekly_similarities(outfile, sims, week, subreddit_names)

 
 

-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
+def pull_weeks(batch):
+    return set(batch.to_pandas()['week'])

 
 

-    tfidf = spark.read.parquet(tempdir.name)
+#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
+    print(outfile)
+    # do this step in parallel if we have the memory for it.
+    # should be doable with pool.map

 
 

-    # 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 = SparkSession.builder.getOrCreate()
+    df = spark.read.parquet(tfidf_path)
+    subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id")
+    nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
+    weeks = df.select(f.col("week")).distinct().toPandas().week.values

     spark.stop()
 
     spark.stop()
 

-    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
-    # do this step in parallel if we have the memory for it.
-    # should be doable with pool.map
+    print(f"computing weekly similarities")
+    week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)

 
 

-    def week_similarities_helper(week):
-        _week_similarities(tempdir, term_colname, week)
+    pool = Pool(cpu_count())
+    
+    list(pool.imap(week_similarities_helper,weeks))
+    pool.close()
+    #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?

 
 

-    with Pool(40) as pool: # maybe it can be done with 40 cores on the huge machine?
-        list(pool.map(weeks,week_similarities_helper))

 
 

-def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_30k.parquet',
+def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',

                                       outfile,
                                       'author',
                                       min_df,
                                       outfile,
                                       'author',
                                       min_df,

+                                      max_df,

                                       included_subreddits,
                                       topN)
 
                                       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_30k.parquet',
-                                      outfile,
-                                      'term',
-                                      min_df,
-                                      included_subreddits,
-                                      topN)
+def term_cosine_similarities_weekly(outfile, min_df=None, max_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,
+                                          max_df,
+                                          included_subreddits,
+                                          topN)

 
 if __name__ == "__main__":
     fire.Fire({'authors':author_cosine_similarities_weekly,
 
 if __name__ == "__main__":
     fire.Fire({'authors':author_cosine_similarities_weekly,