refactor clustring in object oriented style

[cdsc_reddit.git] / similarities / weekly_cosine_similarities.py

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
index 044ee750b5cb2d93f12ff4e9382f9867f686ccaf..e24ceee620568be7ed56c509c4408a680695f643 100644 (file)
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -3,78 +3,78 @@ 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 similarities_helper import *
 from multiprocessing import Pool, cpu_count
 from pathlib import Path
 from similarities_helper import *
 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

 
 

-        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):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+    print(f"loading matrix: {week}")
+    entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
+                                             term_colname=term_colname,
+                                             min_df=min_df,
+                                             max_df=max_df,
+                                             included_subreddits=included_subreddits,
+                                             topN=topN,
+                                             week=week)
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+    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'] = names.subreddit.values
+    outfile = str(Path(outdir) / str(week))
+    write_weekly_similarities(outfile, sims, week, names)

 
 

-        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)
+def pull_weeks(batch):
+    return set(batch.to_pandas()['week'])

 
 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
 
 #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()
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):

     print(outfile)
     print(outfile)

-    tfidf = spark.read.parquet(tfidf_path)
-    
-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df=None, included_subreddits=included_subreddits)
-
-    tfidf = spark.read.parquet(tempdir.name)
+    tfidf_ds = ds.dataset(tfidf_path)
+    tfidf_ds = tfidf_ds.to_table(columns=["week"])
+    batches = tfidf_ds.to_batches()

 
 

-    # 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.stop()
+    with Pool(cpu_count()) as pool:
+        weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))

 
 

-    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
+    weeks = sorted(weeks)

     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 

-    def week_similarities_helper(week):
-        _week_similarities(tempdir, term_colname, week)
+    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)

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

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