X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/2d21ff1137dfaf83c5a51fdcd8900503c50a06ab..b7c39a3494ce214f315fd7e3bb0bf99bc58070d1:/similarities/weekly_cosine_similarities.py

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
index e24ceee..7cafcb9 100644
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -8,32 +8,47 @@ import pandas as pd
 import fire
 from itertools import islice, chain
 from pathlib import Path
-from similarities_helper import *
+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
 
+# 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
 
-def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
+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}")
-    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)))
+
+    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)
+    
+    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('computing similarities')
-    sims = column_similarities(mat)
+    sims = simfunc(mat.T)
     del mat
-    sims = pd.DataFrame(sims.todense())
+    sims = pd.DataFrame(sims)
     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['_subreddit'] = names.subreddit.values
+    sims['_subreddit'] = subreddit_names.subreddit.values
     outfile = str(Path(outdir) / str(week))
-    write_weekly_similarities(outfile, sims, week, names)
+    write_weekly_similarities(outfile, sims, week, subreddit_names)
 
 def pull_weeks(batch):
     return set(batch.to_pandas()['week'])
@@ -41,25 +56,29 @@ def pull_weeks(batch):
 #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)
-    tfidf_ds = ds.dataset(tfidf_path)
-    tfidf_ds = tfidf_ds.to_table(columns=["week"])
-    batches = tfidf_ds.to_batches()
-
-    with Pool(cpu_count()) as pool:
-        weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
-
-    weeks = sorted(weeks)
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 
+    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()
+
     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)
+    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)
+
+    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(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, max_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
                                       outfile,
                                       'author',
                                       min_df,