from pyspark.sql import Window
import numpy as np
import pyarrow
+import pyarrow.dataset as ds
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
+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')
-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)
- 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, 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
- 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(40) as pool: # maybe it can be done with 40 cores on the huge machine?
- list(pool.map(weeks,week_similarities_helper))
+ 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):
- return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_100k.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.parquet',
outfile,
'author',
min_df,
+ max_df,
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_100k.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,
projects / cdsc_reddit.git / blobdiff