X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/b4dd9acbd8e499d87714413b8260240341ebf7d7..8a2248fae1ee5818576b9a8f2849d1ad0efd8187:/similarities/weekly_cosine_similarities.py?ds=sidebyside diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py index 4d496f0..e24ceee 100644 --- a/similarities/weekly_cosine_similarities.py +++ b/similarities/weekly_cosine_similarities.py @@ -3,71 +3,79 @@ from pyspark.sql import SparkSession 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, cpu_count +from functools import partial -#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) - - 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") +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) - 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) - - # 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() - - weeks = sorted(list(subreddit_names.week.drop_duplicates())) - for week in weeks: - print(f"loading matrix: {week}") - mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week) - print('computing similarities') - sims = column_similarities(mat) - del mat +#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() - names = subreddit_names.loc[subreddit_names.week == week] - sims = pd.DataFrame(sims.todense()) + with Pool(cpu_count()) as pool: + weeks = set(chain( * pool.imap_unordered(pull_weeks,batches))) - sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1) - sims['subreddit'] = names.subreddit.values + weeks = sorted(weeks) + # do this step in parallel if we have the memory for it. + # should be doable with pool.map - write_weekly_similarities(outfile, sims, week, names) + 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)) -def author_cosine_similarities_weekly(outfile, min_df=None , 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, + 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.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({'author':author_cosine_similarities_weekly, - 'term':term_cosine_similarities_weekly}) + fire.Fire({'authors':author_cosine_similarities_weekly, + 'terms':term_cosine_similarities_weekly})