from pyspark.sql import functions as f from pyspark.sql import SparkSession from pyspark.sql import Window import numpy as np import pyarrow import pandas as pd import fire from itertools import islice from pathlib import Path from similarities_helper import * from multiprocessing import pool 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()) 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) 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) # 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())) # 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) 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_100k.parquet', outfile, 'author', min_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) if __name__ == "__main__": fire.Fire({'authors':author_cosine_similarities_weekly, 'terms':term_cosine_similarities_weekly})