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 cosine_similarities spark = SparkSession.builder.getOrCreate() conf = spark.sparkContext.getConf() # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0; def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500): ''' Compute similarities between subreddits based on tfi-idf vectors of author comments included_subreddits : string Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits similarity_threshold : double (default = 0) set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm. min_df : int (default = 0.1 * (number of included_subreddits) exclude terms that appear in fewer than this number of documents. outfile: string where to output csv and feather outputs ''' print(outfile) tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet') if included_subreddits is None: included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN)) included_subreddits = {s.strip('\n') for s in included_subreddits} else: included_subreddits = set(open(included_subreddits)) sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold) p = Path(outfile) output_feather = Path(str(p).replace("".join(p.suffixes), ".feather")) output_csv = Path(str(p).replace("".join(p.suffixes), ".csv")) output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet")) sim_dist = sim_dist.entries.toDF() sim_dist = sim_dist.repartition(1) sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy') #instead of toLocalMatrix() why not read as entries and put strait into numpy sim_entries = pd.read_parquet(output_parquet) df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas() spark.stop() df['subreddit_id_new'] = df['subreddit_id_new'] - 1 df = df.sort_values('subreddit_id_new').reset_index(drop=True) df = df.set_index('subreddit_id_new') similarities = sim_entries.join(df, on='i') similarities = similarities.rename(columns={'subreddit':"subreddit_i"}) similarities = similarities.join(df, on='j') similarities = similarities.rename(columns={'subreddit':"subreddit_j"}) similarities.to_feather(output_feather) similarities.to_csv(output_csv) return similarities if __name__ == '__main__': fire.Fire(author_cosine_similarities)