X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/541e125b28dbca5c06d2160a5cd59ce112657b2a..07b0dff9bc0dae2ab6f7fb7334007a5269a512ad:/similarities/weekly_cosine_similarities.py diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py deleted file mode 100755 index 6ce30b8..0000000 --- a/similarities/weekly_cosine_similarities.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -from pyspark.sql import functions as f -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, chain -from pathlib import Path -from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_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_10k.parquet" -tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet" -min_df=None -included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt" -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, subreddit_names, nterms): - term = term_colname - term_id = term + '_id' - term_id_new = term + '_id_new' - print(f"loading matrix: {week}") - - 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, - 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 = simfunc(mat) - del mat - sims = pd.DataFrame(sims) - sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1) - sims['_subreddit'] = subreddit_names.subreddit.values - outfile = str(Path(outdir) / str(week)) - write_weekly_similarities(outfile, sims, week, subreddit_names) - -def pull_weeks(batch): - return set(batch.to_pandas()['week']) - -# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week. -def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs): - term_colname= kwargs.get('term_colname') - #lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl" - - # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model) - - simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model) - - return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs) - -#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, simfunc=column_similarities): - print(outfile) - # 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) - - # load subreddits + topN - - 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=simfunc, 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? - - -def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500): - return cosine_similarities_weekly(infile, - outfile, - 'author', - min_df, - max_df, - included_subreddits, - topN) - -def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None): - return cosine_similarities_weekly(infile, - outfile, - 'term', - min_df, - max_df, - included_subreddits, - topN) - - -def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None): - return cosine_similarities_weekly_lsi(infile, - outfile, - 'author', - min_df, - max_df, - included_subreddits, - topN, - n_components=n_components, - lsi_model=lsi_model) - - -def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None): - return cosine_similarities_weekly_lsi(infile, - outfile, - 'term', - min_df, - max_df, - included_subreddits, - topN, - n_components=n_components, - lsi_model=lsi_model) - -if __name__ == "__main__": - fire.Fire({'authors':author_cosine_similarities_weekly, - 'terms':term_cosine_similarities_weekly, - 'authors-lsi':author_cosine_similarities_weekly_lsi, - 'terms-lsi':term_cosine_similarities_weekly - })