X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/b7c39a3494ce214f315fd7e3bb0bf99bc58070d1..197518a222a321a8027c3dc5a4121350c47d0779:/similarities/weekly_cosine_similarities.py diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py old mode 100644 new mode 100755 index 7cafcb9..6ce30b8 --- a/similarities/weekly_cosine_similarities.py +++ b/similarities/weekly_cosine_similarities.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 from pyspark.sql import functions as f from pyspark.sql import SparkSession from pyspark.sql import Window @@ -8,17 +9,18 @@ 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 +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_test.parquet" -# tfidf_path = infile -# min_df=None -# max_df = None -# topN=100 -# term_colname='author' +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 @@ -34,7 +36,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, - week=week.isoformat(), + week=week, rescale_idf=False) tfidf_colname='tf_idf' @@ -42,7 +44,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, 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.T) + 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) @@ -53,14 +55,28 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, 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): +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 @@ -68,7 +84,7 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, spark.stop() 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, subreddit_names=subreddit_names,nterms=nterms) + 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()) @@ -77,8 +93,8 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, # with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine? -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_test.parquet', +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, @@ -86,8 +102,8 @@ def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_s 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', +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, @@ -95,6 +111,33 @@ def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_ 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}) + 'terms':term_cosine_similarities_weekly, + 'authors-lsi':author_cosine_similarities_weekly_lsi, + 'terms-lsi':term_cosine_similarities_weekly + })