From: Nate E TeBlunthuis Date: Tue, 6 Apr 2021 06:21:06 +0000 (-0700) Subject: Changes for cosine similarities on klone. X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/f0176d9f0de93f0e4f3ab1d676c852c2e5fad3b3 Changes for cosine similarities on klone. --- diff --git a/clustering/clustering.py b/clustering/clustering.py index e652304..4cde717 100755 --- a/clustering/clustering.py +++ b/clustering/clustering.py @@ -14,8 +14,9 @@ def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, conv df = pd.read_feather(similarities) n = df.shape[0] - mat = np.array(df.drop('subreddit',1)) + mat = np.array(df.drop('_subreddit',1)) mat[range(n),range(n)] = 1 + assert(all(np.diag(mat)==1)) preference = np.quantile(mat,preference_quantile) diff --git a/similarities/cosine_similarities.py b/similarities/cosine_similarities.py index 95fa1fb..38b1d7c 100644 --- a/similarities/cosine_similarities.py +++ b/similarities/cosine_similarities.py @@ -9,7 +9,8 @@ def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None): - return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet', + + return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', 'term', outfile, min_df, @@ -22,7 +23,7 @@ def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subredd ) def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None): - return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet', + return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', 'author', outfile, min_df, @@ -35,7 +36,7 @@ def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddi ) def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None): - return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet', + return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', 'author', outfile, min_df, diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py index 9e33c9d..57a36ca 100644 --- a/similarities/similarities_helper.py +++ b/similarities/similarities_helper.py @@ -89,7 +89,8 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non print("loading matrix") # mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname) mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname) - print('computing similarities') + print(f'computing similarities on mat. mat.shape:{mat.shape}') + print(f"size of mat is:{mat.data.nbytes}") sims = simfunc(mat) del mat @@ -387,7 +388,7 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm return df -def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonswf.csv"): +def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"): rankdf = pd.read_csv(path) included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values) return included_subreddits diff --git a/similarities/tfidf.py b/similarities/tfidf.py index f0b5d64..30033a8 100644 --- a/similarities/tfidf.py +++ b/similarities/tfidf.py @@ -24,8 +24,8 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_ def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits): return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits) -def tfidf_weekly(inpath, outpath, topN, term_colname, exclude): - return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, included_subreddits) +def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits): + return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits) def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet', topN=25000): diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py index 4d496f0..f9c9666 100644 --- a/similarities/weekly_cosine_similarities.py +++ b/similarities/weekly_cosine_similarities.py @@ -8,7 +8,22 @@ 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): @@ -36,24 +51,17 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, 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 + # do this step in parallel if we have the memory for it. + # should be doable with pool.map - 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) + 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=None , included_subreddits=None, topN=500): - return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet', +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, @@ -61,7 +69,7 @@ def author_cosine_similarities_weekly(outfile, min_df=None , 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', + return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_100k.parquet', outfile, 'term', min_df, @@ -69,5 +77,5 @@ def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=No 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})