X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/e7942146532ae43d82181d0ed7f60fe60e8dc8b3..13eb95b3b06bd51324e0d05c73a44b5e8e830295:/term_cosine_similarity.py diff --git a/term_cosine_similarity.py b/term_cosine_similarity.py index 48132a8..dd92b2c 100644 --- a/term_cosine_similarity.py +++ b/term_cosine_similarity.py @@ -8,38 +8,23 @@ 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 term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True): - ''' - Compute similarities between subreddits based on tfi-idf vectors of comment texts - - 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 -''' - +from similarities_helper import cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities +import scipy +# outfile='test_similarities_500.feather'; +# min_df = None; +# included_subreddits=None; topN=100; exclude_phrases=True; + +def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500, exclude_phrases=False): + spark = SparkSession.builder.getOrCreate() + conf = spark.sparkContext.getConf() print(outfile) print(exclude_phrases) tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.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} + rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv") + included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values) else: included_subreddits = set(open(included_subreddits)) @@ -47,7 +32,23 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get if exclude_phrases == True: tfidf = tfidf.filter(~f.col(term).contains("_")) - sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, included_subreddits, similarity_threshold) + print("creating temporary parquet with matrix indicies") + tempdir = prep_tfidf_entries(tfidf, 'term', min_df, included_subreddits) + tfidf = spark.read.parquet(tempdir.name) + subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas() + subreddit_names = subreddit_names.sort_values("subreddit_id_new") + subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1 + spark.stop() + + print("loading matrix") + mat = read_tfidf_matrix(tempdir.name,'term') + print('computing similarities') + sims = column_similarities(mat) + del mat + + sims = pd.DataFrame(sims.todense()) + sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)},axis=1) + sims['subreddit'] = subreddit_names.subreddit.values p = Path(outfile) @@ -55,25 +56,72 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get output_csv = Path(str(p).replace("".join(p.suffixes), ".csv")) output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet")) - sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy') + sims.to_feather(outfile) + tempdir.cleanup() + path = "term_tfidf_entriesaukjy5gv.parquet" - #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 +# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0; +# def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True): +# ''' +# Compute similarities between subreddits based on tfi-idf vectors of comment texts + +# 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) +# print(exclude_phrases) + +# tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.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)) + +# if exclude_phrases == True: +# tfidf = tfidf.filter(~f.col(term).contains("_")) + +# sim_dist, tfidf = cosine_similarities(tfidf, 'term', 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.entries.toDF().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(term_cosine_similarities)