import fire
from itertools import islice
from pathlib import Path
-from similarities_helper import build_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))
if exclude_phrases == True:
tfidf = tfidf.filter(~f.col(term).contains("_"))
- sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, include_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)
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"
- spark.stop()
- #instead of toLocalMatrix() why not read as entries and put strait into numpy
- sim_entries = pd.read_parquet(output_parquet)
+# 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)
- df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
- 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')
+# tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
- 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"})
+# 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}
- similarities.write_feather(output_feather)
- similarities.write_csv(output_csv)
- return similarities
+# 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)
projects / cdsc_reddit.git / blobdiff