]> code.communitydata.science - cdsc_reddit.git/blob - author_cosine_similarity.py
08001c2165460bbea2b7f01d32944d67ed36c52f
[cdsc_reddit.git] / author_cosine_similarity.py
1 from pyspark.sql import functions as f
2 from pyspark.sql import SparkSession
3 from pyspark.sql import Window
4 import numpy as np
5 import pyarrow
6 import pandas as pd
7 import fire
8 from itertools import islice
9 from pathlib import Path
10 from similarities_helper import cosine_similarities
11
12 spark = SparkSession.builder.getOrCreate()
13 conf = spark.sparkContext.getConf()
14
15 # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
16 def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500):
17     '''
18     Compute similarities between subreddits based on tfi-idf vectors of author comments
19     
20     included_subreddits : string
21         Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
22
23     similarity_threshold : double (default = 0)
24         set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
25 https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
26
27     min_df : int (default = 0.1 * (number of included_subreddits)
28          exclude terms that appear in fewer than this number of documents.
29
30     outfile: string
31          where to output csv and feather outputs
32 '''
33
34     print(outfile)
35
36     tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')
37
38     if included_subreddits is None:
39         included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
40         included_subreddits = {s.strip('\n') for s in included_subreddits}
41
42     else:
43         included_subreddits = set(open(included_subreddits))
44
45     sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)
46
47     p = Path(outfile)
48
49     output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
50     output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
51     output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
52     sim_dist = sim_dist.entries.toDF()
53
54     sim_dist = sim_dist.repartition(1)
55     sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
56     
57
58
59     #instead of toLocalMatrix() why not read as entries and put strait into numpy
60     sim_entries = pd.read_parquet(output_parquet)
61
62     df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
63
64     spark.stop()
65     df['subreddit_id_new'] = df['subreddit_id_new'] - 1
66     df = df.sort_values('subreddit_id_new').reset_index(drop=True)
67     df = df.set_index('subreddit_id_new')
68
69     similarities = sim_entries.join(df, on='i')
70     similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
71     similarities = similarities.join(df, on='j')
72     similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
73
74     similarities.to_feather(output_feather)
75     similarities.to_csv(output_csv)
76     return similarities
77     
78 if __name__ == '__main__':
79     fire.Fire(author_cosine_similarities)

Community Data Science Collective || Want to submit a patch?