2 from pyspark.sql import functions as f
3 from pyspark.sql import SparkSession
4 from pyspark.sql import Window
7 import pyarrow.dataset as ds
10 from itertools import islice, chain
11 from pathlib import Path
12 from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
13 from scipy.sparse import csr_matrix
14 from multiprocessing import Pool, cpu_count
15 from functools import partial
18 # tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
19 # #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
21 # included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
24 # term_colname='author'
25 # # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
26 # # included_subreddits=None
27 outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors.parquet"; infile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_authors_tfidf.parquet"; included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"; lsi_model="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/2000_authors_LSIMOD.pkl"; n_components=1500; algorithm="randomized"; term_colname='author'; tfidf_path=infile; random_state=1968;
29 # static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
30 # dftest = spark.read.parquet(static_tfidf)
32 def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
34 term_id = term + '_id'
35 term_id_new = term + '_id_new'
36 print(f"loading matrix: {week}")
38 entries = pull_tfidf(infile = tfidf_path,
39 term_colname=term_colname,
40 included_subreddits=included_subreddits,
42 week=week.isoformat(),
45 tfidf_colname='tf_idf'
46 # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
47 mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
48 print('computing similarities')
53 sims = pd.DataFrame(sims)
54 sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
55 sims['_subreddit'] = subreddit_names.subreddit.values
56 outfile = str(Path(outdir) / str(week))
57 write_weekly_similarities(outfile, sims, week, subreddit_names)
59 def pull_weeks(batch):
60 return set(batch.to_pandas()['week'])
62 # This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
63 def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
66 term_colname= kwargs.get('term_colname')
67 # lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
69 lsi_model = pickle.load(open(lsi_model,'rb'))
70 #simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
71 simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
73 return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
75 #tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
76 def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
78 # do this step in parallel if we have the memory for it.
79 # should be doable with pool.map
81 spark = SparkSession.builder.getOrCreate()
82 df = spark.read.parquet(tfidf_path)
84 # load subreddits + topN
86 subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
87 subreddit_names = subreddit_names.sort_values("subreddit_id")
88 nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
89 weeks = df.select(f.col("week")).distinct().toPandas().week.values
92 print(f"computing weekly similarities")
93 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=None, subreddit_names=subreddit_names,nterms=nterms)
96 week_similarities_helper(week)
97 # pool = Pool(cpu_count())
99 # list(pool.imap(week_similarities_helper, weeks))
101 # with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
104 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):
105 return cosine_similarities_weekly(infile,
114 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):
115 return cosine_similarities_weekly(infile,
124 def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', included_subreddits=None, n_components=100,lsi_model=None):
125 return cosine_similarities_weekly_lsi(infile,
128 included_subreddits=included_subreddits,
129 n_components=n_components,
134 def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
135 return cosine_similarities_weekly_lsi(infile,
138 included_subreddits=included_subreddits,
139 n_components=n_components,
143 if __name__ == "__main__":
144 fire.Fire({'authors':author_cosine_similarities_weekly,
145 'terms':term_cosine_similarities_weekly,
146 'authors-lsi':author_cosine_similarities_weekly_lsi,
147 'terms-lsi':term_cosine_similarities_weekly_lsi