from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count
from functools import partial
-
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
-tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
-min_df=None
-included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
-max_df = None
-topN=100
-term_colname='author'
-# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
-# included_subreddits=None
-
-def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
+import pickle
+
+# tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
+# #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
+# min_df=2
+# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+# max_df = None
+# topN=100
+# term_colname='author'
+# # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
+# # included_subreddits=None
+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;
+
+# static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
+# dftest = spark.read.parquet(static_tfidf)
+
+def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
entries = pull_tfidf(infile = tfidf_path,
term_colname=term_colname,
- min_df=min_df,
- max_df=max_df,
included_subreddits=included_subreddits,
topN=topN,
- week=week,
+ week=week.isoformat(),
rescale_idf=False)
tfidf_colname='tf_idf'
# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
-
print('computing similarities')
+ print(simfunc)
sims = simfunc(mat)
del mat
+ sims = next(sims)[0]
sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
sims['_subreddit'] = subreddit_names.subreddit.values
return set(batch.to_pandas()['week'])
# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
-def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
+def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
+ print(args)
+ print(kwargs)
term_colname= kwargs.get('term_colname')
- #lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
-
- # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
+ # lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
- simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
+ lsi_model = pickle.load(open(lsi_model,'rb'))
+ #simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
+ simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities):
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
spark.stop()
print(f"computing weekly similarities")
- 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=topN, subreddit_names=subreddit_names,nterms=nterms)
+ 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)
- pool = Pool(cpu_count())
-
- list(pool.imap(week_similarities_helper,weeks))
- pool.close()
+ for week in weeks:
+ week_similarities_helper(week)
+ # pool = Pool(cpu_count())
+
+ # list(pool.imap(week_similarities_helper, weeks))
+ # pool.close()
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
return cosine_similarities_weekly(infile,
outfile,
'author',
- min_df,
max_df,
included_subreddits,
- topN)
+ topN,
+ min_df=2
+)
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):
return cosine_similarities_weekly(infile,
topN)
-def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None):
+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):
return cosine_similarities_weekly_lsi(infile,
outfile,
'author',
- min_df,
- max_df,
- included_subreddits,
- topN,
+ included_subreddits=included_subreddits,
n_components=n_components,
- lsi_model=lsi_model)
+ lsi_model=lsi_model
+ )
-def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
+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):
return cosine_similarities_weekly_lsi(infile,
outfile,
'term',
- min_df,
- max_df,
- included_subreddits,
- topN,
+ included_subreddits=included_subreddits,
n_components=n_components,
- lsi_model=lsi_model)
+ lsi_model=lsi_model,
+ )
if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly,
'terms':term_cosine_similarities_weekly,
'authors-lsi':author_cosine_similarities_weekly_lsi,
- 'terms-lsi':term_cosine_similarities_weekly
+ 'terms-lsi':term_cosine_similarities_weekly_lsi
})
+
projects / cdsc_reddit.git / blobdiff