Merge remote-tracking branch 'refs/remotes/origin/excise_reindex' into excise_reindex

[cdsc_reddit.git] / similarities / weekly_cosine_similarities.py

diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py
old mode 100644 (file)
new mode 100755 (executable)
index 7cafcb9..45327c7
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python3

 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window


@@ -8,21 +9,27 @@ import pandas as pd
 import fire
 from itertools import islice, chain
 from pathlib import Path
 import fire
 from itertools import islice, chain
 from pathlib import Path

-from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
+from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities

 from scipy.sparse import csr_matrix
 from multiprocessing import Pool, cpu_count
 from functools import partial
 from scipy.sparse import csr_matrix
 from multiprocessing import Pool, cpu_count
 from functools import partial

+import pickle

 
 

-# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
-# tfidf_path = infile 
-# min_df=None
+# 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'
 # 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/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;

 
 

-def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
+# 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'
     term = term_colname
     term_id = term + '_id'
     term_id_new = term + '_id_new'


@@ -30,8 +37,6 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
 
     entries = pull_tfidf(infile = tfidf_path,
                          term_colname=term_colname,
 
     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.isoformat(),
                          included_subreddits=included_subreddits,
                          topN=topN,
                          week=week.isoformat(),


@@ -40,10 +45,11 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
     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]))
     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('computing similarities')

-    sims = simfunc(mat.T)
+    print(simfunc)
+    sims = simfunc(mat)

     del 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
     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


@@ -53,14 +59,30 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
 def pull_weeks(batch):
     return set(batch.to_pandas()['week'])
 
 def pull_weeks(batch):
     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(*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_authors_compex_LSI/1000_author_LSIMOD.pkl"
+
+    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')
 #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):
+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 = SparkSession.builder.getOrCreate()
     df = spark.read.parquet(tfidf_path)
     print(outfile)
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 
     spark = SparkSession.builder.getOrCreate()
     df = spark.read.parquet(tfidf_path)

+
+    # load subreddits + topN
+        

     subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
     subreddit_names = subreddit_names.sort_values("subreddit_id")
     nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
     subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
     subreddit_names = subreddit_names.sort_values("subreddit_id")
     nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max


@@ -68,26 +90,29 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
     spark.stop()
 
     print(f"computing weekly similarities")
     spark.stop()
 
     print(f"computing weekly similarities")

-    week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, 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?
 
 
     #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
 
 

-def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
+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):
+    return cosine_similarities_weekly(infile,

                                       outfile,
                                       'author',
                                       outfile,
                                       'author',

-                                      min_df,

                                       max_df,
                                       included_subreddits,
                                       max_df,
                                       included_subreddits,

-                                      topN)
+                                      topN,
+                                      min_df=2
+)

 
 

-def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
-        return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+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,

                                           outfile,
                                           'term',
                                           min_df,
                                           outfile,
                                           'term',
                                           min_df,


@@ -95,6 +120,30 @@ def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_
                                           included_subreddits,
                                           topN)
 
                                           included_subreddits,
                                           topN)
 

+
+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',
+                                          included_subreddits=included_subreddits,
+                                          n_components=n_components,
+                                          lsi_model=lsi_model
+                                          )
+
+
+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',
+                                              included_subreddits=included_subreddits,
+                                              n_components=n_components,
+                                              lsi_model=lsi_model,
+                                              )
+

 if __name__ == "__main__":
     fire.Fire({'authors':author_cosine_similarities_weekly,
 if __name__ == "__main__":
     fire.Fire({'authors':author_cosine_similarities_weekly,

-               'terms':term_cosine_similarities_weekly})
+               'terms':term_cosine_similarities_weekly,
+               'authors-lsi':author_cosine_similarities_weekly_lsi,
+               'terms-lsi':term_cosine_similarities_weekly_lsi
+               })
+