update pushshift dumps.

[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 4d496f0..6ce30b8
--- a/similarities/weekly_cosine_similarities.py
+++ b/similarities/weekly_cosine_similarities.py
@@ -1,73 +1,143 @@
+#!/usr/bin/env python3

 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
 import numpy as np
 import pyarrow
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
 import numpy as np
 import pyarrow

+import pyarrow.dataset as ds

 import pandas as pd
 import fire
 import pandas as pd
 import fire

-from itertools import islice
+from itertools import islice, chain

 from pathlib import Path
 from pathlib import Path

-from similarities_helper import *
+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

 
 

+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

 
 

-#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(outfile)
-    tfidf = spark.read.parquet(tfidf_path)
+def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+    print(f"loading matrix: {week}")
+
+    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,
+                         rescale_idf=False)

     
     

-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
+    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(f"computing weekly similarities for {len(included_subreddits)} subreddits")
+    print('computing similarities')
+    sims = simfunc(mat)
+    del mat
+    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
+    outfile = str(Path(outdir) / str(week))
+    write_weekly_similarities(outfile, sims, week, subreddit_names)

 
 

-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
+def pull_weeks(batch):
+    return set(batch.to_pandas()['week'])

 
 

-    tfidf = spark.read.parquet(tempdir.name)
+# 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):
+    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"

 
 

-    # the ids can change each week.
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    spark.stop()
+    # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)

 
 

-    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
-    for week in weeks:
-        print(f"loading matrix: {week}")
-        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
-        print('computing similarities')
-        sims = column_similarities(mat)
-        del mat
+    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)

 
 

-        names = subreddit_names.loc[subreddit_names.week == week]
-        sims = pd.DataFrame(sims.todense())
+    return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)

 
 

-        sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
-        sims['subreddit'] = names.subreddit.values
+#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):
+    print(outfile)
+    # do this step in parallel if we have the memory for it.
+    # should be doable with pool.map

 
 

-        write_weekly_similarities(outfile, sims, week, names)
+    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
+    weeks = df.select(f.col("week")).distinct().toPandas().week.values
+    spark.stop()

 
 

-def author_cosine_similarities_weekly(outfile, min_df=None , included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+    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)
+
+    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?
+
+
+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',
                                       min_df,
                                       outfile,
                                       'author',
                                       min_df,

+                                      max_df,

                                       included_subreddits,
                                       topN)
 
                                       included_subreddits,
                                       topN)
 

-def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
-                                      outfile,
-                                      'term',
-                                      min_df,
-                                      included_subreddits,
-                                      topN)
+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,
+                                          max_df,
+                                          included_subreddits,
+                                          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):
+    return cosine_similarities_weekly_lsi(infile,
+                                          outfile,
+                                          'author',
+                                          min_df,
+                                          max_df,
+                                          included_subreddits,
+                                          topN,
+                                          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', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
+        return cosine_similarities_weekly_lsi(infile,
+                                              outfile,
+                                              'term',
+                                              min_df,
+                                              max_df,
+                                              included_subreddits,
+                                              topN,
+                                              n_components=n_components,
+                                              lsi_model=lsi_model)

 
 if __name__ == "__main__":
 
 if __name__ == "__main__":

-    fire.Fire({'author':author_cosine_similarities_weekly,
-               'term':term_cosine_similarities_weekly})
+    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
+               })