Updates to similarities code for smap project.

similarities/cosine_similarities.py

@@ -6,7 +6,7 @@ from functools import partial
 
 def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
 
-    return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+    return similarities(inpath=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
 
 # change so that these take in an input as an optional argument (for speed, but also for idf).
 def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):

similarities/lsi_similarities.py

@@ -1,20 +1,41 @@
 import pandas as pd
 import fire
 from pathlib import Path
-from similarities_helper import similarities, lsi_column_similarities
+from similarities_helper import *
+#from similarities_helper import similarities, lsi_column_similarities
 from functools import partial
 
-def lsi_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack'):
+inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
+term_colname='term'
+outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
+n_components=[10,50,100]
+included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+n_iter=5
+random_state=1968
+algorithm='arpack'
+topN = None
+from_date=None
+to_date=None
+min_df=None
+max_df=None
+def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
     print(n_components,flush=True)
 
-    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm)
+        
+    if lsi_model is None:
+        if type(n_components) == list:
+            lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
+        else:
+            lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
 
-    return similarities(infile=infile, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
+
+    return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
 
 # change so that these take in an input as an optional argument (for speed, but also for idf).
-def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',outfile=None, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, algorithm='arpack', n_components=300,n_iter=5,random_state=1968):
 
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
+    res =  lsi_similarities(inpath,
                             'term',
                             outfile,
                             min_df,
@@ -23,11 +44,13 @@ def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits
                             topN,
                             from_date,
                             to_date,
-                            n_components=n_components
+                            n_components=n_components,
+                            algorithm = algorithm
                             )
+    return res
 
-def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+    return lsi_similarities(inpath,
                             'author',
                             outfile,
                             min_df,
@@ -39,8 +62,8 @@ def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=
                             n_components=n_components
                                )
 
-def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968):
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+    return lsi_similarities(inpath,
                             'author',
                             outfile,
                             min_df,
@@ -50,7 +73,8 @@ def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=N
                             from_date=from_date,
                             to_date=to_date,
                             tfidf_colname='relative_tf',
-                            n_components=n_components
+                            n_components=n_components,
+                            algorithm=algorithm
                             )
 
 

similarities/similarities_helper.py

@@ -15,24 +15,53 @@ import numpy as np
 import pathlib
 from datetime import datetime
 from pathlib import Path
+import pickle
 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
+# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
-cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
+# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
 
 # subreddits missing after this step don't have any terms that have a high enough idf
 # try rewriting without merges
-def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF):
+
-    print("loading tfidf", flush=True)
+# does reindex_tfidf, but without reindexing.
-    tfidf_ds = ds.dataset(infile)
+def reindex_tfidf(*args, **kwargs):
+    df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
+
+    print("assigning names")
+    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+    batches = subreddit_names.to_batches()
+    
+    with Pool(cpu_count()) as pool:
+        chunks = pool.imap_unordered(pull_names,batches) 
+        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
+        subreddit_names = subreddit_names.set_index("subreddit_id")
+
+    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
+    new_ids = new_ids.set_index('subreddit_id')
+    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
+    subreddit_names = subreddit_names.drop("subreddit_id",1)
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    return(df, subreddit_names)
+
+def pull_tfidf(*args, **kwargs):
+    df, _, _ =  _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
+    return df
+
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+    print(f"loading tfidf {infile}", flush=True)
+    if week is not None:
+        tfidf_ds = ds.dataset(infile, partitioning='hive')
+    else: 
+        tfidf_ds = ds.dataset(infile)
 
     if included_subreddits is None:
         included_subreddits = select_topN_subreddits(topN)
     else:
-        included_subreddits = set(open(included_subreddits))
+        included_subreddits = set(map(str.strip,open(included_subreddits)))
 
     ds_filter = ds.field("subreddit").isin(included_subreddits)
 
@@ -68,15 +97,20 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
-    tfidf_ds = ds.dataset(infile)
-
     df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
 
     df = df.to_pandas(split_blocks=True,self_destruct=True)
     print("assigning indexes",flush=True)
-    df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+    if reindex:
-    grouped = df.groupby(term_id)
+        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
-    df[term_id_new] = grouped.ngroup()
+    else:
+        df['subreddit_id_new'] = df['subreddit_id']
+
+    if reindex:
+        grouped = df.groupby(term_id)
+        df[term_id_new] = grouped.ngroup()
+    else:
+        df[term_id_new] = df[term_id]
 
     if rescale_idf:
         print("computing idf", flush=True)
@@ -88,26 +122,13 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
         else: # tf_fam = tf_weight.Norm05
             df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
 
-    print("assigning names")
+    return (df, tfidf_ds, ds_filter)
-    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
-    batches = subreddit_names.to_batches()
 
-    with Pool(cpu_count()) as pool:
-        chunks = pool.imap_unordered(pull_names,batches) 
-        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
-
-    subreddit_names = subreddit_names.set_index("subreddit_id")
-    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
-    new_ids = new_ids.set_index('subreddit_id')
-    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
-    subreddit_names = subreddit_names.drop("subreddit_id",1)
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    return(df, subreddit_names)
 
 def pull_names(batch):
     return(batch.to_pandas().drop_duplicates())
 
-def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
     '''
     tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
     '''
@@ -127,7 +148,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
         output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
         output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
-        outfile.parent.mkdir(exist_ok=True, parents=True)
+        p.parent.mkdir(exist_ok=True, parents=True)
 
         sims.to_feather(outfile)
 
@@ -135,7 +156,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 
-    entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
+    entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
     mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
 
     print("loading matrix")        
@@ -144,6 +165,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
 
     print(f'computing similarities on mat. mat.shape:{mat.shape}')
     print(f"size of mat is:{mat.data.nbytes}",flush=True)
+    # transform this to debug term tfidf
     sims = simfunc(mat)
     del mat
 
@@ -151,7 +173,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         for simmat, name in sims:
             proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
     else:
-        proc_sims(simmat, outfile)
+        proc_sims(sims, outfile)
 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week
@@ -204,7 +226,7 @@ def test_lsi_sims():
 # if n_components is a list we'll return a list of similarities with different latent dimensionalities
 # if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
 # this function takes the svd and then the column similarities of it
-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
+def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
     # first compute the lsi of the matrix
     # then take the column similarities
     print("running LSI",flush=True)
@@ -215,21 +237,32 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     n_components = sorted(n_components,reverse=True)
     
     svd_components = n_components[0]
-    svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
+    
-    mod = svd.fit(tfidfmat.T)
+    if lsi_model_load is not None:
+        mod = pickle.load(open(lsi_model_load ,'rb'))
+
+    else:
+        svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
+        mod = svd.fit(tfidfmat.T)
+
     lsimat = mod.transform(tfidfmat.T)
+    if lsi_model_save is not None:
+        pickle.dump(mod, open(lsi_model_save,'wb'))
+
+    sims_list = []
     for n_dims in n_components:
         sims = column_similarities(lsimat[:,np.arange(n_dims)])
         if len(n_components) > 1:
             yield (sims, n_dims)
         else:
             return sims
-    
 
 def column_similarities(mat):
     return 1 - pairwise_distances(mat,metric='cosine')
 
-
+# need to rewrite this so that subreddit ids and term ids are fixed over the whole thing.
+# this affords taking the LSI similarities.
+# fill all 0s if we don't have it.
 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
     term = term_colname
     term_id = term + '_id'
@@ -254,20 +287,21 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 
     # collect the dictionary to make a pydict of terms to indexes
-    terms = idf.select([term,'week']).distinct() # terms are distinct
+    terms = idf.select([term]).distinct() # terms are distinct
 
-    terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
+    terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 
     # make subreddit ids
-    subreddits = df.select(['subreddit','week']).distinct()
+    subreddits = df.select(['subreddit']).distinct()
-    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 
-    df = df.join(subreddits,on=['subreddit','week'])
+    # df = df.cache()
+    df = df.join(subreddits,on=['subreddit'])
 
     # map terms to indexes in the tfs and the idfs
-    df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
+    df = df.join(terms,on=[term]) # subreddit-term-id is unique
 
-    idf = idf.join(terms,on=[term,'week'])
+    idf = idf.join(terms,on=[term])
 
     # join on subreddit/term to create tf/dfs indexed by term
     df = df.join(idf, on=[term_id, term,'week'])
@@ -327,7 +361,7 @@ def _calc_tfidf(df, term_colname, tf_family):
     return df
     
 
-def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
     term = term_colname
     term_id = term + '_id'
     # aggregate counts by week. now subreddit-term is distinct

similarities/tfidf.py

@@ -1,7 +1,7 @@
 import fire
 from pyspark.sql import SparkSession
 from pyspark.sql import functions as f
-from similarities_helper import build_tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
+from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
 
 def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
     spark = SparkSession.builder.getOrCreate()
@@ -11,7 +11,7 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
     df = df.filter(~ f.col(term_colname).isin(exclude))
 
     if included_subreddits is not None:
-        include_subs = list(open(included_subreddits))
+        include_subs = set(map(str.strip,open(included_subreddits)))
     else:
         include_subs = select_topN_subreddits(topN)
 
@@ -21,42 +21,45 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
     spark.stop()
 
 def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
-    return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+    return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
 
 def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
     return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
 
 def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
-                  topN=25000):
+                  topN=None,
+                  included_subreddits=None):
 
     return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
                  outpath,
                  topN,
                  'author',
                  ['[deleted]','AutoModerator'],
-                 included_subreddits=None
+                 included_subreddits=included_subreddits
                  )
 
 def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
-                topN=25000):
+                topN=None,
+                included_subreddits=None):
 
     return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
                  outpath,
                  topN,
                  'term',
                  [],
-                 included_subreddits=None
+                 included_subreddits=included_subreddits
                  )
 
 def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
-                         topN=25000):
+                         topN=None,
+                         include_subreddits=None):
 
     return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
                         outpath,
                         topN,
                         'author',
                         ['[deleted]','AutoModerator'],
-                        included_subreddits=None
+                        included_subreddits=included_subreddits
                         )
 
 def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',

similarities/weekly_cosine_similarities.py

@@ -8,32 +8,47 @@ import pandas as pd
 import fire
 from itertools import islice, chain
 from pathlib import Path
-from similarities_helper import *
+from similarities_helper import pull_tfidf, column_similarities, write_weekly_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_test.parquet"
+# tfidf_path = infile 
+# min_df=None
+# 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):
+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, subreddit_names = reindex_tfidf(infile = tfidf_path,
+
-                                             term_colname=term_colname,
+    entries = pull_tfidf(infile = tfidf_path,
-                                             min_df=min_df,
+                         term_colname=term_colname,
-                                             max_df=max_df,
+                         min_df=min_df,
-                                             included_subreddits=included_subreddits,
+                         max_df=max_df,
-                                             topN=topN,
+                         included_subreddits=included_subreddits,
-                                             week=week)
+                         topN=topN,
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+                         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')
-    sims = column_similarities(mat)
+    sims = simfunc(mat.T)
     del mat
-    sims = pd.DataFrame(sims.todense())
+    sims = pd.DataFrame(sims)
     sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['_subreddit'] = names.subreddit.values
+    sims['_subreddit'] = subreddit_names.subreddit.values
     outfile = str(Path(outdir) / str(week))
-    write_weekly_similarities(outfile, sims, week, names)
+    write_weekly_similarities(outfile, sims, week, subreddit_names)
 
 def pull_weeks(batch):
     return set(batch.to_pandas()['week'])
@@ -41,25 +56,29 @@ def pull_weeks(batch):
 #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):
     print(outfile)
-    tfidf_ds = ds.dataset(tfidf_path)
-    tfidf_ds = tfidf_ds.to_table(columns=["week"])
-    batches = tfidf_ds.to_batches()
-
-    with Pool(cpu_count()) as pool:
-        weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
-
-    weeks = sorted(weeks)
     # do this step in parallel if we have the memory for it.
     # should be doable with pool.map
 
-    print(f"computing weekly similarities")
+    spark = SparkSession.builder.getOrCreate()
-    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)
+    df = spark.read.parquet(tfidf_path)
+    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()
+
+    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)
+
+    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?
-        list(pool.map(week_similarities_helper,weeks))
 
 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.parquet',
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
                                       outfile,
                                       'author',
                                       min_df,