git-annex in

[cdsc_reddit.git] / similarities / similarities_helper.py

diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index a4983b38ef4ca6d3bb248631ce6e3d8cb7340276..03c10b2310d3984e120eefcc23a6b3d4878bf113 100644 (file)
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -97,6 +97,8 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
+        print(projection)
+
     df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
 
     df = df.to_pandas(split_blocks=True,self_destruct=True)
@@ -124,6 +126,17 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
 
     return (df, tfidf_ds, ds_filter)
 
+    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())
@@ -165,7 +178,6 @@ def similarities(inpath, 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
 
@@ -229,7 +241,6 @@ def test_lsi_sims():
 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)
 
     if type(n_components) is int:
         n_components = [n_components]
@@ -238,15 +249,20 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     
     svd_components = n_components[0]
     
-    if lsi_model_load is not None:
+    if lsi_model_load is not None and Path(lsi_model_load).exists():
+        print("loading LSI")
         mod = pickle.load(open(lsi_model_load ,'rb'))
+        lsi_model_save = lsi_model_load
 
     else:
+        print("running LSI",flush=True)
+
         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:
+        Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
         pickle.dump(mod, open(lsi_model_save,'wb'))
 
     sims_list = []
@@ -256,13 +272,12 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
             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'
@@ -295,7 +310,6 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     subreddits = df.select(['subreddit']).distinct()
     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 
-    # df = df.cache()
     df = df.join(subreddits,on=['subreddit'])
 
     # map terms to indexes in the tfs and the idfs