changes for archiving.

[cdsc_reddit.git] / similarities / similarities_helper.py

diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index 202220c389653de068bc52320c50c249bd18d280..6925a15d55582589a080f1c6351eb823a4a37f2f 100644 (file)
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -95,6 +95,7 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
         projection = {
             'subreddit_id':ds.field('subreddit_id'),
             term_id:ds.field(term_id),
+            'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
     print(projection, flush=True)
@@ -102,7 +103,7 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
     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)
     if reindex:
         print("assigning indexes",flush=True)
         df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
@@ -127,17 +128,6 @@ 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())
@@ -239,8 +229,7 @@ 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
-# lsi_model_load = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model=None):
+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
 
@@ -251,21 +240,24 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     
     svd_components = n_components[0]
     
-    if lsi_model is 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)
-    else:
-        mod = lsi_model
 
-    lsimat = mod.transform(tfidfmat.T)
     if lsi_model_save is not None:
-        Path(lsi_model_save).parent.mkdir(exist_ok=True,parents=True)
+        Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
         pickle.dump(mod, open(lsi_model_save,'wb'))
 
-    print(n_components)
+    print(n_components, flush=True)
+    lsimat = mod.transform(tfidfmat.T)
     for n_dims in n_components:
-        print("computing similarities")
+        print("computing similarities", flush=True)
         sims = column_similarities(lsimat[:,np.arange(n_dims)])
         yield (sims, n_dims)
 
@@ -381,7 +373,7 @@ def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
 def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
     term = term_colname
     term_id = term + '_id'
-
+    # aggregate counts by week. now subreddit-term is distinct
     df = df.filter(df.subreddit.isin(include_subs))
     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 
@@ -390,7 +382,7 @@ def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, mi
     dfwriter = df.write
     return dfwriter
 
-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
+def select_topN_subreddits(topN, path="../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
     rankdf = pd.read_csv(path)
     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
     return included_subreddits