Add download_if_missing argument to fetch_20newsgroups_vectorized (#9425)

lesteve · agramfort · commit 5d040fc583da · 2017-07-20T17:34:35.000+02:00
diff --git a/sklearn/datasets/twenty_newsgroups.py b/sklearn/datasets/twenty_newsgroups.py
@@ -283,7 +283,8 @@ def fetch_20newsgroups(data_home=None, subset='train', categories=None,
     return data
 
 
-def fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None):
+def fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None,
+                                  download_if_missing=True):
     """Load the 20 newsgroups dataset and transform it into tf-idf vectors.
 
     This is a convenience function; the tf-idf transformation is done using the
@@ -313,6 +314,10 @@ def fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None):
         Specify an download and cache folder for the datasets. If None,
         all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
 
+    download_if_missing : optional, True by default
+        If False, raise an IOError if the data is not locally available
+        instead of trying to download the data from the source site.
+
     Returns
     -------
     bunch : Bunch object
@@ -332,14 +337,16 @@ def fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None):
                                     categories=None,
                                     shuffle=True,
                                     random_state=12,
-                                    remove=remove)
+                                    remove=remove,
+                                    download_if_missing=download_if_missing)
 
     data_test = fetch_20newsgroups(data_home=data_home,
                                    subset='test',
                                    categories=None,
                                    shuffle=True,
                                    random_state=12,
-                                   remove=remove)
+                                   remove=remove,
+                                   download_if_missing=download_if_missing)
 
     if os.path.exists(target_file):
         X_train, X_test = joblib.load(target_file)
