Refine code

demo/recommendation/api_train_v2.py

+import paddle.v2 as paddle
+
+
+def main():
+    movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
+    title_word_count = len(movie_title_dict)
+
+    paddle.layer.mixed
+
+
+if __name__ == '__main__':
+    main()

python/paddle/v2/dataset/__init__.py

 import mnist
+import cifar
+import movielens
 
-__all__ = ['mnist']
+__all__ = ['mnist', 'cifar', 'movielens']

python/paddle/v2/dataset/movielens.py

@@ -4,7 +4,7 @@ import re
 import random
 import functools
 
-__all__ = ['train_creator', 'test_creator']
+__all__ = ['train_creator', 'test_creator', 'get_movie_title_dict']
 
 
 class MovieInfo(object):
@@ -40,7 +40,8 @@ USER_INFO = None
 def __initialize_meta_info__():
     fn = download(
         url='http://files.grouplens.org/datasets/movielens/ml-1m.zip',
-        md5='c4d9eecfca2ab87c1945afe126590906')
+        module_name='movielens',
+        md5sum='c4d9eecfca2ab87c1945afe126590906')
     global MOVIE_INFO
     if MOVIE_INFO is None:
         pattern = re.compile(r'^(.*)\((\d+)\)$')
@@ -107,6 +108,11 @@ train_creator = functools.partial(__reader_creator__, is_test=False)
 test_creator = functools.partial(__reader_creator__, is_test=True)
 
 
+def get_movie_title_dict():
+    __initialize_meta_info__()
+    return MOVIE_TITLE_DICT
+
+
 def unittest():
     for train_count, _ in enumerate(train_creator()()):
         pass