Complete api_train_v2

demo/recommendation/api_train_v2.py

 import paddle.v2 as paddle
+import cPickle
+import copy
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=3)
+    paddle.init(use_gpu=False)
     movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
     uid = paddle.layer.data(
         name='user_id',
@@ -86,19 +88,40 @@ def main():
             if event.batch_id % 100 == 0:
                 print "Pass %d Batch %d Cost %.2f" % (
                     event.pass_id, event.batch_id, event.cost)
-        elif isinstance(event, paddle.event.EndPass):
-            result = trainer.test(reader=paddle.reader.batched(
-                paddle.dataset.movielens.test(), batch_size=256))
-            print result.cost
 
     trainer.train(
         reader=paddle.reader.batched(
-            paddle.reader.shuffle(
-                paddle.dataset.movielens.train(), buf_size=8192),
+            paddle.reader.firstn(
+                paddle.reader.shuffle(
+                    paddle.dataset.movielens.train(), buf_size=8192),
+                n=1000),
             batch_size=256),
         event_handler=event_handler,
         reader_dict=reader_dict,
-        num_passes=10)
+        num_passes=1)
+
+    user_id = 234
+    movie_id = 345
+
+    user = paddle.dataset.movielens.user_info()[user_id]
+    movie = paddle.dataset.movielens.movie_info()[movie_id]
+
+    feature = user.value() + movie.value()
+
+    def reader():
+        yield feature
+
+    infer_dict = copy.copy(reader_dict)
+    del infer_dict['score']
+    print infer_dict
+
+    prediction = paddle.infer(
+        output=inference,
+        parameters=parameters,
+        reader=paddle.reader.batched(
+            reader, batch_size=32),
+        reader_dict=infer_dict)
+    print prediction
 
 
 if __name__ == '__main__':

python/paddle/v2/dataset/movielens.py

@@ -6,7 +6,7 @@ import functools
 
 __all__ = [
     'train', 'test', 'get_movie_title_dict', 'max_movie_id', 'max_user_id',
-    'age_table', 'movie_categories', 'max_job_id'
+    'age_table', 'movie_categories', 'max_job_id', 'user_info', 'movie_info'
 ]
 
 age_table = [1, 18, 25, 35, 45, 50, 56]
@@ -24,6 +24,13 @@ class MovieInfo(object):
             [MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()]
         ]
 
+    def __str__(self):
+        return "<MovieInfo id(%d), title(%s), categories(%s)>" % (
+            self.index, self.title, self.categories)
+
+    def __repr__(self):
+        return self.__str__()
+
 
 class UserInfo(object):
     def __init__(self, index, gender, age, job_id):
@@ -35,6 +42,14 @@ class UserInfo(object):
     def value(self):
         return [self.index, 0 if self.is_male else 1, self.age, self.job_id]
 
+    def __str__(self):
+        return "<UserInfo id(%d), gender(%s), age(%d), job(%d)>" % (
+            self.index, "M"
+            if self.is_male else "F", age_table[self.age], self.job_id)
+
+    def __repr__(self):
+        return str(self)
+
 
 MOVIE_INFO = None
 MOVIE_TITLE_DICT = None
@@ -152,6 +167,16 @@ def movie_categories():
     return CATEGORIES_DICT
 
 
+def user_info():
+    __initialize_meta_info__()
+    return USER_INFO
+
+
+def movie_info():
+    __initialize_meta_info__()
+    return MOVIE_INFO
+
+
 def unittest():
     for train_count, _ in enumerate(train()()):
         pass