Remove the overfix of print function in dataset/ folder

python/paddle/dataset/common.py

@@ -74,13 +74,13 @@ def download(url, module_name, md5sum, save_name=None):
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
         if os.path.exists(filename):
-            print(("file md5", md5file(filename), md5sum))
+            print("file md5", md5file(filename), md5sum)
         if retry < retry_limit:
             retry += 1
         else:
             raise RuntimeError("Cannot download {0} within retry limit {1}".
                                format(url, retry_limit))
-        print(("Cache file %s not found, downloading %s" % (filename, url)))
+        print("Cache file %s not found, downloading %s" % (filename, url))
         r = requests.get(url, stream=True)
         total_length = r.headers.get('content-length')
 
@@ -189,7 +189,7 @@ def cluster_files_reader(files_pattern,
         my_file_list = []
         for idx, fn in enumerate(file_list):
             if idx % trainer_count == trainer_id:
-                print(("append file: %s" % fn))
+                print("append file: %s" % fn)
                 my_file_list.append(fn)
         for fn in my_file_list:
             with open(fn, "r") as f:

python/paddle/dataset/movielens.py

@@ -16,7 +16,7 @@ Movielens 1-M dataset.
 
 Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000
 movies, which was collected by GroupLens Research. This module will download
-Movielens 1-M dataset from 
+Movielens 1-M dataset from
 http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training
 set and test set into paddle reader creators.
 
@@ -243,7 +243,7 @@ def unittest():
     for test_count, _ in enumerate(test()()):
         pass
 
-    print((train_count, test_count))
+    print(train_count, test_count)
 
 
 def fetch():

python/paddle/dataset/mq2007.py

@@ -53,7 +53,7 @@ class Query(object):
   ----------
   query_id : int
     query_id in dataset, mapping from query to relevance documents
-  relevance_score : int 
+  relevance_score : int
     relevance score of query and document pair
   feature_vector : array, dense feature
     feature in vector format
@@ -92,7 +92,7 @@ class Query(object):
             sys.stdout.write("expect 48 space split parts, get %d" %
                              (len(parts)))
             return None
-        # format : 0 qid:10 1:0.000272 2:0.000000 .... 
+        # format : 0 qid:10 1:0.000272 2:0.000000 ....
         self.relevance_score = int(parts[0])
         self.query_id = int(parts[1].split(':')[1])
         for p in parts[2:]:
@@ -295,7 +295,7 @@ def __reader__(filepath, format="pairwise", shuffle=False, fill_missing=-1):
   --------
   filename : string
   fill_missing : fill the missing value. default in MQ2007 is -1
-  
+
   Returns
   ------
   yield
@@ -330,4 +330,4 @@ if __name__ == "__main__":
     mytest = functools.partial(
         __reader__, filepath="MQ2007/MQ2007/Fold1/sample", format="listwise")
     for label, query in mytest():
-        print((label, query))
+        print(label, query)

python/paddle/dataset/sentiment.py

@@ -47,7 +47,7 @@ def download_data_if_not_yet():
         nltk.download(
             'movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
         print("Download data set success.....")
-        print(("Path is " + nltk.data.find('corpora/movie_reviews').path))
+        print("Path is " + nltk.data.find('corpora/movie_reviews').path)
 
 
 def get_word_dict():