format by yapf

python/paddle/v2/dataset/sentiment.py

@@ -20,38 +20,30 @@ The script fetch and preprocess movie_reviews data set
 that provided by NLTK
 """
 
+import paddle.v2.dataset.common as common
+import collections
 import nltk
 import numpy as np
 from itertools import chain
 from nltk.corpus import movie_reviews
-from config import DATA_HOME
 
-__all__ = ['train', 'test', 'get_label_dict', 'get_word_dict']
+__all__ = ['train', 'test', 'get_word_dict']
 NUM_TRAINING_INSTANCES = 1600
 NUM_TOTAL_INSTANCES = 2000
 
 
-def get_label_dict():
-    """
-    Define the labels dict for dataset
-    """
-    label_dict = {'neg': 0, 'pos': 1}
-    return label_dict
-
-
 def download_data_if_not_yet():
     """
     Download the data set, if the data set is not download.
     """
     try:
         # make sure that nltk can find the data
-        nltk.data.path.append(DATA_HOME)
+        if common.DATA_HOME not in nltk.data.path:
+            nltk.data.path.append(common.DATA_HOME)
         movie_reviews.categories()
     except LookupError:
         print "Downloading movie_reviews data set, please wait....."
-        nltk.download('movie_reviews', download_dir=DATA_HOME)
-        # make sure that nltk can find the data
-        nltk.data.path.append(DATA_HOME)
+        nltk.download('movie_reviews', download_dir=common.DATA_HOME)
         print "Download data set success....."
         print "Path is " + nltk.data.find('corpora/movie_reviews').path
 
@@ -63,12 +55,17 @@ def get_word_dict():
         words_freq_sorted
     """
     words_freq_sorted = list()
+    word_freq_dict = collections.defaultdict(int)
     download_data_if_not_yet()
-    words_freq = nltk.FreqDist(w.lower() for w in movie_reviews.words())
-    words_sort_list = words_freq.items()
+
+    for category in movie_reviews.categories():
+        for field in movie_reviews.fileids(category):
+            for words in movie_reviews.words(field):
+                word_freq_dict[words] += 1
+    words_sort_list = word_freq_dict.items()
     words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
     for index, word in enumerate(words_sort_list):
-        words_freq_sorted.append((word[0], index + 1))
+        words_freq_sorted.append((word[0], index))
     return words_freq_sorted
 
 
@@ -79,7 +76,6 @@ def sort_files():
         files_list
     """
     files_list = list()
-    download_data_if_not_yet()
     neg_file_list = movie_reviews.fileids('neg')
     pos_file_list = movie_reviews.fileids('pos')
     files_list = list(chain.from_iterable(zip(neg_file_list, pos_file_list)))
@@ -104,9 +100,6 @@ def load_sentiment_data():
     return data_set
 
 
-data_set = load_sentiment_data()
-
-
 def reader_creator(data):
     """
     Reader creator, it format data set to numpy
@@ -114,15 +107,14 @@ def reader_creator(data):
         train data set or test data set
     """
     for each in data:
-        list_of_int = np.array(each[0], dtype=np.int32)
-        label = each[1]
-        yield list_of_int, label
+        yield each[0], each[1]
 
 
 def train():
     """
     Default train set reader creator
     """
+    data_set = load_sentiment_data()
     return reader_creator(data_set[0:NUM_TRAINING_INSTANCES])
 
 
@@ -130,14 +122,5 @@ def test():
     """
     Default test set reader creator
     """
+    data_set = load_sentiment_data()
     return reader_creator(data_set[NUM_TRAINING_INSTANCES:])
-
-
-def unittest():
-    assert len(data_set) == NUM_TOTAL_INSTANCES
-    assert len(list(train())) == NUM_TRAINING_INSTANCES
-    assert len(list(test())) == NUM_TOTAL_INSTANCES - NUM_TRAINING_INSTANCES
-
-
-if __name__ == '__main__':
-    unittest()

python/paddle/v2/dataset/tests/test_sentiment.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import nltk
+import paddle.v2.dataset.sentiment as st
+from nltk.corpus import movie_reviews
+
+
+class TestSentimentMethods(unittest.TestCase):
+    def test_get_word_dict(self):
+        word_dict = st.get_word_dict()[0:10]
+        test_word_list = [(u',', 0), (u'the', 1), (u'.', 2), (u'a', 3),
+                          (u'and', 4), (u'of', 5), (u'to', 6), (u"'", 7),
+                          (u'is', 8), (u'in', 9)]
+        for idx, each in enumerate(word_dict):
+            self.assertEqual(each, test_word_list[idx])
+        self.assertTrue("/root/.cache/paddle/dataset" in nltk.data.path)
+
+    def test_sort_files(self):
+        last_label = ''
+        for sample_file in st.sort_files():
+            current_label = sample_file.split("/")[0]
+            self.assertNotEqual(current_label, last_label)
+            last_label = current_label
+
+    def test_data_set(self):
+        data_set = st.load_sentiment_data()
+        last_label = -1
+        for each in st.test():
+            self.assertNotEqual(each[1], last_label)
+            last_label = each[1]
+        self.assertEqual(len(data_set), st.NUM_TOTAL_INSTANCES)
+        self.assertEqual(len(list(st.train())), st.NUM_TRAINING_INSTANCES)
+        self.assertEqual(
+            len(list(st.test())),
+            (st.NUM_TOTAL_INSTANCES - st.NUM_TRAINING_INSTANCES))
+
+
+if __name__ == '__main__':
+    unittest.main()