add sentiment data package

paddle/setup.py.in

@@ -72,6 +72,8 @@ setup(name="py_paddle",
   packages=['py_paddle'],
   include_dirs = include_dirs,
   install_requires = [
+    'h5py',
+    'nltk',
     'numpy>=1.8.0',      # The numpy is required.
     'protobuf>=3.0.0'    # The paddle protobuf version
   ],

paddle/v2/data_set/config.py

+import os
+
+__all__ = ['DATA_HOME']
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set')
+
+if not os.path.exists(DATA_HOME):
+    os.makedirs(DATA_HOME)

paddle/v2/data_set/sentiment.py

+import random
+import nltk
+import numpy as np
+from nltk.corpus import movie_reviews
+from config import DATA_HOME
+
+__all__ = ['train', 'test', 'get_label_dict', 'get_word_dict']
+SPLIT_NUM = 800
+TOTAL_DATASET_NUM = 1000
+
+
+def get_label_dict():
+    label_dict = {'neg': 0, 'pos': 1}
+    return label_dict
+
+
+def is_download_data():
+    try:
+        nltk.data.path.append(DATA_HOME)
+        movie_reviews.categories()
+    except LookupError:
+        print "dd"
+        nltk.download('movie_reviews', download_dir=DATA_HOME)
+        nltk.data.path.append(DATA_HOME)
+
+
+def get_word_dict():
+    words_freq_sorted = list()
+    is_download_data()
+    words_freq = nltk.FreqDist(w.lower() for w in movie_reviews.words())
+    words_sort_list = words_freq.items()
+    words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
+    print words_sort_list
+    for index, word in enumerate(words_sort_list):
+        words_freq_sorted.append(word[0])
+    return words_freq_sorted
+
+
+def load_sentiment_data():
+    label_dict = get_label_dict()
+    is_download_data()
+    words_freq = nltk.FreqDist(w.lower() for w in movie_reviews.words())
+    data_set = [([words_freq[word]
+                for word in movie_reviews.words(fileid)], label_dict[category])
+                for category in movie_reviews.categories()
+                for fileid in movie_reviews.fileids(category)]
+    random.shuffle(data_set)
+    return data_set
+
+
+data_set = load_sentiment_data()
+
+
+def reader_creator(data_type):
+    if data_type == 'train':
+        for each in data_set[0:SPLIT_NUM]:
+            train_sentences = np.array(each[0], dtype=np.int32)
+            train_label = np.array(each[1], dtype=np.int8)
+            yield train_sentences, train_label
+    else:
+        for each in data_set[SPLIT_NUM:]:
+            test_sentences = np.array(each[0], dtype=np.int32)
+            test_label = np.array(each[1], dtype=np.int8)
+            yield test_sentences, test_label
+
+
+def train():
+    return reader_creator('train')
+
+
+def test():
+    return reader_creator('test')
+
+
+if __name__ == '__main__':
+    for train in train():
+        print "train"
+        print train
+    for test in test():
+        print "test"
+        print test