Merge pull request #2023 from pkuyym/develop

Add dataset PTB into paddle.dataset for language model task.

python/paddle/v2/dataset/imikolov.py

@@ -28,6 +28,11 @@ URL = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz'
 MD5 = '30177ea32e27c525793142b6bf2c8e2d'
 
 
+class DataType(object):
+    NGRAM = 1
+    SEQ = 2
+
+
 def word_count(f, word_freq=None):
     if word_freq is None:
         word_freq = collections.defaultdict(int)
@@ -41,7 +46,7 @@ def word_count(f, word_freq=None):
     return word_freq
 
 
-def build_dict(typo_freq=50):
+def build_dict(min_word_freq=50):
     """
     Build a word dictionary from the corpus,  Keys of the dictionary are words,
     and values are zero-based IDs of these words.
@@ -59,7 +64,7 @@ def build_dict(typo_freq=50):
             # remove <unk> for now, since we will set it as last index
             del word_freq['<unk>']
 
-        word_freq = filter(lambda x: x[1] > typo_freq, word_freq.items())
+        word_freq = filter(lambda x: x[1] > min_word_freq, word_freq.items())
 
         word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
         words, _ = list(zip(*word_freq_sorted))
@@ -69,7 +74,7 @@ def build_dict(typo_freq=50):
     return word_idx
 
 
-def reader_creator(filename, word_idx, n):
+def reader_creator(filename, word_idx, n, data_type):
     def reader():
         with tarfile.open(
                 paddle.v2.dataset.common.download(
@@ -79,16 +84,27 @@ def reader_creator(filename, word_idx, n):
 
             UNK = word_idx['<unk>']
             for l in f:
+                if DataType.NGRAM == data_type:
+                    assert n > -1, 'Invalid gram length'
                     l = ['<s>'] + l.strip().split() + ['<e>']
                     if len(l) >= n:
                         l = [word_idx.get(w, UNK) for w in l]
                         for i in range(n, len(l) + 1):
                             yield tuple(l[i - n:i])
+                elif DataType.SEQ == data_type:
+                    l = l.strip().split()
+                    l = [word_idx.get(w, UNK) for w in l]
+                    src_seq = [word_idx['<s>']] + l
+                    trg_seq = l + [word_idx['<e>']]
+                    if n > 0 and len(src_seq) > n: continue
+                    yield src_seq, trg_seq
+                else:
+                    assert False, 'Unknow data type'
 
     return reader
 
 
-def train(word_idx, n):
+def train(word_idx, n, data_type=DataType.NGRAM):
     """
     imikolov training set creator.
 
@@ -97,15 +113,18 @@ def train(word_idx, n):
 
     :param word_idx: word dictionary
     :type word_idx: dict
-    :param n: sliding window size
+    :param n: sliding window size if type is ngram, otherwise max length of sequence
     :type n: int
+    :param data_type: data type (ngram or sequence)
+    :type data_type: member variable of DataType (NGRAM or SEQ)
     :return: Training reader creator
     :rtype: callable
     """
-    return reader_creator('./simple-examples/data/ptb.train.txt', word_idx, n)
+    return reader_creator('./simple-examples/data/ptb.train.txt', word_idx, n,
+                          data_type)
 
 
-def test(word_idx, n):
+def test(word_idx, n, data_type=DataType.NGRAM):
     """
     imikolov test set creator.
 
@@ -114,12 +133,15 @@ def test(word_idx, n):
 
     :param word_idx: word dictionary
     :type word_idx: dict
-    :param n: sliding window size
+    :param n: sliding window size if type is ngram, otherwise max length of sequence
     :type n: int
+    :param data_type: data type (ngram or sequence)
+    :type data_type: member variable of DataType (NGRAM or SEQ)
     :return: Test reader creator
     :rtype: callable
     """
-    return reader_creator('./simple-examples/data/ptb.valid.txt', word_idx, n)
+    return reader_creator('./simple-examples/data/ptb.valid.txt', word_idx, n,
+                          data_type)
 
 
 def fetch():

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

@@ -13,10 +13,37 @@ class TestMikolov(unittest.TestCase):
         n = 5
         self.check_reader(paddle.v2.dataset.imikolov.train(WORD_DICT, n), n)
 
+        first_line = 'aer banknote berlitz calloway centrust cluett fromstein '\
+            'gitano guterman hydro-quebec ipo kia memotec mlx nahb punts '\
+            'rake regatta rubens sim snack-food ssangyong swapo wachter'
+        first_line = [
+            WORD_DICT.get(ch, WORD_DICT['<unk>'])
+            for ch in first_line.split(' ')
+        ]
+        for l in paddle.v2.dataset.imikolov.train(
+                WORD_DICT, n=-1,
+                data_type=paddle.v2.dataset.imikolov.DataType.SEQ)():
+            read_line = l[0][1:]
+            break
+        self.assertEqual(first_line, read_line)
+
     def test_test(self):
         n = 5
         self.check_reader(paddle.v2.dataset.imikolov.test(WORD_DICT, n), n)
 
+        first_line = 'consumers may want to move their telephones a little '\
+                'closer to the tv set'
+        first_line = [
+            WORD_DICT.get(ch, WORD_DICT['<unk>'])
+            for ch in first_line.split(' ')
+        ]
+        for l in paddle.v2.dataset.imikolov.test(
+                WORD_DICT, n=-1,
+                data_type=paddle.v2.dataset.imikolov.DataType.SEQ)():
+            read_line = l[0][1:]
+            break
+        self.assertEqual(first_line, read_line)
+
     def test_total(self):
         _, idx = zip(*WORD_DICT.items())
         self.assertEqual(sorted(idx)[-1], len(WORD_DICT) - 1)