add unittest of data, fix bug

fastNLP/core/vocabulary.py

@@ -36,9 +36,10 @@ class Vocabulary(object):
                 self.update(w)
         else:
         # it's a word to be added
-            self.word2idx[word] = len(self)
-            if self.idx2word is not None:
-                self.idx2word = None
+            if word not in self.word2idx:
+                self.word2idx[word] = len(self)
+                if self.idx2word is not None:
+                    self.idx2word = None
 
     
     def __getitem__(self, w):
@@ -80,20 +81,5 @@ class Vocabulary(object):
         self.__dict__.update(state)
         self.idx2word = None
     
-if __name__ == '__main__':
-    import _pickle as pickle
-    vocab = Vocabulary()
-    filename = 'vocab'
-    vocab.update(filename)
-    vocab.update([filename, ['a'], [['b']], ['c']])
-    idx = vocab[filename]
-    print('{} {}'.format(vocab.to_word(idx), vocab[filename]))
 
-    with open(filename, 'wb') as f:
-        pickle.dump(vocab, f)
-    with open(filename, 'rb') as f:
-        vocab = pickle.load(f)
-    
-    print('{} {}'.format(vocab.to_word(idx), vocab[filename]))
-    print(vocab.word2idx)
     

test/core/test_field.py

@@ -0,0 +1,69 @@
+import os
+import sys
+sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
+
+import unittest
+import torch
+from fastNLP.data.field import TextField, LabelField
+from fastNLP.data.instance import Instance
+from fastNLP.data.dataset import DataSet
+from fastNLP.data.batch import Batch
+
+
+
+class TestField(unittest.TestCase):
+    def check_batched_data_equal(self, data1, data2):
+        self.assertEqual(len(data1), len(data2))
+        for i in range(len(data1)):
+            self.assertTrue(data1[i].keys(), data2[i].keys())
+        for i in range(len(data1)):
+            for t1, t2 in zip(data1[i].values(), data2[i].values()):
+                self.assertTrue(torch.equal(t1, t2))
+    
+    def test_batchiter(self):
+        texts = [
+            "i am a cat",
+            "this is a test of new batch",
+            "haha"
+        ]
+        labels = [0, 1, 0]
+        
+        # prepare vocabulary
+        vocab = {}
+        for text in texts:
+            for tokens in text.split():
+                if tokens not in vocab:
+                    vocab[tokens] = len(vocab)
+
+        # prepare input dataset    
+        data = DataSet()
+        for text, label in zip(texts, labels):
+            x = TextField(text.split(), False)
+            y = LabelField(label, is_target=True)
+            ins = Instance(text=x, label=y)
+            data.append(ins)
+        
+        # use vocabulary to index data
+        data.index_field("text", vocab)
+
+        # define naive sampler for batch class
+        class SeqSampler:
+            def __call__(self, dataset):
+                return list(range(len(dataset)))
+                
+        # use bacth to iterate dataset
+        batcher = Batch(data, SeqSampler(), 2)
+        TRUE_X = [{'text': torch.tensor([[0, 1, 2, 3, 0, 0, 0], [4, 5, 2, 6, 7, 8, 9]])}, {'text': torch.tensor([[10]])}]
+        TRUE_Y = [{'label': torch.tensor([[0], [1]])}, {'label': torch.tensor([[0]])}]
+        for epoch in range(3):
+            test_x, test_y = [], []
+            for batch_x, batch_y in batcher:
+                test_x.append(batch_x)
+                test_y.append(batch_y)
+            self.check_batched_data_equal(TRUE_X, test_x)
+            self.check_batched_data_equal(TRUE_Y, test_y)
+
+
+if __name__ == "__main__":
+    unittest.main()
+    

test/core/test_vocab.py

@@ -0,0 +1,35 @@
+import os
+import sys
+sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
+
+import unittest
+from fastNLP.data.vocabulary import Vocabulary, DEFAULT_WORD_TO_INDEX
+
+class TestVocabulary(unittest.TestCase):
+    def test_vocab(self):
+        import _pickle as pickle
+        import os
+        vocab = Vocabulary()
+        filename = 'vocab'
+        vocab.update(filename)
+        vocab.update([filename, ['a'], [['b']], ['c']])
+        idx = vocab[filename]
+        before_pic = (vocab.to_word(idx), vocab[filename])
+
+        with open(filename, 'wb') as f:
+            pickle.dump(vocab, f)
+        with open(filename, 'rb') as f:
+            vocab = pickle.load(f)
+        os.remove(filename)
+        
+        vocab.build_reverse_vocab()
+        after_pic = (vocab.to_word(idx), vocab[filename])
+        TRUE_DICT = {'vocab': 5, 'a': 6, 'b': 7, 'c': 8}
+        TRUE_DICT.update(DEFAULT_WORD_TO_INDEX)
+        TRUE_IDXDICT = {0: '<pad>', 1: '<unk>', 2: '<reserved-2>', 3: '<reserved-3>', 4: '<reserved-4>', 5: 'vocab', 6: 'a', 7: 'b', 8: 'c'}
+        self.assertEqual(before_pic, after_pic)
+        self.assertDictEqual(TRUE_DICT, vocab.word2idx)
+        self.assertDictEqual(TRUE_IDXDICT, vocab.idx2word)
+    
+if __name__ == '__main__':
+    unittest.main()