| 30 | from . import imdb |
| 31 | |
| 32 | class IMDBTest(unittest.TestCase): |
| 33 | |
| 34 | def setUp(self): |
| 35 | self._tmp_dir = tempfile.mkdtemp() |
| 36 | super(IMDBTest, self).setUp() |
| 37 | |
| 38 | def tearDown(self): |
| 39 | if os.path.isdir(self._tmp_dir): |
| 40 | shutil.rmtree(self._tmp_dir) |
| 41 | super(IMDBTest, self).tearDown() |
| 42 | |
| 43 | def testGetWordIndexForward(self): |
| 44 | word_index = imdb.get_word_index() |
| 45 | self.assertGreater(word_index['bar'], 0) |
| 46 | |
| 47 | def testIndicesToWordsReverse(self): |
| 48 | forward_index = imdb.get_word_index() |
| 49 | reverse_index = imdb.get_word_index(reverse=True) |
| 50 | self.assertEqual('bar', reverse_index[forward_index['bar']]) |
| 51 | |
| 52 | def testIndicesToWords(self): |
| 53 | forward_index = imdb.get_word_index() |
| 54 | reverse_index = imdb.get_word_index(reverse=True) |
| 55 | indices = [forward_index[word] + imdb.INDEX_FROM |
| 56 | for word in ['one', 'two', 'three']] |
| 57 | self.assertEqual(['one', 'two', 'three'], |
| 58 | imdb.indices_to_words(reverse_index, indices)) |
| 59 | |
| 60 | def testTrainLSTMModel(self): |
| 61 | data_size = 10 |
| 62 | x_train = np.random.randint(0, 100, (data_size,)) |
| 63 | y_train = np.random.randint(0, 2, (data_size,)) |
| 64 | x_test = np.random.randint(0, 100, (data_size,)) |
| 65 | y_test = np.random.randint(0, 2, (data_size,)) |
| 66 | |
| 67 | vocabulary_size = 100 |
| 68 | embedding_size = 32 |
| 69 | epochs = 1 |
| 70 | batch_size = data_size |
| 71 | model = imdb.train_model( |
| 72 | 'lstm', vocabulary_size, embedding_size, |
| 73 | x_train, y_train, x_test, y_test, |
| 74 | epochs, batch_size) |
| 75 | self.assertTrue(model.layers) |
| 76 | |
| 77 | def testTrainModelWithInvalidModelTypeRaisesError(self): |
| 78 | data_size = 10 |
| 79 | x_train = np.random.randint(0, 100, (data_size,)) |
| 80 | y_train = np.random.randint(0, 2, (data_size,)) |
| 81 | x_test = np.random.randint(0, 100, (data_size,)) |
| 82 | y_test = np.random.randint(0, 2, (data_size,)) |
| 83 | |
| 84 | vocabulary_size = 100 |
| 85 | embedding_size = 32 |
| 86 | epochs = 1 |
| 87 | batch_size = data_size |
| 88 | with self.assertRaises(ValueError): |
| 89 | imdb.train_model( |
nothing calls this directly
no outgoing calls
no test coverage detected