wehub-resource-sync
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
import numpy as np
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from keras.src import testing
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from keras.src.datasets import imdb
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class ImdbLoadDataTest(testing.TestCase):
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def test_load_data_default(self):
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(x_train, y_train), (x_test, y_test) = imdb.load_data()
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self.assertIsInstance(x_train, np.ndarray)
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self.assertIsInstance(y_train, np.ndarray)
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self.assertIsInstance(x_test, np.ndarray)
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self.assertIsInstance(y_test, np.ndarray)
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# Check lengths
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self.assertEqual(len(x_train), 25000)
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self.assertEqual(len(y_train), 25000)
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self.assertEqual(len(x_test), 25000)
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self.assertEqual(len(y_test), 25000)
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# Check types within lists for x
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self.assertIsInstance(x_train[0], list)
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self.assertIsInstance(x_test[0], list)
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def test_num_words(self):
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# Only consider the top 1000 words
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(x_train, _), _ = imdb.load_data(num_words=1000)
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# Ensure that no word index exceeds 999 (0-based indexing)
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max_index = max(max(sequence) for sequence in x_train if sequence)
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self.assertLessEqual(max_index, 999)
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def test_skip_top(self):
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# Skip the top 10 most frequent words
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(x_train, _), _ = imdb.load_data(skip_top=10, num_words=1000)
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# Check if top 10 words are skipped properly
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self.assertNotIn(1, x_train[0]) # Assuming 1 is among top 10
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def test_maxlen(self):
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# Only consider sequences shorter than 100
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(x_train, _), _ = imdb.load_data(maxlen=100)
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self.assertTrue(all(len(seq) <= 100 for seq in x_train))
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def test_get_word_index(self):
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word_index = imdb.get_word_index()
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self.assertIsInstance(word_index, dict)
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# Check if word_index contains specific known words
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self.assertIn("the", word_index)
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self.assertIn("and", word_index)
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