wehub-resource-sync
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import numpy as np
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from keras.src import testing
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from keras.src.datasets import reuters
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class ReutersLoadDataTest(testing.TestCase):
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def test_load_data_default(self):
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(x_train, y_train), (x_test, y_test) = reuters.load_data()
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# Check types
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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 shapes
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self.assertGreater(len(x_train), 0)
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self.assertEqual(len(x_train), len(y_train))
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self.assertGreater(len(x_test), 0)
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self.assertEqual(len(x_test), len(y_test))
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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, _), _ = reuters.load_data(num_words=1000)
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# Ensure 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, _), _ = reuters.load_data(skip_top=10, num_words=1000)
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# Assuming 1 is among top 10, check if it's skipped
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self.assertNotIn(1, x_train[0])
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def test_maxlen(self):
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# Only consider sequences shorter than 50
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(x_train, _), _ = reuters.load_data(maxlen=50)
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self.assertTrue(all(len(seq) <= 50 for seq in x_train))
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def test_get_word_index(self):
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word_index = reuters.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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def test_get_label_names(self):
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label_names = reuters.get_label_names()
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self.assertIsInstance(label_names, tuple)
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# Check if the tuple contains specific known labels
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self.assertIn("earn", label_names)
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self.assertIn("acq", label_names)
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