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| """Tests for the metrics module."""
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| import contextlib
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| import numpy as np
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| import tensorflow as tf
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|
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| import metrics
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|
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|
| class AccuracyTest(tf.test.TestCase):
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| def setUp(self):
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| tf.test.TestCase.setUp(self)
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| self.rng = np.random.RandomState([11, 23, 50])
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| self.num_char_classes = 3
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| self.batch_size = 4
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| self.seq_length = 5
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| self.rej_char = 42
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|
|
| @contextlib.contextmanager
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| def initialized_session(self):
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| """Wrapper for test session context manager with required initialization.
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|
|
| Yields:
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| A session object that should be used as a context manager.
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| """
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| with self.cached_session() as sess:
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| sess.run(tf.compat.v1.global_variables_initializer())
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| sess.run(tf.compat.v1.local_variables_initializer())
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| yield sess
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|
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| def _fake_labels(self):
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| return self.rng.randint(
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| low=0,
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| high=self.num_char_classes,
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| size=(self.batch_size, self.seq_length),
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| dtype='int32')
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|
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| def _incorrect_copy(self, values, bad_indexes):
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| incorrect = np.copy(values)
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| incorrect[bad_indexes] = values[bad_indexes] + 1
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| return incorrect
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|
|
| def test_sequence_accuracy_identical_samples(self):
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| labels_tf = tf.convert_to_tensor(value=self._fake_labels())
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|
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| accuracy_tf = metrics.sequence_accuracy(labels_tf, labels_tf,
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| self.rej_char)
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| with self.initialized_session() as sess:
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| accuracy_np = sess.run(accuracy_tf)
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|
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| self.assertAlmostEqual(accuracy_np, 1.0)
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|
|
| def test_sequence_accuracy_one_char_difference(self):
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| ground_truth_np = self._fake_labels()
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| ground_truth_tf = tf.convert_to_tensor(value=ground_truth_np)
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| prediction_tf = tf.convert_to_tensor(
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| value=self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
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|
|
| accuracy_tf = metrics.sequence_accuracy(prediction_tf, ground_truth_tf,
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| self.rej_char)
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| with self.initialized_session() as sess:
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| accuracy_np = sess.run(accuracy_tf)
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|
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|
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| self.assertAlmostEqual(accuracy_np, 1.0 - 1.0 / self.batch_size)
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|
|
| def test_char_accuracy_one_char_difference_with_padding(self):
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| ground_truth_np = self._fake_labels()
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| ground_truth_tf = tf.convert_to_tensor(value=ground_truth_np)
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| prediction_tf = tf.convert_to_tensor(
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| value=self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
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|
|
| accuracy_tf = metrics.char_accuracy(prediction_tf, ground_truth_tf,
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| self.rej_char)
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| with self.initialized_session() as sess:
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| accuracy_np = sess.run(accuracy_tf)
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|
|
| chars_count = self.seq_length * self.batch_size
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| self.assertAlmostEqual(accuracy_np, 1.0 - 1.0 / chars_count)
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|
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|
|
| if __name__ == '__main__':
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| tf.test.main()
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|
|
