def test_clips_observations(self):
history = trax_history.History()
self._append_metrics(history, ('eval', 'loss'), [-10, 10])
observations = online_tune.history_to_observations(
history,
metrics=(('eval', 'loss'), ),
observation_range=(-2, 2),
include_lr=False,
)
np.testing.assert_array_equal(observations, [[-2], [2]])
def test_converts_history_to_observations_without_learning_rate(self):
history = trax_history.History()
self._append_metrics(history, ('train', 'loss'), [3.0, 1.07])
self._append_metrics(history, ('eval', 'accuracy'), [0.12, 0.68])
observations = online_tune.history_to_observations(
history,
metrics=(('eval', 'accuracy'), ('train', 'loss')),
observation_range=(0, 5),
include_lr=False,
)
np.testing.assert_array_equal(observations,
[[0.12, 3.0], [0.68, 1.07]])
def test_converts_history_to_observations_with_learning_rate(self):
history = trax_history.History()
self._append_metrics(history, ('train', 'training/learning_rate'),
[1e-3, 1e-4])
observations = online_tune.history_to_observations(
history,
metrics=(),
observation_range=(0, 5),
include_lr=True,
)
self.assertEqual(observations.shape, (2, 1))
((log_lr_1, ), (log_lr_2, )) = observations
self.assertGreater(log_lr_1, log_lr_2)
