def _current_reward_metric(self):
metric_values = online_tune.historical_metric_values(
self._trainer.state.history,
self._reward_metric,
)
assert metric_values.shape[0] > 0, (
"No values in history for metric {}.".format(self._reward_metric))
return metric_values[-1]
def test_clips_historical_metric_values(self):
history = trax_history.History()
self._append_metrics(history, ("train", "loss"), [-10, 10])
metric_values = online_tune.historical_metric_values(
history, metric=("train", "loss"), observation_range=(-1, 1))
np.testing.assert_array_equal(metric_values, [-1, 1])
def test_retrieves_historical_metric_values(self):
history = trax_history.History()
self._append_metrics(history, ("train", "accuracy"), [0.1, 0.73])
metric_values = online_tune.historical_metric_values(
history, metric=("train", "accuracy"), observation_range=(0, 5))
np.testing.assert_array_equal(metric_values, [0.1, 0.73])