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])