def test_clf_with_nan(self, input_data, expected_value):
params = input_data.copy()
vals = {}
vals["y_pred"] = params.pop("y_pred")
vals["y"] = params.pop("y")
metric = ConfusionMatrixMetric(**params)
result = metric(**vals)
np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)
result, _ = metric.aggregate(reduction="mean_channel")[0]
expected_value, _ = do_metric_reduction(expected_value, "mean_channel")
expected_value = compute_confusion_matrix_metric("tpr", expected_value)
np.testing.assert_allclose(result, expected_value, atol=1e-4, rtol=1e-4)
def _reduce(self, scores) -> Any:
return do_metric_reduction(scores, MetricReduction.MEAN)[0]
