def test_top_k(k: int, preds: torch.Tensor, target: torch.Tensor, reduce: str, expected: torch.Tensor): """ A simple test to check that top_k works as expected """ class_metric = StatScores(top_k=k, reduce=reduce, num_classes=3) class_metric.update(preds, target) assert torch.equal(class_metric.compute(), expected.T) assert torch.equal(stat_scores(preds, target, top_k=k, reduce=reduce, num_classes=3), expected.T)
def test_v1_5_metric_classif_mix(): ConfusionMatrix.__init__._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): ConfusionMatrix(num_classes=1) FBeta.__init__._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): FBeta(num_classes=1) F1.__init__._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): F1(num_classes=1) HammingDistance.__init__._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): HammingDistance() StatScores.__init__._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): StatScores() target = torch.tensor([1, 1, 0, 0]) preds = torch.tensor([0, 1, 0, 0]) confusion_matrix._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): assert torch.equal( confusion_matrix(preds, target, num_classes=2).float(), torch.tensor([[2.0, 0.0], [1.0, 1.0]])) target = torch.tensor([0, 1, 2, 0, 1, 2]) preds = torch.tensor([0, 2, 1, 0, 0, 1]) fbeta._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): assert torch.allclose(fbeta(preds, target, num_classes=3, beta=0.5), torch.tensor(0.3333), atol=1e-4) f1._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): assert torch.allclose(f1(preds, target, num_classes=3), torch.tensor(0.3333), atol=1e-4) target = torch.tensor([[0, 1], [1, 1]]) preds = torch.tensor([[0, 1], [0, 1]]) hamming_distance._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): assert hamming_distance(preds, target) == torch.tensor(0.25) preds = torch.tensor([1, 0, 2, 1]) target = torch.tensor([1, 1, 2, 0]) stat_scores._warned = False with pytest.deprecated_call(match="It will be removed in v1.5.0"): assert torch.equal(stat_scores(preds, target, reduce="micro"), torch.tensor([2, 2, 6, 2, 4]))
def test_wrong_params(reduce, mdmc_reduce, num_classes, inputs, ignore_index): """Test a combination of parameters that are invalid and should raise an error. This includes invalid ``reduce`` and ``mdmc_reduce`` parameter values, not setting ``num_classes`` when ``reduce='macro'`, not setting ``mdmc_reduce`` when inputs are multi-dim multi-class``, setting ``ignore_index`` when inputs are binary, as well as setting ``ignore_index`` to a value higher than the number of classes. """ with pytest.raises(ValueError): stat_scores( inputs.preds[0], inputs.target[0], reduce, mdmc_reduce, num_classes=num_classes, ignore_index=ignore_index ) with pytest.raises(ValueError): sts = StatScores(reduce=reduce, mdmc_reduce=mdmc_reduce, num_classes=num_classes, ignore_index=ignore_index) sts(inputs.preds[0], inputs.target[0])
def test_wrong_threshold(): with pytest.raises(ValueError): StatScores(threshold=1.5)