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fix normalize mode at confusion matrix (replace nans with zeros) #3465

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merged 9 commits into from
Sep 14, 2020
Merged

fix normalize mode at confusion matrix (replace nans with zeros) #3465

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75d3473
replace nans to 0 at conf. matrix & update tests
c00k1ez Sep 11, 2020
3ba5e19
cm.isnan() -> torch.isnan(cm)
c00k1ez Sep 12, 2020
fd19c78
fix row-wise division while normalize
c00k1ez Sep 12, 2020
31d22bc
update tests
c00k1ez Sep 12, 2020
5f23752
pep8 fix
c00k1ez Sep 12, 2020
ef3ad8b
Update tests/metrics/test_classification.py
c00k1ez Sep 13, 2020
c2fe679
Update tests/metrics/functional/test_classification.py
c00k1ez Sep 13, 2020
dbbf0d5
Update pytorch_lightning/metrics/functional/classification.py
c00k1ez Sep 13, 2020
994c677
final update
c00k1ez Sep 13, 2020
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6 changes: 5 additions & 1 deletion pytorch_lightning/metrics/functional/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -312,7 +312,11 @@ def confusion_matrix(
cm = bins.reshape(num_classes, num_classes).squeeze().float()

if normalize:
cm = cm / cm.sum(-1)
cm = cm / cm.sum(-1, keepdim=True)
nan_elements = cm[torch.isnan(cm)].nelement()
if nan_elements != 0:
cm[torch.isnan(cm)] = 0
rank_zero_warn(f'You have {nan_elements} nan values at confusion matrix replaced with zeros.')

return cm

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3 changes: 3 additions & 0 deletions tests/metrics/functional/test_classification.py
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Expand Up @@ -187,6 +187,9 @@ def test_confusion_matrix():
cm = confusion_matrix(pred, target, normalize=False, num_classes=3)
assert torch.allclose(cm, torch.tensor([[5., 0., 0.], [0., 0., 0.], [0., 0., 0.]]))

cm = confusion_matrix(pred, target, normalize=True, num_classes=3)
assert torch.allclose(cm, torch.tensor([[1., 0., 0.], [0., 0., 0.], [0., 0., 0.]]))


@pytest.mark.parametrize(['pred', 'target', 'expected_prec', 'expected_rec'], [
pytest.param(torch.tensor([1., 0., 1., 0.]), torch.tensor([0., 1., 1., 0.]), [0.5, 0.5], [0.5, 0.5]),
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5 changes: 3 additions & 2 deletions tests/metrics/test_classification.py
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Expand Up @@ -41,7 +41,8 @@ def test_accuracy(num_classes):
@pytest.mark.parametrize(['normalize', 'num_classes'], [
pytest.param(False, None),
pytest.param(True, None),
pytest.param(False, 3)
pytest.param(False, 3),
pytest.param(True, 3)
])
def test_confusion_matrix(normalize, num_classes):
conf_matrix = ConfusionMatrix(normalize=normalize, num_classes=num_classes)
Expand All @@ -50,7 +51,7 @@ def test_confusion_matrix(normalize, num_classes):
target = (torch.arange(120) % 3).view(-1, 1)
pred = target.clone()
cm = conf_matrix(pred, target)
assert isinstance(cm, torch.Tensor)
assert isinstance(cm, torch.Tensor) and cm[torch.isnan(cm)].nelement() == 0


@pytest.mark.parametrize('pos_label', [1, 2.])
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