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rmspe: add epsilon to avoid division by zero #2139

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Jun 21, 2022
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rmspe: add epsilon to avoid division by zero #2139

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f7804f9
rmspe: add epsilon to avoid division by zero
jppgks Jun 14, 2022
6c74a27
use test convention
jppgks Jun 14, 2022
98ae13c
Update ludwig/utils/loss_utils.py
justinxzhao Jun 15, 2022
0e0e628
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jun 15, 2022
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11 changes: 9 additions & 2 deletions ludwig/utils/loss_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,15 @@


def rmspe_loss(targets: torch.Tensor, predictions: torch.Tensor) -> torch.Tensor:
"""Root mean square percentage error."""
loss = torch.sqrt(torch.mean(((targets - predictions).float() / targets) ** 2))
"""Root mean square percentage error.

Bad predictions can lead to arbitrarily large RMSPE values, especially if some values of targets are very close to
zero. We return a large value instead of inf when (some) targets are zero.
"""
epsilon = 1e-4
# add epsilon if targets are zero to avoid division by zero
denominator = targets + epsilon * (targets == 0).float()
loss = torch.sqrt(torch.mean(((targets - predictions).float() / denominator) ** 2))
return loss


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8 changes: 8 additions & 0 deletions tests/ludwig/modules/test_loss_modules.py
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Expand Up @@ -36,6 +36,14 @@ def test_rmspe_loss(preds: torch.Tensor, target: torch.Tensor, output: torch.Ten
assert torch.isclose(loss(preds, target), output, rtol=0.0001)


@pytest.mark.parametrize("preds", [torch.tensor([[0.1, 0.2]]).float()])
@pytest.mark.parametrize("target", [torch.tensor([[0.0, 0.2]]).float()])
@pytest.mark.parametrize("output", [torch.tensor(707.1068).float()])
def test_rmspe_loss_zero_targets(preds: torch.Tensor, target: torch.Tensor, output: torch.Tensor):
loss = loss_modules.RMSPELoss()
assert torch.isclose(loss(preds, target), output, rtol=0.0001)


@pytest.mark.parametrize("preds", [torch.arange(6).reshape(3, 2).float()])
@pytest.mark.parametrize("target", [torch.arange(6, 12).reshape(3, 2).float()])
@pytest.mark.parametrize("output", [torch.tensor(-21.4655).float()])
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