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fix: validate inputs and preserve numpy return type in calc_quantile_loss #839

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Merged
WenjieDu merged 2 commits into from
Apr 25, 2026
Merged

fix: validate inputs and preserve numpy return type in calc_quantile_loss #839

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a5dd5cb
fix: validate inputs and preserve numpy return type in calc_quantile_…
Apr 17, 2026
fafdf03
fix: skip shape check in calc_quantile_loss to keep calc_quantile_crp…
Apr 17, 2026
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10 changes: 10 additions & 0 deletions pypots/nn/functional/error.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,6 +260,14 @@ def calc_quantile_loss(
q: float,
eval_points: Union[np.ndarray, torch.Tensor],
) -> Union[float, torch.Tensor]:
# check types and NaN (but not predictions/targets shape, which is
# broadcast here and explicitly differs in the calc_quantile_crps_sum
# caller). Mask shape is still validated against targets by _check_inputs.
_check_inputs(predictions, targets, eval_points, check_shape=False)

# preserve numpy-in/numpy-out contract used by calc_mae/calc_mse/calc_rmse/calc_mre
numpy_in = isinstance(predictions, np.ndarray)

# Handle numpy arrays by converting to torch tensors
if isinstance(predictions, np.ndarray):
predictions = torch.from_numpy(predictions)
Expand All @@ -271,6 +279,8 @@ def calc_quantile_loss(
quantile_loss = 2 * torch.sum(
torch.abs((predictions - targets) * eval_points * ((targets <= predictions) * 1.0 - q))
)
if numpy_in:
return quantile_loss.detach().cpu().numpy()
return quantile_loss


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