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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from torch import Tensor
from torchmetrics.utilities.checks import _check_same_shape
def _weighted_mean_absolute_percentage_error_update(
preds: Tensor,
target: Tensor,
) -> tuple[Tensor, Tensor]:
"""Update and returns variables required to compute Weighted Absolute Percentage Error.
Check for same shape of input tensors.
Args:
preds: Predicted tensor
target: Ground truth tensor
"""
_check_same_shape(preds, target)
sum_abs_error = (preds - target).abs().sum()
sum_scale = target.abs().sum()
return sum_abs_error, sum_scale
def _weighted_mean_absolute_percentage_error_compute(
sum_abs_error: Tensor,
sum_scale: Tensor,
epsilon: float = 1.17e-06,
) -> Tensor:
"""Compute Weighted Absolute Percentage Error.
Args:
sum_abs_error: scalar with sum of absolute errors
sum_scale: scalar with sum of target values
epsilon: small float to prevent division by zero
"""
return sum_abs_error / torch.clamp(sum_scale, min=epsilon)
def weighted_mean_absolute_percentage_error(preds: Tensor, target: Tensor) -> Tensor:
r"""Compute weighted mean absolute percentage error (`WMAPE`_).
The output of WMAPE metric is a non-negative floating point, where the optimal value is 0. It is computes as:
.. math::
\text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| }
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
Args:
preds: estimated labels
target: ground truth labels
Return:
Tensor with WMAPE.
Example:
>>> from torch import randn
>>> preds = randn(20,)
>>> target = randn(20,)
>>> weighted_mean_absolute_percentage_error(preds, target)
tensor(1.3967)
"""
sum_abs_error, sum_scale = _weighted_mean_absolute_percentage_error_update(preds, target)
return _weighted_mean_absolute_percentage_error_compute(sum_abs_error, sum_scale)
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