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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.
from typing import Union
import torch
from torch import Tensor
from torchmetrics.utilities.checks import _check_same_shape
def _mean_absolute_percentage_error_update(
preds: Tensor,
target: Tensor,
epsilon: float = 1.17e-06,
) -> tuple[Tensor, int]:
"""Update and returns variables required to compute Mean Percentage Error.
Check for same shape of input tensors.
Args:
preds: Predicted tensor
target: Ground truth tensor
epsilon: Specifies the lower bound for target values. Any target value below epsilon
is set to epsilon (avoids ``ZeroDivisionError``).
"""
_check_same_shape(preds, target)
abs_diff = torch.abs(preds - target)
abs_per_error = abs_diff / torch.clamp(torch.abs(target), min=epsilon)
sum_abs_per_error = torch.sum(abs_per_error)
num_obs = target.numel()
return sum_abs_per_error, num_obs
def _mean_absolute_percentage_error_compute(sum_abs_per_error: Tensor, num_obs: Union[int, Tensor]) -> Tensor:
"""Compute Mean Absolute Percentage Error.
Args:
sum_abs_per_error: Sum of absolute value of percentage errors over all observations
``(percentage error = (target - prediction) / target)``
num_obs: Number of predictions or observations
Example:
>>> target = torch.tensor([1, 10, 1e6])
>>> preds = torch.tensor([0.9, 15, 1.2e6])
>>> sum_abs_per_error, num_obs = _mean_absolute_percentage_error_update(preds, target)
>>> _mean_absolute_percentage_error_compute(sum_abs_per_error, num_obs)
tensor(0.2667)
"""
return sum_abs_per_error / num_obs
def mean_absolute_percentage_error(preds: Tensor, target: Tensor) -> Tensor:
"""Compute mean absolute percentage error.
Args:
preds: estimated labels
target: ground truth labels
Return:
Tensor with MAPE
Note:
The epsilon value is taken from `scikit-learn's implementation of MAPE`_.
Example:
>>> from torchmetrics.functional.regression import mean_absolute_percentage_error
>>> target = torch.tensor([1, 10, 1e6])
>>> preds = torch.tensor([0.9, 15, 1.2e6])
>>> mean_absolute_percentage_error(preds, target)
tensor(0.2667)
"""
sum_abs_per_error, num_obs = _mean_absolute_percentage_error_update(preds, target)
return _mean_absolute_percentage_error_compute(sum_abs_per_error, num_obs)
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