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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_error_update(preds: Tensor, target: Tensor, num_outputs: int) -> tuple[Tensor, int]:
"""Update and returns variables required to compute Mean Absolute Error.
Check for same shape of input tensors.
Args:
preds: Predicted tensor
target: Ground truth tensor
num_outputs: Number of outputs in multioutput setting
"""
_check_same_shape(preds, target)
if num_outputs == 1:
preds = preds.view(-1)
target = target.view(-1)
preds = preds if preds.is_floating_point else preds.float() # type: ignore[truthy-function] # todo
target = target if target.is_floating_point else target.float() # type: ignore[truthy-function] # todo
sum_abs_error = torch.sum(torch.abs(preds - target), dim=0)
return sum_abs_error, target.shape[0]
def _mean_absolute_error_compute(sum_abs_error: Tensor, num_obs: Union[int, Tensor]) -> Tensor:
"""Compute Mean Absolute Error.
Args:
sum_abs_error: Sum of absolute value of errors over all observations
num_obs: Number of predictions or observations
Example:
>>> preds = torch.tensor([0., 1, 2, 3])
>>> target = torch.tensor([0., 1, 2, 2])
>>> sum_abs_error, num_obs = _mean_absolute_error_update(preds, target, num_outputs=1)
>>> _mean_absolute_error_compute(sum_abs_error, num_obs)
tensor(0.2500)
"""
return sum_abs_error / num_obs
def mean_absolute_error(preds: Tensor, target: Tensor, num_outputs: int = 1) -> Tensor:
"""Compute mean absolute error.
Args:
preds: estimated labels
target: ground truth labels
num_outputs: Number of outputs in multioutput setting
Return:
Tensor with MAE
Example:
>>> from torchmetrics.functional.regression import mean_absolute_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_absolute_error(x, y)
tensor(0.2500)
"""
sum_abs_error, num_obs = _mean_absolute_error_update(preds, target, num_outputs=num_outputs)
return _mean_absolute_error_compute(sum_abs_error, num_obs)
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