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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.functional.regression.r2 import _r2_score_update
def _relative_squared_error_compute(
sum_squared_obs: Tensor,
sum_obs: Tensor,
sum_squared_error: Tensor,
num_obs: Union[int, Tensor],
squared: bool = True,
) -> Tensor:
"""Computes Relative Squared Error.
Args:
sum_squared_obs: Sum of square of all observations
sum_obs: Sum of all observations
sum_squared_error: Residual sum of squares
num_obs: Number of predictions or observations
squared: Returns RRSE value if set to False.
Example:
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> # RSE uses the same update function as R2 score.
>>> sum_squared_obs, sum_obs, rss, num_obs = _r2_score_update(preds, target)
>>> _relative_squared_error_compute(sum_squared_obs, sum_obs, rss, num_obs, squared=True)
tensor(0.0632)
"""
epsilon = torch.finfo(sum_squared_error.dtype).eps
rse = sum_squared_error / torch.clamp(sum_squared_obs - sum_obs * sum_obs / num_obs, min=epsilon)
if not squared:
rse = torch.sqrt(rse)
return torch.mean(rse)
def relative_squared_error(preds: Tensor, target: Tensor, squared: bool = True) -> Tensor:
r"""Computes the relative squared error (RSE).
.. math:: \text{RSE} = \frac{\sum_i^N(y_i - \hat{y_i})^2}{\sum_i^N(y_i - \overline{y})^2}
Where :math:`y` is a tensor of target values with mean :math:`\overline{y}`, and
:math:`\hat{y}` is a tensor of predictions.
If `preds` and `targets` are 2D tensors, the RSE is averaged over the second dim.
Args:
preds: estimated labels
target: ground truth labels
squared: returns RRSE value if set to False
Return:
Tensor with RSE
Example:
>>> from torchmetrics.functional.regression import relative_squared_error
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> relative_squared_error(preds, target)
tensor(0.0514)
"""
sum_squared_obs, sum_obs, rss, num_obs = _r2_score_update(preds, target)
return _relative_squared_error_compute(sum_squared_obs, sum_obs, rss, num_obs, squared=squared)
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