55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple
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import torch
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from pytorch_lightning.metrics.utils import _check_same_shape
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def _mean_relative_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:
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_check_same_shape(preds, target)
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target_nz = target.clone()
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target_nz[target == 0] = 1
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sum_rltv_error = torch.sum(torch.abs((preds - target) / target_nz))
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n_obs = target.numel()
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return sum_rltv_error, n_obs
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def _mean_relative_error_compute(sum_rltv_error: torch.Tensor, n_obs: int) -> torch.Tensor:
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return sum_rltv_error / n_obs
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def mean_relative_error(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""
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Computes mean relative error
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Args:
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pred: estimated labels
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target: ground truth labels
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Return:
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Tensor with mean relative error
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Example:
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>>> x = torch.tensor([0., 1, 2, 3])
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>>> y = torch.tensor([0., 1, 2, 2])
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>>> mean_relative_error(x, y)
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tensor(0.1250)
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"""
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sum_rltv_error, n_obs = _mean_relative_error_update(preds, target)
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return _mean_relative_error_compute(sum_rltv_error, n_obs)
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