86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
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# 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 Union, Tuple, Sequence
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import torch
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from pytorch_lightning.metrics.utils import _check_same_shape
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def _explained_variance_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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_check_same_shape(preds, target)
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return preds, target
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def _explained_variance_compute(preds: torch.Tensor,
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target: torch.Tensor,
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multioutput: str = 'uniform_average',
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) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
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diff_avg = torch.mean(target - preds, dim=0)
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numerator = torch.mean((target - preds - diff_avg) ** 2, dim=0)
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target_avg = torch.mean(target, dim=0)
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denominator = torch.mean((target - target_avg) ** 2, dim=0)
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# Take care of division by zero
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nonzero_numerator = numerator != 0
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nonzero_denominator = denominator != 0
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valid_score = nonzero_numerator & nonzero_denominator
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output_scores = torch.ones_like(diff_avg)
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output_scores[valid_score] = 1.0 - (numerator[valid_score] / denominator[valid_score])
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output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
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# Decide what to do in multioutput case
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# Todo: allow user to pass in tensor with weights
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if multioutput == 'raw_values':
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return output_scores
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if multioutput == 'uniform_average':
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return torch.mean(output_scores)
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if multioutput == 'variance_weighted':
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denom_sum = torch.sum(denominator)
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return torch.sum(denominator / denom_sum * output_scores)
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def explained_variance(preds: torch.Tensor,
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target: torch.Tensor,
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multioutput: str = 'uniform_average',
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) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
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"""
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Computes explained variance.
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Args:
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pred: estimated labels
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target: ground truth labels
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multioutput: Defines aggregation in the case of multiple output scores. Can be one
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of the following strings (default is `'uniform_average'`.):
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* `'raw_values'` returns full set of scores
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* `'uniform_average'` scores are uniformly averaged
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* `'variance_weighted'` scores are weighted by their individual variances
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Example:
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>>> from pytorch_lightning.metrics.functional import explained_variance
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>>> target = torch.tensor([3, -0.5, 2, 7])
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>>> preds = torch.tensor([2.5, 0.0, 2, 8])
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>>> explained_variance(preds, target)
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tensor(0.9572)
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>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
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>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
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>>> explained_variance(preds, target, multioutput='raw_values')
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tensor([0.9677, 1.0000])
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"""
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preds, target = _explained_variance_update(preds, target)
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return _explained_variance_compute(preds, target, multioutput)
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