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Newmetric: NRMSE #2442
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init files
SkafteNicki 58b7577
docs
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requirements for testing
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changelog
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add class interface
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add tests
SkafteNicki 50e65a5
Merge branch 'master' into newmetric/nrmse
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Merge branch 'master' into newmetric/nrmse
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Merge branch 'master' into newmetric/nrmse
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SkafteNicki 642dd27
Update NRMSE computation with normalization options
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try fixing docs
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fix mypy
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fix naming of file
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Merge branch 'master' into newmetric/nrmse
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21 changes: 21 additions & 0 deletions
21
docs/source/regression/normalized_root_mean_squared_error.rst
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| @@ -0,0 +1,21 @@ | ||
| .. customcarditem:: | ||
| :header: Normalized Root Mean Squared Error (NRMSE) | ||
| :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg | ||
| :tags: Regression | ||
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| .. include:: ../links.rst | ||
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| ########################################## | ||
| Normalized Root Mean Squared Error (NRMSE) | ||
| ########################################## | ||
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| Module Interface | ||
| ________________ | ||
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| .. autoclass:: torchmetrics.NormalizedRootMeanSquaredError | ||
| :exclude-members: update, compute | ||
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| Functional Interface | ||
| ____________________ | ||
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| .. autofunction:: torchmetrics.functional.normalized_root_mean_squared_error |
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@@ -20,3 +20,4 @@ | |
| -r classification_test.txt | ||
| -r nominal_test.txt | ||
| -r segmentation_test.txt | ||
| -r regression_test.txt | ||
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| permetrics==2.0.0 |
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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 Tuple, Union | ||
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| import torch | ||
| from torch import Tensor | ||
| from typing_extensions import Literal | ||
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| from torchmetrics.functional.regression.mse import _mean_squared_error_update | ||
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| def _normalized_root_mean_squared_error_update( | ||
| preds: Tensor, target: Tensor, num_outputs: int, normalization: Literal["mean", "range", "std"] = "mean" | ||
| ) -> Tuple[Tensor, int, Tensor]: | ||
| """Updates and returns the sum of squared errors and the number of observations for NRMSE computation. | ||
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| Args: | ||
| preds: Predicted tensor | ||
| target: Ground truth tensor | ||
| num_outputs: Number of outputs in multioutput setting | ||
| normalization: type of normalization to be applied. Choose from "mean", "range", "std" | ||
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| """ | ||
| sum_squared_error, num_obs = _mean_squared_error_update(preds, target, num_outputs) | ||
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| target = target.view(-1) if num_outputs == 1 else target | ||
| if normalization == "mean": | ||
| denom = torch.mean(target, dim=0) | ||
| elif normalization == "range": | ||
| denom = torch.max(target, dim=0).values - torch.min(target, dim=0).values | ||
| elif normalization == "std": | ||
| denom = torch.std(target, correction=0, dim=0) | ||
| else: | ||
| raise ValueError(f"Argument `normalization` should be either 'mean', 'range' or 'std', but got {normalization}") | ||
| return sum_squared_error, num_obs, denom | ||
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| def _normalized_root_mean_squared_error_compute( | ||
| sum_squared_error: Tensor, num_obs: Union[int, Tensor], denom: Tensor | ||
| ) -> Tensor: | ||
| """Calculates RMSE and normalizes it.""" | ||
| rmse = torch.sqrt(sum_squared_error / num_obs) | ||
| return rmse / denom | ||
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| def normalized_root_mean_squared_error( | ||
| preds: Tensor, | ||
| target: Tensor, | ||
| normalization: Literal["mean", "range", "std"] = "mean", | ||
| num_outputs: int = 1, | ||
| ) -> Tensor: | ||
| """Calculates the `Normalized Root Mean Squared Error`_ (NRMSE) also know as scatter index. | ||
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| Args: | ||
| preds: estimated labels | ||
| target: ground truth labels | ||
| normalization: type of normalization to be applied. Choose from "mean", "range", "std" which corresponds to | ||
| normalizing the RMSE by the mean of the target, the range of the target or the standard deviation of the | ||
| target. | ||
| num_outputs: Number of outputs in multioutput setting | ||
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| Return: | ||
| Tensor with the NRMSE score | ||
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| Example: | ||
| >>> import torch | ||
| >>> from torchmetrics.functional.regression import normalized_root_mean_squared_error | ||
| >>> preds = torch.tensor([0., 1, 2, 3]) | ||
| >>> target = torch.tensor([0., 1, 2, 2]) | ||
| >>> normalized_root_mean_squared_error(preds, target, normalization="mean") | ||
| tensor(0.4000) | ||
| >>> normalized_root_mean_squared_error(preds, target, normalization="range") | ||
| tensor(0.2500) | ||
| >>> normalized_root_mean_squared_error(preds, target, normalization="std") | ||
| tensor(0.6030) | ||
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| Example (multioutput): | ||
| >>> import torch | ||
| >>> from torchmetrics.functional.regression import normalized_root_mean_squared_error | ||
| >>> preds = torch.tensor([[0., 1], [2, 3], [4, 5], [6, 7]]) | ||
| >>> target = torch.tensor([[0., 1], [3, 3], [4, 5], [8, 9]]) | ||
| >>> normalized_root_mean_squared_error(preds, target, normalization="mean", num_outputs=2) | ||
| tensor([0.2981, 0.2222]) | ||
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| """ | ||
| sum_squared_error, num_obs, denom = _normalized_root_mean_squared_error_update( | ||
| preds, target, num_outputs=num_outputs, normalization=normalization | ||
| ) | ||
| return _normalized_root_mean_squared_error_compute(sum_squared_error, num_obs, denom) |
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shall this be another PR?