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Newmetric: NRMSE #2442
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Newmetric: NRMSE #2442
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21 changes: 21 additions & 0 deletions
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docs/source/regression/normalized_root_mean_squared_error.rst
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.. 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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-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": | ||
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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?