# 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 collections.abc import Sequence from typing import Any, Optional, Union import torch from torch import Tensor, tensor from torchmetrics.functional.regression.mae import _mean_absolute_error_compute, _mean_absolute_error_update from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["MeanAbsoluteError.plot"] class MeanAbsoluteError(Metric): r"""`Compute Mean Absolute Error`_ (MAE). .. math:: \text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} | Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predictions from model - ``target`` (:class:`~torch.Tensor`): Ground truth values As output of ``forward`` and ``compute`` the metric returns the following output: - ``mean_absolute_error`` (:class:`~torch.Tensor`): A tensor with the mean absolute error over the state Args: num_outputs: Number of outputs in multioutput setting kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import tensor >>> from torchmetrics.regression import MeanAbsoluteError >>> target = tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = tensor([2.5, 0.0, 2.0, 8.0]) >>> mean_absolute_error = MeanAbsoluteError() >>> mean_absolute_error(preds, target) tensor(0.5000) Example:: Multioutput mse computation: >>> from torch import tensor >>> from torchmetrics.regression import MeanAbsoluteError >>> target = tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) >>> preds = tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]) >>> mean_absolute_error = MeanAbsoluteError(num_outputs=3) >>> mean_absolute_error(preds, target) tensor([1., 2., 3.]) """ is_differentiable: bool = True higher_is_better: bool = False full_state_update: bool = False plot_lower_bound: float = 0.0 sum_abs_error: Tensor total: Tensor def __init__( self, num_outputs: int = 1, **kwargs: Any, ) -> None: super().__init__(**kwargs) if not (isinstance(num_outputs, int) and num_outputs > 0): raise ValueError(f"Expected num_outputs to be a positive integer but got {num_outputs}") self.num_outputs = num_outputs self.add_state("sum_abs_error", default=torch.zeros(num_outputs), dist_reduce_fx="sum") self.add_state("total", default=tensor(0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" sum_abs_error, num_obs = _mean_absolute_error_update(preds, target, num_outputs=self.num_outputs) self.sum_abs_error += sum_abs_error self.total += num_obs def compute(self) -> Tensor: """Compute mean absolute error over state.""" return _mean_absolute_error_compute(self.sum_abs_error, self.total) def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import MeanAbsoluteError >>> metric = MeanAbsoluteError() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import MeanAbsoluteError >>> metric = MeanAbsoluteError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) """ return self._plot(val, ax)