iddlmZddlmZmZmZddlZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZmZesd gZGd d e Zy) )Sequence)AnyOptionalUnionN)Tensortensor)_mean_absolute_error_compute_mean_absolute_error_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzMeanAbsoluteError.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed < dd e d e d dffd Zde de d dfdZd e fdZ ddeee ee fdeed efdZxZS)MeanAbsoluteErrora `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.]) Tis_differentiableFhigher_is_betterfull_state_updategplot_lower_bound sum_abs_errortotal num_outputskwargsreturnNc t|di|t|tr|dkDst d|||_|j dtj|d|j dtddy)Nrz6Expected num_outputs to be a positive integer but got rsum)defaultdist_reduce_fxr) super__init__ isinstanceint ValueErrorr add_statetorchzerosr)selfrr __class__s p/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/regression/mae.pyr zMeanAbsoluteError.__init__Ost "6";,qUVaUbcd d&  K0HY^_ wq %Hpredstargetct|||j\}}|xj|z c_|xj|z c_y)z*Update state with predictions and targets.)rN)r rrr)r'r+r,rnum_obss r)updatezMeanAbsoluteError.update]s;!,> KKr*valaxc&|j||S)aPlot 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) )_plot)r'r2r3s r)plotzMeanAbsoluteError.plothsPzz#r""r*))NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrr"rr r/r1rrrr rr6 __classcell__)r(s@r)rrs'R#t""d"#t#!e! M I I I  IFFtLL _c(#E&(6*:":;<(#IQRZI[(# (#r*r)collections.abcrtypingrrrr%rr&torchmetrics.functional.regression.maer r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rrr*r)rGs<%'' l&@@01s#s#r*