a  d@s~ddlmZmZmZmZddlmZmZddlm Z m Z ddl m Z ddl mZddlmZmZesjdgZGdd d e Zd S) )AnyOptionalSequenceUnion)Tensortensor)'_mean_absolute_percentage_error_compute&_mean_absolute_percentage_error_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE MeanAbsolutePercentageError.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <e d d fd d Z e e d dddZe dddZdeee ee feeedddZZS)MeanAbsolutePercentageErroraCompute `Mean Absolute Percentage Error`_ (MAPE). .. math:: \text{MAPE} = \frac{1}{n}\sum_{i=1}^n\frac{| y_i - \hat{y_i} |}{\max(\epsilon, | 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_abs_percentage_error`` (:class:`~torch.Tensor`): A tensor with the mean absolute percentage error over state Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Note: MAPE output is a non-negative floating point. Best result is ``0.0`` . But it is important to note that, bad predictions, can lead to arbitarily large values. Especially when some ``target`` values are close to 0. This `MAPE implementation returns`_ a very large number instead of ``inf``. Example: >>> from torch import tensor >>> from torchmetrics.regression import MeanAbsolutePercentageError >>> target = tensor([1, 10, 1e6]) >>> preds = tensor([0.9, 15, 1.2e6]) >>> mean_abs_percentage_error = MeanAbsolutePercentageError() >>> mean_abs_percentage_error(preds, target) tensor(0.2667) Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_boundsum_abs_per_errortotalN)kwargsreturnc s>tjfi||jdtddd|jdtddddS)Nrrsum)defaultZdist_reduce_fxr)super__init__Z add_stater)selfr __class__e/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/mape.pyrIsz$MeanAbsolutePercentageError.__init__)predstargetrcCs.t||\}}|j|7_|j|7_dS)z*Update state with predictions and targets.N)r rr)rr"r#rZnum_obsr r r!updateRsz"MeanAbsolutePercentageError.update)rcCst|j|jS)z2Compute mean absolute percentage error over state.)rrr)rr r r!computeYsz#MeanAbsolutePercentageError.compute)valaxrcCs |||S)a;Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import MeanAbsolutePercentageError >>> metric = MeanAbsolutePercentageError() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import MeanAbsolutePercentageError >>> metric = MeanAbsolutePercentageError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )Z_plot)rr&r'r r r!plot]s(r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr$r%rrrr r r( __classcell__r r rr!rs" "     rN)typingrrrrZtorchrrZ'torchmetrics.functional.regression.maperr Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr r Z__doctest_skip__rr r r r!s