a  d@sddlmZmZmZmZddlmZmZddlm Z ddl m Z m Z m Z ddlmZddlmZddlmZmZeszdgZGd d d eZd S) )AnyOptionalSequenceUnion)Tensortensor)Literal)ALLOWED_MULTIOUTPUT_explained_variance_compute_explained_variance_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEExplainedVariance.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <e ed <e ed<e ed<de deddfdd Ze e ddddZee ee fdddZd eee ee feeedddZZS)!ExplainedVarianceaCompute `explained variance`_. .. math:: \text{ExplainedVariance} = 1 - \frac{\text{Var}(y - \hat{y})}{\text{Var}(y)} 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 in float tensor with shape ``(N,)`` or ``(N, ...)`` (multioutput) - ``target`` (:class:`~torch.Tensor`): Ground truth values in long tensor with shape ``(N,)`` or ``(N, ...)`` (multioutput) As output of ``forward`` and ``compute`` the metric returns the following output: - ``explained_variance`` (:class:`~torch.Tensor`): A tensor with the explained variance(s) In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions. Please see argument ``multioutput`` for changing this behavior. Args: multioutput: Defines aggregation in the case of multiple output scores. Can be one of the following strings (default is ``'uniform_average'``.): * ``'raw_values'`` returns full set of scores * ``'uniform_average'`` scores are uniformly averaged * ``'variance_weighted'`` scores are weighted by their individual variances kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``. Example: >>> from torch import tensor >>> from torchmetrics.regression import ExplainedVariance >>> target = tensor([3, -0.5, 2, 7]) >>> preds = tensor([2.5, 0.0, 2, 8]) >>> explained_variance = ExplainedVariance() >>> explained_variance(preds, target) tensor(0.9572) >>> target = tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = tensor([[0, 2], [-1, 2], [8, -5]]) >>> explained_variance = ExplainedVariance(multioutput='raw_values') >>> explained_variance(preds, target) tensor([0.9677, 1.0000]) Tis_differentiablehigher_is_betterFfull_state_updateplot_lower_boundg?plot_upper_boundn_obs sum_errorsum_squared_error sum_targetsum_squared_targetuniform_average)Z raw_valuesrZvariance_weightedN) multioutputkwargsreturnc stjfi||tvr(tdt||_|jdtddd|jdtddd|jdtddd|jdtddd|jd tddddS) NzFInvalid input to argument `multioutput`. Choose one of the following: rrsum)defaultZdist_reduce_fxrrrr)super__init__r ValueErrorrZ add_stater)selfrr __class__s/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/explained_variance.pyr$`szExplainedVariance.__init__)predstargetr cCsTt||\}}}}}|j||_|j||_|j||_|j||_|j||_dS)z*Update state with predictions and targets.N)r rrrrr)r&r+r,rrrrrr)r)r*updaters     zExplainedVariance.update)r cCst|j|j|j|j|j|jS)z&Compute explained variance over state.)r rrrrrr)r&r)r)r*compute{szExplainedVariance.compute)valaxr cCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import ExplainedVariance >>> metric = ExplainedVariance() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import ExplainedVariance >>> metric = ExplainedVariance() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )Z_plot)r&r/r0r)r)r*plots(r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrr$r-rrr.rrrr1 __classcell__r)r)r'r*r s0 3       rN)typingrrrrZtorchrrZtyping_extensionsrZ5torchmetrics.functional.regression.explained_variancer r r Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotrrZ__doctest_skip__rr)r)r)r*s