o .wi$@sddlmZddlmZmZmZddlmZmZddl m Z ddl m Z m Z mZddlmZddlmZddlmZmZesAd gZGd d d eZd S) )Sequence)AnyOptionalUnion)Tensortensor)Literal)ALLOWED_MULTIOUTPUT_explained_variance_compute_explained_variance_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEExplainedVariance.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <e ed <e ed<e ed< d"de ddeddffdd Zde de ddfddZdee ee ffddZ d#deee ee fdeedefd d!ZZS)$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_boundnum_obs sum_errorsum_squared_error sum_targetsum_squared_targetuniform_average multioutput) raw_valuesrvariance_weightedkwargsreturnNc stjd i||tvrtdt||_|jdtddd|jdtddd|jdtddd|jdtddd|jd tddddS) NzFInvalid input to argument `multioutput`. Choose one of the following: rrsum)defaultdist_reduce_fxrrrr)super__init__r ValueErrorr add_stater)selfrr! __class__r&g/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/regression/explained_variance.pyr(bszExplainedVariance.__init__predstargetcCsTt||\}}}}}|j||_|j||_|j||_|j||_|j||_dS)z*Update state with predictions and targets.N)r rrrrr)r+r/r0rrrrrr&r&r.updatets    zExplainedVariance.updatecCst|j|j|j|j|j|jS)z&Compute explained variance over state.)r rrrrrr)r+r&r&r.computeszExplainedVariance.computevalaxcCs |||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) )_plot)r+r3r4r&r&r.plots (r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrr(r1rrr2rrrr6 __classcell__r&r&r,r.r!s<  4      rN)collections.abcrtypingrrrtorchrrtyping_extensionsr5torchmetrics.functional.regression.explained_variancer r r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr&r&r&r.s