i$ddlmZddlmZmZmZddlmZmZddl m Z ddl m Z m Z mZddlmZddlmZddlmZmZesd gZGd d eZy ) )Sequence)AnyOptionalUnion)Tensortensor)Literal)ALLOWED_MULTIOUTPUT_explained_variance_compute_explained_variance_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzExplainedVariance.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <e ed <e ed<e ed< dde ddeddffd Zde de ddfdZdee ee ffdZ ddeee ee fdeedefdZxZS)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_target multioutput) raw_valuesuniform_averagevariance_weightedkwargsreturnNc t|d i||tvrtdt||_|j dt dd|j dt dd|j dt dd|j dt dd|j d t ddy) NzFInvalid input to argument `multioutput`. Choose one of the following: rrsum)defaultdist_reduce_fxrrrr)super__init__r ValueErrorr add_stater)selfrr" __class__s /Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/regression/explained_variance.pyr*zExplainedVariance.__init__bs "6" 1 1XYlXmn ' {F3KN *F3KPUV |VC[O +VC[QVW y&+eLpredstargetct||\}}}}}|j|z|_|j|z|_|j|z|_|j|z|_|j |z|_y)z*Update state with predictions and targets.N)r rrrrr)r-r1r2rrrrrs r/updatezExplainedVariance.updatetsyPj 6Q M-z;M||g- )3!%!7!7:K!K//J6"&"9"9>> 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-r7r8s r/plotzExplainedVariance.plotsPzz#r""r0)r )NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr rr*r4rrr6rrrr; __classcell__)r.s@r/rr!s2h#t"!d!#t#!e!!e! OVgMQRMM  M$ OF OF Ot O  vx'778  _c(#E&(6*:":;<(#IQRZI[(# (#r0rN)collections.abcrtypingrrrtorchrrtyping_extensionsr 5torchmetrics.functional.regression.explained_variancer r r torchmetrics.metricr torchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr(r0r/rMsE%'' % '@@01Q#Q#r0