o .wip@sddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZmZes9dgZGd d d e Zd S) )Sequence)AnyOptionalUnion)Tensortensor)_mean_squared_log_error_compute_mean_squared_log_error_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEMeanSquaredLogError.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <d e d d ffdd Z de de d d fddZd e fddZ ddeee ee fdeed efddZZS)MeanSquaredLogErroraCompute `mean squared logarithmic error`_ (MSLE). .. math:: \text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2 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_squared_log_error`` (:class:`~torch.Tensor`): A tensor with the mean squared log error Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import tensor >>> from torchmetrics.regression import MeanSquaredLogError >>> target = tensor([2.5, 5, 4, 8]) >>> preds = tensor([3, 5, 2.5, 7]) >>> mean_squared_log_error = MeanSquaredLogError() >>> mean_squared_log_error(preds, target) tensor(0.0397) .. attention:: Half precision is only support on GPU for this metric. Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_boundsum_squared_log_errortotalkwargsreturnNc s>tjdi||jdtddd|jdtddddS)Nrrsum)defaultdist_reduce_fxrr)super__init__ add_stater)selfr __class__r\/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/regression/log_mse.pyrEszMeanSquaredLogError.__init__predstargetcCs.t||\}}|j|7_|j|7_dS)z*Update state with predictions and targets.N)r rr)r r$r%rnum_obsrrr#updateNszMeanSquaredLogError.updatecCst|j|jS)z2Compute mean squared logarithmic error over state.)rrr)r rrr#computeUszMeanSquaredLogError.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 MeanSquaredLogError >>> metric = MeanSquaredLogError() >>> 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 MeanSquaredLogError >>> metric = MeanSquaredLogError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r r)r*rrr#plotYs (r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr'r(rrrr r r, __classcell__rrr!r#rs.     rN)collections.abcrtypingrrrtorchrr*torchmetrics.functional.regression.log_mserr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrrrr#s