# Copyright The Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections.abc import Sequence from typing import Any, Optional, Union from torch import Tensor, tensor from typing_extensions import Literal from torchmetrics.functional.regression.explained_variance import ( ALLOWED_MULTIOUTPUT, _explained_variance_compute, _explained_variance_update, ) from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["ExplainedVariance.plot"] class ExplainedVariance(Metric): r"""Compute `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]) """ is_differentiable: bool = True higher_is_better: bool = True full_state_update: bool = False plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 num_obs: Tensor sum_error: Tensor sum_squared_error: Tensor sum_target: Tensor sum_squared_target: Tensor def __init__( self, multioutput: Literal["raw_values", "uniform_average", "variance_weighted"] = "uniform_average", **kwargs: Any, ) -> None: super().__init__(**kwargs) if multioutput not in ALLOWED_MULTIOUTPUT: raise ValueError( f"Invalid input to argument `multioutput`. Choose one of the following: {ALLOWED_MULTIOUTPUT}" ) self.multioutput = multioutput self.add_state("sum_error", default=tensor(0.0), dist_reduce_fx="sum") self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum") self.add_state("sum_target", default=tensor(0.0), dist_reduce_fx="sum") self.add_state("sum_squared_target", default=tensor(0.0), dist_reduce_fx="sum") self.add_state("num_obs", default=tensor(0.0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" num_obs, sum_error, sum_squared_error, sum_target, sum_squared_target = _explained_variance_update( preds, target ) self.num_obs = self.num_obs + num_obs self.sum_error = self.sum_error + sum_error self.sum_squared_error = self.sum_squared_error + sum_squared_error self.sum_target = self.sum_target + sum_target self.sum_squared_target = self.sum_squared_target + sum_squared_target def compute(self) -> Union[Tensor, Sequence[Tensor]]: """Compute explained variance over state.""" return _explained_variance_compute( self.num_obs, self.sum_error, self.sum_squared_error, self.sum_target, self.sum_squared_target, self.multioutput, ) def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """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 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) """ return self._plot(val, ax)