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| 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) |
|
|
