# 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 import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.functional.nominal.theils_u import _theils_u_compute, _theils_u_update from torchmetrics.functional.nominal.utils import _nominal_input_validation 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__ = ["TheilsU.plot"] class TheilsU(Metric): r"""Compute `Theil's U`_ statistic measuring the association between two categorical (nominal) data series. .. math:: U(X|Y) = \frac{H(X) - H(X|Y)}{H(X)} where :math:`H(X)` is entropy of variable :math:`X` while :math:`H(X|Y)` is the conditional entropy of :math:`X` given :math:`Y`. It is also know as the Uncertainty Coefficient. Theils's U is an asymmetric coefficient, i.e. :math:`TheilsU(preds, target) \neq TheilsU(target, preds)`, so the order of the inputs matters. The output values lies in [0, 1], where a 0 means y has no information about x while value 1 means y has complete information about x. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the first data series (called X in the above definition) with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively. - ``target`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the second data series (called Y in the above definition) with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively. As output of ``forward`` and ``compute`` the metric returns the following output: - ``theils_u`` (:class:`~torch.Tensor`): Scalar tensor containing the Theil's U statistic. Args: num_classes: Integer specifying the number of classes nan_strategy: Indication of whether to replace or drop ``NaN`` values nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'`` kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example:: >>> from torch import randint >>> from torchmetrics.nominal import TheilsU >>> preds = randint(10, (10,)) >>> target = randint(10, (10,)) >>> metric = TheilsU(num_classes=10) >>> metric(preds, target) tensor(0.8530) """ full_state_update: bool = False is_differentiable: bool = False higher_is_better: bool = True plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 confmat: Tensor def __init__( self, num_classes: int, nan_strategy: Literal["replace", "drop"] = "replace", nan_replace_value: Optional[float] = 0.0, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.num_classes = num_classes _nominal_input_validation(nan_strategy, nan_replace_value) self.nan_strategy = nan_strategy self.nan_replace_value = nan_replace_value self.add_state("confmat", torch.zeros(num_classes, num_classes), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" confmat = _theils_u_update(preds, target, self.num_classes, self.nan_strategy, self.nan_replace_value) self.confmat += confmat def compute(self) -> Tensor: """Compute Theil's U statistic.""" return _theils_u_compute(self.confmat) def plot(self, val: Union[Tensor, Sequence[Tensor], None] = 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 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.nominal import TheilsU >>> metric = TheilsU(num_classes=10) >>> metric.update(torch.randint(10, (10,)), torch.randint(10, (10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.nominal import TheilsU >>> metric = TheilsU(num_classes=10) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(10, (10,)), torch.randint(10, (10,)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)