# 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.tschuprows import _tschuprows_t_compute, _tschuprows_t_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__ = ["TschuprowsT.plot"] class TschuprowsT(Metric): r"""Compute `Tschuprow's T`_ statistic measuring the association between two categorical (nominal) data series. .. math:: T = \sqrt{\frac{\chi^2 / n}{\sqrt{(r - 1) * (k - 1)}}} where .. math:: \chi^2 = \sum_{i,j} \ frac{\left(n_{ij} - \frac{n_{i.} n_{.j}}{n}\right)^2}{\frac{n_{i.} n_{.j}}{n}} where :math:`n_{ij}` denotes the number of times the values :math:`(A_i, B_j)` are observed with :math:`A_i, B_j` represent frequencies of values in ``preds`` and ``target``, respectively. Tschuprow's T is a symmetric coefficient, i.e. :math:`T(preds, target) = T(target, preds)`, so order of input arguments does not matter. The output values lies in [0, 1] with 1 meaning the perfect association. 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 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 with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively. As output of ``forward`` and ``compute`` the metric returns the following output: - ``tschuprows_t`` (:class:`~torch.Tensor`): Scalar tensor containing the Tschuprow's T statistic. Args: num_classes: Integer specifying the number of classes bias_correction: Indication of whether to use bias correction. 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. Raises: ValueError: If `nan_strategy` is not one of `'replace'` and `'drop'` ValueError: If `nan_strategy` is equal to `'replace'` and `nan_replace_value` is not an `int` or `float` Example:: >>> from torch import randint >>> from torchmetrics.nominal import TschuprowsT >>> preds = randint(0, 4, (100,)) >>> target = (preds + torch.randn(100)).round().clamp(0, 4) >>> tschuprows_t = TschuprowsT(num_classes=5) >>> tschuprows_t(preds, target) tensor(0.4930) """ 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, bias_correction: bool = True, nan_strategy: Literal["replace", "drop"] = "replace", nan_replace_value: Optional[float] = 0.0, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.num_classes = num_classes self.bias_correction = bias_correction _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 = _tschuprows_t_update(preds, target, self.num_classes, self.nan_strategy, self.nan_replace_value) self.confmat += confmat def compute(self) -> Tensor: """Compute Tschuprow's T statistic.""" return _tschuprows_t_compute(self.confmat, self.bias_correction) 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 TschuprowsT >>> metric = TschuprowsT(num_classes=5) >>> metric.update(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.nominal import TschuprowsT >>> metric = TschuprowsT(num_classes=5) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)