# Copyright The PyTorch 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 typing import Any, List, Optional, Tuple, Union import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.classification.precision_recall_curve import ( BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve, ) from torchmetrics.functional.classification.roc import ( _binary_roc_compute, _multiclass_roc_compute, _multilabel_roc_compute, ) from torchmetrics.metric import Metric from torchmetrics.utilities.data import dim_zero_cat class BinaryROC(BinaryPrecisionRecallCurve): r"""Computes the Receiver Operating Characteristic (ROC) for binary tasks. The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at different thresholds, such that the tradeoff between the two values can be seen. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of 3 tensors containing: - ``fpr`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with false positive rate values - ``tpr`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with true positive rate values - ``thresholds`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds, )`` with decreasing threshold values .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds})` (constant memory). .. note:: The outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and tpr which are sorted in reversed order during their calculation, such that they are monotome increasing. Args: thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import BinaryROC >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> metric = BinaryROC(thresholds=None) >>> metric(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]), tensor([0.0000, 0.0000, 0.5000, 1.0000, 1.0000]), tensor([1.0000, 0.8000, 0.7000, 0.5000, 0.0000])) >>> broc = BinaryROC(thresholds=5) >>> broc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]), tensor([0., 0., 1., 1., 1.]), tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000])) """ is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False def compute(self) -> Tuple[Tensor, Tensor, Tensor]: if self.thresholds is None: state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] else: state = self.confmat return _binary_roc_compute(state, self.thresholds) class MulticlassROC(MulticlassPrecisionRecallCurve): r"""Computes the Receiver Operating Characteristic (ROC) for binary tasks. The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at different thresholds, such that the tradeoff between the two values can be seen. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified). .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of either 3 tensors or 3 lists containing - ``fpr`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds+1, )`` with false positive rate values (length may differ between classes). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_classes, n_thresholds+1)`` with false positive rate values is returned. - ``tpr`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds+1, )`` with true positive rate values (length may differ between classes). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_classes, n_thresholds+1)`` with true positive rate values is returned. - ``thresholds`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds, )`` with decreasing threshold values (length may differ between classes). If `threshold` is set to something else, then a single 1d tensor of size ``(n_thresholds, )`` is returned with shared threshold values for all classes. .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory). .. note:: Note that outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and tpr which are sorted in reversed order during their calculation, such that they are monotome increasing. Args: num_classes: Integer specifing the number of classes thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import MulticlassROC >>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.05, 0.75, 0.05]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> metric = MulticlassROC(num_classes=5, thresholds=None) >>> fpr, tpr, thresholds = metric(preds, target) >>> fpr # doctest: +NORMALIZE_WHITESPACE [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000]), tensor([0., 1.])] >>> tpr [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0., 0.])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.0500])] >>> mcroc = MulticlassROC(num_classes=5, thresholds=5) >>> mcroc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([[0.0000, 0.0000, 0.0000, 0.0000, 1.0000], [0.0000, 0.0000, 0.0000, 0.0000, 1.0000], [0.0000, 0.3333, 0.3333, 0.3333, 1.0000], [0.0000, 0.3333, 0.3333, 0.3333, 1.0000], [0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]), tensor([[0., 1., 1., 1., 1.], [0., 1., 1., 1., 1.], [0., 0., 0., 0., 1.], [0., 0., 0., 0., 1.], [0., 0., 0., 0., 0.]]), tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000])) """ is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False def compute(self) -> Tuple[Tensor, Tensor, Tensor]: if self.thresholds is None: state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] else: state = self.confmat return _multiclass_roc_compute(state, self.num_classes, self.thresholds) class MultilabelROC(MultilabelPrecisionRecallCurve): r"""Computes the Receiver Operating Characteristic (ROC) for binary tasks. The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at different thresholds, such that the tradeoff between the two values can be seen. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of either 3 tensors or 3 lists containing - ``fpr`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds+1, )`` with false positive rate values (length may differ between labels). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_labels, n_thresholds+1)`` with false positive rate values is returned. - ``tpr`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds+1, )`` with true positive rate values (length may differ between labels). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_labels, n_thresholds+1)`` with true positive rate values is returned. - ``thresholds`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds, )`` with decreasing threshold values (length may differ between labels). If `threshold` is set to something else, then a single 1d tensor of size ``(n_thresholds, )`` is returned with shared threshold values for all labels. .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory). .. note:: The outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and tpr which are sorted in reversed order during their calculation, such that they are monotome increasing. Args: num_labels: Integer specifing the number of labels thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import MultilabelROC >>> preds = torch.tensor([[0.75, 0.05, 0.35], ... [0.45, 0.75, 0.05], ... [0.05, 0.55, 0.75], ... [0.05, 0.65, 0.05]]) >>> target = torch.tensor([[1, 0, 1], ... [0, 0, 0], ... [0, 1, 1], ... [1, 1, 1]]) >>> metric = MultilabelROC(num_labels=3, thresholds=None) >>> fpr, tpr, thresholds = metric(preds, target) >>> fpr # doctest: +NORMALIZE_WHITESPACE [tensor([0.0000, 0.0000, 0.5000, 1.0000]), tensor([0.0000, 0.5000, 0.5000, 0.5000, 1.0000]), tensor([0., 0., 0., 1.])] >>> tpr # doctest: +NORMALIZE_WHITESPACE [tensor([0.0000, 0.5000, 0.5000, 1.0000]), tensor([0.0000, 0.0000, 0.5000, 1.0000, 1.0000]), tensor([0.0000, 0.3333, 0.6667, 1.0000])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([1.0000, 0.7500, 0.4500, 0.0500]), tensor([1.0000, 0.7500, 0.6500, 0.5500, 0.0500]), tensor([1.0000, 0.7500, 0.3500, 0.0500])] >>> mlroc = MultilabelROC(num_labels=3, thresholds=5) >>> mlroc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([[0.0000, 0.0000, 0.0000, 0.5000, 1.0000], [0.0000, 0.5000, 0.5000, 0.5000, 1.0000], [0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]), tensor([[0.0000, 0.5000, 0.5000, 0.5000, 1.0000], [0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.0000, 0.3333, 0.3333, 0.6667, 1.0000]]), tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000])) """ is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False def compute(self) -> Tuple[Tensor, Tensor, Tensor]: if self.thresholds is None: state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] else: state = self.confmat return _multilabel_roc_compute(state, self.num_labels, self.thresholds, self.ignore_index) class ROC: r"""Computes the Receiver Operating Characteristic (ROC). The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at different thresholds, such that the tradeoff between the two values can be seen. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of :mod:`BinaryROC`, :mod:`MulticlassROC` and :mod:`MultilabelROC` for the specific details of each argument influence and examples. Legacy Example: >>> pred = torch.tensor([0.0, 1.0, 2.0, 3.0]) >>> target = torch.tensor([0, 1, 1, 1]) >>> roc = ROC(task="binary") >>> fpr, tpr, thresholds = roc(pred, target) >>> fpr tensor([0., 0., 0., 0., 1.]) >>> tpr tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000]) >>> thresholds tensor([1.0000, 0.9526, 0.8808, 0.7311, 0.5000]) >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05], ... [0.05, 0.05, 0.05, 0.75]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> roc = ROC(task="multiclass", num_classes=4) >>> fpr, tpr, thresholds = roc(pred, target) >>> fpr [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])] >>> tpr [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500])] >>> pred = torch.tensor([[0.8191, 0.3680, 0.1138], ... [0.3584, 0.7576, 0.1183], ... [0.2286, 0.3468, 0.1338], ... [0.8603, 0.0745, 0.1837]]) >>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]]) >>> roc = ROC(task='multilabel', num_labels=3) >>> fpr, tpr, thresholds = roc(pred, target) >>> fpr [tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]), tensor([0., 0., 0., 1., 1.]), tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])] >>> tpr [tensor([0., 0., 1., 1., 1.]), tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]), tensor([0., 1., 1., 1., 1.])] >>> thresholds [tensor([1.0000, 0.8603, 0.8191, 0.3584, 0.2286]), tensor([1.0000, 0.7576, 0.3680, 0.3468, 0.0745]), tensor([1.0000, 0.1837, 0.1338, 0.1183, 0.1138])] """ def __new__( cls, task: Literal["binary", "multiclass", "multilabel"], thresholds: Optional[Union[int, List[float], Tensor]] = None, num_classes: Optional[int] = None, num_labels: Optional[int] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> Metric: kwargs.update(dict(thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)) if task == "binary": return BinaryROC(**kwargs) if task == "multiclass": assert isinstance(num_classes, int) return MulticlassROC(num_classes, **kwargs) if task == "multilabel": assert isinstance(num_labels, int) return MultilabelROC(num_labels, **kwargs) raise ValueError( f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}" )