# 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. import warnings from typing import List, Optional, Union import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.functional.classification.precision_recall_curve import ( _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update, ) from torchmetrics.functional.classification.roc import ( _binary_roc_compute, _multiclass_roc_compute, _multilabel_roc_compute, ) from torchmetrics.utilities.enums import ClassificationTask def _convert_fpr_to_specificity(fpr: Tensor) -> Tensor: """Convert fprs to specificity.""" return 1 - fpr def _specificity_at_sensitivity( specificity: Tensor, sensitivity: Tensor, thresholds: Tensor, min_sensitivity: float, ) -> tuple[Tensor, Tensor]: # get indices where sensitivity is greater than min_sensitivity indices = sensitivity >= min_sensitivity # if no indices are found, max_spec, best_threshold = 0.0, 1e6 if not indices.any(): max_spec = torch.tensor(0.0, device=specificity.device, dtype=specificity.dtype) best_threshold = torch.tensor(1e6, device=thresholds.device, dtype=thresholds.dtype) else: # redefine specificity, sensitivity and threshold tensor based on indices specificity, sensitivity, thresholds = specificity[indices], sensitivity[indices], thresholds[indices] # get argmax idx = torch.argmax(specificity) # get max_spec and best_threshold max_spec, best_threshold = specificity[idx], thresholds[idx] return max_spec, best_threshold def _binary_specificity_at_sensitivity_arg_validation( min_sensitivity: float, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, ) -> None: _binary_precision_recall_curve_arg_validation(thresholds, ignore_index) if not isinstance(min_sensitivity, float) and not (0 <= min_sensitivity <= 1): raise ValueError( f"Expected argument `min_sensitivity` to be an float in the [0,1] range, but got {min_sensitivity}" ) def _binary_specificity_at_sensitivity_compute( state: Union[Tensor, tuple[Tensor, Tensor]], thresholds: Optional[Tensor], min_sensitivity: float, pos_label: int = 1, ) -> tuple[Tensor, Tensor]: fpr, sensitivity, thresholds = _binary_roc_compute(state, thresholds, pos_label) specificity = _convert_fpr_to_specificity(fpr) return _specificity_at_sensitivity(specificity, sensitivity, thresholds, min_sensitivity) def binary_specificity_at_sensitivity( preds: Tensor, target: Tensor, min_sensitivity: float, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, validate_args: bool = True, ) -> tuple[Tensor, Tensor]: r"""Compute the highest possible specificity value given the minimum sensitivity levels provided for binary tasks. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). Additional dimension ``...`` will be flattened into the batch dimension. 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). Args: preds: Tensor with predictions target: Tensor with true labels min_sensitivity: float value specifying minimum sensitivity threshold. 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. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of 2 tensors containing: - specificity: a scalar tensor with the maximum specificity for the given sensitivity level - threshold: a scalar tensor with the corresponding threshold level Example: >>> from torchmetrics.functional.classification import binary_specificity_at_sensitivity >>> preds = torch.tensor([0, 0.5, 0.4, 0.1]) >>> target = torch.tensor([0, 1, 1, 1]) >>> binary_specificity_at_sensitivity(preds, target, min_sensitivity=0.5, thresholds=None) (tensor(1.), tensor(0.4000)) >>> binary_specificity_at_sensitivity(preds, target, min_sensitivity=0.5, thresholds=5) (tensor(1.), tensor(0.2500)) """ if validate_args: _binary_specificity_at_sensitivity_arg_validation(min_sensitivity, thresholds, ignore_index) _binary_precision_recall_curve_tensor_validation(preds, target, ignore_index) preds, target, thresholds = _binary_precision_recall_curve_format(preds, target, thresholds, ignore_index) state = _binary_precision_recall_curve_update(preds, target, thresholds) return _binary_specificity_at_sensitivity_compute(state, thresholds, min_sensitivity) def _multiclass_specificity_at_sensitivity_arg_validation( num_classes: int, min_sensitivity: float, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, ) -> None: _multiclass_precision_recall_curve_arg_validation(num_classes, thresholds, ignore_index) if not isinstance(min_sensitivity, float) and not (0 <= min_sensitivity <= 1): raise ValueError( f"Expected argument `min_sensitivity` to be an float in the [0,1] range, but got {min_sensitivity}" ) def _multiclass_specificity_at_sensitivity_compute( state: Union[Tensor, tuple[Tensor, Tensor]], num_classes: int, thresholds: Optional[Tensor], min_sensitivity: float, ) -> tuple[Tensor, Tensor]: fpr, sensitivity, thresholds = _multiclass_roc_compute(state, num_classes, thresholds) specificity = [_convert_fpr_to_specificity(fpr_) for fpr_ in fpr] if isinstance(state, Tensor): res = [ _specificity_at_sensitivity(sp, sn, thresholds, min_sensitivity) # type: ignore for sp, sn in zip(specificity, sensitivity) ] else: res = [ _specificity_at_sensitivity(sp, sn, t, min_sensitivity) for sp, sn, t in zip(specificity, sensitivity, thresholds) ] specificity = torch.stack([r[0] for r in res]) thresholds = torch.stack([r[1] for r in res]) return specificity, thresholds def multiclass_specificity_at_sensitivity( preds: Tensor, target: Tensor, num_classes: int, min_sensitivity: float, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, validate_args: bool = True, ) -> tuple[Tensor, Tensor]: r"""Compute the highest possible specificity value given minimum sensitivity level provided for multiclass tasks. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(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). Additional dimension ``...`` will be flattened into the batch dimension. 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). Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifying the number of classes min_sensitivity: float value specifying minimum sensitivity threshold. 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. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of either 2 tensors or 2 lists containing - recall: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class Example: >>> from torchmetrics.functional.classification import multiclass_specificity_at_sensitivity >>> 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]) >>> multiclass_specificity_at_sensitivity(preds, target, num_classes=5, min_sensitivity=0.5, thresholds=None) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06])) >>> multiclass_specificity_at_sensitivity(preds, target, num_classes=5, min_sensitivity=0.5, thresholds=5) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06])) """ if validate_args: _multiclass_specificity_at_sensitivity_arg_validation(num_classes, min_sensitivity, thresholds, ignore_index) _multiclass_precision_recall_curve_tensor_validation(preds, target, num_classes, ignore_index) preds, target, thresholds = _multiclass_precision_recall_curve_format( preds, target, num_classes, thresholds, ignore_index ) state = _multiclass_precision_recall_curve_update(preds, target, num_classes, thresholds) return _multiclass_specificity_at_sensitivity_compute(state, num_classes, thresholds, min_sensitivity) def _multilabel_specificity_at_sensitivity_arg_validation( num_labels: int, min_sensitivity: float, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, ) -> None: _multilabel_precision_recall_curve_arg_validation(num_labels, thresholds, ignore_index) if not isinstance(min_sensitivity, float) and not (0 <= min_sensitivity <= 1): raise ValueError( f"Expected argument `min_sensitivity` to be an float in the [0,1] range, but got {min_sensitivity}" ) def _multilabel_specificity_at_sensitivity_compute( state: Union[Tensor, tuple[Tensor, Tensor]], num_labels: int, thresholds: Optional[Tensor], ignore_index: Optional[int], min_sensitivity: float, ) -> tuple[Tensor, Tensor]: fpr, sensitivity, thresholds = _multilabel_roc_compute(state, num_labels, thresholds, ignore_index) specificity = [_convert_fpr_to_specificity(fpr_) for fpr_ in fpr] if isinstance(state, Tensor): res = [ _specificity_at_sensitivity(sp, sn, thresholds, min_sensitivity) # type: ignore for sp, sn in zip(specificity, sensitivity) ] else: res = [ _specificity_at_sensitivity(sp, sn, t, min_sensitivity) for sp, sn, t in zip(specificity, sensitivity, thresholds) ] specificity = torch.stack([r[0] for r in res]) thresholds = torch.stack([r[1] for r in res]) return specificity, thresholds def multilabel_specificity_at_sensitivity( preds: Tensor, target: Tensor, num_labels: int, min_sensitivity: float, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, validate_args: bool = True, ) -> tuple[Tensor, Tensor]: r"""Compute the highest possible specificity value given minimum sensitivity level provided for multilabel tasks. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). Additional dimension ``...`` will be flattened into the batch dimension. 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). Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifying the number of labels min_sensitivity: float value specifying minimum sensitivity threshold. 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. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of either 2 tensors or 2 lists containing - specificity: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class Example: >>> from torchmetrics.functional.classification import multilabel_specificity_at_sensitivity >>> 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]]) >>> multilabel_specificity_at_sensitivity(preds, target, num_labels=3, min_sensitivity=0.5, thresholds=None) (tensor([1.0000, 0.5000, 1.0000]), tensor([0.7500, 0.6500, 0.3500])) >>> multilabel_specificity_at_sensitivity(preds, target, num_labels=3, min_sensitivity=0.5, thresholds=5) (tensor([1.0000, 0.5000, 1.0000]), tensor([0.7500, 0.5000, 0.2500])) """ if validate_args: _multilabel_specificity_at_sensitivity_arg_validation(num_labels, min_sensitivity, thresholds, ignore_index) _multilabel_precision_recall_curve_tensor_validation(preds, target, num_labels, ignore_index) preds, target, thresholds = _multilabel_precision_recall_curve_format( preds, target, num_labels, thresholds, ignore_index ) state = _multilabel_precision_recall_curve_update(preds, target, num_labels, thresholds) return _multilabel_specificity_at_sensitivity_compute(state, num_labels, thresholds, ignore_index, min_sensitivity) def specicity_at_sensitivity( preds: Tensor, target: Tensor, task: Literal["binary", "multiclass", "multilabel"], min_sensitivity: float, 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, ) -> Union[Tensor, tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]: r"""Compute the highest possible specificity value given the minimum sensitivity thresholds provided. .. warning:: This function was deprecated in v1.3.0 of Torchmetrics and will be removed in v2.0.0. Use `specificity_at_sensitivity` instead. """ warnings.warn( "This method has will be removed in 2.0.0. Use `specificity_at_sensitivity` instead.", DeprecationWarning, stacklevel=1, ) return specificity_at_sensitivity( preds=preds, target=target, task=task, min_sensitivity=min_sensitivity, thresholds=thresholds, num_classes=num_classes, num_labels=num_labels, ignore_index=ignore_index, validate_args=validate_args, ) def specificity_at_sensitivity( preds: Tensor, target: Tensor, task: Literal["binary", "multiclass", "multilabel"], min_sensitivity: float, 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, ) -> Union[Tensor, tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]: r"""Compute the highest possible specificity value given the minimum sensitivity thresholds provided. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. 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 :func:`~torchmetrics.functional.classification.binary_specificity_at_sensitivity`, :func:`~torchmetrics.functional.classification.multiclass_specificity_at_sensitivity` and :func:`~torchmetrics.functional.classification.multilabel_specificity_at_sensitivity` for the specific details of each argument influence and examples. """ task = ClassificationTask.from_str(task) if task == ClassificationTask.BINARY: return binary_specificity_at_sensitivity( # type: ignore preds, target, min_sensitivity, thresholds, ignore_index, validate_args ) if task == ClassificationTask.MULTICLASS: if not isinstance(num_classes, int): raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`") return multiclass_specificity_at_sensitivity( # type: ignore preds, target, num_classes, min_sensitivity, thresholds, ignore_index, validate_args ) if task == ClassificationTask.MULTILABEL: if not isinstance(num_labels, int): raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`") return multilabel_specificity_at_sensitivity( # type: ignore preds, target, num_labels, min_sensitivity, thresholds, ignore_index, validate_args ) raise ValueError(f"Not handled value: {task}")