# 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 typing import Any, List, Optional, Union import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.classification.base import _ClassificationTaskWrapper from torchmetrics.functional.classification.auroc import _reduce_auroc from torchmetrics.functional.classification.precision_recall_curve import ( _adjust_threshold_arg, _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_compute, _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_compute, _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_compute, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update, ) from torchmetrics.metric import Metric from torchmetrics.utilities.compute import _auc_compute_without_check from torchmetrics.utilities.data import dim_zero_cat from torchmetrics.utilities.enums import ClassificationTask from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_curve if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = [ "BinaryPrecisionRecallCurve.plot", "MulticlassPrecisionRecallCurve.plot", "MultilabelPrecisionRecallCurve.plot", ] class BinaryPrecisionRecallCurve(Metric): r"""Compute the precision-recall curve for binary tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been 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. .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``precision`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds+1, )`` with precision 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 precision values is returned. - ``recall`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds+1, )`` with recall 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 recall 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 increasing 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})` (constant memory). 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. 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. normalization: Specifies a normalization method that is used for batch-wise update regarding negative logits. Set to ``None`` if negative logits are desired in evaluation. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import BinaryPrecisionRecallCurve >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> bprc = BinaryPrecisionRecallCurve(thresholds=None) >>> bprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]), tensor([0.0000, 0.5000, 0.7000, 0.8000])) >>> bprc = BinaryPrecisionRecallCurve(thresholds=5) >>> bprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([0.5000, 0.6667, 0.6667, 0.0000, nan, 1.0000]), tensor([1., 1., 1., 0., 0., 0.]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) """ is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False preds: List[Tensor] target: List[Tensor] confmat: Tensor def __init__( self, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, validate_args: bool = True, normalization: Optional[Literal["sigmoid", "softmax"]] = "sigmoid", **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _binary_precision_recall_curve_arg_validation(thresholds, ignore_index) self.ignore_index = ignore_index self.validate_args = validate_args self.normalization = normalization thresholds = _adjust_threshold_arg(thresholds) if thresholds is None: self.thresholds = thresholds self.add_state("preds", default=[], dist_reduce_fx="cat") self.add_state("target", default=[], dist_reduce_fx="cat") else: self.register_buffer("thresholds", thresholds, persistent=False) self.add_state( "confmat", default=torch.zeros(len(thresholds), 2, 2, dtype=torch.long), dist_reduce_fx="sum" ) def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states.""" if self.validate_args: _binary_precision_recall_curve_tensor_validation(preds, target, self.ignore_index) preds, target, _ = _binary_precision_recall_curve_format( preds, target, self.thresholds, self.ignore_index, self.normalization, ) state = _binary_precision_recall_curve_update(preds, target, self.thresholds) if isinstance(state, Tensor): self.confmat += state else: self.preds.append(state[0]) self.target.append(state[1]) def compute(self) -> tuple[Tensor, Tensor, Tensor]: """Compute metric.""" state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat return _binary_precision_recall_curve_compute(state, self.thresholds) def plot( self, curve: Optional[tuple[Tensor, Tensor, Tensor]] = None, score: Optional[Union[Tensor, bool]] = None, ax: Optional[_AX_TYPE] = None, ) -> _PLOT_OUT_TYPE: """Plot a single curve from the metric. Args: curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will automatically call `metric.compute` and plot that result. score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided, will automatically compute the score. The score is computed by using the trapezoidal rule to compute the area under the curve. 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 rand, randint >>> from torchmetrics.classification import BinaryPrecisionRecallCurve >>> preds = rand(20) >>> target = randint(2, (20,)) >>> metric = BinaryPrecisionRecallCurve() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot(score=True) """ curve_computed = curve or self.compute() # switch order as the standard way is recall along x-axis and precision along y-axis curve_computed = (curve_computed[1], curve_computed[0], curve_computed[2]) score = ( _auc_compute_without_check(curve_computed[0], curve_computed[1], direction=-1.0) if not curve and score is True else None ) return plot_curve( curve_computed, score=score, ax=ax, label_names=("Recall", "Precision"), name=self.__class__.__name__ ) class MulticlassPrecisionRecallCurve(Metric): r"""Compute the precision-recall curve for multiclass tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen. For multiclass the metric is calculated by iteratively treating each class as the positive class and all other classes as the negative, which is referred to as the one-vs-rest approach. One-vs-one is currently not supported by this metric. 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). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with precision values - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with recall values - ``thresholds`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds, )`` with increasing 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} \times n_{classes})` (constant memory). Args: num_classes: Integer specifying 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 a 1D `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. average: If aggregation of curves should be applied. By default, the curves are not aggregated and a curve for each class is returned. If `average` is set to ``"micro"``, the metric will aggregate the curves by one hot encoding the targets and flattening the predictions, considering all classes jointly as a binary problem. If `average` is set to ``"macro"``, the metric will aggregate the curves by first interpolating the curves from each class at a combined set of thresholds and then average over the classwise interpolated curves. See `averaging curve objects`_ for more info on the different averaging methods. 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. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import MulticlassPrecisionRecallCurve >>> 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]) >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=None) >>> precision, recall, thresholds = mcprc(preds, target) >>> precision # doctest: +NORMALIZE_WHITESPACE [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor(0.0500)] >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=5) >>> mcprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([[0.2500, 1.0000, 1.0000, 1.0000, nan, 1.0000], [0.2500, 1.0000, 1.0000, 1.0000, nan, 1.0000], [0.2500, 0.0000, 0.0000, 0.0000, nan, 1.0000], [0.2500, 0.0000, 0.0000, 0.0000, nan, 1.0000], [0.0000, nan, nan, nan, nan, 1.0000]]), tensor([[1., 1., 1., 1., 0., 0.], [1., 1., 1., 1., 0., 0.], [1., 0., 0., 0., 0., 0.], [1., 0., 0., 0., 0., 0.], [nan, nan, nan, nan, nan, 0.]]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) """ is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False preds: List[Tensor] target: List[Tensor] confmat: Tensor def __init__( self, num_classes: int, thresholds: Optional[Union[int, list[float], Tensor]] = None, average: Optional[Literal["micro", "macro"]] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _multiclass_precision_recall_curve_arg_validation(num_classes, thresholds, ignore_index, average) self.num_classes = num_classes self.average = average self.ignore_index = ignore_index self.validate_args = validate_args thresholds = _adjust_threshold_arg(thresholds) if thresholds is None: self.thresholds = thresholds self.add_state("preds", default=[], dist_reduce_fx="cat") self.add_state("target", default=[], dist_reduce_fx="cat") else: self.register_buffer("thresholds", thresholds, persistent=False) self.add_state( "confmat", default=torch.zeros(len(thresholds), num_classes, 2, 2, dtype=torch.long), dist_reduce_fx="sum", ) def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states.""" if self.validate_args: _multiclass_precision_recall_curve_tensor_validation(preds, target, self.num_classes, self.ignore_index) preds, target, _ = _multiclass_precision_recall_curve_format( preds, target, self.num_classes, self.thresholds, self.ignore_index, self.average ) state = _multiclass_precision_recall_curve_update( preds, target, self.num_classes, self.thresholds, self.average ) if isinstance(state, Tensor): self.confmat += state else: self.preds.append(state[0]) self.target.append(state[1]) def compute(self) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]: """Compute metric.""" state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat return _multiclass_precision_recall_curve_compute(state, self.num_classes, self.thresholds, self.average) def plot( self, curve: Optional[Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]] = None, score: Optional[Union[Tensor, bool]] = None, ax: Optional[_AX_TYPE] = None, ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will automatically call `metric.compute` and plot that result. score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided, will automatically compute the score. The score is computed by using the trapezoidal rule to compute the area under the curve. 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, randint >>> from torchmetrics.classification import MulticlassPrecisionRecallCurve >>> preds = randn(20, 3).softmax(dim=-1) >>> target = randint(3, (20,)) >>> metric = MulticlassPrecisionRecallCurve(num_classes=3) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot(score=True) """ curve_computed = curve or self.compute() # switch order as the standard way is recall along x-axis and precision along y-axis curve_computed = (curve_computed[1], curve_computed[0], curve_computed[2]) score = ( _reduce_auroc(curve_computed[0], curve_computed[1], average=None, direction=-1.0) if not curve and score is True else None ) return plot_curve( curve_computed, score=score, ax=ax, label_names=("Recall", "Precision"), name=self.__class__.__name__ ) class MultilabelPrecisionRecallCurve(Metric): r"""Compute the precision-recall curve for multilabel tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been 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). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following a tuple of either 3 tensors or 3 lists containing: - ``precision`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds+1, )`` with precision 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 precision values is returned. - ``recall`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds+1, )`` with recall 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 recall values is returned. - ``thresholds`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds, )`` with increasing 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). Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifying 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. 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. Example: >>> from torchmetrics.classification import MultilabelPrecisionRecallCurve >>> 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]]) >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=None) >>> precision, recall, thresholds = mlprc(preds, target) >>> precision # doctest: +NORMALIZE_WHITESPACE [tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]), tensor([0.7500, 1.0000, 1.0000, 1.0000])] >>> recall # doctest: +NORMALIZE_WHITESPACE [tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]), tensor([1.0000, 0.6667, 0.3333, 0.0000])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([0.0500, 0.4500, 0.7500]), tensor([0.0500, 0.5500, 0.6500, 0.7500]), tensor([0.0500, 0.3500, 0.7500])] >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=5) >>> mlprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([[0.5000, 0.5000, 1.0000, 1.0000, nan, 1.0000], [0.5000, 0.6667, 0.6667, 0.0000, nan, 1.0000], [0.7500, 1.0000, 1.0000, 1.0000, nan, 1.0000]]), tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000], [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000], [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) """ is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False preds: List[Tensor] target: List[Tensor] confmat: Tensor def __init__( self, num_labels: int, thresholds: Optional[Union[int, list[float], Tensor]] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _multilabel_precision_recall_curve_arg_validation(num_labels, thresholds, ignore_index) self.num_labels = num_labels self.ignore_index = ignore_index self.validate_args = validate_args thresholds = _adjust_threshold_arg(thresholds) if thresholds is None: self.thresholds = thresholds self.add_state("preds", default=[], dist_reduce_fx="cat") self.add_state("target", default=[], dist_reduce_fx="cat") else: self.register_buffer("thresholds", thresholds, persistent=False) self.add_state( "confmat", default=torch.zeros(len(thresholds), num_labels, 2, 2, dtype=torch.long), dist_reduce_fx="sum", ) def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states.""" if self.validate_args: _multilabel_precision_recall_curve_tensor_validation(preds, target, self.num_labels, self.ignore_index) preds, target, _ = _multilabel_precision_recall_curve_format( preds, target, self.num_labels, self.thresholds, self.ignore_index ) state = _multilabel_precision_recall_curve_update(preds, target, self.num_labels, self.thresholds) if isinstance(state, Tensor): self.confmat += state else: self.preds.append(state[0]) self.target.append(state[1]) def compute(self) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]: """Compute metric.""" state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat return _multilabel_precision_recall_curve_compute(state, self.num_labels, self.thresholds, self.ignore_index) def plot( self, curve: Optional[Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]] = None, score: Optional[Union[Tensor, bool]] = None, ax: Optional[_AX_TYPE] = None, ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will automatically call `metric.compute` and plot that result. score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided, will automatically compute the score. The score is computed by using the trapezoidal rule to compute the area under the curve. 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 rand, randint >>> from torchmetrics.classification import MultilabelPrecisionRecallCurve >>> preds = rand(20, 3) >>> target = randint(2, (20,3)) >>> metric = MultilabelPrecisionRecallCurve(num_labels=3) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot(score=True) """ curve_computed = curve or self.compute() # switch order as the standard way is recall along x-axis and precision along y-axis curve_computed = (curve_computed[1], curve_computed[0], curve_computed[2]) score = ( _reduce_auroc(curve_computed[0], curve_computed[1], average=None, direction=-1.0) if not curve and score is True else None ) return plot_curve( curve_computed, score=score, ax=ax, label_names=("Recall", "Precision"), name=self.__class__.__name__ ) class PrecisionRecallCurve(_ClassificationTaskWrapper): r"""Compute the precision-recall curve. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been 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 :class:`~torchmetrics.classification.BinaryPrecisionRecallCurve`, :class:`~torchmetrics.classification.MulticlassPrecisionRecallCurve` and :class:`~torchmetrics.classification.MultilabelPrecisionRecallCurve` for the specific details of each argument influence and examples. Legacy Example: >>> pred = torch.tensor([0, 0.1, 0.8, 0.4]) >>> target = torch.tensor([0, 1, 1, 0]) >>> pr_curve = PrecisionRecallCurve(task="binary") >>> precision, recall, thresholds = pr_curve(pred, target) >>> precision tensor([0.5000, 0.6667, 0.5000, 1.0000, 1.0000]) >>> recall tensor([1.0000, 1.0000, 0.5000, 0.5000, 0.0000]) >>> thresholds tensor([0.0000, 0.1000, 0.4000, 0.8000]) >>> pred = 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]) >>> pr_curve = PrecisionRecallCurve(task="multiclass", num_classes=5) >>> precision, recall, thresholds = pr_curve(pred, target) >>> precision [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor(0.0500)] """ def __new__( # type: ignore[misc] cls: type["PrecisionRecallCurve"], 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: """Initialize task metric.""" task = ClassificationTask.from_str(task) kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args}) if task == ClassificationTask.BINARY: return BinaryPrecisionRecallCurve(**kwargs) 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 MulticlassPrecisionRecallCurve(num_classes, **kwargs) 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 MultilabelPrecisionRecallCurve(num_labels, **kwargs) raise ValueError(f"Task {task} not supported!")