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precision_recall_curve.py
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# 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, Tuple, 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.
.. note::
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.
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 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, 0.0000, 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,
**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
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)
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.
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()
score = (
_auc_compute_without_check(curve_computed[0], curve_computed[1], 1.0)
if not curve and score is True
else None
)
return plot_curve(
curve_computed, score=score, ax=ax, label_names=("Precision", "Recall"), 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).
.. note::
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.
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, 0.0000, 1.0000],
[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
[0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
[0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 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.],
[0., 0., 0., 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,
num_classes: 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:
_multiclass_precision_recall_curve_arg_validation(num_classes, thresholds, ignore_index)
self.num_classes = num_classes
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
)
state = _multiclass_precision_recall_curve_update(preds, target, self.num_classes, 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 _multiclass_precision_recall_curve_compute(state, self.num_classes, self.thresholds)
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.
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()
score = (
_reduce_auroc(curve_computed[0], curve_computed[1], average=None) if not curve and score is True else None
)
return plot_curve(
curve_computed, score=score, ax=ax, label_names=("Precision", "Recall"), 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).
.. note::
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.
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, 0.0000, 1.0000],
[0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000],
[0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 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.
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()
score = (
_reduce_auroc(curve_computed[0], curve_computed[1], average=None) if not curve and score is True else None
)
return plot_curve(
curve_computed, score=score, ax=ax, label_names=("Precision", "Recall"), 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,
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!")