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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 List, Optional, Union
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.classification.precision_recall_curve import (
_binary_clf_curve,
_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.utilities import rank_zero_warn
from torchmetrics.utilities.compute import _safe_divide, interp
from torchmetrics.utilities.enums import ClassificationTask
def _binary_roc_compute(
state: Union[Tensor, tuple[Tensor, Tensor]],
thresholds: Optional[Tensor],
pos_label: int = 1,
) -> tuple[Tensor, Tensor, Tensor]:
if isinstance(state, Tensor) and thresholds is not None:
tps = state[:, 1, 1]
fps = state[:, 0, 1]
fns = state[:, 1, 0]
tns = state[:, 0, 0]
tpr = _safe_divide(tps, tps + fns).flip(0)
fpr = _safe_divide(fps, fps + tns).flip(0)
thres = thresholds.flip(0)
else:
fps, tps, thres = _binary_clf_curve(preds=state[0], target=state[1], pos_label=pos_label)
# Add an extra threshold position to make sure that the curve starts at (0, 0)
tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps])
fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps])
thres = torch.cat([torch.ones(1, dtype=thres.dtype, device=thres.device), thres])
if fps[-1] <= 0:
rank_zero_warn(
"No negative samples in targets, false positive value should be meaningless."
" Returning zero tensor in false positive score",
UserWarning,
)
fpr = torch.zeros_like(thres)
else:
fpr = fps / fps[-1]
if tps[-1] <= 0:
rank_zero_warn(
"No positive samples in targets, true positive value should be meaningless."
" Returning zero tensor in true positive score",
UserWarning,
)
tpr = torch.zeros_like(thres)
else:
tpr = tps / tps[-1]
return fpr, tpr, thres
def binary_roc(
preds: Tensor,
target: Tensor,
thresholds: Optional[Union[int, list[float], Tensor]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> tuple[Tensor, Tensor, Tensor]:
r"""Compute 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.
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). The value 1 always encodes the positive class.
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).
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:
preds: Tensor with predictions
target: Tensor with true 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.
Returns:
(tuple): a tuple of 3 tensors containing:
- fpr: an 1d tensor of size (n_thresholds+1, ) with false positive rate values
- tpr: an 1d tensor of size (n_thresholds+1, ) with true positive rate values
- thresholds: an 1d tensor of size (n_thresholds, ) with decreasing threshold values
Example:
>>> from torchmetrics.functional.classification import binary_roc
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> binary_roc(preds, target, thresholds=None) # 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]))
>>> binary_roc(preds, target, thresholds=5) # 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]))
"""
if validate_args:
_binary_precision_recall_curve_arg_validation(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_roc_compute(state, thresholds)
def _multiclass_roc_compute(
state: Union[Tensor, tuple[Tensor, Tensor]],
num_classes: int,
thresholds: Optional[Tensor],
average: Optional[Literal["micro", "macro"]] = None,
) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]:
if average == "micro":
return _binary_roc_compute(state, thresholds, pos_label=1)
if isinstance(state, Tensor) and thresholds is not None:
tps = state[:, :, 1, 1]
fps = state[:, :, 0, 1]
fns = state[:, :, 1, 0]
tns = state[:, :, 0, 0]
tpr = _safe_divide(tps, tps + fns).flip(0).T
fpr = _safe_divide(fps, fps + tns).flip(0).T
thres = thresholds.flip(0)
tensor_state = True
else:
fpr_list, tpr_list, thres_list = [], [], []
for i in range(num_classes):
res = _binary_roc_compute((state[0][:, i], state[1]), thresholds=None, pos_label=i)
fpr_list.append(res[0])
tpr_list.append(res[1])
thres_list.append(res[2])
tensor_state = False
if average == "macro":
thres = thres.repeat(num_classes) if tensor_state else torch.cat(thres_list, dim=0)
thres = thres.sort(descending=True).values
mean_fpr = fpr.flatten() if tensor_state else torch.cat(fpr_list, dim=0)
mean_fpr = mean_fpr.sort().values
mean_tpr = torch.zeros_like(mean_fpr)
for i in range(num_classes):
mean_tpr += interp(
mean_fpr, fpr[i] if tensor_state else fpr_list[i], tpr[i] if tensor_state else tpr_list[i]
)
mean_tpr /= num_classes
return mean_fpr, mean_tpr, thres
if tensor_state:
return fpr, tpr, thres
return fpr_list, tpr_list, thres_list
def multiclass_roc(
preds: Tensor,
target: Tensor,
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,
) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]:
r"""Compute the Receiver Operating Characteristic (ROC) for multiclass 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.
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).
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:
preds: Tensor with predictions
target: Tensor with true labels
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 an 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.
Returns:
(tuple): a tuple of either 3 tensors or 3 lists containing
- fpr: 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: 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: 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.
Example:
>>> from torchmetrics.functional.classification import multiclass_roc
>>> 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])
>>> fpr, tpr, thresholds = multiclass_roc(
... preds, target, num_classes=5, thresholds=None
... )
>>> 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])]
>>> multiclass_roc(
... preds, target, num_classes=5, thresholds=5
... ) # 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]))
"""
if validate_args:
_multiclass_precision_recall_curve_arg_validation(num_classes, thresholds, ignore_index, average)
_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,
average,
)
state = _multiclass_precision_recall_curve_update(preds, target, num_classes, thresholds, average)
return _multiclass_roc_compute(state, num_classes, thresholds, average)
def _multilabel_roc_compute(
state: Union[Tensor, tuple[Tensor, Tensor]],
num_labels: int,
thresholds: Optional[Tensor],
ignore_index: Optional[int] = None,
) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]:
if isinstance(state, Tensor) and thresholds is not None:
tps = state[:, :, 1, 1]
fps = state[:, :, 0, 1]
fns = state[:, :, 1, 0]
tns = state[:, :, 0, 0]
tpr = _safe_divide(tps, tps + fns).flip(0).T
fpr = _safe_divide(fps, fps + tns).flip(0).T
thres = thresholds.flip(0)
else:
fpr, tpr, thres = [], [], [] # type: ignore[assignment]
for i in range(num_labels):
preds = state[0][:, i]
target = state[1][:, i]
if ignore_index is not None:
idx = target == ignore_index
preds = preds[~idx]
target = target[~idx]
res = _binary_roc_compute((preds, target), thresholds=None, pos_label=1)
fpr.append(res[0])
tpr.append(res[1])
thres.append(res[2])
return fpr, tpr, thres
def multilabel_roc(
preds: Tensor,
target: Tensor,
num_labels: int,
thresholds: Optional[Union[int, list[float], Tensor]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]:
r"""Compute the Receiver Operating Characteristic (ROC) for multilabel 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.
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).
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:
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.
Returns:
(tuple): a tuple of either 3 tensors or 3 lists containing
- fpr: 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: 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: 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.
Example:
>>> from torchmetrics.functional.classification import multilabel_roc
>>> 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]])
>>> fpr, tpr, thresholds = multilabel_roc(
... preds, target, num_labels=3, thresholds=None
... )
>>> 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])]
>>> multilabel_roc(
... preds, target, num_labels=3, thresholds=5
... ) # 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]))
"""
if validate_args:
_multilabel_precision_recall_curve_arg_validation(num_labels, 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_roc_compute(state, num_labels, thresholds, ignore_index)
def roc(
preds: Tensor,
target: Tensor,
task: Literal["binary", "multiclass", "multilabel"],
thresholds: Optional[Union[int, list[float], Tensor]] = None,
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
average: Optional[Literal["micro", "macro"]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Union[tuple[Tensor, Tensor, Tensor], tuple[List[Tensor], List[Tensor], List[Tensor]]]:
r"""Compute 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
:func:`~torchmetrics.functional.classification.binary_roc`,
:func:`~torchmetrics.functional.classification.multiclass_roc` and
:func:`~torchmetrics.functional.classification.multilabel_roc` 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])
>>> fpr, tpr, thresholds = roc(pred, target, task='binary')
>>> 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])
>>> fpr, tpr, thresholds = roc(pred, target, task='multiclass', num_classes=4)
>>> 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
[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]])
>>> fpr, tpr, thresholds = roc(pred, target, task='multilabel', num_labels=3)
>>> 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])]
"""
task = ClassificationTask.from_str(task)
if task == ClassificationTask.BINARY:
return binary_roc(preds, target, 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_roc(preds, target, num_classes, thresholds, average, 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_roc(preds, target, num_labels, thresholds, ignore_index, validate_args)
raise ValueError(f"Task {task} not supported, expected one of {ClassificationTask}.")
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