/
auroc.py
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/
auroc.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor, 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.compute import _auc_compute_without_check, _safe_divide
from torchmetrics.utilities.data import _bincount
from torchmetrics.utilities.prints import rank_zero_warn
def _reduce_auroc(
fpr: Union[Tensor, List[Tensor]],
tpr: Union[Tensor, List[Tensor]],
average: Optional[Literal["macro", "weighted", "none"]] = "macro",
weights: Optional[Tensor] = None,
) -> Tensor:
"""Utility function for reducing multiple average precision score into one number."""
if isinstance(fpr, Tensor):
res = _auc_compute_without_check(fpr, tpr, 1.0, axis=1)
else:
res = [_auc_compute_without_check(x, y, 1.0) for x, y in zip(fpr, tpr)]
res = torch.stack(res)
if average is None or average == "none":
return res
if torch.isnan(res).any():
rank_zero_warn(
f"Average precision score for one or more classes was `nan`. Ignoring these classes in {average}-average",
UserWarning,
)
idx = ~torch.isnan(res)
if average == "macro":
return res[idx].mean()
elif average == "weighted" and weights is not None:
weights = _safe_divide(weights[idx], weights[idx].sum())
return (res[idx] * weights).sum()
else:
raise ValueError("Received an incompatible combinations of inputs to make reduction.")
def _binary_auroc_arg_validation(
max_fpr: Optional[float] = None,
thresholds: Optional[Union[int, List[float], Tensor]] = None,
ignore_index: Optional[int] = None,
) -> None:
_binary_precision_recall_curve_arg_validation(thresholds, ignore_index)
if max_fpr is not None and not isinstance(max_fpr, float) and 0 < max_fpr <= 1:
raise ValueError(f"Arguments `max_fpr` should be a float in range (0, 1], but got: {max_fpr}")
def _binary_auroc_compute(
state: Union[Tensor, Tuple[Tensor, Tensor]],
thresholds: Optional[Tensor],
max_fpr: Optional[float] = None,
pos_label: int = 1,
) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor]]:
fpr, tpr, _ = _binary_roc_compute(state, thresholds, pos_label)
if max_fpr is None or max_fpr == 1:
return _auc_compute_without_check(fpr, tpr, 1.0)
_device = fpr.device if isinstance(fpr, Tensor) else fpr[0].device
max_area: Tensor = tensor(max_fpr, device=_device)
# Add a single point at max_fpr and interpolate its tpr value
stop = torch.bucketize(max_area, fpr, out_int32=True, right=True)
weight = (max_area - fpr[stop - 1]) / (fpr[stop] - fpr[stop - 1])
interp_tpr: Tensor = torch.lerp(tpr[stop - 1], tpr[stop], weight)
tpr = torch.cat([tpr[:stop], interp_tpr.view(1)])
fpr = torch.cat([fpr[:stop], max_area.view(1)])
# Compute partial AUC
partial_auc = _auc_compute_without_check(fpr, tpr, 1.0)
# McClish correction: standardize result to be 0.5 if non-discriminant and 1 if maximal
min_area: Tensor = 0.5 * max_area**2
return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))
def binary_auroc(
preds: Tensor,
target: Tensor,
max_fpr: Optional[float] = None,
thresholds: Optional[Union[int, List[float], Tensor]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tuple[Tensor, Tensor, Tensor]:
r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for binary tasks. The AUROC
score summarizes the ROC curve into an single number that describes the performance of a model for multiple
thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
corresponds to random guessing.
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).
Args:
preds: Tensor with predictions
target: Tensor with true labels
max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``.
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.
Returns:
A single scalar with the auroc score
Example:
>>> from torchmetrics.functional.classification import binary_auroc
>>> preds = torch.tensor([0, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> binary_auroc(preds, target, thresholds=None)
tensor(0.5000)
>>> binary_auroc(preds, target, thresholds=5)
tensor(0.5000)
"""
if validate_args:
_binary_auroc_arg_validation(max_fpr, 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_auroc_compute(state, thresholds, max_fpr)
def _multiclass_auroc_arg_validation(
num_classes: int,
average: Optional[Literal["macro", "weighted", "none"]] = "macro",
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)
allowed_average = ("macro", "weighted", "none", None)
if average not in allowed_average:
raise ValueError(f"Expected argument `average` to be one of {allowed_average} but got {average}")
def _multiclass_auroc_compute(
state: Union[Tensor, Tuple[Tensor, Tensor]],
num_classes: int,
average: Optional[Literal["macro", "weighted", "none"]] = "macro",
thresholds: Optional[Tensor] = None,
) -> Tensor:
fpr, tpr, _ = _multiclass_roc_compute(state, num_classes, thresholds)
return _reduce_auroc(
fpr,
tpr,
average,
weights=_bincount(state[1], minlength=num_classes).float() if thresholds is None else state[0][:, 1, :].sum(-1),
)
def multiclass_auroc(
preds: Tensor,
target: Tensor,
num_classes: int,
average: Optional[Literal["macro", "weighted", "none"]] = "macro",
thresholds: Optional[Union[int, List[float], Tensor]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multiclass tasks. The AUROC
score summarizes the ROC curve into an single number that describes the performance of a model for multiple
thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
corresponds to random guessing.
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 specifing the number of classes
average:
Defines the reduction that is applied over classes. Should be one of the following:
- ``macro``: Calculate score for each class and average them
- ``weighted``: Calculates score for each class and computes weighted average using their support
- ``"none"`` or ``None``: Calculates score for each class and applies no reduction
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.
Returns:
If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class.
If `average="macro"|"weighted"` then a single scalar is returned.
Example:
>>> from torchmetrics.functional.classification import multiclass_auroc
>>> 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_auroc(preds, target, num_classes=5, average="macro", thresholds=None)
tensor(0.5333)
>>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=None)
tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])
>>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=5)
tensor(0.5333)
>>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=5)
tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000])
"""
if validate_args:
_multiclass_auroc_arg_validation(num_classes, average, 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_auroc_compute(state, num_classes, average, thresholds)
def _multilabel_auroc_arg_validation(
num_labels: int,
average: Optional[Literal["micro", "macro", "weighted", "none"]],
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)
allowed_average = ("micro", "macro", "weighted", "none", None)
if average not in allowed_average:
raise ValueError(f"Expected argument `average` to be one of {allowed_average} but got {average}")
def _multilabel_auroc_compute(
state: Union[Tensor, Tuple[Tensor, Tensor]],
num_labels: int,
average: Optional[Literal["micro", "macro", "weighted", "none"]],
thresholds: Optional[Tensor],
ignore_index: Optional[int] = None,
) -> Union[Tuple[Tensor, Tensor, Tensor], Tensor]:
if average == "micro":
if isinstance(state, Tensor) and thresholds is not None:
return _binary_auroc_compute(state.sum(1), thresholds, max_fpr=None)
else:
preds = state[0].flatten()
target = state[1].flatten()
if ignore_index is not None:
idx = target == ignore_index
preds = preds[~idx]
target = target[~idx]
return _binary_auroc_compute((preds, target), thresholds, max_fpr=None)
else:
fpr, tpr, _ = _multilabel_roc_compute(state, num_labels, thresholds, ignore_index)
return _reduce_auroc(
fpr,
tpr,
average,
weights=(state[1] == 1).sum(dim=0).float() if thresholds is None else state[0][:, 1, :].sum(-1),
)
def multilabel_auroc(
preds: Tensor,
target: Tensor,
num_labels: int,
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
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 Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multilabel tasks. The AUROC
score summarizes the ROC curve into an single number that describes the performance of a model for multiple
thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
corresponds to random guessing.
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 specifing the number of labels
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum score over all labels
- ``macro``: Calculate score for each label and average them
- ``weighted``: Calculates score for each label and computes weighted average using their support
- ``"none"`` or ``None``: Calculates score for each label and applies no reduction
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.
Returns:
If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class.
If `average="micro|macro"|"weighted"` then a single scalar is returned.
Example:
>>> from torchmetrics.functional.classification import multilabel_auroc
>>> 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_auroc(preds, target, num_labels=3, average="macro", thresholds=None)
tensor(0.6528)
>>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=None)
tensor([0.6250, 0.5000, 0.8333])
>>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=5)
tensor(0.6528)
>>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=5)
tensor([0.6250, 0.5000, 0.8333])
"""
if validate_args:
_multilabel_auroc_arg_validation(num_labels, average, 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_auroc_compute(state, num_labels, average, thresholds, ignore_index)
def auroc(
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["macro", "weighted", "none"]] = "macro",
max_fpr: Optional[float] = 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 Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_). The AUROC score summarizes the
ROC curve into an single number that describes the performance of a model for multiple thresholds at the same
time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing.
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:`binary_auroc`, :func:`multiclass_auroc` and :func:`multilabel_auroc` for the specific details of
each argument influence and examples.
Legacy Example:
>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> auroc(preds, target, task='binary')
tensor(0.5000)
>>> preds = torch.tensor([[0.90, 0.05, 0.05],
... [0.05, 0.90, 0.05],
... [0.05, 0.05, 0.90],
... [0.85, 0.05, 0.10],
... [0.10, 0.10, 0.80]])
>>> target = torch.tensor([0, 1, 1, 2, 2])
>>> auroc(preds, target, task='multiclass', num_classes=3)
tensor(0.7778)
"""
if task == "binary":
return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
if task == "multiclass":
assert isinstance(num_classes, int)
return multiclass_auroc(preds, target, num_classes, average, thresholds, ignore_index, validate_args)
if task == "multilabel":
assert isinstance(num_labels, int)
return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
raise ValueError(
f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
)