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#
# 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 collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.classification.base import _ClassificationTaskWrapper
from torchmetrics.classification.roc import (
BinaryROC,
MulticlassROC,
MultilabelROC,
)
from torchmetrics.functional.classification.eer import _eer_compute
from torchmetrics.metric import Metric
from torchmetrics.utilities.enums import ClassificationTask
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["BinaryEER.plot", "MulticlassEER.plot", "MultilabelEER.plot"]
class BinaryEER(BinaryROC):
r"""Compute Equal Error Rate (EER) for multiclass classification task.
.. math::
\text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t)
The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are
equal, or in practise minimized. A lower EER value signifies higher system accuracy.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` 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, ...)`` 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.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``b_eer`` (:class:`~torch.Tensor`): A single scalar with the eer score.
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:
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 torch import tensor
>>> from torchmetrics.classification import BinaryEER
>>> preds = tensor([0, 0.5, 0.7, 0.8])
>>> target = tensor([0, 1, 1, 0])
>>> metric = BinaryEER(thresholds=None)
>>> metric(preds, target)
tensor(0.5000)
>>> b_eer = BinaryEER(thresholds=5)
>>> b_eer(preds, target)
tensor(0.7500)
"""
def compute(self) -> Tensor: # type: ignore[override]
"""Compute metric."""
fpr, tpr, _ = super().compute()
return _eer_compute(fpr, tpr)
def plot( # type: ignore[override]
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
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
>>> # Example plotting a single
>>> import torch
>>> from torchmetrics.classification import BinaryEER
>>> metric = BinaryEER()
>>> metric.update(torch.rand(20,), torch.randint(2, (20,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.classification import BinaryEER
>>> metric = BinaryEER()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(20,), torch.randint(2, (20,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MulticlassEER(MulticlassROC):
r"""Compute Equal Error Rate (EER) for multiclass classification task.
.. math::
\text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t)
The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are
equal, or in practise minimized. A lower EER value signifies higher system accuracy.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` 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, ...)`` containing ground truth labels, and
therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``mc_eer`` (:class:`~torch.Tensor`): If `average=None` then a 1d tensor of shape (n_classes, ) will
be returned with eer score per class. If `average="macro"|"micro"` then a single scalar will be returned.
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:
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.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Examples:
>>> from torch import tensor
>>> from torchmetrics.classification import MulticlassEER
>>> preds = 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 = tensor([0, 1, 3, 2])
>>> metric = MulticlassEER(num_classes=5, average="macro", thresholds=None)
>>> metric(preds, target)
tensor(0.4667)
>>> mc_eer = MulticlassEER(num_classes=5, average=None, thresholds=None)
>>> mc_eer(preds, target)
tensor([0.0000, 0.0000, 0.6667, 0.6667, 1.0000])
"""
def compute(self) -> Tensor: # type: ignore[override]
"""Compute metric."""
fpr, tpr, _ = super().compute()
return _eer_compute(fpr, tpr)
def plot( # type: ignore[override]
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
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
>>> # Example plotting a single
>>> import torch
>>> from torchmetrics.classification import MulticlassEER
>>> metric = MulticlassEER(num_classes=3)
>>> metric.update(torch.randn(20, 3), torch.randint(3,(20,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.classification import MulticlassEER
>>> metric = MulticlassEER(num_classes=3)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.randn(20, 3), torch.randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MultilabelEER(MultilabelROC):
r"""Compute Equal Error Rate (EER) for multiclass classification task.
.. math::
\text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t)
The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are
equal, or in practise minimized. A lower EER value signifies higher system accuracy.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` 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, ...)`` containing ground truth labels, and
therefore only contain {0,1} values (except if `ignore_index` is specified).
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``ml_eer`` (:class:`~torch.Tensor`): A 1d tensor of shape (n_classes, ) will be returned with eer score per label.
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:
num_labels: Integer specifying 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.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torch import tensor
>>> from torchmetrics.classification import MultilabelEER
>>> preds = 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 = tensor([[1, 0, 1],
... [0, 0, 0],
... [0, 1, 1],
... [1, 1, 1]])
>>> ml_eer = MultilabelEER(num_labels=3, thresholds=None)
>>> ml_eer(preds, target)
tensor([0.5000, 0.5000, 0.1667])
"""
def compute(self) -> Tensor: # type: ignore[override]
"""Compute metric."""
fpr, tpr, _ = super().compute()
return _eer_compute(fpr, tpr)
def plot( # type: ignore[override]
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
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
>>> # Example plotting a single
>>> import torch
>>> from torchmetrics.classification import MultilabelEER
>>> metric = MultilabelEER(num_labels=3)
>>> metric.update(torch.rand(20,3), torch.randint(2, (20,3)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.classification import MultilabelEER
>>> metric = MultilabelEER(num_labels=3)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(20,3), torch.randint(2, (20,3))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class EER(_ClassificationTaskWrapper):
r"""Compute Equal Error Rate (EER) for multiclass classification task.
.. math::
\text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t)
The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are
equal, or in practise minimized. A lower EER value signifies higher system accuracy.
This module 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.BinaryEER`, :class:`~torchmetrics.classification.MulticlassEER` and
:class:`~torchmetrics.classification.MultilabelEER` for the specific details of each argument influence and
examples.
Legacy Example:
>>> from torch import tensor
>>> preds = tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = tensor([0, 0, 1, 1, 1])
>>> eer = EER(task="binary")
>>> eer(preds, target)
tensor(0.5833)
>>> preds = 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 = tensor([0, 1, 1, 2, 2])
>>> eer = EER(task="multiclass", num_classes=3)
>>> eer(preds, target)
tensor([0.0000, 0.4167, 0.4167])
"""
def __new__( # type: ignore[misc]
cls: type["EER"],
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", "micro"]] = 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 BinaryEER(**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 MulticlassEER(num_classes, average=average, **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 MultilabelEER(num_labels, **kwargs)
raise ValueError(f"Task {task} not supported!")
def update(self, *args: Any, **kwargs: Any) -> None:
"""Update metric state."""
raise NotImplementedError(
f"{self.__class__.__name__} metric does not have a global `update` method. Use the task specific metric."
)
def compute(self) -> None:
"""Compute metric."""
raise NotImplementedError(
f"{self.__class__.__name__} metric does not have a global `compute` method. Use the task specific metric."
)
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