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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 collections.abc import Sequence
from typing import Any, Optional, Union
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.classification.base import _ClassificationTaskWrapper
from torchmetrics.functional.classification.hinge import (
_binary_confusion_matrix_format,
_binary_hinge_loss_arg_validation,
_binary_hinge_loss_tensor_validation,
_binary_hinge_loss_update,
_hinge_loss_compute,
_multiclass_confusion_matrix_format,
_multiclass_hinge_loss_arg_validation,
_multiclass_hinge_loss_tensor_validation,
_multiclass_hinge_loss_update,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["BinaryHingeLoss.plot", "MulticlassHingeLoss.plot"]
class BinaryHingeLoss(Metric):
r"""Compute the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs) for binary tasks.
.. math::
\text{Hinge loss} = \max(0, 1 - y \times \hat{y})
Where :math:`y \in {-1, 1}` is the target, and :math:`\hat{y} \in \mathbb{R}` is the prediction.
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:
- ``bhl`` (:class:`~torch.Tensor`): A tensor containing the hinge loss.
Args:
squared:
If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
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 BinaryHingeLoss
>>> preds = torch.tensor([0.25, 0.25, 0.55, 0.75, 0.75])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> bhl = BinaryHingeLoss()
>>> bhl(preds, target)
tensor(0.6900)
>>> bhl = BinaryHingeLoss(squared=True)
>>> bhl(preds, target)
tensor(0.6905)
"""
is_differentiable: bool = True
higher_is_better: bool = False
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
measures: Tensor
total: Tensor
def __init__(
self,
squared: bool = False,
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if validate_args:
_binary_hinge_loss_arg_validation(squared, ignore_index)
self.validate_args = validate_args
self.squared = squared
self.ignore_index = ignore_index
self.add_state("measures", default=torch.tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update metric state."""
if self.validate_args:
_binary_hinge_loss_tensor_validation(preds, target, self.ignore_index)
preds, target = _binary_confusion_matrix_format(
preds, target, threshold=0.0, ignore_index=self.ignore_index, convert_to_labels=False
)
measures, total = _binary_hinge_loss_update(preds, target, self.squared)
self.measures += measures
self.total += total
def compute(self) -> Tensor:
"""Compute metric."""
return _hinge_loss_compute(self.measures, self.total)
def plot(
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 object and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> from torch import rand, randint
>>> from torchmetrics.classification import BinaryHingeLoss
>>> metric = BinaryHingeLoss()
>>> metric.update(rand(10), randint(2,(10,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torch import rand, randint
>>> from torchmetrics.classification import BinaryHingeLoss
>>> metric = BinaryHingeLoss()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(rand(10), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MulticlassHingeLoss(Metric):
r"""Compute the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs) for multiclass tasks.
The metric can be computed in two ways. Either, the definition by Crammer and Singer is used:
.. math::
\text{Hinge loss} = \max\left(0, 1 - \hat{y}_y + \max_{i \ne y} (\hat{y}_i)\right)
Where :math:`y \in {0, ..., \mathrm{C}}` is the target class (where :math:`\mathrm{C}` is the number of classes),
and :math:`\hat{y} \in \mathbb{R}^\mathrm{C}` is the predicted output per class. Alternatively, the metric can
also be computed in one-vs-all approach, where each class is valued against all other classes in a binary fashion.
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:
- ``mchl`` (:class:`~torch.Tensor`): A tensor containing the multi-class hinge loss.
Args:
num_classes: Integer specifying the number of classes
squared:
If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
multiclass_mode:
Determines how to compute the metric
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 MulticlassHingeLoss
>>> preds = torch.tensor([[0.25, 0.20, 0.55],
... [0.55, 0.05, 0.40],
... [0.10, 0.30, 0.60],
... [0.90, 0.05, 0.05]])
>>> target = torch.tensor([0, 1, 2, 0])
>>> mchl = MulticlassHingeLoss(num_classes=3)
>>> mchl(preds, target)
tensor(0.9125)
>>> mchl = MulticlassHingeLoss(num_classes=3, squared=True)
>>> mchl(preds, target)
tensor(1.1131)
>>> mchl = MulticlassHingeLoss(num_classes=3, multiclass_mode='one-vs-all')
>>> mchl(preds, target)
tensor([0.8750, 1.1250, 1.1000])
"""
is_differentiable: bool = True
higher_is_better: bool = False
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
plot_legend_name: str = "Class"
measures: Tensor
total: Tensor
def __init__(
self,
num_classes: int,
squared: bool = False,
multiclass_mode: Literal["crammer-singer", "one-vs-all"] = "crammer-singer",
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if validate_args:
_multiclass_hinge_loss_arg_validation(num_classes, squared, multiclass_mode, ignore_index)
self.validate_args = validate_args
self.num_classes = num_classes
self.squared = squared
self.multiclass_mode = multiclass_mode
self.ignore_index = ignore_index
self.add_state(
"measures",
default=torch.tensor(0.0)
if self.multiclass_mode == "crammer-singer"
else torch.zeros(
num_classes,
),
dist_reduce_fx="sum",
)
self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update metric state."""
if self.validate_args:
_multiclass_hinge_loss_tensor_validation(preds, target, self.num_classes, self.ignore_index)
preds, target = _multiclass_confusion_matrix_format(preds, target, self.ignore_index, convert_to_labels=False)
measures, total = _multiclass_hinge_loss_update(preds, target, self.squared, self.multiclass_mode)
self.measures += measures
self.total += total
def compute(self) -> Tensor:
"""Compute metric."""
return _hinge_loss_compute(self.measures, self.total)
def plot(
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 object and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value per class
>>> from torch import randint, randn
>>> from torchmetrics.classification import MulticlassHingeLoss
>>> metric = MulticlassHingeLoss(num_classes=3)
>>> metric.update(randn(20, 3), randint(3, (20,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting a multiple values per class
>>> from torch import randint, randn
>>> from torchmetrics.classification import MulticlassHingeLoss
>>> metric = MulticlassHingeLoss(num_classes=3)
>>> values = []
>>> for _ in range(20):
... values.append(metric(randn(20, 3), randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class HingeLoss(_ClassificationTaskWrapper):
r"""Compute the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs).
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'`` or ``'multiclass'``. See the documentation of
:class:`~torchmetrics.classification.BinaryHingeLoss` and :class:`~torchmetrics.classification.MulticlassHingeLoss`
for the specific details of each argument influence and examples.
Legacy Example:
>>> from torch import tensor
>>> target = tensor([0, 1, 1])
>>> preds = tensor([0.5, 0.7, 0.1])
>>> hinge = HingeLoss(task="binary")
>>> hinge(preds, target)
tensor(0.9000)
>>> target = tensor([0, 1, 2])
>>> preds = tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = HingeLoss(task="multiclass", num_classes=3)
>>> hinge(preds, target)
tensor(1.5551)
>>> target = tensor([0, 1, 2])
>>> preds = tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = HingeLoss(task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
>>> hinge(preds, target)
tensor([1.3743, 1.1945, 1.2359])
"""
def __new__( # type: ignore[misc]
cls: type["HingeLoss"],
task: Literal["binary", "multiclass"],
num_classes: Optional[int] = None,
squared: bool = False,
multiclass_mode: Optional[Literal["crammer-singer", "one-vs-all"]] = "crammer-singer",
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> Metric:
"""Initialize task metric."""
task = ClassificationTaskNoMultilabel.from_str(task)
kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
if task == ClassificationTaskNoMultilabel.BINARY:
return BinaryHingeLoss(squared, **kwargs)
if task == ClassificationTaskNoMultilabel.MULTICLASS:
if not isinstance(num_classes, int):
raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
if multiclass_mode not in ("crammer-singer", "one-vs-all"):
raise ValueError(
f"`multiclass_mode` is expected to be one of 'crammer-singer' or 'one-vs-all' but "
f"`{multiclass_mode}` was passed."
)
return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
raise ValueError(f"Unsupported task `{task}`")
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