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hinge.py
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hinge.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 Any, Callable, Optional, Union
from deprecate import deprecated, void
from torch import Tensor, tensor
from torchmetrics.functional.classification.hinge import MulticlassMode, _hinge_compute, _hinge_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import _future_warning
class HingeLoss(Metric):
r"""
Computes the mean `Hinge loss`_, typically used for Support Vector
Machines (SVMs). In the binary case it is defined as:
.. 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.
In the multi-class case, when ``multiclass_mode=None`` (default), ``multiclass_mode=MulticlassMode.CRAMMER_SINGER``
or ``multiclass_mode="crammer-singer"``, this metric will compute the multi-class hinge loss defined by Crammer and
Singer as:
.. 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.
In the multi-class case when ``multiclass_mode=MulticlassMode.ONE_VS_ALL`` or ``multiclass_mode='one-vs-all'``, this
metric will use a one-vs-all approach to compute the hinge loss, giving a vector of C outputs where each entry pits
that class against all remaining classes.
This metric can optionally output the mean of the squared hinge loss by setting ``squared=True``
Only accepts inputs with preds shape of (N) (binary) or (N, C) (multi-class) and target shape of (N).
Args:
squared:
If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss (default).
multiclass_mode:
Which approach to use for multi-class inputs (has no effect in the binary case). ``None`` (default),
``MulticlassMode.CRAMMER_SINGER`` or ``"crammer-singer"``, uses the Crammer Singer multi-class hinge loss.
``MulticlassMode.ONE_VS_ALL`` or ``"one-vs-all"`` computes the hinge loss in a one-vs-all fashion.
Raises:
ValueError:
If ``multiclass_mode`` is not: None, ``MulticlassMode.CRAMMER_SINGER``, ``"crammer-singer"``,
``MulticlassMode.ONE_VS_ALL`` or ``"one-vs-all"``.
Example (binary case):
>>> import torch
>>> from torchmetrics import HingeLoss
>>> target = torch.tensor([0, 1, 1])
>>> preds = torch.tensor([-2.2, 2.4, 0.1])
>>> hinge = HingeLoss()
>>> hinge(preds, target)
tensor(0.3000)
Example (default / multiclass case):
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = HingeLoss()
>>> hinge(preds, target)
tensor(2.9000)
Example (multiclass example, one vs all mode):
>>> target = torch.tensor([0, 1, 2])
>>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
>>> hinge = HingeLoss(multiclass_mode="one-vs-all")
>>> hinge(preds, target)
tensor([2.2333, 1.5000, 1.2333])
"""
is_differentiable = True
higher_is_better = False
measure: Tensor
total: Tensor
def __init__(
self,
squared: bool = False,
multiclass_mode: Optional[Union[str, MulticlassMode]] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.add_state("measure", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
if multiclass_mode not in (None, MulticlassMode.CRAMMER_SINGER, MulticlassMode.ONE_VS_ALL):
raise ValueError(
"The `multiclass_mode` should be either None / 'crammer-singer' / MulticlassMode.CRAMMER_SINGER"
"(default) or 'one-vs-all' / MulticlassMode.ONE_VS_ALL,"
f" got {multiclass_mode}."
)
self.squared = squared
self.multiclass_mode = multiclass_mode
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
measure, total = _hinge_update(preds, target, squared=self.squared, multiclass_mode=self.multiclass_mode)
self.measure = measure + self.measure
self.total = total + self.total
def compute(self) -> Tensor:
return _hinge_compute(self.measure, self.total)
class Hinge(HingeLoss):
r"""
Computes the mean `Hinge loss`_, typically used for Support Vector Machines (SVMs).
.. deprecated:: v0.7
Use :class:`torchmetrics.HingeLoss`. Will be removed in v0.8.
Example (binary case):
>>> import torch
>>> hinge = Hinge()
>>> hinge(torch.tensor([-2.2, 2.4, 0.1]), torch.tensor([0, 1, 1]))
tensor(0.3000)
Example (default / multiclass case):
>>> hinge = Hinge()
>>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
tensor(2.9000)
Example (multiclass example, one vs all mode):
>>> hinge = Hinge(multiclass_mode="one-vs-all")
>>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
tensor([2.2333, 1.5000, 1.2333])
"""
@deprecated(target=HingeLoss, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
def __init__(
self,
squared: bool = False,
multiclass_mode: Optional[Union[str, MulticlassMode]] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
) -> None:
void(squared, multiclass_mode, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)