-
Notifications
You must be signed in to change notification settings - Fork 387
/
hinge.py
377 lines (313 loc) · 15.4 KB
/
hinge.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
# 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 Any, Optional, Sequence, 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.
.. note::
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).
.. note::
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,
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}`")