/
hamming.py
98 lines (76 loc) · 3.67 KB
/
hamming.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
# 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, Dict, Optional
import torch
from torch import Tensor, tensor
from torchmetrics.functional.classification.hamming import _hamming_distance_compute, _hamming_distance_update
from torchmetrics.metric import Metric
class HammingDistance(Metric):
r"""Computes the average `Hamming distance`_ (also known as Hamming loss) between targets and predictions:
.. math::
\text{Hamming distance} = \frac{1}{N \cdot L}\sum_i^N \sum_l^L 1(y_{il} \neq \hat{y_{il}})
Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
tensor.
This is the same as ``1-accuracy`` for binary data, while for all other types of inputs it
treats each possible label separately - meaning that, for example, multi-class data is
treated as if it were multi-label.
Accepts all input types listed in :ref:`pages/classification:input types`.
Args:
threshold:
Threshold for transforming probability or logit predictions to binary ``(0,1)`` predictions, in the case
of binary or multi-label inputs. Default value of ``0.5`` corresponds to input being probabilities.
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If ``threshold`` is not between ``0`` and ``1``.
Example:
>>> from torchmetrics import HammingDistance
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> hamming_distance = HammingDistance()
>>> hamming_distance(preds, target)
tensor(0.2500)
"""
is_differentiable = False
higher_is_better = False
correct: Tensor
total: Tensor
def __init__(
self,
threshold: float = 0.5,
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(compute_on_step=compute_on_step, **kwargs)
self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
self.threshold = threshold
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
See :ref:`pages/classification:input types` for more information on input types.
Args:
preds: Predictions from model (probabilities, logits or labels)
target: Ground truth labels
"""
correct, total = _hamming_distance_update(preds, target, self.threshold)
self.correct += correct
self.total += total
def compute(self) -> Tensor:
"""Computes hamming distance based on inputs passed in to ``update`` previously."""
return _hamming_distance_compute(self.correct, self.total)