72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
# 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 Tuple, Union
|
|
|
|
import torch
|
|
from pytorch_lightning.metrics.classification.helpers import _input_format_classification
|
|
|
|
|
|
def _hamming_distance_update(
|
|
preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5
|
|
) -> Tuple[torch.Tensor, int]:
|
|
preds, target, _ = _input_format_classification(preds, target, threshold=threshold)
|
|
|
|
correct = (preds == target).sum()
|
|
total = preds.numel()
|
|
|
|
return correct, total
|
|
|
|
|
|
def _hamming_distance_compute(correct: torch.Tensor, total: Union[int, torch.Tensor]) -> torch.Tensor:
|
|
return 1 - correct.float() / total
|
|
|
|
|
|
def hamming_distance(preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5) -> torch.Tensor:
|
|
r"""
|
|
Computes the average `Hamming distance <https://en.wikipedia.org/wiki/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:`metrics:Input types`.
|
|
|
|
Args:
|
|
preds: Predictions from model
|
|
target: Ground truth
|
|
threshold:
|
|
Threshold probability value for transforming probability predictions to binary
|
|
(0,1) predictions, in the case of binary or multi-label inputs.
|
|
|
|
Example:
|
|
|
|
>>> from pytorch_lightning.metrics.functional import hamming_distance
|
|
>>> target = torch.tensor([[0, 1], [1, 1]])
|
|
>>> preds = torch.tensor([[0, 1], [0, 1]])
|
|
>>> hamming_distance(preds, target)
|
|
tensor(0.2500)
|
|
|
|
"""
|
|
|
|
correct, total = _hamming_distance_update(preds, target, threshold)
|
|
return _hamming_distance_compute(correct, total)
|