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hamming.py
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hamming.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 Tuple, Union
import torch
from torch import Tensor
from torchmetrics.utilities.checks import _input_format_classification
def _hamming_distance_update(
preds: Tensor,
target: Tensor,
threshold: float = 0.5,
) -> Tuple[Tensor, int]:
"""Returns the number of positions where prediction equals target, and number of predictions.
Args:
preds: Predicted tensor
target: Ground truth tensor
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.
"""
preds, target, _ = _input_format_classification(preds, target, threshold=threshold)
correct = (preds == target).sum()
total = preds.numel()
return correct, total
def _hamming_distance_compute(correct: Tensor, total: Union[int, Tensor]) -> Tensor:
"""Computes the Hamming distance.
Args:
correct: Number of positions where prediction equals target
total: Total number of predictions
Example:
>>> target = torch.tensor([[0, 1], [1, 1]])
>>> preds = torch.tensor([[0, 1], [0, 1]])
>>> correct, total = _hamming_distance_update(preds, target)
>>> _hamming_distance_compute(correct, total)
tensor(0.2500)
"""
return 1 - correct.float() / total
def hamming_distance(preds: Tensor, target: Tensor, threshold: float = 0.5) -> Tensor:
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:
preds: Predictions from model (probabilities, logits or labels)
target: Ground truth
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.
Example:
>>> from torchmetrics.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)