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Source code for pytorch_lightning.metrics.functional.hamming_distance

# 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


[docs]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:`extensions/metrics:input types`. Args: preds: Predictions from model target: Ground truth threshold: Threshold probability value for transforming probability predictions to binary (0 or 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)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision b3b8f95e.

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