73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple, Union
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import torch
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from pytorch_lightning.metrics.classification.helpers import _input_format_classification
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def _hamming_distance_update(
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preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5
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) -> Tuple[torch.Tensor, int]:
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preds, target, _ = _input_format_classification(preds, target, threshold=threshold)
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correct = (preds == target).sum()
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total = preds.numel()
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return correct, total
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def _hamming_distance_compute(correct: torch.Tensor, total: Union[int, torch.Tensor]) -> torch.Tensor:
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return 1 - correct.float() / total
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def hamming_distance(preds: torch.Tensor, target: torch.Tensor, threshold: float = 0.5) -> torch.Tensor:
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r"""
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Computes the average `Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ (also
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known as Hamming loss) between targets and predictions:
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.. math::
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\text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})
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Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
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and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
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tensor.
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This is the same as ``1-accuracy`` for binary data, while for all other types of inputs it
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treats each possible label separately - meaning that, for example, multi-class data is
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treated as if it were multi-label.
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Accepts all input types listed in :ref:`extensions/metrics:input types`.
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Args:
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preds: Predictions from model
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target: Ground truth
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threshold:
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Threshold probability value for transforming probability predictions to binary
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(0 or 1) predictions, in the case of binary or multi-label inputs.
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Example:
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>>> from pytorch_lightning.metrics.functional import hamming_distance
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>>> target = torch.tensor([[0, 1], [1, 1]])
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>>> preds = torch.tensor([[0, 1], [0, 1]])
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>>> hamming_distance(preds, target)
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tensor(0.2500)
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"""
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correct, total = _hamming_distance_update(preds, target, threshold)
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return _hamming_distance_compute(correct, total)
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