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hamming_distance.py
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hamming_distance.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 Any, Callable, Optional
import torch
from torch import Tensor, tensor
from torchmetrics.functional.classification.hamming_distance import _hamming_distance_compute, _hamming_distance_update
from torchmetrics.metric import Metric
class HammingDistance(Metric):
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:`references/modules:input types`.
Args:
threshold:
Threshold probability value for transforming probability predictions to binary
(0 or 1) predictions, in the case of binary or multi-label inputs.
compute_on_step:
Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called.
default: ``None`` (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the all gather.
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)
"""
def __init__(
self,
threshold: float = 0.5,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
if not 0 < threshold < 1:
raise ValueError("The `threshold` should lie in the (0,1) interval.")
self.threshold = threshold
def update(self, preds: Tensor, target: Tensor):
"""
Update state with predictions and targets. See :ref:`references/modules:input types` for more information
on input types.
Args:
preds: Predictions from model (probabilities, 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)