/
confusion_matrix.py
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/
confusion_matrix.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, Dict, Optional
import torch
from torch import Tensor
from torchmetrics.functional.classification.confusion_matrix import _confusion_matrix_compute, _confusion_matrix_update
from torchmetrics.metric import Metric
class ConfusionMatrix(Metric):
r"""Computes the `confusion matrix`_.
Works with binary, multiclass, and multilabel data. Accepts probabilities or logits from a model output
or integer class values in prediction. Works with multi-dimensional preds and target, but it should be noted that
additional dimensions will be flattened.
Forward accepts
- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
- ``target`` (long tensor): ``(N, ...)``
If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
to convert into integer labels. This is the case for binary and multi-label probabilities or logits.
If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
If working with multilabel data, setting the ``is_multilabel`` argument to ``True`` will make sure that a
`confusion matrix gets calculated per label`_.
Args:
num_classes: Number of classes in the dataset.
normalize: Normalization mode for confusion matrix. Choose from:
- ``None`` or ``'none'``: no normalization (default)
- ``'true'``: normalization over the targets (most commonly used)
- ``'pred'``: normalization over the predictions
- ``'all'``: normalization over the whole matrix
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.
multilabel: determines if data is multilabel or not.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (binary data):
>>> from torchmetrics import ConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat = ConfusionMatrix(num_classes=2)
>>> confmat(preds, target)
tensor([[2, 0],
[1, 1]])
Example (multiclass data):
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> confmat = ConfusionMatrix(num_classes=3)
>>> confmat(preds, target)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])
Example (multilabel data):
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> confmat = ConfusionMatrix(num_classes=3, multilabel=True)
>>> confmat(preds, target)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = None
full_state_update: bool = False
confmat: Tensor
def __init__(
self,
num_classes: int,
normalize: Optional[str] = None,
threshold: float = 0.5,
multilabel: bool = False,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(**kwargs)
self.num_classes = num_classes
self.normalize = normalize
self.threshold = threshold
self.multilabel = multilabel
allowed_normalize = ("true", "pred", "all", "none", None)
if self.normalize not in allowed_normalize:
raise ValueError(f"Argument average needs to one of the following: {allowed_normalize}")
if multilabel:
default = torch.zeros(num_classes, 2, 2, dtype=torch.long)
else:
default = torch.zeros(num_classes, num_classes, dtype=torch.long)
self.add_state("confmat", default=default, dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
confmat = _confusion_matrix_update(preds, target, self.num_classes, self.threshold, self.multilabel)
self.confmat += confmat
def compute(self) -> Tensor:
"""Computes confusion matrix.
Returns:
If ``multilabel=False`` this will be a ``[n_classes, n_classes]`` tensor and if ``multilabel=True``
this will be a ``[n_classes, 2, 2]`` tensor.
"""
return _confusion_matrix_compute(self.confmat, self.normalize)