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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 Optional
import torch
from torch import Tensor
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.checks import _input_format_classification
from torchmetrics.utilities.enums import DataType
def _confusion_matrix_update(
preds: Tensor, target: Tensor, num_classes: int, threshold: float = 0.5, multilabel: bool = False
) -> Tensor:
preds, target, mode = _input_format_classification(preds, target, threshold)
if mode not in (DataType.BINARY, DataType.MULTILABEL):
preds = preds.argmax(dim=1)
target = target.argmax(dim=1)
if multilabel:
unique_mapping = ((2 * target + preds) + 4 * torch.arange(num_classes, device=preds.device)).flatten()
minlength = 4 * num_classes
else:
unique_mapping = (target.view(-1) * num_classes + preds.view(-1)).to(torch.long)
minlength = num_classes**2
bins = torch.bincount(unique_mapping, minlength=minlength)
if multilabel:
confmat = bins.reshape(num_classes, 2, 2)
else:
confmat = bins.reshape(num_classes, num_classes)
return confmat
def _confusion_matrix_compute(confmat: Tensor, normalize: Optional[str] = None) -> Tensor:
allowed_normalize = ('true', 'pred', 'all', 'none', None)
assert normalize in allowed_normalize, \
f"Argument average needs to one of the following: {allowed_normalize}"
confmat = confmat.float()
if normalize is not None and normalize != 'none':
cm = None
if normalize == 'true':
cm = confmat / confmat.sum(axis=1, keepdim=True)
elif normalize == 'pred':
cm = confmat / confmat.sum(axis=0, keepdim=True)
elif normalize == 'all':
cm = confmat / confmat.sum()
nan_elements = cm[torch.isnan(cm)].nelement()
if nan_elements != 0:
cm[torch.isnan(cm)] = 0
rank_zero_warn(f'{nan_elements} nan values found in confusion matrix have been replaced with zeros.')
return cm
return confmat
def confusion_matrix(
preds: Tensor,
target: Tensor,
num_classes: int,
normalize: Optional[str] = None,
threshold: float = 0.5,
multilabel: bool = False
) -> Tensor:
"""
Computes the `confusion matrix
<https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix>`_. Works with binary,
multiclass, and multilabel data. Accepts probabilities 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.
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.
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
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html>`_.
Args:
preds: (float or long tensor), Either a ``(N, ...)`` tensor with labels or
``(N, C, ...)`` where C is the number of classes, tensor with labels/probabilities
target: ``target`` (long tensor), tensor with shape ``(N, ...)`` with ground true labels
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 value for binary or multi-label probabilities. default: 0.5
multilabel:
determines if data is multilabel or not.
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) # doctest: +NORMALIZE_WHITESPACE
tensor([[[1., 0.], [0., 1.]],
[[1., 0.], [1., 0.]],
[[0., 1.], [0., 1.]]])
"""
confmat = _confusion_matrix_update(preds, target, num_classes, threshold, multilabel)
return _confusion_matrix_compute(confmat, normalize)