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confusion_matrix.py
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# Copyright The 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, List, Optional
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.classification.base import _ClassificationTaskWrapper
from torchmetrics.functional.classification.confusion_matrix import (
_binary_confusion_matrix_arg_validation,
_binary_confusion_matrix_compute,
_binary_confusion_matrix_format,
_binary_confusion_matrix_tensor_validation,
_binary_confusion_matrix_update,
_multiclass_confusion_matrix_arg_validation,
_multiclass_confusion_matrix_compute,
_multiclass_confusion_matrix_format,
_multiclass_confusion_matrix_tensor_validation,
_multiclass_confusion_matrix_update,
_multilabel_confusion_matrix_arg_validation,
_multilabel_confusion_matrix_compute,
_multilabel_confusion_matrix_format,
_multilabel_confusion_matrix_tensor_validation,
_multilabel_confusion_matrix_update,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.enums import ClassificationTask
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_confusion_matrix
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = [
"BinaryConfusionMatrix.plot",
"MulticlassConfusionMatrix.plot",
"MultilabelConfusionMatrix.plot",
]
class BinaryConfusionMatrix(Metric):
r"""Compute the `confusion matrix`_ for binary tasks.
The confusion matrix :math:`C` is constructed such that :math:`C_{i, j}` is equal to the number of observations
known to be in class :math:`i` but predicted to be in class :math:`j`. Thus row indices of the confusion matrix
correspond to the true class labels and column indices correspond to the predicted class labels.
For binary tasks, the confusion matrix is a 2x2 matrix with the following structure:
- :math:`C_{0, 0}`: True negatives
- :math:`C_{0, 1}`: False positives
- :math:`C_{1, 0}`: False negatives
- :math:`C_{1, 1}`: True positives
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, ...)``. If preds is a floating point
tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per
element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``confusion_matrix`` (:class:`~torch.Tensor`): A tensor containing a ``(2, 2)`` matrix
Additional dimension ``...`` will be flattened into the batch dimension.
Args:
threshold: Threshold for transforming probability to binary (0,1) predictions
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
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
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (preds is int tensor):
>>> from torchmetrics.classification import BinaryConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> bcm = BinaryConfusionMatrix()
>>> bcm(preds, target)
tensor([[2, 0],
[1, 1]])
Example (preds is float tensor):
>>> from torchmetrics.classification import BinaryConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01])
>>> bcm = BinaryConfusionMatrix()
>>> bcm(preds, target)
tensor([[2, 0],
[1, 1]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = None
full_state_update: bool = False
def __init__(
self,
threshold: float = 0.5,
ignore_index: Optional[int] = None,
normalize: Optional[Literal["true", "pred", "all", "none"]] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if validate_args:
_binary_confusion_matrix_arg_validation(threshold, ignore_index, normalize)
self.threshold = threshold
self.ignore_index = ignore_index
self.normalize = normalize
self.validate_args = validate_args
self.add_state("confmat", torch.zeros(2, 2, dtype=torch.long), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
if self.validate_args:
_binary_confusion_matrix_tensor_validation(preds, target, self.ignore_index)
preds, target = _binary_confusion_matrix_format(preds, target, self.threshold, self.ignore_index)
confmat = _binary_confusion_matrix_update(preds, target)
self.confmat += confmat
def compute(self) -> Tensor:
"""Compute confusion matrix."""
return _binary_confusion_matrix_compute(self.confmat, self.normalize)
def plot(
self,
val: Optional[Tensor] = None,
ax: Optional[_AX_TYPE] = None,
add_text: bool = True,
labels: Optional[List[str]] = None,
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
add_text: if the value of each cell should be added to the plot
labels: a list of strings, if provided will be added to the plot to indicate the different classes
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> from torch import randint
>>> from torchmetrics.classification import MulticlassConfusionMatrix
>>> metric = MulticlassConfusionMatrix(num_classes=5)
>>> metric.update(randint(5, (20,)), randint(5, (20,)))
>>> fig_, ax_ = metric.plot()
"""
val = val if val is not None else self.compute()
if not isinstance(val, Tensor):
raise TypeError(f"Expected val to be a single tensor but got {val}")
fig, ax = plot_confusion_matrix(val, ax=ax, add_text=add_text, labels=labels)
return fig, ax
class MulticlassConfusionMatrix(Metric):
r"""Compute the `confusion matrix`_ for multiclass tasks.
The confusion matrix :math:`C` is constructed such that :math:`C_{i, j}` is equal to the number of observations
known to be in class :math:`i` but predicted to be in class :math:`j`. Thus row indices of the confusion matrix
correspond to the true class labels and column indices correspond to the predicted class labels.
For multiclass tasks, the confusion matrix is a NxN matrix, where:
- :math:`C_{i, i}` represents the number of true positives for class :math:`i`
- :math:`\sum_{j=1, j\neq i}^N C_{i, j}` represents the number of false negatives for class :math:`i`
- :math:`\sum_{i=1, i\neq j}^N C_{i, j}` represents the number of false positives for class :math:`i`
- the sum of the remaining cells in the matrix represents the number of true negatives for class :math:`i`
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, ...)``. If preds is a floating point
tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per
element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``confusion_matrix``: [num_classes, num_classes] matrix
Args:
num_classes: Integer specifing the number of classes
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
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
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (pred is integer tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MulticlassConfusionMatrix
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> metric = MulticlassConfusionMatrix(num_classes=3)
>>> metric(preds, target)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])
Example (pred is float tensor):
>>> from torchmetrics.classification import MulticlassConfusionMatrix
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([[0.16, 0.26, 0.58],
... [0.22, 0.61, 0.17],
... [0.71, 0.09, 0.20],
... [0.05, 0.82, 0.13]])
>>> metric = MulticlassConfusionMatrix(num_classes=3)
>>> metric(preds, target)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = None
full_state_update: bool = False
def __init__(
self,
num_classes: int,
ignore_index: Optional[int] = None,
normalize: Optional[Literal["none", "true", "pred", "all"]] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if validate_args:
_multiclass_confusion_matrix_arg_validation(num_classes, ignore_index, normalize)
self.num_classes = num_classes
self.ignore_index = ignore_index
self.normalize = normalize
self.validate_args = validate_args
self.add_state("confmat", torch.zeros(num_classes, num_classes, dtype=torch.long), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
if self.validate_args:
_multiclass_confusion_matrix_tensor_validation(preds, target, self.num_classes, self.ignore_index)
preds, target = _multiclass_confusion_matrix_format(preds, target, self.ignore_index)
confmat = _multiclass_confusion_matrix_update(preds, target, self.num_classes)
self.confmat += confmat
def compute(self) -> Tensor:
"""Compute confusion matrix."""
return _multiclass_confusion_matrix_compute(self.confmat, self.normalize)
def plot(
self,
val: Optional[Tensor] = None,
ax: Optional[_AX_TYPE] = None,
add_text: bool = True,
labels: Optional[List[str]] = None,
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
add_text: if the value of each cell should be added to the plot
labels: a list of strings, if provided will be added to the plot to indicate the different classes
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> from torch import randint
>>> from torchmetrics.classification import MulticlassConfusionMatrix
>>> metric = MulticlassConfusionMatrix(num_classes=5)
>>> metric.update(randint(5, (20,)), randint(5, (20,)))
>>> fig_, ax_ = metric.plot()
"""
val = val if val is not None else self.compute()
if not isinstance(val, Tensor):
raise TypeError(f"Expected val to be a single tensor but got {val}")
fig, ax = plot_confusion_matrix(val, ax=ax, add_text=add_text, labels=labels)
return fig, ax
class MultilabelConfusionMatrix(Metric):
r"""Compute the `confusion matrix`_ for multilabel tasks.
The confusion matrix :math:`C` is constructed such that :math:`C_{i, j}` is equal to the number of observations
known to be in class :math:`i` but predicted to be in class :math:`j`. Thus row indices of the confusion matrix
correspond to the true class labels and column indices correspond to the predicted class labels.
For multilabel tasks, the confusion matrix is a Nx2x2 tensor, where each 2x2 matrix corresponds to the confusion
for that label. The structure of each 2x2 matrix is as follows:
- :math:`C_{0, 0}`: True negatives
- :math:`C_{0, 1}`: False positives
- :math:`C_{1, 0}`: False negatives
- :math:`C_{1, 1}`: True positives
As input to 'update' the metric accepts the following input:
- ``preds`` (int or float tensor): ``(N, C, ...)``. If preds is a floating point tensor with values outside
[0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally,
we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (int tensor): ``(N, C, ...)``
As output of 'compute' the metric returns the following output:
- ``confusion matrix``: [num_labels,2,2] matrix
Args:
num_classes: Integer specifing the number of labels
threshold: Threshold for transforming probability to binary (0,1) predictions
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
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
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MultilabelConfusionMatrix
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelConfusionMatrix(num_labels=3)
>>> metric(preds, target)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])
Example (preds is float tensor):
>>> from torchmetrics.classification import MultilabelConfusionMatrix
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> metric = MultilabelConfusionMatrix(num_labels=3)
>>> metric(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
def __init__(
self,
num_labels: int,
threshold: float = 0.5,
ignore_index: Optional[int] = None,
normalize: Optional[Literal["none", "true", "pred", "all"]] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if validate_args:
_multilabel_confusion_matrix_arg_validation(num_labels, threshold, ignore_index, normalize)
self.num_labels = num_labels
self.threshold = threshold
self.ignore_index = ignore_index
self.normalize = normalize
self.validate_args = validate_args
self.add_state("confmat", torch.zeros(num_labels, 2, 2, dtype=torch.long), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
if self.validate_args:
_multilabel_confusion_matrix_tensor_validation(preds, target, self.num_labels, self.ignore_index)
preds, target = _multilabel_confusion_matrix_format(
preds, target, self.num_labels, self.threshold, self.ignore_index
)
confmat = _multilabel_confusion_matrix_update(preds, target, self.num_labels)
self.confmat += confmat
def compute(self) -> Tensor:
"""Compute confusion matrix."""
return _multilabel_confusion_matrix_compute(self.confmat, self.normalize)
def plot(
self,
val: Optional[Tensor] = None,
ax: Optional[_AX_TYPE] = None,
add_text: bool = True,
labels: Optional[List[str]] = None,
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
add_text: if the value of each cell should be added to the plot
labels: a list of strings, if provided will be added to the plot to indicate the different classes
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> from torch import randint
>>> from torchmetrics.classification import MulticlassConfusionMatrix
>>> metric = MulticlassConfusionMatrix(num_classes=5)
>>> metric.update(randint(5, (20,)), randint(5, (20,)))
>>> fig_, ax_ = metric.plot()
"""
val = val if val is not None else self.compute()
if not isinstance(val, Tensor):
raise TypeError(f"Expected val to be a single tensor but got {val}")
fig, ax = plot_confusion_matrix(val, ax=ax, add_text=add_text, labels=labels)
return fig, ax
class ConfusionMatrix(_ClassificationTaskWrapper):
r"""Compute the `confusion matrix`_.
This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
:mod:`BinaryConfusionMatrix`, :mod:`MulticlassConfusionMatrix` and :func:`MultilabelConfusionMatrix` for
the specific details of each argument influence and examples.
Legacy Example:
>>> from torch import tensor
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
>>> confmat = ConfusionMatrix(task="binary", num_classes=2)
>>> confmat(preds, target)
tensor([[2, 0],
[1, 1]])
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> confmat = ConfusionMatrix(task="multiclass", num_classes=3)
>>> confmat(preds, target)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> confmat = ConfusionMatrix(task="multilabel", num_labels=3)
>>> confmat(preds, target)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])
"""
def __new__(
cls,
task: Literal["binary", "multiclass", "multilabel"],
threshold: float = 0.5,
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
normalize: Optional[Literal["true", "pred", "all", "none"]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> Metric:
"""Initialize task metric."""
task = ClassificationTask.from_str(task)
kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
if task == ClassificationTask.BINARY:
return BinaryConfusionMatrix(threshold, **kwargs)
if task == ClassificationTask.MULTICLASS:
if not isinstance(num_classes, int):
raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
return MulticlassConfusionMatrix(num_classes, **kwargs)
if task == ClassificationTask.MULTILABEL:
if not isinstance(num_labels, int):
raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
return MultilabelConfusionMatrix(num_labels, threshold, **kwargs)
return None