Shortcuts

Confusion Matrix¶

Module Interface¶

ConfusionMatrix¶

class torchmetrics.ConfusionMatrix(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: = None, num_labels: = None, normalize: Optional[Literal['true', 'pred', 'all', 'none']] = None, ignore_index: = None, validate_args: bool = True, **kwargs: Any)[source]

Computes 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 BinaryConfusionMatrix, MulticlassConfusionMatrix and MultilabelConfusionMatrix() for the specific details of each argument influence and examples.

Legacy Example:
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat(preds, target)
tensor([[2, 0],
[1, 1]])

>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> confmat(preds, target)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])

>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> confmat(preds, target)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])


BinaryConfusionMatrix¶

class torchmetrics.classification.BinaryConfusionMatrix(threshold=0.5, ignore_index=None, normalize=None, validate_args=True, **kwargs)[source]

Computes the confusion matrix for binary tasks.

As input to forward and update the metric accepts the following input:

• preds (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 (Tensor): An int tensor of shape (N, ...).

Note

Additional dimension ... will be flattened into the batch dimension.

As output to forward and compute the metric returns the following output:

Parameters
• threshold (float) – Threshold for transforming probability to binary (0,1) predictions

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• normalize (Optional[Literal[‘true’, ‘pred’, ‘all’, ‘none’]]) –

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) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings 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]])


Initializes internal Module state, shared by both nn.Module and ScriptModule.

MulticlassConfusionMatrix¶

class torchmetrics.classification.MulticlassConfusionMatrix(num_classes, ignore_index=None, normalize=None, validate_args=True, **kwargs)[source]

Computes the confusion matrix for multiclass tasks.

As input to forward and update the metric accepts the following input:

• preds (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 (Tensor): An int tensor of shape (N, ...).

Note

Additional dimension ... will be flattened into the batch dimension.

As output to forward and compute the metric returns the following output:

As input to ‘update’ the metric accepts the following input:

• preds: (N, ...) (int tensor) or (N, C, ..) (float tensor). If preds is a floating point we apply torch.argmax along the C dimension to automatically convert probabilities/logits into an int tensor.

• target (int tensor): (N, ...)

Additional dimension ... will be flattened into the batch dimension.

As output of ‘compute’ the metric returns the following output:

• confusion matrix: [num_classes, num_classes] matrix

Parameters
• num_classes (int) – Integer specifing the number of classes

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• normalize (Optional[Literal[‘none’, ‘true’, ‘pred’, ‘all’]]) –

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) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example (pred is integer tensor):
>>> from torchmetrics.classification import MulticlassConfusionMatrix
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.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 = torch.tensor([2, 1, 0, 0])
>>> preds = torch.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]])


Initializes internal Module state, shared by both nn.Module and ScriptModule.

MultilabelConfusionMatrix¶

class torchmetrics.classification.MultilabelConfusionMatrix(num_labels, threshold=0.5, ignore_index=None, normalize=None, validate_args=True, **kwargs)[source]

Computes the confusion matrix for multilabel tasks.

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, ...)

Additional dimension ... will be flattened into the batch dimension.

As output of ‘compute’ the metric returns the following output:

• confusion matrix: [num_labels,2,2] matrix

Parameters
• num_classes – Integer specifing the number of labels

• threshold (float) – Threshold for transforming probability to binary (0,1) predictions

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• normalize (Optional[Literal[‘none’, ‘true’, ‘pred’, ‘all’]]) –

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) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example (preds is int tensor):
>>> from torchmetrics.classification import MultilabelConfusionMatrix
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.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]]])


Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

confusion_matrix¶

torchmetrics.functional.confusion_matrix(preds, target, task, threshold=0.5, num_classes=None, num_labels=None, normalize=None, ignore_index=None, validate_args=True)[source]

Computes 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 binary_confusion_matrix(), multiclass_confusion_matrix() and multilabel_confusion_matrix() for the specific details of each argument influence and examples.

Legacy Example:
>>> from torchmetrics import ConfusionMatrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat(preds, target)
tensor([[2, 0],
[1, 1]])

>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> confmat(preds, target)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])

>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> confmat(preds, target)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])

Return type

Tensor

binary_confusion_matrix¶

torchmetrics.functional.classification.binary_confusion_matrix(preds, target, threshold=0.5, normalize=None, ignore_index=None, validate_args=True)[source]

Computes the confusion matrix for binary tasks.

Accepts the following input tensors:

• preds (int or float tensor): (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 (int tensor): (N, ...)

Additional dimension ... will be flattened into the batch dimension.

Parameters
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• threshold (float) – Threshold for transforming probability to binary (0,1) predictions

• normalize (Optional[Literal[‘true’, ‘pred’, ‘all’, ‘none’]]) –

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

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Tensor

Returns

A [2, 2] tensor

Example (preds is int tensor):
>>> from torchmetrics.functional.classification import binary_confusion_matrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> binary_confusion_matrix(preds, target)
tensor([[2, 0],
[1, 1]])

Example (preds is float tensor):
>>> from torchmetrics.functional.classification import binary_confusion_matrix
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01])
>>> binary_confusion_matrix(preds, target)
tensor([[2, 0],
[1, 1]])


multiclass_confusion_matrix¶

torchmetrics.functional.classification.multiclass_confusion_matrix(preds, target, num_classes, normalize=None, ignore_index=None, validate_args=True)[source]

Computes the confusion matrix for multiclass tasks.

Accepts the following input tensors:

• preds: (N, ...) (int tensor) or (N, C, ..) (float tensor). If preds is a floating point we apply torch.argmax along the C dimension to automatically convert probabilities/logits into an int tensor.

• target (int tensor): (N, ...)

Additional dimension ... will be flattened into the batch dimension.

Parameters
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_classes (int) – Integer specifing the number of classes

• normalize (Optional[Literal[‘true’, ‘pred’, ‘all’, ‘none’]]) –

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

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Tensor

Returns

A [num_classes, num_classes] tensor

Example (pred is integer tensor):
>>> from torchmetrics.functional.classification import multiclass_confusion_matrix
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> multiclass_confusion_matrix(preds, target, num_classes=3)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])

Example (pred is float tensor):
>>> from torchmetrics.functional.classification import multiclass_confusion_matrix
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.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],
... ])
>>> multiclass_confusion_matrix(preds, target, num_classes=3)
tensor([[1, 1, 0],
[0, 1, 0],
[0, 0, 1]])


multilabel_confusion_matrix¶

torchmetrics.functional.classification.multilabel_confusion_matrix(preds, target, num_labels, threshold=0.5, normalize=None, ignore_index=None, validate_args=True)[source]

Computes the confusion matrix for multilabel tasks.

Accepts the following input tensors:

• 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, ...)

Additional dimension ... will be flattened into the batch dimension.

Parameters
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_labels (int) – Integer specifing the number of labels

• threshold (float) – Threshold for transforming probability to binary (0,1) predictions

• normalize (Optional[Literal[‘true’, ‘pred’, ‘all’, ‘none’]]) –

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

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Tensor

Returns

A [num_labels, 2, 2] tensor

Example (preds is int tensor):
>>> from torchmetrics.functional.classification import multilabel_confusion_matrix
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_confusion_matrix(preds, target, num_labels=3)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])

Example (preds is float tensor):
>>> from torchmetrics.functional.classification import multilabel_confusion_matrix
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> multilabel_confusion_matrix(preds, target, num_labels=3)
tensor([[[1, 0], [0, 1]],
[[1, 0], [1, 0]],
[[0, 1], [0, 1]]])


© Copyright Copyright (c) 2020-2023, Lightning-AI et al... Revision 825d17f3.

Built with Sphinx using a theme provided by Read the Docs.
Versions
latest
stable
v0.11.3
v0.11.2
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0