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Cohen Kappa

Module Interface

CohenKappa

class torchmetrics.CohenKappa(task: Literal['binary', 'multiclass'], threshold: float = 0.5, num_classes: Optional[int] = None, weights: Optional[Literal['linear', 'quadratic', 'none']] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any)[source]

Calculates Cohen’s kappa score that measures inter-annotator agreement. It is defined as.

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

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' or 'multiclass'. See the documentation of BinaryCohenKappa and MulticlassCohenKappa 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])
>>> cohenkappa = CohenKappa(task="multiclass", num_classes=2)
>>> cohenkappa(preds, target)
tensor(0.5000)

BinaryCohenKappa

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

Calculates Cohen’s kappa score that measures inter-annotator agreement for binary tasks. It is defined as.

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

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

  • preds (Tensor): A 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:

  • bck (Tensor): A tensor containing cohen kappa score

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

  • weights (Optional[Literal[‘linear’, ‘quadratic’, ‘none’]]) –

    Weighting type to calculate the score. Choose from:

    • None or 'none': no weighting

    • 'linear': linear weighting

    • 'quadratic': quadratic weighting

  • 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 BinaryCohenKappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> metric = BinaryCohenKappa()
>>> metric(preds, target)
tensor(0.5000)
Example (preds is float tensor):
>>> from torchmetrics.classification import BinaryCohenKappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01])
>>> metric = BinaryCohenKappa()
>>> metric(preds, target)
tensor(0.5000)

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

MulticlassCohenKappa

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

Calculates Cohen’s kappa score that measures inter-annotator agreement for multiclass tasks. It is defined as.

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

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

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

  • 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:

  • mcck (Tensor): A tensor containing cohen kappa score

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

  • weights (Optional[Literal[‘linear’, ‘quadratic’, ‘none’]]) –

    Weighting type to calculate the score. Choose from:

    • None or 'none': no weighting

    • 'linear': linear weighting

    • 'quadratic': quadratic weighting

  • 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 MulticlassCohenKappa
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> metric(preds, target)
tensor(0.6364)
Example (pred is float tensor):
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> 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 = MulticlassCohenKappa(num_classes=3)
>>> metric(preds, target)
tensor(0.6364)

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

Functional Interface

cohen_kappa

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

Calculates Cohen’s kappa score that measures inter-annotator agreement. It is defined as.

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

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' or 'multiclass'. See the documentation of binary_cohen_kappa() and multiclass_cohen_kappa() 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])
>>> cohen_kappa(preds, target, task="multiclass", num_classes=2)
tensor(0.5000)
Return type

Tensor

binary_cohen_kappa

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

Calculates Cohen’s kappa score that measures inter-annotator agreement for binary tasks. It is defined as.

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

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

  • weights (Optional[Literal[‘linear’, ‘quadratic’, ‘none’]]) –

    Weighting type to calculate the score. Choose from:

    • None or 'none': no weighting

    • 'linear': linear weighting

    • 'quadratic': quadratic weighting

  • 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.

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

Example (preds is int tensor):
>>> from torchmetrics.functional.classification import binary_cohen_kappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> binary_cohen_kappa(preds, target)
tensor(0.5000)
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import binary_cohen_kappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01])
>>> binary_cohen_kappa(preds, target)
tensor(0.5000)
Return type

Tensor

multiclass_cohen_kappa

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

Calculates Cohen’s kappa score that measures inter-annotator agreement for multiclass tasks. It is defined as.

\kappa = (p_o - p_e) / (1 - p_e)

where p_o is the empirical probability of agreement and p_e is the expected agreement when both annotators assign labels randomly. Note that p_e is estimated using a per-annotator empirical prior over the class labels.

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

  • weights (Optional[Literal[‘linear’, ‘quadratic’, ‘none’]]) –

    Weighting type to calculate the score. Choose from:

    • None or 'none': no weighting

    • 'linear': linear weighting

    • 'quadratic': quadratic weighting

  • 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.

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

Example (pred is integer tensor):
>>> from torchmetrics.functional.classification import multiclass_cohen_kappa
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> multiclass_cohen_kappa(preds, target, num_classes=3)
tensor(0.6364)
Example (pred is float tensor):
>>> from torchmetrics.functional.classification import multiclass_cohen_kappa
>>> 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_cohen_kappa(preds, target, num_classes=3)
tensor(0.6364)
Return type

Tensor

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