# Cohen Kappa¶

## Module Interface¶

class torchmetrics.CohenKappa(**kwargs)[source]

Calculate Cohen’s kappa score that measures inter-annotator agreement.

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

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:
>>> from torch import tensor
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
>>> cohenkappa(preds, target)
tensor(0.5000)

static __new__(cls, task, threshold=0.5, num_classes=None, weights=None, ignore_index=None, validate_args=True, **kwargs)[source]

Return type:

Metric

### BinaryCohenKappa¶

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

Calculate Cohen’s kappa score that measures inter-annotator agreement for binary tasks.

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

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. Additionally, 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

• 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 torch import tensor
>>> from torchmetrics.classification import BinaryCohenKappa
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
>>> metric = BinaryCohenKappa()
>>> metric(preds, target)
tensor(0.5000)

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

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import BinaryCohenKappa
>>> metric = BinaryCohenKappa()
>>> metric.update(rand(10), randint(2,(10,)))
>>> fig_, ax_ = metric.plot()

>>> from torch import rand, randint
>>> # Example plotting multiple values
>>> from torchmetrics.classification import BinaryCohenKappa
>>> metric = BinaryCohenKappa()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(rand(10), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)


### MulticlassCohenKappa¶

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

Calculate Cohen’s kappa score that measures inter-annotator agreement for multiclass tasks.

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

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:

Parameters:
• num_classes (int) – Integer specifying 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 torch import tensor
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> target = tensor([2, 1, 0, 0])
>>> preds = 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 = 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 = MulticlassCohenKappa(num_classes=3)
>>> metric(preds, target)
tensor(0.6364)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> metric.update(randn(20,3).softmax(dim=-1), randint(3, (20,)))
>>> fig_, ax_ = metric.plot()

>>> from torch import randn, randint
>>> # Example plotting a multiple values
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> values = []
>>> for _ in range(20):
...     values.append(metric(randn(20,3).softmax(dim=-1), randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)


## 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]

Calculate Cohen’s kappa score that measures inter-annotator agreement. It is defined as. :rtype: Tensor

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

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:
>>> from torch import tensor
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
tensor(0.5000)


### binary_cohen_kappa¶

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

Calculate Cohen’s kappa score that measures inter-annotator agreement for binary tasks.

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

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. Additionally, 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:
Return type:

Tensor

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


### multiclass_cohen_kappa¶

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

Calculate Cohen’s kappa score that measures inter-annotator agreement for multiclass tasks.

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

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:
Return type:

Tensor

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