Adjusted Mutual Information Score

Module Interface

class torchmetrics.clustering.AdjustedMutualInfoScore(average_method='arithmetic', **kwargs)

Compute Adjusted Mutual Information Score.

\[AMI(U,V) = \frac{MI(U,V) - E(MI(U,V))}{avg(H(U), H(V)) - E(MI(U,V))}\]

Where \(U\) is a tensor of target values, \(V\) is a tensor of predictions, \(M_p(U,V)\) is the generalized mean of order \(p\) of \(U\) and \(V\), and \(MI(U,V)\) is the mutual information score between clusters \(U\) and \(V\). The metric is symmetric, therefore swapping \(U\) and \(V\) yields the same mutual information score.

This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not be available in practice since clustering in generally is used for unsupervised learning.

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

• preds (Tensor): single integer tensor with shape (N,) with predicted cluster labels

• target (Tensor): single integer tensor with shape (N,) with ground truth cluster labels

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

• ami_score (Tensor): A tensor with the Adjusted Mutual Information Score

Parameters:

• average_method (Literal['min', 'geometric', 'arithmetic', 'max']) – Method used to calculate generalized mean for normalization. Choose between 'min', 'geometric', 'arithmetic', 'max'.

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

Example::

>>> import torch
>>> from torchmetrics.clustering import AdjustedMutualInfoScore
>>> preds = torch.tensor([2, 1, 0, 1, 0])
>>> target = torch.tensor([0, 2, 1, 1, 0])
>>> ami_score = AdjustedMutualInfoScore(average_method="arithmetic")
>>> ami_score(preds, target)
tensor(-0.2500)

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – 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 (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.clustering import AdjustedMutualInfoScore
>>> metric = AdjustedMutualInfoScore()
>>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
>>> fig_, ax_ = metric.plot(metric.compute())

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.clustering import AdjustedMutualInfoScore
>>> metric = AdjustedMutualInfoScore()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))))
>>> fig_, ax_ = metric.plot(values)

higher_is_better: Optional[bool] = None

Functional Interface

torchmetrics.functional.clustering.adjusted_mutual_info_score(preds, target, average_method='arithmetic')

Compute adjusted mutual information between two clusterings.

Parameters:

• preds (Tensor) – predicted cluster labels

• target (Tensor) – ground truth cluster labels

• average_method (Literal['min', 'geometric', 'arithmetic', 'max']) – normalizer computation method

Return type:

Tensor

Returns:

Scalar tensor with adjusted mutual info score between 0.0 and 1.0

Example

>>> from torchmetrics.functional.clustering import adjusted_mutual_info_score
>>> preds = torch.tensor([2, 1, 0, 1, 0])
>>> target = torch.tensor([0, 2, 1, 1, 0])
>>> adjusted_mutual_info_score(preds, target, "arithmetic")
tensor(-0.2500)