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Adjusted Mutual Information between two clusterings.

Adjusted Mutual Information (AMI) is an adjustment of the Mutual Information score that accounts for chance. It corrects the effect of agreement solely due to chance between clusterings, similar to the way the Adjusted Rand Index corrects the Rand Index. It is closely related to variation of information. The adjusted measure, however, is no longer metrical.

For two clusterings \(U\) and \(V\), the Adjusted Mutual Information is calculated as:

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

This metric is independent of the permutation of the class or cluster label values; furthermore, it is also symmetric. This can be useful to measure the agreement of two label assignments strategies on the same dataset, regardless of the ground truth.

However, due to the complexity of the Expected Mutual Info Score, the computation of this metric is an order of magnitude slower than most other metrics, in general.


  • cm – defaults to None

    This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.

  • average_method – defaults to arithmetic

    This parameter defines how to compute the normalizer in the denominator. Possible options include min, max, arithmetic and geometric.


  • bigger_is_better

    Indicate if a high value is better than a low one or not.

  • requires_labels

    Indicates if labels are required, rather than probabilities.

  • works_with_weights

    Indicate whether the model takes into consideration the effect of sample weights


>>> from river import metrics

>>> y_true = [1, 1, 2, 2, 3, 3]
>>> y_pred = [1, 1, 1, 2, 2, 2]

>>> metric = metrics.AdjustedMutualInfo()
>>> for yt, yp in zip(y_true, y_pred):
...     print(metric.update(yt, yp).get())

>>> metric
AdjustedMutualInfo: 0.298792



Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.


Return the current value of the metric.


Revert the metric.


  • y_true
  • y_pred
  • sample_weight – defaults to 1.0

Update the metric.


  • y_true
  • y_pred
  • sample_weight – defaults to 1.0

Indicates whether or not a metric can work with a given model.


  • model (river.base.estimator.Estimator)


  1. Wikipedia contributors. (2021, March 17). Mutual information. In Wikipedia, The Free Encyclopedia, from