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NormalizedMutualInfo

Normalized Mutual Information between two clusterings.

Normalized Mutual Information (NMI) is a normalized version of the Mutual Information (MI) score to scale the results between the range of 0 (no mutual information) and 1 (perfectly mutual information). In the formula, the mutual information will be normalized by a generalized mean of the entropy of true and predicted labels, defined by the average_method.

We note that this measure is not adjusted for chance (i.e corrected the effect of result agreement solely due to chance); as a result, the Adjusted Mutual Info Score will mostly be preferred. However, this metric is still symmetric, which means that switching true and predicted labels will not alter the score value. This fact can be useful when the metric is used to measure the agreement between two indepedent label solutions on the same dataset, when the ground truth remains unknown.

Another advantage of the metric is that as it is based on the calculation of entropy-related measures, it is independent of the permutation of class/cluster labels.

Parameters

  • cm

    Default → 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

    Default → arithmetic

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

Attributes

  • 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

Examples

from river import metrics

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

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

1.0
1.0
0.0
0.343711
0.458065
0.515803


metric

NormalizedMutualInfo: 0.515804

Methods

get

Return the current value of the metric.

is_better_than

Indicate if the current metric is better than another one.

Parameters

  • other

revert

Revert the metric.

Parameters

  • y_true
  • y_pred
  • sample_weight — defaults to 1.0

update

Update the metric.

Parameters

  • y_true
  • y_pred
  • sample_weight — defaults to 1.0

works_with

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

Parameters

  1. Wikipedia contributors. (2021, March 17). Mutual information. In Wikipedia, The Free Encyclopedia, from https://en.wikipedia.org/w/index.php?title=Mutual_information&oldid=1012714929 ↩