sklearn.metrics
.mutual_info_score¶

sklearn.metrics.
mutual_info_score
(labels_true, labels_pred, contingency=None)[source]¶ Mutual Information between two clusterings
The Mutual Information is a measure of the similarity between two labels of the same data. Where is the probability of a random sample occurring in cluster and is the probability of a random sample occurring in cluster , the Mutual Information between clusterings and is given as:
This is equal to the KullbackLeibler divergence of the joint distribution with the product distribution of the marginals.
This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way.
This metric is furthermore symmetric: switching
label_true
withlabel_pred
will return the same score value. This can be useful to measure the agreement of two independent label assignments strategies on the same dataset when the real ground truth is not known.Read more in the User Guide.
Parameters: labels_true : int array, shape = [n_samples]
A clustering of the data into disjoint subsets.
labels_pred : array, shape = [n_samples]
A clustering of the data into disjoint subsets.
contingency: None or array, shape = [n_classes_true, n_classes_pred] :
A contingency matrix given by the
contingency_matrix
function. If value isNone
, it will be computed, otherwise the given value is used, withlabels_true
andlabels_pred
ignored.Returns: mi: float :
Mutual information, a nonnegative value
See also
adjusted_mutual_info_score
 Adjusted against chance Mutual Information
normalized_mutual_info_score
 Normalized Mutual Information