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 \(U_i\) is the number of the samples in cluster \(U_i\) and \(V_j\) is the number of the samples in cluster \(V_j\), the Mutual Information between clusterings \(U\) and \(V\) is given as:
\[MI(U,V)=\sum_{i=1}^{U} \sum_{j=1}^{V} \frac{U_i\cap V_j}{N} \log\frac{NU_i \cap V_j}{U_iV_j}\]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_trueint array, shape = [n_samples]
A clustering of the data into disjoint subsets.
 labels_predint arraylike of shape (n_samples,)
A clustering of the data into disjoint subsets.
 contingency{None, array, sparse matrix}, 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
 mifloat
Mutual information, a nonnegative value
See also
adjusted_mutual_info_score
Adjusted against chance Mutual Information
normalized_mutual_info_score
Normalized Mutual Information
Notes
The logarithm used is the natural logarithm (basee).