# sklearn.metrics.homogeneity_score¶

sklearn.metrics.homogeneity_score(labels_true, labels_pred)[source]

Homogeneity metric of a cluster labeling given a ground truth.

A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.

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 not symmetric: switching label_true with label_pred will return the completeness_score which will be different in general.

Read more in the User Guide.

Parameters
labels_trueint array, shape = [n_samples]

Ground truth class labels to be used as a reference.

labels_predarray-like of shape (n_samples,)

Cluster labels to evaluate.

Returns
homogeneityfloat

Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling.

completeness_score

Completeness metric of cluster labeling.

v_measure_score

V-Measure (NMI with arithmetic mean option).

References

1

Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A conditional entropy-based external cluster evaluation measure

Examples

Perfect labelings are homogeneous:

>>> from sklearn.metrics.cluster import homogeneity_score
>>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0])
1.0


Non-perfect labelings that further split classes into more clusters can be perfectly homogeneous:

>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
1.000000
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
1.000000


Clusters that include samples from different classes do not make for an homogeneous labeling:

>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
0.0...
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
0.0...