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sklearn.metrics.homogeneity_score

sklearn.metrics.homogeneity_score(labels_true, labels_pred)

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.

Parameters:

labels_true : int array, shape = [n_samples]

ground truth class labels to be used as a reference

labels_pred : array, shape = [n_samples]

cluster labels to evaluate

Returns:

homogeneity: float :

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

References

[R155]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]))
...                                                  
1.0...
>>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
...                                                  
1.0...

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...
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