sklearn.metrics
.homogeneity_completeness_v_measure¶

sklearn.metrics.
homogeneity_completeness_v_measure
(labels_true, labels_pred, beta=1.0)[source]¶ Compute the homogeneity and completeness and VMeasure scores at once.
Those metrics are based on normalized conditional entropy measures of the clustering labeling to evaluate given the knowledge of a Ground Truth class labels of the same samples.
A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.
A clustering result satisfies completeness if all the data points that are members of a given class are elements of the same cluster.
Both scores have positive values between 0.0 and 1.0, larger values being desirable.
Those 3 metrics are independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score values in any way.
VMeasure is furthermore symmetric: swapping
labels_true
andlabel_pred
will give the same score. This does not hold for homogeneity and completeness. VMeasure is identical tonormalized_mutual_info_score
with the arithmetic averaging method.Read more in the User Guide.
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
 beta : float
Ratio of weight attributed to
homogeneity
vscompleteness
. Ifbeta
is greater than 1,completeness
is weighted more strongly in the calculation. Ifbeta
is less than 1,homogeneity
is weighted more strongly.
Returns:  homogeneity : float
score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous labeling
 completeness : float
score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
 v_measure : float
harmonic mean of the first two