completeness_score#
- sklearn.metrics.completeness_score(labels_true,labels_pred)[source]#
Compute completeness metric of a cluster labeling given a ground truth.
A clustering result satisfies completeness if all the data pointsthat are members of a given class are elements of the same cluster.
This metric is independent of the absolute values of the labels:a permutation of the class or cluster label values won’t change thescore value in any way.
This metric is not symmetric: switching
label_truewithlabel_predwill return thehomogeneity_scorewhich will be different ingeneral.Read more in theUser Guide.
- Parameters:
- labels_truearray-like of 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:
- completenessfloat
Score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling.
See also
homogeneity_scoreHomogeneity metric of cluster labeling.
v_measure_scoreV-Measure (NMI with arithmetic mean option).
References
Examples
Perfect labelings are complete:
>>>fromsklearn.metrics.clusterimportcompleteness_score>>>completeness_score([0,0,1,1],[1,1,0,0])1.0
Non-perfect labelings that assign all classes members to the same clustersare still complete:
>>>print(completeness_score([0,0,1,1],[0,0,0,0]))1.0>>>print(completeness_score([0,1,2,3],[0,0,1,1]))0.999
If classes members are split across different clusters, theassignment cannot be complete:
>>>print(completeness_score([0,0,1,1],[0,1,0,1]))0.0>>>print(completeness_score([0,0,0,0],[0,1,2,3]))0.0
Gallery examples#
A demo of K-Means clustering on the handwritten digits data
