RuleTree.evaluation.evaluate_clustering

evaluate_clustering(labels_true, labels_pred) → dict

Agreement between a clustering and known classes (external metrics).

Parameters:

• labels_true (array-like of shape (n_samples,)) – Known classes.

• labels_pred (array-like of shape (n_samples,)) – Cluster labels.

Returns:

scores – adjusted_rand, rand_score, adjusted_mutual_info, mutual_info, normalized_mutual_info, homogeneity, completeness, v_measure and fowlkes_mallows.

Return type:

dict

See also

evaluate_clustering_internal

metrics that do not need the classes.

Examples

>>> from sklearn.datasets import make_blobs
>>> from RuleTree import RuleTreeCluster
>>> from RuleTree.evaluation import evaluate_clustering
>>> X, y = make_blobs(n_samples=300, centers=3, cluster_std=0.5, random_state=0)
>>> labels = RuleTreeCluster(max_depth=3, random_state=0).fit_predict(X)
>>> round(evaluate_clustering(y, labels)["adjusted_rand"], 2)
0.95