RuleTree.evaluation.evaluate_clustering_internal

evaluate_clustering_internal(X, labels, metric='euclidean') → dict

Quality of a clustering from the data alone (internal metrics).

Parameters:

• X (array-like of shape (n_samples, n_features), or (n_samples, n_samples) if metric='precomputed') – Numerical data, or the matrix of the distances between the instances.

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

• metric (str or callable, default='euclidean') – Distance used by the silhouette, as in sklearn.metrics.silhouette_score; ‘precomputed’ if X is a distance matrix (then only the silhouette is computed).

Returns:

scores – silhouette_score (the higher the better), calinski_harabasz (the higher the better) and davies_bouldin (the lower the better); nan with a single cluster.

Return type:

dict

See also

evaluate_clustering

metrics that compare the clusters with known classes.

Examples

>>> from sklearn.datasets import make_blobs
>>> from RuleTree import RuleTreeCluster
>>> from RuleTree.evaluation import evaluate_clustering_internal
>>> X, _ = 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_internal(X, labels)["silhouette_score"], 2)
0.59