test_unsupervised.py 文件源码

def test_silhouette():
    # Tests the Silhouette Coefficient.
    dataset = datasets.load_iris()
    X = dataset.data
    y = dataset.target
    D = pairwise_distances(X, metric='euclidean')
    # Given that the actual labels are used, we can assume that S would be
    # positive.
    silhouette = silhouette_score(D, y, metric='precomputed')
    assert(silhouette > 0)
    # Test without calculating D
    silhouette_metric = silhouette_score(X, y, metric='euclidean')
    assert_almost_equal(silhouette, silhouette_metric)
    # Test with sampling
    silhouette = silhouette_score(D, y, metric='precomputed',
                                  sample_size=int(X.shape[0] / 2),
                                  random_state=0)
    silhouette_metric = silhouette_score(X, y, metric='euclidean',
                                         sample_size=int(X.shape[0] / 2),
                                         random_state=0)
    assert(silhouette > 0)
    assert(silhouette_metric > 0)
    assert_almost_equal(silhouette_metric, silhouette)
    # Test with sparse X
    X_sparse = csr_matrix(X)
    D = pairwise_distances(X_sparse, metric='euclidean')
    silhouette = silhouette_score(D, y, metric='precomputed')
    assert(silhouette > 0)