test_neighbors.py 文件源码

def test_neighbors_badargs():
    # Test bad argument values: these should all raise ValueErrors
    assert_raises(ValueError,
                  neighbors.NearestNeighbors,
                  algorithm='blah')

    X = rng.random_sample((10, 2))
    Xsparse = csr_matrix(X)
    y = np.ones(10)

    for cls in (neighbors.KNeighborsClassifier,
                neighbors.RadiusNeighborsClassifier,
                neighbors.KNeighborsRegressor,
                neighbors.RadiusNeighborsRegressor):
        assert_raises(ValueError,
                      cls,
                      weights='blah')
        assert_raises(ValueError,
                      cls, p=-1)
        assert_raises(ValueError,
                      cls, algorithm='blah')
        nbrs = cls(algorithm='ball_tree', metric='haversine')
        assert_raises(ValueError,
                      nbrs.predict,
                      X)
        assert_raises(ValueError,
                      ignore_warnings(nbrs.fit),
                      Xsparse, y)
        nbrs = cls()
        assert_raises(ValueError,
                      nbrs.fit,
                      np.ones((0, 2)), np.ones(0))
        assert_raises(ValueError,
                      nbrs.fit,
                      X[:, :, None], y)
        nbrs.fit(X, y)
        assert_raises(ValueError,
                      nbrs.predict,
                      [[]])
        if (isinstance(cls, neighbors.KNeighborsClassifier) or
                isinstance(cls, neighbors.KNeighborsRegressor)):
            nbrs = cls(n_neighbors=-1)
            assert_raises(ValueError, nbrs.fit, X, y)

    nbrs = neighbors.NearestNeighbors().fit(X)

    assert_raises(ValueError, nbrs.kneighbors_graph, X, mode='blah')
    assert_raises(ValueError, nbrs.radius_neighbors_graph, X, mode='blah')