test_logistic.py 文件源码

def test_multinomial_grad_hess():
    rng = np.random.RandomState(0)
    n_samples, n_features, n_classes = 100, 5, 3
    X = rng.randn(n_samples, n_features)
    w = rng.rand(n_classes, n_features)
    Y = np.zeros((n_samples, n_classes))
    ind = np.argmax(np.dot(X, w.T), axis=1)
    Y[range(0, n_samples), ind] = 1
    w = w.ravel()
    sample_weights = np.ones(X.shape[0])
    grad, hessp = _multinomial_grad_hess(w, X, Y, alpha=1.,
                                         sample_weight=sample_weights)
    # extract first column of hessian matrix
    vec = np.zeros(n_features * n_classes)
    vec[0] = 1
    hess_col = hessp(vec)

    # Estimate hessian using least squares as done in
    # test_logistic_grad_hess
    e = 1e-3
    d_x = np.linspace(-e, e, 30)
    d_grad = np.array([
        _multinomial_grad_hess(w + t * vec, X, Y, alpha=1.,
                               sample_weight=sample_weights)[0]
        for t in d_x
        ])
    d_grad -= d_grad.mean(axis=0)
    approx_hess_col = linalg.lstsq(d_x[:, np.newaxis], d_grad)[0].ravel()
    assert_array_almost_equal(hess_col, approx_hess_col)