def test_kernel_theta():
    """ Check that parameter vector theta of kernel is set correctly. """
    for kernel in kernels:
        if isinstance(kernel, KernelOperator) \
           or isinstance(kernel, Exponentiation):  # skip non-basic kernels
            continue
        theta = kernel.theta
        _, K_gradient = kernel(X, eval_gradient=True)

        # Determine kernel parameters that contribute to theta
        init_sign = signature(kernel.__class__.__init__).parameters.values()
        args = [p.name for p in init_sign if p.name != 'self']
        theta_vars = map(lambda s: s.rstrip("_bounds"),
                         filter(lambda s: s.endswith("_bounds"), args))
        assert_equal(
            set(hyperparameter.name
                for hyperparameter in kernel.hyperparameters),
            set(theta_vars))

        # Check that values returned in theta are consistent with
        # hyperparameter values (being their logarithms)
        for i, hyperparameter in enumerate(kernel.hyperparameters):
            assert_equal(theta[i],
                         np.log(getattr(kernel, hyperparameter.name)))

        # Fixed kernel parameters must be excluded from theta and gradient.
        for i, hyperparameter in enumerate(kernel.hyperparameters):
            # create copy with certain hyperparameter fixed
            params = kernel.get_params()
            params[hyperparameter.name + "_bounds"] = "fixed"
            kernel_class = kernel.__class__
            new_kernel = kernel_class(**params)
            # Check that theta and K_gradient are identical with the fixed
            # dimension left out
            _, K_gradient_new = new_kernel(X, eval_gradient=True)
            assert_equal(theta.shape[0], new_kernel.theta.shape[0] + 1)
            assert_equal(K_gradient.shape[2], K_gradient_new.shape[2] + 1)
            if i > 0:
                assert_equal(theta[:i], new_kernel.theta[:i])
                assert_array_equal(K_gradient[..., :i],
                                   K_gradient_new[..., :i])
            if len(kernel.hyperparameters) < i + 1:
                assert_equal(theta[i+1:], new_kernel.theta[i:])
                assert_array_equal(K_gradient[..., i+1:],
                                   K_gradient_new[..., i:])

        # Check that values of theta are modified correctly
        for i, hyperparameter in enumerate(kernel.hyperparameters):
            theta[i] = np.log(42)
            kernel.theta = theta
            assert_almost_equal(getattr(kernel, hyperparameter.name), 42)

            setattr(kernel, hyperparameter.name, 43)
            assert_almost_equal(kernel.theta[i], np.log(43))
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/gaussian_process/tests/test_kernels.py/test_kernel_theta/swapped operands of `<`