def _dim_two_gp(mean_shift: Tuple[float, float] = (0.0, 0.0)) -> GaussianProcess: matern52 = tfp.math.psd_kernels.MaternFiveHalves( amplitude=tf.cast(2.3, tf.float64), length_scale=tf.cast(0.5, tf.float64) ) return GaussianProcess( [lambda x: mean_shift[0] + branin(x), lambda x: mean_shift[1] + quadratic(x)], [matern52, rbf()], )
def test_batch_monte_carlo_expected_improvement_raises_for_model_with_wrong_event_shape( ) -> None: builder = BatchMonteCarloExpectedImprovement(100) data = mk_dataset([(0.0, 0.0)], [(0.0, 0.0)]) matern52 = tfp.math.psd_kernels.MaternFiveHalves( amplitude=tf.cast(2.3, tf.float64), length_scale=tf.cast(0.5, tf.float64)) model = GaussianProcess([lambda x: branin(x), lambda x: quadratic(x)], [matern52, rbf()]) with pytest.raises(TF_DEBUGGING_ERROR_TYPES): builder.prepare_acquisition_function(data, model)
def test_expected_constrained_improvement_is_less_for_constrained_points() -> None: class _Constraint(AcquisitionFunctionBuilder): def prepare_acquisition_function( self, datasets: Mapping[str, Dataset], models: Mapping[str, ProbabilisticModel] ) -> AcquisitionFunction: return lambda x: tf.cast(x >= 0, x.dtype) def two_global_minima(x: tf.Tensor) -> tf.Tensor: return x ** 4 / 4 - x ** 2 / 2 initial_query_points = tf.constant([[-2.0], [0.0], [1.2]]) data = {"foo": Dataset(initial_query_points, two_global_minima(initial_query_points))} models_ = {"foo": GaussianProcess([two_global_minima], [rbf()])} eci = ExpectedConstrainedImprovement("foo", _Constraint()).prepare_acquisition_function( data, models_ ) npt.assert_array_less(eci(tf.constant([-1.0])), eci(tf.constant([1.0])))
def __init__(self): super().__init__([lambda x: 2 * x], [rbf()])
def __init__(self): super().__init__([lambda x: tf.exp(-x)], [rbf()])