def test_acq_optimizer_with_time_api(base_estimator, acq_func):
    # TODO: Refactor - Use PyTest
    opt = Optimizer(
        [(-2.0, 2.0)],
        base_estimator=base_estimator,
        acq_func=acq_func,
        acq_optimizer="sampling",
        n_initial_points=2,
    )
    x1 = opt.ask()
    opt.tell(x1, (bench1(x1), 1.0))
    x2 = opt.ask()
    res = opt.tell(x2, (bench1(x2), 2.0))

    # `x1` and `x2` are random.
    assert x1 != x2

    assert len(res.models) == 1
    assert_array_equal(res.func_vals.shape, (2,))
    assert_array_equal(res.log_time.shape, (2,))

    # x3 = opt.ask()
    # TODO: Refactor - Split into separate error test
    with pytest.raises(TypeError):
        opt.tell(x2, bench1(x2))
示例#2
0
def test_acq_optimizer_with_time_api(base_estimator, acq_func):
    opt = Optimizer([(-2.0, 2.0),], base_estimator=base_estimator,
                    acq_func=acq_func,
                    acq_optimizer="sampling", n_initial_points=2)
    x1 = opt.ask()
    opt.tell(x1, (bench1(x1), 1.0))
    x2 = opt.ask()
    res = opt.tell(x2, (bench1(x2), 2.0))

    # x1 and x2 are random.
    assert_true(x1 != x2)

    assert_true(len(res.models) == 1)
    assert_array_equal(res.func_vals.shape, (2,))
    assert_array_equal(res.log_time.shape, (2,))

    # x3 = opt.ask()

    with pytest.raises(TypeError) as e:
        opt.tell(x2, bench1(x2))
示例#3
0
def test_acq_optimizer_with_time_api(base_estimator, acq_func):
    opt = Optimizer([(-2.0, 2.0),], base_estimator=base_estimator,
                    acq_func=acq_func,
                    acq_optimizer="sampling", n_initial_points=2)
    x1 = opt.ask()
    opt.tell(x1, (bench1(x1), 1.0))
    x2 = opt.ask()
    res = opt.tell(x2, (bench1(x2), 2.0))

    # x1 and x2 are random.
    assert x1 != x2

    assert len(res.models) == 1
    assert_array_equal(res.func_vals.shape, (2,))
    assert_array_equal(res.log_time.shape, (2,))

    # x3 = opt.ask()

    with pytest.raises(TypeError) as e:
        opt.tell(x2, bench1(x2))
示例#4
0
 def bench1_with_time(x):
     return bench1(x), np.abs(x[0])