def test_optimizer_simple_resampling_noresample(self):
"""Tests that the _select_next_parameters_method works as expected
when using simple resampling strategy and the parameters should not be resampled"""
np.random.seed(1)
bb_obj = BBOptimizer(
black_box=self.parabola,
heuristic="surrogate_model",
max_iteration=nbr_iteration,
initial_sample_size=2,
parameter_space=parameter_space,
next_parameter_strategy=expected_improvement,
regression_model=GaussianProcessRegressor,
resampling_policy="simple_resampling",
nbr_resamples=2,
)
bb_obj.history = {
"fitness": np.array([10, 5, 4, 2, 15, 20]),
"parameters": np.array(
[[1, 2, 3], [2, 3, 3], [1, 3, 3], [1, 5, 3], [1, 1, 3], [1, 5, 3]]
),
"truncated": np.array([True, True, True, True, True, True]),
"resampled": np.array([True, True, True, True, True, True]),
"initialization": np.array([True, True, True, True, True, True]),
}
parameter = bb_obj._select_next_parameters()
np.testing.assert_array_equal(parameter, [1, 3, 2])
def test_select_parameters(self):
"""Test the function _select_next_parameters when there is no retry."""
np.random.seed(10)
bb_obj = BBOptimizer(
black_box=self.parabola,
heuristic="surrogate_model",
max_iteration=1,
initial_sample_size=2,
parameter_space=parameter_space,
next_parameter_strategy=expected_improvement,
regression_model=GaussianProcessRegressor,
)
bb_obj._initialize()
parameter = bb_obj._select_next_parameters()
np.testing.assert_array_equal(parameter, np.array([-5, 2, -6]))