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]))
def test_initialize(self):
"""Tests that the initialization step happens correctly, by appending the proper
number of fitness and parametrization values."""
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,
)
bb_obj._initialize(callbacks=[lambda x: x])
self.assertTrue(len(bb_obj.history["fitness"]), 2)
self.assertTrue(len(bb_obj.history["parameters"]), 2)
def test_initialize_callback(self):
"""Tests that the initialization step happens correctly when using a callback function,
which prints the square root of the sum of the fitness values and the sum of the parameters"""
np.random.seed(10)
# Capture the stdout to check if the print happenned correctly
captured_output = io.StringIO()
sys.stdout = captured_output
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,
)
bb_obj._initialize(callbacks=[mock_callback_1])
# Redirect the stdout to not mess with the system
sys.stdout = sys.__stdout__
expected = "Result: 3 + 5\nResult: 4 + 6\n"
self.assertEqual(expected, captured_output.getvalue())
