def test_transform_function(self):
"""
Tests that the transform function works properly by transforming the output of the
compute method of the black-box function.
"""
def perf_function(x):
return x + 2
bb_obj = BBOptimizer(
black_box=self.parabola,
heuristic="surrogate_model",
max_iteration=nbr_iteration,
parameter_space=parameter_space,
perf_function=perf_function,
regression_model=GaussianProcessRegressor,
next_parameter_strategy=expected_improvement,
)
self.assertEqual(
bb_obj.compute_result(np.array([5, 0])),
27,
"Use of a transformation "
"function black-box output "
"did not work properly.",
)
def test_evaluate_fitness_single_item(self):
"""
Tests that the fitness is properly evaluated when applied on a single numpy array.
"""
test_parameters = np.array([2, 3])
expected_result = np.array([13])
bb_obj = BBOptimizer(
black_box=self.parabola,
heuristic="surrogate_model",
max_iteration=nbr_iteration,
time_out=time_out,
parameter_space=parameter_space,
next_parameter_strategy=expected_improvement,
regression_model=GaussianProcessRegressor,
)
result = bb_obj.compute_result(test_parameters)
np.testing.assert_array_equal(
expected_result,
result,
"Fitness evaluation on a parameter "
"single item did not work as "
"expected.",
)