def test_fitness_gain_per_iteration(self):
"""
Tests that the fitness gain per iteration is properly computed.
"""
history = {
"fitness": np.array([10, 5, 6, 2, 15, 4]),
"parameters": np.array([[1, 2], [2, 3], [1, 3], [4, 3], [2, 1], [1, 5]]),
"truncated": np.array([True, True, True, True, True, True]),
}
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.history = history
bb_obj.launched = True
expected_gain_per_iteration = np.array([-5, 1, -4, 13, -11])
np.testing.assert_array_equal(
expected_gain_per_iteration,
bb_obj.fitness_gain_per_iteration,
"Computation of fitness gain per iteration did not work as " "expected.",
)
def test_fitness_gain_per_iteration_not_launched(self):
"""
Tests that the fitness gain per iteration is None if the experiment is not launched.
"""
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.launched = False
self.assertEqual(bb_obj.fitness_gain_per_iteration, None)
