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_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_global_exploration_cost(self):
"""
Tests that the global exploration cost 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
expected_number_states = 3
expected_performance_cost = 16
real_number_states, real_performance_cost = bb_obj.global_exploration_cost
self.assertEqual(
expected_number_states,
real_number_states,
"Percentage of static moves " "was not computed properly.",
)
self.assertEqual(
expected_performance_cost,
real_performance_cost,
"Percentage of explored " "space was not computed " "properly.",
)
def test_size_explored_space(self):
"""
Tests that the computation of the size of the explored space works properly, as well as
the percentage of static moves.
"""
history = {
"fitness": np.array([10, 5, 4]),
"parameters": np.array([[1, 2, 3], [2, 3, 4], [1, 2, 3]]),
"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
expected_static_moves = 1 / 3 * 100
expected_explored_space = 2 / 1440 * 100
real_explored_space, real_static_moves = bb_obj.size_explored_space
self.assertEqual(
expected_static_moves,
real_static_moves,
"Percentage of static moves " "was not computed properly.",
)
self.assertEqual(
expected_explored_space,
real_explored_space,
"Percentage of explored " "space was not computed " "properly.",
)
def test_append_performance(self):
"""
Tests that appending a new fitness value on the performance history works properly.
"""
# Manually create the history of the BBOptimizer
bb_obj = BBOptimizer(
black_box=self.parabola,
heuristic="surrogate_model",
max_iteration=nbr_iteration,
parameter_space=parameter_space,
next_parameter_strategy=expected_improvement,
regression_model=GaussianProcessRegressor,
)
bb_obj.history = fake_history
# Tests the append method
bb_obj._append_fitness(10)
np.testing.assert_array_equal(
bb_obj.history["fitness"], np.array(
np.array([10, 5, 4, 2, 15, 20, 10]))
)
def test_append_parameters(self):
"""
Tests that the parameters are correctly added to the history.
"""
# Manually create the history of the BBOptimizer
bb_obj = BBOptimizer(
black_box=self.parabola,
heuristic="surrogate_model",
max_iteration=nbr_iteration,
parameter_space=parameter_space,
next_parameter_strategy=expected_improvement,
regression_model=GaussianProcessRegressor,
)
bb_obj.history = fake_history
# Test the append method
bb_obj._append_parameters([1, 3])
np.testing.assert_array_equal(
bb_obj.history["parameters"],
np.array([[1, 2], [2, 3], [1, 3], [4, 3], [2, 1], [1, 5], [1, 3]]),
)
def test_fitness_aggregation_std(self):
"""Tests that the fitness aggregation is performed properly when using the std
as the estimator for the fitness aggregation.
"""
np.random.seed(5)
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,
fitness_aggregation="simple_fitness_aggregation",
estimator=np.std,
)
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]),
}
aggregated_history = bb_obj.fitness_aggregation.transform(
bb_obj.history)
np.testing.assert_array_equal(
aggregated_history["fitness"], np.array([0.0, 0.0, 0.0, 9.0, 0.0])
)
np.testing.assert_array_equal(
aggregated_history["parameters"],
np.array([[1, 2, 3], [2, 3, 3], [1, 3, 3], [1, 5, 3], [1, 1, 3]]),
)