def test_multistart_hyperparameter_optimization(self):
"""Check that multistart optimization (gradient descent) can find the optimum hyperparameters."""
random_state = numpy.random.get_state()
numpy.random.seed(87612)
max_num_steps = 200 # this is generally *too few* steps; we configure it this way so the test will run quickly
max_num_restarts = 5
num_steps_averaged = 0
gamma = 0.2
pre_mult = 1.0
max_relative_change = 0.3
tolerance = 1.0e-11
gd_parameters = GradientDescentParameters(
max_num_steps,
max_num_restarts,
num_steps_averaged,
gamma,
pre_mult,
max_relative_change,
tolerance,
)
num_multistarts = 3 # again, too few multistarts; but we want the test to run reasonably quickly
num_sampled = 10
self.gp_test_environment_input.num_sampled = num_sampled
_, gaussian_process = self._build_gaussian_process_test_data(
self.gp_test_environment_input)
python_cov, historical_data = gaussian_process.get_core_data_copy()
lml = GaussianProcessLogMarginalLikelihood(python_cov, historical_data)
domain = TensorProductDomain(
[ClosedInterval(1.0, 4.0)] *
self.gp_test_environment_input.num_hyperparameters)
hyperparameter_optimizer = GradientDescentOptimizer(
domain, lml, gd_parameters)
best_hyperparameters = multistart_hyperparameter_optimization(
hyperparameter_optimizer, num_multistarts)
# Check that gradients are small
lml.hyperparameters = best_hyperparameters
gradient = lml.compute_grad_log_likelihood()
self.assert_vector_within_relative(
gradient, numpy.zeros(self.num_hyperparameters), tolerance)
# Check that output is in the domain
assert domain.check_point_inside(best_hyperparameters) is True
numpy.random.set_state(random_state)
def test_multistart_hyperparameter_optimization(self):
"""Check that multistart optimization (gradient descent) can find the optimum hyperparameters."""
random_state = numpy.random.get_state()
numpy.random.seed(87612)
max_num_steps = 200 # this is generally *too few* steps; we configure it this way so the test will run quickly
max_num_restarts = 5
num_steps_averaged = 0
gamma = 0.2
pre_mult = 1.0
max_relative_change = 0.3
tolerance = 1.0e-11
gd_parameters = GradientDescentParameters(
max_num_steps,
max_num_restarts,
num_steps_averaged,
gamma,
pre_mult,
max_relative_change,
tolerance,
)
num_multistarts = 3 # again, too few multistarts; but we want the test to run reasonably quickly
num_sampled = 10
self.gp_test_environment_input.num_sampled = num_sampled
_, gaussian_process = self._build_gaussian_process_test_data(self.gp_test_environment_input)
python_cov, historical_data = gaussian_process.get_core_data_copy()
lml = GaussianProcessLogMarginalLikelihood(python_cov, historical_data)
domain = TensorProductDomain([ClosedInterval(1.0, 4.0)] * self.gp_test_environment_input.num_hyperparameters)
hyperparameter_optimizer = GradientDescentOptimizer(domain, lml, gd_parameters)
best_hyperparameters = multistart_hyperparameter_optimization(hyperparameter_optimizer, num_multistarts)
# Check that gradients are small
lml.hyperparameters = best_hyperparameters
gradient = lml.compute_grad_log_likelihood()
self.assert_vector_within_relative(gradient, numpy.zeros(self.num_hyperparameters), tolerance)
# Check that output is in the domain
assert domain.check_point_inside(best_hyperparameters) is True
numpy.random.set_state(random_state)
