class NullOptimizerTest(OptimalLearningTestCase):
"""Test the NullOptimizer on a simple objective.
NullOptimizer should do nothing.
Multistarting it should be the same as a 'dumb' search over points.
"""
@T.class_setup
def base_setup(self):
"""Set up a test case for optimizing a simple quadratic polynomial."""
self.dim = 3
domain_bounds = [ClosedInterval(-1.0, 1.0)] * self.dim
self.domain = TensorProductDomain(domain_bounds)
maxima_point = numpy.full(self.dim, 0.5)
current_point = numpy.zeros(self.dim)
self.polynomial = QuadraticFunction(maxima_point, current_point)
self.null_optimizer = NullOptimizer(self.domain, self.polynomial)
def test_null_optimizer(self):
"""Test that null optimizer does not change current_point."""
current_point_old = self.null_optimizer.objective_function.current_point
self.null_optimizer.optimize()
current_point_new = self.null_optimizer.objective_function.current_point
self.assert_vector_within_relative(current_point_old,
current_point_new, 0.0)
def test_multistarted_null_optimizer(self):
"""Test that multistarting null optimizer just evalutes the function and indentifies the max."""
num_points = 15
points = self.domain.generate_uniform_random_points_in_domain(
num_points)
truth = numpy.empty(num_points)
for i, point in enumerate(points):
self.null_optimizer.objective_function.current_point = point
truth[
i] = self.null_optimizer.objective_function.compute_objective_function(
)
best_index = numpy.argmax(truth)
truth_best_point = points[best_index, ...]
test_best_point, test_values = multistart_optimize(
self.null_optimizer, starting_points=points)
self.assert_vector_within_relative(test_best_point, truth_best_point,
0.0)
self.assert_vector_within_relative(test_values, truth, 0.0)
class NullOptimizerTest(OptimalLearningTestCase):
"""Test the NullOptimizer on a simple objective.
NullOptimizer should do nothing.
Multistarting it should be the same as a 'dumb' search over points.
"""
@T.class_setup
def base_setup(self):
"""Set up a test case for optimizing a simple quadratic polynomial."""
self.dim = 3
domain_bounds = [ClosedInterval(-1.0, 1.0)] * self.dim
self.domain = TensorProductDomain(domain_bounds)
maxima_point = numpy.full(self.dim, 0.5)
current_point = numpy.zeros(self.dim)
self.polynomial = QuadraticFunction(maxima_point, current_point)
self.null_optimizer = NullOptimizer(self.domain, self.polynomial)
def test_null_optimizer(self):
"""Test that null optimizer does not change current_point."""
current_point_old = self.null_optimizer.objective_function.current_point
self.null_optimizer.optimize()
current_point_new = self.null_optimizer.objective_function.current_point
self.assert_vector_within_relative(current_point_old, current_point_new, 0.0)
def test_multistarted_null_optimizer(self):
"""Test that multistarting null optimizer just evalutes the function and indentifies the max."""
num_points = 15
points = self.domain.generate_uniform_random_points_in_domain(num_points)
truth = numpy.empty(num_points)
for i, point in enumerate(points):
self.null_optimizer.objective_function.current_point = point
truth[i] = self.null_optimizer.objective_function.compute_objective_function()
best_index = numpy.argmax(truth)
truth_best_point = points[best_index, ...]
test_best_point, test_values = multistart_optimize(self.null_optimizer, starting_points=points)
self.assert_vector_within_relative(test_best_point, truth_best_point, 0.0)
self.assert_vector_within_relative(test_values, truth, 0.0)
