def test_get_random_component(self):
mutator = mutators.XCFunctionalMutator(self.mutator_x,
self.mutator_css,
self.mutator_cos,
seed=1)
self.assertEqual([mutator.get_random_component() for _ in range(10)], [
'f_css', 'f_cos', 'f_x', 'f_x', 'f_x', 'f_x', 'f_x', 'f_css',
'f_css', 'f_css'
])
def test_initialization_with_unnormalized_probability(self):
with self.assertRaisesRegex(
ValueError,
'component_mutation_probabilities not normalized to 1'):
mutators.XCFunctionalMutator(
self.mutator_x,
self.mutator_css,
self.mutator_cos,
component_mutation_probabilities=[0.4, 0.4, 0.4])
def test_initialization_with_negative_probability(self):
with self.assertRaisesRegex(
ValueError,
'component_mutation_probabilities contains negative probabilities'
):
mutators.XCFunctionalMutator(
self.mutator_x,
self.mutator_css,
self.mutator_cos,
component_mutation_probabilities=[-0.2, 0.6, 0.6])
def test_initialization_with_wrong_probability_length(self):
with self.assertRaisesRegex(
ValueError,
'Wrong length for component_mutation_probabilities. Expected 3, got 2'
):
mutators.XCFunctionalMutator(
self.mutator_x,
self.mutator_css,
self.mutator_cos,
component_mutation_probabilities=[0.5, 0.5])
def make_mutator(instruction_pool,
mutation_pool,
max_num_instructions,
max_num_bound_parameters,
num_fixed_instructions,
component_mutation_probabilities=None,
seed=None):
"""Constructs mutator for functional forms.
Args:
instruction_pool: Dict, the pool of possible instructions.
mutation_pool: Dict, the pool of possible mutation rules.
max_num_instructions: Integer, the maximum number of instructions.
max_num_bound_parameters: Integer, the maximum number of bound parameters.
num_fixed_instructions: Integer, the number of fixed instructions.
component_mutation_probabilities: Sequence of 3 floats, the probabilities
for mutating exchange, same-spin or opposite-spin component of the
functional.
seed: Integer, random seed.
Returns:
Instance of mutators.XCFunctionalMutator, the resulting mutator.
"""
return mutators.XCFunctionalMutator(
mutator_x=mutators.EnhancementFactorMutator(
instruction_pool=instruction_pool,
mutation_pool=mutation_pool,
max_num_instructions=max_num_instructions,
num_fixed_instructions=num_fixed_instructions,
max_num_bound_parameters=max_num_bound_parameters),
mutator_css=mutators.EnhancementFactorMutator(
instruction_pool=instruction_pool,
mutation_pool=mutation_pool,
max_num_instructions=max_num_instructions,
num_fixed_instructions=num_fixed_instructions,
max_num_bound_parameters=max_num_bound_parameters),
mutator_cos=mutators.EnhancementFactorMutator(
instruction_pool=instruction_pool,
mutation_pool=mutation_pool,
max_num_instructions=max_num_instructions,
num_fixed_instructions=num_fixed_instructions,
max_num_bound_parameters=max_num_bound_parameters),
component_mutation_probabilities=component_mutation_probabilities,
seed=seed)
def test_mutate(self, component):
with mock.patch.object(mutators.XCFunctionalMutator,
'get_random_component',
return_value=component), mock.patch.object(
mutators.EnhancementFactorMutator,
'get_random_mutation_type',
return_value='remove_instruction'):
mutator = mutators.XCFunctionalMutator(
mutator_x=mutators.EnhancementFactorMutator(),
mutator_css=mutators.EnhancementFactorMutator(),
mutator_cos=mutators.EnhancementFactorMutator())
functional = copy.deepcopy(xc_functionals.b97_u)
new_functional, mutated_component, _, instruction_index, change = (
mutator.mutate(functional, verbose=False))
self.assertEqual(functional, xc_functionals.b97_u)
self.assertEqual(mutated_component, component)
new_enhancement_factor = getattr(new_functional, component)
self.assertEqual(new_enhancement_factor.num_instructions, 4)
new_enhancement_factor.instruction_list.insert(
instruction_index, change[0])
self.assertEqual(new_functional, functional)