def test_is_eq_archive():
archive_chain_first = chain_third()
archive_chain_second = chain_first()
archive_chain_third = chain_third()
archive_chain_forth = chain_third()
population_first = [
archive_chain_first, archive_chain_second, archive_chain_third
]
population_second = deepcopy(population_first)
population_first.append(archive_chain_forth)
eval_fitness = [(-0.9821, 0.8), (-0.8215, 0.6), (-0.21111, 0.4),
(-0.92, 0.9)]
for population in (population_first, population_second):
for chain_num, chain in enumerate(population):
fitness = MultiObjFitness(
values=eval_fitness[chain_num],
weights=tuple(
[-1 for _ in range(len(eval_fitness[chain_num]))]))
chain.fitness = fitness
archive_first = tools.ParetoFront()
archive_first.update(population_first)
assert len(archive_first.items) == 3
archive_second = tools.ParetoFront()
archive_second.update(population_second)
assert is_equal_archive(archive_first, archive_second)
new_fitness = (-0.9821, 0.80001)
population_second[0].fitness.values = new_fitness
archive_third = tools.ParetoFront()
archive_third.update(population_second)
assert not is_equal_archive(archive_first, archive_third)
def test_evaluate_individuals():
project_root_path = str(project_root())
file_path_train = os.path.join(project_root_path,
'test/data/simple_classification.csv')
full_path_train = os.path.join(str(project_root()), file_path_train)
task = Task(TaskTypesEnum.classification)
dataset_to_compose = InputData.from_csv(full_path_train, task=task)
available_model_types, _ = OperationTypesRepository().suitable_operation(
task_type=task.task_type)
metric_function = ClassificationMetricsEnum.ROCAUC_penalty
composer_requirements = GPComposerRequirements(
primary=available_model_types, secondary=available_model_types)
builder = GPComposerBuilder(task=task).with_requirements(composer_requirements). \
with_metrics(metric_function)
composer = builder.build()
train_data, test_data = train_test_data_setup(
dataset_to_compose,
sample_split_ration_for_tasks[dataset_to_compose.task.task_type])
metric_function_for_nodes = partial(composer.composer_metric,
composer.metrics, train_data,
test_data)
population = [chain_first(), chain_second(), chain_third(), chain_fourth()]
max_lead_time = datetime.timedelta(minutes=0.001)
with CompositionTimer(max_lead_time=max_lead_time) as t:
evaluate_individuals(individuals_set=population,
objective_function=metric_function_for_nodes,
is_multi_objective=False,
timer=t)
assert len(population) == 1
assert population[0].fitness is not None
population = [chain_first(), chain_second(), chain_third(), chain_fourth()]
max_lead_time = datetime.timedelta(minutes=5)
with CompositionTimer(max_lead_time=max_lead_time) as t:
evaluate_individuals(individuals_set=population,
objective_function=metric_function_for_nodes,
is_multi_objective=False,
timer=t)
assert len(population) == 4
assert all([ind.fitness is not None for ind in population])
def test_filter_duplicates():
archive = tools.ParetoFront()
archive_items = [chain_first(), chain_second(), chain_third()]
population = [chain_first(), chain_second(), chain_third(), chain_fourth()]
archive_items_fitness = ((-0.80001, 0.25), (-0.7, 0.1), (-0.9, 0.7))
population_fitness = ((-0.8, 0.25), (-0.59, 0.25), (-0.9, 0.7), (-0.7,
0.1))
weights = tuple([-1 for _ in range(len(population_fitness[0]))])
for ind_num in range(len(archive_items)):
archive_items[ind_num].fitness = MultiObjFitness(
values=archive_items_fitness[ind_num], weights=weights)
for ind_num in range(len(population)):
population[ind_num].fitness = MultiObjFitness(
values=population_fitness[ind_num], weights=weights)
archive.update(archive_items)
filtered_archive = filter_duplicates(archive, population)
assert len(filtered_archive) == 1
assert filtered_archive[0].fitness.values[0] == -0.80001
assert filtered_archive[0].fitness.values[1] == 0.25
def test_mutation():
chain = chain_first()
mutation_types = [MutationTypesEnum.none]
log = default_log(__name__)
chain_gener_params = ChainGenerationParams()
task = Task(TaskTypesEnum.classification)
primary_model_types, _ = OperationTypesRepository().suitable_operation(
task_type=task.task_type)
secondary_model_types = ['xgboost', 'knn', 'lda', 'qda']
composer_requirements = GPComposerRequirements(
primary=primary_model_types,
secondary=secondary_model_types,
mutation_prob=1)
new_chain = mutation(mutation_types,
chain_gener_params,
chain,
composer_requirements,
log=log,
max_depth=3)
assert new_chain == chain
mutation_types = [MutationTypesEnum.growth]
composer_requirements = GPComposerRequirements(
primary=primary_model_types,
secondary=secondary_model_types,
mutation_prob=0)
new_chain = mutation(mutation_types,
chain_gener_params,
chain,
composer_requirements,
log=log,
max_depth=3)
assert new_chain == chain
chain = chain_fifth()
assert not constraint_function(chain)
new_chain = mutation(mutation_types,
chain_gener_params,
chain,
composer_requirements,
log=log,
max_depth=3)
assert new_chain == chain
def test_pareto_front():
archive_chain_first = chain_third()
archive_chain_second = chain_first()
archive_chain_third = chain_third()
population = [
archive_chain_first, archive_chain_second, archive_chain_third
]
eval_fitness = [(-0.9821, 0.8), (-0.8215, 0.6), (-0.9821, 0.8)]
for chain_num, chain in enumerate(population):
fitness = MultiObjFitness(
values=eval_fitness[chain_num],
weights=tuple([-1 for _ in range(len(eval_fitness[chain_num]))]))
chain.fitness = fitness
front = tools.ParetoFront()
front.__class__ = FedotParetoFront
front.update(population)
assert len(front) == 2
def test_crossover():
chain_example_first = chain_first()
chain_example_second = chain_second()
log = default_log(__name__)
crossover_types = [CrossoverTypesEnum.none]
new_chains = crossover(crossover_types,
chain_example_first,
chain_example_second,
max_depth=3,
log=log,
crossover_prob=1)
assert new_chains[0] == chain_example_first
assert new_chains[1] == chain_example_second
crossover_types = [CrossoverTypesEnum.subtree]
new_chains = crossover(crossover_types,
chain_example_first,
chain_example_second,
max_depth=3,
log=log,
crossover_prob=0)
assert new_chains[0] == chain_example_first
assert new_chains[1] == chain_example_second