def get_model(train_file_path: str,
cur_lead_time: datetime.timedelta = timedelta(seconds=60)):
task = Task(task_type=TaskTypesEnum.classification)
dataset_to_compose = InputData.from_csv(train_file_path, task=task)
# the search of the models provided by the framework
# that can be used as nodes in a chain for the selected task
models_repo = ModelTypesRepository()
available_model_types, _ = models_repo.suitable_model(
task_type=task.task_type, tags=['simple'])
metric_function = MetricsRepository(). \
metric_by_id(ClassificationMetricsEnum.ROCAUC_penalty)
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_lead_time=cur_lead_time)
# Create the genetic programming-based composer, that allow to find
# the optimal structure of the composite model
builder = GPComposerBuilder(task).with_requirements(
composer_requirements).with_metrics(metric_function)
composer = builder.build()
# run the search of best suitable model
chain_evo_composed = composer.compose_chain(data=dataset_to_compose,
is_visualise=False)
chain_evo_composed.fit(input_data=dataset_to_compose)
return chain_evo_composed
def test_parameter_free_composer_build_chain_correct(data_fixture, request):
random.seed(1)
np.random.seed(1)
data = request.getfixturevalue(data_fixture)
dataset_to_compose = data
dataset_to_validate = data
available_model_types, _ = ModelTypesRepository().suitable_model(
task_type=TaskTypesEnum.classification)
metric_function = MetricsRepository().metric_by_id(
ClassificationMetricsEnum.ROCAUC)
req = GPComposerRequirements(primary=available_model_types,
secondary=available_model_types,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=1,
crossover_prob=0.4,
mutation_prob=0.5)
opt_params = GPChainOptimiserParameters(
genetic_scheme_type=GeneticSchemeTypesEnum.parameter_free)
builder = GPComposerBuilder(task=Task(
TaskTypesEnum.classification)).with_requirements(req).with_metrics(
metric_function).with_optimiser_parameters(opt_params)
gp_composer = builder.build()
chain_gp_composed = gp_composer.compose_chain(data=dataset_to_compose)
chain_gp_composed.fit_from_scratch(input_data=dataset_to_compose)
predicted_gp_composed = chain_gp_composed.predict(dataset_to_validate)
roc_on_valid_gp_composed = roc_auc(y_true=dataset_to_validate.target,
y_score=predicted_gp_composed.predict)
assert roc_on_valid_gp_composed > 0.6
def test_composer_cv_correct():
""" Checks if the composer works correctly when using cross validation for
time series """
folds = 2
_, forecast_len, validation_blocks, time_series = configure_experiment()
primary_operations, secondary_operations = get_available_operations()
# Composer parameters
composer_requirements = GPComposerRequirements(
primary=primary_operations,
secondary=secondary_operations,
max_arity=3,
max_depth=3,
pop_size=2,
num_of_generations=2,
crossover_prob=0.8,
mutation_prob=0.8,
timeout=datetime.timedelta(seconds=5),
cv_folds=folds,
validation_blocks=validation_blocks)
init_pipeline = get_simple_ts_pipeline()
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.RMSE)
builder = GPComposerBuilder(task=time_series.task). \
with_requirements(composer_requirements). \
with_metrics(metric_function).with_initial_pipeline(init_pipeline)
composer = builder.build()
obtained_pipeline = composer.compose_pipeline(data=time_series,
is_visualise=False)
assert isinstance(obtained_pipeline, Pipeline)
def test_composer_with_cv_optimization_correct():
task = Task(task_type=TaskTypesEnum.classification)
dataset_to_compose, dataset_to_validate = get_data(task)
models_repo = OperationTypesRepository()
available_model_types, _ = models_repo.suitable_operation(
task_type=task.task_type, tags=['simple'])
metric_function = [
ClassificationMetricsEnum.ROCAUC_penalty,
ClassificationMetricsEnum.accuracy, ClassificationMetricsEnum.logloss
]
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
timeout=timedelta(minutes=1),
num_of_generations=2,
cv_folds=3)
builder = GPComposerBuilder(task).with_requirements(
composer_requirements).with_metrics(metric_function)
composer = builder.build()
pipeline_evo_composed = composer.compose_pipeline(data=dataset_to_compose,
is_visualise=False)[0]
assert isinstance(pipeline_evo_composed, Pipeline)
pipeline_evo_composed.fit(input_data=dataset_to_compose)
predicted = pipeline_evo_composed.predict(dataset_to_validate)
roc_on_valid_evo_composed = roc_auc(y_score=predicted.predict,
y_true=dataset_to_validate.target)
assert roc_on_valid_evo_composed > 0
def get_composed_chain(dataset_to_compose, task, metric_function):
# the search of the models provided by the framework that can be used as nodes in a chain for the selected task
available_model_types = get_operations_for_task(task=task, mode='models')
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
pop_size=20,
num_of_generations=20,
crossover_prob=0.8,
mutation_prob=0.8,
allow_single_operations=False)
# GP optimiser parameters choice
scheme_type = GeneticSchemeTypesEnum.steady_state
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type)
# Create builder for composer and set composer params
builder = GPComposerBuilder(
task=task).with_requirements(composer_requirements).with_metrics(
metric_function).with_optimiser_parameters(optimiser_parameters)
# Create GP-based composer
composer = builder.build()
# the optimal chain generation by composition - the most time-consuming task
chain_evo_composed = composer.compose_chain(data=dataset_to_compose,
is_visualise=True)
return chain_evo_composed
def test_gp_composer_build_pipeline_correct(data_fixture, request):
random.seed(1)
np.random.seed(1)
data = request.getfixturevalue(data_fixture)
dataset_to_compose = data
dataset_to_validate = data
task = Task(TaskTypesEnum.classification)
available_model_types, _ = OperationTypesRepository().suitable_operation(
task_type=task.task_type)
metric_function = ClassificationMetricsEnum.ROCAUC
req = GPComposerRequirements(primary=available_model_types,
secondary=available_model_types,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=1,
crossover_prob=0.4,
mutation_prob=0.5)
builder = GPComposerBuilder(task).with_requirements(req).with_metrics(
metric_function)
gp_composer = builder.build()
pipeline_gp_composed = gp_composer.compose_pipeline(
data=dataset_to_compose)
pipeline_gp_composed.fit_from_scratch(input_data=dataset_to_compose)
predicted_gp_composed = pipeline_gp_composed.predict(dataset_to_validate)
roc_on_valid_gp_composed = roc_auc(y_true=dataset_to_validate.target,
y_score=predicted_gp_composed.predict)
assert roc_on_valid_gp_composed > 0.6
def test_gp_composer_with_start_depth(data_fixture, request):
random.seed(1)
np.random.seed(1)
data = request.getfixturevalue(data_fixture)
dataset_to_compose = data
available_model_types = ['xgboost', 'knn']
quality_metric = ClassificationMetricsEnum.ROCAUC
req = GPComposerRequirements(primary=available_model_types,
secondary=available_model_types,
max_arity=2,
max_depth=5,
pop_size=5,
num_of_generations=1,
crossover_prob=0.4,
mutation_prob=0.5,
start_depth=2)
scheme_type = GeneticSchemeTypesEnum.steady_state
optimiser_parameters = GPGraphOptimiserParameters(
genetic_scheme_type=scheme_type)
builder = GPComposerBuilder(task=Task(
TaskTypesEnum.classification)).with_requirements(req).with_metrics(
quality_metric).with_optimiser_parameters(optimiser_parameters)
composer = builder.build()
composer.compose_pipeline(data=dataset_to_compose, is_visualise=True)
assert all(
[ind.graph.depth <= 3 for ind in composer.history.individuals[0]])
assert composer.optimiser.max_depth == 5
def test_gp_composer_builder():
task = Task(TaskTypesEnum.classification)
available_model_types, _ = ModelTypesRepository().suitable_model(
task_type=task.task_type)
metric_function = MetricsRepository().metric_by_id(
ClassificationMetricsEnum.ROCAUC)
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
pop_size=5,
num_of_generations=4,
crossover_prob=0.8,
mutation_prob=1,
max_lead_time=datetime.timedelta(minutes=5))
scheme_type = GeneticSchemeTypesEnum.steady_state
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type)
builder_with_custom_params = GPComposerBuilder(
task=task).with_requirements(composer_requirements).with_metrics(
metric_function).with_optimiser_parameters(optimiser_parameters)
composer_with_custom_params = builder_with_custom_params.build()
assert composer_with_custom_params.optimiser.parameters.genetic_scheme_type == scheme_type
assert composer_with_custom_params.metrics == metric_function
assert composer_with_custom_params.composer_requirements.pop_size == 5
assert composer_with_custom_params.composer_requirements.mutation_prob == 1
builder_with_default_params = GPComposerBuilder(task=task)
composer_with_default_params = builder_with_default_params.build()
default_metric = MetricsRepository().metric_by_id(
ClassificationMetricsEnum.ROCAUC.ROCAUC_penalty)
assert composer_with_default_params.optimiser.parameters.genetic_scheme_type == GeneticSchemeTypesEnum.generational
assert composer_with_default_params.metrics == default_metric
assert composer_with_default_params.composer_requirements.pop_size == 20
assert composer_with_default_params.composer_requirements.mutation_prob == 0.8
def test_evaluate_individuals():
project_root_path = str(fedot_project_root())
file_path_train = os.path.join(project_root_path,
'test/data/simple_classification.csv')
full_path_train = os.path.join(str(fedot_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()
pipelines_to_evaluate = [
pipeline_first(),
pipeline_second(),
pipeline_third(),
pipeline_fourth()
]
train_data, test_data = train_test_data_setup(
dataset_to_compose,
sample_split_ratio_for_tasks[dataset_to_compose.task.task_type])
metric_function_for_nodes = partial(composer.composer_metric,
composer.metrics, train_data,
test_data)
adapter = PipelineAdapter()
population = [Individual(adapter.adapt(c)) for c in pipelines_to_evaluate]
timeout = datetime.timedelta(minutes=0.001)
params = GraphGenerationParams(adapter=PipelineAdapter(),
advisor=PipelineChangeAdvisor())
with OptimisationTimer(timeout=timeout) as t:
evaluate_individuals(individuals_set=population,
objective_function=metric_function_for_nodes,
graph_generation_params=params,
is_multi_objective=False,
timer=t)
assert len(population) == 1
assert population[0].fitness is not None
population = [Individual(adapter.adapt(c)) for c in pipelines_to_evaluate]
timeout = datetime.timedelta(minutes=5)
with OptimisationTimer(timeout=timeout) as t:
evaluate_individuals(individuals_set=population,
objective_function=metric_function_for_nodes,
graph_generation_params=params,
is_multi_objective=False,
timer=t)
assert len(population) == 4
assert all([ind.fitness is not None for ind in population])
def test_composition_time(data_fixture, request):
random.seed(1)
np.random.seed(1)
data = request.getfixturevalue(data_fixture)
task = Task(TaskTypesEnum.classification)
models_impl = ['mlp', 'knn']
metric_function = MetricsRepository().metric_by_id(
ClassificationMetricsEnum.ROCAUC)
req_terminated_evolution = GPComposerRequirements(
primary=models_impl,
secondary=models_impl,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=5,
crossover_prob=0.9,
mutation_prob=0.9,
max_lead_time=datetime.timedelta(minutes=0.000001))
builder = GPComposerBuilder(task).with_requirements(
req_terminated_evolution).with_metrics(metric_function)
gp_composer_terminated_evolution = builder.build()
_ = gp_composer_terminated_evolution.compose_chain(data=data)
req_completed_evolution = GPComposerRequirements(primary=models_impl,
secondary=models_impl,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=2,
crossover_prob=0.4,
mutation_prob=0.5)
builder = GPComposerBuilder(task).with_requirements(
req_completed_evolution).with_metrics(metric_function)
gp_composer_completed_evolution = builder.build()
_ = gp_composer_completed_evolution.compose_chain(data=data)
assert len(gp_composer_terminated_evolution.history.chains) == len(
gp_composer_completed_evolution.history.chains)
def test_gp_composer_builder_default_params_correct():
task = Task(TaskTypesEnum.regression)
builder = GPComposerBuilder(task=task)
# Initialise default parameters
builder.set_default_composer_params()
composer_with_default_params = builder.build()
# Get default available operations for regression task
primary_operations = composer_with_default_params.composer_requirements.primary
# Data operations and models must be in this default primary operations list
assert 'ridge' in primary_operations
assert 'scaling' in primary_operations
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_cv_ts_and_cluster_raise():
task = Task(task_type=TaskTypesEnum.clustering)
dataset_to_compose, dataset_to_validate = get_data(task)
metric_function = ClusteringMetricsEnum.silhouette
operations_repo = OperationTypesRepository()
available_model_types, _ = operations_repo.suitable_operation(
task_type=task.task_type)
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
cv_folds=4)
builder = GPComposerBuilder(task).with_requirements(
composer_requirements).with_metrics(metric_function)
composer = builder.build()
with pytest.raises(NotImplementedError):
composer.compose_pipeline(data=dataset_to_compose, is_visualise=False)
def _get_gp_composer_builder(task: Task, metric_function,
composer_requirements: GPComposerRequirements,
optimizer_parameters: GPChainOptimiserParameters,
logger: Log):
""" Return GPComposerBuilder with parameters and if it is necessary
init_chain in it """
builder = GPComposerBuilder(task=task). \
with_requirements(composer_requirements). \
with_optimiser_parameters(optimizer_parameters). \
with_metrics(metric_function).with_logger(logger)
init_chain = _obtain_initial_assumption(task)
if init_chain is not None:
builder = builder.with_initial_chain(init_chain)
return builder
def test_multi_objective_composer(data_fixture, request):
random.seed(1)
np.random.seed(1)
data = request.getfixturevalue(data_fixture)
dataset_to_compose = data
dataset_to_validate = data
available_model_types, _ = OperationTypesRepository().suitable_operation(
task_type=TaskTypesEnum.classification)
quality_metric = ClassificationMetricsEnum.ROCAUC
complexity_metric = ComplexityMetricsEnum.node_num
metrics = [quality_metric, complexity_metric]
req = GPComposerRequirements(primary=available_model_types,
secondary=available_model_types,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=1,
crossover_prob=0.4,
mutation_prob=0.5)
scheme_type = GeneticSchemeTypesEnum.steady_state
optimiser_parameters = GPGraphOptimiserParameters(
genetic_scheme_type=scheme_type,
selection_types=[SelectionTypesEnum.nsga2])
builder = GPComposerBuilder(task=Task(
TaskTypesEnum.classification)).with_requirements(req).with_metrics(
metrics).with_optimiser_parameters(optimiser_parameters)
composer = builder.build()
pipelines_evo_composed = composer.compose_pipeline(data=dataset_to_compose)
pipelines_roc_auc = []
for pipeline_evo_composed in pipelines_evo_composed:
pipeline_evo_composed.fit_from_scratch(input_data=dataset_to_compose)
predicted_gp_composed = pipeline_evo_composed.predict(
dataset_to_validate)
roc_on_valid_gp_composed = roc_auc(
y_true=dataset_to_validate.target,
y_score=predicted_gp_composed.predict)
pipelines_roc_auc.append(roc_on_valid_gp_composed)
assert type(composer.metrics) is list and len(composer.metrics) > 1
assert type(pipelines_evo_composed) is list
assert composer.optimiser.parameters.multi_objective
assert all([roc_auc > 0.6 for roc_auc in pipelines_roc_auc])
def _get_gp_composer_builder(task: Task, metric_function,
composer_requirements: GPComposerRequirements,
optimizer_parameters: GPGraphOptimiserParameters,
data: Union[InputData, MultiModalData],
initial_pipeline: Pipeline,
logger: Log):
""" Return GPComposerBuilder with parameters and if it is necessary
init_pipeline in it """
builder = GPComposerBuilder(task=task). \
with_requirements(composer_requirements). \
with_optimiser_parameters(optimizer_parameters). \
with_metrics(metric_function).with_logger(logger)
init_pipeline = _obtain_initial_assumption(task, data) if not initial_pipeline else initial_pipeline
if init_pipeline is not None:
builder = builder.with_initial_pipeline(init_pipeline)
return builder
def test_parameter_free_composer_build_pipeline_correct(data_fixture, request):
random.seed(1)
np.random.seed(1)
data = request.getfixturevalue(data_fixture)
dataset_to_compose = data
dataset_to_validate = data
available_model_types, _ = OperationTypesRepository().suitable_operation(
task_type=TaskTypesEnum.classification)
metric_function = ClassificationMetricsEnum.ROCAUC
req = GPComposerRequirements(primary=available_model_types,
secondary=available_model_types,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=4,
crossover_prob=0.4,
mutation_prob=0.5)
opt_params = GPGraphOptimiserParameters(
genetic_scheme_type=GeneticSchemeTypesEnum.parameter_free)
builder = GPComposerBuilder(task=Task(
TaskTypesEnum.classification)).with_requirements(req).with_metrics(
metric_function).with_optimiser_parameters(opt_params)
gp_composer = builder.build()
pipeline_gp_composed = gp_composer.compose_pipeline(
data=dataset_to_compose)
pipeline_gp_composed.fit_from_scratch(input_data=dataset_to_compose)
predicted_gp_composed = pipeline_gp_composed.predict(dataset_to_validate)
roc_on_valid_gp_composed = roc_auc(y_true=dataset_to_validate.target,
y_score=predicted_gp_composed.predict)
population_len = sum([
len(history) for history in gp_composer.history.individuals
]) / len(gp_composer.history.individuals)
assert population_len != len(gp_composer.history.individuals[0])
assert roc_on_valid_gp_composed > 0.6
def test_gp_composer_saving_info_from_process(data_fixture, request):
data = request.getfixturevalue(data_fixture)
dataset_to_compose = data
available_model_types = ['xgboost', 'knn']
quality_metric = ClassificationMetricsEnum.ROCAUC
req = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=2,
max_depth=2,
pop_size=2,
num_of_generations=1,
crossover_prob=0.4,
mutation_prob=0.5,
start_depth=2,
max_chain_fit_time=datetime.timedelta(minutes=5),
allow_single_operations=False)
scheme_type = GeneticSchemeTypesEnum.steady_state
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type)
builder = GPComposerBuilder(
task=Task(TaskTypesEnum.classification)).with_requirements(
req).with_metrics(quality_metric).with_optimiser_parameters(
optimiser_parameters).with_cache()
composer = builder.build()
train_data, test_data = train_test_data_setup(
data, sample_split_ration_for_tasks[data.task.task_type])
composer.compose_chain(data=dataset_to_compose, is_visualise=True)
with shelve.open(composer.cache.db_path) as cache:
global_cache_len_before = len(cache.dict)
new_chain = chain_first()
composer.composer_metric([quality_metric], dataset_to_compose, test_data,
new_chain)
with shelve.open(composer.cache.db_path) as cache:
global_cache_len_after = len(cache.dict)
assert global_cache_len_before < global_cache_len_after
assert new_chain.computation_time is not None
assert new_chain.fitted_on_data is not None
def run_multi_modal_case(files_path,
is_visualise=False,
timeout=datetime.timedelta(minutes=2)):
task = Task(TaskTypesEnum.classification)
images_size = (128, 128)
train_num, test_num, train_img, test_img, train_text, test_text = prepare_multi_modal_data(
files_path, task, images_size)
pipeline, fit_data, predict_data = generate_initial_pipeline_and_data(
images_size, train_num, test_num, train_img, test_img, train_text,
test_text)
# the search of the models provided by the framework that can be used as nodes in a pipeline for the selected task
available_model_types = get_operations_for_task(task=task, mode='model')
# the choice of the metric for the pipeline quality assessment during composition
metric_function = ClassificationMetricsEnum.ROCAUC_penalty
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
pop_size=5,
num_of_generations=5,
crossover_prob=0.8,
mutation_prob=0.8,
timeout=timeout)
# GP optimiser parameters choice
scheme_type = GeneticSchemeTypesEnum.parameter_free
optimiser_parameters = GPGraphOptimiserParameters(
genetic_scheme_type=scheme_type)
# Create builder for composer and set composer params
logger = default_log('FEDOT logger', verbose_level=4)
# the multi modal template (with data sources) is passed as inital assumption for composer
builder = GPComposerBuilder(task=task).with_requirements(composer_requirements). \
with_metrics(metric_function).with_optimiser_parameters(optimiser_parameters).with_logger(logger=logger). \
with_initial_pipeline(pipeline).with_cache('multi_modal_opt.cache')
# Create GP-based composer
composer = builder.build()
# the optimal pipeline generation by composition - the most time-consuming task
pipeline_evo_composed = composer.compose_pipeline(data=fit_data,
is_visualise=True)
pipeline_evo_composed.fit(input_data=fit_data)
if is_visualise:
pipeline_evo_composed.show()
prediction = pipeline_evo_composed.predict(predict_data)
err = calculate_validation_metric(prediction, test_num)
print(f'ROC AUC for validation sample is {err}')
return err
def run_credit_scoring_problem(
train_file_path,
test_file_path,
max_lead_time: datetime.timedelta = datetime.timedelta(minutes=5),
is_visualise=False,
with_tuning=False):
task = Task(TaskTypesEnum.classification)
dataset_to_compose = InputData.from_csv(train_file_path, task=task)
dataset_to_validate = InputData.from_csv(test_file_path, task=task)
# the search of the models provided by the framework that can be used as nodes in a chain for the selected task
available_model_types, _ = ModelTypesRepository().suitable_model(
task_type=task.task_type)
# the choice of the metric for the chain quality assessment during composition
metric_function = MetricsRepository().metric_by_id(
ClassificationMetricsEnum.ROCAUC_penalty)
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
pop_size=20,
num_of_generations=20,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=max_lead_time)
# GP optimiser parameters choice
scheme_type = GeneticSchemeTypesEnum.steady_state
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type)
# Create builder for composer and set composer params
builder = GPComposerBuilder(
task=task).with_requirements(composer_requirements).with_metrics(
metric_function).with_optimiser_parameters(optimiser_parameters)
# Create GP-based composer
composer = builder.build()
# the optimal chain generation by composition - the most time-consuming task
chain_evo_composed = composer.compose_chain(data=dataset_to_compose,
is_visualise=True)
if with_tuning:
chain_evo_composed.fine_tune_primary_nodes(
input_data=dataset_to_compose, iterations=50, verbose=True)
chain_evo_composed.fit(input_data=dataset_to_compose, verbose=True)
if is_visualise:
visualiser = ChainVisualiser()
composer.log.info('History visualization started')
visualiser.visualise_history(composer.history)
composer.log.info('History visualization finished')
composer.history.write_composer_history_to_csv()
composer.log.info('Best chain visualization started')
visualiser.visualise(chain_evo_composed)
composer.log.info('Best chain visualization finished')
# the quality assessment for the obtained composite models
roc_on_valid_evo_composed = calculate_validation_metric(
chain_evo_composed, dataset_to_validate)
print(f'Composed ROC AUC is {round(roc_on_valid_evo_composed, 3)}')
return roc_on_valid_evo_composed
def run_credit_scoring_problem(
train_file_path,
test_file_path,
max_lead_time: datetime.timedelta = datetime.timedelta(minutes=5),
is_visualise=False):
task = Task(TaskTypesEnum.classification)
dataset_to_compose = InputData.from_csv(train_file_path, task=task)
dataset_to_validate = InputData.from_csv(test_file_path, task=task)
# the search of the models provided by the framework that can be used as nodes in a chain for the selected task
available_model_types = get_operations_for_task(task=task, mode='models')
# the choice of the metric for the chain quality assessment during composition
quality_metric = ClassificationMetricsEnum.ROCAUC
complexity_metric = ComplexityMetricsEnum.node_num
metrics = [quality_metric, complexity_metric]
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
pop_size=20,
num_of_generations=20,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=max_lead_time,
start_depth=2,
allow_single_operations=False)
# GP optimiser parameters choice
scheme_type = GeneticSchemeTypesEnum.parameter_free
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type,
selection_types=[SelectionTypesEnum.spea2])
# Create builder for composer and set composer params
builder = GPComposerBuilder(
task=task).with_requirements(composer_requirements).with_metrics(
metrics).with_optimiser_parameters(optimiser_parameters)
# Create GP-based composer
composer = builder.build()
# the optimal chain generation by composition - the most time-consuming task
chains_evo_composed = composer.compose_chain(data=dataset_to_compose,
is_visualise=True)
composer.history.write_composer_history_to_csv()
if is_visualise:
results_visualization(composed_chains=chains_evo_composed,
history=composer.history)
chains_roc_auc = []
for chain_num, chain_evo_composed in enumerate(chains_evo_composed):
chain_evo_composed.fine_tune_primary_nodes(
input_data=dataset_to_compose, iterations=50)
chain_evo_composed.fit(input_data=dataset_to_compose)
# the quality assessment for the obtained composite models
roc_on_valid_evo_composed = calculate_validation_metric(
chain_evo_composed, dataset_to_validate)
chains_roc_auc.append(roc_on_valid_evo_composed)
if len(chains_evo_composed) > 1:
print(
f'Composed ROC AUC of chain {chain_num + 1} is {round(roc_on_valid_evo_composed, 3)}'
)
else:
print(f'Composed ROC AUC is {round(roc_on_valid_evo_composed, 3)}')
return max(chains_roc_auc)
def run_fedot(params: 'ExecutionParams'):
train_file_path = params.train_file
test_file_path = params.test_file
case_label = params.case_label
task_type = params.task
if task_type == TaskTypesEnum.classification:
metric = ClassificationMetricsEnum.ROCAUC
elif task_type == TaskTypesEnum.regression:
metric = RegressionMetricsEnum.RMSE
else:
raise NotImplementedError()
metric_func = MetricsRepository().metric_by_id(metric)
task = Task(task_type)
dataset_to_compose = InputData.from_csv(train_file_path, task=task)
dataset_to_validate = InputData.from_csv(test_file_path, task=task)
models_hyperparameters = get_models_hyperparameters()['FEDOT']
cur_lead_time = models_hyperparameters['MAX_RUNTIME_MINS']
saved_model_name = f'fedot_{case_label}_{task_type.name}_{cur_lead_time}_{metric.name}'
loaded_model = load_fedot_model(saved_model_name)
if not loaded_model:
generations = models_hyperparameters['GENERATIONS']
population_size = models_hyperparameters['POPULATION_SIZE']
# the search of the models provided by the framework that can be used as nodes in a chain'
models_repo = ModelTypesRepository()
available_model_types, _ = models_repo.suitable_model(task.task_type)
heavy_models = ['svc', 'multinb', 'tfidf', 'qda']
available_model_types = [
model for model in available_model_types
if model not in heavy_models
]
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=2,
pop_size=population_size,
num_of_generations=generations,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=datetime.timedelta(minutes=cur_lead_time),
add_single_model_chains=True)
# Create GP-based composer
builder = GPComposerBuilder(task).with_requirements(
composer_requirements).with_metrics(metric_func)
gp_composer = builder.build()
chain_gp_composed = gp_composer.compose_chain(data=dataset_to_compose)
chain_gp_composed.fit_from_scratch(input_data=dataset_to_compose)
save_fedot_model(chain_gp_composed, saved_model_name)
else:
chain_gp_composed = loaded_model
evo_predicted = chain_gp_composed.predict(dataset_to_validate)
evo_predicted_labels = chain_gp_composed.predict(dataset_to_validate,
output_mode='labels')
return dataset_to_validate.target, evo_predicted.predict, evo_predicted_labels.predict
def make_forecast(df, len_forecast: int):
"""
Function for making time series forecasting with AutoTS library
:param df: dataframe to process
:param len_forecast: forecast length
:return predicted_values: forecast
:return model_name: name of the model (always 'AutoTS')
"""
time_series = np.array(df['value'])
train_input, predict_input, task = prepare_input_data(len_forecast=len_forecast,
train_data_features=time_series,
train_data_target=time_series,
test_data_features=time_series)
# Get chain with pre-defined structure
init_chain = get_source_chain()
# Init check
preds = fit_predict_for_chain(chain=init_chain,
train_input=train_input,
predict_input=predict_input)
# Get available_operations type
primary_operations, secondary_operations = get_available_operations()
# Composer parameters
composer_requirements = GPComposerRequirements(
primary=primary_operations,
secondary=secondary_operations, max_arity=3,
max_depth=7, pop_size=10, num_of_generations=10,
crossover_prob=0.8, mutation_prob=0.8,
max_lead_time=datetime.timedelta(minutes=5),
allow_single_operations=False)
mutation_types = [MutationTypesEnum.parameter_change,
MutationTypesEnum.simple,
MutationTypesEnum.reduce]
optimiser_parameters = GPChainOptimiserParameters(
mutation_types=mutation_types)
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.MAE)
builder = GPComposerBuilder(task=task). \
with_optimiser_parameters(optimiser_parameters). \
with_requirements(composer_requirements). \
with_metrics(metric_function).with_initial_chain(init_chain)
composer = builder.build()
obtained_chain = composer.compose_chain(data=train_input,
is_visualise=False)
chain_tuner = ChainTuner(chain=obtained_chain,
task=task,
iterations=10)
tuned_chain = chain_tuner.tune_chain(input_data=train_input,
loss_function=mean_squared_error,
loss_params={'squared': False})
preds = fit_predict_for_chain(chain=tuned_chain,
train_input=train_input,
predict_input=predict_input)
list_with_nodes = display_chain_info(obtained_chain)
model_name = str(list_with_nodes)
return preds, model_name
def run_credit_scoring_problem(
train_file_path,
test_file_path,
max_lead_time: datetime.timedelta = datetime.timedelta(minutes=5),
is_visualise=False,
with_tuning=False,
cache_path=None):
task = Task(TaskTypesEnum.classification)
dataset_to_compose = InputData.from_csv(train_file_path, task=task)
dataset_to_validate = InputData.from_csv(test_file_path, task=task)
# the search of the models provided by the framework that can be used as nodes in a chain for the selected task
available_model_types = get_operations_for_task(task=task, mode='models')
# the choice of the metric for the chain quality assessment during composition
metric_function = ClassificationMetricsEnum.ROCAUC_penalty
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
pop_size=20,
num_of_generations=20,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=max_lead_time)
# GP optimiser parameters choice
scheme_type = GeneticSchemeTypesEnum.parameter_free
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type)
# Create builder for composer and set composer params
logger = default_log('FEDOT logger', verbose_level=4)
builder = GPComposerBuilder(task=task).with_requirements(composer_requirements). \
with_metrics(metric_function).with_optimiser_parameters(optimiser_parameters).with_logger(logger=logger)
if cache_path:
builder = builder.with_cache(cache_path)
# Create GP-based composer
composer = builder.build()
# the optimal chain generation by composition - the most time-consuming task
chain_evo_composed = composer.compose_chain(data=dataset_to_compose,
is_visualise=True)
if with_tuning:
# TODO Add tuning
raise NotImplementedError(f'Tuning is not supported')
chain_evo_composed.fit(input_data=dataset_to_compose)
composer.history.write_composer_history_to_csv()
if is_visualise:
visualiser = ChainVisualiser()
composer.log.debug('History visualization started')
visualiser.visualise_history(composer.history)
composer.log.debug('History visualization finished')
composer.log.debug('Best chain visualization started')
visualiser.visualise(chain_evo_composed)
composer.log.debug('Best chain visualization finished')
# the quality assessment for the obtained composite models
roc_on_valid_evo_composed = calculate_validation_metric(
chain_evo_composed, dataset_to_validate)
print(f'Composed ROC AUC is {round(roc_on_valid_evo_composed, 3)}')
return roc_on_valid_evo_composed
def run_river_composer_experiment(file_path, init_pipeline, file_to_save,
iterations=20, tuner=None):
""" Function launch experiment for river level prediction. Composing and
tuner processes are available for such experiment.
:param file_path: path to the file with river level data
:param init_pipeline: pipeline to start composing process
:param file_to_save: path to the file and file name to save report
:param iterations: amount of iterations to process
:param tuner: if tuning after composing process is required or not. tuner -
NodesTuner or PipelineTuner.
"""
# Read dataframe and prepare train and test data
data = InputData.from_csv(file_path, target_columns='level_station_2',
task=Task(TaskTypesEnum.regression),
columns_to_drop=['date'])
train_input, predict_input = train_test_data_setup(data)
y_data_test = np.array(predict_input.target)
available_secondary_operations = ['ridge', 'lasso', 'dtreg',
'xgbreg', 'adareg', 'knnreg',
'linear', 'svr', 'poly_features',
'scaling', 'ransac_lin_reg', 'rfe_lin_reg',
'pca', 'ransac_non_lin_reg',
'rfe_non_lin_reg', 'normalization']
available_primary_operations = ['one_hot_encoding']
# Report arrays
obtained_pipelines = []
depths = []
maes = []
for i in range(0, iterations):
print(f'Iteration {i}\n')
composer_requirements = GPComposerRequirements(
primary=available_primary_operations,
secondary=available_secondary_operations, max_arity=3,
max_depth=8, pop_size=10, num_of_generations=5,
crossover_prob=0.8, mutation_prob=0.8,
timeout=datetime.timedelta(minutes=5))
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.MAE)
builder = GPComposerBuilder(task=data.task). \
with_requirements(composer_requirements). \
with_metrics(metric_function).with_initial_pipeline(init_pipeline)
composer = builder.build()
obtained_pipeline = composer.compose_pipeline(data=train_input, is_visualise=False)
# Display info about obtained pipeline
obtained_models, depth = get_pipeline_info(pipeline=obtained_pipeline)
preds = fit_predict_for_pipeline(pipeline=obtained_pipeline,
train_input=train_input,
predict_input=predict_input)
mse_value = mean_squared_error(y_data_test, preds, squared=False)
mae_value = mean_absolute_error(y_data_test, preds)
print(f'Obtained metrics for current iteration {i}:')
print(f'RMSE - {mse_value:.2f}')
print(f'MAE - {mae_value:.2f}\n')
if tuner is not None:
print(f'Start tuning process ...')
pipeline_tuner = tuner(pipeline=obtained_pipeline, task=data.task,
iterations=100)
tuned_pipeline = pipeline_tuner.tune_pipeline(input_data=train_input,
loss_function=mean_absolute_error)
preds_tuned = fit_predict_for_pipeline(pipeline=tuned_pipeline,
train_input=train_input,
predict_input=predict_input)
mse_value = mean_squared_error(y_data_test, preds_tuned, squared=False)
mae_value = mean_absolute_error(y_data_test, preds_tuned)
print(f'Obtained metrics for current iteration {i} after tuning:')
print(f'RMSE - {mse_value:.2f}')
print(f'MAE - {mae_value:.2f}\n')
obtained_pipelines.append(obtained_models)
maes.append(mae_value)
depths.append(depth)
report = pd.DataFrame({'Pipeline': obtained_pipelines,
'Depth': depths,
'MAE': maes})
report.to_csv(file_to_save, index=False)
def run(dataset, config):
log.info("\n**** FEDOT ****\n")
is_classification = config.type == 'classification'
# Mapping of benchmark metrics to FEDOT metrics
metrics_mapping = dict(acc='accuracy',
auc='roc_auc',
f1='f1',
logloss='neg_log_loss',
mae='neg_mean_absolute_error',
mse='neg_mean_squared_error',
msle='neg_mean_squared_log_error',
r2='r2',
rmse='neg_mean_squared_error')
scoring_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if scoring_metric is None:
raise ValueError("Performance metric {} not supported.".format(
config.metric))
if is_classification:
metric = ClassificationMetricsEnum.ROCAUC
task_type = TaskTypesEnum.classification
else:
metric = RegressionMetricsEnum.RMSE
task_type = TaskTypesEnum.regression
task = Task(task_type)
x_train = dataset.train.X_enc
y_train = dataset.train.y_enc
x_test = dataset.test.X_enc
x_train, y_train = shuffle(dataset.train.X_enc,
dataset.train.y_enc,
random_state=0)
if len(y_train.shape) > 1 and y_train.shape[1] == 1:
y_train = np.squeeze(y_train, axis=1)
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
dataset_to_compose = \
InputData(idx=[_ for _ in range(len(y_train))],
features=x_train,
target=y_train,
task=task,
data_type=DataTypesEnum.table)
dataset_to_test = \
InputData(idx=[_ for _ in range(len(y_train))],
features=x_test,
target=None,
task=task,
data_type=DataTypesEnum.table)
n_jobs = config.framework_params.get(
'_n_jobs', config.cores
) # useful to disable multicore, regardless of the dataset config
log.info(
'Running FEDOT with a maximum time of %ss on %s cores, optimizing %s.',
config.max_runtime_seconds, n_jobs, scoring_metric)
runtime_min = (config.max_runtime_seconds / 60)
available_model_types, _ = ModelTypesRepository().suitable_model(
task_type=task.task_type)
metric_function = MetricsRepository().metric_by_id(metric)
# Create GP-based composer
composer = GPComposer()
if False:
# the choice and initialisation of the GP search
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=3,
max_depth=3,
max_lead_time=datetime.timedelta(minutes=runtime_min * 0.8))
# GP optimiser parameters choice
scheme_type = GeneticSchemeTypesEnum.parameter_free
optimiser_parameters = GPChainOptimiserParameters(
genetic_scheme_type=scheme_type)
# Create builder for composer and set composer params
builder = GPComposerBuilder(
task=task).with_requirements(composer_requirements).with_metrics(
metric_function).with_optimiser_parameters(
optimiser_parameters)
composer = builder.build()
# the optimal chain generation by composition - the most time-consuming task
chain_evo_composed = composer.compose_chain(data=dataset_to_compose,
is_visualise=False)
else:
if is_classification:
chain_evo_composed = Chain(PrimaryNode('logit'))
else:
chain_evo_composed = Chain(PrimaryNode('lasso'))
chain_evo_composed.fit(input_data=dataset_to_compose, verbose=False)
log.info('Predicting on the test set.')
y_test = dataset.test.y_enc
predictions = chain_evo_composed.predict(dataset_to_test,
output_mode='labels').predict
if not is_classification:
probabilities = None
else:
probabilities = chain_evo_composed.predict(
dataset_to_test, output_mode='full_probs').predict
return result(output_file=config.output_predictions_file,
predictions=predictions,
truth=y_test,
probabilities=probabilities,
target_is_encoded=is_classification,
models_count=1,
training_duration=1)
def run_ts_forecasting_problem(forecast_length=50,
with_visualisation=True,
cv_folds=None) -> None:
""" Function launch time series task with composing
:param forecast_length: length of the forecast
:param with_visualisation: is it needed to show the plots
:param cv_folds: is it needed apply cross validation and what number
of folds to use
"""
file_path = '../cases/data/metocean/metocean_data_test.csv'
df = pd.read_csv(file_path)
time_series = np.array(df['sea_height'])
# Train/test split
train_part = time_series[:-forecast_length]
test_part = time_series[-forecast_length:]
# Prepare data for train and test
train_input, predict_input, task = prepare_train_test_input(
train_part, forecast_length)
# Get pipeline with pre-defined structure
init_pipeline = get_source_pipeline()
# Init check
preds = fit_predict_for_pipeline(pipeline=init_pipeline,
train_input=train_input,
predict_input=predict_input)
display_validation_metric(predicted=preds,
real=test_part,
actual_values=time_series,
is_visualise=with_visualisation)
# Get available_operations type
primary_operations, secondary_operations = get_available_operations()
# Composer parameters
composer_requirements = GPComposerRequirements(
primary=primary_operations,
secondary=secondary_operations,
max_arity=3,
max_depth=8,
pop_size=10,
num_of_generations=10,
crossover_prob=0.8,
mutation_prob=0.8,
timeout=datetime.timedelta(minutes=10),
cv_folds=cv_folds,
validation_blocks=3)
mutation_types = [
parameter_change_mutation, MutationTypesEnum.simple,
MutationTypesEnum.reduce
]
optimiser_parameters = GPGraphOptimiserParameters(
mutation_types=mutation_types)
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.RMSE)
builder = GPComposerBuilder(task=task). \
with_optimiser_parameters(optimiser_parameters).\
with_requirements(composer_requirements).\
with_metrics(metric_function).with_initial_pipeline(init_pipeline)
composer = builder.build()
obtained_pipeline = composer.compose_pipeline(data=train_input,
is_visualise=False)
###################################
# Obtained pipeline visualisation #
###################################
if with_visualisation:
obtained_pipeline.show()
preds = fit_predict_for_pipeline(pipeline=obtained_pipeline,
train_input=train_input,
predict_input=predict_input)
display_validation_metric(predicted=preds,
real=test_part,
actual_values=time_series,
is_visualise=with_visualisation)
obtained_pipeline.print_structure()
def run_metocean_forecasting_problem(train_file_path,
test_file_path,
forecast_length=1,
max_window_size=64,
with_visualisation=True):
# specify the task to solve
task_to_solve = Task(
TaskTypesEnum.ts_forecasting,
TsForecastingParams(forecast_length=forecast_length,
max_window_size=max_window_size,
return_all_steps=False))
full_path_train = os.path.join(str(project_root()), train_file_path)
dataset_to_train = InputData.from_csv(full_path_train,
task=task_to_solve,
data_type=DataTypesEnum.ts)
# a dataset for a final validation of the composed model
full_path_test = os.path.join(str(project_root()), test_file_path)
dataset_to_validate = InputData.from_csv(full_path_test,
task=task_to_solve,
data_type=DataTypesEnum.ts)
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.RMSE)
time_limit_min = 10
available_model_types = [
'linear', 'ridge', 'lasso', 'rfr', 'dtreg', 'knnreg', 'svr'
]
if max_window_size == 1:
# unit test model
available_model_types = ['linear', 'ridge']
time_limit_min = 0.001
# each possible single-model chain
for model in available_model_types:
chain = TsForecastingChain(PrimaryNode(model))
chain.fit(input_data=dataset_to_train, verbose=False)
calculate_validation_metric(chain.predict(dataset_to_validate),
dataset_to_validate,
is_visualise=with_visualisation,
label=model)
# static multiscale chain
multiscale_chain = get_composite_multiscale_chain()
multiscale_chain.fit(input_data=dataset_to_train, verbose=False)
calculate_validation_metric(multiscale_chain.predict(dataset_to_validate),
dataset_to_validate,
is_visualise=with_visualisation,
label='Fixed multiscale')
# static all-in-one ensemble chain
ens_chain = get_ensemble_chain()
ens_chain.fit(input_data=dataset_to_train, verbose=False)
calculate_validation_metric(ens_chain.predict(dataset_to_validate),
dataset_to_validate,
is_visualise=with_visualisation,
label='Ensemble composite')
# optimized ensemble chain
composer_requirements = GPComposerRequirements(
primary=available_model_types,
secondary=available_model_types,
max_arity=5,
max_depth=2,
pop_size=10,
num_of_generations=10,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=datetime.timedelta(minutes=time_limit_min),
add_single_model_chains=False)
builder = GPComposerBuilder(task=task_to_solve).with_requirements(
composer_requirements).with_metrics(metric_function)
composer = builder.build()
chain = composer.compose_chain(data=dataset_to_train, is_visualise=False)
chain.fit_from_scratch(input_data=dataset_to_train, verbose=False)
if with_visualisation:
ComposerVisualiser.visualise(chain)
calculate_validation_metric(chain.predict(dataset_to_validate),
dataset_to_validate,
is_visualise=with_visualisation,
label='Automated ensemble')
# optimized multiscale chain
available_model_types_primary = ['trend_data_model', 'residual_data_model']
available_model_types_secondary = [
'linear', 'ridge', 'lasso', 'rfr', 'dtreg', 'knnreg', 'svr'
]
available_model_types_all = available_model_types_primary + available_model_types_secondary
composer_requirements = GPComposerRequirements(
primary=available_model_types_all,
secondary=available_model_types_secondary,
max_arity=5,
max_depth=2,
pop_size=10,
num_of_generations=30,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=datetime.timedelta(minutes=time_limit_min))
builder = GPComposerBuilder(task=task_to_solve).with_requirements(
composer_requirements).with_metrics(
metric_function).with_initial_chain(multiscale_chain)
composer = builder.build()
chain = composer.compose_chain(data=dataset_to_train, is_visualise=False)
chain.fit_from_scratch(input_data=dataset_to_train, verbose=False)
if with_visualisation:
visualiser = ChainVisualiser()
visualiser.visualise(chain)
rmse_on_valid = calculate_validation_metric(
chain.predict(dataset_to_validate),
dataset_to_validate,
is_visualise=with_visualisation,
label='Automated multiscale')
return rmse_on_valid
def run_ts_forecasting_problem(forecast_length=50,
with_visualisation=True) -> None:
""" Function launch time series task with composing
:param forecast_length: length of the forecast
:param with_visualisation: is it needed to show the plots
"""
file_path = '../cases/data/metocean/metocean_data_test.csv'
df = pd.read_csv(file_path)
time_series = np.array(df['sea_height'])
# Train/test split
train_part = time_series[:-forecast_length]
test_part = time_series[-forecast_length:]
# Prepare data for train and test
train_input, predict_input, task = prepare_train_test_input(
train_part, forecast_length)
# Get chain with pre-defined structure
init_chain = get_source_chain()
# Init check
preds = fit_predict_for_chain(chain=init_chain,
train_input=train_input,
predict_input=predict_input)
display_validation_metric(predicted=preds,
real=test_part,
actual_values=time_series,
is_visualise=with_visualisation)
# Get available_operations type
primary_operations, secondary_operations = get_available_operations()
# Composer parameters
composer_requirements = GPComposerRequirements(
primary=primary_operations,
secondary=secondary_operations,
max_arity=3,
max_depth=8,
pop_size=10,
num_of_generations=15,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=datetime.timedelta(minutes=10),
allow_single_operations=False)
mutation_types = [
MutationTypesEnum.parameter_change, MutationTypesEnum.simple,
MutationTypesEnum.reduce
]
optimiser_parameters = GPChainOptimiserParameters(
mutation_types=mutation_types)
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.MAE)
builder = GPComposerBuilder(task=task). \
with_optimiser_parameters(optimiser_parameters).\
with_requirements(composer_requirements).\
with_metrics(metric_function).with_initial_chain(init_chain)
composer = builder.build()
obtained_chain = composer.compose_chain(data=train_input,
is_visualise=False)
################################
# Obtained chain visualisation #
################################
if with_visualisation:
visualiser = ChainVisualiser()
visualiser.visualise(obtained_chain)
preds = fit_predict_for_chain(chain=obtained_chain,
train_input=train_input,
predict_input=predict_input)
display_validation_metric(predicted=preds,
real=test_part,
actual_values=time_series,
is_visualise=with_visualisation)
display_chain_info(obtained_chain)
def run_experiment(file_path, init_chain):
# Read dataframe and prepare train and test data
df = pd.read_csv(file_path)
features = np.array(df[['level_station_1', 'mean_temp', 'month',
'precip']])
target = np.array(df['level_station_2'])
x_data_train, x_data_test, y_data_train, y_data_test = train_test_split(
features, target, test_size=0.2, shuffle=True, random_state=10)
y_data_test = np.ravel(y_data_test)
# Define regression task
task = Task(TaskTypesEnum.regression)
# Prepare data to train the model
train_input = InputData(idx=np.arange(0, len(x_data_train)),
features=x_data_train,
target=y_data_train,
task=task,
data_type=DataTypesEnum.table)
predict_input = InputData(idx=np.arange(0, len(x_data_test)),
features=x_data_test,
target=None,
task=task,
data_type=DataTypesEnum.table)
available_operations_types = [
'ridge', 'lasso', 'dtreg', 'xgbreg', 'adareg', 'knnreg', 'linear',
'svr', 'poly_features', 'scaling', 'ransac_lin_reg', 'rfe_lin_reg',
'pca', 'ransac_non_lin_reg', 'rfe_non_lin_reg', 'normalization'
]
composer_requirements = GPComposerRequirements(
primary=['one_hot_encoding'],
secondary=available_operations_types,
max_arity=3,
max_depth=8,
pop_size=10,
num_of_generations=5,
crossover_prob=0.8,
mutation_prob=0.8,
max_lead_time=datetime.timedelta(minutes=5),
allow_single_operations=True)
metric_function = MetricsRepository().metric_by_id(
RegressionMetricsEnum.MAE)
builder = GPComposerBuilder(
task=task).with_requirements(composer_requirements).with_metrics(
metric_function).with_initial_chain(init_chain)
composer = builder.build()
obtained_chain = composer.compose_chain(data=train_input,
is_visualise=False)
# Display info about obtained chain
obtained_models, depth = get_chain_info(chain=obtained_chain)
preds = fit_predict_for_chain(chain=obtained_chain,
train_input=train_input,
predict_input=predict_input)
mse_value = mean_squared_error(y_data_test, preds, squared=False)
mae_value = mean_absolute_error(y_data_test, preds)
print(f'RMSE - {mse_value:.2f}')
print(f'MAE - {mae_value:.2f}\n')