def test_smbo_metalearning_configurations(backend, context, dask_client):
# Get the inputs to the optimizer
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris')
config_space = AutoML(backend=backend,
metric=autosklearn.metrics.accuracy,
time_left_for_this_task=20,
per_run_time_limit=5).fit(
X_train,
Y_train,
task=BINARY_CLASSIFICATION,
only_return_configuration_space=True)
watcher = StopWatch()
# Create an optimizer
smbo = AutoMLSMBO(
config_space=config_space,
dataset_name='iris',
backend=backend,
total_walltime_limit=10,
func_eval_time_limit=5,
memory_limit=4096,
metric=autosklearn.metrics.accuracy,
watcher=watcher,
n_jobs=1,
dask_client=dask_client,
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
start_num_run=1,
data_memory_limit=None,
num_metalearning_cfgs=25,
pynisher_context=context,
)
assert smbo.pynisher_context == context
# Create the inputs to metalearning
datamanager = XYDataManager(
X_train,
Y_train,
X_test,
Y_test,
task=BINARY_CLASSIFICATION,
dataset_name='iris',
feat_type={i: 'numerical'
for i in range(X_train.shape[1])},
)
backend.save_datamanager(datamanager)
smbo.task = BINARY_CLASSIFICATION
smbo.reset_data_manager()
metalearning_configurations = smbo.get_metalearning_suggestions()
# We should have 25 metalearning configurations
assert len(metalearning_configurations) == 25
assert [
isinstance(config, Configuration)
for config in metalearning_configurations
]
def get_meta_learning_configs(X,
y,
task_type,
dataset_name='default',
metric='accuracy',
num_cfgs=5):
if X is None or y is None:
X, y, _ = load_data(dataset_name)
backend = create(temporary_directory=None,
output_directory=None,
delete_tmp_folder_after_terminate=False,
delete_output_folder_after_terminate=False,
shared_mode=True)
dm = XYDataManager(X, y, None, None, task_type, None, dataset_name)
configuration_space = pipeline.get_configuration_space(
dm.info,
include_estimators=None,
exclude_estimators=None,
include_preprocessors=None,
exclude_preprocessors=None)
watcher = StopWatch()
name = os.path.basename(dm.name)
watcher.start_task(name)
def reset_data_manager(max_mem=None):
pass
automlsmbo = AutoMLSMBO(
config_space=configuration_space,
dataset_name=dataset_name,
backend=backend,
total_walltime_limit=1e5,
func_eval_time_limit=1e5,
memory_limit=1e5,
metric=metric,
watcher=watcher,
metadata_directory='components/meta_learning/meta_resource',
num_metalearning_cfgs=num_cfgs)
automlsmbo.reset_data_manager = reset_data_manager
automlsmbo.task = task_type
automlsmbo.datamanager = dm
configs = automlsmbo.get_metalearning_suggestions()
return configs
