def test_refit_shuffle_on_fail(self):
backend_api = self._create_backend('test_refit_shuffle_on_fail')
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
failing_model.fit_transformer.side_effect = [
ValueError(), ValueError(), (None, {})]
failing_model.get_max_iter.return_value = 100
auto = AutoML(backend_api, 20, 5)
ensemble_mock = unittest.mock.Mock()
ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
auto.ensemble_ = ensemble_mock
for budget_type in [None, 'iterations']:
auto._budget_type = budget_type
auto.models_ = {(1, 1, 50.0): failing_model}
# Make sure a valid 2D array is given to automl
X = np.array([1, 2, 3]).reshape(-1, 1)
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
self.assertEqual(failing_model.fit_transformer.call_count, 3)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)
def test_refit_shuffle_on_fail(backend, dask_client):
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
failing_model.fit_transformer.side_effect = [
ValueError(), ValueError(), (None, {})]
failing_model.get_max_iter.return_value = 100
auto = AutoML(backend, 30, 5, dask_client=dask_client)
ensemble_mock = unittest.mock.Mock()
ensemble_mock.get_selected_model_identifiers.return_value = [(1, 1, 50.0)]
auto.ensemble_ = ensemble_mock
auto.InputValidator = InputValidator()
for budget_type in [None, 'iterations']:
auto._budget_type = budget_type
auto.models_ = {(1, 1, 50.0): failing_model}
# Make sure a valid 2D array is given to automl
X = np.array([1, 2, 3]).reshape(-1, 1)
y = np.array([1, 2, 3])
auto.InputValidator.fit(X, y)
auto.refit(X, y)
assert failing_model.fit.call_count == 3
assert failing_model.fit_transformer.call_count == 3
del auto
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 test_refit_shuffle_on_fail(self):
output = os.path.join(self.test_dir, '..', '.tmp_refit_shuffle_on_fail')
context = BackendContext(output, output, False, False)
backend = Backend(context)
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
auto = AutoML(backend, 20, 5)
ensemble_mock = unittest.mock.Mock()
auto.ensemble_ = ensemble_mock
ensemble_mock.get_model_identifiers.return_value = [1]
auto.models_ = {1: failing_model}
X = np.array([1, 2, 3])
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
def test_refit_shuffle_on_fail(self):
backend_api = self._create_backend('test_refit_shuffle_on_fail')
failing_model = unittest.mock.Mock()
failing_model.fit.side_effect = [ValueError(), ValueError(), None]
auto = AutoML(backend_api, 20, 5)
ensemble_mock = unittest.mock.Mock()
auto.ensemble_ = ensemble_mock
ensemble_mock.get_selected_model_identifiers.return_value = [1]
auto.models_ = {1: failing_model}
X = np.array([1, 2, 3])
y = np.array([1, 2, 3])
auto.refit(X, y)
self.assertEqual(failing_model.fit.call_count, 3)
del auto
self._tearDown(backend_api.temporary_directory)
self._tearDown(backend_api.output_directory)