def test_finish_up_model_predicts_NaN(self):
'''Tests by handing in predictions which contain NaNs'''
rs = np.random.RandomState(1)
D = get_multiclass_classification_datamanager()
backend_api = unittest.mock.Mock()
backend_api.load_datamanager.return_value = D
queue_mock = unittest.mock.Mock()
ae = AbstractEvaluator(backend=backend_api,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy)
ae.Y_optimization = rs.rand(33, 3)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
# NaNs in prediction ensemble
predictions_ensemble[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(0.1,
predictions_ensemble,
predictions_valid,
predictions_test)
self.assertEqual(loss, 1.0)
self.assertEqual(
additional_run_info, {
'error': 'Model predictions for optimization set '
'contains NaNs.'
})
# NaNs in prediction validation
predictions_ensemble[5, 2] = 0.5
predictions_valid[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(0.1,
predictions_ensemble,
predictions_valid,
predictions_test)
self.assertEqual(loss, 1.0)
self.assertEqual(
additional_run_info, {
'error': 'Model predictions for validation set '
'contains NaNs.'
})
# NaNs in prediction test
predictions_valid[5, 2] = 0.5
predictions_test[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(0.1,
predictions_ensemble,
predictions_valid,
predictions_test)
self.assertEqual(loss, 1.0)
self.assertEqual(
additional_run_info,
{'error': 'Model predictions for test set contains '
'NaNs.'})
self.assertEqual(backend_api.save_predictions_as_npy.call_count, 0)
def test_add_additional_components(self):
shutil.rmtree(self.working_directory, ignore_errors=True)
os.mkdir(self.working_directory)
queue_mock = unittest.mock.Mock()
context = BackendContext(
temporary_directory=os.path.join(self.working_directory, 'tmp'),
delete_tmp_folder_after_terminate=True,
)
with unittest.mock.patch.object(
Backend, 'load_datamanager') as load_datamanager_mock:
load_datamanager_mock.return_value = get_multiclass_classification_datamanager(
)
backend = Backend(context)
with unittest.mock.patch.object(_addons['classification'],
'add_component') as _:
# If the components in the argument `additional_components` are an empty dict
# there is no call to `add_component`, if there's something in it, `add_component
# is called (2nd case)
for fixture, case in ((0, dict()), (1, dict(abc='def'))):
thirdparty_components_patch = unittest.mock.Mock()
thirdparty_components_patch.components = case
additional_components = dict(
classification=thirdparty_components_patch)
AbstractEvaluator(
backend=backend,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy,
port=self.port,
additional_components=additional_components,
)
self.assertEqual(
_addons['classification'].add_component.call_count,
fixture)
def test_file_output(self):
shutil.rmtree(self.working_directory, ignore_errors=True)
os.mkdir(self.working_directory)
queue_mock = unittest.mock.Mock()
context = BackendContext(
temporary_directory=os.path.join(self.working_directory, 'tmp'),
output_directory=os.path.join(self.working_directory, 'out'),
delete_tmp_folder_after_terminate=True,
delete_output_folder_after_terminate=True,
)
with unittest.mock.patch.object(
Backend, 'load_datamanager') as load_datamanager_mock:
load_datamanager_mock.return_value = get_multiclass_classification_datamanager(
)
backend = Backend(context)
ae = AbstractEvaluator(
backend=backend,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy,
port=self.port,
)
ae.model = sklearn.dummy.DummyClassifier()
rs = np.random.RandomState()
ae.Y_optimization = rs.rand(33, 3)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
ae.file_output(
Y_optimization_pred=predictions_ensemble,
Y_valid_pred=predictions_valid,
Y_test_pred=predictions_test,
)
self.assertTrue(
os.path.exists(
os.path.join(self.working_directory, 'tmp',
'.auto-sklearn', 'runs', '1_0_None')))
shutil.rmtree(self.working_directory, ignore_errors=True)
def test_finish_up_model_predicts_NaN(self):
'''Tests by handing in predictions which contain NaNs'''
rs = np.random.RandomState(1)
queue_mock = unittest.mock.Mock()
ae = AbstractEvaluator(backend=self.backend_mock,
port=self.port,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy)
ae.Y_optimization = rs.rand(33, 3)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
# NaNs in prediction ensemble
predictions_ensemble[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(
loss=0.1,
train_loss=0.1,
opt_pred=predictions_ensemble,
valid_pred=predictions_valid,
test_pred=predictions_test,
additional_run_info=None,
final_call=True,
file_output=True,
status=StatusType.SUCCESS,
)
self.assertEqual(loss, 1.0)
self.assertEqual(
additional_run_info, {
'error': 'Model predictions for optimization set '
'contains NaNs.'
})
# NaNs in prediction validation
predictions_ensemble[5, 2] = 0.5
predictions_valid[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(
loss=0.1,
train_loss=0.1,
opt_pred=predictions_ensemble,
valid_pred=predictions_valid,
test_pred=predictions_test,
additional_run_info=None,
final_call=True,
file_output=True,
status=StatusType.SUCCESS,
)
self.assertEqual(loss, 1.0)
self.assertEqual(
additional_run_info, {
'error': 'Model predictions for validation set '
'contains NaNs.'
})
# NaNs in prediction test
predictions_valid[5, 2] = 0.5
predictions_test[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(
loss=0.1,
train_loss=0.1,
opt_pred=predictions_ensemble,
valid_pred=predictions_valid,
test_pred=predictions_test,
additional_run_info=None,
final_call=True,
file_output=True,
status=StatusType.SUCCESS,
)
self.assertEqual(loss, 1.0)
self.assertEqual(
additional_run_info,
{'error': 'Model predictions for test set contains '
'NaNs.'})
self.assertEqual(self.backend_mock.save_predictions_as_npy.call_count,
0)
def test_disable_file_output(self):
queue_mock = unittest.mock.Mock()
rs = np.random.RandomState(1)
ae = AbstractEvaluator(
backend=self.backend_mock,
queue=queue_mock,
disable_file_output=True,
metric=accuracy,
port=self.port,
)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
loss_, additional_run_info_ = (ae.file_output(
predictions_ensemble,
predictions_valid,
predictions_test,
))
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
# This function is never called as there is a return before
self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count, 0)
for call_count, disable in enumerate(['model', 'cv_model'], start=1):
ae = AbstractEvaluator(
backend=self.backend_mock,
output_y_hat_optimization=False,
queue=queue_mock,
disable_file_output=[disable],
metric=accuracy,
port=self.port,
)
ae.Y_optimization = predictions_ensemble
ae.model = unittest.mock.Mock()
ae.models = [unittest.mock.Mock()]
loss_, additional_run_info_ = (ae.file_output(
predictions_ensemble,
predictions_valid,
predictions_test,
))
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
self.assertEqual(self.backend_mock.save_numrun_to_dir.call_count,
call_count)
if disable == 'model':
self.assertIsNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['model'])
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['cv_model'])
else:
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['model'])
self.assertIsNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['cv_model'])
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['ensemble_predictions'])
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['valid_predictions'])
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['test_predictions'])
ae = AbstractEvaluator(
backend=self.backend_mock,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy,
disable_file_output=['y_optimization'],
port=self.port,
)
ae.Y_optimization = predictions_ensemble
ae.model = 'model'
ae.models = [unittest.mock.Mock()]
loss_, additional_run_info_ = (ae.file_output(
predictions_ensemble,
predictions_valid,
predictions_test,
))
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
self.assertIsNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['ensemble_predictions'])
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['valid_predictions'])
self.assertIsNotNone(
self.backend_mock.save_numrun_to_dir.call_args_list[-1][1]
['test_predictions'])
def test_disable_file_output(self, exists_mock):
backend_mock = unittest.mock.Mock()
backend_mock.get_model_dir.return_value = 'abc'
D = get_multiclass_classification_datamanager()
backend_mock.load_datamanager.return_value = D
queue_mock = unittest.mock.Mock()
rs = np.random.RandomState(1)
ae = AbstractEvaluator(
backend=backend_mock,
queue=queue_mock,
disable_file_output=True,
metric=accuracy,
)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
loss_, additional_run_info_ = ae.file_output(predictions_ensemble,
predictions_valid,
predictions_test)
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
# This function is not guarded by a an if statement
self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 0)
self.assertEqual(backend_mock.save_model.call_count, 0)
ae = AbstractEvaluator(
backend=backend_mock,
output_y_hat_optimization=False,
queue=queue_mock,
disable_file_output=['model'],
metric=accuracy,
)
ae.Y_optimization = predictions_ensemble
loss_, additional_run_info_ = ae.file_output(predictions_ensemble,
predictions_valid,
predictions_test)
self.assertIsNone(loss_)
self.assertIsNone(additional_run_info_)
# This function is not guarded by a an if statement
self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 3)
self.assertEqual(backend_mock.save_model.call_count, 0)
ae = AbstractEvaluator(
backend=backend_mock,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy,
disable_file_output=['y_optimization'],
)
exists_mock.return_value = True
ae.Y_optimization = predictions_ensemble
ae.model = 'model'
loss_, additional_run_info_ = ae.file_output(predictions_ensemble,
predictions_valid,
predictions_test)
self.assertIsNone(loss_)
self.assertIsNone(additional_run_info_)
# This function is not guarded by a an if statement
self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 5)
self.assertEqual(backend_mock.save_model.call_count, 1)
def test_disable_file_output(self, exists_mock):
backend_mock = unittest.mock.Mock()
backend_mock.get_model_dir.return_value = 'abc'
D = get_multiclass_classification_datamanager()
backend_mock.load_datamanager.return_value = D
queue_mock = unittest.mock.Mock()
rs = np.random.RandomState(1)
ae = AbstractEvaluator(
backend=backend_mock,
queue=queue_mock,
disable_file_output=True,
metric=accuracy,
)
predictions_train = rs.rand(66, 3)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
loss_, additional_run_info_ = (
ae.file_output(
predictions_train,
predictions_ensemble,
predictions_valid,
predictions_test,
)
)
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
# This function is not guarded by a an if statement
self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 0)
self.assertEqual(backend_mock.save_model.call_count, 0)
ae = AbstractEvaluator(
backend=backend_mock,
output_y_hat_optimization=False,
queue=queue_mock,
disable_file_output=['model'],
metric=accuracy,
)
ae.Y_optimization = predictions_ensemble
loss_, additional_run_info_ = (
ae.file_output(
predictions_train,
predictions_ensemble,
predictions_valid,
predictions_test,
)
)
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
# This function is not guarded by a an if statement
self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 3)
self.assertEqual(backend_mock.save_model.call_count, 0)
ae = AbstractEvaluator(
backend=backend_mock,
output_y_hat_optimization=False,
queue=queue_mock,
metric=accuracy,
disable_file_output=['y_optimization'],
)
exists_mock.return_value = True
ae.Y_optimization = predictions_ensemble
ae.model = 'model'
loss_, additional_run_info_ = (
ae.file_output(
predictions_train,
predictions_ensemble,
predictions_valid,
predictions_test,
)
)
self.assertIsNone(loss_)
self.assertEqual(additional_run_info_, {})
# This function is not guarded by a an if statement
self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 5)
self.assertEqual(backend_mock.save_model.call_count, 1)
def test_finish_up_model_predicts_NaN(self):
'''Tests by handing in predictions which contain NaNs'''
rs = np.random.RandomState(1)
D = get_multiclass_classification_datamanager()
backend_api = unittest.mock.Mock()
backend_api.load_datamanager.return_value = D
queue_mock = unittest.mock.Mock()
ae = AbstractEvaluator(backend=backend_api,
output_y_hat_optimization=False,
queue=queue_mock, metric=accuracy)
ae.Y_optimization = rs.rand(33, 3)
predictions_train = rs.rand(66, 3)
predictions_ensemble = rs.rand(33, 3)
predictions_test = rs.rand(25, 3)
predictions_valid = rs.rand(25, 3)
# NaNs in prediction ensemble
predictions_ensemble[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(
loss=0.1,
train_pred=predictions_train,
opt_pred=predictions_ensemble,
valid_pred=predictions_valid,
test_pred=predictions_test,
additional_run_info=None,
final_call=True,
file_output=True,
)
self.assertEqual(loss, 1.0)
self.assertEqual(additional_run_info,
{'error': 'Model predictions for optimization set '
'contains NaNs.'})
# NaNs in prediction validation
predictions_ensemble[5, 2] = 0.5
predictions_valid[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(
loss=0.1,
train_pred=predictions_train,
opt_pred=predictions_ensemble,
valid_pred=predictions_valid,
test_pred=predictions_test,
additional_run_info=None,
final_call=True,
file_output=True,
)
self.assertEqual(loss, 1.0)
self.assertEqual(additional_run_info,
{'error': 'Model predictions for validation set '
'contains NaNs.'})
# NaNs in prediction test
predictions_valid[5, 2] = 0.5
predictions_test[5, 2] = np.NaN
_, loss, _, additional_run_info = ae.finish_up(
loss=0.1,
train_pred=predictions_train,
opt_pred=predictions_ensemble,
valid_pred=predictions_valid,
test_pred=predictions_test,
additional_run_info=None,
final_call=True,
file_output=True,
)
self.assertEqual(loss, 1.0)
self.assertEqual(additional_run_info,
{'error': 'Model predictions for test set contains '
'NaNs.'})
self.assertEqual(backend_api.save_predictions_as_npy.call_count, 0)
