def test_get_tunable_hyperparameters(self): mlpipeline = MLPipeline(['a_primitive']) tunable = dict() mlpipeline._tunable_hyperparameters = tunable returned = mlpipeline.get_tunable_hyperparameters() assert returned == tunable assert returned is not tunable
def test_get_tunable_hyperparameters_flat(self): mlpipeline = MLPipeline(['a_primitive']) mlpipeline._tunable_hyperparameters = { 'block_1': { 'hp_1': { 'type': 'int', 'range': [ 1, 10 ], } }, 'block_2': { 'hp_1': { 'type': 'str', 'default': 'a', 'values': [ 'a', 'b', 'c' ], }, 'hp_2': { 'type': 'bool', 'default': True, } } } returned = mlpipeline.get_tunable_hyperparameters(flat=True) expected = { ('block_1', 'hp_1'): { 'type': 'int', 'range': [ 1, 10 ], }, ('block_2', 'hp_1'): { 'type': 'str', 'default': 'a', 'values': [ 'a', 'b', 'c' ], }, ('block_2', 'hp_2'): { 'type': 'bool', 'default': True, } } assert returned == expected