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
