def test_pipeline_should_update_hyperparams_space():
p = Pipeline([
SomeStep().set_name('step_1'),
SomeStep().set_name('step_2')
])
p.set_hyperparams_space({
'hp': RandInt(1, 2),
'step_1__hp': RandInt(2, 3),
'step_2__hp': RandInt(3, 4)
})
p.update_hyperparams_space({
'hp': RandInt(4, 6),
'step_2__hp': RandInt(6, 8)
})
assert isinstance(p.hyperparams_space, HyperparameterSpace)
assert p.hyperparams_space['hp'].min_included == 4
assert p.hyperparams_space['hp'].max_included == 6
assert p[0].hyperparams_space['hp'].min_included == 2
assert p[0].hyperparams_space['hp'].max_included == 3
assert p[1].hyperparams_space['hp'].min_included == 6
assert p[1].hyperparams_space['hp'].max_included == 8
def main():
p = Pipeline([
('step1', MultiplyByN()),
('step2', MultiplyByN()),
Pipeline([
Identity(),
Identity(),
PCA(n_components=4)
])
])
p.set_hyperparams_space({
'step1__multiply_by': RandInt(42, 50),
'step2__multiply_by': RandInt(-10, 0),
'Pipeline__PCA__n_components': RandInt(2, 3)
})
samples = p.get_hyperparams_space().rvs()
p.set_hyperparams(samples)
samples = p.get_hyperparams().to_flat_as_dict_primitive()
assert 42 <= samples['step1__multiply_by'] <= 50
assert -10 <= samples['step2__multiply_by'] <= 0
assert samples['Pipeline__PCA__n_components'] in [2, 3]
assert p['Pipeline']['PCA'].get_wrapped_sklearn_predictor().n_components in [2, 3]
def test_pipeline_should_get_hyperparams_space():
p = Pipeline([
SomeStep().set_name('step_1'),
SomeStep().set_name('step_2')
])
p.set_hyperparams_space({
'hp': RandInt(1, 2),
'step_1__hp': RandInt(2, 3),
'step_2__hp': RandInt(3, 4)
})
hyperparams_space = p.get_hyperparams_space()
assert isinstance(hyperparams_space, HyperparameterSpace)
assert hyperparams_space['hp'].min_included == 1
assert hyperparams_space['hp'].max_included == 2
assert hyperparams_space['step_1__hp'].min_included == 2
assert hyperparams_space['step_1__hp'].max_included == 3
assert hyperparams_space['step_2__hp'].min_included == 3
assert hyperparams_space['step_2__hp'].max_included == 4
