def test_hyperparam_space():
p = Pipeline([
AddFeatures([
SomeStep(hyperparams_space=HyperparameterSpace({"n_components": RandInt(1, 5)})),
SomeStep(hyperparams_space=HyperparameterSpace({"n_components": RandInt(1, 5)}))
]),
ModelStacking([
SomeStep(hyperparams_space=HyperparameterSpace({"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace({"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace({"max_depth": RandInt(1, 100)})),
SomeStep(hyperparams_space=HyperparameterSpace({"max_depth": RandInt(1, 100)}))
],
joiner=NumpyTranspose(),
judge=SomeStep(hyperparams_space=HyperparameterSpace({"alpha": LogUniform(0.1, 10.0)}))
)
])
rvsed = p.get_hyperparams_space()
p.set_hyperparams(rvsed)
hyperparams = p.get_hyperparams()
assert "AddFeatures" in hyperparams.keys()
assert "SomeStep" in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep"]
assert "SomeStep1" in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep1"]
assert "SomeStep" in hyperparams["ModelStacking"]
assert "n_estimators" in hyperparams["ModelStacking"]["SomeStep"]
assert "SomeStep1" in hyperparams["ModelStacking"]
assert "max_depth" in hyperparams["ModelStacking"]["SomeStep2"]
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_hyperparam_space():
p = Pipeline([
AddFeatures([
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_components": RandInt(1, 5)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_components": RandInt(1, 5)}))
]),
ModelStacking([
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"n_estimators": RandInt(1, 1000)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"max_depth": RandInt(1, 100)})),
SomeStep(hyperparams_space=HyperparameterSpace(
{"max_depth": RandInt(1, 100)}))
],
joiner=NumpyTranspose(),
judge=SomeStep(hyperparams_space=HyperparameterSpace(
{"alpha": LogUniform(0.1, 10.0)})))
])
rvsed = p.get_hyperparams_space()
p.set_hyperparams(rvsed)
hyperparams = p.get_hyperparams()
flat_hyperparams_keys = hyperparams.to_flat_dict().keys()
assert 'AddFeatures' in hyperparams
assert 'SomeStep' in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep"]
assert 'SomeStep1' in hyperparams["AddFeatures"]
assert "n_components" in hyperparams["AddFeatures"]["SomeStep1"]
assert 'ModelStacking' in hyperparams
assert 'SomeStep' in hyperparams["ModelStacking"]
assert 'n_estimators' in hyperparams["ModelStacking"]["SomeStep"]
assert 'SomeStep1' in hyperparams["ModelStacking"]
assert 'n_estimators' in hyperparams["ModelStacking"]["SomeStep1"]
assert 'SomeStep2' in hyperparams["ModelStacking"]
assert 'max_depth' in hyperparams["ModelStacking"]["SomeStep2"]
assert 'SomeStep3' in hyperparams["ModelStacking"]
assert 'max_depth' in hyperparams["ModelStacking"]["SomeStep3"]
assert 'AddFeatures__SomeStep1__n_components' in flat_hyperparams_keys
assert 'AddFeatures__SomeStep__n_components' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep__n_estimators' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep1__n_estimators' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep2__max_depth' in flat_hyperparams_keys
assert 'ModelStacking__SomeStep3__max_depth' in flat_hyperparams_keys
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