コード例 #1
0
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"]
コード例 #2
0
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]
コード例 #3
0
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
コード例 #4
0
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