53da02f8d5923d32f3c11a28b0e11f64d905399d,ParamSklearn/components/regression/gradient_boosting.py,GradientBoosting,get_hyperparameter_search_space,#Any#,127

Before Change

        cs = ConfigurationSpace()
        cs.add_hyperparameter(n_estimators)
        cs.add_hyperparameter(loss)
        cs.add_hyperparameter(learning_rate)
        cs.add_hyperparameter(max_features)
        cs.add_hyperparameter(max_depth)
        cs.add_hyperparameter(min_samples_split)
        cs.add_hyperparameter(min_samples_leaf)

After Change

    @staticmethod
    def get_hyperparameter_search_space(dataset_properties=None):
        cs = ConfigurationSpace()
        loss = cs.add_hyperparameter(CategoricalHyperparameter(
            "loss", ["ls", "lad", "huber", "quantile"], default="ls"))
        learning_rate = cs.add_hyperparameter(UniformFloatHyperparameter(
            name="learning_rate", lower=0.0001, upper=1, default=0.1, log=True))
        n_estimators = cs.add_hyperparameter(Constant("n_estimators", 100))
        max_depth = cs.add_hyperparameter(UniformIntegerHyperparameter(
            name="max_depth", lower=1, upper=10, default=3))
        min_samples_split = cs.add_hyperparameter(UniformIntegerHyperparameter(
            name="min_samples_split", lower=2, upper=20, default=2, log=False))
        min_samples_leaf = cs.add_hyperparameter(UniformIntegerHyperparameter(
            name="min_samples_leaf", lower=1, upper=20, default=1, log=False))
        min_weight_fraction_leaf = cs.add_hyperparameter(
            UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.))
        subsample = cs.add_hyperparameter(UniformFloatHyperparameter(
            name="subsample", lower=0.01, upper=1.0, default=1.0, log=False))
        max_features = cs.add_hyperparameter(UniformFloatHyperparameter(
            "max_features", 0.5, 5, default=1))
        max_leaf_nodes = cs.add_hyperparameter(UnParametrizedHyperparameter(
            name="max_leaf_nodes", value="None"))
        alpha = cs.add_hyperparameter(UniformFloatHyperparameter(
            "alpha", lower=0.75, upper=0.99, default=0.9))

        cs.add_condition(InCondition(alpha, loss, ["huber", "quantile"]))
        return cs

Instances