53da02f8d5923d32f3c11a28b0e11f64d905399d,ParamSklearn/components/classification/gradient_boosting.py,GradientBoostingClassifier,get_hyperparameter_search_space,#Any#,139

Before Change

    def get_hyperparameter_search_space(dataset_properties=None):
        learning_rate = UniformFloatHyperparameter(
            name="learning_rate", lower=0.0001, upper=1, default=0.1, log=True)
        subsample = UniformFloatHyperparameter(
            name="subsample", lower=0.01, upper=1.0, default=1.0, log=False)

        // Unparametrized
        //max_leaf_nodes_or_max_depth = UnParametrizedHyperparameter(
        //    name="max_leaf_nodes_or_max_depth", value="max_depth")
            // CategoricalHyperparameter("max_leaf_nodes_or_max_depth",
            // choices=["max_leaf_nodes", "max_depth"], default="max_depth")

        max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes",
                                                      value="None")


        // Copied from random_forest.py
        //n_estimators = UniformIntegerHyperparameter(
        //    name="n_estimators", lower=10, upper=100, default=10, log=False)
        n_estimators = Constant("n_estimators", 100)
        //max_features = UniformFloatHyperparameter(
        //    name="max_features", lower=0.01, upper=0.5, default=0.1)
        max_features = UniformFloatHyperparameter(
            "max_features", 0.5, 5, default=1)
        max_depth = UniformIntegerHyperparameter(
            name="max_depth", lower=1, upper=10, default=3)
        min_samples_split = UniformIntegerHyperparameter(
            name="min_samples_split", lower=2, upper=20, default=2, log=False)
        min_samples_leaf = UniformIntegerHyperparameter(
            name="min_samples_leaf", lower=1, upper=20, default=1, log=False)

        cs = ConfigurationSpace()
        cs.add_hyperparameter(n_estimators)
        cs.add_hyperparameter(learning_rate)
        cs.add_hyperparameter(max_features)
        //cs.add_hyperparameter(max_leaf_nodes_or_max_depth)
        //cs.add_hyperparameter(max_leaf_nodes)
        cs.add_hyperparameter(max_depth)
        cs.add_hyperparameter(min_samples_split)
        cs.add_hyperparameter(min_samples_leaf)
        cs.add_hyperparameter(subsample)

        // Conditions
        //cond_max_leaf_nodes_or_max_depth = \
        //    EqualsCondition(child=max_leaf_nodes,

After Change

    @staticmethod
    def get_hyperparameter_search_space(dataset_properties=None):
        cs = ConfigurationSpace()
        loss = cs.add_hyperparameter(Constant("loss", "deviance"))
        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"))

        return cs

Instances