8565a366de87d82dc19c3386b4a83359be1aa275,autosklearn/pipeline/components/classification/random_forest.py,RandomForest,get_hyperparameter_search_space,#Any#,124

Before Change

    @staticmethod
    def get_hyperparameter_search_space(dataset_properties=None):
        cs = ConfigurationSpace()
        cs.add_hyperparameter(Constant("n_estimators", 100))
        cs.add_hyperparameter(CategoricalHyperparameter(
            "criterion", ["gini", "entropy"], default="gini"))
        cs.add_hyperparameter(UniformFloatHyperparameter(
            "max_features", 0.5, 5, default=1))
        cs.add_hyperparameter(UnParametrizedHyperparameter("max_depth", "None"))
        cs.add_hyperparameter(UniformIntegerHyperparameter(
            "min_samples_split", 2, 20, default=2))
        cs.add_hyperparameter(UniformIntegerHyperparameter(
            "min_samples_leaf", 1, 20, default=1))
        cs.add_hyperparameter(UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.))
        cs.add_hyperparameter(UnParametrizedHyperparameter("max_leaf_nodes", "None"))
        cs.add_hyperparameter(CategoricalHyperparameter(
            "bootstrap", ["True", "False"], default="True"))
        return cs

After Change

    @staticmethod
    def get_hyperparameter_search_space(dataset_properties=None):
        cs = ConfigurationSpace()
        n_estimators = Constant("n_estimators", 100)
        criterion = CategoricalHyperparameter(
            "criterion", ["gini", "entropy"], default="gini")
        max_features = UniformFloatHyperparameter(
            "max_features", 0.5, 5, default=1)
        max_depth = UnParametrizedHyperparameter("max_depth", "None")
        min_samples_split = UniformIntegerHyperparameter(
            "min_samples_split", 2, 20, default=2)
        min_samples_leaf = UniformIntegerHyperparameter(
            "min_samples_leaf", 1, 20, default=1)
        min_weight_fraction_leaf = UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.)
        max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None")
        bootstrap = CategoricalHyperparameter(
            "bootstrap", ["True", "False"], default="True")
        cs.add_hyperparameters([n_estimators, criterion, max_features,
                                max_depth, min_samples_split, min_samples_leaf,
                                min_weight_fraction_leaf, max_leaf_nodes,
                                bootstrap])
        return cs

Instances