Source code for evalml.pipelines.components.estimators.regressors.et_regressor

from sklearn.ensemble import ExtraTreesRegressor as SKExtraTreesRegressor
from skopt.space import Integer

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes


class ExtraTreesRegressor(Estimator):
    """Extra Trees Regressor."""

    name = "Extra Trees Regressor"
    hyperparameter_ranges = {
        "n_estimators": Integer(10, 1000),
        "max_features": ["auto", "sqrt", "log2"],
        "max_depth": Integer(4, 10),
    }
    model_family = ModelFamily.EXTRA_TREES
    supported_problem_types = [
        ProblemTypes.REGRESSION,
        ProblemTypes.TIME_SERIES_REGRESSION,
    ]

    def __init__(
        self,
        n_estimators=100,
        max_features="auto",
        max_depth=6,
        min_samples_split=2,
        min_weight_fraction_leaf=0.0,
        n_jobs=-1,
        random_seed=0,
        **kwargs
    ):
        parameters = {
            "n_estimators": n_estimators,
            "max_features": max_features,
            "max_depth": max_depth,
            "min_samples_split": min_samples_split,
            "min_weight_fraction_leaf": min_weight_fraction_leaf,
            "n_jobs": n_jobs,
        }
        parameters.update(kwargs)

        et_regressor = SKExtraTreesRegressor(random_state=random_seed, **parameters)
        super().__init__(
            parameters=parameters, component_obj=et_regressor, random_seed=random_seed
        )