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

from sklearn.ensemble import RandomForestRegressor as SKRandomForestRegressor
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 RandomForestRegressor(Estimator):
    """Random Forest Regressor."""

    name = "Random Forest Regressor"
    hyperparameter_ranges = {
        "n_estimators": Integer(10, 1000),
        "max_depth": Integer(1, 32),
    }
    model_family = ModelFamily.RANDOM_FOREST
    supported_problem_types = [
        ProblemTypes.REGRESSION,
        ProblemTypes.TIME_SERIES_REGRESSION,
    ]

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

        rf_regressor = SKRandomForestRegressor(random_state=random_seed, **parameters)
        super().__init__(
            parameters=parameters, component_obj=rf_regressor, random_seed=random_seed
        )