Source code for evalml.pipelines.components.estimators.classifiers.rf_classifier

from sklearn.ensemble import RandomForestClassifier as SKRandomForestClassifier
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 RandomForestClassifier(Estimator):
    """
    Random Forest Classifier.

    Arguments:
        n_estimators (float): The number of trees in the forest. Defaults to 100.
        max_depth (int): Maximum tree depth for base learners. Defaults to 6.
        n_jobs (int or None): Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.
        random_seed (int): Seed for the random number generator. Defaults to 0.
    """

    name = "Random Forest Classifier"
    hyperparameter_ranges = {
        "n_estimators": Integer(10, 1000),
        "max_depth": Integer(1, 10),
    }
    """{
        "n_estimators": Integer(10, 1000),
        "max_depth": Integer(1, 10),
    }"""
    model_family = ModelFamily.RANDOM_FOREST
    """ModelFamily.RANDOM_FOREST"""
    supported_problem_types = [
        ProblemTypes.BINARY,
        ProblemTypes.MULTICLASS,
        ProblemTypes.TIME_SERIES_BINARY,
        ProblemTypes.TIME_SERIES_MULTICLASS,
    ]
    """[
        ProblemTypes.BINARY,
        ProblemTypes.MULTICLASS,
        ProblemTypes.TIME_SERIES_BINARY,
        ProblemTypes.TIME_SERIES_MULTICLASS,
    ]"""

    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_classifier = SKRandomForestClassifier(random_state=random_seed, **parameters)
        super().__init__(
            parameters=parameters, component_obj=rf_classifier, random_seed=random_seed
        )