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

"""Random Forest Regressor."""
from typing import Dict, List, Optional, Union

import pandas as pd
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.pipelines.components.utils import (
    get_prediction_intevals_for_tree_regressors,
)
from evalml.problem_types import ProblemTypes


[docs]class RandomForestRegressor(Estimator): """Random Forest Regressor. Args: 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 Regressor" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 32), } """{ "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 32), }""" model_family = ModelFamily.RANDOM_FOREST """ModelFamily.RANDOM_FOREST""" supported_problem_types = [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ] """[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ]""" def __init__( self, n_estimators: int = 100, max_depth: int = 6, n_jobs: int = -1, random_seed: Union[int, float] = 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, )
[docs] def get_prediction_intervals( self, X: pd.DataFrame, y: Optional[pd.Series] = None, coverage: List[float] = None, ) -> Dict[str, pd.Series]: """Find the prediction intervals using the fitted RandomForestRegressor. Args: X (pd.DataFrame): Data of shape [n_samples, n_features]. y (pd.Series): Target data. Optional. coverage (list[float]): A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for. Returns: dict: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. """ if coverage is None: coverage = [0.95] X, _ = self._manage_woodwork(X, y) X = X.ww.select(exclude="Datetime") predictions = self._component_obj.predict(X) estimators = self._component_obj.estimators_ return get_prediction_intevals_for_tree_regressors( X, predictions, coverage, estimators, )