Source code for evalml.pipelines.regression.random_forest

import numpy as np
from skopt.space import Integer, Real

from evalml.model_types import ModelTypes
from evalml.pipelines import PipelineBase
from evalml.pipelines.components import (
    OneHotEncoder,
    RandomForestRegressor,
    RFRegressorSelectFromModel,
    SimpleImputer
)
from evalml.problem_types import ProblemTypes


[docs]class RFRegressionPipeline(PipelineBase): """Random Forest Pipeline for regression problems""" name = "Random Forest Regressor w/ One Hot Encoder + Simple Imputer + RF Regressor Select From Model" model_type = ModelTypes.RANDOM_FOREST problem_types = [ProblemTypes.REGRESSION] hyperparameters = { "n_estimators": Integer(10, 1000), "max_depth": Integer(1, 32), "impute_strategy": ["mean", "median", "most_frequent"], "percent_features": Real(.01, 1) }
[docs] def __init__(self, objective, n_estimators, max_depth, impute_strategy, percent_features, number_features, n_jobs=-1, random_state=0): imputer = SimpleImputer(impute_strategy=impute_strategy) enc = OneHotEncoder() feature_selection = RFRegressorSelectFromModel(n_estimators=n_estimators, max_depth=max_depth, number_features=number_features, percent_features=percent_features, threshold=-np.inf, n_jobs=n_jobs, random_state=random_state) estimator = RandomForestRegressor(random_state=random_state, n_estimators=n_estimators, max_depth=max_depth, n_jobs=n_jobs) super().__init__(objective=objective, component_list=[enc, imputer, feature_selection, estimator], n_jobs=n_jobs, random_state=random_state)