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

import pandas as pd
from skopt.space import Integer, Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
from evalml.utils import get_random_seed, import_or_raise


[docs]class XGBoostRegressor(Estimator): """XGBoost Regressor.""" name = "XGBoost Regressor" hyperparameter_ranges = { "eta": Real(0.000001, 1), "max_depth": Integer(1, 20), "min_child_weight": Real(1, 10), "n_estimators": Integer(1, 1000), } model_family = ModelFamily.XGBOOST supported_problem_types = [ProblemTypes.REGRESSION] # xgboost supports seeds from -2**31 to 2**31 - 1 inclusive. these limits ensure the random seed generated below # is within that range. SEED_MIN = -2**31 SEED_MAX = 2**31 - 1
[docs] def __init__(self, eta=0.1, max_depth=6, min_child_weight=1, n_estimators=100, random_state=0, **kwargs): random_seed = get_random_seed(random_state, self.SEED_MIN, self.SEED_MAX) parameters = {"eta": eta, "max_depth": max_depth, "min_child_weight": min_child_weight, "n_estimators": n_estimators} parameters.update(kwargs) xgb_error_msg = "XGBoost is not installed. Please install using `pip install xgboost.`" xgb = import_or_raise("xgboost", error_msg=xgb_error_msg) xgb_Regressor = xgb.XGBRegressor(random_state=random_seed, **parameters) super().__init__(parameters=parameters, component_obj=xgb_Regressor, random_state=random_state)
[docs] def fit(self, X, y=None): # necessary to convert to numpy in case input DataFrame has column names that contain symbols ([, ], <) that XGBoost cannot properly handle if isinstance(X, pd.DataFrame): X = X.to_numpy() return super().fit(X, y)
[docs] def predict(self, X): if isinstance(X, pd.DataFrame): X = X.to_numpy() return super().predict(X)
@property def feature_importance(self): return self._component_obj.feature_importances_