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

"""XGBoost Regressor."""
from typing import Dict, List, Optional, Union

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.gen_utils import _rename_column_names_to_numeric, import_or_raise


[docs]class XGBoostRegressor(Estimator): """XGBoost Regressor. Args: eta (float): Boosting learning rate. Defaults to 0.1. max_depth (int): Maximum tree depth for base learners. Defaults to 6. min_child_weight (float): Minimum sum of instance weight (hessian) needed in a child. Defaults to 1.0 n_estimators (int): Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100. random_seed (int): Seed for the random number generator. Defaults to 0. n_jobs (int): Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to 12. """ 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), } """{ "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 """ModelFamily.XGBOOST""" supported_problem_types = [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ] """[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_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 def __init__( self, eta: float = 0.1, max_depth: int = 6, min_child_weight: int = 1, n_estimators: int = 100, random_seed: Union[int, float] = 0, n_jobs: int = 12, **kwargs, ): parameters = { "eta": eta, "max_depth": max_depth, "min_child_weight": min_child_weight, "n_estimators": n_estimators, "n_jobs": n_jobs, } 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_seed=random_seed, )
[docs] def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None): """Fits XGBoost regressor component to data. Args: X (pd.DataFrame): The input training data of shape [n_samples, n_features]. y (pd.Series, optional): The target training data of length [n_samples]. Returns: self """ X, y = super()._manage_woodwork(X, y) self.input_feature_names = list(X.columns) X = _rename_column_names_to_numeric(X) self._component_obj.fit(X, y) return self
[docs] def predict(self, X: pd.DataFrame) -> pd.Series: """Make predictions using fitted XGBoost regressor. Args: X (pd.DataFrame): Data of shape [n_samples, n_features]. Returns: pd.Series: Predicted values. """ X, _ = super()._manage_woodwork(X) X = _rename_column_names_to_numeric(X) return super().predict(X)
[docs] def get_prediction_intervals( self, X: pd.DataFrame, y: Optional[pd.Series] = None, coverage: List[float] = None, predictions: pd.Series = None, ) -> Dict[str, pd.Series]: """Find the prediction intervals using the fitted XGBoostRegressor. Args: X (pd.DataFrame): Data of shape [n_samples, n_features]. y (pd.Series): Target data. Ignored. 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. predictions (pd.Series): Optional list of predictions to use. If None, will generate predictions using `X`. Returns: dict: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. """ X = _rename_column_names_to_numeric(X) prediction_interval_result = super().get_prediction_intervals( X=X, y=y, coverage=coverage, predictions=predictions, ) return prediction_interval_result
@property def feature_importance(self) -> pd.Series: """Feature importance of fitted XGBoost regressor.""" return pd.Series(self._component_obj.feature_importances_)