"""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.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_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_)
