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):
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}
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,
eta=eta,
max_depth=max_depth,
n_estimators=n_estimators,
min_child_weight=min_child_weight)
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_importances(self):
return self._component_obj.feature_importances_