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."""
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,
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
[docs] def __init__(
self,
eta=0.1,
max_depth=6,
min_child_weight=1,
n_estimators=100,
random_seed=0,
**kwargs
):
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_seed=random_seed
)
[docs] def fit(self, X, y=None):
X, y = super()._manage_woodwork(X, y)
self.input_feature_names = list(X.columns)
X = _rename_column_names_to_numeric(X, flatten_tuples=False)
self._component_obj.fit(X, y)
return self
[docs] def predict(self, X):
X = _rename_column_names_to_numeric(X, flatten_tuples=False)
return super().predict(X)
@property
def feature_importance(self):
return self._component_obj.feature_importances_