from skopt.space import Integer, Real
from evalml.model_types import ModelTypes
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
from evalml.utils import import_or_raise
[docs]class XGBoostClassifier(Estimator):
"""XGBoost Classifier"""
name = "XGBoost Classifier"
hyperparameter_ranges = {
"eta": Real(0, 1),
"max_depth": Integer(1, 20),
"min_child_weight": Real(1, 10),
"n_estimators": Integer(1, 1000),
}
model_type = ModelTypes.XGBOOST
problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
[docs] def __init__(self, eta=0.1, max_depth=3, min_child_weight=1, n_estimators=100, random_state=0):
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_classifier = xgb.XGBClassifier(random_state=random_state,
eta=eta,
max_depth=max_depth,
n_estimators=n_estimators,
min_child_weight=min_child_weight)
super().__init__(parameters=parameters,
component_obj=xgb_classifier,
random_state=random_state)
@property
def feature_importances(self):
return self._component_obj.feature_importances_