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,
deprecate_arg,
import_or_raise
)
[docs]class XGBoostClassifier(Estimator):
"""XGBoost Classifier."""
name = "XGBoost Classifier"
hyperparameter_ranges = {
"eta": Real(0.000001, 1),
"max_depth": Integer(1, 10),
"min_child_weight": Real(1, 10),
"n_estimators": Integer(1, 1000),
}
model_family = ModelFamily.XGBOOST
supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS]
# 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=None,
random_seed=0, **kwargs):
random_seed = deprecate_arg("random_state", "random_seed", random_state, random_seed)
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_classifier = xgb.XGBClassifier(random_state=random_seed,
**parameters)
super().__init__(parameters=parameters,
component_obj=xgb_classifier,
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)
[docs] def predict_proba(self, X):
X = _rename_column_names_to_numeric(X, flatten_tuples=False)
return super().predict_proba(X)
@property
def feature_importance(self):
return self._component_obj.feature_importances_