Source code for evalml.pipelines.components.estimators.classifiers.xgboost_classifier

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_