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,
    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_seed=0, n_jobs=-1, **kwargs ): """XGBoost Classifier. Arguments: eta (float): Learning rate. Defaults to 0.1. max_depth (int): Maximum tree depth for base learners. Defaults to 6. min_child_weight (float): Minimum sum of instance weight(hessian) needed in a child. Defaults to 1. n_estimators (int): Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100. random_seed (int): Seed for the random number generator. Defaults to 0. n_jobs (int): Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to -1. """ parameters = { "eta": eta, "max_depth": max_depth, "min_child_weight": min_child_weight, "n_estimators": n_estimators, "n_jobs": n_jobs, } 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_