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

import pandas as pd
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 import get_random_seed, import_or_raise
from evalml.utils.gen_utils import _rename_column_names_to_numeric


[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] # 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=0, **kwargs): random_seed = get_random_seed(random_state, self.SEED_MIN, self.SEED_MAX) 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(**parameters, random_state=random_seed) super().__init__(parameters=parameters, component_obj=xgb_classifier, random_state=random_state)
[docs] def fit(self, X, y=None): # rename column names to column number if input is a pd.DataFrame in case it has column names that contain symbols ([, ], <) that XGBoost cannot properly handle if isinstance(X, pd.DataFrame): X = _rename_column_names_to_numeric(X) return super().fit(X, y)
[docs] def predict(self, X): # rename column names to column number if input is a pd.DataFrame in case it has column names that contain symbols ([, ], <) that XGBoost cannot properly handle if isinstance(X, pd.DataFrame): X = _rename_column_names_to_numeric(X) predictions = super().predict(X) return predictions
[docs] def predict_proba(self, X): if isinstance(X, pd.DataFrame): X = _rename_column_names_to_numeric(X) predictions = super().predict_proba(X) return predictions
@property def feature_importance(self): return self._component_obj.feature_importances_