task.py 文件源码

def _get_learner(self):
        # xgboost
        if self.learner_name in ["reg_xgb_linear", "reg_xgb_tree", "reg_xgb_tree_best_single_model"]:
            return XGBRegressor(**self.param_dict)
        if self.learner_name in ["clf_xgb_linear", "clf_xgb_tree"]:
            return XGBClassifier(**self.param_dict)
        # sklearn
        if self.learner_name == "reg_skl_lasso":
            return Lasso(**self.param_dict)
        if self.learner_name == "reg_skl_ridge":
            return Ridge(**self.param_dict)
        if self.learner_name == "reg_skl_random_ridge":
            return RandomRidge(**self.param_dict)
        if self.learner_name == "reg_skl_bayesian_ridge":
            return BayesianRidge(**self.param_dict)
        if self.learner_name == "reg_skl_svr":
            return SVR(**self.param_dict)
        if self.learner_name == "reg_skl_lsvr":
            return LinearSVR(**self.param_dict)
        if self.learner_name == "reg_skl_knn":
            return KNNRegressor(**self.param_dict)
        if self.learner_name == "reg_skl_etr":
            return ExtraTreesRegressor(**self.param_dict)
        if self.learner_name == "reg_skl_rf":
            return RandomForestRegressor(**self.param_dict)
        if self.learner_name == "reg_skl_gbm":
            return GradientBoostingRegressor(**self.param_dict)
        if self.learner_name == "reg_skl_adaboost":
            return AdaBoostRegressor(**self.param_dict)
        # keras
        if self.learner_name == "reg_keras_dnn":
            try:
                return KerasDNNRegressor(**self.param_dict)
            except:
                return None
        # rgf
        if self.learner_name == "reg_rgf":
            return RGFRegressor(**self.param_dict)
        # ensemble
        if self.learner_name == "reg_ensemble":
            return EnsembleLearner(**self.param_dict)

        return None