Stock_Prediction_Model_XgBoost.py 文件源码

def GBM(self, argsDict):
        max_depth = argsDict["max_depth"] + 10
        subsample = argsDict["subsample"] * 0.1 + 0.5
        #n_estimators = argsDict['n_estimators'] * 5 + 50
        learning_rate = argsDict["learning_rate"] * 0.02 + 0.12
        #gamma = argsDict["gamma"] * 0.1
        #min_child_weight = argsDict["min_child_weight"] + 1

        print("max_depth:" + str(max_depth), "learning_rate:" + str(learning_rate), "subsample:" + str(subsample))
        params={
            "max_depth":max_depth,
            #"gamma":gamma,
             'subsample' : subsample,
            'learning_rate' : learning_rate,
            #'subsample' : subsample,
            #'min_child_weight': min_child_weight,
            'objective': "multi:softmax",
            'num_class': 7 ,
            "eval_metric":'merror',
            'silent':False,

            # 'gpu_id':1,
            # 'max_bin':16,
            # 'tree_method': "gpu_exact",
            # 'updater':'grow_gpu',
            # 'n_gpus':-1,
            # 'predictor' : "gpu_predictor",

        }
        num_round = 1
        model=xgb.train(params,self.train, num_round, self.watchlist, feval=Xg_iter_precision)
        cov_res=xgb.cv(params,self.train, num_round, nfold=5, feval=Xg_iter_precision)
        #print(cov_res.head())
        cov_rec=cov_res.tail(1)['test-precision_4_5_6-mean'].values
        predicted=model.predict(self.test)
        scoring=precision_score( self.test_y,predicted,average='micro',labels=[4,5,6])
        print('precision is ',scoring)
        print('cv_precision_4_5_6',cov_rec[0])
        return -cov_rec[0]