使用Xgboost時出現錯誤 TypeError: 'str' object is not callable

在Xgboost進行調參代碼時，出現錯誤，TypeError: 'str' object is not callable

def modelfit(alg,dtrain,predictors,useTrainCV=True,cv_folds=5,early_stopping_rounds=50):
    if useTrainCV:
        xgb_param = alg.get_xgb_params()
        xgbtrain = xgb.DMatrix(dtrain[predictors].values,label=dtrain[target].values)
        cvresult = xgb.cv(xgb_param,xgbtrain,num_boost_round=alg.get_params()['n_estimators'],
                          nfold=cv_folds,metrics='auc',early_stopping_rounds=early_stopping_rounds,
                          show_stdv=False)
        alg.set_params(n_estimators=cvresult.shape[0])
    # Fit the algorithm on the data
    alg.fit(dtrain[predictors],dtrain['FLAG'],eval_metric='auc')
    
    #Predict training set:
    dtrain_predictions = alg.predict(dtrain[predictors])
    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]

    #Print model report:
    print("\nModel Report") 
    print("Accuracy : %.4g" % metrics.accuracy_score(dtrain['FLAG'].values, dtrain_predictions)) 
    print("AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['FLAG'], dtrain_predprob)) 
    
    print('sucessful')

    feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
    feat_imp.plot(kind='bar', title='Feature Importances')
    plt.ylabel('Feature Importance Score')

解決方法：

將代碼中的 alg.booster() 改成alg.get_booster() 即可。