使用Xgboost时出现错误 TypeError: 'str' object is not callable_raise typeerror('not supported type for data.' + s

def modelfit(alg, dtrain, dtest, predictors,useTrainCV=True, cv_folds=3, early_stopping_rounds=50):
    
    if useTrainCV:
        xgb_param = alg.get_xgb_params()
        xgtrain = xgb.DMatrix(dtrain[predictors], label=dtrain[target])
        xgtest = xgb.DMatrix(dtest[predictors].values)
        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
            metrics='auc', early_stopping_rounds=early_stopping_rounds)
        print (cvresult.shape[0])
        alg.set_params(n_estimators=cvresult.shape[0])
    
    #Fit the algorithm on the data
    alg.fit(dtrain[predictors], dtrain['Disbursed'],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['Disbursed'].values, dtrain_predictions))
    print ("AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob))        
    feat_imp = pd.Series(alg.get_booster().get_fscore()).sort_values(ascending=False)#booster所有弱分类器、准确率+召回率+F值、排序
    feat_imp.plot(kind='bar', title='Feature Importances')
    plt.ylabel('Feature Importance Score')

解决方法：

将代码中的 alg.booster() 改成alg.get_booster() 即可。

转载自http://www.cnblogs.com/xiaodongsuibi/p/9303959.html