XGBoostClassifier.py 文件源码

def predict(self, X): 
    if  self.k_models!=None and len(self.k_models)<2:
        X1 = self.build_matrix(X)
        return self.bst.predict(X1)
    else :
        dtest = xgb.DMatrix(X)
        preds= [0.0 for k in X.shape[0]]
        for gbdt in self.k_models:
            predsnew = gbdt.predict(dtest, ntree_limit=(gbdt.best_iteration+1)*self.num_parallel_tree)  
            for g in range (0, predsnew.shape[0]):
                preds[g]+=predsnew[g]
        for g in range (0, len(preds)):
            preds[g]/=float(len(self.k_models))