xgb_model.py 文件源码

def fit(self,X,y,Xt=None,yt=None,
        load_model=None,save_model=None,
        obj=None,feval=None,print_fscore=True,evalx=None):
        print(X.shape,y.shape)

        num_round = self.params.get('num_round',100)
        early_stopping_rounds = self.params.get('early_stopping_rounds',None)
        maximize = self.params.get('maximize',False)
        dtrain = xgb.DMatrix(X, y)
        vb = self.params.get('verbose_eval',1)
        if Xt is not None:
            dvalid = xgb.DMatrix(Xt, yt)
            watchlist = [(dtrain, 'train'), (dvalid, 'valid')]
            bst = xgb.train(self.params, dtrain, num_round, evals = watchlist,
                early_stopping_rounds=early_stopping_rounds,verbose_eval=vb,
                xgb_model=load_model,obj=obj,feval=feval,maximize=maximize)
        else:
            watchlist = [(dtrain, 'train')]
            bst = xgb.train(self.params, dtrain, num_round, evals = watchlist,
                verbose_eval=vb,xgb_model=load_model,obj=obj,feval=feval)
        self.bst = bst
        if save_model is not None:
            bst.save_model(save_model)            

        fscore = self.feature_importance()
        if print_fscore:
            print("Feature Importance:")
            for i in fscore:
                print(i) 
        if Xt is not None and evalx is not None:
            yp = self.predict(Xt)
            score = evalx(yt,yp)
            print(score)
            return score
        return 0