                           axis=1).values.astype(str)
        
            NULLSTR=np.array([&#39;&#39;]*len(modelneg.feature_names))
            s_background=qsample(NULLSTR,modelneg,steps=nullsteps)
            explainer = shap.KernelExplainer(funcw_,np.array([s_background]))
            shap_values = explainer.shap_values(X[:shapnum])
            
            self.shap_index=pd.DataFrame(shap_values.mean(axis=0),
                                         columns=[&#39;shap&#39;])\
                        