5890bf32602d818e33c7e0fe4c24bffedc849c9e,cheml/wrappers/sklearn_skl/sklearn_wrapper.py,evaluate_regression,fit,#evaluate_regression#,1108

Before Change

                self._reg_evaluate(dfy, dfy_pred, self.evaluator)
                evaluation_results_ = self.results
                self.Base.send[(self.iblock, token)] = [pd.DataFrame(evaluation_results_), order.count(token),
                                                        (self.iblock,token,self.Host,self.Function)]
            else:
                msg = "@Task //%i(%s): non valid output token "%s"" % (self.iblock+1, self.SuperFunction, token)

After Change

                self.Base.send[(self.iblock, token)] = self.outputs[token]
            elif token == "evaluation_results_":
                // step1: check inputs
                self.required("dfy", req=True)
                self.required("dfy_pred", req=True)

                // step3: check the dimension of input data frame
                dfy, _ = self.data_check("dfy", dfy, ndim=2, n0=None, n1=None, format_out="df")

Instances