8768184ae39b420292acc25eda9ae43c3af0752d,python-package/mlbox/model/supervised/regression/stacking_regressor.py,StackingRegressor,fit_transform,#StackingRegressor#,98

Before Change

                print("")
                print("> fitting estimator n°"+ str(c+1) + " : "+ str(clf.get_params())+" ...")

            start_time = time.time()
            y_pred = cross_val_predict(estimator = clf, X = X, y = y, cv = cv)     //for each base estimator, we create the meta feature on train set
            end_time = time.time()

            preds["est"+str(c+1)+"_("+str(end_time-start_time)+"sec)"] = y_pred

            clf.fit(X, y)  // and we refit the base estimator on entire train set

After Change

            reg.fit(X, y)  // and we refit the base estimator on entire train set

        layer = 1
        while(len(np.intersect1d(X.columns, ["layer"+str(layer)+"_"+s for s in preds.columns]))>0):
            layer = layer + 1
        preds.columns = ["layer"+str(layer)+"_"+s for s in preds.columns]            
            
        self.__fittransformOK = True

Instances