scoring = "mean_squared_error" ext = ".npz" file_cross_val = file_name+ext fine_name_weights = file_name+"ranking"+ext results_cross_val = Result(file_cross_val, "lasso") results_weighted_lasso = Result(fine_name_weights, "lasso") mses = results_weighted_lasso.extract_mses() mses_int = list(map(int, mses)) iter = np.argmin(mses_int) print ("iter chosen:",iter, "with mse:",mses_int[iter]) print("--------------") indexes_beta = results_weighted_lasso.extract_beta_div_zeros()[iter] ##get transformed data XTrain, XTest = results_cross_val.extract_data_transf() _,YTrain,_, YTest = results_cross_val.extract_train_test() ### centratura dei dati XTrain, YTrain, X_mean, y_mean, X_std = center_data(XTrain, YTrain, fit_intercept=True, normalize = True) XTest, YTest = center_test(XTest,YTest,X_mean,y_mean,X_std) values_TM = np.array([[24,281], [24,214]]) ##ranking verbose = True dict_ = results_cross_val.extract_dict() weights_data = results_cross_val.extract_weights()