extract_chosen_indexes_from_start, center_test import sys sys.argv[1:2] = [str(x) for x in sys.argv[1:2]] output_folder = sys.argv[1] threshold_dir = (int)(sys.argv[2]) num_blocks = (int)(sys.argv[3]) compute_mse_current = (int)(sys.argv[4]) cycles = (int)(sys.argv[5]) max_active_set = (int)(sys.argv[6]) ####load data file = "ENEL_2014/Enel_dataset.npz" results = Result(file, "lasso") XTrain, YTrain, XTest, YTest = results.extract_train_test() enel_dict = results.extract_dict() Coord, Coord_turb, power_curve = results.extract_coords() angles_coord_turb, _ = compute_angle(Coord, Coord_turb) ##transformation of data X = np.concatenate((XTrain, XTest), axis=0) enel_transf = Enel_powerCurveTransformation() X_angle, _, _ = enel_transf.compute_angle_matrix(X) output_dict = dict.fromkeys(np.arange(0, 49), np.array([[]], dtype="int64")) k_levels = np.arange(0, 12).reshape([12, 1]) for key in np.arange(0, 49): current_values = np.arange(key * 12, key * 12 + 12).reshape([12, 1])
score_f = r2_score scoring = "r2" else: score_f = mean_squared_error scoring = "mean_squared_error" folder = "ENEL_2014/" ext = ".npz" file_cross_val = folder+file_name+ext results_cross_val = Result(file_cross_val, "lasso") ##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) ##ranking verbose = True dict_ = results_cross_val.extract_dict() weights_data = results_cross_val.extract_weights() index_mse = len(weights_data)-1 weights_data = weights_data[index_mse]