diff options
| -rw-r--r-- | do_ml.py | 30 |
1 files changed, 22 insertions, 8 deletions
@@ -36,25 +36,34 @@ def printc(text, color): def do_ml(desc_data, - desc_type, energy_data, training_size, test_size, - sigma=1000.0): + sigma=1000.0, + desc_type=None, + show_msgs=True): """ Does the ML methodology. desc_data: descriptor (or representation) data. - desc_type: string with the name of the descriptor used. energy_data: energy data associated with desc_data. training_size: size of the training set to use. test_size: size of the test set to use. sigma: depth of the kernel. + desc_type: string with the name of the descriptor used. + show_msgs: Show debug messages or not. NOTE: desc_type is just a string and is only for identification purposes. Also, training is done with the first part of the data and testing with the ending part of the data. """ + if not desc_type: + desc_type = 'NOT SPECIFIED' + tic = time.perf_counter() - printc('{} ML started.'.format(desc_type), Fore.CYAN) + if show_msgs: + printc('{} ML started, with parameters:'.format(desc_type), Fore.CYAN) + printc('\tTraining size: {}'.format(training_size), Fore.BLUE) + printc('\tTest size: {}'.format(test_size), Fore.BLUE) + printc('\tSigma: {}'.format(sigma), Fore.BLUE) Xcm_training = desc_data[:training_size] Ycm_training = energy_data[:training_size] @@ -66,9 +75,14 @@ def do_ml(desc_data, Kcm_test = gauss_kernel(Xcm_test, Xcm_training, sigma) Ycm_predicted = np.dot(Kcm_test, alpha_cm) - print('\tMAE for {}: {}'.format(desc_type, - np.mean(np.abs(Ycm_predicted - Ycm_test)))) + mae = np.mean(np.abs(Ycm_predicted - Ycm_test)) + if show_msgs: + print('\tMAE for {}: {}'.format(desc_type, mae)) toc = time.perf_counter() - printc('\t{} ML took {:.4f} seconds.'.format(desc_type, toc-tic), - Fore.GREEN) + tictoc = toc - tic + if show_msgs: + printc('\t{} ML took {:.4f} seconds.'.format(desc_type, tictoc), + Fore.GREEN) + + return mae, tictoc |