'batch_size': 50, 'learn_rate': 1e-3, 'max_epochs': 1500, 'early_stop': 5, 'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_rmses, eval_lls = run_experiment(model_names, 'Parkinsons', dataset, **training_settings) print(eval_rmses, eval_lls) for model_name in model_names: rmse_mu = np.mean(eval_rmses[model_name]) rmse_std = np.std(eval_rmses[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> RMSE = {:.4f} \pm {:.4f}'.format(rmse_mu, 1.96 * rmse_std)) print('>> NLPD = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std))
'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_rmses, eval_lls = run_experiment(model_names, 'Power Plant', dataset, **training_settings) print(eval_rmses, eval_lls) for model_name in model_names: rmse_mu = np.mean(eval_rmses[model_name]) rmse_std = np.std(eval_rmses[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> RMSE = {:.4f} \pm {:.4f}'.format(rmse_mu, 1.96 * rmse_std)) print('>> NLPD = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std)) ''' Result: >>> VIBayesNN >> RMSE = 3.6585 \pm 0.5116 >> NLPD = 2.6769 \pm 0.1125
'batch_size': 10, 'learn_rate': 1e-3, 'max_epochs': 1500, 'early_stop': 5, 'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_err_rates, eval_lls = run_experiment(model_names, 'Bank Marketing', dataset, **training_settings) print(eval_err_rates, eval_lls) for model_name in model_names: errt_mu = np.mean(eval_err_rates[model_name]) errt_std = np.std(eval_err_rates[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> ERRT = {:.4f} \pm {:.4f}'.format(errt_mu, 1.96 * errt_std)) print('>> AUC = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std))
'n_hiddens': [50, 25], 'batch_size': 10, 'learn_rate': 1e-3, 'max_epochs': 1500, 'early_stop': 5, 'check_freq': 5, 'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (argv[eq_ind + 1:] == 'True') print(training_settings) eval_err_rates, eval_lls = run_experiment(model_names, 'Spambase', dataset, **training_settings) print(eval_err_rates, eval_lls) for model_name in model_names: errt_mu = np.mean(eval_err_rates[model_name]) errt_std = np.std(eval_err_rates[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> ACC = {:.4f} \pm {:.4f}'.format(errt_mu, 1.96 * errt_std)) print('>> AUC = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std))
'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_err_rates, eval_lls = run_experiment(model_names, 'Car Evaluation', dataset, **training_settings) print(eval_err_rates, eval_lls) for model_name in model_names: errt_mu = np.mean(eval_err_rates[model_name]) errt_std = np.std(eval_err_rates[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> ERRT = {:.4f} \pm {:.4f}'.format(errt_mu, 1.96 * errt_std)) print('>> AUC = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std)) ''' Result: >>> BayesNN >> err_rate = 0.0378 p/m 0.0058 >> log_likelihood = -0.3359 p/m 0.0650
'batch_size': 10, 'learn_rate': 1e-3, 'max_epochs': 1500, 'early_stop': 5, 'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_rmses, eval_lls = run_experiment(model_names, 'Airfoil', dataset, **training_settings) print(eval_rmses, eval_lls) for model_name in model_names: rmse_mu = np.mean(eval_rmses[model_name]) rmse_std = np.std(eval_rmses[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> RMSE = {:.4f} \pm {:.4f}'.format(rmse_mu, 1.96 * rmse_std)) print('>> NLPD = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std))
'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_err_rates, eval_lls = run_experiment(model_names, 'Wine Quality', dataset, **training_settings) print(eval_err_rates, eval_lls) for model_name in model_names: errt_mu = np.mean(eval_err_rates[model_name]) errt_std = np.std(eval_err_rates[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> ERRT = {:.4f} \pm {:.4f}'.format(errt_mu, 1.96 * errt_std)) print('>> AUC = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std)) ''' Result: >>> VIBayesNN >> ERRT = 0.4465 \pm 0.0116 >> AUC = 0.9024 \pm 0.0005
'check_freq': 5, } for argv in sys.argv: if ('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind + 1:] if (setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value == 'True') if (setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_rmses, eval_lls = run_experiment(model_names, 'Concrete', dataset, **training_settings) print(eval_rmses, eval_lls) for model_name in model_names: rmse_mu = np.mean(eval_rmses[model_name]) rmse_std = np.std(eval_rmses[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> ' + model_name) print('>> RMSE = {:.4f} \pm {:.4f}'.format(rmse_mu, 1.96 * rmse_std)) print('>> NLPD = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96 * ll_std)) ''' Result: >>> VIBayesNN >> RMSE = 3.6585 \pm 0.5116 >> NLPD = 2.6769 \pm 0.1125
'batch_size': 100, 'learn_rate': 1e-3, 'max_epochs': 1500, 'early_stop': 5, 'check_freq': 5, } for argv in sys.argv: if('--' == argv[:2] and '=' in argv): eq_ind = argv.index('=') setting_feature = argv[2:eq_ind] setting_value = argv[eq_ind+1:] if(setting_feature in ['save', 'plot']): training_settings[setting_feature] = (setting_value=='True') if(setting_feature == 'model'): model_names = [setting_value] print(training_settings) eval_rmses, eval_lls = run_experiment( model_names, 'Protein', dataset, **training_settings) print(eval_rmses, eval_lls) for model_name in model_names: rmse_mu = np.mean(eval_rmses[model_name]) rmse_std = np.std(eval_rmses[model_name]) ll_mu = np.mean(eval_lls[model_name]) ll_std = np.std(eval_lls[model_name]) print('>>> '+model_name) print('>> RMSE = {:.4f} \pm {:.4f}'.format(rmse_mu, 1.96*rmse_std)) print('>> NLPD = {:.4f} \pm {:.4f}'.format(ll_mu, 1.96*ll_std))