'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))
