#if os.path.isfile(base_path1):
info['n_iter'] += n_iter
row = '%(n_iter)i\t%(time)g\t%(loss)f\t%(val_loss)f\t%(mae_train)g\t%(rmse_train)g\t%(mae_test)g\t%(rmse_test)g' % info
results = open('result.txt', 'a')
print row
results.write(row + '\n')
results.close()
with open('pars.pkl', 'wb') as fp:
cp.dump((info['n_iter'], info['best_pars']), fp)
m.parameters.data[...] = info['best_pars']
with open('best_pars.pkl', 'wb') as bp:
cp.dump(info['best_pars'], bp)
Y = m.predict(X)
TY = m.predict(TX)
output_train = Y * np.std(train_labels) + np.mean(train_labels)
output_test = TY * np.std(train_labels) + np.mean(train_labels)
print 'TRAINING SET\n'
print('MAE: %5.2f kcal/mol' %
np.abs(output_train - train_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol' %
np.square(output_train - train_labels).mean(axis=0)**.5)
print 'TESTING SET\n'
print('MAE: %5.2f kcal/mol' % np.abs(output_test - test_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol' %
np.square(output_test - test_labels).mean(axis=0)**.5)
results = open('result.txt','a')
print row
results.write(row + '\n')
results.close()
with open('pars.pkl', 'wb') as fp:
cp.dump((info['n_iter'], info['best_pars']), fp)
m.parameters.data[...] = info['best_pars']
with open('best_pars.pkl', 'wb') as bp:
cp.dump(info['best_pars'], bp)
Y = m.predict(X)
TY = m.predict(TX)
output_train = Y * np.std(train_labels) + np.mean(train_labels)
output_test = TY * np.std(train_labels) + np.mean(train_labels)
print 'TRAINING SET\n'
print('MAE: %5.2f kcal/mol'%np.abs(output_train - train_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol'%np.square(output_train - train_labels).mean(axis=0) ** .5)
print 'TESTING SET\n'
print('MAE: %5.2f kcal/mol'%np.abs(output_test - test_labels).mean(axis=0))
print('RMSE: %5.2f kcal/mol'%np.square(output_test - test_labels).mean(axis=0) ** .5)
