def train_test(phi_train, y_train, phi_test, y_test, lam_ridge=0.0, scale_price=1.0, dates=None, bool_draw=False):
beta = np.linalg.inv(phi_train.T.dot(phi_train) + np.identity(phi_train.shape[1])*lam_ridge).dot(phi_train.T).dot(y_train)
y_pred = phi_test.dot(beta)
if bool_draw:
dates = [datetime.datetime.strptime(date, '%Y-%m-%d').strftime('%Y-%m-%d %a') for date in dates]
draw_compare(y_pred*scale, Y_test*scale, dates, node=NODE_NAME)
draw_heat(beta, NODE_NAME)
return cal_mse(phi_train.dot(beta), y_train)/(scale_price**2), cal_mse(y_pred, y_test)/(scale_price**2)
def train_test(phi, yy, idx_train, idx_test, lam_ridge=0.0, price_mu=0.0, price_std=1.0, verbose=False, case='rt'):
phi_train = phi[idx_train, :]
y_train = yy[idx_train, :]
phi_test = phi[idx_test, :]
y_test = yy[idx_test, :]
beta = np.linalg.inv(phi_train.T.dot(phi_train) + np.identity(phi_train.shape[1])*lam_ridge).dot(phi_train.T).dot(y_train)
y_pred = phi_test.dot(beta)
if verbose:
if case == 'da':
y_pred = y_pred.reshape((y_pred.shape[0]*24, 1))
y_test = y_test.reshape((y_test.shape[0]*24, 1))
draw_compare(y_pred*price_std+price_mu, y_test*price_std+price_mu)
draw_heat(beta, NODE_NAME)
return cal_mse(phi_train.dot(beta), y_train), cal_mse(y_pred, y_test)