k = gp.kernels.RBF() * gp.kernels.ConstantKernel() + gp.kernels.WhiteKernel()
gpr = gp.GaussianProcessRegressor(kernel=k,\
                            n_restarts_optimizer=10,\
                            alpha=0.1,\
                            normalize_y=True)
gpr.fit(train[features],train[label])
r2 = gpr.score(test[features],test[label])
print('gpr r2:',r2)