example1 result

[min13s]

for the MT-GBM
def mymse2(preds, train_data, ep = 0):
labels = train_data.get_label()
labels2 = labels.reshape((num_labels,-1)).transpose()
preds2 = preds.reshape((num_labels,-1)).transpose()
grad2 = (preds2 - labels2)
grad = grad2 * np.array([20,0.001])
grad = np.sum(grad,axis = 1)
grad2 = grad2.transpose().reshape((-1))
hess = grad * 0. + 1
hess2 = grad2 * 0. + 1
return grad, hess, grad2, hess2

it seems that parameter will change the best mape, rmse,
compared to common lightgbm, it has one more parameter ,

[min13s]

is there something wrong with the hessian matrix， since grad is weighted, so should the hessian?
it seems that grad and hess is the ensampled gradient and hessian(the algorithm 2 ), why still need grad2 and hess2 ?