Prediction-MindSpore/stock-forcast-models/utils/metrics.py

import mindspore.numpy as mnp

def RSE(pred, true):
    return mnp.sqrt(mnp.sum((true-pred)**2)) / mnp.sqrt(mnp.sum((true-true.mean())**2))

def CORR(pred, true):
    u = ((true-true.mean(0))*(pred-pred.mean(0))).sum(0) 
    d = mnp.sqrt(((true-true.mean(0))**2*(pred-pred.mean(0))**2).sum(0))
    return (u/d).mean(-1)

def MAE(pred, true):
    return mnp.mean(mnp.abs(pred-true))

def MSE(pred, true):
    return mnp.mean((pred-true)**2)

def RMSE(pred, true):
    return mnp.sqrt(MSE(pred, true))

def MAPE(pred, true):
    return mnp.mean(mnp.abs((pred - true) / true))

def MSPE(pred, true):
    return mnp.mean(mnp.square((pred - true) / true))

def metric(pred, true):
    mae = MAE(pred, true)
    mse = MSE(pred, true)
    rmse = RMSE(pred, true)
    mape = MAPE(pred, true)
    mspe = MSPE(pred, true)
    
    return mae, mse, rmse, mape, mspe