MAPE pytorch

MAPE（Mean Absolute Percentage Error）是一种衡量预测值与真实值之间误差指标，通常用于评估回归模型的性能。在PyTorch中计算MAPE可以使用以下代码实现:

def masked_mape(preds, labels, null_val=np.nan):
    if np.isnan(null_val):
        mask = ~torch.isnan(labels)
    else:
        mask = (labels!=null_val)
    mask = mask.float()
    mask /= torch.mean((mask))
    mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
    loss = torch.abs((preds-labels)/labels)
    loss = loss * mask
    loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
    return torch.mean(loss)

这个函数接受两个参数`preds`和`labels`分别表示预测值和真实值，并可选的参数`null_val`用于指定无效值的情况。函数首先根据是否存在无效值来生成掩码，然后将掩码归一化，并用掩码乘以误差计算得到一个带有掩码的误差张量，最后取平均值作为MAPE值。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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