如何有效地计算 PyTorch 中的混淆矩阵? [英] How do I calculate the confusion matrix in PyTorch efficiently?

问题描述

我有一个包含我的预测的张量和一个包含我的二元分类问题的实际标签的张量.如何有效地计算混淆矩阵?

I have a tensor that contains my predictions and a tensor that contains the actual labels for my binary classification problem. How can I calculate the confusion matrix efficiently?

推荐答案

在我使用 for 循环的第一个版本被证明效率低下之后，这是我目前想到的最快的解决方案，对于两个等维张量 预测和真相:

After my first version using a for-loop has proven inefficient, this is the fastest solution I came up with so far, for two equal-dimensional tensors prediction and truth:

def confusion(prediction, truth):
    confusion_vector = prediction / truth

    true_positives = torch.sum(confusion_vector == 1).item()
    false_positives = torch.sum(confusion_vector == float('inf')).item()
    true_negatives = torch.sum(torch.isnan(confusion_vector)).item()
    false_negatives = torch.sum(confusion_vector == 0).item()

    return true_positives, false_positives, true_negatives, false_negatives

在https://gist.github.com/the-的评论版本和测试用例低音/cae9f3976866776dea17a5049013258d

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