自定义损失函数 sklearn [英] Customize loss-function sklearn

问题描述

我想在一个数据科学项目中做预测，误差是通过一个非对称函数计算出来的.

I want to make prediction in a data science project, and the error is calculate through an asymmetric function.

是否可以调整随机森林的损失函数或梯度提升(sklearn)?

Is it possible to tune the loss function of random forest or gradient boosting (of sklearn) ?

我读到需要修改 .pyx 文件，但我在 sklearn 文件夹中找不到任何文件(我使用的是 ubuntu 14.04 LTS).

I have read that it is required to modify a .pyx file but I cannot find any in my sklearn folder (I am on ubuntu 14.04 LTS).

你有什么建议吗?

推荐答案

是的，可以调整.例如:

Yes, it is possible to tune. For example:

class ExponentialPairwiseLoss(object):
    def __init__(self, groups):
        self.groups = groups

    def __call__(self, preds, dtrain):
        labels = dtrain.get_label().astype(np.int)
        rk = len(np.bincount(labels))
        plus_exp = np.exp(preds)
        minus_exp = np.exp(-preds)
        grad = np.zeros(preds.shape)
        hess = np.zeros(preds.shape)
        pos = 0
        for size in self.groups:
            sum_plus_exp = np.zeros((rk,))
            sum_minus_exp = np.zeros((rk,))
            for i in range(pos, pos + size, 1):
                sum_plus_exp[labels[i]] += plus_exp[i]
                sum_minus_exp[labels[i]] += minus_exp[i]
            for i in range(pos, pos + size, 1):
                grad[i] = -minus_exp[i] * np.sum(sum_plus_exp[:labels[i]]) +\
                          plus_exp[i] * np.sum(sum_minus_exp[labels[i] + 1:])
                hess[i] = minus_exp[i] * np.sum(sum_plus_exp[:labels[i]]) +\
                          plus_exp[i] * np.sum(sum_minus_exp[labels[i] + 1:])
            pos += size
        return grad, hess

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