带有张量流的雅卡德距离矩阵 [英] Jaccard's distance matrix with tensorflow

问题描述

我想使用 Jaccard 距离来计算距离矩阵.并尽快这样做.我曾经使用 scikit-learn的pairwise_distances 功能.但是scikit-learn并不打算支持GPU，甚至还有一个已知错误使并行运行时的功能变慢.

I would like to compute a distance matrix using the Jaccard distance. And do so as fast as possible. I used to use scikit-learn's pairwise_distances function. But scikit-learn doesn't plan to support GPU, and there's even a known bug that makes the function slower when run in parallel.

我唯一的约束是可以将生成的距离矩阵输入到

My only constraint is that the resulting distance matrix can then be fed to scikit-learn's DBSCAN clustering algorithm. I was thinking about implementing the computation with tensorflow but couldn't find a nice and simple way to do it.

PS:我有理由预先计算距离矩阵，而不是让DBSCAN根据需要进行计算.

PS: I have reasons to precompute the distance matrix instead of letting DBSCAN do it as needed.

推荐答案

嘿，我遇到了同样的问题.

Hej I was facing the same problem.

鉴于jaccard相似度是真实阳性(tp)与真实阳性，假阴性(fn)和假阳性(fp)之和的比率，我想出了以下解决方案:

Given the idea that the jaccard similarity is the ratio of true postives (tp) to the sum of true positives, false negatives (fn) and false positives (fp), I came up with this solution:

    def jaccard_distance(self):
        tp = tf.reduce_sum(tf.mul(self.target, self.prediction), 1)
        fn = tf.reduce_sum(tf.mul(self.target, 1-self.prediction), 1)
        fp = tf.reduce_sum(tf.mul(1-self.target, self.prediction), 1)
        return 1 - (tp / (tp + fn + fp))

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