蓄水池抽样

最新推荐文章于 2025-07-13 18:18:52 发布

转载最新推荐文章于 2025-07-13 18:18:52 发布 · 538 阅读

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本文通过逐步解析从数据流中随机选取样本的过程，介绍了储水池抽样算法的基本原理。该算法能够确保每个元素被选中的概率相等，适用于处理大量数据集的情况。

http://blog.cloudera.com/blog/2013/04/hadoop-stratified-randosampling-algorithm/

A Primer on Reservoir Sampling

For this problem, the simplest concrete example would be a stream that only contained a single item. In this case, our algorithm should return this single element with probability 1. Now let’s try a slightly harder problem, a stream with exactly two elements. We know that we have to hold on to the first element we see from this stream, because we don’t know if we’re in the case that the stream only has one element. When the second element comes along, we know that we want to return one of the two elements, each with probability 1/2. So let’s generate a random number R between 0 and 1, and return the first element if R is less than 0.5 and return the second element if R is greater than 0.5.

Now let’s try to generalize this approach to a stream with three elements. After we’ve seen the second element in the stream, we’re now holding on to either the first element or the second element, each with probability 1/2. When the third element arrives, what should we do? Well, if we know that there are only three elements in the stream, we need to return this third element with probability 1/3, which means that we’ll return the other element we’re holding with probability 1 – 1/3 = 2/3. That means that the probability of returning each element in the stream is as follows:

First Element: (1/2) * (2/3) = 1/3
Second Element: (1/2) * (2/3) = 1/3
Third Element: 1/3

By considering the stream of three elements, we see how to generalize this algorithm to any N: at every step N, keep the next element in the stream with probability 1/N. This means that we have an (N-1)/N probability of keeping the element we are currently holding on to, which means that we keep it with probability (1/(N-1)) * (N-1)/N = 1/N.

This general technique is called reservoir sampling, and it is useful in a number of applications that require us to analyze very large data sets. You can find an excellent overview of a set of algorithms for performing reservoir sampling in this blog post by Greg Grothaus. I’d like to focus on two of those algorithms in particular, and talk about how they are used in Cloudera ML, our open-source collection of data preparation and machine learning algorithms for Hadoop.