如何在 Pyspark 中随时间序列数据使用滑动窗口转

问题描述

我正在尝试根据时间序列数据的滑动窗口提取特征.在 Scala 中，似乎有一个基于 this post 和 文档

I am trying to extract features based on sliding window over time series data. In Scala, it seems like there is a sliding function based on this post and the documentation

import org.apache.spark.mllib.rdd.RDDFunctions._

sc.parallelize(1 to 100, 10)
  .sliding(3)
  .map(curSlice => (curSlice.sum / curSlice.size))
  .collect()

我的问题是 PySpark 中是否有类似的功能?或者如果还没有这样的功能，我们如何实现类似的滑动窗口变换?

My questions is there similar functions in PySpark? Or how do we achieve similar sliding window transformations if there is no such function yet?

推荐答案

据我所知 sliding 函数在 Python 中不可用并且 SlidingRDD 是一个私有类并且不能在 MLlib 之外访问.

As far as I can tell sliding function is not available from Python and SlidingRDD is a private class and cannot be accessed outside MLlib.

如果你在现有的 RDD 上使用 sliding，你可以像这样创建穷人的 sliding:

If you to use sliding on an existing RDD you can create poor man's sliding like this:

def sliding(rdd, n):
    assert n > 0
    def gen_window(xi, n):
        x, i = xi
        return [(i - offset, (i, x)) for offset in xrange(n)]

    return (
        rdd.
        zipWithIndex(). # Add index
        flatMap(lambda xi: gen_window(xi, n)). # Generate pairs with offset
        groupByKey(). # Group to create windows
        # Sort values to ensure order inside window and drop indices
        mapValues(lambda vals: [x for (i, x) in sorted(vals)]).
        sortByKey(). # Sort to makes sure we keep original order
        values(). # Get values
        filter(lambda x: len(x) == n)) # Drop beginning and end

或者，您可以尝试这样的事情(在 toolz)

Alternatively you can try something like this (with a small help of toolz)

from toolz.itertoolz import sliding_window, concat

def sliding2(rdd, n):
    assert n > 1

    def get_last_el(i, iter):
        """Return last n - 1 elements from the partition"""
        return  [(i, [x for x in iter][(-n + 1):])]

    def slide(i, iter):
        """Prepend previous items and return sliding window"""
        return sliding_window(n, concat([last_items.value[i - 1], iter]))

    def clean_last_items(last_items):
        """Adjust for empty or to small partitions"""
        clean = {-1: [None] * (n - 1)}
        for i in range(rdd.getNumPartitions()):
            clean[i] = (clean[i - 1] + list(last_items[i]))[(-n + 1):]
        return {k: tuple(v) for k, v in clean.items()}

    last_items = sc.broadcast(clean_last_items(
        rdd.mapPartitionsWithIndex(get_last_el).collectAsMap()))

    return rdd.mapPartitionsWithIndex(slide)

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