是否可以在Spark中按组缩放数据？

Question

我想用StandardScaler（from pyspark.mllib.feature import StandardScaler）缩放数据，现在我可以通过将RDD的值传递给transform函数来做到这一点，但是问题是我想保留键。无论如何，我是否通过保留数据密钥来扩展数据？

样本数据集

0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,235,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,29,29,1.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,smurf.

进口货

import sys
import os
from collections import OrderedDict
from numpy import array
from math import sqrt
try:
    from pyspark import SparkContext, SparkConf
    from pyspark.mllib.clustering import KMeans
    from pyspark.mllib.feature import StandardScaler
    from pyspark.statcounter import StatCounter

    print ("Successfully imported Spark Modules")
except ImportError as e:
    print ("Can not import Spark Modules", e)
    sys.exit(1)

代码部分

    sc = SparkContext(conf=conf)   
    raw_data = sc.textFile(data_file)
    parsed_data = raw_data.map(Parseline)

Parseline 功能：

def Parseline(line):
    line_split = line.split(",")
    clean_line_split = [line_split[0]]+line_split[4:-1]
    return (line_split[-1], array([float(x) for x in clean_line_split]))

Answer 1

这不是一个很好的解决方案，但是您可以调整我对类似Scala问题的答案。让我们从一个示例数据开始：

import numpy as np

np.random.seed(323)

keys = ["foo"] * 50 + ["bar"] * 50
values = (
    np.vstack([np.repeat(-10, 500), np.repeat(10, 500)]).reshape(100, -1) +
    np.random.rand(100, 10)
)

rdd = sc.parallelize(zip(keys, values))

不幸的MultivariateStatisticalSummary是，它只是围绕JVM模型的包装，并且它并不是真正的Python友好。幸运的是，有了NumPy数组，我们可以使用standardStatCounter通过键来计算统计信息：

from pyspark.statcounter import StatCounter

def compute_stats(rdd):
    return rdd.aggregateByKey(
        StatCounter(), StatCounter.merge, StatCounter.mergeStats
    ).collectAsMap()

最后我们可以map归一化：

def scale(rdd, stats):
    def scale_(kv):
        k, v = kv
        return (v - stats[k].mean()) / stats[k].stdev()
    return rdd.map(scale_)

scaled = scale(rdd, compute_stats(rdd))
scaled.first()

## array([ 1.59879188, -1.66816084,  1.38546532,  1.76122047,  1.48132643,
##    0.01512487,  1.49336769,  0.47765982, -1.04271866,  1.55288814])