我想用StandardScaler(来自pyspark.mllib.feature导入StandardScaler)来扩展数据,现在我可以通过将RDD的值传递给transform函数来实现,但问题是我想保留密钥.无论如何,我通过保留其密钥来扩展我的数据?
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
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)
部分代码
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]))
解决方法:
不是一个漂亮的解决方案,但你可以调整我的答案the similar Scala question.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))
不幸的是,MultivariateStatisticalSummary只是一个JVM模型的包装器,并不是真正的Python友好.幸运的是,使用NumPy数组,我们可以使用标准的StatCounter按键计算统计数据:
from pyspark.statcounter import StatCounter
def compute_stats(rdd):
return rdd.aggregateByKey(
StatCounter(), StatCounter.merge, StatCounter.mergeStats
).collectAsMap()
最后我们可以映射到规范化:
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])