1、Spark MLlib Statistics统计
Spark Mllib
统计模块代码结构如下：
1.1
列统计汇总
计算每列最大值、最小值、平均值、方差值、L1范数、L2范数。
//读取数据，转换成RDD[Vector]类型
val
data_path =
"/home/jb-huangmeiling/sample_stat.txt"
val
data =
sc.textFile(data_path).map(_.split("\t")).map(f
=> f.map(f => f.toDouble))
val
data1 =
data.map(f => Vectors.dense(f))
//计算每列最大值、最小值、平均值、方差值、L1范数、L2范数
val
stat1 = Statistics.colStats(data1)
stat1.max
stat1.min
stat1.mean
stat1.variance
stat1.normL1
stat1.normL2
执行结果：
数据
|
1 |
2 |
3 |
4 |
5 |
|
6 |
7 |
1 |
5 |
9 |
|
3 |
5 |
6 |
3 |
1 |
|
3 |
1 |
1 |
5 |
6 |
scala> data1.collect
res19: Array[org.apache.spark.mllib.linalg.Vector] = Array([1.0,2.0,3.0,4.0,5.0], [6.0,7.0,1.0,5.0,9.0],
[3.0,5.0,6.0,3.0,1.0], [3.0,1.0,1.0,5.0,6.0])
scala>
stat1.max
res20: org.apache.spark.mllib.linalg.Vector = [6.0,7.0,6.0,5.0,9.0]
scala>
stat1.min
res21: org.apache.spark.mllib.linalg.Vector = [1.0,1.0,1.0,3.0,1.0]
scala>
stat1.mean
res22: org.apache.spark.mllib.linalg.Vector = [3.25,3.75,2.75,4.25,5.25]
scala>
stat1.variance
res23: org.apache.spark.mllib.linalg.Vector = [4.25,7.583333333333333,5.583333333333333,0.9166666666666666,10.916666666666666]
scala>
stat1.normL1
res24: org.apache.spark.mllib.linalg.Vector = [13.0,15.0,11.0,17.0,21.0]
scala>
stat1.normL2
res25: org.apache.spark.mllib.linalg.Vector = [7.416198487095663,8.888194417315589,6.855654600401044,8.660254037844387,11.958260743101398]
1.2
相关系数
Pearson相关系数表达的是两个数值变量的线性相关性,
它一般适用于正态分布。其取值范围是[-1, 1], 当取值为0表示不相关，取值为(0~-1]表示负相关，取值为(0, 1]表示正相关。
Spearman相关系数也用来表达两个变量的相关性，但是它没有Pearson相关系数对变量的分布要求那么严格，另外Spearman相关系数可以更好地用于测度变量的排序关系。其计算公式为：
//计算pearson系数、spearman相关系数
val
corr1 = Statistics.corr(data1,
"pearson")
val
corr2 = Statistics.corr(data1,
"spearman")
val
x1 =
sc.parallelize(Array(1.0,
2.0,
3.0,
4.0))
val
y1 =
sc.parallelize(Array(5.0,
6.0,
6.0,
6.0))
val
corr3 = Statistics.corr(x1,
y1,
"pearson")
scala> corr1
res6: org.apache.spark.mllib.linalg.Matrix =
1.0
0.7779829610026362 -0.39346431156047523
... (5 total)
0.7779829610026362
1.0 0.14087521363240252
...
-0.39346431156047523
0.14087521363240252 1.0
...
0.4644203640128242
-0.09482093118615205 -0.9945577827230707
...
0.5750122832421579
0.19233705001984078 -0.9286374704669208
...
scala> corr2
res7: org.apache.spark.mllib.linalg.Matrix =
1.0
0.632455532033675 -0.5000000000000001
... (5 total)
0.632455532033675
1.0 0.10540925533894883
...
-0.5000000000000001
0.10540925533894883 1.0
...
0.5000000000000001
-0.10540925533894883 -1.0000000000000002
...
0.6324555320336723
0.20000000000000429 -0.9486832980505085
...
scala> corr3
res8: Double = 0.7745966692414775
1.3
假设检验
MLlib当前支持用于判断拟合度或者独立性的Pearson卡方(chi-squared
( χ2) )检验。不同的输入类型决定了是做拟合度检验还是独立性检验。拟合度检验要求输入为Vector,
独立性检验要求输入是Matrix。
//卡方检验
val
v1 = Vectors.dense(43.0,
9.0)
val
v2 = Vectors.dense(44.0,
4.0)
val
c1 = Statistics.chiSqTest(v1,
v2)
执行结果：
c1: org.apache.spark.mllib.stat.test.ChiSqTestResult =
Chi squared test summary:
method: pearson
degrees of freedom = 1
statistic = 5.482517482517483
pValue = 0.01920757707591003
Strong presumption against null hypothesis: observed follows the same distribution as expected..
结果返回：统计量：pearson、自由度：1、值：5.48、概率：0.019。