Flink实现高斯朴素贝叶斯
在之前的文章中提到了多项式的朴素贝叶斯，在spark的ML里也实现了多项式的朴素贝叶斯和伯努利朴素贝叶斯，在实际情况当中我们处理的变量除了离散型，还有连续型。在对这类数据使用朴素贝叶斯的时候，我们通常会假定变量服从高斯分布。然后再进行概率计算。
Flink代码实现
在这里，使用的数据集是鸢尾花数据集
// 创建一个鸢尾花数据类
/**
* Created by WZZC on 2019/4/26
**/
case class Iris(
species: String,
features: Vector[Double]
) {
}
object Iris {
def apply(irisInfo: String): Iris = {
val splited = irisInfo.split(",")
new Iris(
splited.head,
splited.tail.map(_.toDouble).toVector
)
}
}
flink DataSet
import bean.Iris
import breeze.stats.distributions.Gaussian
import org.apache.flink.api.scala.ExecutionEnvironment
import org.apache.flink.api.scala._
val env = ExecutionEnvironment.getExecutionEnvironment
// 读取鸢尾花数据并解析为Iris对象作为训练集
val irisDataSet: DataSet[Iris] = env.readTextFile("F:\\DataSource\\iris.csv")
.map(Iris.apply(_))
// 样本量大小 样本有150个 分为三类 Iris-setosa、Iris-versicolor、Iris-virginica，各占50条
val sampleSize: Long = irisDataSet.count()
// 分组
val grouped = irisDataSet.groupBy("species")
//计算正态分布的概率密度
def pdf(x: Double, mu: Double, sigma2: Double) = {
Gaussian(mu, math.sqrt(sigma2)).pdf(x)
}
val reduced: Seq[(Double, Vector[(Double, Double)], String)] = grouped
.reduceGroup(elmes => {
// compute mu and sigma
val itris = elmes.toSeq
val features = itris.map(_.features)
val num = features.length.toDouble
//pprob 计算先验概率
val pprob = num / sampleSize
val folded: Vector[Double] = features.tail.fold(features.head) { (v1, v2) =>
v1.zip(v2).map(tp => tp._1 + tp._2)
}
// 计算特征的均值向量
val muVec = folded.map(_ / num)
// 计算特征的方差向量
val ssr = features.map(feature => {
feature.zip(muVec).map { case (v1, v2) => math.pow(v1 - v2, 2) }
})
val sigma2 = ssr.tail.fold(ssr.head) { (v1, v2) =>
v1.zip(v2).map(tp => tp._1 + tp._2)
}
.map(_ / num)
(pprob, muVec.zip(sigma2), itris.head.species)
})
.collect()
// 我们还是以训练集作为测试
val result = irisDataSet.map(iris => {
val prob = reduced.map(probs => {
// 计算条件概率的乘积
val cprob = probs._2.zip(iris.features).map {
case ((mu, sigma2), x) => pdf(x, mu, sigma2)
}.product
(probs._1 * cprob, probs._3)
})
.maxBy(_._1)
(iris,prob)
})
// 结果查看 ，我们只打印出分类错误的记录
result.filter(tp=> tp._1.species != tp._2._2).print()
结果查看，最终只有6个样本被错误的分类，正确率达到96%
(Iris(Iris-versicolor,Vector(6.9, 3.1, 4.9, 1.5)),(0.0174740386794776,Iris-virginica))
(Iris(Iris-versicolor,Vector(5.9, 3.2, 4.8, 1.8)),(0.03325849601325018,Iris-virginica))
(Iris(Iris-versicolor,Vector(6.7, 3.0, 5.0, 1.7)),(0.08116596041911653,Iris-virginica))
(Iris(Iris-virginica,Vector(4.9, 2.5, 4.5, 1.7)),(0.007235803888178925,Iris-versicolor))
(Iris(Iris-virginica,Vector(6.0, 2.2, 5.0, 1.5)),(0.020461656241466127,Iris-versicolor))
(Iris(Iris-virginica,Vector(6.3, 2.8, 5.1, 1.5)),(0.05952315832865549,Iris-versicolor))
Spark代码实现
import breeze.stats.distributions.Gaussian
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.DenseVector
import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.stat.Summarizer.{
mean => summaryMean,
variance => summaryVar
}
import org.apache.spark.sql.functions.udf
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName(s"${this.getClass.getSimpleName}")
.master("local[*]")
.getOrCreate()
import spark.implicits._
val sc = spark.sparkContext
// 数据加载
val irisData = spark.read
.option("header", true)
.option("inferSchema", true)
// .csv("F:\\DataSource\\person_naiveBayes.csv")
.csv("F:\\DataSource\\iris.csv")
val rolluped = irisData.rollup($"class").count()
// 样本量
val sampleSize = rolluped.where($"class".isNull).head().getAs[Long](1)
// 计算先验概率
val pprobMap = rolluped
.where($"class".isNotNull)
.withColumn("pprob", $"count" / sampleSize)
.collect()
.map(row => {
row.getAs[String]("class") -> row.getAs[Double]("pprob")
})
.toMap
val schema = irisData.schema
val fts = schema.filterNot(_.name == """class""").map(_.name).toArray
// 数据转换
val amountVectorAssembler: VectorAssembler = new VectorAssembler()
.setInputCols(fts)
.setOutputCol("features")
val ftsDF = amountVectorAssembler
.transform(irisData)
.select("class", "features")
// 聚合计算：计算特征均值向量和方差向量
val irisAggred = ftsDF
.groupBy($"class")
.agg(
summaryMean($"features") as "mfts",
summaryVar($"features") as "vfts"
)
val cprobs: Array[(Array[(Double, Double)], String)] = irisAggred
.collect()
.map(row => {
val cl = row.getAs[String]("class")
val mus = row.getAs[DenseVector]("mfts").toArray
val vars = row.getAs[DenseVector]("vfts").toArray
(mus.zip(vars), cl)
})
def pdf(x: Double, mu: Double, sigma2: Double) = {
Gaussian(mu, math.sqrt(sigma2)).pdf(x)
}
val predictUDF = udf((vec: DenseVector) => {
cprobs
.map(tp => {
val tuples: Array[((Double, Double), Double)] = tp._1.zip(vec.toArray)
val cp: Double = tuples.map {
case ((mu, sigma), x) => pdf(x, mu, sigma)
}.product
val pprob: Double = pprobMap.getOrElse(tp._2, 0)
(cp * pprob, tp._2)
})
.maxBy(_._1)
._2
})
val predictDF = ftsDF
.withColumn("predict", predictUDF($"features"))
predictDF.where($"class" =!= $"predict").show(truncate = false)
spark.stop()
}
结果查看，与Flink计算的结果一致
+---------------+-----------------+---------------+
|class |features |predict |
+---------------+-----------------+---------------+
|Iris-versicolor|[6.9,3.1,4.9,1.5]|Iris-virginica |
|Iris-versicolor|[5.9,3.2,4.8,1.8]|Iris-virginica |
|Iris-versicolor|[6.7,3.0,5.0,1.7]|Iris-virginica |
|Iris-virginica |[4.9,2.5,4.5,1.7]|Iris-versicolor|
|Iris-virginica |[6.0,2.2,5.0,1.5]|Iris-versicolor|
|Iris-virginica |[6.3,2.8,5.1,1.5]|Iris-versicolor|
+---------------+-----------------+---------------+
我们可以对比之前的贝叶斯判别分析。在之前的贝叶斯判别中使用同样的鸢尾花数据集，分类的准确率是98%。因为朴素贝叶斯假定了变量之间是独立的，所以在计算上实现了很多简化（不用计算协方差矩阵）。但是分类效果仅仅比普通的贝叶斯判别差一点点。
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