因为项目的需要，将数据库中表的属性向量化，然后进行机器学习，所以去spark官网学习了一下OneHotEncoder,官网的相关介绍比较少，主要是针对单属性的处理，但是项目的要求是多属性的处理，网上找了很多的资料，研究了大半天终于将它集成到了自己的项目之中，下面分享一下自己的学习心得，说的不好的地方，还请各位大神多多指教。
介绍：将类别映射为二进制向量，其中至多一个值为1（其余为零），这种编码可供期望连续特征的算法使用，比如逻辑回归，这些分类的算法。
好处：1.解决分类器不好处理属性数据的问题（分类器往往默认数据是连续的，并且是有序的）
2.在一定程度上也起到了扩充特征的作用
原理：1.String字符串转换成索引IndexDouble
2.索引转化成SparseVector
总结：OneHotEncoder=String->IndexDouble->SparseVector
单属性的官网实现：
package com.iflytek.features
import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer}
import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.linalg.SparseVector
object OneHotEncoder {
val spark=SparkSession.builder().appName("pca").master("local").getOrCreate()
def main(args: Array[String]): Unit = {
val df = spark.createDataFrame(Seq(
(0, "a"),
(1, "b"),
(2, "c"),
(3, "a"),
(4, "a"),
(5, "c")
)).toDF("id", "category")
//可以把一个属性列里的值映射成数值类型
val indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df)
val indexed = indexer.transform(df)
indexed.select("category", "categoryIndex").show()
val encoder = new OneHotEncoder()
.setInputCol("categoryIndex")
.setOutputCol("categoryVec")
val encoded = encoder.transform(indexed)
encoded.select("id","categoryIndex", "categoryVec").show()
encoded.select("categoryVec").foreach {
x => println(x.getAs[SparseVector]("categoryVec").toArray.foreach {
x => print(x+" ")
}
)
}
}
}
输出结果如下：
+--------+-------------+ |category|categoryIndex| +--------+-------------+ | a| 0.0| | b| 2.0| | c| 1.0| | a| 0.0| | a| 0.0| | c| 1.0| +--------+-------------+
+---+-------------+-------------+ | id|categoryIndex| categoryVec| +---+-------------+-------------+ | 0| 0.0|(2,[0],[1.0])| | 1| 2.0| (2,[],[])| | 2| 1.0|(2,[1],[1.0])| | 3| 0.0|(2,[0],[1.0])| | 4| 0.0|(2,[0],[1.0])| | 5| 1.0|(2,[1],[1.0])| +---+-------------+-------------+
1.0 0.0 () 0.0 0.0 () 0.0 1.0 () 1.0 0.0 () 1.0 0.0 () 0.0 1.0 ()
多属性的找了很多资料，业务需求一般都是多属性的：
import sc.implicits._
val vectorData = dataRDD
//将 枚举的值 转化为 Double
.map( x => ( enum2Double("是否已流失",x._1), x._2(0) , x._2(1) ,x._2(2),x._2(3) ) )
//ml.feature.LabeledPoint
.toDF("loss","gender","age","grade","region")
//indexing columns
val stringColumns = Array("gender","age","grade","region")
val index_transformers: Array[org.apache.spark.ml.PipelineStage] = stringColumns.map(
cname => new StringIndexer()
.setInputCol(cname)
.setOutputCol(s"${cname}_index")
)
// Add the rest of your pipeline like VectorAssembler and algorithm
val index_pipeline = new Pipeline().setStages(index_transformers)
val index_model = index_pipeline.fit(vectorData)
val df_indexed = index_model.transform(vectorData)
//encoding columns
val indexColumns = df_indexed.columns.filter(x => x contains "index")
val one_hot_encoders: Array[org.apache.spark.ml.PipelineStage] = indexColumns.map(
cname => new OneHotEncoder()
.setInputCol(cname)
.setOutputCol(s"${cname}_vec")
)
val pipeline = new Pipeline().setStages(index_transformers ++ one_hot_encoders)
val model = pipeline.fit(vectorData)
model.transform(vectorData).select("loss","gender_index_vec","age_index_vec","grade_index_vec","region_index_vec")
.map (
x=>
ml.feature.LabeledPoint(x.apply(0).toString().toDouble ,ml.linalg.Vectors.dense(x.getAs[SparseVector] ("gender_index_vec").toArray++x.getAs[SparseVector]("age_index_vec").toArray++x.getAs[SparseVector]("grade_index_vec").toArray++x.getAs[SparseVector]("region_index_vec").toArray))
)
