Spark特征(提取，转换，选择)extracting, transforming and selecting features
VectorAssembler字段转换成特征向量
import org.apache.spark.ml.feature.VectorAssembler
val colArray = Array("age", "yearsmarried", "religiousness", "education", "occupation", "rating")
// 字段转换成特征向量
val assembler = new VectorAssembler().setInputCols(colArray).setOutputCol("features")
val vecDF: DataFrame = assembler.transform(data)
vecDF: org.apache.spark.sql.DataFrame = [affairs: double, gender: string ... 8 more fields]
vecDF.select("features", colArray: _*).show(10, truncate = false)
+----------------------------+----+------------+-------------+---------+----------+------+
|features |age |yearsmarried|religiousness|education|occupation|rating|
+----------------------------+----+------------+-------------+---------+----------+------+
|[37.0,10.0,3.0,18.0,7.0,4.0]|37.0|10.0 |3.0 |18.0 |7.0 |4.0 |
|[27.0,4.0,4.0,14.0,6.0,4.0] |27.0|4.0 |4.0 |14.0 |6.0 |4.0 |
|[32.0,15.0,1.0,12.0,1.0,4.0]|32.0|15.0 |1.0 |12.0 |1.0 |4.0 |
|[57.0,15.0,5.0,18.0,6.0,5.0]|57.0|15.0 |5.0 |18.0 |6.0 |5.0 |
|[22.0,0.75,2.0,17.0,6.0,3.0]|22.0|0.75 |2.0 |17.0 |6.0 |3.0 |
|[32.0,1.5,2.0,17.0,5.0,5.0] |32.0|1.5 |2.0 |17.0 |5.0 |5.0 |
|[22.0,0.75,2.0,12.0,1.0,3.0]|22.0|0.75 |2.0 |12.0 |1.0 |3.0 |
|[57.0,15.0,2.0,14.0,4.0,4.0]|57.0|15.0 |2.0 |14.0 |4.0 |4.0 |
|[32.0,15.0,4.0,16.0,1.0,2.0]|32.0|15.0 |4.0 |16.0 |1.0 |2.0 |
|[22.0,1.5,4.0,14.0,4.0,5.0] |22.0|1.5 |4.0 |14.0 |4.0 |5.0 |
+----------------------------+----+------------+-------------+---------+----------+------+
only showing top 10 rows
VectorIndexer自动识别分类的特征，并对它们进行索引
import org.apache.spark.ml.feature.VectorIndexer
val colArray = Array("age", "yearsmarried", "religiousness", "education", "occupation", "rating")
// 自动识别分类的特征，并对它们进行索引
// 具有大于7个不同的值的特征被视为连续。
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(7)
.fit(vecDF)
val categoricalFeatures: Set[Int] = featureIndexer.categoryMaps.keys.toSet
categoricalFeatures: Set[Int] = Set(2, 3, 4, 5)
println(s"Chose ${categoricalFeatures.size} categorical features: " +
categoricalFeatures.mkString(", "))
Chose 4 categorical features: 2, 3, 4, 5
// 由此看出，当MaxCategories=7，从6个字段中识别出了4个“类别特征字段”，
// 他们的下标索引为(2, 3, 4, 5),分别对应colArray中的(2, 3, 4, 5)元素，即"religiousness", "education", "occupation", "rating"
// 为什么识别出了4个“类别特征字段”呢，请看本人博客http://www.cnblogs.com/wwxbi/p/6125363.html“统计字段中元素的个数”
// 从“统计字段中元素的个数”看出，("religiousness", "education", "occupation", "rating")这4个字段的元素个数<=7
// Create new column "indexedFeatures" with categorical values transformed to indices
val indexedData = featureIndexer.transform(vecDF)
indexedData: org.apache.spark.sql.DataFrame = [affairs: double, gender: string ... 9 more fields]
val resColArray = Array("indexedFeatures", "features", "age", "yearsmarried", "religiousness", "education", "occupation", "rating")
resColArray: Array[String] = Array(indexedFeatures, features, age, yearsmarried, religiousness, education, occupation, rating)
indexedData.selectExpr(resColArray: _*).show(10, truncate = false)
+---------------------------+----------------------------+----+------------+-------------+---------+----------+------+
|indexedFeatures |features |age |yearsmarried|religiousness|education|occupation|rating|
+---------------------------+----------------------------+----+------------+-------------+---------+----------+------+
|[37.0,10.0,2.0,5.0,6.0,3.0]|[37.0,10.0,3.0,18.0,7.0,4.0]|37.0|10.0 |3.0 |18.0 |7.0 |4.0 |
|[27.0,4.0,3.0,2.0,5.0,3.0] |[27.0,4.0,4.0,14.0,6.0,4.0] |27.0|4.0 |4.0 |14.0 |6.0 |4.0 |
|[32.0,15.0,0.0,1.0,0.0,3.0]|[32.0,15.0,1.0,12.0,1.0,4.0]|32.0|15.0 |1.0 |12.0 |1.0 |4.0 |
|[57.0,15.0,4.0,5.0,5.0,4.0]|[57.0,15.0,5.0,18.0,6.0,5.0]|57.0|15.0 |5.0 |18.0 |6.0 |5.0 |
|[22.0,0.75,1.0,4.0,5.0,2.0]|[22.0,0.75,2.0,17.0,6.0,3.0]|22.0|0.75 |2.0 |17.0 |6.0 |3.0 |
|[32.0,1.5,1.0,4.0,4.0,4.0] |[32.0,1.5,2.0,17.0,5.0,5.0] |32.0|1.5 |2.0 |17.0 |5.0 |5.0 |
|[22.0,0.75,1.0,1.0,0.0,2.0]|[22.0,0.75,2.0,12.0,1.0,3.0]|22.0|0.75 |2.0 |12.0 |1.0 |3.0 |
|[57.0,15.0,1.0,2.0,3.0,3.0]|[57.0,15.0,2.0,14.0,4.0,4.0]|57.0|15.0 |2.0 |14.0 |4.0 |4.0 |
|[32.0,15.0,3.0,3.0,0.0,1.0]|[32.0,15.0,4.0,16.0,1.0,2.0]|32.0|15.0 |4.0 |16.0 |1.0 |2.0 |
|[22.0,1.5,3.0,2.0,3.0,4.0] |[22.0,1.5,4.0,14.0,4.0,5.0] |22.0|1.5 |4.0 |14.0 |4.0 |5.0 |
+---------------------------+----------------------------+----+------------+-------------+---------+----------+------+
only showing top 10 rows
import org.apache.spark.ml.feature.VectorSlicer
val slicer = new VectorSlicer().setInputCol("indexedFeatures").setOutputCol("slicerFeatures")
slicer.setIndices(Array(3)) // 此处的3对应“索引化”之前的字段“education”
val output = slicer.transform(indexedData)
output.select("indexedFeatures",
"slicerFeatures",
"education").limit(10).orderBy($"education").show(10, truncate = false)
+---------------------------+--------------+---------+
|indexedFeatures |slicerFeatures|education|
+---------------------------+--------------+---------+
|[32.0,15.0,0.0,1.0,0.0,3.0]|[1.0] |12.0 |
|[22.0,0.75,1.0,1.0,0.0,2.0]|[1.0] |12.0 |
|[27.0,4.0,3.0,2.0,5.0,3.0] |[2.0] |14.0 |
|[57.0,15.0,1.0,2.0,3.0,3.0]|[2.0] |14.0 |
|[22.0,1.5,3.0,2.0,3.0,4.0] |[2.0] |14.0 |
|[32.0,15.0,3.0,3.0,0.0,1.0]|[3.0] |16.0 |
|[32.0,1.5,1.0,4.0,4.0,4.0] |[4.0] |17.0 |
|[22.0,0.75,1.0,4.0,5.0,2.0]|[4.0] |17.0 |
|[37.0,10.0,2.0,5.0,6.0,3.0]|[5.0] |18.0 |
|[57.0,15.0,4.0,5.0,5.0,4.0]|[5.0] |18.0 |
+---------------------------+--------------+---------+
// 由此看出，“类别特征字段”被索引化后，索引的编号是跟“原字段值的大小顺序”对照的，索引从0开始
// 索引编号(0,1,2,3,4,5,6)对应[9.0, 12.0, 14.0, 16.0, 17.0, 18.0, 20.0]
VectorSlicer向量切割
import org.apache.spark.ml.feature.VectorSlicer
val colArray = Array("age", "yearsmarried", "religiousness", "education", "occupation", "rating")
// 字段转换成特征向量
val assembler = new VectorAssembler().setInputCols(colArray).setOutputCol("features")
val vecDF = assembler.transform(data)
val slicer = new VectorSlicer().setInputCol("features").setOutputCol("slicerFeatures")
// 指定“向量字段features”中的下标索引
// (2, 3, 4)分别对应字段("religiousness", "education", "occupation")
slicer.setIndices(Array(2, 3, 4))
val output = slicer.transform(vecDF)
output.select("features", "slicerFeatures","religiousness", "education", "occupation").show(10, truncate = false)
+----------------------------+--------------+-------------+---------+----------+
|features |slicerFeatures|religiousness|education|occupation|
+----------------------------+--------------+-------------+---------+----------+
|[37.0,10.0,3.0,18.0,7.0,4.0]|[3.0,18.0,7.0]|3.0 |18.0 |7.0 |
|[27.0,4.0,4.0,14.0,6.0,4.0] |[4.0,14.0,6.0]|4.0 |14.0 |6.0 |
|[32.0,15.0,1.0,12.0,1.0,4.0]|[1.0,12.0,1.0]|1.0 |12.0 |1.0 |
|[57.0,15.0,5.0,18.0,6.0,5.0]|[5.0,18.0,6.0]|5.0 |18.0 |6.0 |
|[22.0,0.75,2.0,17.0,6.0,3.0]|[2.0,17.0,6.0]|2.0 |17.0 |6.0 |
|[32.0,1.5,2.0,17.0,5.0,5.0] |[2.0,17.0,5.0]|2.0 |17.0 |5.0 |
|[22.0,0.75,2.0,12.0,1.0,3.0]|[2.0,12.0,1.0]|2.0 |12.0 |1.0 |
|[57.0,15.0,2.0,14.0,4.0,4.0]|[2.0,14.0,4.0]|2.0 |14.0 |4.0 |
|[32.0,15.0,4.0,16.0,1.0,2.0]|[4.0,16.0,1.0]|4.0 |16.0 |1.0 |
|[22.0,1.5,4.0,14.0,4.0,5.0] |[4.0,14.0,4.0]|4.0 |14.0 |4.0 |
+----------------------------+--------------+-------------+---------+----------+
only showing top 10 rows
output.printSchema()
root
|-- affairs: double (nullable = false)
|-- gender: string (nullable = true)
|-- age: double (nullable = false)
|-- yearsmarried: double (nullable = false)
|-- children: string (nullable = true)
|-- religiousness: double (nullable = false)
|-- education: double (nullable = false)
|-- occupation: double (nullable = false)
|-- rating: double (nullable = false)
|-- features: vector (nullable = true)
|-- slicerFeatures: vector (nullable = true)
Bucketizer将连续数据离散化到指定的范围区间
import org.apache.spark.ml.feature.Bucketizer // Double.NegativeInfinity：负无穷；Double.PositiveInfinity：正无穷
// 分为6个组：[负无穷,-100),[-100,-10),[-10,0),[0,10),[10,90),[90,正无穷)
val splits = Array(Double.NegativeInfinity, -100, -10, 0.0, 10, 90, Double.PositiveInfinity) val data: Array[Double] = Array(-180,-160,-100,-50,-70,-20,-8,-5,-3, 0.0, 1,3,7,10,30,60,90,100,120,150) val dataFrame = spark.createDataFrame(data.map(Tuple1.apply)).toDF("features")
dataFrame: org.apache.spark.sql.DataFrame = [features: double] val bucketizer = new Bucketizer()
.setInputCol("features")
.setOutputCol("bucketedFeatures")
.setSplits(splits) // 将原始数据转换为桶索引
val bucketedData = bucketizer.transform(dataFrame)
bucketedData: org.apache.spark.sql.DataFrame = [features: double, bucketedFeatures: double] bucketedData.show(50,truncate=false)
+--------+----------------+
|features|bucketedFeatures|
+--------+----------------+
|-180.0 |0.0 |
|-160.0 |0.0 |
|-100.0 |1.0 |
|-50.0 |1.0 |
|-70.0 |1.0 |
|-20.0 |1.0 |
|-8.0 |2.0 |
|-5.0 |2.0 |
|-3.0 |2.0 |
|0.0 |3.0 |
|1.0 |3.0 |
|3.0 |3.0 |
|7.0 |3.0 |
|10.0 |4.0 |
|30.0 |4.0 |
|60.0 |4.0 |
|90.0 |5.0 |
|100.0 |5.0 |
|120.0 |5.0 |
|150.0 |5.0 |
+--------+----------------+
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