如何在Spark ML中创建正确的分类数据框架

Question

我试图使用Spark ML api运行随机林分类，但在创建正确的数据帧输入到管道时遇到了问题。

以下是示例数据：

age,hours_per_week,education,sex,salaryRange
38,40,"hs-grad","male","A"
28,40,"bachelors","female","A"
52,45,"hs-grad","male","B"
31,50,"masters","female","B"
42,40,"bachelors","male","B"

age和hours_per_week是整数，而包括标签salaryRange在内的其他功能是分类（字符串）

加载这个csv文件（我们称之为sample.csv）可以通过Spark csv库完成，如下所示：

val data = sqlContext.csvFile("/home/dusan/sample.csv")

默认情况下，所有列都作为字符串导入，因此我们需要将"age"one_answers"hours_per_week"更改为Int:

val toInt    = udf[Int, String]( _.toInt)
val dataFixed = data.withColumn("age", toInt(data("age"))).withColumn("hours_per_week",toInt(data("hours_per_week")))

只是为了检查模式现在的样子：

scala> dataFixed.printSchema
root
 |-- age: integer (nullable = true)
 |-- hours_per_week: integer (nullable = true)
 |-- education: string (nullable = true)
 |-- sex: string (nullable = true)
 |-- salaryRange: string (nullable = true)

然后让我们设置交叉验证器和管道：

val rf = new RandomForestClassifier()
val pipeline = new Pipeline().setStages(Array(rf)) 
val cv = new CrossValidator().setNumFolds(10).setEstimator(pipeline).setEvaluator(new BinaryClassificationEvaluator)

运行此行时出现错误：

val cmModel = cv.fit(dataFixed)

java.lang.IollegalArgumentException：字段"features"不存在

可以在RandomForestClassifier中设置标签列和特征列，但我有4列作为预测因子（特征），而不仅仅是一列。

我应该如何组织我的数据框架，使其具有正确组织的标签和特征列

为了您的方便，这里有完整的代码：

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.tuning.CrossValidator
import org.apache.spark.ml.Pipeline
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
import org.apache.spark.mllib.linalg.{Vector, Vectors}

object SampleClassification {
  def main(args: Array[String]): Unit = {
    //set spark context
    val conf = new SparkConf().setAppName("Simple Application").setMaster("local");
    val sc = new SparkContext(conf)
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    import sqlContext.implicits._
    import com.databricks.spark.csv._
    //load data by using databricks "Spark CSV Library" 
    val data = sqlContext.csvFile("/home/dusan/sample.csv")
    //by default all columns are imported as string so we need to change "age" and  "hours_per_week" to Int
    val toInt    = udf[Int, String]( _.toInt)
    val dataFixed = data.withColumn("age", toInt(data("age"))).withColumn("hours_per_week",toInt(data("hours_per_week")))

    val rf = new RandomForestClassifier()
    val pipeline = new Pipeline().setStages(Array(rf))
    val cv = new CrossValidator().setNumFolds(10).setEstimator(pipeline).setEvaluator(new BinaryClassificationEvaluator)
    // this fails with error
    //java.lang.IllegalArgumentException: Field "features" does not exist.
    val cmModel = cv.fit(dataFixed) 
  }
}

谢谢你的帮助！

Answer 1

从Spark 1.4开始，您可以使用Transformer org.apache.Spark.ml.feature.VectorAssembler。只需提供要成为功能的列名即可。

val assembler = new VectorAssembler()
  .setInputCols(Array("col1", "col2", "col3"))
  .setOutputCol("features")

并将其添加到您的管道中。

Answer 2

您只需要确保数据帧中有一个类型为VectorUDF的"features"列，如下所示：

scala> val df2 = dataFixed.withColumnRenamed("age", "features")
df2: org.apache.spark.sql.DataFrame = [features: int, hours_per_week: int, education: string, sex: string, salaryRange: string]
scala> val cmModel = cv.fit(df2) 
java.lang.IllegalArgumentException: requirement failed: Column features must be of type org.apache.spark.mllib.linalg.VectorUDT@1eef but was actually IntegerType.
    at scala.Predef$.require(Predef.scala:233)
    at org.apache.spark.ml.util.SchemaUtils$.checkColumnType(SchemaUtils.scala:37)
    at org.apache.spark.ml.PredictorParams$class.validateAndTransformSchema(Predictor.scala:50)
    at org.apache.spark.ml.Predictor.validateAndTransformSchema(Predictor.scala:71)
    at org.apache.spark.ml.Predictor.transformSchema(Predictor.scala:118)
    at org.apache.spark.ml.Pipeline$$anonfun$transformSchema$4.apply(Pipeline.scala:164)
    at org.apache.spark.ml.Pipeline$$anonfun$transformSchema$4.apply(Pipeline.scala:164)
    at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
    at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
    at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:108)
    at org.apache.spark.ml.Pipeline.transformSchema(Pipeline.scala:164)
    at org.apache.spark.ml.tuning.CrossValidator.transformSchema(CrossValidator.scala:142)
    at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:59)
    at org.apache.spark.ml.tuning.CrossValidator.fit(CrossValidator.scala:107)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:67)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:72)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:74)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:76)

EDIT1

本质上，数据帧中需要有两个字段"features"作为特征向量，"label"作为实例标签。实例的类型必须为Double。

要使用Vector创建"功能"字段，请首先创建udf，如下所示：

val toVec4    = udf[Vector, Int, Int, String, String] { (a,b,c,d) => 
  val e3 = c match {
    case "hs-grad" => 0
    case "bachelors" => 1
    case "masters" => 2
  }
  val e4 = d match {case "male" => 0 case "female" => 1}
  Vectors.dense(a, b, e3, e4) 
}

现在还要对"标签"字段进行编码，创建另一个udf，如下所示：

val encodeLabel    = udf[Double, String]( _ match { case "A" => 0.0 case "B" => 1.0} )

现在我们使用这两个udf:来转换原始数据帧

val df = dataFixed.withColumn(
  "features",
  toVec4(
    dataFixed("age"),
    dataFixed("hours_per_week"),
    dataFixed("education"),
    dataFixed("sex")
  )
).withColumn("label", encodeLabel(dataFixed("salaryRange"))).select("features", "label")

注意，数据帧中可能存在额外的列/字段，但在这种情况下，我只选择了features和label:

scala> df.show()
+-------------------+-----+
|           features|label|
+-------------------+-----+
|[38.0,40.0,0.0,0.0]|  0.0|
|[28.0,40.0,1.0,1.0]|  0.0|
|[52.0,45.0,0.0,0.0]|  1.0|
|[31.0,50.0,2.0,1.0]|  1.0|
|[42.0,40.0,1.0,0.0]|  1.0|
+-------------------+-----+

现在，你可以为你的学习算法设置正确的参数，使其发挥作用。

Answer 3

根据mllib-随机树上的spark文档，在我看来，你应该定义你正在使用的特征图，并且这些点应该是一个标记点。

这将告诉算法哪个列应该用作预测，哪些列是特征。

https://spark.apache.org/docs/latest/mllib-decision-tree.html