字段"功能"不存在.SparkML

Question

我试图用Zeppelin在Spark ML中建立一个模型.我是这个领域的新手,想要一些帮助.我想我需要将正确的数据类型设置为列并将第一列设置为标签.非常感谢任何帮助,谢谢

val training = sc.textFile("hdfs:///ford/fordTrain.csv")
val header = training.first
val inferSchema = true  
val df = training.toDF

val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)

 val lrModel = lr.fit(df)

// Print the coefficients and intercept for multinomial logistic regression
println(s"Coefficients: \n${lrModel.coefficientMatrix}")
println(s"Intercepts: ${lrModel.interceptVector}")

我正在使用的csv文件的片段是:

IsAlert,P1,P2,P3,P4,P5,P6,P7,P8,E1,E2
0,34.7406,9.84593,1400,42.8571,0.290601,572,104.895,0,0,0,

Answer 1

正如您所提到的,您缺少该features列.它是包含所有预测变量的向量.你必须使用它来创建它VectorAssembler.

IsAlert是标签,所有其他变量(p1,p2,...)都是预测变量,您可以通过以下方式创建features列(实际上您可以将其命名为任何您想要的名称features):

import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors

//creating features column
val assembler = new VectorAssembler()
  .setInputCols(Array("P1","P2","P3","P4","P5","P6","P7","P8","E1","E2"))
  .setOutputCol("features")


val lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.3)
  .setElasticNetParam(0.8)
  .setFeaturesCol("features")   // setting features column
  .setLabelCol("IsAlert")       // setting label column

//creating pipeline
val pipeline = new Pipeline().setStages(Array(assembler,lr))

//fitting the model
val lrModel = pipeline.fit(df)

请参阅:https://spark.apache.org/docs/latest/ml-features.html#vectorassembler.