字段 "features" 不存在。 SparkML

Question

我正在尝试使用 Zeppelin 在 Spark ML 中构建模型。我是这个领域的新手，需要一些帮助。我想我需要为列设置正确的数据类型并将第一列设置为标签。任何帮助将不胜感激，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 features 列。它是一个包含所有预测变量的向量。您必须使用 VectorAssembler.

创建它

IsAlert 是标签，所有其他变量 (p1,p2,...) 是预测变量，您可以创建 features 列（实际上您可以随意命名它而不是features) 作者：

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)

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

字段 "features" 不存在。 SparkML

Field "features" does not exist. SparkML

scala

apache-zeppelin

apache-spark-ml