how to retain part of the features in LogisticRegressionModel (spark2.0)

[jinhong lu]

I train my LogisticRegressionModel like this,  I want my model to retain only 
some of the features(e.g. 500 of them), not all the 5555 features. What shou I 
do? 
I use .setElasticNetParam(1.0), but still all the features is in 
lrModel.coefficients.

          import org.apache.spark.ml.classification.LogisticRegression
          val 
data=spark.read.format("libsvm").option("numFeatures","5555").load("/tmp/data/training_data3")
 
          val Array(trainingData, testData) = data.randomSplit(Array(0.5, 0.5), 
seed = 1234L)

          val lr = new LogisticRegression()
          val lrModel = lr.fit(trainingData)
          println(s"Coefficients: ${lrModel.coefficients} Intercept: 
${lrModel.intercept}")

          val predictions = lrModel.transform(testData)
          predictions.show()


Thanks, 
lujinhong