title: How to provide multiple columns to setInputCol()
body: <p>I am very new to Spark Machine Learning I want to pass multiple columns to features, in my below code I am only passing the Date column to features but now I want to pass Userid and Date columns to features. I tried to Use Vector but It only support Double data type but in My case I have Int and String </p>
  
  <p>I would be thankful if anyone provide any suggestion/solution or any code example which will fulfill my requirement </p>
  
  <p><strong>Code:</strong>   </p>
  
  <pre><code> case class LabeledDocument(Userid: Double, Date: String, label: Double)
   val training = spark.read.option("inferSchema", true).csv("/root/Predictiondata3.csv").toDF("Userid","Date","label").toDF().as[LabeledDocument]
   import scala.beans.BeanInfo
   import org.apache.spark.{SparkConf, SparkContext}
   import org.apache.spark.ml.Pipeline
   import org.apache.spark.ml.classification.LogisticRegression
   import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
   import org.apache.spark.mllib.linalg.Vector
   import org.apache.spark.sql.{Row, SQLContext}
   val tokenizer = new Tokenizer().setInputCol("Date").setOutputCol("words")
   val hashingTF = new HashingTF().setNumFeatures(1000).setInputCol(tokenizer.getOutputCol).setOutputCol("features")
   import org.apache.spark.ml.regression.LinearRegression
   val lr = new LinearRegression().setMaxIter(100).setRegParam(0.001).setElasticNetParam(0.0001)
   val pipeline = new Pipeline().setStages(Array(tokenizer, hashingTF, lr))
   val model = pipeline.fit(training.toDF())
   case class Document(Userid: Integer, Date: String)
   val test = sc.parallelize(Seq(Document(4, "04-Jan-18"),Document(5, "01-Jan-17"),Document(2, "03-Jan-17")))
   model.transform(test.toDF()).show()
  </code></pre>
  
  <p><strong>Input Data</strong> with Columns</p>
  
  <pre><code>Userid,Date,SwipeIntime
  1, 1-Jan-2017,9.30
  1, 2-Jan-2017,9.35
  1, 3-Jan-2017,9.45
  1, 4-Jan-2017,9.26
  2, 1-Jan-2017,9.37
  2, 2-Jan-2017,9.35
  2, 3-Jan-2017,9.45
  2, 4-Jan-2017,9.46  
  </code></pre>