When trying to create a DataFrameL Exception in thread "main" java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:C:/Developer/Scala%20Projects/Spark2/spark-warehouse

DigitRecognizer1.scala

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.feature.{IndexToString, VectorIndexer, StringIndexer}
import org.apache.spark.sql.SparkSession
//import spark.implicits._

/**
  * Created by Janis Rumnieks on 15/08/2016.
  */

object DigitRecognizer1 {

  def main(args: Array[String]): Unit = {

    //val trainDataFile = """C:\Developer\Kaggle\DigitRecogniser\train.csv"""
    //val testDataFile = """C:\Developer\Kaggle\DigitRecogniser\test.csv"""

    val trainDataFile = """/C:/Developer/Kaggle/DigitRecogniser/train.csv"""
    val testDataFile = """/C:/Developer/Kaggle/DigitRecogniser/test.csv"""

    //val trainDataFileURI = new java.io.File(trainDataFile).toURI
    //val testDataFileURI = new java.io.File(testDataFile).toURI

    //val trainDataFile = """file:///C:\Developer\Kaggle\DigitRecogniser\train.csv"""
    //val testDataFile = """file:///C:\Developer\Kaggle\DigitRecogniser\test.csv"""

    //val trainDataFile = "train.csv"
    //val testDataFile = "test.csv"

    //val trainDataFile = """///train.csv"""
    //val testDataFile = """///test.csv"""


    val spark = SparkSession
      .builder()
      .appName("Spark SQL - Digit Recognition")
      .master("local[*]")
      .getOrCreate()

    //val trainDataFrame = spark.read.csv(trainDataFileURI.toString)
    //val testDataFrame = spark.read.csv(testDataFileURI.toString)

    val trainDataFrame = spark.read.csv(trainDataFile)
    val testDataFrame = spark.read.csv(testDataFile)

    val labelIndexer = new StringIndexer()
      .setInputCol("label")
      .setOutputCol("indexedLabel")
      .fit(trainDataFrame)

    val featureIndexer = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("indexedFeatures")
      .setMaxCategories(10)
      .fit(trainDataFrame)

    ///////val Array(trainingData, testData) = testDataFrame.randomSplit(Array(0.7, 0.3))

    val rf = new RandomForestClassifier()
      .setLabelCol("indexedLabel")
      .setFeaturesCol("indexedPixel")
      .setNumTrees(10)

    val labelConverter = new IndexToString()
      .setInputCol("prediction")
      .setOutputCol("predictedLabel")
      .setLabels(labelIndexer.labels)

    val pipeline = new Pipeline()
      .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))

    val model = pipeline.fit(trainDataFrame)

    val predictions = model.transform(testDataFrame)

    //predictions.select("predictedLabel", "label", "features").show(5)

    println(s"Predictions: ${predictions.count()}")



  }

}