[jvm-packages] update methods in test cases to be consistent (#1780)

* add back train method but mark as deprecated

* fix scalastyle error

* change class to object in examples

* fix compilation error

* update methods in test cases to be consistent

* add blank lines

* fix

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/Utils.scala

@@ -38,38 +38,45 @@ trait Utils extends Serializable {
     }
   }
 
-  protected def loadLabelPoints(filePath: String): List[LabeledPoint] = {
+  protected def loadLabelPoints(filePath: String, zeroBased: Boolean = false):
+      List[LabeledPoint] = {
     val file = Source.fromFile(new File(filePath))
     val sampleList = new ListBuffer[LabeledPoint]
     for (sample <- file.getLines()) {
-      sampleList += fromSVMStringToLabeledPoint(sample)
+      sampleList += fromColValueStringToLabeledPoint(sample, zeroBased)
     }
     sampleList.toList
   }
 
-  protected def loadLabelAndVector(filePath: String): List[(Double, SparkVector)] = {
+  protected def loadLabelAndVector(filePath: String, zeroBased: Boolean = false):
+      List[(Double, SparkVector)] = {
     val file = Source.fromFile(new File(filePath))
     val sampleList = new ListBuffer[(Double, SparkVector)]
     for (sample <- file.getLines()) {
-      sampleList += fromSVMStringToLabelAndVector(sample)
+      sampleList += fromColValueStringToLabelAndVector(sample, zeroBased)
     }
     sampleList.toList
   }
 
-  protected def fromSVMStringToLabelAndVector(line: String): (Double, SparkVector) = {
+  protected def fromColValueStringToLabelAndVector(line: String, zeroBased: Boolean):
+      (Double, SparkVector) = {
     val labelAndFeatures = line.split(" ")
     val label = labelAndFeatures(0).toDouble
     val features = labelAndFeatures.tail
-    val denseFeature = new Array[Double](129)
+    val denseFeature = new Array[Double](126)
     for (feature <- features) {
       val idAndValue = feature.split(":")
-      denseFeature(idAndValue(0).toInt) = idAndValue(1).toDouble
+      if (!zeroBased) {
+        denseFeature(idAndValue(0).toInt - 1) = idAndValue(1).toDouble
+      } else {
+        denseFeature(idAndValue(0).toInt) = idAndValue(1).toDouble
+      }
     }
     (label, new DenseVector(denseFeature))
   }
 
-  protected def fromSVMStringToLabeledPoint(line: String): LabeledPoint = {
-    val (label, sv) = fromSVMStringToLabelAndVector(line)
+  protected def fromColValueStringToLabeledPoint(line: String, zeroBased: Boolean): LabeledPoint = {
+    val (label, sv) = fromColValueStringToLabelAndVector(line, zeroBased)
     LabeledPoint(label, sv)
   }
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala

@@ -95,12 +95,14 @@ class XGBoostDFSuite extends SharedSparkContext with Utils {
   test("test schema of XGBoostRegressionModel") {
     val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
       "objective" -> "reg:linear")
-    val testItr = loadLabelPoints(getClass.getResource("/machine.txt.test").getFile).iterator.
+    val testItr = loadLabelPoints(getClass.getResource("/machine.txt.test").getFile,
+      zeroBased = true).iterator.
       zipWithIndex.map { case (instance: LabeledPoint, id: Int) =>
       (id, instance.features, instance.label)
     }
     val trainingDF = {
-      val rowList = loadLabelPoints(getClass.getResource("/machine.txt.train").getFile)
+      val rowList = loadLabelPoints(getClass.getResource("/machine.txt.train").getFile,
+        zeroBased = true)
       val labeledPointsRDD = sc.parallelize(rowList, numWorkers)
       val sparkSession = SparkSession.builder().appName("XGBoostDFSuite").getOrCreate()
       import sparkSession.implicits._
@@ -183,4 +185,5 @@ class XGBoostDFSuite extends SharedSparkContext with Utils {
   }
 
 
+
 }