Support instance weights for xgboost4j-spark (#2642)

* Support instance weights for xgboost4j-spark

* Use 0.001 instead of 0 for weights

* Address CR comments

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

@@ -18,11 +18,11 @@ package ml.dmlc.xgboost4j.scala.spark
 
 import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost => ScalaXGBoost}
 import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
-
 import org.apache.spark.ml.linalg.DenseVector
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DataTypes
 import org.scalatest.FunSuite
 
 class XGBoostDFSuite extends FunSuite with PerTest {
@@ -213,4 +213,24 @@ class XGBoostDFSuite extends FunSuite with PerTest {
     val predWithMargin = trainPredict(trainingDfWithMargin)
     assert((pred, predWithMargin).zipped.exists { case (p, pwm) => p !== pwm })
   }
+
+  test("test use weight") {
+    import DataUtils._
+    val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "reg:linear", "weightCol" -> "weight")
+
+    val getWeightFromId = udf({id: Int => if (id == 0) 1.0f else 0.001f}, DataTypes.FloatType)
+    val trainingDF = buildDataFrame(Regression.train)
+      .withColumn("weight", getWeightFromId(col("id")))
+
+    val model = XGBoost.trainWithDataFrame(trainingDF, paramMap, round = 5,
+      nWorkers = numWorkers, useExternalMemory = true)
+      .setPredictionCol("final_prediction")
+      .setExternalMemory(true)
+    val testRDD = sc.parallelize(Regression.test.map(_.features))
+    val predictions = model.predict(testRDD).collect().flatten
+
+    // The predictions heavily relies on the first training instance, and thus are very close.
+    predictions.foreach(pred => assert(math.abs(pred - predictions.head) <= 0.01f))
+  }
 }