[jvm-packages] support spark 2.4 and compatibility test with previous xgboost version

jvm-packages/pom.xml

@@ -34,7 +34,7 @@
         <maven.compiler.source>1.7</maven.compiler.source>
         <maven.compiler.target>1.7</maven.compiler.target>
         <flink.version>1.5.0</flink.version>
-        <spark.version>2.3.3</spark.version>
+        <spark.version>2.4.1</spark.version>
         <scala.version>2.11.12</scala.version>
         <scala.binary.version>2.11</scala.binary.version>
     </properties>

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsReader.scala

@@ -22,12 +22,17 @@ import org.json4s.JsonAST.JObject
 import org.json4s.jackson.JsonMethods.{compact, parse, render}
 
 import org.apache.spark.SparkContext
-import org.apache.spark.ml.param.Params
+import org.apache.spark.ml.param.{Param, Params}
 import org.apache.spark.ml.util.MLReader
 
 // This originates from apache-spark DefaultPramsReader copy paste
 private[spark] object DefaultXGBoostParamsReader {
 
+  private val paramNameCompatibilityMap: Map[String, String] = Map("silent" -> "verbosity")
+
+  private val paramValueCompatibilityMap: Map[String, Map[Any, Any]] =
+    Map("objective" -> Map("reg:linear" -> "reg:squarederror"))
+
   /**
    * All info from metadata file.
    *
@@ -103,6 +108,14 @@ private[spark] object DefaultXGBoostParamsReader {
     Metadata(className, uid, timestamp, sparkVersion, params, metadata, metadataStr)
   }
 
+  private def handleBrokenlyChangedValue[T](paramName: String, value: T): T = {
+    paramValueCompatibilityMap.getOrElse(paramName, Map()).getOrElse(value, value).asInstanceOf[T]
+  }
+
+  private def handleBrokenlyChangedName(paramName: String): String = {
+    paramNameCompatibilityMap.getOrElse(paramName, paramName)
+  }
+
   /**
    * Extract Params from metadata, and set them in the instance.
    * This works if all Params implement [[org.apache.spark.ml.param.Param.jsonDecode()]].
@@ -113,9 +126,9 @@ private[spark] object DefaultXGBoostParamsReader {
     metadata.params match {
       case JObject(pairs) =>
         pairs.foreach { case (paramName, jsonValue) =>
-          val param = instance.getParam(paramName)
+          val param = instance.getParam(handleBrokenlyChangedName(paramName))
           val value = param.jsonDecode(compact(render(jsonValue)))
-          instance.set(param, value)
+          instance.set(param, handleBrokenlyChangedValue(paramName, value))
         }
       case _ =>
         throw new IllegalArgumentException(

jvm-packages/xgboost4j-spark/src/test/resources/model/0.82/model/metadata/part-00000

@@ -0,0 +1 @@
+{"class":"ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel","timestamp":1555350539033,"sparkVersion":"2.3.2-uber-109","uid":"xgbc_5e7bec215a4c","paramMap":{"useExternalMemory":false,"trainTestRatio":1.0,"alpha":0.0,"seed":0,"numWorkers":100,"skipDrop":0.0,"treeLimit":0,"silent":0,"trackerConf":{"workerConnectionTimeout":0,"trackerImpl":"python"},"missing":"NaN","colsampleBylevel":1.0,"probabilityCol":"probability","checkpointPath":"","lambda":1.0,"rawPredictionCol":"rawPrediction","eta":0.3,"numEarlyStoppingRounds":0,"growPolicy":"depthwise","gamma":0.0,"sampleType":"uniform","maxDepth":6,"rateDrop":0.0,"objective":"reg:linear","customObj":null,"lambdaBias":0.0,"baseScore":0.5,"labelCol":"label","minChildWeight":1.0,"customEval":null,"normalizeType":"tree","maxBin":16,"nthread":4,"numRound":20,"colsampleBytree":1.0,"predictionCol":"prediction","subsample":1.0,"timeoutRequestWorkers":1800000,"featuresCol":"features","evalMetric":"error","sketchEps":0.03,"scalePosWeight":1.0,"checkpointInterval":-1,"maxDeltaStep":0.0,"treeMethod":"approx"}}

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

@@ -19,9 +19,11 @@ package ml.dmlc.xgboost4j.scala.spark
 import java.io.{File, FileNotFoundException}
 import java.util.Arrays
 
-import ml.dmlc.xgboost4j.scala.DMatrix
+import scala.io.Source
 
+import ml.dmlc.xgboost4j.scala.DMatrix
 import scala.util.Random
+
 import org.apache.spark.ml.feature._
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.apache.spark.network.util.JavaUtils
@@ -162,5 +164,17 @@ class PersistenceSuite extends FunSuite with PerTest with BeforeAndAfterAll {
     assert(xgbModel.getNumRound === xgbModel2.getNumRound)
     assert(xgbModel.getRawPredictionCol === xgbModel2.getRawPredictionCol)
   }
+
+  test("cross-version model loading (0.82)") {
+    val modelPath = getClass.getResource("/model/0.82/model").getPath
+    val model = XGBoostClassificationModel.read.load(modelPath)
+    val r = new Random(0)
+    val df = ss.createDataFrame(Seq.fill(100)(r.nextInt(2)).map(i => (i, i))).
+      toDF("feature", "label")
+    val assembler = new VectorAssembler()
+      .setInputCols(df.columns.filter(!_.contains("label")))
+      .setOutputCol("features")
+    model.transform(assembler.transform(df)).show()
+  }
 }
 

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

@@ -261,6 +261,7 @@ class XGBoostGeneralSuite extends FunSuite with PerTest {
     val vectorAssembler = new VectorAssembler()
       .setInputCols(Array("col1", "col2", "col3"))
       .setOutputCol("features")
+      .setHandleInvalid("keep")
     val inputDF = vectorAssembler.transform(testDF).select("features", "label")
     val paramMap = List("eta" -> "1", "max_depth" -> "2",
       "objective" -> "binary:logistic", "num_workers" -> 1).toMap