[jvm-packages] Fix "obj_type" error to enable custom objectives and evaluations (#3646)

credits to @mmui

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

@@ -227,9 +227,9 @@ object XGBoost extends Serializable {
     }
     require(nWorkers > 0, "you must specify more than 0 workers")
     if (obj != null) {
-      require(params.get("obj_type").isDefined, "parameter \"obj_type\" is not defined," +
+      require(params.get("objective_type").isDefined, "parameter \"objective_type\" is not" +
-        " you have to specify the objective type as classification or regression with a" +
+        " defined, you have to specify the objective type as classification or regression" +
-        " customized objective function")
+        " with a customized objective function")
     }
     val trackerConf = params.get("tracker_conf") match {
       case None => TrackerConf()

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

@@ -130,6 +130,8 @@ class XGBoostClassifier (
   // setters for learning params
   def setObjective(value: String): this.type = set(objective, value)
 
+  def setObjectiveType(value: String): this.type = set(objectiveType, value)
+
   def setBaseScore(value: Double): this.type = set(baseScore, value)
 
   def setEvalMetric(value: String): this.type = set(evalMetric, value)
@@ -160,6 +162,10 @@ class XGBoostClassifier (
       set(evalMetric, setupDefaultEvalMetric())
     }
 
+    if (isDefined(customObj) && $(customObj) != null) {
+      set(objectiveType, "classification")
+    }
+
     val _numClasses = getNumClasses(dataset)
     if (isDefined(numClass) && $(numClass) != _numClasses) {
       throw new Exception("The number of classes in dataset doesn't match " +

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

@@ -130,6 +130,8 @@ class XGBoostRegressor (
   // setters for learning params
   def setObjective(value: String): this.type = set(objective, value)
 
+  def setObjectiveType(value: String): this.type = set(objectiveType, value)
+
   def setBaseScore(value: Double): this.type = set(baseScore, value)
 
   def setEvalMetric(value: String): this.type = set(evalMetric, value)
@@ -158,6 +160,10 @@ class XGBoostRegressor (
       set(evalMetric, setupDefaultEvalMetric())
     }
 
+    if (isDefined(customObj) && $(customObj) != null) {
+      set(objectiveType, "regression")
+    }
+
     val weight = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
     val baseMargin = if (!isDefined(baseMarginCol) || $(baseMarginCol).isEmpty) {
       lit(Float.NaN)

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

@@ -33,6 +33,18 @@ private[spark] trait LearningTaskParams extends Params {
 
   final def getObjective: String = $(objective)
 
+  /**
+   * The learning objective type of the specified custom objective and eval.
+   * Corresponding type will be assigned if custom objective is defined
+   * options: regression, classification. default: null
+   */
+  final val objectiveType = new Param[String](this, "objectiveType", "objective type used for " +
+    s"training, options: {${LearningTaskParams.supportedObjectiveType.mkString(",")}",
+    (value: String) => LearningTaskParams.supportedObjectiveType.contains(value))
+
+  final def getObjectiveType: String = $(objectiveType)
+
+
   /**
    * the initial prediction score of all instances, global bias. default=0.5
    */
@@ -84,6 +96,8 @@ private[spark] object LearningTaskParams {
     "binary:logitraw", "count:poisson", "multi:softmax", "multi:softprob", "rank:pairwise",
     "rank:ndcg", "rank:map", "reg:gamma", "reg:tweedie")
 
+  val supportedObjectiveType = HashSet("regression", "classification")
+
   val supportedEvalMetrics = HashSet("rmse", "mae", "logloss", "error", "merror", "mlogloss",
     "auc", "aucpr", "ndcg", "map", "gamma-deviance")
 }

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

@@ -140,15 +140,18 @@ class XGBoostClassifierSuite extends FunSuite with PerTest {
   }
 
   test("XGBoost and Spark parameters synchronize correctly") {
-    val xgbParamMap = Map("eta" -> "1", "objective" -> "binary:logistic")
+    val xgbParamMap = Map("eta" -> "1", "objective" -> "binary:logistic",
+      "objective_type" -> "classification")
     // from xgboost params to spark params
     val xgb = new XGBoostClassifier(xgbParamMap)
     assert(xgb.getEta === 1.0)
     assert(xgb.getObjective === "binary:logistic")
+    assert(xgb.getObjectiveType === "classification")
     // from spark to xgboost params
     val xgbCopy = xgb.copy(ParamMap.empty)
     assert(xgbCopy.MLlib2XGBoostParams("eta").toString.toDouble === 1.0)
     assert(xgbCopy.MLlib2XGBoostParams("objective").toString === "binary:logistic")
+    assert(xgbCopy.MLlib2XGBoostParams("objective_type").toString === "classification")
     val xgbCopy2 = xgb.copy(ParamMap.empty.put(xgb.evalMetric, "logloss"))
     assert(xgbCopy2.MLlib2XGBoostParams("eval_metric").toString === "logloss")
   }