[jvm-packages] Fix wrong method name setAllowZeroForMissingValue. (#5740)

* Allow non-zero for missing value when training.

* Fix wrong method names.

* Add a unit test

* Move the getter/setter unit test to MissingValueHandlingSuite

Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>

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

@@ -141,6 +141,11 @@ class XGBoostClassifier (
 
   def setCustomEval(value: EvalTrait): this.type = set(customEval, value)
 
+  def setAllowNonZeroForMissing(value: Boolean): this.type = set(
+    allowNonZeroForMissing,
+    value
+  )
+
   def setSinglePrecisionHistogram(value: Boolean): this.type =
     set(singlePrecisionHistogram, value)
 
@@ -248,7 +253,7 @@ class XGBoostClassificationModel private[ml](
 
   def setMissing(value: Float): this.type = set(missing, value)
 
-  def setAllowZeroForMissingValue(value: Boolean): this.type = set(
+  def setAllowNonZeroForMissing(value: Boolean): this.type = set(
     allowNonZeroForMissing,
     value
   )

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

@@ -145,6 +145,11 @@ class XGBoostRegressor (
 
   def setCustomEval(value: EvalTrait): this.type = set(customEval, value)
 
+  def setAllowNonZeroForMissing(value: Boolean): this.type = set(
+    allowNonZeroForMissing,
+    value
+  )
+
   def setSinglePrecisionHistogram(value: Boolean): this.type =
     set(singlePrecisionHistogram, value)
 
@@ -244,7 +249,7 @@ class XGBoostRegressionModel private[ml] (
 
   def setMissing(value: Float): this.type = set(missing, value)
 
-  def setAllowZeroForMissingValue(value: Boolean): this.type = set(
+  def setAllowNonZeroForMissing(value: Boolean): this.type = set(
     allowNonZeroForMissing,
     value
   )

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

@@ -198,4 +198,38 @@ class MissingValueHandlingSuite extends FunSuite with PerTest {
     val model = new XGBoostClassifier(paramMap).fit(inputDF)
     model.transform(inputDF).collect()
   }
+
+  test("Getter and setter for AllowNonZeroForMissingValue works") {
+    {
+      val paramMap = Map("eta" -> "1", "max_depth" -> "6",
+        "objective" -> "binary:logistic", "num_round" -> 5, "num_workers" -> numWorkers)
+      val training = buildDataFrame(Classification.train)
+      val classifier = new XGBoostClassifier(paramMap)
+      classifier.setAllowNonZeroForMissing(true)
+      assert(classifier.getAllowNonZeroForMissingValue)
+      classifier.setAllowNonZeroForMissing(false)
+      assert(!classifier.getAllowNonZeroForMissingValue)
+      val model = classifier.fit(training)
+      model.setAllowNonZeroForMissing(true)
+      assert(model.getAllowNonZeroForMissingValue)
+      model.setAllowNonZeroForMissing(false)
+      assert(!model.getAllowNonZeroForMissingValue)
+    }
+
+    {
+      val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
+        "objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
+      val training = buildDataFrame(Regression.train)
+      val regressor = new XGBoostRegressor(paramMap)
+      regressor.setAllowNonZeroForMissing(true)
+      assert(regressor.getAllowNonZeroForMissingValue)
+      regressor.setAllowNonZeroForMissing(false)
+      assert(!regressor.getAllowNonZeroForMissingValue)
+      val model = regressor.fit(training)
+      model.setAllowNonZeroForMissing(true)
+      assert(model.getAllowNonZeroForMissingValue)
+      model.setAllowNonZeroForMissing(false)
+      assert(!model.getAllowNonZeroForMissingValue)
+    }
+  }
 }