[jvm-packages] fix the prediction issue for multi:softmax (#7694)

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

@@ -385,18 +385,7 @@ class XGBoostClassificationModel private[ml](
       Vectors.dense(rawPredictions)
     }
 
-    val probabilityUDF = udf { probability: mutable.WrappedArray[Float] =>
-      val prob = probability.map(_.toDouble).toArray
-      val probabilities = if (numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
-      Vectors.dense(probabilities)
-    }
 
-    val predictUDF = udf { probability: mutable.WrappedArray[Float] =>
-      // From XGBoost probability to MLlib prediction
-      val prob = probability.map(_.toDouble).toArray
-      val probabilities = if (numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
-      probability2prediction(Vectors.dense(probabilities))
-    }
 
     if ($(rawPredictionCol).nonEmpty) {
       outputData = outputData
@@ -404,16 +393,41 @@ class XGBoostClassificationModel private[ml](
       numColsOutput += 1
     }
 
-    if ($(probabilityCol).nonEmpty) {
-      outputData = outputData
-        .withColumn(getProbabilityCol, probabilityUDF(col(_probabilityCol)))
-      numColsOutput += 1
-    }
+    if (getObjective.equals("multi:softmax")) {
+      // For objective=multi:softmax scenario, there is no probability predicted from xgboost.
+      // Instead, the probability column will be filled with real prediction
+      val predictUDF = udf { probability: mutable.WrappedArray[Float] =>
+        probability(0)
+      }
+      if ($(predictionCol).nonEmpty) {
+        outputData = outputData
+          .withColumn($(predictionCol), predictUDF(col(_probabilityCol)))
+        numColsOutput += 1
+      }
 
-    if ($(predictionCol).nonEmpty) {
-      outputData = outputData
-        .withColumn($(predictionCol), predictUDF(col(_probabilityCol)))
-      numColsOutput += 1
+    } else {
+      val probabilityUDF = udf { probability: mutable.WrappedArray[Float] =>
+        val prob = probability.map(_.toDouble).toArray
+        val probabilities = if (numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
+        Vectors.dense(probabilities)
+      }
+      if ($(probabilityCol).nonEmpty) {
+        outputData = outputData
+          .withColumn(getProbabilityCol, probabilityUDF(col(_probabilityCol)))
+        numColsOutput += 1
+      }
+
+      val predictUDF = udf { probability: mutable.WrappedArray[Float] =>
+        // From XGBoost probability to MLlib prediction
+        val prob = probability.map(_.toDouble).toArray
+        val probabilities = if (numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
+        probability2prediction(Vectors.dense(probabilities))
+      }
+      if ($(predictionCol).nonEmpty) {
+        outputData = outputData
+          .withColumn($(predictionCol), predictUDF(col(_probabilityCol)))
+        numColsOutput += 1
+      }
     }
 
     if (numColsOutput == 0) {