[SPARK-23152][ML] - Correctly guard against empty datasets

## What changes were proposed in this pull request?

Correctly guard against empty datasets in `org.apache.spark.ml.classification.Classifier`

## How was this patch tested?

existing tests

Author: Matthew Tovbin <mtovbin@salesforce.com>

Closes #20321 from tovbinm/SPARK-23152.

mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala

@@ -109,7 +109,7 @@ abstract class Classifier[
       case None =>
         // Get number of classes from dataset itself.
         val maxLabelRow: Array[Row] = dataset.select(max($(labelCol))).take(1)
-        if (maxLabelRow.isEmpty) {
+        if (maxLabelRow.isEmpty || maxLabelRow(0).get(0) == null) {
           throw new SparkException("ML algorithm was given empty dataset.")
         }
         val maxDoubleLabel: Double = maxLabelRow.head.getDouble(0)

mllib/src/test/scala/org/apache/spark/ml/classification/ClassifierSuite.scala

@@ -90,6 +90,13 @@ class ClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
       }
       assert(e.getMessage.contains("requires integers in range"))
     }
+    val df3 = getTestData(Seq.empty[Double])
+    withClue("getNumClasses should fail if dataset is empty") {
+      val e: SparkException = intercept[SparkException] {
+        c.getNumClasses(df3)
+      }
+      assert(e.getMessage == "ML algorithm was given empty dataset.")
+    }
   }
 }