[SPARK-21770][ML] ProbabilisticClassificationModel fix corner case: normalization of all-zero raw predictions

## What changes were proposed in this pull request?

Fix probabilisticClassificationModel corner case: normalization of all-zero raw predictions, throw IllegalArgumentException with description.

## How was this patch tested?

Test case added.

Author: WeichenXu <[email protected]>

Closes apache#19106 from WeichenXu123/SPARK-21770.

Author: WeichenXu123

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

@@ -230,21 +230,23 @@ private[ml] object ProbabilisticClassificationModel {
    * Normalize a vector of raw predictions to be a multinomial probability vector, in place.
    *
    * The input raw predictions should be nonnegative.
-   * The output vector sums to 1, unless the input vector is all-0 (in which case the output is
-   * all-0 too).
+   * The output vector sums to 1.
    *
    * NOTE: This is NOT applicable to all models, only ones which effectively use class
    *       instance counts for raw predictions.
+   *
+   * @throws IllegalArgumentException if the input vector is all-0 or including negative values
    */
   def normalizeToProbabilitiesInPlace(v: DenseVector): Unit = {
+    v.values.foreach(value => require(value >= 0,
+      "The input raw predictions should be nonnegative."))
     val sum = v.values.sum
-    if (sum != 0) {
-      var i = 0
-      val size = v.size
-      while (i < size) {
-        v.values(i) /= sum
-        i += 1
-      }
+    require(sum > 0, "Can't normalize the 0-vector.")
+    var i = 0
+    val size = v.size
+    while (i < size) {
+      v.values(i) /= sum
+      i += 1
     }
   }
 }

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

@@ -80,6 +80,24 @@ class ProbabilisticClassifierSuite extends SparkFunSuite {
       new TestProbabilisticClassificationModel("myuid", 2, 2).setThresholds(Array(-0.1, 0.1))
     }
   }
+
+  test("normalizeToProbabilitiesInPlace") {
+    val vec1 = Vectors.dense(1.0, 2.0, 3.0).toDense
+    ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(vec1)
+    assert(vec1 ~== Vectors.dense(1.0 / 6, 2.0 / 6, 3.0 / 6) relTol 1e-3)
+
+    // all-0 input test
+    val vec2 = Vectors.dense(0.0, 0.0, 0.0).toDense
+    intercept[IllegalArgumentException] {
+      ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(vec2)
+    }
+
+    // negative input test
+    val vec3 = Vectors.dense(1.0, -1.0, 2.0).toDense
+    intercept[IllegalArgumentException] {
+      ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(vec3)
+    }
+  }
 }
 
 object ProbabilisticClassifierSuite {