[SPARK-17141][ML] MinMaxScaler should remain NaN value.

## What changes were proposed in this pull request?
In the existing code, ```MinMaxScaler``` handle ```NaN``` value indeterminately.
* If a column has identity value, that is ```max == min```, ```MinMaxScalerModel``` transformation will output ```0.5``` for all rows even the original value is ```NaN```.
* Otherwise, it will remain ```NaN``` after transformation.

I think we should unify the behavior by remaining ```NaN``` value at any condition, since we don't know how to transform a ```NaN``` value. In Python sklearn, it will throw exception when there is ```NaN``` in the dataset.

## How was this patch tested?
Unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14716 from yanboliang/spark-17141.

mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala

@@ -186,8 +186,10 @@ class MinMaxScalerModel private[ml] (
       val size = values.length
       var i = 0
       while (i < size) {
-        val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) / originalRange(i) else 0.5
-        values(i) = raw * scale + $(min)
+        if (!values(i).isNaN) {
+          val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) / originalRange(i) else 0.5
+          values(i) = raw * scale + $(min)
+        }
         i += 1
       }
       Vectors.dense(values)

mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala

@@ -90,4 +90,31 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with De
     assert(newInstance.originalMin === instance.originalMin)
     assert(newInstance.originalMax === instance.originalMax)
   }
+
+  test("MinMaxScaler should remain NaN value") {
+    val data = Array(
+      Vectors.dense(1, Double.NaN, 2.0, 2.0),
+      Vectors.dense(2, 2.0, 0.0, 3.0),
+      Vectors.dense(3, Double.NaN, 0.0, 1.0),
+      Vectors.dense(6, 2.0, 2.0, Double.NaN))
+
+    val expected: Array[Vector] = Array(
+      Vectors.dense(-5.0, Double.NaN, 5.0, 0.0),
+      Vectors.dense(-3.0, 0.0, -5.0, 5.0),
+      Vectors.dense(-1.0, Double.NaN, -5.0, -5.0),
+      Vectors.dense(5.0, 0.0, 5.0, Double.NaN))
+
+    val df = spark.createDataFrame(data.zip(expected)).toDF("features", "expected")
+    val scaler = new MinMaxScaler()
+      .setInputCol("features")
+      .setOutputCol("scaled")
+      .setMin(-5)
+      .setMax(5)
+
+    val model = scaler.fit(df)
+    model.transform(df).select("expected", "scaled").collect()
+      .foreach { case Row(vector1: Vector, vector2: Vector) =>
+        assert(vector1.equals(vector2), "Transformed vector is different with expected.")
+      }
+  }
 }