[SPARK-15318][ML][EXAMPLE] spark.ml Collaborative Filtering example does not work in spark-shell

## What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)

copy & paste example in ml-collaborative-filtering.html into spark-shell, we see the following errors.
scala> case class Rating(userId: Int, movieId: Int, rating: Float, timestamp: Long)
defined class Rating

scala> object Rating {
def parseRating(str: String): Rating = { | val fields = str.split("::") | assert(fields.size == 4) | Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat, fields(3).toLong) | }
}
<console>:29: error: Rating.type does not take parameters
Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat, fields(3).toLong)
^
In standard scala repl, it has the same error.

Scala/spark-shell repl has some quirks (e.g. packages are also not well supported).

The reason of errors is that scala/spark-shell repl discards previous definitions when we define the Object with the same class name. Solution: We can rename the Object Rating.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

Manually test it: 1). ./bin/run-example ALSExample
2). copy & paste example in the generated document. It works fine.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #13110 from wangmiao1981/repl.

examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala

@@ -24,16 +24,21 @@ import org.apache.spark.ml.recommendation.ALS
 // $example off$
 import org.apache.spark.sql.SparkSession
 
+/**
+ * An example demonstrating ALS.
+ * Run with
+ * {{{
+ * bin/run-example ml.ALSExample
+ * }}}
+ */
 object ALSExample {
 
   // $example on$
   case class Rating(userId: Int, movieId: Int, rating: Float, timestamp: Long)
-  object Rating {
-    def parseRating(str: String): Rating = {
-      val fields = str.split("::")
-      assert(fields.size == 4)
-      Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat, fields(3).toLong)
-    }
+  def parseRating(str: String): Rating = {
+    val fields = str.split("::")
+    assert(fields.size == 4)
+    Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat, fields(3).toLong)
   }
   // $example off$
 
@@ -46,7 +51,7 @@ object ALSExample {
 
     // $example on$
     val ratings = spark.read.text("data/mllib/als/sample_movielens_ratings.txt")
-      .map(Rating.parseRating)
+      .map(parseRating)
       .toDF()
     val Array(training, test) = ratings.randomSplit(Array(0.8, 0.2))