SPARK-3568 [mllib] add ranking metrics

Add common metrics for ranking algorithms (http://www-nlp.stanford.edu/IR-book/), including:
 - Mean Average Precision
 - Precision@n: top-n precision
 - Discounted cumulative gain (DCG) and NDCG 

The following methods and the corresponding tests are implemented:

```
class RankingMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
  /* Returns the precsion@k for each query */
  lazy val precAtK: RDD[Array[Double]]

  /* Returns the average precision for each query */
  lazy val avePrec: RDD[Double]

  /*Returns the mean average precision (MAP) of all the queries*/
  lazy val meanAvePrec: Double

  /*Returns the normalized discounted cumulative gain for each query */
  lazy val ndcg: RDD[Double]

  /* Returns the mean NDCG of all the queries */
  lazy val meanNdcg: Double
}
```

Given my previous comment, maybe I'm missing something, but isn't one of the two arguments always going to be 1 to n? either you are ranking the predicted top n versus real rankings, or evaluating the predicted ranking of the known top n... ? I think I would have expected the input to be the predicted top n items by ID or something, and the IDs of the real top n, and then making a set and `contains` makes some sense.