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[T](predictionAndLabels: RDD[(Array[T], Array[T])]) {
  /* Returns the precsion@k for each query */
  lazy val precAtK: RDD[Array[Double]]

  /**
   * @param k the position to compute the truncated precision
   * @return the average precision at the first k ranking positions
   */
  def precision(k: Int): 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 ndcgAtK: RDD[Array[Double]]

  /**
   * @param k the position to compute the truncated ndcg
   * @return the average ndcg at the first k ranking positions
   */
  def ndcg(k: Int): Double
}
```

If `k` is given, we only need to compute `topKPrec(k-1)` for each record. We can do

1. compute on-demand, every call requires a pass.
2. compute average `prec@k` for all possible k and cache the result. Then simply return the cached result.

The first approach may be good for this PR. We can optimize it later.