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https://issues.apache.org/jira/browse/SPARK-3568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14160022#comment-14160022
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Apache Spark commented on SPARK-3568:
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User 'coderxiang' has created a pull request for this issue:
https://github.com/apache/spark/pull/2667
> Add metrics for ranking algorithms
> ----------------------------------
>
> Key: SPARK-3568
> URL: https://issues.apache.org/jira/browse/SPARK-3568
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Shuo Xiang
> Assignee: Shuo Xiang
>
> Include 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
> This implementation attempts to create a new class called *RankingMetrics*
> under *org.apache.spark.mllib.evaluation*, which accepts input (prediction
> and label pairs) as *RDD[Array[Double], Array[Double]]*. The following
> methods will be implemented:
> {code:title=RankingMetrics.scala|borderStyle=solid}
> 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
> }
> {code}
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