[GitHub] srowen commented on a change in pull request #23881: [SPARK-26981][MLlib] Add 'Recall_at_k' metric to RankingMetrics

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srowen commented on a change in pull request #23881: [SPARK-26981][MLlib] Add 'Recall_at_k' metric to RankingMetrics
URL: https://github.com/apache/spark/pull/23881#discussion_r259622175
 
 

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 File path: mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
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 @@ -157,6 +157,46 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
     }.mean()
   }
 
+  /**
+    * Compute the average recall of all the queries, truncated at ranking position k.
+    *
+    * If for a query, the ranking algorithm returns n results, the recall value will be
+    * computed as #(relevant items retrieved) / #(ground truth set). This formula
+    * also applies when the size of the ground truth set is less than k.
+    *
+    * If a query has an empty ground truth set, zero will be used as recall together with
+    * a log warning.
+    *
+    * See the following paper for detail:
+    *
+    * IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
+    *
+    * @param k the position to compute the truncated recall, must be positive
+    * @return the average recall at the first k ranking positions
+    */
+  @Since("2.5.0")
+  def recallAt(k: Int): Double = {
+    require(k > 0, "ranking position k should be positive")
+    predictionAndLabels.map { case (pred, lab) =>
 
 Review comment:
   This is so similar to precision at k that they should be refactored to use one common private implementation method for 90% of it.

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