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_r259622126
##########
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")
Review comment:
There is no 2.5.0; this would have to be 3.0.0
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