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rank_metric: add AUC-PR (#3172)

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* rank_metric: add AUC-PR

Implementation of the AUC-PR calculation for weighted data, proposed by Keilwagen, Grosse and Grau (https://doi.org/10.1371/journal.pone.0092209)

* rank_metric: fix lint warnings

* Implement tests for AUC-PR and fix implementation

* add aucpr to documentation for other languages
This commit is contained in:
Arjan van der Velde
2018-03-23 10:43:47 -04:00
committed by Yuan (Terry) Tang
parent 8fb3388af2
commit 04221a7469
7 changed files with 121 additions and 2 deletions
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5
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
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@@ -45,7 +45,8 @@ trait LearningTaskParams extends Params {
/**
* evaluation metrics for validation data, a default metric will be assigned according to
* objective(rmse for regression, and error for classification, mean average precision for
* ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, ndcg, map, gamma-deviance
* ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map,
* gamma-deviance
*/
val evalMetric = new Param[String](this, "eval_metric", "evaluation metrics for validation" +
" data, a default metric will be assigned according to objective (rmse for regression, and" +
@@ -97,5 +98,5 @@ private[spark] object LearningTaskParams {
"reg:gamma")
val supportedEvalMetrics = HashSet("rmse", "mae", "logloss", "error", "merror", "mlogloss",
"auc", "ndcg", "map", "gamma-deviance")
"auc", "aucpr", "ndcg", "map", "gamma-deviance")
}