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[jvm-packages] automatically set the max/min direction for best score (#9404)

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Bobby Wang
2023-07-27 11:09:55 +08:00
committed by GitHub
parent 7579905e18
commit 8f0efb4ab3
4 changed files with 194 additions and 42 deletions
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23
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
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@@ -23,7 +23,6 @@ import scala.util.Random
import scala.collection.JavaConverters._
import ml.dmlc.xgboost4j.java.{Communicator, IRabitTracker, XGBoostError, RabitTracker => PyRabitTracker}
import ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams
import ml.dmlc.xgboost4j.scala.ExternalCheckpointManager
import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
@@ -55,9 +54,6 @@ object TrackerConf {
def apply(): TrackerConf = TrackerConf(0L)
}
private[scala] case class XGBoostExecutionEarlyStoppingParams(numEarlyStoppingRounds: Int,
maximizeEvalMetrics: Boolean)
private[scala] case class XGBoostExecutionInputParams(trainTestRatio: Double, seed: Long)
private[scala] case class XGBoostExecutionParams(
@@ -71,7 +67,7 @@ private[scala] case class XGBoostExecutionParams(
trackerConf: TrackerConf,
checkpointParam: Option[ExternalCheckpointParams],
xgbInputParams: XGBoostExecutionInputParams,
earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
earlyStoppingRounds: Int,
cacheTrainingSet: Boolean,
device: Option[String],
isLocal: Boolean,
@@ -146,15 +142,8 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
val numEarlyStoppingRounds = overridedParams.getOrElse(
"num_early_stopping_rounds", 0).asInstanceOf[Int]
overridedParams += "num_early_stopping_rounds" -> numEarlyStoppingRounds
if (numEarlyStoppingRounds > 0 &&
!overridedParams.contains("maximize_evaluation_metrics")) {
if (overridedParams.getOrElse("custom_eval", null) != null) {
if (numEarlyStoppingRounds > 0 && overridedParams.getOrElse("custom_eval", null) != null) {
throw new IllegalArgumentException("custom_eval does not support early stopping")
}
val eval_metric = overridedParams("eval_metric").toString
val maximize = LearningTaskParams.evalMetricsToMaximize contains eval_metric
logger.info("parameter \"maximize_evaluation_metrics\" is set to " + maximize)
overridedParams += ("maximize_evaluation_metrics" -> maximize)
}
overridedParams
}
@@ -213,10 +202,6 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
val earlyStoppingRounds = overridedParams.getOrElse(
"num_early_stopping_rounds", 0).asInstanceOf[Int]
val maximizeEvalMetrics = overridedParams.getOrElse(
"maximize_evaluation_metrics", true).asInstanceOf[Boolean]
val xgbExecEarlyStoppingParams = XGBoostExecutionEarlyStoppingParams(earlyStoppingRounds,
maximizeEvalMetrics)
val cacheTrainingSet = overridedParams.getOrElse("cache_training_set", false)
.asInstanceOf[Boolean]
@@ -232,7 +217,7 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
missing, allowNonZeroForMissing, trackerConf,
checkpointParam,
inputParams,
xgbExecEarlyStoppingParams,
earlyStoppingRounds,
cacheTrainingSet,
device,
isLocal,
@@ -319,7 +304,7 @@ object XGBoost extends Serializable {
watches = buildWatchesAndCheck(buildWatches)
val numEarlyStoppingRounds = xgbExecutionParam.earlyStoppingParams.numEarlyStoppingRounds
val numEarlyStoppingRounds = xgbExecutionParam.earlyStoppingRounds
val metrics = Array.tabulate(watches.size)(_ => Array.ofDim[Float](numRounds))
val externalCheckpointParams = xgbExecutionParam.checkpointParam

4
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
View File

@@ -112,8 +112,4 @@ private[spark] object LearningTaskParams {
val supportedObjectiveType = HashSet("regression", "classification")
val evalMetricsToMaximize = HashSet("auc", "aucpr", "ndcg", "map")
val evalMetricsToMinimize = HashSet("rmse", "rmsle", "mae", "mape", "logloss", "error", "merror",
"mlogloss", "gamma-deviance")
}