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[Breaking] Remove rabit support for custom reductions and grow_local_histmaker updater (#7992)

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This commit is contained in:
Rong Ou
2022-06-21 00:08:23 -07:00
committed by GitHub
parent 4a87ea49b8
commit e5ec546da5
17 changed files with 36 additions and 1100 deletions
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2
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala
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@@ -100,8 +100,6 @@ class XGBoostClassifier (
def setMaxLeaves(value: Int): this.type = set(maxLeaves, value)
def setSketchEps(value: Double): this.type = set(sketchEps, value)
def setScalePosWeight(value: Double): this.type = set(scalePosWeight, value)
def setSampleType(value: String): this.type = set(sampleType, value)

2
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala
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@@ -102,8 +102,6 @@ class XGBoostRegressor (
def setMaxLeaves(value: Int): this.type = set(maxLeaves, value)
def setSketchEps(value: Double): this.type = set(sketchEps, value)
def setScalePosWeight(value: Double): this.type = set(scalePosWeight, value)
def setSampleType(value: String): this.type = set(sampleType, value)

14
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala
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@@ -182,20 +182,6 @@ private[spark] trait BoosterParams extends Params {
final def getSinglePrecisionHistogram: Boolean = $(singlePrecisionHistogram)
/**
* This is only used for approximate greedy algorithm.
* This roughly translated into O(1 / sketch_eps) number of bins. Compared to directly select
* number of bins, this comes with theoretical guarantee with sketch accuracy.
* [default=0.03] range: (0, 1)
*/
final val sketchEps = new DoubleParam(this, "sketchEps",
"This is only used for approximate greedy algorithm. This roughly translated into" +
" O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes" +
" with theoretical guarantee with sketch accuracy.",
(value: Double) => value < 1 && value > 0)
final def getSketchEps: Double = $(sketchEps)
/**
* Control the balance of positive and negative weights, useful for unbalanced classes. A typical
* value to consider: sum(negative cases) / sum(positive cases). [default=1]