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[jvm-packages] XGBoost Spark integration refactor #3387

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merged 9 commits into from
Jun 18, 2018
Merged

[jvm-packages] XGBoost Spark integration refactor #3387

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8d0e14d
add back train method but mark as deprecated
CodingCat Sep 14, 2016
2b862e2
fix scalastyle error
CodingCat Oct 10, 2016
1c4685f
add back train method but mark as deprecated
CodingCat Sep 14, 2016
2fe34d2
fix scalastyle error
CodingCat Oct 10, 2016
7300f5e
[jvm-packages] XGBoost Spark integration refactor. (#3313)
yanboliang May 12, 2018
af3b980
[jvm-packages] Refactor XGBoost-Spark params to make it compatible wi…
yanboliang May 23, 2018
6697cfc
[jvm-packages] XGBoost Spark supports ranking with group data. (#3369)
yanboliang Jun 15, 2018
404f60b
Update CheckpointManagerSuite.scala
yanboliang Jun 15, 2018
c1ff626
Resolve conflicts
yanboliang Jun 15, 2018
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6 changes: 3 additions & 3 deletions ...j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkModelTuningTool.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ import scala.collection.mutable
import scala.collection.mutable.ListBuffer
import scala.io.Source

import ml.dmlc.xgboost4j.scala.spark.{XGBoostEstimator, XGBoost}
import ml.dmlc.xgboost4j.scala.spark.XGBoostRegressor
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.{VectorAssembler, StringIndexer}
Expand Down Expand Up @@ -160,10 +160,10 @@ object SparkModelTuningTool {
private def crossValidation(
xgboostParam: Map[String, Any],
trainingData: Dataset[_]): TrainValidationSplitModel = {
val xgbEstimator = new XGBoostEstimator(xgboostParam).setFeaturesCol("features").
val xgbEstimator = new XGBoostRegressor(xgboostParam).setFeaturesCol("features").
setLabelCol("logSales")
val paramGrid = new ParamGridBuilder()
.addGrid(xgbEstimator.round, Array(20, 50))
.addGrid(xgbEstimator.numRound, Array(20, 50))
.addGrid(xgbEstimator.eta, Array(0.1, 0.4))
.build()
val tv = new TrainValidationSplit()
Expand Down
9 changes: 5 additions & 4 deletions ...t4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkWithDataFrame.scala
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Expand Up @@ -17,7 +17,7 @@
package ml.dmlc.xgboost4j.scala.example.spark

import ml.dmlc.xgboost4j.scala.Booster
import ml.dmlc.xgboost4j.scala.spark.XGBoost
import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
import org.apache.spark.sql.SparkSession
import org.apache.spark.SparkConf

Expand Down Expand Up @@ -45,9 +45,10 @@ object SparkWithDataFrame {
val paramMap = List(
"eta" -> 0.1f,
"max_depth" -> 2,
"objective" -> "binary:logistic").toMap
val xgboostModel = XGBoost.trainWithDataFrame(
trainDF, paramMap, numRound, nWorkers = args(1).toInt, useExternalMemory = true)
"objective" -> "binary:logistic",
"num_round" -> numRound,
"nWorkers" -> args(1).toInt).toMap
val xgboostModel = new XGBoostClassifier(paramMap).fit(trainDF)
// xgboost-spark appends the column containing prediction results
xgboostModel.transform(testDF).show()
}
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58 changes: 0 additions & 58 deletions ...xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkWithRDD.scala
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15 changes: 8 additions & 7 deletions ...ages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/CheckpointManager.scala
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Expand Up @@ -17,6 +17,7 @@
package ml.dmlc.xgboost4j.scala.spark

import ml.dmlc.xgboost4j.scala.Booster
import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost}
import org.apache.commons.logging.LogFactory
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.SparkContext
Expand Down Expand Up @@ -63,9 +64,9 @@ private[spark] class CheckpointManager(sc: SparkContext, checkpointPath: String)
val version = versions.max
val fullPath = getPath(version)
logger.info(s"Start training from previous booster at $fullPath")
val model = XGBoost.loadModelFromHadoopFile(fullPath)(sc)
model.booster.booster.setVersion(version)
model.booster
val booster = SXGBoost.loadModel(fullPath)
booster.booster.setVersion(version)
booster
} else {
null
}
Expand All @@ -76,12 +77,12 @@ private[spark] class CheckpointManager(sc: SparkContext, checkpointPath: String)
*
* @param checkpoint the checkpoint to save as an XGBoostModel
*/
private[spark] def updateCheckpoint(checkpoint: XGBoostModel): Unit = {
private[spark] def updateCheckpoint(checkpoint: Booster): Unit = {
val fs = FileSystem.get(sc.hadoopConfiguration)
val prevModelPaths = getExistingVersions.map(version => new Path(getPath(version)))
val fullPath = getPath(checkpoint.version)
logger.info(s"Saving checkpoint model with version ${checkpoint.version} to $fullPath")
checkpoint.saveModelAsHadoopFile(fullPath)(sc)
val fullPath = getPath(checkpoint.getVersion)
logger.info(s"Saving checkpoint model with version ${checkpoint.getVersion} to $fullPath")
checkpoint.saveModel(fullPath)
prevModelPaths.foreach(path => fs.delete(path, true))
}

Expand Down
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