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[jvm-packages] Support Ranker (#10823)
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124
...packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRanker.scala
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| /* | ||
| Copyright (c) 2024 by Contributors | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| */ | ||
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| package ml.dmlc.xgboost4j.scala.spark | ||
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| import org.apache.spark.ml.{PredictionModel, Predictor} | ||
| import org.apache.spark.ml.linalg.Vector | ||
| import org.apache.spark.ml.param.ParamMap | ||
| import org.apache.spark.ml.util.{DefaultParamsReadable, Identifiable, MLReadable, MLReader} | ||
| import org.apache.spark.ml.xgboost.SparkUtils | ||
| import org.apache.spark.sql.Dataset | ||
| import ml.dmlc.xgboost4j.scala.Booster | ||
| import ml.dmlc.xgboost4j.scala.spark.XGBoostRanker._uid | ||
| import ml.dmlc.xgboost4j.scala.spark.params.HasGroupCol | ||
| import ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams.RANKER_OBJS | ||
| import org.apache.spark.sql.types.{DataType, DoubleType, StructType} | ||
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| class XGBoostRanker(override val uid: String, | ||
| private val xgboostParams: Map[String, Any]) | ||
| extends Predictor[Vector, XGBoostRanker, XGBoostRankerModel] | ||
| with XGBoostEstimator[XGBoostRanker, XGBoostRankerModel] with HasGroupCol { | ||
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| def this() = this(_uid, Map[String, Any]()) | ||
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| def this(uid: String) = this(uid, Map[String, Any]()) | ||
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| def this(xgboostParams: Map[String, Any]) = this(_uid, xgboostParams) | ||
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| def setGroupCol(value: String): XGBoostRanker = set(groupCol, value) | ||
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| xgboost2SparkParams(xgboostParams) | ||
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| /** | ||
| * Validate the parameters before training, throw exception if possible | ||
| */ | ||
| override protected[spark] def validate(dataset: Dataset[_]): Unit = { | ||
| super.validate(dataset) | ||
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| require(isDefinedNonEmpty(groupCol), "groupCol needs to be set") | ||
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| // If the objective is set explicitly, it must be in RANKER_OBJS | ||
| if (isSet(objective)) { | ||
| val tmpObj = getObjective | ||
| require(RANKER_OBJS.contains(tmpObj), | ||
| s"Wrong objective for XGBoostRanker, supported objs: ${RANKER_OBJS.mkString(",")}") | ||
| } else { | ||
| setObjective("rank:ndcg") | ||
| } | ||
| } | ||
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| /** | ||
| * Sort partition for Ranker issue. | ||
| * | ||
| * @param dataset | ||
| * @return | ||
| */ | ||
| override private[spark] def sortPartitionIfNeeded(dataset: Dataset[_]) = { | ||
| dataset.sortWithinPartitions(getGroupCol) | ||
| } | ||
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| override protected def createModel( | ||
| booster: Booster, | ||
| summary: XGBoostTrainingSummary): XGBoostRankerModel = { | ||
| new XGBoostRankerModel(uid, booster, Option(summary)) | ||
| } | ||
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| override protected def validateAndTransformSchema( | ||
| schema: StructType, | ||
| fitting: Boolean, | ||
| featuresDataType: DataType): StructType = | ||
| SparkUtils.appendColumn(schema, $(predictionCol), DoubleType) | ||
| } | ||
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| object XGBoostRanker extends DefaultParamsReadable[XGBoostRanker] { | ||
| private val _uid = Identifiable.randomUID("xgbranker") | ||
| } | ||
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| class XGBoostRankerModel private[ml](val uid: String, | ||
| val nativeBooster: Booster, | ||
| val summary: Option[XGBoostTrainingSummary] = None) | ||
| extends PredictionModel[Vector, XGBoostRankerModel] | ||
| with RankerRegressorBaseModel[XGBoostRankerModel] with HasGroupCol { | ||
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| def this(uid: String) = this(uid, null) | ||
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| def setGroupCol(value: String): XGBoostRankerModel = set(groupCol, value) | ||
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| override def copy(extra: ParamMap): XGBoostRankerModel = { | ||
| val newModel = copyValues(new XGBoostRankerModel(uid, nativeBooster, summary), extra) | ||
| newModel.setParent(parent) | ||
| } | ||
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| override def predict(features: Vector): Double = { | ||
| val values = predictSingleInstance(features) | ||
| values(0) | ||
| } | ||
| } | ||
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| object XGBoostRankerModel extends MLReadable[XGBoostRankerModel] { | ||
| override def read: MLReader[XGBoostRankerModel] = new ModelReader | ||
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| private class ModelReader extends XGBoostModelReader[XGBoostRankerModel] { | ||
| override def load(path: String): XGBoostRankerModel = { | ||
| val xgbModel = loadBooster(path) | ||
| val meta = SparkUtils.loadMetadata(path, sc) | ||
| val model = new XGBoostRankerModel(meta.uid, xgbModel, None) | ||
| meta.getAndSetParams(model) | ||
| model | ||
| } | ||
| } | ||
| } |
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