feat: add custom objective function to lightgbm learners

src/main/scala/com/microsoft/lightgbm/SWIG.scala

@@ -0,0 +1,10 @@
+// Copyright (C) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License. See LICENSE in project root for information.
+
+package com.microsoft.lightgbm
+
+import com.microsoft.ml.lightgbm.SWIGTYPE_p_void
+
+class SwigPtrWrapper(val value: SWIGTYPE_p_void) extends SWIGTYPE_p_void {
+  def getCPtrValue(): Long = SWIGTYPE_p_void.getCPtr(value)
+}

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMBase.scala

@@ -4,6 +4,9 @@
 package com.microsoft.ml.spark.lightgbm
 
 import com.microsoft.ml.spark.core.utils.ClusterUtil
+import com.microsoft.ml.spark.lightgbm.booster.LightGBMBooster
+import com.microsoft.ml.spark.lightgbm.params.{DartModeParams, ExecutionParams, LightGBMParams,
+  ObjectiveParams, TrainParams}
 import com.microsoft.ml.spark.logging.BasicLogging
 import org.apache.spark.ml.attribute.AttributeGroup
 import org.apache.spark.ml.linalg.SQLDataTypes.VectorType
@@ -180,14 +183,30 @@ trait LightGBMBase[TrainedModel <: Model[TrainedModel]] extends Estimator[Traine
     }
   }
 
+  /**
+    * Constructs the DartModeParams
+    * @return DartModeParams object containing parameters related to dart mode.
+    */
   protected def getDartParams(): DartModeParams = {
     DartModeParams(getDropRate, getMaxDrop, getSkipDrop, getXGBoostDartMode, getUniformDrop)
   }
 
+  /**
+    * Constructs the ExecutionParams.
+    * @return ExecutionParams object containing parameters related to LightGBM execution.
+    */
   protected def getExecutionParams(): ExecutionParams = {
     ExecutionParams(getChunkSize, getMatrixType)
   }
 
+  /**
+    * Constructs the ObjectiveParams.
+    * @return ObjectiveParams object containing parameters related to the objective function.
+    */
+  protected def getObjectiveParams(): ObjectiveParams = {
+    ObjectiveParams(getObjective, if (isDefined(fobj)) Some(getFObj) else None)
+  }
+
   /**
     * Inner train method for LightGBM learners.  Calculates the number of workers,
     * creates a driver thread, and runs mapPartitions on the dataset.

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMClassifier.scala

@@ -3,6 +3,9 @@
 
 package com.microsoft.ml.spark.lightgbm
 
+import com.microsoft.ml.spark.lightgbm.booster.LightGBMBooster
+import com.microsoft.ml.spark.lightgbm.params.{ClassifierTrainParams, LightGBMModelParams,
+  LightGBMPredictionParams, TrainParams}
 import com.microsoft.ml.spark.logging.BasicLogging
 import org.apache.spark.ml.{ComplexParamsReadable, ComplexParamsWritable}
 import org.apache.spark.ml.param._
@@ -46,11 +49,11 @@ class LightGBMClassifier(override val uid: String)
     ClassifierTrainParams(getParallelism, getTopK, getNumIterations, getLearningRate, getNumLeaves, getMaxBin,
       getBinSampleCount, getBaggingFraction, getPosBaggingFraction, getNegBaggingFraction,
       getBaggingFreq, getBaggingSeed, getEarlyStoppingRound, getImprovementTolerance,
-      getFeatureFraction, getMaxDepth, getMinSumHessianInLeaf, numTasks, getObjective, modelStr,
+      getFeatureFraction, getMaxDepth, getMinSumHessianInLeaf, numTasks, modelStr,
       getIsUnbalance, getVerbosity, categoricalIndexes, actualNumClasses, getBoostFromAverage,
       getBoostingType, getLambdaL1, getLambdaL2, getIsProvideTrainingMetric,
       getMetric, getMinGainToSplit, getMaxDeltaStep, getMaxBinByFeature, getMinDataInLeaf, getSlotNames,
-      getDelegate, getDartParams(), getExecutionParams())
+      getDelegate, getDartParams(), getExecutionParams(), getObjectiveParams())
   }
 
   def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMClassificationModel = {
@@ -187,7 +190,7 @@ class LightGBMClassificationModel(override val uid: String)
 
 object LightGBMClassificationModel extends ComplexParamsReadable[LightGBMClassificationModel] {
   def loadNativeModelFromFile(filename: String): LightGBMClassificationModel = {
-    val uid = Identifiable.randomUID("LightGBMClassifier")
+    val uid = Identifiable.randomUID("LightGBMClassificationModel")
     val session = SparkSession.builder().getOrCreate()
     val textRdd = session.read.text(filename)
     val text = textRdd.collect().map { row => row.getString(0) }.mkString("\n")
@@ -197,7 +200,7 @@ object LightGBMClassificationModel extends ComplexParamsReadable[LightGBMClassif
   }
 
   def loadNativeModelFromString(model: String): LightGBMClassificationModel = {
-    val uid = Identifiable.randomUID("LightGBMClassifier")
+    val uid = Identifiable.randomUID("LightGBMClassificationModel")
     val lightGBMBooster = new LightGBMBooster(model)
     val actualNumClasses = lightGBMBooster.numClasses
     new LightGBMClassificationModel(uid).setLightGBMBooster(lightGBMBooster).setActualNumClasses(actualNumClasses)

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMDelegate.scala

@@ -3,7 +3,8 @@
 
 package com.microsoft.ml.spark.lightgbm
 
-import com.microsoft.ml.lightgbm.SWIGTYPE_p_void
+import com.microsoft.ml.spark.lightgbm.booster.LightGBMBooster
+import com.microsoft.ml.spark.lightgbm.params.TrainParams
 import org.apache.spark.sql.Dataset
 import org.apache.spark.sql.types.StructType
 import org.slf4j.Logger
@@ -40,12 +41,12 @@ trait LightGBMDelegate extends Serializable {
   }
 
   def beforeTrainIteration(batchIndex: Int, partitionId: Int, curIters: Int, log: Logger,
-                           trainParams: TrainParams, boosterPtr: Option[SWIGTYPE_p_void], hasValid: Boolean): Unit = {
+                           trainParams: TrainParams, booster: LightGBMBooster, hasValid: Boolean): Unit = {
     // override this function and write code
   }
 
   def afterTrainIteration(batchIndex: Int, partitionId: Int, curIters: Int, log: Logger,
-                          trainParams: TrainParams, boosterPtr: Option[SWIGTYPE_p_void], hasValid: Boolean,
+                          trainParams: TrainParams, booster: LightGBMBooster, hasValid: Boolean,
                           isFinished: Boolean,
                           trainEvalResults: Option[Map[String, Double]],
                           validEvalResults: Option[Map[String, Double]]): Unit = {

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMModelMethods.scala

@@ -3,6 +3,7 @@
 
 package com.microsoft.ml.spark.lightgbm
 
+import com.microsoft.ml.spark.lightgbm.params.LightGBMModelParams
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.linalg.{Vector, Vectors}
 

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMRanker.scala

@@ -3,6 +3,9 @@
 
 package com.microsoft.ml.spark.lightgbm
 
+import com.microsoft.ml.spark.lightgbm.booster.LightGBMBooster
+import com.microsoft.ml.spark.lightgbm.params.{LightGBMModelParams, LightGBMPredictionParams,
+  RankerTrainParams, TrainParams}
 import com.microsoft.ml.spark.logging.BasicLogging
 import org.apache.spark.ml.{ComplexParamsReadable, ComplexParamsWritable, Ranker, RankerModel}
 import org.apache.spark.ml.param._
@@ -51,12 +54,13 @@ class LightGBMRanker(override val uid: String)
   def getTrainParams(numTasks: Int, categoricalIndexes: Array[Int], dataset: Dataset[_]): TrainParams = {
     val modelStr = if (getModelString == null || getModelString.isEmpty) None else get(modelString)
     RankerTrainParams(getParallelism, getTopK, getNumIterations, getLearningRate, getNumLeaves,
-      getObjective, getMaxBin, getBinSampleCount, getBaggingFraction, getPosBaggingFraction, getNegBaggingFraction,
+      getMaxBin, getBinSampleCount, getBaggingFraction, getPosBaggingFraction, getNegBaggingFraction,
       getBaggingFreq, getBaggingSeed, getEarlyStoppingRound, getImprovementTolerance,
       getFeatureFraction, getMaxDepth, getMinSumHessianInLeaf, numTasks, modelStr,
       getVerbosity, categoricalIndexes, getBoostingType, getLambdaL1, getLambdaL2, getMaxPosition, getLabelGain,
       getIsProvideTrainingMetric, getMetric, getEvalAt, getMinGainToSplit, getMaxDeltaStep,
-      getMaxBinByFeature, getMinDataInLeaf, getSlotNames, getDelegate, getDartParams(), getExecutionParams())
+      getMaxBinByFeature, getMinDataInLeaf, getSlotNames, getDelegate, getDartParams(), getExecutionParams(),
+      getObjectiveParams())
   }
 
   def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMRankerModel = {
@@ -157,7 +161,7 @@ class LightGBMRankerModel(override val uid: String)
 
 object LightGBMRankerModel extends ComplexParamsReadable[LightGBMRankerModel] {
   def loadNativeModelFromFile(filename: String): LightGBMRankerModel = {
-    val uid = Identifiable.randomUID("LightGBMRanker")
+    val uid = Identifiable.randomUID("LightGBMRankerModel")
     val session = SparkSession.builder().getOrCreate()
     val textRdd = session.read.text(filename)
     val text = textRdd.collect().map { row => row.getString(0) }.mkString("\n")
@@ -166,7 +170,7 @@ object LightGBMRankerModel extends ComplexParamsReadable[LightGBMRankerModel] {
   }
 
   def loadNativeModelFromString(model: String): LightGBMRankerModel = {
-    val uid = Identifiable.randomUID("LightGBMRanker")
+    val uid = Identifiable.randomUID("LightGBMRankerModel")
     val lightGBMBooster = new LightGBMBooster(model)
     new LightGBMRankerModel(uid).setLightGBMBooster(lightGBMBooster)
   }

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMRegressor.scala

@@ -3,6 +3,9 @@
 
 package com.microsoft.ml.spark.lightgbm
 
+import com.microsoft.ml.spark.lightgbm.booster.LightGBMBooster
+import com.microsoft.ml.spark.lightgbm.params.{LightGBMModelParams, LightGBMPredictionParams,
+  RegressorTrainParams, TrainParams}
 import com.microsoft.ml.spark.logging.BasicLogging
 import org.apache.spark.ml.{BaseRegressor, ComplexParamsReadable, ComplexParamsWritable}
 import org.apache.spark.ml.param._
@@ -58,12 +61,12 @@ class LightGBMRegressor(override val uid: String)
   def getTrainParams(numTasks: Int, categoricalIndexes: Array[Int], dataset: Dataset[_]): TrainParams = {
     val modelStr = if (getModelString == null || getModelString.isEmpty) None else get(modelString)
     RegressorTrainParams(getParallelism, getTopK, getNumIterations, getLearningRate, getNumLeaves,
-      getObjective, getAlpha, getTweedieVariancePower, getMaxBin, getBinSampleCount,
-      getBaggingFraction, getPosBaggingFraction, getNegBaggingFraction, getBaggingFreq, getBaggingSeed,
-      getEarlyStoppingRound, getImprovementTolerance, getFeatureFraction, getMaxDepth, getMinSumHessianInLeaf,
-      numTasks, modelStr, getVerbosity, categoricalIndexes, getBoostFromAverage, getBoostingType, getLambdaL1,
-      getLambdaL2, getIsProvideTrainingMetric, getMetric, getMinGainToSplit, getMaxDeltaStep,
-      getMaxBinByFeature, getMinDataInLeaf, getSlotNames, getDelegate, getDartParams(), getExecutionParams())
+      getAlpha, getTweedieVariancePower, getMaxBin, getBinSampleCount, getBaggingFraction, getPosBaggingFraction,
+      getNegBaggingFraction, getBaggingFreq, getBaggingSeed, getEarlyStoppingRound, getImprovementTolerance,
+      getFeatureFraction, getMaxDepth, getMinSumHessianInLeaf, numTasks, modelStr, getVerbosity, categoricalIndexes,
+      getBoostFromAverage, getBoostingType, getLambdaL1, getLambdaL2, getIsProvideTrainingMetric, getMetric,
+      getMinGainToSplit, getMaxDeltaStep, getMaxBinByFeature, getMinDataInLeaf, getSlotNames, getDelegate,
+      getDartParams(), getExecutionParams(), getObjectiveParams())
   }
 
   def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMRegressionModel = {
@@ -134,7 +137,7 @@ class LightGBMRegressionModel(override val uid: String)
 
 object LightGBMRegressionModel extends ComplexParamsReadable[LightGBMRegressionModel] {
   def loadNativeModelFromFile(filename: String): LightGBMRegressionModel = {
-    val uid = Identifiable.randomUID("LightGBMRegressor")
+    val uid = Identifiable.randomUID("LightGBMRegressionModel")
     val session = SparkSession.builder().getOrCreate()
     val textRdd = session.read.text(filename)
     val text = textRdd.collect().map { row => row.getString(0) }.mkString("\n")
@@ -143,7 +146,7 @@ object LightGBMRegressionModel extends ComplexParamsReadable[LightGBMRegressionM
   }
 
   def loadNativeModelFromString(model: String): LightGBMRegressionModel = {
-    val uid = Identifiable.randomUID("LightGBMRegressor")
+    val uid = Identifiable.randomUID("LightGBMRegressionModel")
     val lightGBMBooster = new LightGBMBooster(model)
     new LightGBMRegressionModel(uid).setLightGBMBooster(lightGBMBooster)
   }

src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMUtils.scala

@@ -11,6 +11,8 @@ import com.microsoft.ml.lightgbm._
 import com.microsoft.ml.spark.core.env.NativeLoader
 import com.microsoft.ml.spark.core.utils.ClusterUtil
 import com.microsoft.ml.spark.featurize.{Featurize, FeaturizeUtilities}
+import com.microsoft.ml.spark.lightgbm.dataset.LightGBMDataset
+import com.microsoft.ml.spark.lightgbm.params.TrainParams
 import org.apache.spark.{SparkEnv, TaskContext}
 import org.apache.spark.ml.PipelineModel
 import org.apache.spark.ml.attribute._
@@ -245,7 +247,7 @@ object LightGBMUtils {
       LightGBMUtils.validate(lightgbmlib.LGBM_DatasetCreateFromMats(featuresArray.get_chunks_count().toInt,
         featuresArray.data_as_void(), data64bitType,
         numRowsForChunks, numCols,
-        isRowMajor, datasetParams, referenceDataset.map(_.dataset).orNull, datasetOutPtr),
+        isRowMajor, datasetParams, referenceDataset.map(_.datasetPtr).orNull, datasetOutPtr),
         "Dataset create")
     } finally {
       featuresArray.release()
@@ -275,7 +277,7 @@ object LightGBMUtils {
     LightGBMUtils.validate(lightgbmlib.LGBM_DatasetCreateFromCSRSpark(
       sparseRows.asInstanceOf[Array[Object]],
       sparseRows.length,
-      numCols, datasetParams, referenceDataset.map(_.dataset).orNull,
+      numCols, datasetParams, referenceDataset.map(_.datasetPtr).orNull,
       datasetOutPtr),
       "Dataset create")
     val dataset = new LightGBMDataset(lightgbmlib.voidpp_value(datasetOutPtr))