add seed parameters to lightgbm

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMBase.scala

@@ -255,6 +255,11 @@ trait LightGBMBase[TrainedModel <: Model[TrainedModel]] extends Estimator[Traine
     ExecutionParams(getChunkSize, getMatrixType, execNumThreads, getUseSingleDatasetMode)
   }
 
+  /**
+    * Constructs the ColumnParams.
+    *
+    * @return ColumnParams object containing the parameters related to LightGBM columns.
+    */
   protected def getColumnParams: ColumnParams = {
     ColumnParams(getLabelCol, getFeaturesCol, get(weightCol), get(initScoreCol), getOptGroupCol)
   }
@@ -268,6 +273,16 @@ trait LightGBMBase[TrainedModel <: Model[TrainedModel]] extends Estimator[Traine
     ObjectiveParams(getObjective, if (isDefined(fobj)) Some(getFObj) else None)
   }
 
+  /**
+    * Constructs the SeedParams.
+    *
+    * @return SeedParams object containing the parameters related to LightGBM seeds and determinism.
+    */
+  protected def getSeedParams: SeedParams = {
+    SeedParams(get(seed), get(deterministic), get(baggingSeed), get(featureFractionSeed),
+      get(extraSeed), get(dropSeed), get(dataRandomSeed), get(objectiveSeed), boostingType)
+  }
+
   def getDatasetParams(categoricalIndexes: Array[Int], numThreads: Int): String = {
     val datasetParams = s"max_bin=$getMaxBin is_pre_partition=True " +
       s"bin_construct_sample_cnt=$getBinSampleCount " +

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.scala

@@ -54,7 +54,7 @@ class LightGBMClassifier(override val uid: String)
       getIsUnbalance, getVerbosity, categoricalIndexes, actualNumClasses, getBoostFromAverage,
       getBoostingType, get(lambdaL1), get(lambdaL2), get(isProvideTrainingMetric),
       get(metric), get(minGainToSplit), get(maxDeltaStep), getMaxBinByFeature, get(minDataInLeaf), getSlotNames,
-      getDelegate, getDartParams, getExecutionParams(numTasksPerExec), getObjectiveParams)
+      getDelegate, getDartParams, getExecutionParams(numTasksPerExec), getObjectiveParams, getSeedParams)
   }
 
   def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMClassificationModel = {

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.scala

@@ -60,7 +60,7 @@ class LightGBMRanker(override val uid: String)
       getVerbosity, categoricalIndexes, getBoostingType, get(lambdaL1), get(lambdaL2), getMaxPosition, getLabelGain,
       get(isProvideTrainingMetric), get(metric), getEvalAt, get(minGainToSplit), get(maxDeltaStep),
       getMaxBinByFeature, get(minDataInLeaf), getSlotNames, getDelegate, getDartParams,
-      getExecutionParams(numTasksPerExec), getObjectiveParams)
+      getExecutionParams(numTasksPerExec), getObjectiveParams, getSeedParams)
   }
 
   def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMRankerModel = {

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.scala

@@ -70,7 +70,7 @@ class LightGBMRegressor(override val uid: String)
       getBoostFromAverage, getBoostingType, get(lambdaL1), get(lambdaL2), get(isProvideTrainingMetric),
       get(metric), get(minGainToSplit), get(maxDeltaStep),
       getMaxBinByFeature, get(minDataInLeaf), getSlotNames, getDelegate,
-      getDartParams, getExecutionParams(numTasksPerExec), getObjectiveParams)
+      getDartParams, getExecutionParams(numTasksPerExec), getObjectiveParams, getSeedParams)
   }
 
   def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMRegressionModel = {

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/params/LightGBMParams.scala

@@ -317,12 +317,71 @@ trait LightGBMObjectiveParams extends Wrappable {
   def setFObj(value: FObjTrait): this.type = set(fobj, value)
 }
 
+/** Defines common parameters related to seed and determinism
+  */
+trait LightGBMSeedParams extends Wrappable {
+  val seed = new IntParam(this, "seed", "Main seed, used to generate other seeds")
+
+  def getSeed: Int = $(seed)
+  def setSeed(value: Int): this.type = set(seed, value)
+
+  val deterministic = new BooleanParam(this, "deterministic", "Used only with cpu " +
+    "devide type. Setting this to true should ensure stable results when using the same data and the " +
+    "same parameters.  Note: setting this to true may slow down training.  To avoid potential instability " +
+    "due to numerical issues, please set force_col_wise=true or force_row_wise=true when setting " +
+    "deterministic=true")
+  setDefault(deterministic->false)
+
+  def getDeterministic: Boolean = $(deterministic)
+  def setDeterministic(value: Boolean): this.type = set(deterministic, value)
+
+  val baggingSeed = new IntParam(this, "baggingSeed", "Bagging seed")
+  setDefault(baggingSeed->3)
+
+  def getBaggingSeed: Int = $(baggingSeed)
+  def setBaggingSeed(value: Int): this.type = set(baggingSeed, value)
+
+  val featureFractionSeed = new IntParam(this, "featureFractionSeed", "Feature fraction seed")
+  setDefault(featureFractionSeed->2)
+
+  def getFeatureFractionSeed: Int = $(featureFractionSeed)
+  def setFeatureFractionSeed(value: Int): this.type = set(featureFractionSeed, value)
+
+  val extraSeed = new IntParam(this, "extraSeed", "Random seed for selecting threshold " +
+    "when extra_trees is true")
+  setDefault(extraSeed->6)
+
+  def getExtraSeed: Int = $(extraSeed)
+  def setExtraSeed(value: Int): this.type = set(extraSeed, value)
+
+  val dropSeed = new IntParam(this, "dropSeed", "Random seed to choose dropping models. " +
+    "Only used in dart.")
+  setDefault(dropSeed->4)
+
+  def getDropSeed: Int = $(dropSeed)
+  def setDropSeed(value: Int): this.type = set(dropSeed, value)
+
+  val dataRandomSeed = new IntParam(this, "dataRandomSeed", "Random seed for sampling " +
+    "data to construct histogram bins.")
+  setDefault(dataRandomSeed->1)
+
+  def getDataRandomSeed: Int = $(dataRandomSeed)
+  def setDataRandomSeed(value: Int): this.type = set(dataRandomSeed, value)
+
+  val objectiveSeed = new IntParam(this, "objectiveSeed", "Random seed for objectives, " +
+    "if random process is needed.  Currently used only for rank_xendcg objective.")
+  setDefault(objectiveSeed->5)
+
+  def getObjectiveSeed: Int = $(objectiveSeed)
+  def setObjectiveSeed(value: Int): this.type = set(objectiveSeed, value)
+}
+
 /** Defines common parameters across all LightGBM learners.
   */
 trait LightGBMParams extends Wrappable with DefaultParamsWritable with HasWeightCol
   with HasValidationIndicatorCol with HasInitScoreCol with LightGBMExecutionParams
   with LightGBMSlotParams with LightGBMFractionParams with LightGBMBinParams with LightGBMLearnerParams
-  with LightGBMDartParams with LightGBMPredictionParams with LightGBMObjectiveParams {
+  with LightGBMDartParams with LightGBMPredictionParams with LightGBMObjectiveParams with LightGBMSeedParams {
   val numIterations = new IntParam(this, "numIterations",
     "Number of iterations, LightGBM constructs num_class * num_iterations trees")
   setDefault(numIterations->100)
@@ -348,12 +407,6 @@ trait LightGBMParams extends Wrappable with DefaultParamsWritable with HasWeight
   def getBaggingFreq: Int = $(baggingFreq)
   def setBaggingFreq(value: Int): this.type = set(baggingFreq, value)
 
-  val baggingSeed = new IntParam(this, "baggingSeed", "Bagging seed")
-  setDefault(baggingSeed->3)
-
-  def getBaggingSeed: Int = $(baggingSeed)
-  def setBaggingSeed(value: Int): this.type = set(baggingSeed, value)
-
   val maxDepth = new IntParam(this, "maxDepth", "Max depth")
   setDefault(maxDepth-> -1)
 

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/params/TrainParams.scala

@@ -5,6 +5,22 @@ package com.microsoft.azure.synapse.ml.lightgbm.params
 
 import com.microsoft.azure.synapse.ml.lightgbm.{LightGBMConstants, LightGBMDelegate}
 
+/** Helper utilities for converting params to a string, to be passed to LightGBM. */
+object ParamUtils {
+  def paramToString[T](paramName: String, paramValueOpt: Option[T]): String = {
+    paramValueOpt match {
+      case Some(paramValue) => s"$paramName=$paramValue"
+      case None => ""
+    }
+  }
+
+  def paramsToString(paramNamesToValues: Array[(String, Option[_])]): String = {
+    paramNamesToValues.map {
+      case (paramName: String, paramValue: Option[_]) => paramToString(paramName, paramValue)
+    }.mkString(" ")
+  }
+}
+
 /** Defines the common Booster parameters passed to the LightGBM learners.
   */
 abstract class TrainParams extends Serializable {
@@ -43,25 +59,13 @@ abstract class TrainParams extends Serializable {
   def dartModeParams: DartModeParams
   def executionParams: ExecutionParams
   def objectiveParams: ObjectiveParams
-
-  def paramToString[T](paramName: String, paramValueOpt: Option[T]): String = {
-    paramValueOpt match {
-      case Some(paramValue) => s"$paramName=$paramValue"
-      case None => ""
-    }
-  }
-
-  def paramsToString(paramNamesToValues: Array[(String, Option[_])]): String = {
-    paramNamesToValues.map {
-      case (paramName: String, paramValue: Option[_]) => paramToString(paramName, paramValue)
-    }.mkString(" ")
-  }
+  def seedParams: SeedParams
 
   override def toString: String = {
     // Since passing `isProvideTrainingMetric` to LightGBM as a config parameter won't work,
     // let's fetch and print training metrics in `TrainUtils.scala` through JNI.
     s"is_pre_partition=True boosting_type=$boostingType tree_learner=$parallelism " +
-      paramsToString(Array(("top_k", topK), ("num_leaves", numLeaves), ("max_bin", maxBin),
+      ParamUtils.paramsToString(Array(("top_k", topK), ("num_leaves", numLeaves), ("max_bin", maxBin),
         ("bagging_fraction", baggingFraction), ("pos_bagging_fraction", posBaggingFraction),
         ("neg_bagging_fraction", negBaggingFraction), ("bagging_freq", baggingFreq),
         ("bagging_seed", baggingSeed), ("feature_fraction", featureFraction), ("max_depth", maxDepth),
@@ -75,7 +79,8 @@ abstract class TrainParams extends Serializable {
       (if (categoricalFeatures.isEmpty) "" else s"categorical_feature=${categoricalFeatures.mkString(",")} ") +
       (if (maxBinByFeature.isEmpty) "" else s"max_bin_by_feature=${maxBinByFeature.mkString(",")} ") +
       (if (boostingType == "dart") s"${dartModeParams.toString()} " else "") +
-      executionParams.toString()
+      executionParams.toString() +
+      seedParams.toString()
   }
 }
 
@@ -118,7 +123,8 @@ case class ClassifierTrainParams(parallelism: String,
                                  delegate: Option[LightGBMDelegate],
                                  dartModeParams: DartModeParams,
                                  executionParams: ExecutionParams,
-                                 objectiveParams: ObjectiveParams)
+                                 objectiveParams: ObjectiveParams,
+                                 seedParams: SeedParams)
   extends TrainParams {
   override def toString: String = {
     val extraStr =
@@ -167,7 +173,8 @@ case class RegressorTrainParams(parallelism: String,
                                 delegate: Option[LightGBMDelegate],
                                 dartModeParams: DartModeParams,
                                 executionParams: ExecutionParams,
-                                objectiveParams: ObjectiveParams)
+                                objectiveParams: ObjectiveParams,
+                                seedParams: SeedParams)
   extends TrainParams {
   override def toString: String = {
     s"alpha=$alpha tweedie_variance_power=$tweedieVariancePower boost_from_average=${boostFromAverage.toString} " +
@@ -214,7 +221,8 @@ case class RankerTrainParams(parallelism: String,
                              delegate: Option[LightGBMDelegate],
                              dartModeParams: DartModeParams,
                              executionParams: ExecutionParams,
-                             objectiveParams: ObjectiveParams)
+                             objectiveParams: ObjectiveParams,
+                             seedParams: SeedParams)
   extends TrainParams {
   override def toString: String = {
     val labelGainStr =
@@ -266,3 +274,34 @@ case class ObjectiveParams(objective: String, fobj: Option[FObjTrait]) extends S
     }
   }
 }
+
+/** Defines parameters related to seed and determinism for lightgbm.
+  *
+  * @param seed                Main seed, used to generate other seeds.
+  *
+  * @param deterministic       Setting this to true should ensure stable results when using the
+  *                            same data and the same parameters.
+  * @param baggingSeed         Bagging seed.
+  * @param featureFractionSeed Feature fraction seed.
+  * @param extraSeed           Random seed for selecting threshold when extra_trees is true.
+  * @param dropSeed            Random seed to choose dropping models. Only used in dart.
+  * @param dataRandomSeed      Random seed for sampling data to construct histogram bins.
+  * @param objectiveSeed       Random seed for objectives, if random process is needed.
+  *                            Currently used only for rank_xendcg objective.
+  * @param boostingType        Boosting type, used to determine if drop seed should be set.
+  * @param objective           Objective, used to determine if objective seed should be set.
+  */
+case class SeedParams(seed: Option[Int], deterministic: Option[Boolean],
+                      baggingSeed: Option[Int], featureFractionSeed: Option[Int],
+                      extraSeed: Option[Int], dropSeed: Option[Int],
+                      dataRandomSeed: Option[Int], objectiveSeed: Option[Int],
+                      boostingType: String, objective: String) extends Serializable {
+  override def toString: String = {
+    ParamUtils.paramsToString(Array(("seed", seed), ("deterministic", deterministic),
+      ("bagging_seed", baggingSeed), ("feature_fraction_seed", featureFractionSeed),
+      ("extra_seed", extraSeed), ("data_random_seed", dataRandomSeed))) +
+      (if (boostingType == "dart" && dropSeed.isDefined) s"drop_seed=${dropSeed.toString()} " else "") +
+      (if (objective == "rank_xendcg" && objectiveSeed.isDefined)
+        s"objective_seed=${objectiveSeed.toString()} " else "")
+  }
+}