fix: lightgbm default params should not be specified if optional (#1232)

microsoft · Oct 28, 2021 · 336eff5 · 336eff5
1 parent 3d92dd7
commit 336eff5
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Showing 5 changed files with 189 additions and 97 deletions.
diff --git a/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.scala b/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.scala
@@ -47,13 +47,13 @@ class LightGBMClassifier(override val uid: String)
     val actualNumClasses = getNumClasses(dataset)
     val categoricalIndexes = getCategoricalIndexes(dataset.schema(getFeaturesCol))
     val modelStr = if (getModelString == null || getModelString.isEmpty) None else get(modelString)
-    ClassifierTrainParams(getParallelism, getTopK, getNumIterations, getLearningRate, getNumLeaves, getMaxBin,
-      getBinSampleCount, getBaggingFraction, getPosBaggingFraction, getNegBaggingFraction,
-      getBaggingFreq, getBaggingSeed, getEarlyStoppingRound, getImprovementTolerance,
-      getFeatureFraction, getMaxDepth, getMinSumHessianInLeaf, numTasks, modelStr,
+    ClassifierTrainParams(getParallelism, get(topK), getNumIterations, getLearningRate,
+      get(numLeaves), get(maxBin), get(binSampleCount), get(baggingFraction), get(posBaggingFraction),
+      get(negBaggingFraction), get(baggingFreq), get(baggingSeed), getEarlyStoppingRound, getImprovementTolerance,
+      get(featureFraction), get(maxDepth), get(minSumHessianInLeaf), numTasks, modelStr,
       getIsUnbalance, getVerbosity, categoricalIndexes, actualNumClasses, getBoostFromAverage,
-      getBoostingType, getLambdaL1, getLambdaL2, getIsProvideTrainingMetric,
-      getMetric, getMinGainToSplit, getMaxDeltaStep, getMaxBinByFeature, getMinDataInLeaf, getSlotNames,
+      getBoostingType, get(lambdaL1), get(lambdaL2), get(isProvideTrainingMetric),
+      get(metric), get(minGainToSplit), get(maxDeltaStep), getMaxBinByFeature, get(minDataInLeaf), getSlotNames,
       getDelegate, getDartParams, getExecutionParams, getObjectiveParams)
   }
 

diff --git a/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.scala b/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.scala
@@ -53,13 +53,13 @@ class LightGBMRanker(override val uid: String)
   def getTrainParams(numTasks: Int, dataset: Dataset[_], numTasksPerExec: Int): TrainParams = {
     val categoricalIndexes = getCategoricalIndexes(dataset.schema(getFeaturesCol))
     val modelStr = if (getModelString == null || getModelString.isEmpty) None else get(modelString)
-    RankerTrainParams(getParallelism, getTopK, getNumIterations, getLearningRate, getNumLeaves,
-      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,
+    RankerTrainParams(getParallelism, get(topK), getNumIterations, getLearningRate,
+      get(numLeaves), get(maxBin), get(binSampleCount), get(baggingFraction), get(posBaggingFraction),
+      get(negBaggingFraction), get(baggingFreq), get(baggingSeed), getEarlyStoppingRound, getImprovementTolerance,
+      get(featureFraction), get(maxDepth), get(minSumHessianInLeaf), numTasks, modelStr,
+      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, getObjectiveParams)
   }
 

diff --git a/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.scala b/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.scala
@@ -61,12 +61,15 @@ class LightGBMRegressor(override val uid: String)
   def getTrainParams(numTasks: Int, dataset: Dataset[_], numTasksPerExec: Int): TrainParams = {
     val categoricalIndexes = getCategoricalIndexes(dataset.schema(getFeaturesCol))
     val modelStr = if (getModelString == null || getModelString.isEmpty) None else get(modelString)
-    RegressorTrainParams(getParallelism, getTopK, getNumIterations, getLearningRate, getNumLeaves,
-      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,
+    RegressorTrainParams(getParallelism, get(topK), getNumIterations, getLearningRate,
+      get(numLeaves), getAlpha, getTweedieVariancePower,
+      get(maxBin), get(binSampleCount), get(baggingFraction), get(posBaggingFraction),
+      get(negBaggingFraction), get(baggingFreq), get(baggingSeed), getEarlyStoppingRound, getImprovementTolerance,
+      get(featureFraction), get(maxDepth), get(minSumHessianInLeaf),
+      numTasks, modelStr, getVerbosity, categoricalIndexes,
+      getBoostFromAverage, getBoostingType, get(lambdaL1), get(lambdaL2), get(isProvideTrainingMetric),
+      get(metric), get(minGainToSplit), get(maxDeltaStep),
+      getMaxBinByFeature, get(minDataInLeaf), getSlotNames, getDelegate,
       getDartParams, getExecutionParams, getObjectiveParams)
   }
 

diff --git a/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/TrainUtils.scala b/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/TrainUtils.scala
@@ -114,13 +114,14 @@ private object TrainUtils extends Serializable {
 
       isFinished = updateOneIteration(trainParams, booster, log, iters)
 
-      val trainEvalResults: Option[Map[String, Double]] = if (trainParams.isProvideTrainingMetric && !isFinished) {
-        val evalResults: Array[(String, Double)] = booster.getEvalResults(evalNames, 0)
-        evalResults.foreach { case (evalName: String, score: Double) => log.info(s"Train $evalName=$score") }
-        Option(Map(evalResults:_*))
-      } else {
-        None
-      }
+      val trainEvalResults: Option[Map[String, Double]] =
+        if (trainParams.isProvideTrainingMetric.getOrElse(false) && !isFinished) {
+          val evalResults: Array[(String, Double)] = booster.getEvalResults(evalNames, 0)
+          evalResults.foreach { case (evalName: String, score: Double) => log.info(s"Train $evalName=$score") }
+          Option(Map(evalResults:_*))
+        } else {
+          None
+        }
 
       val validEvalResults: Option[Map[String, Double]] = if (hasValid && !isFinished) {
         val evalResults: Array[(String, Double)] = booster.getEvalResults(evalNames, 1)