[SPARK-22060][ML] Fix CrossValidator/TrainValidationSplit param persist/load bug
## What changes were proposed in this pull request? Currently the param of CrossValidator/TrainValidationSplit persist/loading is hardcoding, which is different with other ML estimators. This cause persist bug for new added `parallelism` param. I refactor related code, avoid hardcoding persist/load param. And in the same time, it solve the `parallelism` persisting bug. This refactoring is very useful because we will add more new params in #19208 , hardcoding param persisting/loading making the thing adding new params very troublesome. ## How was this patch tested? Test added. Author: WeichenXu <weichen.xu@databricks.com> Closes #19278 from WeichenXu123/fix-tuning-param-bug.
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@ -212,14 +212,13 @@ object CrossValidator extends MLReadable[CrossValidator] {
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val (metadata, estimator, evaluator, estimatorParamMaps) =
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ValidatorParams.loadImpl(path, sc, className)
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val numFolds = (metadata.params \ "numFolds").extract[Int]
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val seed = (metadata.params \ "seed").extract[Long]
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new CrossValidator(metadata.uid)
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val cv = new CrossValidator(metadata.uid)
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.setEstimator(estimator)
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.setEvaluator(evaluator)
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.setEstimatorParamMaps(estimatorParamMaps)
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.setNumFolds(numFolds)
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.setSeed(seed)
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DefaultParamsReader.getAndSetParams(cv, metadata,
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skipParams = Option(List("estimatorParamMaps")))
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cv
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}
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}
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}
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@ -302,17 +301,17 @@ object CrossValidatorModel extends MLReadable[CrossValidatorModel] {
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val (metadata, estimator, evaluator, estimatorParamMaps) =
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ValidatorParams.loadImpl(path, sc, className)
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val numFolds = (metadata.params \ "numFolds").extract[Int]
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val seed = (metadata.params \ "seed").extract[Long]
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val bestModelPath = new Path(path, "bestModel").toString
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val bestModel = DefaultParamsReader.loadParamsInstance[Model[_]](bestModelPath, sc)
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val avgMetrics = (metadata.metadata \ "avgMetrics").extract[Seq[Double]].toArray
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val model = new CrossValidatorModel(metadata.uid, bestModel, avgMetrics)
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model.set(model.estimator, estimator)
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.set(model.evaluator, evaluator)
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.set(model.estimatorParamMaps, estimatorParamMaps)
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.set(model.numFolds, numFolds)
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.set(model.seed, seed)
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DefaultParamsReader.getAndSetParams(model, metadata,
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skipParams = Option(List("estimatorParamMaps")))
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model
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}
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}
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}
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@ -17,6 +17,7 @@
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package org.apache.spark.ml.tuning
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import java.io.IOException
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import java.util.{List => JList}
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import scala.collection.JavaConverters._
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@ -207,14 +208,13 @@ object TrainValidationSplit extends MLReadable[TrainValidationSplit] {
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val (metadata, estimator, evaluator, estimatorParamMaps) =
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ValidatorParams.loadImpl(path, sc, className)
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val trainRatio = (metadata.params \ "trainRatio").extract[Double]
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val seed = (metadata.params \ "seed").extract[Long]
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new TrainValidationSplit(metadata.uid)
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val tvs = new TrainValidationSplit(metadata.uid)
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.setEstimator(estimator)
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.setEvaluator(evaluator)
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.setEstimatorParamMaps(estimatorParamMaps)
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.setTrainRatio(trainRatio)
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.setSeed(seed)
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DefaultParamsReader.getAndSetParams(tvs, metadata,
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skipParams = Option(List("estimatorParamMaps")))
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tvs
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}
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}
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}
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@ -295,17 +295,17 @@ object TrainValidationSplitModel extends MLReadable[TrainValidationSplitModel] {
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val (metadata, estimator, evaluator, estimatorParamMaps) =
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ValidatorParams.loadImpl(path, sc, className)
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val trainRatio = (metadata.params \ "trainRatio").extract[Double]
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val seed = (metadata.params \ "seed").extract[Long]
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val bestModelPath = new Path(path, "bestModel").toString
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val bestModel = DefaultParamsReader.loadParamsInstance[Model[_]](bestModelPath, sc)
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val validationMetrics = (metadata.metadata \ "validationMetrics").extract[Seq[Double]].toArray
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val model = new TrainValidationSplitModel(metadata.uid, bestModel, validationMetrics)
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model.set(model.estimator, estimator)
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.set(model.evaluator, evaluator)
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.set(model.estimatorParamMaps, estimatorParamMaps)
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.set(model.trainRatio, trainRatio)
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.set(model.seed, seed)
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DefaultParamsReader.getAndSetParams(model, metadata,
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skipParams = Option(List("estimatorParamMaps")))
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model
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}
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}
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}
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@ -150,20 +150,14 @@ private[ml] object ValidatorParams {
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}.toSeq
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))
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val validatorSpecificParams = instance match {
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case cv: CrossValidatorParams =>
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List("numFolds" -> parse(cv.numFolds.jsonEncode(cv.getNumFolds)))
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case tvs: TrainValidationSplitParams =>
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List("trainRatio" -> parse(tvs.trainRatio.jsonEncode(tvs.getTrainRatio)))
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case _ =>
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// This should not happen.
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throw new NotImplementedError("ValidatorParams.saveImpl does not handle type: " +
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instance.getClass.getCanonicalName)
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}
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val jsonParams = validatorSpecificParams ++ List(
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"estimatorParamMaps" -> parse(estimatorParamMapsJson),
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"seed" -> parse(instance.seed.jsonEncode(instance.getSeed)))
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val params = instance.extractParamMap().toSeq
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val skipParams = List("estimator", "evaluator", "estimatorParamMaps")
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val jsonParams = render(params
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.filter { case ParamPair(p, v) => !skipParams.contains(p.name)}
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.map { case ParamPair(p, v) =>
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p.name -> parse(p.jsonEncode(v))
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}.toList ++ List("estimatorParamMaps" -> parse(estimatorParamMapsJson))
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)
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DefaultParamsWriter.saveMetadata(instance, path, sc, extraMetadata, Some(jsonParams))
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@ -396,17 +396,27 @@ private[ml] object DefaultParamsReader {
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/**
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* Extract Params from metadata, and set them in the instance.
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* This works if all Params implement [[org.apache.spark.ml.param.Param.jsonDecode()]].
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* This works if all Params (except params included by `skipParams` list) implement
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* [[org.apache.spark.ml.param.Param.jsonDecode()]].
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*
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* @param skipParams The params included in `skipParams` won't be set. This is useful if some
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* params don't implement [[org.apache.spark.ml.param.Param.jsonDecode()]]
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* and need special handling.
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* TODO: Move to [[Metadata]] method
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*/
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def getAndSetParams(instance: Params, metadata: Metadata): Unit = {
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def getAndSetParams(
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instance: Params,
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metadata: Metadata,
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skipParams: Option[List[String]] = None): Unit = {
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implicit val format = DefaultFormats
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metadata.params match {
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case JObject(pairs) =>
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pairs.foreach { case (paramName, jsonValue) =>
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val param = instance.getParam(paramName)
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val value = param.jsonDecode(compact(render(jsonValue)))
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instance.set(param, value)
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if (skipParams == None || !skipParams.get.contains(paramName)) {
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val param = instance.getParam(paramName)
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val value = param.jsonDecode(compact(render(jsonValue)))
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instance.set(param, value)
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}
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}
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case _ =>
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throw new IllegalArgumentException(
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@ -159,12 +159,15 @@ class CrossValidatorSuite
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.setEvaluator(evaluator)
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.setNumFolds(20)
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.setEstimatorParamMaps(paramMaps)
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.setSeed(42L)
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.setParallelism(2)
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val cv2 = testDefaultReadWrite(cv, testParams = false)
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assert(cv.uid === cv2.uid)
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assert(cv.getNumFolds === cv2.getNumFolds)
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assert(cv.getSeed === cv2.getSeed)
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assert(cv.getParallelism === cv2.getParallelism)
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assert(cv2.getEvaluator.isInstanceOf[BinaryClassificationEvaluator])
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val evaluator2 = cv2.getEvaluator.asInstanceOf[BinaryClassificationEvaluator]
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@ -23,7 +23,7 @@ import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressio
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import org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInput
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import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
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import org.apache.spark.ml.linalg.Vectors
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import org.apache.spark.ml.param.{ParamMap}
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import org.apache.spark.ml.param.ParamMap
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import org.apache.spark.ml.param.shared.HasInputCol
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import org.apache.spark.ml.regression.LinearRegression
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import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
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@ -160,11 +160,13 @@ class TrainValidationSplitSuite
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.setTrainRatio(0.5)
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.setEstimatorParamMaps(paramMaps)
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.setSeed(42L)
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.setParallelism(2)
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val tvs2 = testDefaultReadWrite(tvs, testParams = false)
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assert(tvs.getTrainRatio === tvs2.getTrainRatio)
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assert(tvs.getSeed === tvs2.getSeed)
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assert(tvs.getParallelism === tvs2.getParallelism)
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ValidatorParamsSuiteHelpers
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.compareParamMaps(tvs.getEstimatorParamMaps, tvs2.getEstimatorParamMaps)
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