[jira] [Updated] (SPARK-5807) Parallel grid search
[ https://issues.apache.org/jira/browse/SPARK-5807?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Peter Rudenko updated SPARK-5807: - Description: Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} import scala.collection.immutable.{ Vector = ScalaVec } val metrics = ScalaVec.fill(numModels)(0.0) //Scala vector is thread safe val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) = { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(sTrain split $splitIndex with multiple sets of parameters.) val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(sGot metric $metric for model trained with ${epm(i)}.) metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations (pretty much memory), so maybe it's possible to cache each fold separately. was: Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} import scala.collection.immutable.{ Vector = ScalaVec } val metrics = ScalaVec.fill(numModels)(0.0) //Scala vector is thread safe val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) = { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(sTrain split $splitIndex with multiple sets of parameters.) val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(sGot metric $metric for model trained with ${epm(i)}.) metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations (pretty much memory), so maybe it's possible to cache each fold separately. Parallel grid search - Key: SPARK-5807 URL: https://issues.apache.org/jira/browse/SPARK-5807 Project: Spark Issue Type: New Feature Components: ML Affects Versions: 1.3.0 Reporter: Peter Rudenko Priority: Minor Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} import scala.collection.immutable.{ Vector = ScalaVec } val metrics = ScalaVec.fill(numModels)(0.0) //Scala vector is thread safe val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) = { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(sTrain split $splitIndex with multiple sets of parameters.) val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(sGot metric $metric for model trained with ${epm(i)}.) metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel)
[jira] [Updated] (SPARK-5807) Parallel grid search
[ https://issues.apache.org/jira/browse/SPARK-5807?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Peter Rudenko updated SPARK-5807: - Description: Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} val metrics = val metrics = new ArrayBuffer[Double](numModels) with mutable.SynchronizedBuffer[Double] val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) = { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(sTrain split $splitIndex with multiple sets of parameters.) val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(sGot metric $metric for model trained with ${epm(i)}.) metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations (pretty much memory), so maybe it's possible to cache each fold separately. was: Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} import scala.collection.immutable.{ Vector = ScalaVec } val metrics = ScalaVec.fill(numModels)(0.0) //Scala vector is thread safe val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) = { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(sTrain split $splitIndex with multiple sets of parameters.) val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(sGot metric $metric for model trained with ${epm(i)}.) metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations (pretty much memory), so maybe it's possible to cache each fold separately. Parallel grid search - Key: SPARK-5807 URL: https://issues.apache.org/jira/browse/SPARK-5807 Project: Spark Issue Type: New Feature Components: ML Affects Versions: 1.3.0 Reporter: Peter Rudenko Priority: Minor Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} val metrics = val metrics = new ArrayBuffer[Double](numModels) with mutable.SynchronizedBuffer[Double] val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) = { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(sTrain split $splitIndex with multiple sets of parameters.) val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(sGot metric $metric for model trained with ${epm(i)}.) metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations