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[SPARK-10015][MLlib]: ML model broadcasts should be stored in private vars: spark.ml tree ensembles #8243

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[SPARK-10015][MLlib]: ML model broadcasts should be stored in private vars: spark.ml tree ensembles #8243

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0f3f577
Add private var to store broadcast variable
Aug 17, 2015
2245df6
Create a local ref to model object to avoid sending the entire object…
Aug 18, 2015
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11 changes: 9 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
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Expand Up @@ -21,6 +21,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}

import org.apache.spark.Logging
import org.apache.spark.annotation.Experimental
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.param.{Param, ParamMap}
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
Expand Down Expand Up @@ -175,14 +176,20 @@ final class GBTClassificationModel(
require(_trees.length == _treeWeights.length, "GBTClassificationModel given trees, treeWeights" +
s" of non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")

private var bcastModel: Option[Broadcast[GBTClassificationModel]] = None

override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]

override def treeWeights: Array[Double] = _treeWeights

override protected def transformImpl(dataset: DataFrame): DataFrame = {
val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
bcastModel match {
case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
case _ =>
}
val lclBcastModel = bcastModel
val predictUDF = udf { (features: Any) =>
bcastModel.value.predict(features.asInstanceOf[Vector])
lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
}
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
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11 changes: 9 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
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Expand Up @@ -18,6 +18,7 @@
package org.apache.spark.ml.classification

import org.apache.spark.annotation.Experimental
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.tree.impl.RandomForest
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
Expand Down Expand Up @@ -132,6 +133,8 @@ final class RandomForestClassificationModel private[ml] (

require(numTrees > 0, "RandomForestClassificationModel requires at least 1 tree.")

private var bcastModel: Option[Broadcast[RandomForestClassificationModel]] = None

/**
* Construct a random forest classification model, with all trees weighted equally.
* @param trees Component trees
Expand All @@ -150,9 +153,13 @@ final class RandomForestClassificationModel private[ml] (
override def treeWeights: Array[Double] = _treeWeights

override protected def transformImpl(dataset: DataFrame): DataFrame = {
val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
bcastModel match {
case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
case _ =>
}
val lclBcastModel = bcastModel
val predictUDF = udf { (features: Any) =>
bcastModel.value.predict(features.asInstanceOf[Vector])
lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
}
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
Expand Down
11 changes: 9 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
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Expand Up @@ -21,6 +21,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}

import org.apache.spark.Logging
import org.apache.spark.annotation.Experimental
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.param.{Param, ParamMap}
import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeEnsembleModel, TreeRegressorParams}
Expand Down Expand Up @@ -165,14 +166,20 @@ final class GBTRegressionModel(
require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")

private var bcastModel: Option[Broadcast[GBTRegressionModel]] = None

override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]

override def treeWeights: Array[Double] = _treeWeights

override protected def transformImpl(dataset: DataFrame): DataFrame = {
val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
bcastModel match {
case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
case _ =>
}
val lclBcastModel = bcastModel
val predictUDF = udf { (features: Any) =>
bcastModel.value.predict(features.asInstanceOf[Vector])
lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
}
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
Expand Down
11 changes: 9 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
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Expand Up @@ -18,6 +18,7 @@
package org.apache.spark.ml.regression

import org.apache.spark.annotation.Experimental
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams}
Expand Down Expand Up @@ -121,6 +122,8 @@ final class RandomForestRegressionModel private[ml] (

require(numTrees > 0, "RandomForestRegressionModel requires at least 1 tree.")

private var bcastModel: Option[Broadcast[RandomForestRegressionModel]] = None

/**
* Construct a random forest regression model, with all trees weighted equally.
* @param trees Component trees
Expand All @@ -136,9 +139,13 @@ final class RandomForestRegressionModel private[ml] (
override def treeWeights: Array[Double] = _treeWeights

override protected def transformImpl(dataset: DataFrame): DataFrame = {
val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
bcastModel match {
case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
case _ =>
}
val lclBcastModel = bcastModel
val predictUDF = udf { (features: Any) =>
bcastModel.value.predict(features.asInstanceOf[Vector])
lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
}
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
Expand Down