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[SPARK-59162][ML] Replace BLAS calls with while loops in GBT models #58461

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[SPARK-59162][ML] Replace BLAS calls with while loops in GBT models #58461

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11 changes: 8 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
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Expand Up @@ -22,7 +22,7 @@ import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.internal.Logging
import org.apache.spark.ml.linalg.{BLAS, DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.tree._
Expand Down Expand Up @@ -367,8 +367,13 @@ class GBTClassificationModel private[ml](

/** Raw prediction for the positive class. */
private def margin(features: Vector): Double = {
val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
BLAS.nativeBLAS.ddot(getNumTrees, treePredictions, 1, _treeWeights, 1)
var prediction = 0.0
var i = 0
while (i < _trees.length) {
prediction += _trees(i).rootNode.predictImpl(features).prediction * _treeWeights(i)
i += 1
}
prediction
}

/** (private[ml]) Convert to a model in the old API */
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11 changes: 8 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
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Expand Up @@ -22,7 +22,7 @@ import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.internal.{Logging, LogKeys}
import org.apache.spark.ml.linalg.{BLAS, Vector}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.impl.GradientBoostedTrees
Expand Down Expand Up @@ -301,8 +301,13 @@ class GBTRegressionModel private[ml](
override def predict(features: Vector): Double = {
// TODO: When we add a generic Boosting class, handle transform there? SPARK-7129
// Classifies by thresholding sum of weighted tree predictions
val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
BLAS.nativeBLAS.ddot(getNumTrees, treePredictions, 1, _treeWeights, 1)
var prediction = 0.0
var i = 0
while (i < _trees.length) {
prediction += _trees(i).rootNode.predictImpl(features).prediction * _treeWeights(i)
i += 1
}
prediction
}

@Since("1.4.0")
Expand Down