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[SPARK-7279] Removed diffSum which is theoretical zero in LinearRegression and coding formating #5809

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[SPARK-7279] Removed diffSum which is theoretical zero in LinearRegression and coding formating #5809

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9146e19
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Apr 30, 2015
6904eed
triger jenkins
Apr 30, 2015
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18 changes: 6 additions & 12 deletions mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,7 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction}

import org.apache.spark.annotation.AlphaComponent
import org.apache.spark.ml.param.{Params, ParamMap}
import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter,
HasRegParam}
import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, HasRegParam}
import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.BLAS._
Expand Down Expand Up @@ -103,9 +102,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
case LabeledPoint(label: Double, features: Vector) => (label, features)
}
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
if (handlePersistence) {
instances.persist(StorageLevel.MEMORY_AND_DISK)
}
if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)

val (summarizer, statCounter) = instances.treeAggregate(
(new MultivariateOnlineSummarizer, new StatCounter))( {
Expand Down Expand Up @@ -146,8 +143,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol))
} else {
new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam,
paramMap(tol))
new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol))
}

val initialWeights = Vectors.zeros(numFeatures)
Expand Down Expand Up @@ -304,9 +300,8 @@ private class LeastSquaresAggregator(
featuresStd: Array[Double],
featuresMean: Array[Double]) extends Serializable {

private var totalCnt: Long = 0
private var totalCnt: Long = 0L
private var lossSum = 0.0
private var diffSum = 0.0

private val (effectiveWeightsArray: Array[Double], offset: Double, dim: Int) = {
val weightsArray = weights.toArray.clone()
Expand All @@ -323,9 +318,10 @@ private class LeastSquaresAggregator(
}
(weightsArray, -sum + labelMean / labelStd, weightsArray.length)
}

private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray)

private val gradientSumArray: Array[Double] = Array.ofDim[Double](dim)
private val gradientSumArray = Array.ofDim[Double](dim)

/**
* Add a new training data to this LeastSquaresAggregator, and update the loss and gradient
Expand All @@ -350,7 +346,6 @@ private class LeastSquaresAggregator(
}
}
lossSum += diff * diff / 2.0
diffSum += diff
}

totalCnt += 1
Expand All @@ -372,7 +367,6 @@ private class LeastSquaresAggregator(
if (other.totalCnt != 0) {
totalCnt += other.totalCnt
lossSum += other.lossSum
diffSum += other.diffSum

var i = 0
val localThisGradientSumArray = this.gradientSumArray
Expand Down