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[SPARK-3317][MLlib] The loss of regularization in Updater should use the oldWeights #2207

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[SPARK-3317][MLlib] The loss of regularization in Updater should use the oldWeights #2207

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5 changes: 3 additions & 2 deletions mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -112,6 +112,7 @@ class L1Updater extends Updater {
val thisIterStepSize = stepSize / math.sqrt(iter)
// Take gradient step
val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
val norm = brzNorm(brzWeights, 1.0)
brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
// Apply proximal operator (soft thresholding)
val shrinkageVal = regParam * thisIterStepSize
Expand All @@ -122,7 +123,7 @@ class L1Updater extends Updater {
i += 1
}

(Vectors.fromBreeze(brzWeights), brzNorm(brzWeights, 1.0) * regParam)
(Vectors.fromBreeze(brzWeights), regParam * norm)
}
}

Expand All @@ -146,9 +147,9 @@ class SquaredL2Updater extends Updater {
// w' = (1 - thisIterStepSize * regParam) * w - thisIterStepSize * gradient
val thisIterStepSize = stepSize / math.sqrt(iter)
val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector
val norm = brzNorm(brzWeights, 2.0)
brzWeights :*= (1.0 - thisIterStepSize * regParam)
brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights)
val norm = brzNorm(brzWeights, 2.0)

(Vectors.fromBreeze(brzWeights), 0.5 * regParam * norm * norm)
}
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