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[SPARK-9835] [ML] Implement IterativelyReweightedLeastSquares solver #10639

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[SPARK-9835] [ML] Implement IterativelyReweightedLeastSquares solver #10639

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b7d24c5
Initial draft of IterativelyReweightedLeastSquares
yanboliang Jan 7, 2016
ca4f16d
fix typos
yanboliang Jan 8, 2016
abed2f3
Make Link as object
yanboliang Jan 8, 2016
f1485ca
Add wrapper function: predict & fitted
yanboliang Jan 8, 2016
d7653d6
Rename z to adjusted
yanboliang Jan 11, 2016
65133ad
add Binomial and Poisson object
yanboliang Jan 11, 2016
446b1cb
update calculation of Family.weights.
yanboliang Jan 12, 2016
6d108cd
IRLS refactor
yanboliang Jan 21, 2016
8ecb763
Update doc
yanboliang Jan 21, 2016
2191d2a
Fix typos
yanboliang Jan 21, 2016
6074fd6
clean IRLS tests
yanboliang Jan 25, 2016
cb2057e
make the convergence check more efficient
yanboliang Jan 28, 2016
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Add wrapper function: predict & fitted
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@yanboliang
yanboliang committed Jan 11, 2016
commit f1485ca620395810ac25300c4dd0b17ecac56031
6 changes: 6 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/optim/GLMFamilies.scala
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Expand Up @@ -41,6 +41,12 @@ private[ml] abstract class Family(val link: Link) extends Serializable {

/** The working dependent variable. */
def z(y: Double, mu: Double, eta: Double): Double

/** Linear predictors based on given mu. */
def predict(mu: Double): Double = this.link.link(mu)

/** Fitted values based on linear predictors eta. */
def fitted(eta: Double): Double = this.link.unlink(eta)
}

/**
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4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala
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Expand Up @@ -66,7 +66,7 @@ private[ml] class IterativelyReweightedLeastSquares(
while (iter < maxIter && !converged) {

zw = y.zip(mu).map { case (y, mu) =>
val eta = family.link.link(mu)
val eta = family.predict(mu)
val z = family.z(y, mu, eta)
val w = family.weights(mu)
(z, w)
Expand All @@ -80,7 +80,7 @@ private[ml] class IterativelyReweightedLeastSquares(
eta = newInstances.map { instance =>
dot(instance.features, model.coefficients) + model.intercept
}
mu = eta.map { mu => family.link.unlink(mu) }
mu = eta.map { eta => family.fitted(eta) }

oldDev = dev
dev = family.deviance(y, mu)
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