Github user actuaryzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/16699#discussion_r124402141
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquaresSuite.scala
---
@@ -169,29 +169,29 @@ class IterativelyReweightedLeastSquaresSuite extends
SparkFunSuite with MLlibTes
object IterativelyReweightedLeastSquaresSuite {
def BinomialReweightFunc(
- instance: Instance,
+ instance: OffsetInstance,
model: WeightedLeastSquaresModel): (Double, Double) = {
- val eta = model.predict(instance.features)
+ val eta = model.predict(instance.features) + instance.offset
val mu = 1.0 / (1.0 + math.exp(-1.0 * eta))
- val z = eta + (instance.label - mu) / (mu * (1.0 - mu))
+ val z = eta - instance.offset + (instance.label - mu) / (mu * (1.0 -
mu))
--- End diff --
Indeed this is the correct implementation: in the IRWLS, we only include
offset when computing `mu` and use `Xb` (without offset) when updating the
working label. To see this clearly, one would have to derive the IRWLS. But for
a quick reference, below is R's implementation:
```
eta <- drop(x %*% start)
mu <- linkinv(eta <- eta + offset)
z <- (eta - offset)[good] + (y - mu)[good]/mu.eta.val[good]
w <- sqrt((weights[good] * mu.eta.val[good]^2)/variance(mu)[good])
fit <- .Call(C_Cdqrls, x[good, , drop = FALSE] *
w, z * w, min(1e-07, control$epsilon/1000), check = FALSE)
```
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