Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/14834#discussion_r78128374
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -460,33 +564,74 @@ class LogisticRegression @Since("1.2.0") (
as a result, no scaling is needed.
*/
val rawCoefficients = state.x.toArray.clone()
- var i = 0
- while (i < numFeatures) {
- rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 /
featuresStd(i) else 0.0 }
- i += 1
+ val coefficientArray = Array.tabulate(numCoefficientSets *
numFeatures) { i =>
+ // flatIndex will loop though rawCoefficients, and skip the
intercept terms.
+ val flatIndex = if ($(fitIntercept)) i + i / numFeatures else i
+ val featureIndex = i % numFeatures
+ if (featuresStd(featureIndex) != 0.0) {
+ rawCoefficients(flatIndex) / featuresStd(featureIndex)
+ } else {
+ 0.0
+ }
+ }
+ val coefficientMatrix =
+ new DenseMatrix(numCoefficientSets, numFeatures,
coefficientArray, isTransposed = true)
+
+ if ($(regParam) == 0.0 && isMultinomial) {
+ /*
+ When no regularization is applied, the coefficients lack
identifiability because
+ we do not use a pivot class. We can add any constant value to
the coefficients and
+ get the same likelihood. So here, we choose the mean centered
coefficients for
+ reproducibility. This method follows the approach in glmnet,
described here:
+
+ Friedman, et al. "Regularization Paths for Generalized Linear
Models via
+ Coordinate Descent,"
https://core.ac.uk/download/files/153/6287975.pdf
+ */
+ val coefficientMean = coefficientMatrix.values.sum /
coefficientMatrix.values.length
+ coefficientMatrix.update(_ - coefficientMean)
}
- bcFeaturesStd.destroy(blocking = false)
- if ($(fitIntercept)) {
- (Vectors.dense(rawCoefficients.dropRight(1)).compressed,
rawCoefficients.last,
- arrayBuilder.result())
+ val interceptsArray: Array[Double] = if ($(fitIntercept)) {
+ Array.tabulate(numCoefficientSets) { i =>
+ val coefIndex = (i + 1) * numFeaturesPlusIntercept - 1
+ rawCoefficients(coefIndex)
+ }
+ } else {
+ Array[Double]()
+ }
+ /*
+ The intercepts are never regularized, so we always center the
mean.
+ */
+ val interceptVector = if (interceptsArray.nonEmpty &&
isMultinomial) {
+ val interceptMean = interceptsArray.sum / numClasses
+ interceptsArray.indices.foreach { i => interceptsArray(i) -=
interceptMean }
+ Vectors.dense(interceptsArray)
--- End diff --
Let's have it in TODO. This will not work if one of the class is not in
training. Since the intercept corresponding to that class will be negative
infinity, and there is no well defined interceptMean.
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