Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/14834#discussion_r78128581
--- 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)
+ } else if (interceptsArray.length == 1) {
+ Vectors.dense(interceptsArray)
} else {
- (Vectors.dense(rawCoefficients).compressed, 0.0,
arrayBuilder.result())
+ Vectors.sparse(numCoefficientSets, Seq())
}
+ (coefficientMatrix, interceptVector, arrayBuilder.result())
}
}
if (handlePersistence) instances.unpersist()
- val model = copyValues(new LogisticRegressionModel(uid, coefficients,
intercept))
- val (summaryModel, probabilityColName) =
model.findSummaryModelAndProbabilityCol()
- val logRegSummary = new BinaryLogisticRegressionTrainingSummary(
- summaryModel.transform(dataset),
- probabilityColName,
- $(labelCol),
- $(featuresCol),
- objectiveHistory)
--- End diff --
Change the outer
```scala
val (coefficients, intercept, objectiveHistory) = {
.....
}
```
to
```scala
val (coefficientMatrix, interceptVector, objectiveHistory) = {
.....
}
```
for clarity.
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