Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5767#discussion_r29374063
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -110,59 +110,70 @@ class LinearRegression extends Regressor[Vector,
LinearRegression, LinearRegress
val yMean = statCounter.mean
val yStd = math.sqrt(statCounter.variance)
- val featuresMean = summarizer.mean.toArray
- val featuresStd = summarizer.variance.toArray.map(math.sqrt)
-
- // Since we implicitly do the feature scaling when we compute the cost
function
- // to improve the convergence, the effective regParam will be changed.
- val effectiveRegParam = paramMap(regParam) / yStd
- val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam
- val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) *
effectiveRegParam
-
- val costFun = new LeastSquaresCostFun(instances, yStd, yMean,
- featuresStd, featuresMean, effectiveL2RegParam)
-
- val optimizer = if (paramMap(elasticNetParam) == 0.0 ||
effectiveRegParam == 0.0) {
- new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol))
- } else {
- new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10,
effectiveL1RegParam, paramMap(tol))
- }
+ // If the yStd is zero, then the intercept is yMean with zero weights;
+ // as a result, training is not needed.
+ val model = if (yStd != 0.0) {
--- End diff --
Is it really rare? If `yStd` is `0.0` and then the optimal model would be
empty with intercept `yMean`. In this case, a warning would be proper. Having
this giant `if ... else` block making the code hard to read.
~~~scala
if (yStd == 0.0) {
logWarning(...)
if (handlePersistence) ...
return new LinearRegressionModel(...)
}
// actual implementation
~~~
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