Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10702#discussion_r51077516
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -219,33 +219,43 @@ class LinearRegression @Since("1.3.0")
(@Since("1.3.0") override val uid: String
}
val yMean = ySummarizer.mean(0)
- val yStd = math.sqrt(ySummarizer.variance(0))
-
- // If the yStd is zero, then the intercept is yMean with zero
coefficient;
- // as a result, training is not needed.
- if (yStd == 0.0) {
- logWarning(s"The standard deviation of the label is zero, so the
coefficients will be " +
- s"zeros and the intercept will be the mean of the label; as a
result, " +
- s"training is not needed.")
- if (handlePersistence) instances.unpersist()
- val coefficients = Vectors.sparse(numFeatures, Seq())
- val intercept = yMean
-
- val model = new LinearRegressionModel(uid, coefficients, intercept)
- // Handle possible missing or invalid prediction columns
- val (summaryModel, predictionColName) =
model.findSummaryModelAndPredictionCol()
-
- val trainingSummary = new LinearRegressionTrainingSummary(
- summaryModel.transform(dataset),
- predictionColName,
- $(labelCol),
- model,
- Array(0D),
- $(featuresCol),
- Array(0D))
- return copyValues(model.setSummary(trainingSummary))
+ val rawYStd = math.sqrt(ySummarizer.variance(0))
+ if (rawYStd == 0.0) {
+ if ($(fitIntercept)) {
+ // If the rawYStd is zero and fitIntercept=true, then the
intercept is yMean with
+ // zero coefficient; as a result, training is not needed.
+ logWarning(s"The standard deviation of the label is zero, so the
coefficients will be " +
+ s"zeros and the intercept will be the mean of the label; as a
result, " +
+ s"training is not needed.")
+ if (handlePersistence) instances.unpersist()
+ val coefficients = Vectors.sparse(numFeatures, Seq())
+ val intercept = yMean
+
+ val model = new LinearRegressionModel(uid, coefficients, intercept)
+ // Handle possible missing or invalid prediction columns
+ val (summaryModel, predictionColName) =
model.findSummaryModelAndPredictionCol()
+
+ val trainingSummary = new LinearRegressionTrainingSummary(
+ summaryModel.transform(dataset),
+ predictionColName,
+ $(labelCol),
+ model,
+ Array(0D),
+ $(featuresCol),
+ Array(0D))
+ return copyValues(model.setSummary(trainingSummary))
+ } else {
+ require(!($(regParam) > 0.0 && $(standardization)),
--- End diff --
Your PR for `WeightedLeastSquares` is doing the right behavior, and
consistent to `require($(regParam) != 0.0)`. The problem only happens when
`standardizationLabel = true`, and in your `WeightedLeastSquares`, you already
throw exception in this case.
GLMNET will do `standardizationLabel = true` even `standardization ==
false`, and that's why you see inconsistent solution between GLMNET and your
analytical solution.
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