Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10702#discussion_r51354767
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -219,33 +219,44 @@ 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) || yMean==0.0) {
+ // If the rawYStd is zero and fitIntercept=true, then the
intercept is yMean with
+ // zero coefficient; as a result, training is not needed.
+ // Also, if yMean==0 and rawYStd==0, all the coefficients are zero
regardless of
+ // the fitIntercept
+ 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, "The standard deviation of the label
is zero. " +
+ "Model cannot be regularized.")
+ logWarning(s"The standard deviation of the label is zero. " +
+ "Consider setting fitIntercept=true.")
+ }
}
+ // if y is constant (rawYStd is zero), then y cannot be scaled. In
this case
+ // setting yStd=1.0 ensures that y is not scaled anymore in l-bfgs
algorithm.
+ val yStd = if (rawYStd > 0) rawYStd else if (yMean != 0.0)
math.abs(yMean) else 1.0
--- End diff --
`val yStd = if (rawYStd > 0) rawYStd else math.abs(yMean)' since you
already check the condition before.
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