Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10743#discussion_r49955755
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -276,113 +276,123 @@ class LogisticRegression @Since("1.2.0") (
val numClasses = histogram.length
val numFeatures = summarizer.mean.size
- if (numInvalid != 0) {
- val msg = s"Classification labels should be in {0 to ${numClasses -
1} " +
- s"Found $numInvalid invalid labels."
- logError(msg)
- throw new SparkException(msg)
- }
-
- if (numClasses > 2) {
- val msg = s"Currently, LogisticRegression with ElasticNet in ML
package only supports " +
- s"binary classification. Found $numClasses in the input dataset."
- logError(msg)
- throw new SparkException(msg)
- }
+ val (coefficients, intercept, objectiveHistory) = {
+ if (numInvalid != 0) {
+ val msg = s"Classification labels should be in {0 to ${numClasses
- 1} " +
+ s"Found $numInvalid invalid labels."
+ logError(msg)
+ throw new SparkException(msg)
+ }
- val featuresMean = summarizer.mean.toArray
- val featuresStd = summarizer.variance.toArray.map(math.sqrt)
+ if (numClasses > 2) {
+ val msg = s"Currently, LogisticRegression with ElasticNet in ML
package only supports " +
+ s"binary classification. Found $numClasses in the input dataset."
+ logError(msg)
+ throw new SparkException(msg)
+ } else if ($(fitIntercept) && numClasses == 2 && histogram(0) ==
0.0) {
+ logWarning(s"All labels are one and fitIntercept=true, so the
coefficients will be " +
+ s"zeros and the intercept will be positive infinity; as a
result, " +
+ s"training is not needed.")
+ (Vectors.sparse(numFeatures, Seq()), Double.PositiveInfinity,
Array.empty[Double])
+ } else if ($(fitIntercept) && numClasses == 1) {
+ logWarning(s"All labels are one and fitIntercept=true, so the
coefficients will be " +
--- End diff --
"All labels are zero...."
---
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