Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/6386#discussion_r30956281
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
---
@@ -363,4 +370,34 @@ class LogisticRegressionWithLBFGS
new LogisticRegressionModel(weights, intercept, numFeatures,
numOfLinearPredictor + 1)
}
}
+
+ /**
+ * Run the algorithm with the configured parameters on an input RDD
+ * of LabeledPoint entries starting from the initial weights provided.
+ * If a known updater is used calls the ml implementation, to avoid
+ * applying a regularization penalty to the intercept, otherwise
+ * defaults to the mllib implementation. If more than two classes
+ * or feature scaling is disabled, always uses mllib implementation.
+ */
+ override def run(input: RDD[LabeledPoint], initialWeights: Vector):
LogisticRegressionModel = {
+ // ml's Logisitic regression only supports binary classifcation
currently.
+ if (numOfLinearPredictor == 1 && useFeatureScaling) {
+ def runWithMlLogisitcRegression(elasticNetParam: Double) = {
--- End diff --
We can do that, the only downside is that on the other side its ripped back
out right away. This would also loose the initial weights but I could either
modify the signature on the other side to take initial weights or require the
the initial weights or zero (which do you think is better)?
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