Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/6386#discussion_r30955191
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
---
@@ -363,4 +370,36 @@ 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
+ * 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) {
+ def runWithMlLogisitcRegression(elasticNetParam: Double) = {
+ val lr = new
org.apache.spark.ml.classification.LogisticRegression()
+ lr.setRegParam(optimizer.getRegParam())
+ val handlePersistence = input.getStorageLevel == StorageLevel.NONE
+ val instances = input.map {
+ case LabeledPoint(label: Double, features: Vector) => (label,
features)
+ }
+ val mlLogisticRegresionModel = lr.trainOnInstances(instances,
handlePersistence,
+ Some(initialWeights))
--- End diff --
Yes
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