Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10788#discussion_r50370414
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
---
@@ -374,4 +384,82 @@ class LogisticRegressionWithLBFGS
new LogisticRegressionModel(weights, intercept, numFeatures,
numOfLinearPredictor + 1)
}
}
+
+ /**
+ * Run Logistic Regression with the configured parameters on an input RDD
+ * of LabeledPoint entries.
+ *
+ * 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.
+ * If using ml implementation, uses ml code to generate initial weights.
+ */
+ override def run(input: RDD[LabeledPoint]): LogisticRegressionModel = {
+ run(input, generateInitialWeights(input), userSuppliedWeights = false)
+ }
+
+ /**
+ * Run Logistic Regression 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.
+ * Uses user provided weights.
+ */
+ override def run(input: RDD[LabeledPoint], initialWeights: Vector):
LogisticRegressionModel = {
+ run(input, initialWeights, userSuppliedWeights = true)
+ }
+
+ private def run(input: RDD[LabeledPoint], initialWeights: Vector,
userSuppliedWeights: Boolean):
+ LogisticRegressionModel = {
+ // ml's Logisitic regression only supports binary classifcation
currently.
+ if (numOfLinearPredictor == 1) {
+ def runWithMlLogisitcRegression(elasticNetParam: Double) = {
+ // Prepare the ml LogisticRegression based on our settings
+ val lr = new
org.apache.spark.ml.classification.LogisticRegression()
+ lr.setRegParam(optimizer.getRegParam())
+ lr.setElasticNetParam(elasticNetParam)
+ lr.setStandardization(useFeatureScaling)
+ if (userSuppliedWeights) {
+ val uid = Identifiable.randomUID("logreg-static")
+ lr.setInitialModel(new
org.apache.spark.ml.classification.LogisticRegressionModel(
+ uid, initialWeights, 1.0))
+ }
+ lr.setFitIntercept(addIntercept)
+ lr.setMaxIter(optimizer.getNumIterations())
+ lr.setTol(optimizer.getConvergenceTol())
+ // Convert our input into a DataFrame
+ val sqlContext = new SQLContext(input.context)
+ import sqlContext.implicits._
+ val df = input.toDF()
+ // Determine if we should cache the DF
+ val handlePersistence = input.getStorageLevel == StorageLevel.NONE
+ if (handlePersistence) {
+ df.persist(StorageLevel.MEMORY_AND_DISK)
+ }
+ // Train our model
+ val mlLogisticRegresionModel = lr.train(df)
+ // unpersist if we persisted
+ if (handlePersistence) {
+ df.unpersist()
+ }
+ // convert the model
+ val weights = mlLogisticRegresionModel.weights match {
+ case x: DenseVector => x
+ case y: Vector => Vectors.dense(y.toArray)
+ }
+ createModel(weights, mlLogisticRegresionModel.intercept)
+ }
+ optimizer.getUpdater() match {
--- End diff --
when `optimizer.getRegParam() == 0.0`, run the old version.
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