Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/6386#discussion_r31650853 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala --- @@ -363,4 +371,79 @@ 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. + * If using ml implementation, uses ml code to generate initial weights. + */ + override def run(input: RDD[LabeledPoint]): LogisticRegressionModel = { + run(input, generateInitialWeights(), false) + } + + /** + * 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. + * Uses user provided weights. + */ + override def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel = { + run(input, initialWeights, true) + } + + private def run(input: RDD[LabeledPoint], initialWeights: Vector, userSupplied: Boolean): + LogisticRegressionModel = { + // ml's Logisitic regression only supports binary classifcation currently. + if (numOfLinearPredictor == 1 && useFeatureScaling) { + 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) + val initialWeightsWithIntercept = Vectors.dense(0.0, initialWeights.toArray: _*) + if (userSuppliedWeights) { --- End diff -- `initialWeightsWithIntercept` should be ```scala val initialWeightsWithIntercept = if(addIntercept) { appendBias(initialWeights) } else { initialWeights } ``` we add the last element as `1` as intercept, not the first element. Please move it into `if` block. Thanks.
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