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[SPARK-6496] [MLLIB] GeneralizedLinearAlgorithm.run(input, initialWeights) should initialize numFeatures #5167

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[SPARK-6496] [MLLIB] GeneralizedLinearAlgorithm.run(input, initialWeights) should initialize numFeatures #5167

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4 changes: 4 additions & 0 deletions mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
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Expand Up @@ -211,6 +211,10 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
*/
def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {

if (numFeatures < 0) {
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@srowen srowen Mar 24, 2015

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Ah! I get it now. This check is only carried out in one of the two run() methods. LGTM.

numFeatures = input.map(_.features.size).first()
}

if (input.getStorageLevel == StorageLevel.NONE) {
logWarning("The input data is not directly cached, which may hurt performance if its"
+ " parent RDDs are also uncached.")
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6 changes: 6 additions & 0 deletions mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
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Expand Up @@ -425,6 +425,12 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext with M

val model = lr.run(testRDD)

val numFeatures = testRDD.map(_.features.size).first()
val initialWeights = Vectors.dense(new Array[Double]((numFeatures + 1) * 2))
val model2 = lr.run(testRDD, initialWeights)

LogisticRegressionSuite.checkModelsEqual(model, model2)

/**
* The following is the instruction to reproduce the model using R's glmnet package.
*
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