Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/8884#discussion_r41808787 --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala --- @@ -130,9 +131,54 @@ class LinearRegression(override val uid: String) def setWeightCol(value: String): this.type = set(weightCol, value) setDefault(weightCol -> "") + /** + * Set the solver algorithm used for optimization. + * In case of linear regression, this can be "l-bfgs", "normal" and "auto". + * The default value is "auto" which means that the solver algorithm is + * selected automatically. + * @group setParam + */ + def setSolver(value: String): this.type = set(solver, value) + setDefault(solver -> "auto") + override protected def train(dataset: DataFrame): LinearRegressionModel = { + // Extract the number of features before deciding optimization solver. + val numFeatures = dataset.select(col($(featuresCol))).limit(1).map { + case Row(features: Vector) => + features.size + }.toArray()(0) // Extract columns from data. If dataset is persisted, do not persist instances. val w = if ($(weightCol).isEmpty) lit(1.0) else col($(weightCol)) + + if ($(solver) == "normal" || ($(solver) == "auto" + && $(elasticNetParam) == 0.0 && numFeatures <= 4096)) { --- End diff -- For readability, ```scala if (($(solver) == "auto" && $(elasticNetParam) == 0.0 && numFeatures <= 4096) || $(solver) == "normal") { ```
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