Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8884#discussion_r41809032
--- 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"
+ && $(elasticNetParam) == 0.0 && numFeatures <= 4096)) {
+ require($(elasticNetParam) == 0.0, "Only L2 regularization can be
used when normal " +
+ "solver is selected.'")
+ // In case of feature size is small, WeightedLeastSquares can train
more efficiently
+ // because it requires one pass through to the data. (SPARK-10668)
--- End diff --
For low dimensional data, WeightedLeastSquares is more efficiently since
the training algorithm only requires one pass through the data.
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