Github user Lewuathe commented on a diff in the pull request: https://github.com/apache/spark/pull/8884#discussion_r41856633 --- 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) + val instances: RDD[WeightedLeastSquares.Instance] = dataset.select( + col($(labelCol)), w, col($(featuresCol))).map { + case Row(label: Double, weight: Double, features: Vector) => + WeightedLeastSquares.Instance(weight, features, label) + } + + val optimizer = new WeightedLeastSquares($(fitIntercept), $(regParam), + $(standardization), true) + val model = optimizer.fit(instances) + // When it is trained by WeightedLeastSquares, training summary does not + // attached returned model. + val lrModel = new LinearRegressionModel(uid, model.coefficients, model.intercept) --- End diff -- The finally returned value is copied at L.177. Is it also necessary to copy here?
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