Repository: spark Updated Branches: refs/heads/branch-1.5 6d8ebc801 -> 08d390f45
[SPARK-10235] [MLLIB] update since versions in mllib.regression Same as #8421 but for `mllib.regression`. cc freeman-lab dbtsai Author: Xiangrui Meng <m...@databricks.com> Closes #8426 from mengxr/SPARK-10235 and squashes the following commits: 6cd28e4 [Xiangrui Meng] update since versions in mllib.regression (cherry picked from commit 4657fa1f37d41dd4c7240a960342b68c7c591f48) Signed-off-by: DB Tsai <d...@netflix.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/08d390f4 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/08d390f4 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/08d390f4 Branch: refs/heads/branch-1.5 Commit: 08d390f457f80ffdc2dfce61ea579d9026047f12 Parents: 6d8ebc8 Author: Xiangrui Meng <m...@databricks.com> Authored: Tue Aug 25 22:49:33 2015 -0700 Committer: DB Tsai <d...@netflix.com> Committed: Tue Aug 25 22:49:46 2015 -0700 ---------------------------------------------------------------------- .../regression/GeneralizedLinearAlgorithm.scala | 6 ++++-- .../spark/mllib/regression/IsotonicRegression.scala | 16 +++++++++------- .../spark/mllib/regression/LabeledPoint.scala | 5 +++-- .../org/apache/spark/mllib/regression/Lasso.scala | 9 ++++++--- .../spark/mllib/regression/LinearRegression.scala | 9 ++++++--- .../spark/mllib/regression/RidgeRegression.scala | 12 +++++++----- .../mllib/regression/StreamingLinearAlgorithm.scala | 8 +++----- .../StreamingLinearRegressionWithSGD.scala | 11 +++++++++-- 8 files changed, 47 insertions(+), 29 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 509f6a2..7e3b4d5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -38,7 +38,9 @@ import org.apache.spark.storage.StorageLevel */ @Since("0.8.0") @DeveloperApi -abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double) +abstract class GeneralizedLinearModel @Since("1.0.0") ( + @Since("1.0.0") val weights: Vector, + @Since("0.8.0") val intercept: Double) extends Serializable { /** @@ -107,7 +109,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] * The optimizer to solve the problem. * */ - @Since("1.0.0") + @Since("0.8.0") def optimizer: Optimizer /** Whether to add intercept (default: false). */ http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala index 31ca7c2..877d31b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala @@ -50,10 +50,10 @@ import org.apache.spark.sql.SQLContext */ @Since("1.3.0") @Experimental -class IsotonicRegressionModel ( - val boundaries: Array[Double], - val predictions: Array[Double], - val isotonic: Boolean) extends Serializable with Saveable { +class IsotonicRegressionModel @Since("1.3.0") ( + @Since("1.3.0") val boundaries: Array[Double], + @Since("1.3.0") val predictions: Array[Double], + @Since("1.3.0") val isotonic: Boolean) extends Serializable with Saveable { private val predictionOrd = if (isotonic) Ordering[Double] else Ordering[Double].reverse @@ -63,7 +63,6 @@ class IsotonicRegressionModel ( /** * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter. - * */ @Since("1.4.0") def this(boundaries: java.lang.Iterable[Double], @@ -214,8 +213,6 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] { } } - /** - */ @Since("1.4.0") override def load(sc: SparkContext, path: String): IsotonicRegressionModel = { implicit val formats = DefaultFormats @@ -256,6 +253,7 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] { * @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]] */ @Experimental +@Since("1.3.0") class IsotonicRegression private (private var isotonic: Boolean) extends Serializable { /** @@ -263,6 +261,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali * * @return New instance of IsotonicRegression. */ + @Since("1.3.0") def this() = this(true) /** @@ -271,6 +270,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali * @param isotonic Isotonic (increasing) or antitonic (decreasing) sequence. * @return This instance of IsotonicRegression. */ + @Since("1.3.0") def setIsotonic(isotonic: Boolean): this.type = { this.isotonic = isotonic this @@ -286,6 +286,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali * the algorithm is executed. * @return Isotonic regression model. */ + @Since("1.3.0") def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel = { val preprocessedInput = if (isotonic) { input @@ -311,6 +312,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali * the algorithm is executed. * @return Isotonic regression model. */ + @Since("1.3.0") def run(input: JavaRDD[(JDouble, JDouble, JDouble)]): IsotonicRegressionModel = { run(input.rdd.retag.asInstanceOf[RDD[(Double, Double, Double)]]) } http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala index f7fe1b7..c284ad2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala @@ -29,11 +29,12 @@ import org.apache.spark.SparkException * * @param label Label for this data point. * @param features List of features for this data point. - * */ @Since("0.8.0") @BeanInfo -case class LabeledPoint(label: Double, features: Vector) { +case class LabeledPoint @Since("1.0.0") ( + @Since("0.8.0") label: Double, + @Since("1.0.0") features: Vector) { override def toString: String = { s"($label,$features)" } http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala index 556411a..a9aba17 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala @@ -34,9 +34,9 @@ import org.apache.spark.rdd.RDD * */ @Since("0.8.0") -class LassoModel ( - override val weights: Vector, - override val intercept: Double) +class LassoModel @Since("1.1.0") ( + @Since("1.0.0") override val weights: Vector, + @Since("0.8.0") override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable with Saveable with PMMLExportable { @@ -84,6 +84,7 @@ object LassoModel extends Loader[LassoModel] { * its corresponding right hand side label y. * See also the documentation for the precise formulation. */ +@Since("0.8.0") class LassoWithSGD private ( private var stepSize: Double, private var numIterations: Int, @@ -93,6 +94,7 @@ class LassoWithSGD private ( private val gradient = new LeastSquaresGradient() private val updater = new L1Updater() + @Since("0.8.0") override val optimizer = new GradientDescent(gradient, updater) .setStepSize(stepSize) .setNumIterations(numIterations) @@ -103,6 +105,7 @@ class LassoWithSGD private ( * Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100, * regParam: 0.01, miniBatchFraction: 1.0}. */ + @Since("0.8.0") def this() = this(1.0, 100, 0.01, 1.0) override protected def createModel(weights: Vector, intercept: Double) = { http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala index 00ab06e..4996ace 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala @@ -34,9 +34,9 @@ import org.apache.spark.rdd.RDD * */ @Since("0.8.0") -class LinearRegressionModel ( - override val weights: Vector, - override val intercept: Double) +class LinearRegressionModel @Since("1.1.0") ( + @Since("1.0.0") override val weights: Vector, + @Since("0.8.0") override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable with Saveable with PMMLExportable { @@ -85,6 +85,7 @@ object LinearRegressionModel extends Loader[LinearRegressionModel] { * its corresponding right hand side label y. * See also the documentation for the precise formulation. */ +@Since("0.8.0") class LinearRegressionWithSGD private[mllib] ( private var stepSize: Double, private var numIterations: Int, @@ -93,6 +94,7 @@ class LinearRegressionWithSGD private[mllib] ( private val gradient = new LeastSquaresGradient() private val updater = new SimpleUpdater() + @Since("0.8.0") override val optimizer = new GradientDescent(gradient, updater) .setStepSize(stepSize) .setNumIterations(numIterations) @@ -102,6 +104,7 @@ class LinearRegressionWithSGD private[mllib] ( * Construct a LinearRegression object with default parameters: {stepSize: 1.0, * numIterations: 100, miniBatchFraction: 1.0}. */ + @Since("0.8.0") def this() = this(1.0, 100, 1.0) override protected[mllib] def createModel(weights: Vector, intercept: Double) = { http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index 21a791d..0a44ff5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -35,9 +35,9 @@ import org.apache.spark.rdd.RDD * */ @Since("0.8.0") -class RidgeRegressionModel ( - override val weights: Vector, - override val intercept: Double) +class RidgeRegressionModel @Since("1.1.0") ( + @Since("1.0.0") override val weights: Vector, + @Since("0.8.0") override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable with Saveable with PMMLExportable { @@ -85,6 +85,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] { * its corresponding right hand side label y. * See also the documentation for the precise formulation. */ +@Since("0.8.0") class RidgeRegressionWithSGD private ( private var stepSize: Double, private var numIterations: Int, @@ -94,7 +95,7 @@ class RidgeRegressionWithSGD private ( private val gradient = new LeastSquaresGradient() private val updater = new SquaredL2Updater() - + @Since("0.8.0") override val optimizer = new GradientDescent(gradient, updater) .setStepSize(stepSize) .setNumIterations(numIterations) @@ -105,6 +106,7 @@ class RidgeRegressionWithSGD private ( * Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100, * regParam: 0.01, miniBatchFraction: 1.0}. */ + @Since("0.8.0") def this() = this(1.0, 100, 0.01, 1.0) override protected def createModel(weights: Vector, intercept: Double) = { @@ -134,7 +136,7 @@ object RidgeRegressionWithSGD { * the number of features in the data. * */ - @Since("0.8.0") + @Since("1.0.0") def train( input: RDD[LabeledPoint], numIterations: Int, http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala index cd3ed8a..73948b2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala @@ -22,7 +22,7 @@ import scala.reflect.ClassTag import org.apache.spark.Logging import org.apache.spark.annotation.{DeveloperApi, Since} import org.apache.spark.api.java.JavaSparkContext.fakeClassTag -import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.streaming.api.java.{JavaDStream, JavaPairDStream} import org.apache.spark.streaming.dstream.DStream @@ -83,9 +83,8 @@ abstract class StreamingLinearAlgorithm[ * batch of data from the stream. * * @param data DStream containing labeled data - * */ - @Since("1.3.0") + @Since("1.1.0") def trainOn(data: DStream[LabeledPoint]): Unit = { if (model.isEmpty) { throw new IllegalArgumentException("Model must be initialized before starting training.") @@ -105,7 +104,6 @@ abstract class StreamingLinearAlgorithm[ /** * Java-friendly version of `trainOn`. - * */ @Since("1.3.0") def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream) @@ -129,7 +127,7 @@ abstract class StreamingLinearAlgorithm[ * Java-friendly version of `predictOn`. * */ - @Since("1.1.0") + @Since("1.3.0") def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = { JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]]) } http://git-wip-us.apache.org/repos/asf/spark/blob/08d390f4/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala index 26654e4..fe1d487 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala @@ -17,7 +17,7 @@ package org.apache.spark.mllib.regression -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.mllib.linalg.Vector /** @@ -41,6 +41,7 @@ import org.apache.spark.mllib.linalg.Vector * .trainOn(DStream) */ @Experimental +@Since("1.1.0") class StreamingLinearRegressionWithSGD private[mllib] ( private var stepSize: Double, private var numIterations: Int, @@ -54,8 +55,10 @@ class StreamingLinearRegressionWithSGD private[mllib] ( * Initial weights must be set before using trainOn or predictOn * (see `StreamingLinearAlgorithm`) */ + @Since("1.1.0") def this() = this(0.1, 50, 1.0) + @Since("1.1.0") val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction) protected var model: Option[LinearRegressionModel] = None @@ -63,6 +66,7 @@ class StreamingLinearRegressionWithSGD private[mllib] ( /** * Set the step size for gradient descent. Default: 0.1. */ + @Since("1.1.0") def setStepSize(stepSize: Double): this.type = { this.algorithm.optimizer.setStepSize(stepSize) this @@ -71,6 +75,7 @@ class StreamingLinearRegressionWithSGD private[mllib] ( /** * Set the number of iterations of gradient descent to run per update. Default: 50. */ + @Since("1.1.0") def setNumIterations(numIterations: Int): this.type = { this.algorithm.optimizer.setNumIterations(numIterations) this @@ -79,6 +84,7 @@ class StreamingLinearRegressionWithSGD private[mllib] ( /** * Set the fraction of each batch to use for updates. Default: 1.0. */ + @Since("1.1.0") def setMiniBatchFraction(miniBatchFraction: Double): this.type = { this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction) this @@ -87,6 +93,7 @@ class StreamingLinearRegressionWithSGD private[mllib] ( /** * Set the initial weights. */ + @Since("1.1.0") def setInitialWeights(initialWeights: Vector): this.type = { this.model = Some(algorithm.createModel(initialWeights, 0.0)) this @@ -95,9 +102,9 @@ class StreamingLinearRegressionWithSGD private[mllib] ( /** * Set the convergence tolerance. Default: 0.001. */ + @Since("1.5.0") def setConvergenceTol(tolerance: Double): this.type = { this.algorithm.optimizer.setConvergenceTol(tolerance) this } - } --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
