Repository: spark Updated Branches: refs/heads/master 6b87acd66 -> f80f7b69a
[SPARK-10265][DOCUMENTATION, ML] Fixed @Since annotation to ml.regression Here is my first commit. Author: Ehsan M.Kermani <ehsanmo1...@gmail.com> Closes #8728 from ehsanmok/SinceAnn. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/f80f7b69 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/f80f7b69 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/f80f7b69 Branch: refs/heads/master Commit: f80f7b69a3f81d0ea879a31c769d17ffbbac74aa Parents: 6b87acd Author: Ehsan M.Kermani <ehsanmo1...@gmail.com> Authored: Thu Nov 5 12:11:57 2015 -0800 Committer: Xiangrui Meng <m...@databricks.com> Committed: Thu Nov 5 12:11:57 2015 -0800 ---------------------------------------------------------------------- .../ml/regression/DecisionTreeRegressor.scala | 20 ++++++++++-- .../spark/ml/regression/GBTRegressor.scala | 33 +++++++++++++++++--- .../ml/regression/IsotonicRegression.scala | 26 +++++++++++++-- .../spark/ml/regression/LinearRegression.scala | 28 +++++++++++++++-- .../ml/regression/RandomForestRegressor.scala | 30 +++++++++++++++--- 5 files changed, 119 insertions(+), 18 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/f80f7b69/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala index 88b79a4..04420fc 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -17,7 +17,7 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeRegressorParams} @@ -36,30 +36,39 @@ import org.apache.spark.sql.DataFrame * for regression. * It supports both continuous and categorical features. */ +@Since("1.4.0") @Experimental -final class DecisionTreeRegressor(override val uid: String) +final class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String) extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] with DecisionTreeParams with TreeRegressorParams { + @Since("1.4.0") def this() = this(Identifiable.randomUID("dtr")) // Override parameter setters from parent trait for Java API compatibility. - + @Since("1.4.0") override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + @Since("1.4.0") override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + @Since("1.4.0") override def setMinInstancesPerNode(value: Int): this.type = super.setMinInstancesPerNode(value) + @Since("1.4.0") override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + @Since("1.4.0") override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + @Since("1.4.0") override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + @Since("1.4.0") override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) + @Since("1.4.0") override def setImpurity(value: String): this.type = super.setImpurity(value) override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = { @@ -78,9 +87,11 @@ final class DecisionTreeRegressor(override val uid: String) subsamplingRate = 1.0) } + @Since("1.4.0") override def copy(extra: ParamMap): DecisionTreeRegressor = defaultCopy(extra) } +@Since("1.4.0") @Experimental object DecisionTreeRegressor { /** Accessor for supported impurities: variance */ @@ -93,6 +104,7 @@ object DecisionTreeRegressor { * It supports both continuous and categorical features. * @param rootNode Root of the decision tree */ +@Since("1.4.0") @Experimental final class DecisionTreeRegressionModel private[ml] ( override val uid: String, @@ -115,10 +127,12 @@ final class DecisionTreeRegressionModel private[ml] ( rootNode.predictImpl(features).prediction } + @Since("1.4.0") override def copy(extra: ParamMap): DecisionTreeRegressionModel = { copyValues(new DecisionTreeRegressionModel(uid, rootNode, numFeatures), extra).setParent(parent) } + @Since("1.4.0") override def toString: String = { s"DecisionTreeRegressionModel (uid=$uid) of depth $depth with $numNodes nodes" } http://git-wip-us.apache.org/repos/asf/spark/blob/f80f7b69/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala index 65b5b3e..07144cc 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.regression import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.Logging -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeEnsembleModel, TreeRegressorParams} @@ -42,54 +42,65 @@ import org.apache.spark.sql.types.DoubleType * learning algorithm for regression. * It supports both continuous and categorical features. */ +@Since("1.4.0") @Experimental -final class GBTRegressor(override val uid: String) +final class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String) extends Predictor[Vector, GBTRegressor, GBTRegressionModel] with GBTParams with TreeRegressorParams with Logging { + @Since("1.4.0") def this() = this(Identifiable.randomUID("gbtr")) // Override parameter setters from parent trait for Java API compatibility. // Parameters from TreeRegressorParams: - + @Since("1.4.0") override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + @Since("1.4.0") override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + @Since("1.4.0") override def setMinInstancesPerNode(value: Int): this.type = super.setMinInstancesPerNode(value) + @Since("1.4.0") override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + @Since("1.4.0") override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + @Since("1.4.0") override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + @Since("1.4.0") override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) /** * The impurity setting is ignored for GBT models. * Individual trees are built using impurity "Variance." */ + @Since("1.4.0") override def setImpurity(value: String): this.type = { logWarning("GBTRegressor.setImpurity should NOT be used") this } // Parameters from TreeEnsembleParams: - + @Since("1.4.0") override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value) + @Since("1.4.0") override def setSeed(value: Long): this.type = { logWarning("The 'seed' parameter is currently ignored by Gradient Boosting.") super.setSeed(value) } // Parameters from GBTParams: - + @Since("1.4.0") override def setMaxIter(value: Int): this.type = super.setMaxIter(value) + @Since("1.4.0") override def setStepSize(value: Double): this.type = super.setStepSize(value) // Parameters for GBTRegressor: @@ -100,6 +111,7 @@ final class GBTRegressor(override val uid: String) * (default = squared) * @group param */ + @Since("1.4.0") val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + " tries to minimize (case-insensitive). Supported options:" + s" ${GBTRegressor.supportedLossTypes.mkString(", ")}", @@ -108,9 +120,11 @@ final class GBTRegressor(override val uid: String) setDefault(lossType -> "squared") /** @group setParam */ + @Since("1.4.0") def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ + @Since("1.4.0") def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ @@ -135,13 +149,16 @@ final class GBTRegressor(override val uid: String) GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures, numFeatures) } + @Since("1.4.0") override def copy(extra: ParamMap): GBTRegressor = defaultCopy(extra) } +@Since("1.4.0") @Experimental object GBTRegressor { // The losses below should be lowercase. /** Accessor for supported loss settings: squared (L2), absolute (L1) */ + @Since("1.4.0") final val supportedLossTypes: Array[String] = Array("squared", "absolute").map(_.toLowerCase) } @@ -154,6 +171,7 @@ object GBTRegressor { * @param _trees Decision trees in the ensemble. * @param _treeWeights Weights for the decision trees in the ensemble. */ +@Since("1.4.0") @Experimental final class GBTRegressionModel private[ml]( override val uid: String, @@ -172,11 +190,14 @@ final class GBTRegressionModel private[ml]( * @param _trees Decision trees in the ensemble. * @param _treeWeights Weights for the decision trees in the ensemble. */ + @Since("1.4.0") def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) = this(uid, _trees, _treeWeights, -1) + @Since("1.4.0") override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]] + @Since("1.4.0") override def treeWeights: Array[Double] = _treeWeights override protected def transformImpl(dataset: DataFrame): DataFrame = { @@ -194,11 +215,13 @@ final class GBTRegressionModel private[ml]( blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1) } + @Since("1.4.0") override def copy(extra: ParamMap): GBTRegressionModel = { copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures), extra).setParent(parent) } + @Since("1.4.0") override def toString: String = { s"GBTRegressionModel (uid=$uid) with $numTrees trees" } http://git-wip-us.apache.org/repos/asf/spark/blob/f80f7b69/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala index f4a17c8..a1fe01b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala @@ -18,7 +18,7 @@ package org.apache.spark.ml.regression import org.apache.spark.Logging -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol} @@ -124,32 +124,42 @@ private[regression] trait IsotonicRegressionBase extends Params with HasFeatures * * Uses [[org.apache.spark.mllib.regression.IsotonicRegression]]. */ +@Since("1.5.0") @Experimental -class IsotonicRegression(override val uid: String) extends Estimator[IsotonicRegressionModel] - with IsotonicRegressionBase { +class IsotonicRegression @Since("1.5.0") (@Since("1.5.0") override val uid: String) + extends Estimator[IsotonicRegressionModel] with IsotonicRegressionBase { + @Since("1.5.0") def this() = this(Identifiable.randomUID("isoReg")) /** @group setParam */ + @Since("1.5.0") def setLabelCol(value: String): this.type = set(labelCol, value) /** @group setParam */ + @Since("1.5.0") def setFeaturesCol(value: String): this.type = set(featuresCol, value) /** @group setParam */ + @Since("1.5.0") def setPredictionCol(value: String): this.type = set(predictionCol, value) /** @group setParam */ + @Since("1.5.0") def setIsotonic(value: Boolean): this.type = set(isotonic, value) /** @group setParam */ + @Since("1.5.0") def setWeightCol(value: String): this.type = set(weightCol, value) /** @group setParam */ + @Since("1.5.0") def setFeatureIndex(value: Int): this.type = set(featureIndex, value) + @Since("1.5.0") override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra) + @Since("1.5.0") override def fit(dataset: DataFrame): IsotonicRegressionModel = { validateAndTransformSchema(dataset.schema, fitting = true) // Extract columns from data. If dataset is persisted, do not persist oldDataset. @@ -163,6 +173,7 @@ class IsotonicRegression(override val uid: String) extends Estimator[IsotonicReg copyValues(new IsotonicRegressionModel(uid, oldModel).setParent(this)) } + @Since("1.5.0") override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema, fitting = true) } @@ -178,6 +189,7 @@ class IsotonicRegression(override val uid: String) extends Estimator[IsotonicReg * @param oldModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]] * model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]]. */ +@Since("1.5.0") @Experimental class IsotonicRegressionModel private[ml] ( override val uid: String, @@ -185,27 +197,34 @@ class IsotonicRegressionModel private[ml] ( extends Model[IsotonicRegressionModel] with IsotonicRegressionBase { /** @group setParam */ + @Since("1.5.0") def setFeaturesCol(value: String): this.type = set(featuresCol, value) /** @group setParam */ + @Since("1.5.0") def setPredictionCol(value: String): this.type = set(predictionCol, value) /** @group setParam */ + @Since("1.5.0") def setFeatureIndex(value: Int): this.type = set(featureIndex, value) /** Boundaries in increasing order for which predictions are known. */ + @Since("1.5.0") def boundaries: Vector = Vectors.dense(oldModel.boundaries) /** * Predictions associated with the boundaries at the same index, monotone because of isotonic * regression. */ + @Since("1.5.0") def predictions: Vector = Vectors.dense(oldModel.predictions) + @Since("1.5.0") override def copy(extra: ParamMap): IsotonicRegressionModel = { copyValues(new IsotonicRegressionModel(uid, oldModel), extra).setParent(parent) } + @Since("1.5.0") override def transform(dataset: DataFrame): DataFrame = { val predict = dataset.schema($(featuresCol)).dataType match { case DoubleType => @@ -217,6 +236,7 @@ class IsotonicRegressionModel private[ml] ( dataset.withColumn($(predictionCol), predict(col($(featuresCol)))) } + @Since("1.5.0") override def transformSchema(schema: StructType): StructType = { validateAndTransformSchema(schema, fitting = false) } http://git-wip-us.apache.org/repos/asf/spark/blob/f80f7b69/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 6638313..913140e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -24,9 +24,9 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, import breeze.stats.distributions.StudentsT import org.apache.spark.{Logging, SparkException} -import org.apache.spark.annotation.Experimental import org.apache.spark.ml.feature.Instance import org.apache.spark.ml.optim.WeightedLeastSquares +import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.PredictorParams import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared._ @@ -61,11 +61,13 @@ private[regression] trait LinearRegressionParams extends PredictorParams * - L1 (Lasso) * - L2 + L1 (elastic net) */ +@Since("1.3.0") @Experimental -class LinearRegression(override val uid: String) +class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String) extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams with Logging { + @Since("1.4.0") def this() = this(Identifiable.randomUID("linReg")) /** @@ -73,6 +75,7 @@ class LinearRegression(override val uid: String) * Default is 0.0. * @group setParam */ + @Since("1.3.0") def setRegParam(value: Double): this.type = set(regParam, value) setDefault(regParam -> 0.0) @@ -81,6 +84,7 @@ class LinearRegression(override val uid: String) * Default is true. * @group setParam */ + @Since("1.5.0") def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) setDefault(fitIntercept -> true) @@ -93,6 +97,7 @@ class LinearRegression(override val uid: String) * Default is true. * @group setParam */ + @Since("1.5.0") def setStandardization(value: Boolean): this.type = set(standardization, value) setDefault(standardization -> true) @@ -103,6 +108,7 @@ class LinearRegression(override val uid: String) * Default is 0.0 which is an L2 penalty. * @group setParam */ + @Since("1.4.0") def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value) setDefault(elasticNetParam -> 0.0) @@ -111,6 +117,7 @@ class LinearRegression(override val uid: String) * Default is 100. * @group setParam */ + @Since("1.3.0") def setMaxIter(value: Int): this.type = set(maxIter, value) setDefault(maxIter -> 100) @@ -120,6 +127,7 @@ class LinearRegression(override val uid: String) * Default is 1E-6. * @group setParam */ + @Since("1.4.0") def setTol(value: Double): this.type = set(tol, value) setDefault(tol -> 1E-6) @@ -129,6 +137,7 @@ class LinearRegression(override val uid: String) * Default is empty, so all instances have weight one. * @group setParam */ + @Since("1.6.0") def setWeightCol(value: String): this.type = set(weightCol, value) setDefault(weightCol -> "") @@ -139,6 +148,7 @@ class LinearRegression(override val uid: String) * selected automatically. * @group setParam */ + @Since("1.6.0") def setSolver(value: String): this.type = set(solver, value) setDefault(solver -> "auto") @@ -329,6 +339,7 @@ class LinearRegression(override val uid: String) model.setSummary(trainingSummary) } + @Since("1.4.0") override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra) } @@ -336,6 +347,7 @@ class LinearRegression(override val uid: String) * :: Experimental :: * Model produced by [[LinearRegression]]. */ +@Since("1.3.0") @Experimental class LinearRegressionModel private[ml] ( override val uid: String, @@ -355,6 +367,7 @@ class LinearRegressionModel private[ml] ( * Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is * thrown if `trainingSummary == None`. */ + @Since("1.5.0") def summary: LinearRegressionTrainingSummary = trainingSummary match { case Some(summ) => summ case None => @@ -369,6 +382,7 @@ class LinearRegressionModel private[ml] ( } /** Indicates whether a training summary exists for this model instance. */ + @Since("1.5.0") def hasSummary: Boolean = trainingSummary.isDefined /** @@ -402,6 +416,7 @@ class LinearRegressionModel private[ml] ( dot(features, coefficients) + intercept } + @Since("1.4.0") override def copy(extra: ParamMap): LinearRegressionModel = { val newModel = copyValues(new LinearRegressionModel(uid, coefficients, intercept), extra) if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get) @@ -416,6 +431,7 @@ class LinearRegressionModel private[ml] ( * @param predictions predictions outputted by the model's `transform` method. * @param objectiveHistory objective function (scaled loss + regularization) at each iteration. */ +@Since("1.5.0") @Experimental class LinearRegressionTrainingSummary private[regression] ( predictions: DataFrame, @@ -428,6 +444,7 @@ class LinearRegressionTrainingSummary private[regression] ( extends LinearRegressionSummary(predictions, predictionCol, labelCol, model, diagInvAtWA) { /** Number of training iterations until termination */ + @Since("1.5.0") val totalIterations = objectiveHistory.length } @@ -437,6 +454,7 @@ class LinearRegressionTrainingSummary private[regression] ( * Linear regression results evaluated on a dataset. * @param predictions predictions outputted by the model's `transform` method. */ +@Since("1.5.0") @Experimental class LinearRegressionSummary private[regression] ( @transient val predictions: DataFrame, @@ -455,33 +473,39 @@ class LinearRegressionSummary private[regression] ( * explainedVariance = 1 - variance(y - \hat{y}) / variance(y) * Reference: [[http://en.wikipedia.org/wiki/Explained_variation]] */ + @Since("1.5.0") val explainedVariance: Double = metrics.explainedVariance /** * Returns the mean absolute error, which is a risk function corresponding to the * expected value of the absolute error loss or l1-norm loss. */ + @Since("1.5.0") val meanAbsoluteError: Double = metrics.meanAbsoluteError /** * Returns the mean squared error, which is a risk function corresponding to the * expected value of the squared error loss or quadratic loss. */ + @Since("1.5.0") val meanSquaredError: Double = metrics.meanSquaredError /** * Returns the root mean squared error, which is defined as the square root of * the mean squared error. */ + @Since("1.5.0") val rootMeanSquaredError: Double = metrics.rootMeanSquaredError /** * Returns R^2^, the coefficient of determination. * Reference: [[http://en.wikipedia.org/wiki/Coefficient_of_determination]] */ + @Since("1.5.0") val r2: Double = metrics.r2 /** Residuals (label - predicted value) */ + @Since("1.5.0") @transient lazy val residuals: DataFrame = { val t = udf { (pred: Double, label: Double) => label - pred } predictions.select(t(col(predictionCol), col(labelCol)).as("residuals")) http://git-wip-us.apache.org/repos/asf/spark/blob/f80f7b69/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala index 64fc172..71e40b5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala @@ -17,7 +17,7 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.Experimental +import org.apache.spark.annotation.{Experimental, Since} import org.apache.spark.ml.{PredictionModel, Predictor} import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams} @@ -37,44 +37,55 @@ import org.apache.spark.sql.functions._ * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning algorithm for regression. * It supports both continuous and categorical features. */ +@Since("1.4.0") @Experimental -final class RandomForestRegressor(override val uid: String) +final class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String) extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel] with RandomForestParams with TreeRegressorParams { + @Since("1.4.0") def this() = this(Identifiable.randomUID("rfr")) // Override parameter setters from parent trait for Java API compatibility. // Parameters from TreeRegressorParams: - + @Since("1.4.0") override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + @Since("1.4.0") override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + @Since("1.4.0") override def setMinInstancesPerNode(value: Int): this.type = super.setMinInstancesPerNode(value) + @Since("1.4.0") override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + @Since("1.4.0") override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + @Since("1.4.0") override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + @Since("1.4.0") override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) + @Since("1.4.0") override def setImpurity(value: String): this.type = super.setImpurity(value) // Parameters from TreeEnsembleParams: - + @Since("1.4.0") override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value) + @Since("1.4.0") override def setSeed(value: Long): this.type = super.setSeed(value) // Parameters from RandomForestParams: - + @Since("1.4.0") override def setNumTrees(value: Int): this.type = super.setNumTrees(value) + @Since("1.4.0") override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) @@ -91,15 +102,19 @@ final class RandomForestRegressor(override val uid: String) new RandomForestRegressionModel(trees, numFeatures) } + @Since("1.4.0") override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra) } +@Since("1.4.0") @Experimental object RandomForestRegressor { /** Accessor for supported impurity settings: variance */ + @Since("1.4.0") final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities /** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */ + @Since("1.4.0") final val supportedFeatureSubsetStrategies: Array[String] = RandomForestParams.supportedFeatureSubsetStrategies } @@ -111,6 +126,7 @@ object RandomForestRegressor { * @param _trees Decision trees in the ensemble. * @param numFeatures Number of features used by this model */ +@Since("1.4.0") @Experimental final class RandomForestRegressionModel private[ml] ( override val uid: String, @@ -128,11 +144,13 @@ final class RandomForestRegressionModel private[ml] ( private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) = this(Identifiable.randomUID("rfr"), trees, numFeatures) + @Since("1.4.0") override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]] // Note: We may add support for weights (based on tree performance) later on. private lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0) + @Since("1.4.0") override def treeWeights: Array[Double] = _treeWeights override protected def transformImpl(dataset: DataFrame): DataFrame = { @@ -150,10 +168,12 @@ final class RandomForestRegressionModel private[ml] ( _trees.map(_.rootNode.predictImpl(features).prediction).sum / numTrees } + @Since("1.4.0") override def copy(extra: ParamMap): RandomForestRegressionModel = { copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra).setParent(parent) } + @Since("1.4.0") override def toString: String = { s"RandomForestRegressionModel (uid=$uid) with $numTrees trees" } --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
