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‎mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala‎

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@@ -104,6 +104,7 @@ object DecisionTreeRegressor {
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* It supports both continuous and categorical features.
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* @param rootNode Root of the decision tree
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*/
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@Since("1.4.0")
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@Experimental
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final class DecisionTreeRegressionModel private[ml] (
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override val uid: String,
@@ -126,10 +127,12 @@ final class DecisionTreeRegressionModel private[ml] (
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rootNode.predictImpl(features).prediction
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}
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@Since("1.4.0")
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override def copy(extra: ParamMap): DecisionTreeRegressionModel = {
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copyValues(new DecisionTreeRegressionModel(uid, rootNode, numFeatures), extra).setParent(parent)
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}
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135+
@Since("1.4.0")
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override def toString: String = {
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s"DecisionTreeRegressionModel (uid=$uid) of depth $depth with $numNodes nodes"
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}

‎mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala‎

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@@ -169,6 +169,7 @@ object GBTRegressor {
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* @param _trees Decision trees in the ensemble.
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* @param _treeWeights Weights for the decision trees in the ensemble.
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*/
172+
@Since("1.4.0")
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@Experimental
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final class GBTRegressionModel private[ml](
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override val uid: String,
@@ -187,11 +188,14 @@ final class GBTRegressionModel private[ml](
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* @param _trees Decision trees in the ensemble.
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* @param _treeWeights Weights for the decision trees in the ensemble.
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*/
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@Since("1.4.0")
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def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) =
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this(uid, _trees, _treeWeights, -1)
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@Since("1.4.0")
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override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
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@Since("1.4.0")
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override def treeWeights: Array[Double] = _treeWeights
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override protected def transformImpl(dataset: DataFrame): DataFrame = {
@@ -209,11 +213,13 @@ final class GBTRegressionModel private[ml](
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blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
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}
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@Since("1.4.0")
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override def copy(extra: ParamMap): GBTRegressionModel = {
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copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures),
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extra).setParent(parent)
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}
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@Since("1.4.0")
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override def toString: String = {
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s"GBTRegressionModel (uid=$uid) with $numTrees trees"
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}

‎mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala‎

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@@ -189,34 +189,42 @@ class IsotonicRegression @Since("1.5.0") (@Since("1.5.0") override val uid: Stri
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* @param oldModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
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* model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
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*/
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@Since("1.5.0")
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@Experimental
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class IsotonicRegressionModel private[ml] (
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override val uid: String,
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private val oldModel: MLlibIsotonicRegressionModel)
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extends Model[IsotonicRegressionModel] with IsotonicRegressionBase {
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/** @group setParam */
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@Since("1.5.0")
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def setFeaturesCol(value: String): this.type = set(featuresCol, value)
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/** @group setParam */
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@Since("1.5.0")
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def setPredictionCol(value: String): this.type = set(predictionCol, value)
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/** @group setParam */
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@Since("1.5.0")
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def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
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/** Boundaries in increasing order for which predictions are known. */
212+
@Since("1.5.0")
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def boundaries: Vector = Vectors.dense(oldModel.boundaries)
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/**
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* Predictions associated with the boundaries at the same index, monotone because of isotonic
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* regression.
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*/
219+
@Since("1.5.0")
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def predictions: Vector = Vectors.dense(oldModel.predictions)
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222+
@Since("1.5.0")
216223
override def copy(extra: ParamMap): IsotonicRegressionModel = {
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copyValues(new IsotonicRegressionModel(uid, oldModel), extra).setParent(parent)
218225
}
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227+
@Since("1.5.0")
220228
override def transform(dataset: DataFrame): DataFrame = {
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val predict = dataset.schema($(featuresCol)).dataType match {
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case DoubleType =>
@@ -228,6 +236,7 @@ class IsotonicRegressionModel private[ml] (
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dataset.withColumn($(predictionCol), predict(col($(featuresCol))))
229237
}
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239+
@Since("1.5.0")
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override def transformSchema(schema: StructType): StructType = {
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validateAndTransformSchema(schema, fitting = false)
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}

‎mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala‎

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@@ -136,6 +136,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
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* Default is empty, so all instances have weight one.
137137
* @group setParam
138138
*/
139+
@Since("1.6.0")
139140
def setWeightCol(value: String): this.type = set(weightCol, value)
140141
setDefault(weightCol -> "")
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@@ -146,6 +147,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
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* selected automatically.
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* @group setParam
148149
*/
150+
@Since("1.6.0")
149151
def setSolver(value: String): this.type = set(solver, value)
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setDefault(solver -> "auto")
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@@ -329,14 +331,15 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
329331
model.setSummary(trainingSummary)
330332
}
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332-
@Since("1.3.0")
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@Since("1.4.0")
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override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra)
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}
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/**
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* :: Experimental ::
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* Model produced by [[LinearRegression]].
339341
*/
342+
@Since("1.3.0")
340343
@Experimental
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class LinearRegressionModel private[ml] (
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override val uid: String,
@@ -353,6 +356,7 @@ class LinearRegressionModel private[ml] (
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* Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is
354357
* thrown if `trainingSummary == None`.
355358
*/
359+
@Since("1.5.0")
356360
def summary: LinearRegressionTrainingSummary = trainingSummary match {
357361
case Some(summ) => summ
358362
case None =>
@@ -367,6 +371,7 @@ class LinearRegressionModel private[ml] (
367371
}
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369373
/** Indicates whether a training summary exists for this model instance. */
374+
@Since("1.5.0")
370375
def hasSummary: Boolean = trainingSummary.isDefined
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372377
/**
@@ -399,6 +404,7 @@ class LinearRegressionModel private[ml] (
399404
dot(features, weights) + intercept
400405
}
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407+
@Since("1.4.0")
402408
override def copy(extra: ParamMap): LinearRegressionModel = {
403409
val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept), extra)
404410
if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
@@ -413,6 +419,7 @@ class LinearRegressionModel private[ml] (
413419
* @param predictions predictions outputted by the model's `transform` method.
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* @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
415421
*/
422+
@Since("1.5.0")
416423
@Experimental
417424
class LinearRegressionTrainingSummary private[regression] (
418425
predictions: DataFrame,
@@ -432,6 +439,7 @@ class LinearRegressionTrainingSummary private[regression] (
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* Linear regression results evaluated on a dataset.
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* @param predictions predictions outputted by the model's `transform` method.
434441
*/
442+
@Since("1.5.0")
435443
@Experimental
436444
class LinearRegressionSummary private[regression] (
437445
@transient val predictions: DataFrame,

‎mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala‎

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@@ -124,6 +124,7 @@ object RandomForestRegressor {
124124
* @param _trees Decision trees in the ensemble.
125125
* @param numFeatures Number of features used by this model
126126
*/
127+
@Since("1.4.0")
127128
@Experimental
128129
final class RandomForestRegressionModel private[ml] (
129130
override val uid: String,
@@ -141,11 +142,13 @@ final class RandomForestRegressionModel private[ml] (
141142
private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
142143
this(Identifiable.randomUID("rfr"), trees, numFeatures)
143144

145+
@Since("1.4.0")
144146
override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
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146148
// Note: We may add support for weights (based on tree performance) later on.
147149
private lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0)
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151+
@Since("1.4.0")
149152
override def treeWeights: Array[Double] = _treeWeights
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override protected def transformImpl(dataset: DataFrame): DataFrame = {
@@ -163,10 +166,12 @@ final class RandomForestRegressionModel private[ml] (
163166
_trees.map(_.rootNode.predictImpl(features).prediction).sum / numTrees
164167
}
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169+
@Since("1.4.0")
166170
override def copy(extra: ParamMap): RandomForestRegressionModel = {
167171
copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra).setParent(parent)
168172
}
169173

174+
@Since("1.4.0")
170175
override def toString: String = {
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s"RandomForestRegressionModel (uid=$uid) with $numTrees trees"
172177
}

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