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[SPARK-10259] [ML] Add @since annotation to ml.classification #8534

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[SPARK-10259] [ML] Add @since annotation to ml.classification #8534

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b107213
Add @Since annotation to ml.classification
Oct 1, 2015
6831e7a
modify my miss
Nov 13, 2015
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30 changes: 24 additions & 6 deletions mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@

package org.apache.spark.ml.classification

import org.apache.spark.annotation.Experimental
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
import org.apache.spark.ml.tree.impl.RandomForest
Expand All @@ -36,32 +36,44 @@ import org.apache.spark.sql.DataFrame
* It supports both binary and multiclass labels, as well as both continuous and categorical
* features.
*/
@Since("1.4.0")
@Experimental
final class DecisionTreeClassifier(override val uid: String)
final class DecisionTreeClassifier @Since("1.4.0") (
@Since("1.4.0") override val uid: String)
extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
with DecisionTreeParams with TreeClassifierParams {

@Since("1.4.0")
def this() = this(Identifiable.randomUID("dtc"))

// 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)

@Since("1.6.0")
override def setSeed(value: Long): this.type = super.setSeed(value)

override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = {
Expand Down Expand Up @@ -89,12 +101,15 @@ final class DecisionTreeClassifier(override val uid: String)
subsamplingRate = 1.0)
}

@Since("1.4.1")
override def copy(extra: ParamMap): DecisionTreeClassifier = defaultCopy(extra)
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@feynmanliang feynmanliang Sep 8, 2015

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Annotate all public methods

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@feynmanliang feynmanliang Sep 10, 2015

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This was introduced in 1.5

}

@Since("1.4.0")
@Experimental
object DecisionTreeClassifier {
/** Accessor for supported impurities: entropy, gini */
@Since("1.4.0")
final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
}

Expand All @@ -104,12 +119,13 @@ object DecisionTreeClassifier {
* It supports both binary and multiclass labels, as well as both continuous and categorical
* features.
*/
@Since("1.4.0")
@Experimental
final class DecisionTreeClassificationModel private[ml] (
override val uid: String,
override val rootNode: Node,
override val numFeatures: Int,
override val numClasses: Int)
@Since("1.4.0")override val uid: String,
@Since("1.4.0")override val rootNode: Node,
@Since("1.6.0")override val numFeatures: Int,
@Since("1.5.0")override val numClasses: Int)
extends ProbabilisticClassificationModel[Vector, DecisionTreeClassificationModel]
with DecisionTreeModel with Serializable {

Expand Down Expand Up @@ -142,11 +158,13 @@ final class DecisionTreeClassificationModel private[ml] (
}
}

@Since("1.4.0")
override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
copyValues(new DecisionTreeClassificationModel(uid, rootNode, numFeatures, numClasses), extra)
.setParent(parent)
}

@Since("1.4.0")
override def toString: String = {
s"DecisionTreeClassificationModel (uid=$uid) of depth $depth with $numNodes nodes"
}
Expand Down
35 changes: 31 additions & 4 deletions mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ package org.apache.spark.ml.classification
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.regression.DecisionTreeRegressionModel
Expand All @@ -44,54 +44,69 @@ import org.apache.spark.sql.types.DoubleType
* It supports binary labels, as well as both continuous and categorical features.
* Note: Multiclass labels are not currently supported.
*/
@Since("1.4.0")
@Experimental
final class GBTClassifier(override val uid: String)
final class GBTClassifier @Since("1.4.0") (
@Since("1.4.0") override val uid: String)
extends Predictor[Vector, GBTClassifier, GBTClassificationModel]
with GBTParams with TreeClassifierParams with Logging {

@Since("1.4.0")
def this() = this(Identifiable.randomUID("gbtc"))

// Override parameter setters from parent trait for Java API compatibility.

// Parameters from TreeClassifierParams:

@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("GBTClassifier.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 GBTClassifier:
Expand All @@ -102,6 +117,7 @@ final class GBTClassifier(override val uid: String)
* (default = logistic)
* @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" ${GBTClassifier.supportedLossTypes.mkString(", ")}",
Expand All @@ -110,9 +126,11 @@ final class GBTClassifier(override val uid: String)
setDefault(lossType -> "logistic")

/** @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. */
Expand Down Expand Up @@ -145,13 +163,16 @@ final class GBTClassifier(override val uid: String)
GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures, numFeatures)
}

@Since("1.4.1")
override def copy(extra: ParamMap): GBTClassifier = defaultCopy(extra)
}

@Since("1.4.0")
@Experimental
object GBTClassifier {
// The losses below should be lowercase.
/** Accessor for supported loss settings: logistic */
@Since("1.4.0")
final val supportedLossTypes: Array[String] = Array("logistic").map(_.toLowerCase)
}

Expand All @@ -164,12 +185,13 @@ object GBTClassifier {
* @param _trees Decision trees in the ensemble.
* @param _treeWeights Weights for the decision trees in the ensemble.
*/
@Since("1.6.0")
@Experimental
final class GBTClassificationModel private[ml](
override val uid: String,
@Since("1.6.0") override val uid: String,
private val _trees: Array[DecisionTreeRegressionModel],
private val _treeWeights: Array[Double],
override val numFeatures: Int)
@Since("1.6.0") override val numFeatures: Int)
extends PredictionModel[Vector, GBTClassificationModel]
with TreeEnsembleModel with Serializable {

Expand All @@ -182,11 +204,14 @@ final class GBTClassificationModel private[ml](
* @param _trees Decision trees in the ensemble.
* @param _treeWeights Weights for the decision trees in the ensemble.
*/
@Since("1.6.0")
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 = {
Expand All @@ -205,11 +230,13 @@ final class GBTClassificationModel private[ml](
if (prediction > 0.0) 1.0 else 0.0
}

@Since("1.4.0")
override def copy(extra: ParamMap): GBTClassificationModel = {
copyValues(new GBTClassificationModel(uid, _trees, _treeWeights, numFeatures),
extra).setParent(parent)
}

@Since("1.4.0")
override def toString: String = {
s"GBTClassificationModel (uid=$uid) with $numTrees trees"
}
Expand Down
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