ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

[SPARK-10243] [MLLIB] update since versions in mllib.tree

Browse source 
Same as #8421 but for `mllib.tree`.

cc jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8442 from mengxr/SPARK-10236.
This commit is contained in:
Xiangrui Meng 2015-08-25 22:35:49 -07:00
parent d703372f86
commit fb7e12fe2e
12 changed files with 57 additions and 44 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
View file

@ -46,7 +46,8 @@ import org.apache.spark.util.random.XORShiftRandom
*/
@Since("1.0.0")
@Experimental
class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
class DecisionTree @Since("1.0.0") (private val strategy: Strategy)
extends Serializable with Logging {
strategy.assertValid()

2
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
View file

@ -51,7 +51,7 @@ import org.apache.spark.storage.StorageLevel
*/
@Since("1.2.0")
@Experimental
class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
class GradientBoostedTrees @Since("1.2.0") (private val boostingStrategy: BoostingStrategy)
extends Serializable with Logging {
/**

2
mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
View file

@ -26,7 +26,9 @@ import org.apache.spark.annotation.{Experimental, Since}
@Since("1.0.0")
@Experimental
object Algo extends Enumeration {
@Since("1.0.0")
type Algo = Value
@Since("1.0.0")
val Classification, Regression = Value
private[mllib] def fromString(name: String): Algo = name match {

12
mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
View file

@ -41,14 +41,14 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
*/
@Since("1.2.0")
@Experimental
case class BoostingStrategy(
case class BoostingStrategy @Since("1.4.0") (
// Required boosting parameters
@BeanProperty var treeStrategy: Strategy,
@BeanProperty var loss: Loss,
@Since("1.2.0") @BeanProperty var treeStrategy: Strategy,
@Since("1.2.0") @BeanProperty var loss: Loss,
// Optional boosting parameters
@BeanProperty var numIterations: Int = 100,
@BeanProperty var learningRate: Double = 0.1,
@BeanProperty var validationTol: Double = 1e-5) extends Serializable {
@Since("1.2.0") @BeanProperty var numIterations: Int = 100,
@Since("1.2.0") @BeanProperty var learningRate: Double = 0.1,
@Since("1.4.0") @BeanProperty var validationTol: Double = 1e-5) extends Serializable {
/**
* Check validity of parameters.

2
mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
View file

@ -26,6 +26,8 @@ import org.apache.spark.annotation.{Experimental, Since}
@Since("1.0.0")
@Experimental
object FeatureType extends Enumeration {
@Since("1.0.0")
type FeatureType = Value
@Since("1.0.0")
val Continuous, Categorical = Value
}

2
mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
View file

@ -26,6 +26,8 @@ import org.apache.spark.annotation.{Experimental, Since}
@Since("1.0.0")
@Experimental
object QuantileStrategy extends Enumeration {
@Since("1.0.0")
type QuantileStrategy = Value
@Since("1.0.0")
val Sort, MinMax, ApproxHist = Value
}

29
mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
View file

@ -69,20 +69,20 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
*/
@Since("1.0.0")
@Experimental
class Strategy (
@BeanProperty var algo: Algo,
@BeanProperty var impurity: Impurity,
@BeanProperty var maxDepth: Int,
@BeanProperty var numClasses: Int = 2,
@BeanProperty var maxBins: Int = 32,
@BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
@BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
@BeanProperty var minInstancesPerNode: Int = 1,
@BeanProperty var minInfoGain: Double = 0.0,
@BeanProperty var maxMemoryInMB: Int = 256,
@BeanProperty var subsamplingRate: Double = 1,
@BeanProperty var useNodeIdCache: Boolean = false,
@BeanProperty var checkpointInterval: Int = 10) extends Serializable {
class Strategy @Since("1.3.0") (
@Since("1.0.0") @BeanProperty var algo: Algo,
@Since("1.0.0") @BeanProperty var impurity: Impurity,
@Since("1.0.0") @BeanProperty var maxDepth: Int,
@Since("1.2.0") @BeanProperty var numClasses: Int = 2,
@Since("1.0.0") @BeanProperty var maxBins: Int = 32,
@Since("1.0.0") @BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
@Since("1.0.0") @BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
@Since("1.2.0") @BeanProperty var minInstancesPerNode: Int = 1,
@Since("1.2.0") @BeanProperty var minInfoGain: Double = 0.0,
@Since("1.0.0") @BeanProperty var maxMemoryInMB: Int = 256,
@Since("1.2.0") @BeanProperty var subsamplingRate: Double = 1,
@Since("1.2.0") @BeanProperty var useNodeIdCache: Boolean = false,
@Since("1.2.0") @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
/**
*/
@ -206,6 +206,7 @@ object Strategy {
}
@deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
@Since("1.2.0")
def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
}

5
mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
View file

@ -43,7 +43,9 @@ import org.apache.spark.util.Utils
*/
@Since("1.0.0")
@Experimental
class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
class DecisionTreeModel @Since("1.0.0") (
@Since("1.0.0") val topNode: Node,
@Since("1.0.0") val algo: Algo) extends Serializable with Saveable {
/**
* Predict values for a single data point using the model trained.
@ -110,6 +112,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
/**
* Print the full model to a string.
*/
@Since("1.2.0")
def toDebugString: String = {
val header = toString + "\n"
header + topNode.subtreeToString(2)

18
mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
View file

@ -41,15 +41,15 @@ import org.apache.spark.mllib.linalg.Vector
*/
@Since("1.0.0")
@DeveloperApi
class Node (
val id: Int,
var predict: Predict,
var impurity: Double,
var isLeaf: Boolean,
var split: Option[Split],
var leftNode: Option[Node],
var rightNode: Option[Node],
var stats: Option[InformationGainStats]) extends Serializable with Logging {
class Node @Since("1.2.0") (
@Since("1.0.0") val id: Int,
@Since("1.0.0") var predict: Predict,
@Since("1.2.0") var impurity: Double,
@Since("1.0.0") var isLeaf: Boolean,
@Since("1.0.0") var split: Option[Split],
@Since("1.0.0") var leftNode: Option[Node],
@Since("1.0.0") var rightNode: Option[Node],
@Since("1.0.0") var stats: Option[InformationGainStats]) extends Serializable with Logging {
override def toString: String = {
s"id = $id, isLeaf = $isLeaf, predict = $predict, impurity = $impurity, " +

6
mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
View file

@ -26,9 +26,9 @@ import org.apache.spark.annotation.{DeveloperApi, Since}
*/
@Since("1.2.0")
@DeveloperApi
class Predict(
val predict: Double,
val prob: Double = 0.0) extends Serializable {
class Predict @Since("1.2.0") (
@Since("1.2.0") val predict: Double,
@Since("1.2.0") val prob: Double = 0.0) extends Serializable {
override def toString: String = s"$predict (prob = $prob)"

8
mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
View file

@ -34,10 +34,10 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
@Since("1.0.0")
@DeveloperApi
case class Split(
feature: Int,
threshold: Double,
featureType: FeatureType,
categories: List[Double]) {
@Since("1.0.0") feature: Int,
@Since("1.0.0") threshold: Double,
@Since("1.0.0") featureType: FeatureType,
@Since("1.0.0") categories: List[Double]) {
override def toString: String = {
s"Feature = $feature, threshold = $threshold, featureType = $featureType, " +

12
mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
View file

@ -48,7 +48,9 @@ import org.apache.spark.util.Utils
*/
@Since("1.2.0")
@Experimental
class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
class RandomForestModel @Since("1.2.0") (
@Since("1.2.0") override val algo: Algo,
@Since("1.2.0") override val trees: Array[DecisionTreeModel])
extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0),
combiningStrategy = if (algo == Classification) Vote else Average)
with Saveable {
@ -115,10 +117,10 @@ object RandomForestModel extends Loader[RandomForestModel] {
*/
@Since("1.2.0")
@Experimental
class GradientBoostedTreesModel(
override val algo: Algo,
override val trees: Array[DecisionTreeModel],
override val treeWeights: Array[Double])
class GradientBoostedTreesModel @Since("1.2.0") (
@Since("1.2.0") override val algo: Algo,
@Since("1.2.0") override val trees: Array[DecisionTreeModel],
@Since("1.2.0") override val treeWeights: Array[Double])
extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy = Sum)
with Saveable {