mark decision tree APIs

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -41,7 +41,7 @@ class NaiveBayesModel(
   private val brzTheta = new BDM[Double](theta.length, theta(0).length)
 
   {
-    // Need to put an extra pair of braces to prevent Scala treat `i` as a member.
+    // Need to put an extra pair of braces to prevent Scala treating `i` as a member.
     var i = 0
     while (i < theta.length) {
       var j = 0

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -33,13 +33,15 @@ import org.apache.spark.util.random.XORShiftRandom
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
+ * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ *
  * A class that implements a decision tree algorithm for classification and regression. It
  * supports both continuous and categorical features.
  * @param strategy The configuration parameters for the tree algorithm which specify the type
  *                 of algorithm (classification, regression, etc.), feature type (continuous,
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
  */
-class DecisionTree private(val strategy: Strategy) extends Serializable with Logging {
+class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
 
   /**
    * Method to train a decision tree model over an RDD

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

@@ -40,4 +40,4 @@ class Strategy (
     val maxDepth: Int,
     val maxBins: Int = 100,
     val quantileCalculationStrategy: QuantileStrategy = Sort,
-    val categoricalFeaturesInfo: Map[Int,Int] = Map[Int,Int]()) extends Serializable
+    val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int]()) extends Serializable

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala

@@ -18,20 +18,24 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
+ * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ *
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
  */
 object Entropy extends Impurity {
 
-   def log2(x: Double) = scala.math.log(x) / scala.math.log(2)
+   private[tree] def log2(x: Double) = scala.math.log(x) / scala.math.log(2)
 
   /**
+   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   *
    * entropy calculation
    * @param c0 count of instances with label 0
    * @param c1 count of instances with label 1
    * @return entropy value
    */
-   def calculate(c0: Double, c1: Double): Double = {
+   override def calculate(c0: Double, c1: Double): Double = {
      if (c0 == 0 || c1 == 0) {
        0
      } else {
@@ -42,6 +46,6 @@ object Entropy extends Impurity {
      }
    }
 
-  def calculate(count: Double, sum: Double, sumSquares: Double): Double =
+  override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Entropy.calculate")
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala

@@ -18,13 +18,17 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
+ * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ *
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
  * during binary classification.
  */
 object Gini extends Impurity {
 
   /**
+   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   *
    * Gini coefficient calculation
    * @param c0 count of instances with label 0
    * @param c1 count of instances with label 1
@@ -41,6 +45,6 @@ object Gini extends Impurity {
     }
   }
 
-  def calculate(count: Double, sum: Double, sumSquares: Double): Double =
+  override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Gini.calculate")
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala

@@ -18,12 +18,14 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
+ * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ *
  * Trait for calculating information gain.
  */
 trait Impurity extends Serializable {
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API - UNSTABLE</span>
+   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
    *
    * information calculation for binary classification
    * @param c0 count of instances with label 0
@@ -33,7 +35,7 @@ trait Impurity extends Serializable {
   def calculate(c0 : Double, c1 : Double): Double
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API - UNSTABLE</span>
+   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
    *
    * information calculation for regression
    * @param count number of instances

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala

@@ -18,13 +18,17 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
+ * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ *
  * Class for calculating variance during regression
  */
 object Variance extends Impurity {
    override def calculate(c0: Double, c1: Double): Double =
      throw new UnsupportedOperationException("Variance.calculate")
 
   /**
+   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   *
    * variance calculation
    * @param count number of instances
    * @param sum sum of labels

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala

@@ -30,4 +30,5 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
  * @param featureType type of feature -- categorical or continuous
  * @param category categorical label value accepted in the bin
  */
+private[tree]
 case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double)

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

@@ -22,6 +22,8 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.Vector
 
 /**
+ * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ *
  * Model to store the decision tree parameters
  * @param topNode root node
  * @param algo algorithm type -- classification or regression

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Filter.scala

@@ -22,7 +22,7 @@ package org.apache.spark.mllib.tree.model
  * @param split split specifying the feature index, type and threshold
  * @param comparison integer specifying <,=,>
  */
-case class Filter(split: Split, comparison: Int) {
+private[tree] case class Filter(split: Split, comparison: Int) {
   // Comparison -1,0,1 signifies <.=,>
   override def toString = " split = " + split + "comparison = " + comparison
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala

@@ -18,6 +18,8 @@
 package org.apache.spark.mllib.tree.model
 
 /**
+ * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+ *
  * Information gain statistics for each split
  * @param gain information gain value
  * @param impurity current node impurity

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

@@ -22,6 +22,8 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vector
 
 /**
+ * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ *
  * Node in a decision tree
  * @param id integer node id
  * @param predict predicted value at the node

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

@@ -30,7 +30,7 @@ case class Split(
     feature: Int,
     threshold: Double,
     featureType: FeatureType,
-    categories: List[Double]){
+    categories: List[Double]) {
 
   override def toString =
     "Feature = " + feature + ", threshold = " + threshold + ", featureType =  " + featureType +
@@ -42,15 +42,15 @@ case class Split(
  * @param feature feature index
  * @param featureType type of feature -- categorical or continuous
  */
-class DummyLowSplit(feature: Int, featureType: FeatureType)
+private[tree] class DummyLowSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MinValue, featureType, List())
 
 /**
  * Split with maximum threshold for continuous features. Helps with the highest bin creation.
  * @param feature feature index
  * @param featureType type of feature -- categorical or continuous
  */
-class DummyHighSplit(feature: Int, featureType: FeatureType)
+private[tree] class DummyHighSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MaxValue, featureType, List())
 
 /**
@@ -59,6 +59,6 @@ class DummyHighSplit(feature: Int, featureType: FeatureType)
  * @param feature feature index
  * @param featureType type of feature -- categorical or continuous
  */
-class DummyCategoricalSplit(feature: Int, featureType: FeatureType)
+private[tree] class DummyCategoricalSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MaxValue, featureType, List())