code documentation

Signed-off-by: Manish Amde <[email protected]>

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

@@ -29,7 +29,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.Algo._
 
-/*
+/**
 A class that implements a decision tree algorithm for classification and regression.
 It supports both continuous and categorical features.
 
@@ -40,7 +40,7 @@ quantile calculation strategy, etc.
  */
 class DecisionTree(val strategy : Strategy) extends Serializable with Logging {
 
-  /*
+  /**
   Method to train a decision tree model over an RDD
 
   @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
@@ -157,14 +157,14 @@ class DecisionTree(val strategy : Strategy) extends Serializable with Logging {
 
 object DecisionTree extends Serializable with Logging {
 
-  /*
+  /**
   Returns an Array[Split] of optimal splits for all nodes at a given level
 
   @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
-  for DecisionTree
+                            for DecisionTree
   @param parentImpurities Impurities for all parent nodes for the current level
   @param strategy [[org.apache.spark.mllib.tree.configuration.Strategy]] instance containing
-  parameters for construction the DecisionTree
+                                 parameters for construction the DecisionTree
   @param level Level of the tree
   @param filters Filter for all nodes at a given level
   @param splits possible splits for all features
@@ -200,7 +200,7 @@ object DecisionTree extends Serializable with Logging {
       }
     }
 
-    /*
+    /**
     Find whether the sample is valid input for the current node.
     In other words, does it pass through all the filters for the current node.
     */
@@ -236,7 +236,9 @@ object DecisionTree extends Serializable with Logging {
       true
     }
 
-    /*Finds the right bin for the given feature*/
+    /**
+    Finds the right bin for the given feature
+    */
     def findBin(featureIndex: Int, labeledPoint: LabeledPoint, isFeatureContinuous : Boolean) : Int = {
 
       if (isFeatureContinuous){
@@ -266,7 +268,8 @@ object DecisionTree extends Serializable with Logging {
 
     }
 
-    /*Finds bins for all nodes (and all features) at a given level
+    /**
+     Finds bins for all nodes (and all features) at a given level
      k features, l nodes (level = log2(l))
      Storage label, b11, b12, b13, .., bk, b21, b22, .. ,bl1, bl2, .. ,blk
      Denotes invalid sample for tree by noting bin for feature 1 as -1
@@ -343,7 +346,8 @@ object DecisionTree extends Serializable with Logging {
       }
     }
 
-    /*Performs a sequential aggregation over a partition.
+    /**
+    Performs a sequential aggregation over a partition.
 
     for p bins, k features, l nodes (level = log2(l)) storage is of the form:
     b111_left_count,b111_right_count, .... , ..
@@ -370,7 +374,8 @@ object DecisionTree extends Serializable with Logging {
     }
     logDebug("binAggregateLength = " + binAggregateLength)
 
-    /*Combines the aggregates from partitions
+    /**
+    Combines the aggregates from partitions
     @param agg1 Array containing aggregates from one or more partitions
     @param agg2 Array containing aggregates from one or more partitions
 
@@ -507,7 +512,7 @@ object DecisionTree extends Serializable with Logging {
       }
     }
 
-    /*
+    /**
     Extracts left and right split aggregates
 
     @param binData Array[Double] of size 2*numFeatures*numSplits
@@ -604,7 +609,7 @@ object DecisionTree extends Serializable with Logging {
       gains
     }
 
-    /*
+    /**
     Find the best split for a node given bin aggregate data
 
     @param binData Array[Double] of size 2*numSplits*numFeatures
@@ -669,7 +674,7 @@ object DecisionTree extends Serializable with Logging {
     bestSplits
   }
 
-  /*
+  /**
   Returns split and bins for decision tree calculation.
 
   @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data

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

@@ -16,6 +16,9 @@
  */
 package org.apache.spark.mllib.tree.configuration
 
+/**
+ * Enum to select the algorithm for the decision tree
+ */
 object Algo extends Enumeration {
   type Algo = Value
   val Classification, Regression = Value

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

@@ -16,6 +16,9 @@
  */
 package org.apache.spark.mllib.tree.configuration
 
+/**
+ * Enum to describe whether a feature is "continuous" or "categorical"
+ */
 object FeatureType extends Enumeration {
   type FeatureType = Value
   val Continuous, Categorical = Value

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

@@ -16,6 +16,9 @@
  */
 package org.apache.spark.mllib.tree.configuration
 
+/**
+ * Enum for selecting the quantile calculation strategy
+ */
 object QuantileStrategy extends Enumeration {
   type QuantileStrategy = Value
   val Sort, MinMax, ApproxHist = Value

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

@@ -20,6 +20,19 @@ import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 
+/**
+ * Stores all the configuration options for tree construction
+ * @param algo classification or regression
+ * @param impurity criterion used for information gain calculation
+ * @param maxDepth maximum depth of the tree
+ * @param maxBins maximum number of bins used for splitting features
+ * @param quantileCalculationStrategy algorithm for calculating quantiles
+ * @param categoricalFeaturesInfo A map storing information about the categorical variables and the
+ *                                number of discrete values they take. For example, an entry (n ->
+ *                                k) implies the feature n is categorical with k categories 0,
+ *                                1, 2, ... , k-1. It's important to note that features are
+ *                                zero-indexed.
+ */
 class Strategy  (
                 val algo : Algo,
                 val impurity : Impurity,

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

@@ -18,10 +18,20 @@ package org.apache.spark.mllib.tree.impurity
 
 import javax.naming.OperationNotSupportedException
 
+/**
+ * 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)
 
+  /**
+   * 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 = {
      if (c0 == 0 || c1 == 0) {
        0

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

@@ -18,8 +18,18 @@ package org.apache.spark.mllib.tree.impurity
 
 import javax.naming.OperationNotSupportedException
 
+/**
+ * Class for calculating the [[http://en.wikipedia.org/wiki/Gini_coefficient Gini
+ * coefficent]] during binary classification
+ */
 object Gini extends Impurity {
 
+  /**
+   * gini coefficient calculation
+   * @param c0 count of instances with label 0
+   * @param c1 count of instances with label 1
+   * @return gini coefficient value
+   */
   def calculate(c0 : Double, c1 : Double): Double = {
     if (c0 == 0 || c1 == 0) {
       0

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

@@ -19,9 +19,19 @@ package org.apache.spark.mllib.tree.impurity
 import javax.naming.OperationNotSupportedException
 import org.apache.spark.Logging
 
+/**
+ * Class for calculating variance during regression
+ */
 object Variance extends Impurity with Logging {
    def calculate(c0: Double, c1: Double): Double = throw new OperationNotSupportedException("Variance.calculate")
 
+  /**
+   * variance calculation
+   * @param count number of instances
+   * @param sum sum of labels
+   * @param sumSquares summation of squares of the labels
+   * @return
+   */
   def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
     val squaredLoss = sumSquares - (sum*sum)/count
     squaredLoss/count

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

@@ -18,6 +18,17 @@ package org.apache.spark.mllib.tree.model
 
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 
+/**
+ * Used for "binning" the features bins for faster best split calculation. For a continuous
+ * feature, a bin is determined by a low and a high "split". For a categorical feature,
+ * the a bin is determined using a single label value (category).
+ * @param lowSplit signifying the lower threshold for the continuous feature to be
+ *                 accepted in the bin
+ * @param highSplit signifying the upper threshold for the continuous feature to be
+ *                 accepted in the bin
+ * @param featureType type of feature -- categorical or continuous
+ * @param category categorical label value accepted in the bin
+ */
 case class Bin(lowSplit : Split, highSplit : Split, featureType : FeatureType, category : Double) {
 
 }

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

@@ -16,12 +16,23 @@
  */
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.rdd.RDD
 
+/**
+ * Model to store the decision tree parameters
+ * @param topNode root node
+ * @param algo algorithm type -- classification or regression
+ */
 class DecisionTreeModel(val topNode : Node, val algo : Algo) extends Serializable {
 
-  def predict(features : Array[Double]) = {
+  /**
+   * Predict values for a single data point using the model trained.
+   *
+   * @param features array representing a single data point
+   * @return Double prediction from the trained model
+   */
+  def predict(features : Array[Double]) : Double = {
     algo match {
       case Classification => {
         if (topNode.predictIfLeaf(features) < 0.5) 0.0 else 1.0
@@ -32,4 +43,15 @@ class DecisionTreeModel(val topNode : Node, val algo : Algo) extends Serializabl
     }
   }
 
+  /**
+   * Predict values for the given data set using the model trained.
+   *
+   * @param features RDD representing data points to be predicted
+   * @return RDD[Int] where each entry contains the corresponding prediction
+   */
+  def predict(features: RDD[Array[Double]]): RDD[Double] = {
+    features.map(x => predict(x))
+  }
+
+
 }

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

@@ -16,6 +16,11 @@
  */
 package org.apache.spark.mllib.tree.model
 
+/**
+ * Filter specifying a split and type of comparison to be applied on features
+ * @param split split specifying the feature index, type and threshold
+ * @param comparison integer specifying <,=,>
+ */
 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

@@ -16,6 +16,14 @@
  */
 package org.apache.spark.mllib.tree.model
 
+/**
+ * Information gain statistics for each split
+ * @param gain information gain value
+ * @param impurity current node impurity
+ * @param leftImpurity left node impurity
+ * @param rightImpurity right node impurity
+ * @param predict predicted value
+ */
 class InformationGainStats(
     val gain : Double,
     val impurity: Double,

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

@@ -20,6 +20,16 @@ import org.apache.spark.Logging
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 
+/**
+ * Node in a decision tree
+ * @param id integer node id
+ * @param predict predicted value at the node
+ * @param isLeaf whether the leaf is a node
+ * @param split split to calculate left and right nodes
+ * @param leftNode  left child
+ * @param rightNode right child
+ * @param stats information gain stats
+ */
 class Node ( val id : Int,
              val predict : Double,
              val isLeaf : Boolean,

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

@@ -18,6 +18,13 @@ package org.apache.spark.mllib.tree.model
 
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 
+/**
+ * Split applied to a feature
+ * @param feature feature index
+ * @param threshold threshold for continuous feature
+ * @param featureType type of feature -- categorical or continuous
+ * @param categories accepted values for categorical variables
+ */
 case class Split(
     feature: Int,
     threshold : Double,
@@ -29,12 +36,28 @@ case class Split(
       ", categories = " + categories
 }
 
-class DummyLowSplit(feature: Int, kind : FeatureType)
-  extends Split(feature, Double.MinValue, kind, List())
+/**
+ * Split with minimum threshold for continuous features. Helps with the smallest bin creation.
+ * @param feature feature index
+ * @param featureType type of feature -- categorical or continuous
+ */
+class DummyLowSplit(feature: Int, featureType : FeatureType)
+  extends Split(feature, Double.MinValue, featureType, List())
 
-class DummyHighSplit(feature: Int, kind : FeatureType)
-  extends Split(feature, Double.MaxValue, kind, 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)
+  extends Split(feature, Double.MaxValue, featureType, List())
 
-class DummyCategoricalSplit(feature: Int, kind : FeatureType)
-  extends Split(feature, Double.MaxValue, kind, List())
+/**
+ * Split with no acceptable feature values for categorical features. Helps with the first bin
+ * creation.
+ * @param feature feature index
+ * @param featureType type of feature -- categorical or continuous
+ */
+class DummyCategoricalSplit(feature: Int, featureType : FeatureType)
+  extends Split(feature, Double.MaxValue, featureType, List())