Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/79#discussion_r10439850
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -0,0 +1,1055 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.tree
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.tree.model._
+import org.apache.spark.{SparkContext, Logging}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.model.Split
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
+import org.apache.spark.mllib.tree.configuration.FeatureType._
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini,
Impurity}
+import scala.util.control.Breaks._
+
+/**
+ * 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 {
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+ * for DecisionTree
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(input: RDD[LabeledPoint]): DecisionTreeModel = {
+
+ //Cache input RDD for speedup during multiple passes
+ input.cache()
+ logDebug("algo = " + strategy.algo)
+
+ //Finding the splits and the corresponding bins (interval between the
splits) using a sample
+ // of the input data.
+ val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+ logDebug("numSplits = " + bins(0).length)
+
+ //Noting numBins for the input data
+ strategy.numBins = bins(0).length
+
+ //The depth of the decision tree
+ val maxDepth = strategy.maxDepth
+ //The max number of nodes possible given the depth of the tree
+ val maxNumNodes = scala.math.pow(2, maxDepth).toInt - 1
+ //Initalizing an array to hold filters applied to points for each node
+ val filters = new Array[List[Filter]](maxNumNodes)
+ //The filter at the top node is an empty list
+ filters(0) = List()
+ //Initializing an array to hold parent impurity calculations for each
node
+ val parentImpurities = new Array[Double](maxNumNodes)
+ //Dummy value for top node (updated during first split calculation)
+ val nodes = new Array[Node](maxNumNodes)
+
+ //The main-idea here is to perform level-wise training of the decision
tree nodes thus
+ // reducing the passes over the data from l to log2(l) where l is the
total number of nodes.
+ // Each data sample is checked for validity w.r.t to each node at a
given level -- i.e.,
+ // the sample is only used for the split calculation at the node if
the sampled would have
+ // still survived the filters of the parent nodes.
+ breakable {
+ for (level <- 0 until maxDepth) {
+
+ logDebug("#####################################")
+ logDebug("level = " + level)
+ logDebug("#####################################")
+
+ //Find best split for all nodes at a level
+ val splitsStatsForLevel = DecisionTree.findBestSplits(input,
parentImpurities, strategy,
+ level, filters, splits, bins)
+
+ for ((nodeSplitStats, index) <-
splitsStatsForLevel.view.zipWithIndex) {
+ //Extract info for nodes at the current level
+ extractNodeInfo(nodeSplitStats, level, index, nodes)
+ //Extract info for nodes at the next lower level
+ extractInfoForLowerLevels(level, index, maxDepth,
nodeSplitStats, parentImpurities,
+ filters)
+ logDebug("final best split = " + nodeSplitStats._1)
+
+ }
+ require(scala.math.pow(2, level) == splitsStatsForLevel.length)
+ //Check whether all the nodes at the current level at leaves
+ val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
+ logDebug("all leaf = " + allLeaf)
+ if (allLeaf) break //no more tree construction
+
+ }
+ }
+
+ //Initialize the top or root node of the tree
+ val topNode = nodes(0)
+ //Build the full tree using the node info calculated in the level-wise
best split calculations
+ topNode.build(nodes)
+
+ //Return a decision tree model
+ return new DecisionTreeModel(topNode, strategy.algo)
+ }
+
+ /**
+ * Extract the decision tree node information for th given tree level
and node index
+ */
+ private def extractNodeInfo(
+ nodeSplitStats: (Split, InformationGainStats),
+ level: Int,
+ index: Int,
+ nodes: Array[Node])
+ : Unit = {
+
+ val split = nodeSplitStats._1
+ val stats = nodeSplitStats._2
+ val nodeIndex = scala.math.pow(2, level).toInt - 1 + index
+ val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth - 1)
+ val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split),
None, None, Some(stats))
+ logDebug("Node = " + node)
+ nodes(nodeIndex) = node
+ }
+
+ /**
+ * Extract the decision tree node information for the children of the
node
+ */
+ private def extractInfoForLowerLevels(
+ level: Int,
+ index: Int,
+ maxDepth: Int,
+ nodeSplitStats: (Split, InformationGainStats),
+ parentImpurities: Array[Double],
+ filters: Array[List[Filter]])
+ : Unit = {
+
+ // 0 corresponds to the left child node and 1 corresponds to the right
child node.
+ for (i <- 0 to 1) {
+ //Calculating the index of the node from the node level and the index
at the current level
+ val nodeIndex = scala.math.pow(2, level + 1).toInt - 1 + 2 * index +
i
+ if (level < maxDepth - 1) {
+ val impurity = if (i == 0) {
+ nodeSplitStats._2.leftImpurity
+ } else {
+ nodeSplitStats._2.rightImpurity
+ }
+ logDebug("nodeIndex = " + nodeIndex + ", impurity = " + impurity)
+ //noting the parent impurities
+ parentImpurities(nodeIndex) = impurity
+ //noting the parents filters for the child nodes
+ val childFilter = new Filter(nodeSplitStats._1, if (i == 0) -1
else 1)
+ filters(nodeIndex) = childFilter :: filters((nodeIndex - 1) / 2)
+ for (filter <- filters(nodeIndex)) {
+ logDebug("Filter = " + filter)
+ }
+ }
+ }
+ }
+}
+
+object DecisionTree 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
+ * for DecisionTree
+ * @param strategy The configuration parameters for the tree algorithm
which specify the type
+ * of algoritm (classification, regression, etc.),
feature type (continuous,
+ * categorical), depth of the tree, quantile calculation
strategy, etc.
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(input: RDD[LabeledPoint], strategy: Strategy):
DecisionTreeModel = {
+ new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+ }
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as
+ * training data
+ * @param algo algo classification or regression
+ * @param impurity impurity criterion used for information gain
calculation
+ * @param maxDepth maxDepth maximum depth of the tree
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(
+ input: RDD[LabeledPoint],
+ algo: Algo,
+ impurity: Impurity,
+ maxDepth: Int)
+ : DecisionTreeModel = {
+ val strategy = new Strategy(algo,impurity,maxDepth)
+ new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+ }
+
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as
+ * training data for DecisionTree
+ * @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.
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(
+ input: RDD[LabeledPoint],
+ algo: Algo,
+ impurity: Impurity,
+ maxDepth: Int,
+ maxBins: Int,
+ quantileCalculationStrategy: QuantileStrategy,
+ categoricalFeaturesInfo: Map[Int,Int])
+ : DecisionTreeModel = {
+ val strategy = new Strategy(algo, impurity, maxDepth, maxBins,
quantileCalculationStrategy,
+ categoricalFeaturesInfo)
+ new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+ }
+
+ /**
+ * Returns an array 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
+ * @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
+ * @param level Level of the tree
+ * @param filters Filters for all nodes at a given level
+ * @param splits possible splits for all features
+ * @param bins possible bins for all features
+ * @return array of splits with best splits for all nodes at a given
level.
+ */
+ def findBestSplits(
+ input: RDD[LabeledPoint],
+ parentImpurities: Array[Double],
+ strategy: Strategy,
+ level: Int,
+ filters: Array[List[Filter]],
+ splits: Array[Array[Split]],
+ bins: Array[Array[Bin]])
+ : Array[(Split, InformationGainStats)] = {
+
+ //Common calculations for multiple nested methods
+ val numNodes = scala.math.pow(2, level).toInt
+ logDebug("numNodes = " + numNodes)
+ //Find the number of features by looking at the first sample
+ val numFeatures = input.take(1)(0).features.length
+ logDebug("numFeatures = " + numFeatures)
+ val numBins = strategy.numBins
+ logDebug("numBins = " + numBins)
+
+ /*Find the filters used before reaching the current code*/
+ def findParentFilters(nodeIndex: Int): List[Filter] = {
+ if (level == 0) {
+ List[Filter]()
+ } else {
+ val nodeFilterIndex = scala.math.pow(2, level).toInt - 1 +
nodeIndex
+ filters(nodeFilterIndex)
+ }
+ }
+
+ /**
+ * 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.
+ */
+ def isSampleValid(parentFilters: List[Filter], labeledPoint:
LabeledPoint): Boolean = {
+
+ //Leaf
+ if ((level > 0) & (parentFilters.length == 0) ){
+ return false
+ }
+
+ for (filter <- parentFilters) {
+ val features = labeledPoint.features
+ val featureIndex = filter.split.feature
+ val threshold = filter.split.threshold
+ val comparison = filter.comparison
+ val categories = filter.split.categories
+ val isFeatureContinuous = filter.split.featureType == Continuous
+ val feature = features(featureIndex)
+ if (isFeatureContinuous){
+ comparison match {
+ case(-1) => if (feature > threshold) return false
+ case(1) => if (feature <= threshold) return false
+ }
+ } else {
+ val containsFeature = categories.contains(feature)
+ comparison match {
+ case(-1) => if (!containsFeature) return false
+ case(1) => if (containsFeature) return false
+ }
+
+ }
+ }
+ true
+ }
+
+ /**
+ * Finds the right bin for the given feature
+ */
+ def findBin(
+ featureIndex: Int,
+ labeledPoint: LabeledPoint,
+ isFeatureContinuous: Boolean)
+ : Int = {
+
+ if (isFeatureContinuous){
+ for (binIndex <- 0 until strategy.numBins) {
+ val bin = bins(featureIndex)(binIndex)
+ val lowThreshold = bin.lowSplit.threshold
+ val highThreshold = bin.highSplit.threshold
+ val features = labeledPoint.features
+ if ((lowThreshold < features(featureIndex)) & (highThreshold >=
features(featureIndex))) {
+ return binIndex
+ }
+ }
+ throw new UnknownError("no bin was found for continuous variable.")
+ } else {
+
+ for (binIndex <- 0 until strategy.numBins) {
+ val bin = bins(featureIndex)(binIndex)
+ val category = bin.category
+ val features = labeledPoint.features
+ if (category == features(featureIndex)) {
+ return binIndex
+ }
+ }
+ throw new UnknownError("no bin was found for categorical
variable.")
+
+ }
+
+ }
+
+ /**
+ * Finds bins for all nodes (and all features) at a given level k
features,
+ * l nodes (level = log2(l)).
+ * Storage label, b11, b12, b13, .., b1k,
+ * b21, b22, .. , b2k,
+ * bl1, bl2, .. , blk
+ * Denotes invalid sample for tree by noting bin for feature 1 as -1
+ */
+ def findBinsForLevel(labeledPoint: LabeledPoint): Array[Double] = {
+
+
+ // calculating bin index and label per feature per node
+ val arr = new Array[Double](1 + (numFeatures * numNodes))
+ arr(0) = labeledPoint.label
+ for (nodeIndex <- 0 until numNodes) {
+ val parentFilters = findParentFilters(nodeIndex)
+ //Find out whether the sample qualifies for the particular node
+ val sampleValid = isSampleValid(parentFilters, labeledPoint)
+ val shift = 1 + numFeatures * nodeIndex
+ if (!sampleValid) {
+ //Add to invalid bin index -1
+ breakable {
+ for (featureIndex <- 0 until numFeatures) {
+ arr(shift + featureIndex) = -1
+ //Breaking since marking one bin is sufficient
+ break()
+ }
+ }
+ } else {
+ for (featureIndex <- 0 until numFeatures) {
+ //logDebug("shift+featureIndex =" + (shift+featureIndex))
+ val isFeatureContinous =
strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
+ arr(shift + featureIndex) = findBin(featureIndex,
labeledPoint,isFeatureContinous)
+ }
+ }
+
+ }
+ arr
+ }
+
+ /**
+ Performs a sequential aggregation over a partition for classification.
+
+ for p bins, k features, l nodes (level = log2(l)) storage is of the
form:
+ b111_left_count,b111_right_count, .... , ..
+ .. bpk1_left_count, bpk1_right_count, .... , ..
+ .. bpkl_left_count, bpkl_right_count
+
+ @param agg Array[Double] storing aggregate calculation of size
+ 2*numSplits*numFeatures*numNodes for classification
+ @param arr Array[Double] of size 1+(numFeatures*numNodes)
+ @return Array[Double] storing aggregate calculation of size
2*numSplits*numFeatures*numNodes
+ for classification
+ */
+ def classificationBinSeqOp(arr: Array[Double], agg: Array[Double]) {
+ for (node <- 0 until numNodes) {
+ val validSignalIndex = 1 + numFeatures * node
+ val isSampleValidForNode = if (arr(validSignalIndex) != -1) true
else false
+ if (isSampleValidForNode) {
+ val label = arr(0)
+ for (featureIndex <- 0 until numFeatures) {
+ val arrShift = 1 + numFeatures * node
+ val aggShift = 2 * numBins * numFeatures * node
+ val arrIndex = arrShift + featureIndex
+ val aggIndex = aggShift + 2 * featureIndex * numBins +
arr(arrIndex).toInt * 2
+ label match {
+ case (0.0) => agg(aggIndex) = agg(aggIndex) + 1
+ case (1.0) => agg(aggIndex + 1) = agg(aggIndex + 1) + 1
+ }
+ }
+ }
+ }
+ }
+
+ /**
+ Performs a sequential aggregation over a partition for regression.
+
+ for p bins, k features, l nodes (level = log2(l)) storage is of the
form:
+ b111_count,b111_sum, b111_sum_squares .... , ..
+ .. bpk1_count, bpk1_sum, bpk1_sum_squares, .... , ..
+ .. bpkl_count, bpkl_sum, bpkl_sum_squares
+
+ @param agg Array[Double] storing aggregate calculation of size
+ 3*numSplits*numFeatures*numNodes for classification
+ @param arr Array[Double] of size 1+(numFeatures*numNodes)
+ @return Array[Double] storing aggregate calculation of size
3*numSplits*numFeatures*numNodes
+ for regression
+ */
+ def regressionBinSeqOp(arr: Array[Double], agg: Array[Double]) {
+ for (node <- 0 until numNodes) {
+ val validSignalIndex = 1 + numFeatures * node
+ val isSampleValidForNode = if (arr(validSignalIndex) != -1) true
else false
+ if (isSampleValidForNode) {
+ val label = arr(0)
+ for (feature <- 0 until numFeatures) {
+ val arrShift = 1 + numFeatures * node
+ val aggShift = 3 * numBins * numFeatures * node
+ val arrIndex = arrShift + feature
+ val aggIndex = aggShift + 3 * feature * numBins +
arr(arrIndex).toInt * 3
+ //count, sum, sum^2
+ agg(aggIndex) = agg(aggIndex) + 1
+ agg(aggIndex + 1) = agg(aggIndex + 1) + label
+ agg(aggIndex + 2) = agg(aggIndex + 2) + label*label
+ }
+ }
+ }
+ }
+
+ /**
+ * 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, .... , ....
+ * bpk1_left_count, bpk1_right_count, .... , ...., bpkl_left_count,
bpkl_right_count
+ * @param agg Array[Double] storing aggregate calculation of size
+ * 2*numSplits*numFeatures*numNodes for classification and
+ * 3*numSplits*numFeatures*numNodes for regression
+ * @param arr Array[Double] of size 1+(numFeatures*numNodes)
+ * @return Array[Double] storing aggregate calculation of size
+ * 2*numSplits*numFeatures*numNodes for classification and
+ * 3*numSplits*numFeatures*numNodes for regression
+ */
+ def binSeqOp(agg: Array[Double], arr: Array[Double]): Array[Double] = {
+ strategy.algo match {
+ case Classification => classificationBinSeqOp(arr, agg)
+ case Regression => regressionBinSeqOp(arr, agg)
+ }
+ agg
+ }
+
+ val binAggregateLength = strategy.algo match {
+ case Classification => 2*numBins * numFeatures * numNodes
+ case Regression => 3*numBins * numFeatures * numNodes
+ }
+ logDebug("binAggregateLength = " + binAggregateLength)
+
+ /**
+ * 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
+ * @return Combined aggregate from agg1 and agg2
+ */
+ def binCombOp(agg1: Array[Double], agg2: Array[Double]): Array[Double]
= {
+ strategy.algo match {
+ case Classification => {
+ val combinedAggregate = new Array[Double](binAggregateLength)
+ for (index <- 0 until binAggregateLength){
+ combinedAggregate(index) = agg1(index) + agg2(index)
+ }
+ combinedAggregate
+ }
+ case Regression => {
+ val combinedAggregate = new Array[Double](binAggregateLength)
+ for (index <- 0 until binAggregateLength){
+ combinedAggregate(index) = agg1(index) + agg2(index)
+ }
+ combinedAggregate
+ }
+ }
+ }
+
+ logDebug("input = " + input.count)
+ val binMappedRDD = input.map(x => findBinsForLevel(x))
+ logDebug("binMappedRDD.count = " + binMappedRDD.count)
+ //calculate bin aggregates
+
+ val binAggregates = {
+
binMappedRDD.aggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp,binCombOp)
+ }
+ logDebug("binAggregates.length = " + binAggregates.length)
+ //binAggregates.foreach(x => logDebug(x))
+
+ /**
+ * Calculates the information gain for all splits
+ * @param leftNodeAgg left node aggregates
+ * @param featureIndex feature index
+ * @param splitIndex split index
+ * @param rightNodeAgg right node aggregate
+ * @param topImpurity impurity of the parent node
+ * @return information gain and statistics for all splits
+ */
+ def calculateGainForSplit(
+ leftNodeAgg: Array[Array[Double]],
+ featureIndex: Int,
+ splitIndex: Int,
+ rightNodeAgg: Array[Array[Double]],
+ topImpurity: Double)
+ : InformationGainStats = {
+
+ strategy.algo match {
+ case Classification => {
+
+ val left0Count = leftNodeAgg(featureIndex)(2 * splitIndex)
+ val left1Count = leftNodeAgg(featureIndex)(2 * splitIndex + 1)
+ val leftCount = left0Count + left1Count
+
+ val right0Count = rightNodeAgg(featureIndex)(2 * splitIndex)
+ val right1Count = rightNodeAgg(featureIndex)(2 * splitIndex + 1)
+ val rightCount = right0Count + right1Count
+
+ val impurity = {
+ if (level > 0) {
+ topImpurity
+ } else {
+ strategy.impurity.calculate(left0Count + right0Count,
left1Count + right1Count)
+ }
+ }
+
+ if (leftCount == 0) {
+ return new InformationGainStats(0, topImpurity,
Double.MinValue, topImpurity,1)
+ }
+ if (rightCount == 0) {
+ return new InformationGainStats(0, topImpurity, topImpurity,
Double.MinValue,0)
+ }
+
+ val leftImpurity = strategy.impurity.calculate(left0Count,
left1Count)
+ val rightImpurity = strategy.impurity.calculate(right0Count,
right1Count)
+
+ val leftWeight = leftCount.toDouble / (leftCount + rightCount)
+ val rightWeight = rightCount.toDouble / (leftCount + rightCount)
+
+ val gain = {
+ if (level > 0) {
+ impurity - leftWeight * leftImpurity - rightWeight *
rightImpurity
+ } else {
+ impurity - leftWeight * leftImpurity - rightWeight *
rightImpurity
+ }
+ }
+
+ //val predict = leftCount / (leftCount + rightCount)
+ val predict = (left1Count + right1Count) / (leftCount +
rightCount)
+
+ new InformationGainStats(gain, impurity, leftImpurity,
rightImpurity, predict)
+ }
+ case Regression => {
+ val leftCount = leftNodeAgg(featureIndex)(3 * splitIndex)
+ val leftSum = leftNodeAgg(featureIndex)(3 * splitIndex + 1)
+ val leftSumSquares = leftNodeAgg(featureIndex)(3 * splitIndex +
2)
+
+ val rightCount = rightNodeAgg(featureIndex)(3 * splitIndex)
+ val rightSum = rightNodeAgg(featureIndex)(3 * splitIndex + 1)
+ val rightSumSquares = rightNodeAgg(featureIndex)(3 * splitIndex
+ 2)
+
+ val impurity = {
+ if (level > 0) {
+ topImpurity
+ } else {
+ val count = leftCount + rightCount
+ val sum = leftSum + rightSum
+ val sumSquares = leftSumSquares + rightSumSquares
+ strategy.impurity.calculate(count, sum, sumSquares)
+ }
+ }
+
+ if (leftCount == 0) {
+ return new InformationGainStats(0, topImpurity,
Double.MinValue, topImpurity,
+ rightSum/rightCount)
+ }
+ if (rightCount == 0) {
+ return new InformationGainStats(0, topImpurity ,topImpurity,
+ Double.MinValue, leftSum/leftCount)
+ }
+
+ val leftImpurity = strategy.impurity.calculate(leftCount,
leftSum, leftSumSquares)
+ val rightImpurity = strategy.impurity.calculate(rightCount,
rightSum, rightSumSquares)
+
+ val leftWeight = leftCount.toDouble / (leftCount + rightCount)
+ val rightWeight = rightCount.toDouble / (leftCount + rightCount)
+
+ val gain = {
+ if (level > 0) {
+ impurity - leftWeight * leftImpurity - rightWeight *
rightImpurity
+ } else {
+ impurity - leftWeight * leftImpurity - rightWeight *
rightImpurity
+ }
+ }
+
+ val predict = (leftSum + rightSum)/(leftCount + rightCount)
+ new InformationGainStats(gain, impurity, leftImpurity,
rightImpurity, predict)
+
+ }
+ }
+ }
+
+ /**
+ * Extracts left and right split aggregates
+ * @param binData Array[Double] of size 2*numFeatures*numSplits
+ * @return (leftNodeAgg, rightNodeAgg) tuple of type (Array[Double],
+ * Array[Double]) where each array is of
size(numFeature,2*(numSplits-1))
+ */
+ def extractLeftRightNodeAggregates(
+ binData: Array[Double]): (Array[Array[Double]],
Array[Array[Double]]) = {
+
+ strategy.algo match {
+ case Classification => {
+
+ val leftNodeAgg = Array.ofDim[Double](numFeatures, 2 * (numBins
- 1))
+ val rightNodeAgg = Array.ofDim[Double](numFeatures, 2 * (numBins
- 1))
+ for (featureIndex <- 0 until numFeatures) {
+ val shift = 2*featureIndex*numBins
+ leftNodeAgg(featureIndex)(0) = binData(shift + 0)
+ leftNodeAgg(featureIndex)(1) = binData(shift + 1)
+ rightNodeAgg(featureIndex)(2 * (numBins - 2))
+ = binData(shift + (2 * (numBins - 1)))
+ rightNodeAgg(featureIndex)(2 * (numBins - 2) + 1)
+ = binData(shift + (2 * (numBins - 1)) + 1)
+ for (splitIndex <- 1 until numBins - 1) {
+ leftNodeAgg(featureIndex)(2 * splitIndex) = binData(shift +
2*splitIndex) +
+ leftNodeAgg(featureIndex)(2 * splitIndex - 2)
+ leftNodeAgg(featureIndex)(2 * splitIndex + 1) =
binData(shift + 2*splitIndex + 1) +
+ leftNodeAgg(featureIndex)(2 * splitIndex - 2 + 1)
+ rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex)) =
+ binData(shift + (2 *(numBins - 2 - splitIndex))) +
+ rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex))
+ rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex) +
1) =
+ binData(shift + (2* (numBins - 2 - splitIndex) + 1)) +
+ rightNodeAgg(featureIndex)(2 * (numBins - 1 -
splitIndex) + 1)
+ }
+ }
+ (leftNodeAgg, rightNodeAgg)
+ }
+
+ case Regression => {
+
+ val leftNodeAgg = Array.ofDim[Double](numFeatures, 3 * (numBins
- 1))
+ val rightNodeAgg = Array.ofDim[Double](numFeatures, 3 * (numBins
- 1))
+ for (featureIndex <- 0 until numFeatures) {
+ val shift = 3*featureIndex*numBins
+ leftNodeAgg(featureIndex)(0) = binData(shift + 0)
+ leftNodeAgg(featureIndex)(1) = binData(shift + 1)
+ leftNodeAgg(featureIndex)(2) = binData(shift + 2)
+ rightNodeAgg(featureIndex)(3 * (numBins - 2)) =
+ binData(shift + (3 * (numBins - 1)))
+ rightNodeAgg(featureIndex)(3 * (numBins - 2) + 1) =
+ binData(shift + (3 * (numBins - 1)) + 1)
+ rightNodeAgg(featureIndex)(3 * (numBins - 2) + 2) =
+ binData(shift + (3 * (numBins - 1)) + 2)
+ for (splitIndex <- 1 until numBins - 1) {
+ leftNodeAgg(featureIndex)(3 * splitIndex)
+ = binData(shift + 3*splitIndex) +
+ leftNodeAgg(featureIndex)(3 * splitIndex - 3)
+ leftNodeAgg(featureIndex)(3 * splitIndex + 1)
+ = binData(shift + 3*splitIndex + 1) +
+ leftNodeAgg(featureIndex)(3 * splitIndex - 3 + 1)
+ leftNodeAgg(featureIndex)(3 * splitIndex + 2)
+ = binData(shift + 3*splitIndex + 2) +
+ leftNodeAgg(featureIndex)(3 * splitIndex - 3 + 2)
+ rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex))
+ = binData(shift + (3 * (numBins - 2 - splitIndex))) +
+ rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex))
+ rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) +
1)
+ = binData(shift + (3 * (numBins - 2 - splitIndex) + 1)) +
+ rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex)
+ 1)
+ rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) +
2)
+ = binData(shift + (3 * (numBins - 2 - splitIndex) + 2)) +
+ rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex)
+ 2)
+ }
+ }
+ (leftNodeAgg, rightNodeAgg)
+ }
+ }
+ }
+
+ def calculateGainsForAllNodeSplits(
+ leftNodeAgg: Array[Array[Double]],
+ rightNodeAgg: Array[Array[Double]],
+ nodeImpurity: Double)
+ : Array[Array[InformationGainStats]] = {
+
+ val gains = Array.ofDim[InformationGainStats](numFeatures, numBins -
1)
+
+ for (featureIndex <- 0 until numFeatures) {
+ for (splitIndex <- 0 until numBins -1) {
+ gains(featureIndex)(splitIndex) =
calculateGainForSplit(leftNodeAgg, featureIndex,
+ splitIndex, rightNodeAgg, nodeImpurity)
+ }
+ }
+ gains
+ }
+
+ /**
+ * Find the best split for a node given bin aggregate data
+ * @param binData Array[Double] of size 2*numSplits*numFeatures
+ * @param nodeImpurity impurity of the top node
+ * @return
+ */
+ def binsToBestSplit(
+ binData: Array[Double],
+ nodeImpurity: Double)
+ : (Split, InformationGainStats) = {
+
+ logDebug("node impurity = " + nodeImpurity)
+ val (leftNodeAgg, rightNodeAgg) =
extractLeftRightNodeAggregates(binData)
+ val gains = calculateGainsForAllNodeSplits(leftNodeAgg,
rightNodeAgg, nodeImpurity)
+
+ val (bestFeatureIndex,bestSplitIndex, gainStats) = {
+ var bestFeatureIndex = 0
+ var bestSplitIndex = 0
+ //Initialization with infeasible values
+ var bestGainStats = new
InformationGainStats(Double.MinValue,-1.0,-1.0,-1.0,-1)
+ for (featureIndex <- 0 until numFeatures) {
+ for (splitIndex <- 0 until numBins - 1){
+ val gainStats = gains(featureIndex)(splitIndex)
+ if(gainStats.gain > bestGainStats.gain) {
+ bestGainStats = gainStats
+ bestFeatureIndex = featureIndex
+ bestSplitIndex = splitIndex
+ }
+ }
+ }
+ (bestFeatureIndex,bestSplitIndex,bestGainStats)
+ }
+
+ logDebug("best split bin = " +
bins(bestFeatureIndex)(bestSplitIndex))
+ logDebug("best split bin = " +
splits(bestFeatureIndex)(bestSplitIndex))
+ (splits(bestFeatureIndex)(bestSplitIndex),gainStats)
+ }
+
+ //Calculate best splits for all nodes at a given level
+ val bestSplits = new Array[(Split, InformationGainStats)](numNodes)
+ def getBinDataForNode(node: Int): Array[Double] = {
+ strategy.algo match {
+ case Classification => {
+ val shift = 2 * node * numBins * numFeatures
+ val binsForNode = binAggregates.slice(shift, shift + 2 * numBins
* numFeatures)
+ binsForNode
+ }
+ case Regression => {
+ val shift = 3 * node * numBins * numFeatures
+ val binsForNode = binAggregates.slice(shift, shift + 3 * numBins
* numFeatures)
+ binsForNode
+ }
+ }
+ }
+
+ for (node <- 0 until numNodes){
+ val nodeImpurityIndex = scala.math.pow(2, level).toInt - 1 + node
+ val binsForNode: Array[Double] = getBinDataForNode(node)
+ logDebug("nodeImpurityIndex = " + nodeImpurityIndex)
+ val parentNodeImpurity = parentImpurities(nodeImpurityIndex)
+ logDebug("node impurity = " + parentNodeImpurity)
+ bestSplits(node) = binsToBestSplit(binsForNode, parentNodeImpurity)
+ }
+ bestSplits
+ }
+
+
+
+ /**
+ * Returns split and bins for decision tree calculation.
+ * @param input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+ * for DecisionTree
+ * @param strategy
[[org.apache.spark.mllib.tree.configuration.Strategy]] instance containing
+ * parameters for construction the DecisionTree
+ * @return a tuple of (splits,bins) where splits is an Array of
[org.apache.spark.mllib.tree
+ * .model.Split] of size (numFeatures,numSplits-1) and bins is
an Array of [org.apache
+ * .spark.mllib.tree.model.Bin] of size (numFeatures,numSplits1)
+ */
+ def findSplitsBins(
+ input: RDD[LabeledPoint],
+ strategy: Strategy)
+ : (Array[Array[Split]], Array[Array[Bin]]) = {
+
+ val count = input.count()
+
+ //Find the number of features by looking at the first sample
+ val numFeatures = input.take(1)(0).features.length
--- End diff --
use RDD.first() here
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