private void BuildRegressionTree(BoostTreeLoss boostTreeLoss, int iTree, IFindSplit findSplit, RandomSampler featureSampler, RandomSampler dataSampler)
{
this.responses = boostTreeLoss.PseudoResponse(iTree);
TreeNode root = new TreeNode();
root.isTerminal = true;
root.dataPoints = Vector.IndexArray(this.workIndex.Length);
this.tree = new TreeNode[2 * maxTreeSize - 1];
this.tree[0] = root;
for (int i = 0; i < maxTreeSize - 1; i++)
{
float maxGain = -1;
int bestRegion = -1;
TreeNode leftNode = new TreeNode();
TreeNode rightNode = new TreeNode();
//qiangwu: compute the best split for new nodes
// We only need to explore the last two nodes because they are and only they are new nodes i.e.
// for (int j = 2*i; j >= 0; j--)
for (int j = 0; j < 2 * i + 1; j++)
{
TreeNode curNode = this.tree[j];
//qiangwu: (assert curNode.split<0 && curNode.isTerminal) <==> (2*i-1 <= j <= 2*i)
if (curNode.split<0 && curNode.isTerminal && curNode.dataPoints.Length >= this.minNumSamples)
{
dataSampler.Shuffle(curNode.dataPoints.Length);
featureSampler.Shuffle(this.numFeatures);
Split bestSplit = findSplit.FindBestSplit(this.labelFeatureDataCoded, this.responses, curNode.dataPoints, this.workIndex, featureSampler, dataSampler, this.minNumSamples);
//qiangwu: the only way (bestSplit.feature < 0) not slippint is because this.dataColRange[dim]=1 for all
// dimensions. I.e. the values all of data points in every dimension are the same (or in one bin)
if (bestSplit.feature >= 0)
{
curNode.split = bestSplit.feature;
curNode.gain = (float)bestSplit.gain;
curNode.splitValueCoded = bestSplit.iThresh + 0.2F; // add 0.2 to avoid boundary check or floating point rounding
curNode.splitValue = this.labelFeatureDataCoded.ConvertToOrigData(curNode.split, curNode.splitValueCoded);
//SplitOneDim(curNode.dataPoints, regionSplitDim, regionSplitPoint, out curNode.leftPoints, out curNode.rightPoints);
}
}
if (curNode.gain > maxGain)
{
maxGain = curNode.gain;
bestRegion = j;
}
}
if (bestRegion == -1)
break;
TreeNode bestNode = this.tree[bestRegion];
SplitOneDim(bestNode.dataPoints, bestNode.split, (int)bestNode.splitValueCoded, out bestNode.leftPoints, out bestNode.rightPoints);
leftNode.isTerminal = true; leftNode.parent = bestRegion;
leftNode.dataPoints = bestNode.leftPoints;
rightNode.isTerminal = true; rightNode.parent = bestRegion;
rightNode.dataPoints = bestNode.rightPoints;
this.tree[2 * i + 1] = leftNode; this.tree[2 * i + 2] = rightNode;
this.featureImportance[bestNode.split] += bestNode.gain;
bestNode.leftChild = 2 * i + 1;
bestNode.rightChild = 2 * i + 2;
bestNode.isTerminal = false;
bestNode.gain = -1;
bestNode.dataPoints = null;
bestNode.leftPoints = null;
bestNode.rightPoints = null;
GC.Collect(); // hope for the best.
}
//qiangwu: compute the response of newly created region (node)
for (int i = 0; i < this.tree.Length; i++)
{
if (this.tree[i] != null && this.tree[i].isTerminal)
{
Debug.Assert(this.tree[i].dataPoints.Length >= this.minNumSamples, "Regression Tree split has problems");
float v = boostTreeLoss.Response(this.tree[i].dataPoints, this.workIndex, iTree);
//round the regional value to 5 decimal point
//to remove/alleviate the differences due to floating point precision
//so that different algorithms produces the same model/results
#if ROUND
this.tree[i].regionValue = (float)Math.Round(v, 5);
#else
this.tree[i].regionValue = v;
#endif //ROUND
this.tree[i].dataPoints = null;
this.tree[i].leftPoints = null;
this.tree[i].rightPoints = null;
GC.Collect();
}
}
}
public RegressionTree(LabelFeatureDataCoded labelFeatureDataCoded, BoostTreeLoss boostTreeLoss, int iTree, int[] workIndex,
RandomSampler featureSampler, RandomSampler dataSampler,
int maxTreeSize, int minNumSamples,
IFindSplit findSplit, TempSpace tempSpace)
{
this.labelFeatureDataCoded = labelFeatureDataCoded;
this.workIndex = workIndex;
this.numFeatures = labelFeatureDataCoded.NumFeatures;
this.maxTreeSize = maxTreeSize;
this.featureImportance = new float[this.numFeatures];
this.minNumSamples = minNumSamples;
//distributed setting
this.adjustFactor = 1.0F;
InitTempSpace(tempSpace);
BuildRegressionTree(boostTreeLoss, iTree, findSplit, dataSampler, featureSampler);
GC.Collect(); // hope for the best!!!
}