/// <summary>
/// This method implements the main functionality of stochastic gradient boosting
/// </summary>
private void BuildBoostTree(Metrics metrics, BoostTreeLoss boostTreeLoss, DataFeatureSampleRate dataFeatureSampleRate,
int maxTreeSize, int minNumSamples, int numIter,
int cThreads, Random r)
{
float minValidationErr = 100;
float[] funValueGain = new float[this.numSamples];
//(1) compute scores produced by the sub-model
boostTreeLoss.ModelEval(this.subModel, this.labelFeatureDataCoded, this.subModelScore);
//(2) compute the corresponding function values;
boostTreeLoss.ModelScoresToFuncValues();
//(3) compute the metrics of the sub-model
int m = optIter = 0;
metrics.ComputeMetrics(boostTreeLoss.ModelScores, m, false);
#if VERBOSE
Console.WriteLine(metrics.ResultsHeaderStr());
Console.WriteLine(metrics.ResultsStr(m));
#endif
//(4) creat samplers to sub-sampl the features and data during node spliting
RandomSampler featureSampler = new RandomSampler(r);
RandomSampler dataSampler = new RandomSampler(r);
//(5) creat the object that does node splitting
#if SINGLE_THREAD
// single-threaded
this.findSplit = new FindSplitSync();
#else
// multi-threaded
this.findSplit = new FindSplitAsync(cThreads);
#endif //SINGLE_THREAD
//(6) Iteratively building boosted trees
for (m = 0; m < numIter; m++)
{
// selecting a fraction of data groups for each iteration
float sampleRate = dataFeatureSampleRate.SampleDataGroupRate(m);
DataSet workDataSet = this.labelFeatureDataCoded.DataGroups.GetDataPartition(DataPartitionType.Train, sampleRate, r);
workDataSet.Sort(); // sorting gains some noticable speedup.
// compute the pseudo response of the current system
boostTreeLoss.ComputePseudoResponse(workDataSet);
//set the data and feature sampling rate for node spliting in this iteration
featureSampler.SampleRate = dataFeatureSampleRate.SampleFeatureRate(m);
dataSampler.SampleRate = dataFeatureSampleRate.SampleDataRate(m);
// fit a residual model (regression trees) from the pesuso response
// to compensate the error of the current system
for (int k = 0; k < boostTreeLoss.NumTreesPerIteration; k++)
{
//only use the important data points if necessary
int[] trimIndex = boostTreeLoss.TrimIndex(workDataSet, k, m);
//build a regression tree according to the pseduo-response
this.regressionTrees[m, k] = new RegressionTree(this.labelFeatureDataCoded, boostTreeLoss, k, trimIndex,
dataSampler, featureSampler, maxTreeSize, minNumSamples, this.findSplit, this.tempSpace);
//compute the function value of all data points produced by the newly generated regression tree
this.regressionTrees[m, k].PredictFunValue(this.labelFeatureDataCoded, ref funValueGain);
//try to do a more global optimalization - refine the leaf node response of a decision tree
//by looking at all the training data points, instead of only the ones falling into the regaion.
//Here we are estimate and apply a global mutiplication factor for all leaf nodes
float adjFactor = (m>0) ? boostTreeLoss.ComputeResponseAdjust(funValueGain) : 1.0F;
//apply the multiplication factor to the leaf nodes of the newly generated regression tree
this.regressionTrees[m, k].AdjustResponse(adjFactor);
//update the function value for all data points given the new regression tree
boostTreeLoss.AccFuncValueGain(funValueGain, adjFactor, k);
}
//compute the metrics of the current system
boostTreeLoss.FuncValuesToModelScores();
metrics.ComputeMetrics(boostTreeLoss.ModelScores, m + 1, false);
#if VERBOSE
Console.WriteLine(metrics.ResultsStr(m+1));
#endif
//keep track of the best (minimal Error) iteration on the Validation data set
this.optIter = metrics.GetBest(DataPartitionType.Validation, ref minValidationErr);
if ((m+1) % 5 == 0) // save the tree every 5 iterations
SaveBoostTree();
}
if (this.findSplit != null)
{
this.findSplit.Cleanup();
}
}
public void Predict(LabelFeatureData labelFeatureData, int numIter,
BoostTreeLoss boostTreeLoss,
Metrics metrics, //reporting the error for each iteration if the following are set
bool silent // If true, only report results on the last iteration
)
{
if (numIter > this.TotalIter)
numIter = this.TotalIter;
boostTreeLoss.Reset(labelFeatureData.NumDataPoint);
//(1) compute the probabilities produced by the sub-model
boostTreeLoss.ModelEval(this.subModel, labelFeatureData, null);
//(2) compute the corresponding function values;
boostTreeLoss.ModelScoresToFuncValues();
if (metrics != null)
{
metrics.ComputeMetrics(boostTreeLoss.ModelScores, 0, this.optIter == 0);
#if VERBOSE
Console.WriteLine(metrics.ResultsHeaderStr());
Console.WriteLine(metrics.ResultsStr(0));
#endif
}
//(3) accumulate the function values for each boosted regression tree
int numSamples = labelFeatureData.NumDataPoint;
float[] funValueGain = new float[numSamples];
#if GET_PER_DOC_PER_ITER_SCORES
float[][] saveScores = ArrayUtils.FloatMatrix(numIter+2, labelFeatureData.NumDataPoint); // We will take transpose when we print
for (int i = 0; i < labelFeatureData.NumDataPoint; ++i)
{
saveScores[0][i] = labelFeatureData.GetGroupId(i);
saveScores[1][i] = labelFeatureData.GetLabel(i);
}
#endif
for (int m = 0; m < numIter; m++)
{
// fit a residual model (regression trees) from the pesuso response
// to compensate the error of the current system
for (int k = 0; k < boostTreeLoss.NumTreesPerIteration; k++)
{
if (this.regressionTrees[m, 0] == null)
break;
#if GET_PER_DOC_PER_ITER_SCORES
this.regressionTrees[m, k].PredictFunValueNKeepScores(labelFeatureData, this.Train2TestIdx, funValueGain, saveScores[m+2]);
#else
this.regressionTrees[m, k].PredictFunValue(labelFeatureData, this.Train2TestIdx, funValueGain);
#endif
boostTreeLoss.AccFuncValueGain(funValueGain, 1.0f, k);
}
if (metrics != null)
{
//compute the metrics of the current system
boostTreeLoss.FuncValuesToModelScores();
metrics.ComputeMetrics(boostTreeLoss.ModelScores, m + 1, this.optIter == m + 1);
if(m==numIter-1 || !silent)
Console.WriteLine(metrics.ResultsStr(m + 1));
}
}
#if GET_PER_DOC_PER_ITER_SCORES
using (StreamWriter sw = new StreamWriter("allScores.tsv"))
{
sw.Write("m:QueryID\tm:Rating"); // Write the header (with no tab at the end!)
for (int j = 1; j < numIter+1; ++j)
sw.Write("\tFtr_" + j.ToString("0000"));
sw.WriteLine();
for (int j = 0; j < labelFeatureData.NumDataPoint; ++j)
{
sw.Write("{0}\t{1}", saveScores[0][j], saveScores[1][j]); // Write the query ID and label
for (int m = 2; m < numIter + 2; ++m)
sw.Write("\t{0:G6}", saveScores[m][j]);
sw.WriteLine();
}
}
#endif
if (metrics == null)
{
boostTreeLoss.FuncValuesToModelScores();
}
else
metrics.SaveScores("DataScores.txt", boostTreeLoss.ModelScores);
}
public void AddWeakLearner(RegressionTree[] candidateTree, float[] funValueGain, int m, Metrics metrics, BoostTreeLoss boostTreeLoss, DataFeatureSampleRate dataFeatureSampleRate, int maxTreeSize, int minNumSamples, int cThreads, Random r)
{
//update the function value for all data points given the new regression tree
for (int i = 0; i < boostTreeLoss.NumTreesPerIteration; i++)
{
candidateTree[i].PredictFunValue(this.labelFeatureDataCoded, true, ref funValueGain);
this.regressionTrees[m, i] = candidateTree[i];
boostTreeLoss.AccFuncValueGain(funValueGain, candidateTree[i].AdjustFactor, i);
}
}
