public void DistributedBuild(Metrics metrics, DataFeatureSampleRate dataFeatureSampleRate,
int maxTreeSize, int minNumSamples, int numIter,
int cThreads, Random r)
{
this.regressionTrees = new RegressionTree[numIter, this.boostTreeLoss.NumTreesPerIteration];
this.tempSpace = new TempSpace(this.numSamples);
DistributedBuildBoostTree(metrics, this.boostTreeLoss, dataFeatureSampleRate, maxTreeSize, minNumSamples, numIter, cThreads, r);
SaveBoostTree();
}
/// <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 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);
}
}
public RegressionTree[] GetNextWeakLearner(int m, float[] funValueGain, Metrics metrics, BoostTreeLoss boostTreeLoss, DataFeatureSampleRate dataFeatureSampleRate, RandomSampler dataSampler, RandomSampler featureSampler,
int maxTreeSize, int minNumSamples, int cThreads, Random r)
{
// select a fraction of data groups for this 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 pseudo response
// to compensate the error of the current system
RegressionTree[] newTree = new RegressionTree[boostTreeLoss.NumTreesPerIteration];
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
newTree[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
newTree[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
newTree[k].AdjustResponse(adjFactor);
newTree[k].AdjustFactor = adjFactor;
}
//return the k regression trees
return newTree;
}
/// <summary>
/// This method implements the main functionality of stochastic gradient boosting, for distributed computing
/// </summary>
private void DistributedBuildBoostTree(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++)
{
//returns array of regression trees (one per class k) for this iteration
RegressionTree[] candidateTree = GetNextWeakLearner(m, funValueGain, metrics,boostTreeLoss,dataFeatureSampleRate, dataSampler, featureSampler, maxTreeSize,minNumSamples,cThreads,r);
AddWeakLearner(candidateTree, funValueGain, m, metrics, boostTreeLoss, dataFeatureSampleRate, maxTreeSize, minNumSamples, cThreads, r);
//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();
}
}
/// <summary>
/// Main Program
/// </summary>
/// <param name="args">
/// Should contain five parameters in the order of
/// file name for training source data (DataProcess class),
/// file name for saving the tree
/// tree size, i.e., maximum number of terminal nodes, usually 16 - 20
/// learning rate, usually 0.02 - 0.06
/// number of iterations, usallay >500(can be pruned later)
/// </param>
public static void Main(string[] args)
{
try
{
TrainArgs cmd = new TrainArgs(args);
Random r = null;
if (cmd.seed == -1)
{
r = new Random();
}
else
{
r = new Random(cmd.seed);
}
string[] activeFeatureNames = null;
//read and process only a subset of activated features as specified in the activeFeatureFile
if (cmd.activeFeatureFile != null)
{
activeFeatureNames = TsvFileLoader.ReadFeatureNames(cmd.activeFeatureFile);
}
//feature parser: special module that understand MSN style value encoding
MsnFeatureParser featureParser = new MsnFeatureParser(activeFeatureNames);
//the column name for label:
string[] labelName = { cmd.labelName };
//label/rating parser:
IParser<float> RateParser = new MsnLabelParser(labelName, cmd.labelNameValueFile);
//data boundary: no boundary
OnelineGroup noBounadry = new OnelineGroup();
//Load coded data if exist
LabelFeatureDataCoded trainLabelFeatureDataCoded = (CLabelFeatureDataCoded)CLabelFeatureData.Load(cmd.trainFile, featureParser, RateParser, noBounadry, typeof(CLabelFeatureDataCoded), activeFeatureNames, cmd.cThreads, cmd.SparseCoded);
LabelFeatureData validLabelFeatureData = (CLabelFeatureData)CLabelFeatureData.Load(cmd.validFile, featureParser, RateParser, noBounadry, typeof(CLabelFeatureData), activeFeatureNames, cmd.cThreads, cmd.SparseCoded);
LabelFeatureData testLabelFeatureData = (CLabelFeatureData)CLabelFeatureData.Load(cmd.testFile, featureParser, RateParser, noBounadry, typeof(CLabelFeatureData), activeFeatureNames, cmd.cThreads, cmd.SparseCoded);
//build composite data - an aggregated data object that keeps tract of train/valid/test data
CLabelFeatureDataCodedComposite labelFeatureDataCoded = CLabelFeatureDataCodedComposite.Create(trainLabelFeatureDataCoded, validLabelFeatureData, testLabelFeatureData);
//initial submodel to boost on
Model subModel = null;
if (cmd.subModel != null && cmd.subModel.Length > 0)
{
if (cmd.cLayer > 0)
{
string[] layerNames = new string[cmd.cLayer];
for (int i = 0; i < cmd.cLayer; i++)
{
string num = "";
if (cmd.cLayer > 1)
{
num = (i + 1).ToString();
}
layerNames[i] = cmd.subModel + "layer" + num + ".txt";
}
subModel = new NNModel(layerNames);
}
else
{
string iniName = cmd.subModel + ".ini";
subModel = NNModelMSN.Create(iniName);
}
}
if (subModel != null && !subModel.SetFeatureNames(labelFeatureDataCoded.FeatureNames))
{
Console.WriteLine("Fail to initialize specified submodel - training with empty submodel");
subModel = null;
}
LabelConverter labelConvert = new LabelConverterNull();
LabelFeatureData subModelScore = null;
McBoostTreeLoss boostTreeLoss = new McBoostTreeLoss(labelFeatureDataCoded, labelConvert, cmd.learnRate);
BoostTree boostTree = new BoostTree(labelFeatureDataCoded, subModelScore, subModel, boostTreeLoss,
cmd.binaryTreeFile, cmd.textTreeFile);
//set up the error metrics that we like to keep tract of during testing
DataPartitionType[] dataTypes = { DataPartitionType.Train, DataPartitionType.Validation, DataPartitionType.Test };
//dp.LabelFeatureData is the data we are evaluating
Metrics metrics = new ClassError(labelFeatureDataCoded, labelConvert, dataTypes);
DataFeatureSampleRate dataFeatureSampleRate = new DataFeatureSampleRate(cmd.sampleFeatureRate, cmd.sampleDataRate, cmd.sampleDataGroupRate);
boostTree.Build(metrics, dataFeatureSampleRate, cmd.cLeafNodes, cmd.minNumSamples, cmd.numIter, cmd.cThreads, r);
metrics.SaveAllResults("TrainingErrHistory.txt");
}
catch (Exception exc)
{
Console.WriteLine(exc.Message);
}
}