private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
: base(original, cloner) {
if (original.actualModel != null) this.actualModel = cloner.Clone(original.actualModel);
this.trainingProblemData = cloner.Clone(original.trainingProblemData);
this.lossFunction = cloner.Clone(original.lossFunction);
this.seed = original.seed;
this.iterations = original.iterations;
this.maxSize = original.maxSize;
this.r = original.r;
this.m = original.m;
this.nu = original.nu;
}
private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
: base(original, cloner)
{
if (original.actualModel != null)
{
this.actualModel = cloner.Clone(original.actualModel);
}
this.trainingProblemData = cloner.Clone(original.trainingProblemData);
this.lossFunction = cloner.Clone(original.lossFunction);
this.seed = original.seed;
this.iterations = original.iterations;
this.maxSize = original.maxSize;
this.r = original.r;
this.m = original.m;
this.nu = original.nu;
}
private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
: base(original, cloner)
{
IGradientBoostedTreesModel clonedModel = null;
if (original.actualModel.IsValueCreated)
{
clonedModel = cloner.Clone(original.ActualModel);
}
actualModel = new Lazy <IGradientBoostedTreesModel>(CreateLazyInitFunc(clonedModel)); // only capture clonedModel in the closure
this.trainingProblemData = cloner.Clone(original.trainingProblemData);
this.lossFunction = cloner.Clone(original.lossFunction);
this.seed = original.seed;
this.iterations = original.iterations;
this.maxSize = original.maxSize;
this.r = original.r;
this.m = original.m;
this.nu = original.nu;
}
protected override void Run(CancellationToken cancellationToken)
{
// Set up the algorithm
if (SetSeedRandomly)
{
Seed = Random.RandomSeedGenerator.GetSeed();
}
// Set up the results display
var iterations = new IntValue(0);
Results.Add(new Result("Iterations", iterations));
var table = new DataTable("Qualities");
table.Rows.Add(new DataRow("Loss (train)"));
table.Rows.Add(new DataRow("Loss (test)"));
table.Rows["Loss (train)"].VisualProperties.StartIndexZero = true;
table.Rows["Loss (test)"].VisualProperties.StartIndexZero = true;
Results.Add(new Result("Qualities", table));
var curLoss = new DoubleValue();
Results.Add(new Result("Loss (train)", curLoss));
// init
var problemData = (IRegressionProblemData)Problem.ProblemData.Clone();
var lossFunction = LossFunctionParameter.Value;
var state = GradientBoostedTreesAlgorithmStatic.CreateGbmState(problemData, lossFunction, (uint)Seed, MaxSize, R, M, Nu);
var updateInterval = UpdateIntervalParameter.Value.Value;
// Loop until iteration limit reached or canceled.
for (int i = 0; i < Iterations; i++)
{
cancellationToken.ThrowIfCancellationRequested();
GradientBoostedTreesAlgorithmStatic.MakeStep(state);
// iteration results
if (i % updateInterval == 0)
{
curLoss.Value = state.GetTrainLoss();
table.Rows["Loss (train)"].Values.Add(curLoss.Value);
table.Rows["Loss (test)"].Values.Add(state.GetTestLoss());
iterations.Value = i;
}
}
// final results
iterations.Value = Iterations;
curLoss.Value = state.GetTrainLoss();
table.Rows["Loss (train)"].Values.Add(curLoss.Value);
table.Rows["Loss (test)"].Values.Add(state.GetTestLoss());
// produce variable relevance
var orderedImpacts = state.GetVariableRelevance().Select(t => new { name = t.Key, impact = t.Value }).ToList();
var impacts = new DoubleMatrix();
var matrix = impacts as IStringConvertibleMatrix;
matrix.Rows = orderedImpacts.Count;
matrix.RowNames = orderedImpacts.Select(x => x.name);
matrix.Columns = 1;
matrix.ColumnNames = new string[] { "Relative variable relevance" };
int rowIdx = 0;
foreach (var p in orderedImpacts)
{
matrix.SetValue(string.Format("{0:N2}", p.impact), rowIdx++, 0);
}
Results.Add(new Result("Variable relevance", impacts));
Results.Add(new Result("Loss (test)", new DoubleValue(state.GetTestLoss())));
// produce solution
if (ModelCreation == ModelCreation.SurrogateModel || ModelCreation == ModelCreation.Model)
{
IRegressionModel model = state.GetModel();
if (ModelCreation == ModelCreation.SurrogateModel)
{
model = new GradientBoostedTreesModelSurrogate((GradientBoostedTreesModel)model, problemData, (uint)Seed, lossFunction, Iterations, MaxSize, R, M, Nu);
}
// for logistic regression we produce a classification solution
if (lossFunction is LogisticRegressionLoss)
{
var classificationModel = new DiscriminantFunctionClassificationModel(model,
new AccuracyMaximizationThresholdCalculator());
var classificationProblemData = new ClassificationProblemData(problemData.Dataset,
problemData.AllowedInputVariables, problemData.TargetVariable, problemData.Transformations);
classificationProblemData.TrainingPartition.Start = Problem.ProblemData.TrainingPartition.Start;
classificationProblemData.TrainingPartition.End = Problem.ProblemData.TrainingPartition.End;
classificationProblemData.TestPartition.Start = Problem.ProblemData.TestPartition.Start;
classificationProblemData.TestPartition.End = Problem.ProblemData.TestPartition.End;
classificationModel.SetThresholdsAndClassValues(new double[] { double.NegativeInfinity, 0.0 }, new[] { 0.0, 1.0 });
var classificationSolution = new DiscriminantFunctionClassificationSolution(classificationModel, classificationProblemData);
Results.Add(new Result("Solution", classificationSolution));
}
else
{
// otherwise we produce a regression solution
Results.Add(new Result("Solution", new GradientBoostedTreesSolution(model, problemData)));
}
}
else if (ModelCreation == ModelCreation.QualityOnly)
{
//Do nothing
}
else
{
throw new NotImplementedException("Selected parameter for CreateSolution isn't implemented yet");
}
}
