public void TestDecisionTreePersistence()
{
var provider = new HeuristicLab.Problems.Instances.DataAnalysis.RegressionRealWorldInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name.Contains("Tower"));
var regProblem = new RegressionProblem();
regProblem.Load(provider.LoadData(instance));
var problemData = regProblem.ProblemData;
var state = GradientBoostedTreesAlgorithmStatic.CreateGbmState(problemData, new SquaredErrorLoss(), randSeed: 31415, maxSize: 100, r: 0.5, m: 1, nu: 1);
GradientBoostedTreesAlgorithmStatic.MakeStep(state);
var model = ((IGradientBoostedTreesModel)state.GetModel());
var treeM = model.Models.Skip(1).First();
var origStr = treeM.ToString();
using (var memStream = new MemoryStream()) {
Persistence.Default.Xml.XmlGenerator.Serialize(treeM, memStream);
var buf = memStream.GetBuffer();
using (var restoreStream = new MemoryStream(buf)) {
var restoredTree = Persistence.Default.Xml.XmlParser.Deserialize(restoreStream);
var restoredStr = restoredTree.ToString();
Assert.AreEqual(origStr, restoredStr);
}
}
}
private void BuildTree(double[,] xy, string[] allVariables, int maxSize)
{
int nRows = xy.GetLength(0);
var allowedInputs = allVariables.Skip(1);
var dataset = new Dataset(allVariables, xy);
var problemData = new RegressionProblemData(dataset, allowedInputs, allVariables.First());
problemData.TrainingPartition.Start = 0;
problemData.TrainingPartition.End = nRows;
problemData.TestPartition.Start = nRows;
problemData.TestPartition.End = nRows;
var solution = GradientBoostedTreesAlgorithmStatic.TrainGbm(problemData, new SquaredErrorLoss(), maxSize, nu: 1, r: 1, m: 1, maxIterations: 1, randSeed: 31415);
var model = solution.Model;
var treeM = model.Models.Skip(1).First() as RegressionTreeModel;
Console.WriteLine(treeM.ToString());
Console.WriteLine();
}
public void TestDecisionTreePartialDependence()
{
var provider = new HeuristicLab.Problems.Instances.DataAnalysis.RegressionRealWorldInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name.Contains("Tower"));
var regProblem = new RegressionProblem();
regProblem.Load(provider.LoadData(instance));
var problemData = regProblem.ProblemData;
var state = GradientBoostedTreesAlgorithmStatic.CreateGbmState(problemData, new SquaredErrorLoss(), randSeed: 31415, maxSize: 10, r: 0.5, m: 1, nu: 0.02);
for (int i = 0; i < 1000; i++)
{
GradientBoostedTreesAlgorithmStatic.MakeStep(state);
}
var mostImportantVar = state.GetVariableRelevance().OrderByDescending(kvp => kvp.Value).First();
Console.WriteLine("var: {0} relevance: {1}", mostImportantVar.Key, mostImportantVar.Value);
var model = ((IGradientBoostedTreesModel)state.GetModel());
var treeM = model.Models.Skip(1).First();
Console.WriteLine(treeM.ToString());
Console.WriteLine();
var mostImportantVarValues = problemData.Dataset.GetDoubleValues(mostImportantVar.Key).OrderBy(x => x).ToArray();
var ds = new ModifiableDataset(new string[] { mostImportantVar.Key },
new IList[] { mostImportantVarValues.ToList <double>() });
var estValues = model.GetEstimatedValues(ds, Enumerable.Range(0, mostImportantVarValues.Length)).ToArray();
for (int i = 0; i < mostImportantVarValues.Length; i += 10)
{
Console.WriteLine("{0,-5:N3} {1,-5:N3}", mostImportantVarValues[i], estValues[i]);
}
}
protected override void Run(CancellationToken cancellationToken)
{
// Set up the algorithm
if (SetSeedRandomly)
{
Seed = new System.Random().Next();
}
// 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)"));
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 (CreateSolution)
{
var model = state.GetModel();
// 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);
classificationModel.RecalculateModelParameters(classificationProblemData, classificationProblemData.TrainingIndices);
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 RegressionSolution(model, problemData)));
}
}
}
private IGradientBoostedTreesModel RecalculateModel()
{
return(GradientBoostedTreesAlgorithmStatic.TrainGbm(trainingProblemData, lossFunction, maxSize, nu, r, m, iterations, seed).Model);
}