protected DiscriminantFunctionClassificationSolution(DiscriminantFunctionClassificationSolution original, Cloner cloner)
: base(original, cloner)
{
valueEvaluationCache = new Dictionary <int, double>(original.valueEvaluationCache);
classValueEvaluationCache = new Dictionary <int, double>(original.classValueEvaluationCache);
}
protected DiscriminantFunctionClassificationSolution(DiscriminantFunctionClassificationSolution original, Cloner cloner)
: base(original, cloner) {
valueEvaluationCache = new Dictionary<int, double>(original.valueEvaluationCache);
classValueEvaluationCache = new Dictionary<int, double>(original.classValueEvaluationCache);
}
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)));
}
}
}
