public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int numberOfParameters)
{
if (pd.Dataset.Rows < MinLeafSize(pd))
{
throw new ArgumentException("The number of training instances is too small to create a Gaussian process model");
}
Regression.Problem = new RegressionProblem {
ProblemData = pd
};
var cvscore = double.MaxValue;
GaussianProcessRegressionSolution sol = null;
for (var i = 0; i < Tries; i++)
{
var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
var t = res.Select(x => x.Value).OfType <GaussianProcessRegressionSolution>().FirstOrDefault();
var score = ((DoubleValue)res["Negative log pseudo-likelihood (LOO-CV)"].Value).Value;
if (score >= cvscore || t == null || double.IsNaN(t.TrainingRSquared))
{
continue;
}
cvscore = score;
sol = t;
}
Regression.Runs.Clear();
if (sol == null)
{
throw new ArgumentException("Could not create Gaussian process model");
}
numberOfParameters = pd.Dataset.Rows + 1
+ Regression.CovarianceFunction.GetNumberOfParameters(pd.AllowedInputVariables.Count())
+ Regression.MeanFunction.GetNumberOfParameters(pd.AllowedInputVariables.Count());
return(sol.Model);
}
public void FillLeafs(RegressionNodeTreeModel tree, IReadOnlyList <int> trainingRows, IDataset data)
{
var helperQueue = new Queue <RegressionNodeModel>();
var trainingHelperQueue = new Queue <IReadOnlyList <int> >();
nodeQueue.Clear();
trainingRowsQueue.Clear();
helperQueue.Enqueue(tree.Root);
trainingHelperQueue.Enqueue(trainingRows);
while (helperQueue.Count != 0)
{
var n = helperQueue.Dequeue();
var t = trainingHelperQueue.Dequeue();
if (n.IsLeaf)
{
nodeQueue.Enqueue(n);
trainingRowsQueue.Enqueue(t);
continue;
}
IReadOnlyList <int> leftTraining, rightTraining;
RegressionTreeUtilities.SplitRows(t, data, n.SplitAttribute, n.SplitValue, out leftTraining, out rightTraining);
helperQueue.Enqueue(n.Left);
helperQueue.Enqueue(n.Right);
trainingHelperQueue.Enqueue(leftTraining);
trainingHelperQueue.Enqueue(rightTraining);
}
}
private static SymbolicExpressionTreeNode MirrorTree(RegressionNodeModel regressionNode, IDictionary <int, RegressionNodeModel> dict,
IDictionary <int, IReadOnlyList <int> > trainingLeafRows,
IDictionary <int, IReadOnlyList <int> > testLeafRows,
IntValue nextId, IDataset data, IReadOnlyList <int> trainingRows, IReadOnlyList <int> testRows)
{
if (regressionNode.IsLeaf)
{
var i = nextId.Value++;
dict.Add(i, regressionNode);
trainingLeafRows.Add(i, trainingRows);
testLeafRows.Add(i, testRows);
return(new SymbolicExpressionTreeNode(new TextSymbol("Model " + i + "\n(" + trainingRows.Count + "/" + testRows.Count + ")")));
}
var pftext = "\npf = " + regressionNode.PruningStrength.ToString("0.###");
var text = regressionNode.SplitAttribute + " <= " + regressionNode.SplitValue.ToString("0.###");
if (!double.IsNaN(regressionNode.PruningStrength))
{
text += pftext;
}
var textNode = new SymbolicExpressionTreeNode(new TextSymbol(text));
IReadOnlyList <int> lTrainingRows, rTrainingRows;
IReadOnlyList <int> lTestRows, rTestRows;
RegressionTreeUtilities.SplitRows(trainingRows, data, regressionNode.SplitAttribute, regressionNode.SplitValue, out lTrainingRows, out rTrainingRows);
RegressionTreeUtilities.SplitRows(testRows, data, regressionNode.SplitAttribute, regressionNode.SplitValue, out lTestRows, out rTestRows);
textNode.AddSubtree(MirrorTree(regressionNode.Left, dict, trainingLeafRows, testLeafRows, nextId, data, lTrainingRows, lTestRows));
textNode.AddSubtree(MirrorTree(regressionNode.Right, dict, trainingLeafRows, testLeafRows, nextId, data, rTrainingRows, rTestRows));
return(textNode);
}
private static IRegressionProblemData Subselect(IRegressionProblemData data, IReadOnlyList <int> training, IReadOnlyList <int> test)
{
var dataset = RegressionTreeUtilities.ReduceDataset(data.Dataset, training.Concat(test).ToList(), data.AllowedInputVariables.ToList(), data.TargetVariable);
var res = new RegressionProblemData(dataset, data.AllowedInputVariables, data.TargetVariable);
res.TrainingPartition.Start = 0;
res.TrainingPartition.End = training.Count;
res.TestPartition.Start = training.Count;
res.TestPartition.End = training.Count + test.Count;
return(res);
}
public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int noParameters)
{
if (pd.Dataset.Rows < MinLeafSize(pd))
{
throw new ArgumentException("The number of training instances is too small to create a linear model");
}
noParameters = pd.Dataset.Rows + 1;
Regression.Problem = new RegressionProblem {
ProblemData = pd
};
var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
var t = res.Select(x => x.Value).OfType <IRegressionSolution>().FirstOrDefault();
if (t == null)
{
throw new ArgumentException("No RegressionSolution was provided by the algorithm");
}
return(t.Model);
}
public IRegressionModel BuildModel(IReadOnlyList <int> rows, RegressionTreeParameters parameters, CancellationToken cancellation, out int numberOfParameters)
{
var reducedData = RegressionTreeUtilities.ReduceDataset(parameters.Data, rows, parameters.AllowedInputVariables.ToArray(), parameters.TargetVariable);
var pd = new RegressionProblemData(reducedData, parameters.AllowedInputVariables.ToArray(), parameters.TargetVariable);
pd.TrainingPartition.Start = 0;
pd.TrainingPartition.End = pd.TestPartition.Start = pd.TestPartition.End = reducedData.Rows;
int numP;
var model = Build(pd, parameters.Random, cancellation, out numP);
if (UseDampening && Dampening > 0.0)
{
model = DampenedModel.DampenModel(model, pd, Dampening);
}
numberOfParameters = numP;
cancellation.ThrowIfCancellationRequested();
return(model);
}
public void Split(RegressionNodeTreeModel tree, IReadOnlyList <int> trainingRows, IScope stateScope, CancellationToken cancellationToken)
{
var regressionTreeParams = (RegressionTreeParameters)stateScope.Variables[DecisionTreeRegression.RegressionTreeParameterVariableName].Value;
var splittingState = (SplittingState)stateScope.Variables[SplittingStateVariableName].Value;
var variables = regressionTreeParams.AllowedInputVariables.ToArray();
var target = regressionTreeParams.TargetVariable;
if (splittingState.Code <= 0)
{
splittingState.nodeQueue.Enqueue(tree.Root);
splittingState.trainingRowsQueue.Enqueue(trainingRows);
splittingState.Code = 1;
}
while (splittingState.nodeQueue.Count != 0)
{
var n = splittingState.nodeQueue.Dequeue();
var rows = splittingState.trainingRowsQueue.Dequeue();
string attr;
double splitValue;
var isLeaf = !DecideSplit(new RegressionProblemData(RegressionTreeUtilities.ReduceDataset(regressionTreeParams.Data, rows, variables, target), variables, target), regressionTreeParams.MinLeafSize, out attr, out splitValue);
if (isLeaf)
{
continue;
}
IReadOnlyList <int> leftRows, rightRows;
RegressionTreeUtilities.SplitRows(rows, regressionTreeParams.Data, attr, splitValue, out leftRows, out rightRows);
n.Split(regressionTreeParams, attr, splitValue, rows.Count);
splittingState.nodeQueue.Enqueue(n.Left);
splittingState.nodeQueue.Enqueue(n.Right);
splittingState.trainingRowsQueue.Enqueue(leftRows);
splittingState.trainingRowsQueue.Enqueue(rightRows);
cancellationToken.ThrowIfCancellationRequested();
}
}