source: branches/GBT/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs @ 12372

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace GradientBoostedTrees {
  [StorableClass]
  [Item("RegressionTreeModel", "Represents a decision tree for regression.")]
  // TODO: Implement a view for this
  public class RegressionTreeModel : NamedItem, IRegressionModel {

    // trees are represented as a flat array
    // object-graph-travesal has problems if this is defined as a struct. TODO investigate...
    [StorableClass]
    public class TreeNode {
      public readonly static string NO_VARIABLE = string.Empty;
      [Storable]
      public string varName; // name of the variable for splitting or -1 if terminal node
      [Storable]
      public double val; // threshold
      [Storable]
      public int leftIdx;
      [Storable]
      public int rightIdx;

      public TreeNode() {
        varName = NO_VARIABLE;
        leftIdx = -1;
        rightIdx = -1;
      }
      [StorableConstructor]
      private TreeNode(bool deserializing) { }
    }

    [Storable]
    public readonly TreeNode[] tree;

    [StorableConstructor]
    private RegressionTreeModel(bool serializing) : base(serializing) { }
    // cloning ctor
    public RegressionTreeModel(RegressionTreeModel original, Cloner cloner)
      : base(original, cloner) {
      this.tree = original.tree; // shallow clone, tree must be readonly
    }

    public RegressionTreeModel(TreeNode[] tree)
      : base("RegressionTreeModel", "Represents a decision tree for regression.") {
      this.tree = tree;
    }

    private static double GetPredictionForRow(TreeNode[] t, int nodeIdx, Dataset ds, int row) {
      var node = t[nodeIdx];
      if (node.varName == TreeNode.NO_VARIABLE)
        return node.val;
      else if (ds.GetDoubleValue(node.varName, row) <= node.val)
        return GetPredictionForRow(t, node.leftIdx, ds, row);
      else
        return GetPredictionForRow(t, node.rightIdx, ds, row);
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new RegressionTreeModel(this, cloner);
    }

    public IEnumerable<double> GetEstimatedValues(Dataset ds, IEnumerable<int> rows) {
      return rows.Select(r => GetPredictionForRow(tree, 0, ds, r));
    }

    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new RegressionSolution(this, new RegressionProblemData(problemData));
    }
  }

}