1 | using System.Collections.Generic;
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2 | using System.Linq;
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3 | using HeuristicLab.Common;
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4 | using HeuristicLab.Core;
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5 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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6 | using HeuristicLab.Problems.DataAnalysis;
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7 |
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8 | namespace GradientBoostedTrees {
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9 | [StorableClass]
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10 | [Item("RegressionTreeModel", "Represents a decision tree for regression.")]
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11 | // TODO: Implement a view for this
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12 | public class RegressionTreeModel : NamedItem, IRegressionModel {
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13 |
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14 | [StorableClass]
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15 | public class TreeNode {
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16 | public readonly static string NO_VARIABLE = string.Empty;
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17 | [Storable]
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18 | public readonly string varName; // name of the variable for splitting or -1 if terminal node
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19 | [Storable]
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20 | public readonly double val; // threshold
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21 | [Storable]
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22 | public readonly TreeNode left;
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23 | [Storable]
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24 | public readonly TreeNode right;
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25 |
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26 | public TreeNode(string varName, double value, TreeNode left = null, TreeNode right = null) {
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27 | this.varName = varName;
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28 | this.val = value;
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29 | this.left = left;
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30 | this.right = right;
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31 | }
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32 | }
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33 |
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34 | [Storable]
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35 | public readonly TreeNode tree;
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36 |
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37 | [StorableConstructor]
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38 | private RegressionTreeModel(bool serializing) : base(serializing) { }
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39 | // cloning ctor
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40 | public RegressionTreeModel(RegressionTreeModel original, Cloner cloner)
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41 | : base(original, cloner) {
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42 | this.tree = original.tree;
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43 | }
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44 |
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45 | public RegressionTreeModel(TreeNode tree)
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46 | : base() {
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47 | this.name = ItemName;
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48 | this.description = ItemDescription;
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49 |
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50 | this.tree = tree;
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51 | }
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52 |
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53 | private static double GetPredictionForRow(TreeNode t, Dataset ds, int row) {
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54 | if (t.varName == TreeNode.NO_VARIABLE)
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55 | return t.val;
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56 | else if (ds.GetDoubleValue(t.varName, row) <= t.val)
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57 | return GetPredictionForRow(t.left, ds, row);
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58 | else
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59 | return GetPredictionForRow(t.right, ds, row);
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60 | }
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61 |
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62 | public override IDeepCloneable Clone(Cloner cloner) {
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63 | return new RegressionTreeModel(this, cloner);
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64 | }
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65 |
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66 | public IEnumerable<double> GetEstimatedValues(Dataset ds, IEnumerable<int> rows) {
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67 | return rows.Select(r => GetPredictionForRow(tree, ds, r));
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68 | }
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69 |
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70 | public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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71 | return new RegressionSolution(this, new RegressionProblemData(problemData));
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72 | }
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73 | }
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74 |
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75 | }
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