Changeset 17081 for trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees

Timestamp:

07/05/19 11:14:32 (5 years ago)

Author:

gkronber

Message:

#2847: more changes for renaming the M5 plugin

Location:

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4

Files:

• DecisionTreeRegression.cs (modified) (4 diffs)
• Interfaces/IDecisionTreeModel.cs (modified) (1 diff)
• LeafModels/DampenedModel.cs (modified) (1 diff)
• LeafTypes/Leaf.cs (modified) (2 diffs)
• LeafTypes/RegularizedLeaf.cs (modified) (1 diff)
• MetaModels/RegressionNodeTreeModel.cs (modified) (2 diffs)
• MetaModels/RegressionRuleModel.cs (modified) (1 diff)
• MetaModels/RegressionRuleSetModel.cs (modified) (2 diffs)
• Properties/AssemblyInfo.cs.frame (modified) (1 diff)
• Splitting/Splitter.cs (modified) (2 diffs)

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/DecisionTreeRegression.cs

@@ -145 +145 @@
       Parameters.Add(new FixedValueParameter<BoolValue>(GenerateRulesParameterName, "Whether a set of rules or a decision tree shall be created (default=false)", new BoolValue(false)));
       Parameters.Add(new FixedValueParameter<PercentValue>(HoldoutSizeParameterName, "How much of the training set shall be reserved for pruning (default=20%).", new PercentValue(0.2)));
-      Parameters.Add(new ConstrainedValueParameter<ISplitter>(SplitterParameterName, "The type of split function used to create node splits (default='Splitter').", splitterSet, splitterSet.OfType<M5Splitter>().First()));
+      Parameters.Add(new ConstrainedValueParameter<ISplitter>(SplitterParameterName, "The type of split function used to create node splits (default='Splitter').", splitterSet, splitterSet.OfType<Splitter>().First()));
       Parameters.Add(new FixedValueParameter<IntValue>(MinimalNodeSizeParameterName, "The minimal number of samples in a leaf node (default=1).", new IntValue(1)));
       Parameters.Add(new ConstrainedValueParameter<ILeafModel>(LeafModelParameterName, "The type of model used for the nodes (default='LinearLeaf').", modelSet, modelSet.OfType<LinearLeaf>().First()));
@@ -178 +178 @@
       bool useHoldout = false, double holdoutSize = 0.2, int minimumLeafSize = 1, bool generateRules = false, ResultCollection results = null, CancellationToken? cancellationToken = null) {
       if (leafModel == null) leafModel = new LinearLeaf();
-      if (splitter == null) splitter = new M5Splitter();
+      if (splitter == null) splitter = new Splitter();
       if (cancellationToken == null) cancellationToken = CancellationToken.None;
       if (pruning == null) pruning = new ComplexityPruning();
@@ -187 +187 @@
     }
 
-    public static void UpdateModel(IM5Model model, IRegressionProblemData problemData, IRandom random, ILeafModel leafModel, CancellationToken? cancellationToken = null) {
+    public static void UpdateModel(IDecisionTreeModel model, IRegressionProblemData problemData, IRandom random, ILeafModel leafModel, CancellationToken? cancellationToken = null) {
       if (cancellationToken == null) cancellationToken = CancellationToken.None;
       var regressionTreeParameters = new RegressionTreeParameters(leafModel, problemData, random);
@@ -244 +244 @@
     private static IRegressionModel Build(IScope stateScope, ResultCollection results, CancellationToken cancellationToken) {
       var regressionTreeParams = (RegressionTreeParameters)stateScope.Variables[RegressionTreeParameterVariableName].Value;
-      var model = (IM5Model)stateScope.Variables[ModelVariableName].Value;
+      var model = (IDecisionTreeModel)stateScope.Variables[ModelVariableName].Value;
       var trainingRows = (IntArray)stateScope.Variables[TrainingSetVariableName].Value;
       var pruningRows = (IntArray)stateScope.Variables[PruningSetVariableName].Value;

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/Interfaces/IDecisionTreeModel.cs

@@ -29 +29 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableType("A5399E6A-6A4D-4616-A1CD-CE12FE670F12")]
-  public interface IM5Model : IRegressionModel {
+  public interface IDecisionTreeModel : IRegressionModel {
     void Build(IReadOnlyList<int> trainingRows, IReadOnlyList<int> pruningRows, IScope stateScope, ResultCollection results, CancellationToken cancellationToken);
     void Update(IReadOnlyList<int> rows, IScope stateScope, CancellationToken cancellationToken);

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/LeafModels/DampenedModel.cs

@@ -28 +28 @@
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
-  //mulitdimensional extension of http://www2.stat.duke.edu/~tjl13/s101/slides/unit6lec3H.pdf
+  // mulitdimensional extension of http://www2.stat.duke.edu/~tjl13/s101/slides/unit6lec3H.pdf
   [StorableType("42E9766F-207F-47B1-890C-D5DFCF469838")]
   public class DampenedModel : RegressionModel {

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/LeafTypes/Leaf.cs

@@ -31 +31 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableType("58517042-5318-4087-B098-AC75F0208BA0")]
-  [Item("M5Leaf", "A leaf type that uses regularized linear models with feature selection as leaf models.")]
-  public sealed class M5Leaf : LeafBase {
+  [Item("Leaf", "A leaf type that uses regularized linear models with feature selection as leaf models.")]
+  public sealed class Leaf : LeafBase {
     #region Constructors & Cloning
     [StorableConstructor]
-    private M5Leaf(StorableConstructorFlag _) : base(_) { }
-    private M5Leaf(M5Leaf original, Cloner cloner) : base(original, cloner) { }
-    public M5Leaf() { }
+    private Leaf(StorableConstructorFlag _) : base(_) { }
+    private Leaf(Leaf original, Cloner cloner) : base(original, cloner) { }
+    public Leaf() { }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new M5Leaf(this, cloner);
+      return new Leaf(this, cloner);
     }
     #endregion
@@ -48 +48 @@
     }
     public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int numberOfParameters) {
-      if (pd.Dataset.Rows == 0) throw new ArgumentException("The number of training instances is too small to create an M5 leaf model");
+      if (pd.Dataset.Rows == 0) throw new ArgumentException("The number of training instances is too small to create a leaf model");
 
       if (pd.Dataset.Rows == 1)

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/LeafTypes/RegularizedLeaf.cs

@@ -31 +31 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableType("0AED959D-78C3-4927-BDCF-473D0AEE32AA")]
-  [Item("M5regLeaf", "A leaf type that uses regularized linear models as leaf models.")]
-  public sealed class M5regLeaf : LeafBase {
+  [Item("RegularizedLeaf", "A leaf type that uses regularized linear models as leaf models.")]
+  public sealed class RegularizedLeaf : LeafBase {
     #region Constructors & Cloning
     [StorableConstructor]
-    private M5regLeaf(StorableConstructorFlag _) : base(_) { }
-    private M5regLeaf(M5regLeaf original, Cloner cloner) : base(original, cloner) { }
-    public M5regLeaf() { }
+    private RegularizedLeaf(StorableConstructorFlag _) : base(_) { }
+    private RegularizedLeaf(RegularizedLeaf original, Cloner cloner) : base(original, cloner) { }
+    public RegularizedLeaf() { }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new M5regLeaf(this, cloner);
+      return new RegularizedLeaf(this, cloner);
     }
     #endregion

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/MetaModels/RegressionNodeTreeModel.cs

@@ -32 +32 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableType("FAF1F955-82F3-4824-9759-9D2846E831AE")]
-  public class RegressionNodeTreeModel : RegressionModel, IM5Model {
+  public class RegressionNodeTreeModel : RegressionModel, IDecisionTreeModel {
     public const string NumCurrentLeafsResultName = "Number of current leafs";
     public const string RootVariableName = "Root";
@@ -69 +69 @@
     #endregion
 
-    #region IM5Model
+    #region IDecisionTreeModel
     public void Build(IReadOnlyList<int> trainingRows, IReadOnlyList<int> pruningRows, IScope statescope, ResultCollection results, CancellationToken cancellationToken) {
       var regressionTreeParams = (RegressionTreeParameters)statescope.Variables[DecisionTreeRegression.RegressionTreeParameterVariableName].Value;

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/MetaModels/RegressionRuleModel.cs

@@ -33 +33 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableType("425AF262-A756-4E9A-B76F-4D2480BEA4FD")]
-  public class RegressionRuleModel : RegressionModel, IM5Model {
+  public class RegressionRuleModel : RegressionModel, IDecisionTreeModel {
     #region Properties
     [Storable]

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/MetaModels/RegressionRuleSetModel.cs

@@ -33 +33 @@
 namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableType("7B4D9AE9-0456-4029-80A6-CCB5E33CE356")]
-  public class RegressionRuleSetModel : RegressionModel, IM5Model {
+  public class RegressionRuleSetModel : RegressionModel, IDecisionTreeModel {
     private const string NumRulesResultName = "Number of rules";
     private const string CoveredInstancesResultName = "Covered instances";
@@ -75 +75 @@
     #endregion
 
-    #region IM5Model
+    #region IDecisionTreeModel
     public void Build(IReadOnlyList<int> trainingRows, IReadOnlyList<int> pruningRows, IScope stateScope, ResultCollection results, CancellationToken cancellationToken) {
       var regressionTreeParams = (RegressionTreeParameters)stateScope.Variables[DecisionTreeRegression.RegressionTreeParameterVariableName].Value;

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/Properties/AssemblyInfo.cs.frame

@@ -28 +28 @@
 // set of attributes. Change these attribute values to modify the information
 // associated with an assembly.
-[assembly: AssemblyTitle("HeuristicLab.Algorithms.DataAnalysis.M5")]
-[assembly: AssemblyDescription("Provides the M5' regression algorithm")]
+[assembly: AssemblyTitle("HeuristicLab.Algorithms.DataAnalysis.DecisionTrees")]
+[assembly: AssemblyDescription("Provides algorithms for learning decision trees for regression.")]
 [assembly: AssemblyConfiguration("")]
 [assembly: AssemblyCompany("")]

trunk/HeuristicLab.Algorithms.DataAnalysis.DecisionTrees/3.4/Splitting/Splitter.cs

@@ -31 +31 @@
   [StorableType("502B1429-7A28-45C1-A60A-93E72CB3AF4A")]
   [Item("Splitter", "A split selector that uses the ratio between Variances^(1/Order) to determine good splits.")]
-  public sealed class M5Splitter : SplitterBase {
+  public sealed class Splitter : SplitterBase {
     public const string OrderParameterName = "Order";
     public IFixedValueParameter<DoubleValue> OrderParameter {
@@ -43 +43 @@
     #region Constructors & Cloning
     [StorableConstructor]
-    private M5Splitter(StorableConstructorFlag _) { }
-    private M5Splitter(M5Splitter original, Cloner cloner) : base(original, cloner) { }
-    public M5Splitter() {
+    private Splitter(StorableConstructorFlag _) { }
+    private Splitter(Splitter original, Cloner cloner) : base(original, cloner) { }
+    public Splitter() {
       Parameters.Add(new FixedValueParameter<DoubleValue>(OrderParameterName, "The exponent in the split calculation sum (x_i - x_avg)^Order (default=5).", new DoubleValue(5)));
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new M5Splitter(this, cloner);
+      return new Splitter(this, cloner);
     }
     #endregion