Changeset 14027 for stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification

Timestamp:

07/08/16 14:37:15 (8 years ago)

Author:

mkommend

Message:

#2604: Merged r13826,r13921, r13922, r13941, r13992, r13993, r14000 intos table.

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs (modified) (5 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationModel.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationModel.cs) (1 diff)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs (modified) (4 diffs)

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleModel.cs

@@ -32 +32 @@
   [StorableClass]
   [Item("ClassificationEnsembleModel", "A classification model that contains an ensemble of multiple classification models")]
-  public class ClassificationEnsembleModel : NamedItem, IClassificationEnsembleModel {
+  public class ClassificationEnsembleModel : ClassificationModel, IClassificationEnsembleModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
+    }
 
     [Storable]
@@ -49 +52 @@
     public ClassificationEnsembleModel() : this(Enumerable.Empty<IClassificationModel>()) { }
     public ClassificationEnsembleModel(IEnumerable<IClassificationModel> models)
-      : base() {
+      : base(string.Empty) {
       this.name = ItemName;
       this.description = ItemDescription;
       this.models = new List<IClassificationModel>(models);
+
+      if (this.models.Any()) this.TargetVariable = this.models.First().TargetVariable;
     }
 
@@ -59 +64 @@
     }
 
-    #region IClassificationEnsembleModel Members
     public void Add(IClassificationModel model) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
       models.Add(model);
     }
     public void Remove(IClassificationModel model) {
       models.Remove(model);
+      if (!models.Any()) TargetVariable = string.Empty;
     }
 
@@ -78 +84 @@
     }
 
-    #endregion
 
-    #region IClassificationModel Members
-
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       foreach (var estimatedValuesVector in GetEstimatedClassValueVectors(dataset, rows)) {
         // return the class which is most often occuring
@@ -94 +97 @@
     }
 
-    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
       return new ClassificationEnsembleSolution(models, new ClassificationEnsembleProblemData(problemData));
     }
-    #endregion
+
+
   }
 }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationModel.cs

@@ -55 +55 @@
     }
 
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.4
+      targetVariable = string.Empty;
+      #endregion
+    }
+
     public abstract IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows);
     public abstract IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData);

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs

@@ -33 +33 @@
   [StorableClass]
   [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")]
-  public class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
+  public class DiscriminantFunctionClassificationModel : ClassificationModel, IDiscriminantFunctionClassificationModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return model.VariablesUsedForPrediction; }
+    }
+
     [Storable]
     private IRegressionModel model;
@@ -73 +77 @@
 
     public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator)
-      : base() {
+      : base(model.TargetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
+
       this.model = model;
       this.classValues = new double[0];
@@ -115 +120 @@
     }
 
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current classification model.");
       foreach (var x in GetEstimatedValues(dataset, rows)) {
@@ -135 +140 @@
     #endregion
 
-    public virtual IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData) {
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
+      return CreateDiscriminantFunctionClassificationSolution(problemData);
+    }
+    public virtual IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(
+      IClassificationProblemData problemData) {
       return new DiscriminantFunctionClassificationSolution(this, new ClassificationProblemData(problemData));
-    }
-
-    public virtual IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
-      return CreateDiscriminantFunctionClassificationSolution(problemData);
     }
   }