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

Timestamp:

07/08/16 14:37:15 (9 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/Regression/ConstantRegressionModel.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleModel.cs (modified) (7 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionModel.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionModel.cs) (1 diff)

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs

@@ -32 +32 @@
   [Item("Constant Regression Model", "A model that always returns the same constant value regardless of the presented input data.")]
   [Obsolete]
-  public class ConstantRegressionModel : NamedItem, IRegressionModel, IStringConvertibleValue {
+  public class ConstantRegressionModel : RegressionModel, IStringConvertibleValue {
+    public override IEnumerable<string> VariablesUsedForPrediction { get { return Enumerable.Empty<string>(); } }
+
     [Storable]
     private double constant;
@@ -46 +48 @@
       this.constant = original.constant;
     }
+
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantRegressionModel(this, cloner); }
 
-    public ConstantRegressionModel(double constant)
-      : base() {
+    public ConstantRegressionModel(double constant, string targetVariable)
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
@@ -56 +59 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       return rows.Select(row => Constant);
     }
 
-    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
-      return new ConstantRegressionSolution(new ConstantModel(constant), new RegressionProblemData(problemData));
+    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+      return new ConstantRegressionSolution(new ConstantModel(constant, TargetVariable), new RegressionProblemData(problemData));
     }
 

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleModel.cs

@@ -33 +33 @@
   [StorableClass]
   [Item("RegressionEnsembleModel", "A regression model that contains an ensemble of multiple regression models")]
-  public sealed class RegressionEnsembleModel : NamedItem, IRegressionEnsembleModel {
+  public sealed class RegressionEnsembleModel : RegressionModel, IRegressionEnsembleModel {
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
+    }
 
     private List<IRegressionModel> models;
@@ -100 +103 @@
     public RegressionEnsembleModel(IEnumerable<IRegressionModel> models) : this(models, models.Select(m => 1.0)) { }
     public RegressionEnsembleModel(IEnumerable<IRegressionModel> models, IEnumerable<double> modelWeights)
-      : base() {
+      : base(string.Empty) {
       this.name = ItemName;
       this.description = ItemDescription;
 
-
       this.models = new List<IRegressionModel>(models);
       this.modelWeights = new List<double>(modelWeights);
+
+      if (this.models.Any()) this.TargetVariable = this.models.First().TargetVariable;
     }
 
     public void Add(IRegressionModel model) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
       Add(model, 1.0);
     }
     public void Add(IRegressionModel model, double weight) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
+
       models.Add(model);
       modelWeights.Add(weight);
@@ -122 +129 @@
     }
     public void AddRange(IEnumerable<IRegressionModel> models, IEnumerable<double> weights) {
+      if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = models.First().TargetVariable;
+
       this.models.AddRange(models);
       modelWeights.AddRange(weights);
@@ -131 +140 @@
       models.RemoveAt(index);
       modelWeights.RemoveAt(index);
+
+      if (!models.Any()) TargetVariable = string.Empty;
       OnChanged();
     }
@@ -139 +150 @@
         modelWeights.RemoveAt(index);
       }
+
+      if (!models.Any()) TargetVariable = string.Empty;
       OnChanged();
     }
@@ -165 +178 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       double weightsSum = modelWeights.Sum();
       var summedEstimates = from estimatedValuesVector in GetEstimatedValueVectors(dataset, rows)
@@ -213 +226 @@
 
 
-    public RegressionEnsembleSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
       return new RegressionEnsembleSolution(this, new RegressionEnsembleProblemData(problemData));
-    }
-    IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
-      return CreateRegressionSolution(problemData);
     }
   }

stable/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionModel.cs

@@ -56 +56 @@
     }
 
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.4
+      targetVariable = string.Empty;
+      #endregion
+    }
     public abstract IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows);
     public abstract IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData);