Changeset 13941 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear

Timestamp:

06/28/16 13:33:17 (8 years ago)

Author:

mkommend

Message:

#2604:

• Base classes for data analysis, classification, and regression models
• Added target variable to classification and regression models
• Switched parameter order in data analysis solutions (model, problemdata)

Location:

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear

Files:

• LinearDiscriminantAnalysis.cs (modified) (1 diff)
• LinearRegression.cs (modified) (1 diff)
• MultinomialLogitClassification.cs (modified) (1 diff)
• MultinomialLogitClassificationSolution.cs (modified) (1 diff)
• MultinomialLogitModel.cs (modified) (5 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearDiscriminantAnalysis.cs

@@ -111 +111 @@
       IClassificationProblemData problemData,
       IEnumerable<int> rows) {
-      var model = new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter, new AccuracyMaximizationThresholdCalculator());
+      var model = new SymbolicDiscriminantFunctionClassificationModel(problemData.TargetVariable, tree, interpreter, new AccuracyMaximizationThresholdCalculator());
       model.RecalculateModelParameters(problemData, rows);
       return model;

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

@@ -110 +110 @@
       addition.AddSubtree(cNode);
 
-      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone());
+      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(problemData.TargetVariable, tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone());
       solution.Model.Name = "Linear Regression Model";
       solution.Name = "Linear Regression Solution";

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs

@@ -95 +95 @@
       relClassError = alglib.mnlrelclserror(lm, inputMatrix, nRows);
 
-      MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution((IClassificationProblemData)problemData.Clone(), new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues));
+      MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution(new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues), (IClassificationProblemData)problemData.Clone());
       return solution;
     }

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassificationSolution.cs

@@ -43 +43 @@
       : base(original, cloner) {
     }
-    public MultinomialLogitClassificationSolution(IClassificationProblemData problemData, MultinomialLogitModel logitModel)
+    public MultinomialLogitClassificationSolution( MultinomialLogitModel logitModel,IClassificationProblemData problemData)
       : base(logitModel, problemData) {
     }

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs

@@ -34 +34 @@
   [StorableClass]
   [Item("Multinomial Logit Model", "Represents a multinomial logit model for classification.")]
-  public sealed class MultinomialLogitModel : NamedItem, IClassificationModel {
+  public sealed class MultinomialLogitModel : ClassificationModel {
 
     private alglib.logitmodel logitModel;
@@ -48 +48 @@
     }
 
-    public IEnumerable<string> VariablesUsedForPrediction {
+    public override IEnumerable<string> VariablesUsedForPrediction {
       get { return allowedInputVariables; }
     }
 
-    public string TargetVariable {
-      get { return targetVariable; }
-    }
-
-    [Storable]
-    private string targetVariable;
     [Storable]
     private string[] allowedInputVariables;
@@ -72 +66 @@
       logitModel = new alglib.logitmodel();
       logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
-      targetVariable = original.targetVariable;
       allowedInputVariables = (string[])original.allowedInputVariables.Clone();
       classValues = (double[])original.classValues.Clone();
     }
     public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
-      : base() {
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
       this.logitModel = logitModel;
-      this.targetVariable = targetVariable;
       this.allowedInputVariables = allowedInputVariables.ToArray();
       this.classValues = (double[])classValues.Clone();
@@ -90 +82 @@
     }
 
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
 
@@ -116 +108 @@
     }
 
-    public MultinomialLogitClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
-      return new MultinomialLogitClassificationSolution(new ClassificationProblemData(problemData), this);
-    }
-
-
-
-    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
-      return CreateClassificationSolution(problemData);
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
+      return new MultinomialLogitClassificationSolution(this, new ClassificationProblemData(problemData));
     }