Changeset 15973 for branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs

Timestamp:

06/28/18 11:13:37 (6 years ago)

Author:

gkronber

Message:

#2522: merged trunk changes from r13402:15972 to branch resolving conflicts where necessary

Location:

branches/2522_RefactorPluginInfrastructure

Files:

• . (modified) (2 props)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4 (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs (modified) (7 diffs)

branches/2522_RefactorPluginInfrastructure

@@ -24 +24 @@
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 obj
+.vs

@@ -24 +24 @@
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+.vs

branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -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 @@
     }
 
-    [Storable]
-    private string targetVariable;
+    public override IEnumerable<string> VariablesUsedForPrediction {
+      get { return allowedInputVariables; }
+    }
+
     [Storable]
     private string[] allowedInputVariables;
     [Storable]
     private double[] classValues;
+    [Storable]
+    private List<KeyValuePair<string, IEnumerable<string>>> factorVariables;
+
     [StorableConstructor]
     private MultinomialLogitModel(bool deserializing)
@@ -64 +69 @@
       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();
+      this.factorVariables = original.factorVariables.Select(kvp => new KeyValuePair<string, IEnumerable<string>>(kvp.Key, new List<string>(kvp.Value))).ToList();
     }
-    public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
-      : base() {
+    public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> doubleInputVariables, IEnumerable<KeyValuePair<string, IEnumerable<string>>> factorVariables, double[] classValues)
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
       this.logitModel = logitModel;
-      this.targetVariable = targetVariable;
-      this.allowedInputVariables = allowedInputVariables.ToArray();
+      this.allowedInputVariables = doubleInputVariables.ToArray();
+      this.factorVariables = factorVariables.Select(kvp => new KeyValuePair<string, IEnumerable<string>>(kvp.Key, new List<string>(kvp.Value))).ToList();
       this.classValues = (double[])classValues.Clone();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.4
+      factorVariables = new List<KeyValuePair<string, IEnumerable<string>>>();
+      #endregion
     }
 
@@ -82 +95 @@
     }
 
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
-      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+
+      double[,] inputData = dataset.ToArray(allowedInputVariables, rows);
+      double[,] factorData = dataset.ToArray(factorVariables, rows);
+
+      inputData = factorData.HorzCat(inputData);
 
       int n = inputData.GetLength(0);
@@ -108 +125 @@
     }
 
-    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));
     }
 
@@ -135 +149 @@
     }
     #endregion
+
   }
 }

@@ -24 +24 @@
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+.vs