Changeset 15134 for stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/RandomForestModelView.cs

Timestamp:

07/06/17 10:31:09 (7 years ago)

Author:

gkronber

Message:

#2690: merged r14839,r15103:15106,r15123,r15124 from trunk to stable

File:

• stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/RandomForestModelView.cs (modified) (3 diffs)

stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/RandomForestModelView.cs

@@ -18 +18 @@
  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
  */
-#endregion
+#endregion            
 
-using System.Drawing;
+using HeuristicLab.Common;
+using HeuristicLab.Core.Views;
 using HeuristicLab.MainForm;
 using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;                       
 using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Problems.DataAnalysis.Views;
 
 namespace HeuristicLab.Algorithms.DataAnalysis.Views {
   [View("Random forest model")]
-  [Content(typeof(IRandomForestRegressionSolution), false)]
-  [Content(typeof(IRandomForestClassificationSolution), false)]
-  public partial class RandomForestModelView : DataAnalysisSolutionEvaluationView {
-    public override Image ViewImage {
-      get { return HeuristicLab.Common.Resources.VSImageLibrary.Function; }
+  [Content(typeof(IRandomForestModel), true)]
+  public partial class RandomForestModelView : ItemView {
+
+    public new IRandomForestModel Content {
+      get { return (IRandomForestModel)base.Content; }
+      set { base.Content = value; }
     }
 
@@ -56 +56 @@
         viewHost.Content = null;
         listBox.Items.Clear();
-        var classSol = Content as IRandomForestClassificationSolution;
-        var regSol = Content as IRandomForestRegressionSolution;
-        var numTrees = classSol != null ? classSol.NumberOfTrees : regSol != null ? regSol.NumberOfTrees : 0;
+        var rfModel = Content;
+        var numTrees = rfModel.NumberOfTrees;
         for (int i = 0; i < numTrees; i++) {
           listBox.Items.Add(i + 1);
@@ -69 +68 @@
       else {
         var idx = (int)listBox.SelectedItem;
-        idx -= 1;
-        var rfModel = Content.Model as RandomForestModel;
-        var regProblemData = Content.ProblemData as IRegressionProblemData;
-        var classProblemData = Content.ProblemData as IClassificationProblemData;
-        if (rfModel != null) {
-          if (idx < 0 || idx >= rfModel.NumberOfTrees) return;
-          if (regProblemData != null) {
-            var syModel = new SymbolicRegressionModel(regProblemData.TargetVariable, rfModel.ExtractTree(idx),
-              new SymbolicDataAnalysisExpressionTreeLinearInterpreter());
-            viewHost.Content = syModel.CreateRegressionSolution(regProblemData);
-          } else if (classProblemData != null) {
-            var syModel = new SymbolicDiscriminantFunctionClassificationModel(classProblemData.TargetVariable, rfModel.ExtractTree(idx),
-              new SymbolicDataAnalysisExpressionTreeLinearInterpreter(), new NormalDistributionCutPointsThresholdCalculator());
-            syModel.RecalculateModelParameters(classProblemData, classProblemData.TrainingIndices);
-            viewHost.Content = syModel.CreateClassificationSolution(classProblemData);
-          }
-        }
+        viewHost.Content = CreateModel(idx);
       }
+    }
+
+    private void listBox_DoubleClick(object sender, System.EventArgs e) {
+      var selectedItem = listBox.SelectedItem;
+      if (selectedItem == null) return;
+      var idx = (int)listBox.SelectedItem;
+      MainFormManager.MainForm.ShowContent(CreateModel(idx));
+    }
+
+    private IContent CreateModel(int idx) {
+      idx -= 1;
+      var rfModel = Content;
+      var rfClassModel = rfModel as IClassificationModel; // rfModel is always a IRegressionModel and a IClassificationModel
+      var targetVariable = rfClassModel.TargetVariable;
+      if (rfModel == null) return null;
+      if (idx < 0 || idx >= rfModel.NumberOfTrees)
+        return null;
+      var syModel = new SymbolicRegressionModel(targetVariable, rfModel.ExtractTree(idx),
+          new SymbolicDataAnalysisExpressionTreeLinearInterpreter());
+      return syModel;
     }
   }