Changeset 7028 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/SymbolicDataAnalysisSolutionResponseFunctionView.cs

Timestamp:

11/21/11 19:08:06 (12 years ago)

Author:

gkronber

Message:

#1621: fixed bugs in response function view and added scatter plot of original data as a reference.

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/SymbolicDataAnalysisSolutionResponseFunctionView.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Views/3.4/SymbolicDataAnalysisSolutionResponseFunctionView.cs) (4 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/SymbolicDataAnalysisSolutionResponseFunctionView.cs

@@ -30 +30 @@
 using HeuristicLab.MainForm.WindowsForms;
 
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Views {
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
   [View("Response Function View")]
-  [Content(typeof(RegressionSolutionBase), false)]
+  [Content(typeof(ISymbolicRegressionSolution), false)]
   public partial class SymbolicDataAnalysisSolutionResponseFunctionView : AsynchronousContentView {
     private Dictionary<string, List<ISymbolicExpressionTreeNode>> variableNodes;
@@ -44 +44 @@
     }
 
-    public new ISymbolicDataAnalysisSolution Content {
-      get { return (ISymbolicDataAnalysisSolution)base.Content; }
+    public new ISymbolicRegressionSolution Content {
+      get { return (ISymbolicRegressionSolution)base.Content; }
       set { base.Content = value; }
     }
@@ -122 +122 @@
         constNode.Value = value;
 
-      UpdateChart();
-    }
-
-    private void UpdateChart() {
+      UpdateResponseSeries();
+    }
+
+    private void UpdateScatterPlot() {
       string freeVariable = (string)comboBox.SelectedItem;
       IEnumerable<string> fixedVariables = comboBox.Items.OfType<string>()
         .Except(new string[] { freeVariable });
+      
+      // scatter plots for subset of samples that have values near the median values for all variables
+      Func<int, bool> NearMedianValue = (r) => {
+        foreach (var fixedVar in fixedVariables) {
+          double med = medianValues[fixedVar];
+          if (!(Content.ProblemData.Dataset.GetDoubleValue(fixedVar, r) < med + 0.1 * Math.Abs(med) &&
+            Content.ProblemData.Dataset.GetDoubleValue(fixedVar, r) > med - 0.1 * Math.Abs(med)))
+            return false;
+        }
+        return true;
+      };
+
+      var mainTrainingIndizes = (from row in Content.ProblemData.TrainingIndizes
+                                 where NearMedianValue(row)
+                                 select row)
+        .ToArray();
+      var mainTestIndizes = (from row in Content.ProblemData.TestIndizes
+                             where NearMedianValue(row)
+                             select row)
+        .ToArray();
+
+      var freeVariableValues = Content.ProblemData.Dataset.GetDoubleValues(freeVariable, mainTrainingIndizes).ToArray();
+      var trainingValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable,
+                                                                     mainTrainingIndizes).ToArray();
+      Array.Sort(freeVariableValues, trainingValues);
+      responseChart.Series["Training Data"].Points.DataBindXY(freeVariableValues, trainingValues);
+
+      freeVariableValues = Content.ProblemData.Dataset.GetDoubleValues(freeVariable, mainTestIndizes).ToArray();
+      var testValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable,
+                                                                     mainTestIndizes).ToArray();
+      Array.Sort(freeVariableValues, testValues);
+      responseChart.Series["Test Data"].Points.DataBindXY(freeVariableValues, testValues);
+
+      // draw scatter plots of remaining values
+      freeVariableValues = Content.ProblemData.Dataset.GetDoubleValues(freeVariable, Content.ProblemData.TrainingIndizes).ToArray();
+      trainingValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable,
+                                                                     Content.ProblemData.TrainingIndizes).ToArray();
+      Array.Sort(freeVariableValues, trainingValues);
+      responseChart.Series["Training Data (edge)"].Points.DataBindXY(freeVariableValues, trainingValues);
+
+      freeVariableValues = Content.ProblemData.Dataset.GetDoubleValues(freeVariable, Content.ProblemData.TestIndizes).ToArray();
+      testValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable,
+                                                                     Content.ProblemData.TestIndizes).ToArray();
+      Array.Sort(freeVariableValues, testValues);
+      responseChart.Series["Test Data (edge)"].Points.DataBindXY(freeVariableValues, testValues);
+
+
+
+      responseChart.ChartAreas[0].AxisX.Maximum = Math.Ceiling(freeVariableValues.Max());
+      responseChart.ChartAreas[0].AxisX.Minimum = Math.Floor(freeVariableValues.Min());
+      responseChart.ChartAreas[0].AxisY.Maximum = Math.Ceiling(Math.Max(testValues.Max(), trainingValues.Max()));
+      responseChart.ChartAreas[0].AxisY.Minimum = Math.Floor(Math.Min(testValues.Min(), trainingValues.Min()));
+    }
+
+    private void UpdateResponseSeries() {
+      string freeVariable = (string)comboBox.SelectedItem;
 
       var freeVariableValues = Content.ProblemData.Dataset.GetDoubleValues(freeVariable, Content.ProblemData.TrainingIndizes).ToArray();
       var responseValues = Content.Model.Interpreter.GetSymbolicExpressionTreeValues(clonedTree,
                                                                               Content.ProblemData.Dataset,
-                                                                              Content.ProblemData.TrainingIndizes).ToArray();
+                                                                              Content.ProblemData.TrainingIndizes)
+                                                                              .ToArray();
       Array.Sort(freeVariableValues, responseValues);
       responseChart.Series["Model Response"].Points.DataBindXY(freeVariableValues, responseValues);
+    }
+
+    private void ComboBoxSelectedIndexChanged(object sender, EventArgs e) {
+      string freeVariable = (string)comboBox.SelectedItem;
+      IEnumerable<string> fixedVariables = comboBox.Items.OfType<string>()
+        .Except(new string[] { freeVariable });
+
+      variableNodes.Clear();
+      clonedTree = (ISymbolicExpressionTree)Content.Model.SymbolicExpressionTree.Clone();
+
+      foreach (var varNode in clonedTree.IterateNodesPrefix().OfType<VariableTreeNode>()) {
+        if (fixedVariables.Contains(varNode.VariableName)) {
+          if (!variableNodes.ContainsKey(varNode.VariableName))
+            variableNodes.Add(varNode.VariableName, new List<ISymbolicExpressionTreeNode>());
+
+          int childIndex = varNode.Parent.IndexOfSubtree(varNode);
+          var replacementNode = MakeConstantTreeNode(medianValues[varNode.VariableName]);
+          var parent = varNode.Parent;
+          parent.RemoveSubtree(childIndex);
+          parent.InsertSubtree(childIndex, MakeProduct(replacementNode, varNode.Weight));
+          variableNodes[varNode.VariableName].Add(replacementNode);
+        }
+      }
+
+      CreateSliders(fixedVariables);
+      UpdateScatterPlot();
+      UpdateResponseSeries();
+    }
+
+    private ISymbolicExpressionTreeNode MakeProduct(ConstantTreeNode c, double weight) {
+      var mul = new Multiplication();
+      var prod = mul.CreateTreeNode();
+      prod.AddSubtree(MakeConstantTreeNode(weight));
+      prod.AddSubtree(c);
+      return prod;
     }
 
@@ -146 +238 @@
       return constantTreeNode;
     }
-
-    private void ComboBoxSelectedIndexChanged(object sender, EventArgs e) {
-      string freeVariable = (string)comboBox.SelectedItem;
-      IEnumerable<string> fixedVariables = comboBox.Items.OfType<string>()
-        .Except(new string[] { freeVariable });
-
-      variableNodes.Clear();
-      clonedTree = (ISymbolicExpressionTree)Content.Model.SymbolicExpressionTree.Clone();
-
-      foreach (var varNode in clonedTree.IterateNodesPrefix().OfType<VariableTreeNode>()) {
-        if (fixedVariables.Contains(varNode.VariableName)) {
-          if (!variableNodes.ContainsKey(varNode.VariableName))
-            variableNodes.Add(varNode.VariableName, new List<ISymbolicExpressionTreeNode>());
-
-          int childIndex = varNode.Parent.IndexOfSubtree(varNode);
-          var replacementNode = MakeConstantTreeNode(medianValues[varNode.VariableName]);
-          var parent = varNode.Parent;
-          parent.RemoveSubtree(childIndex);
-          parent.InsertSubtree(childIndex, replacementNode);
-          variableNodes[varNode.VariableName].Add(replacementNode);
-        }
-      }
-
-      CreateSliders(fixedVariables);
-      UpdateChart();
-    }
   }
 }