Changeset 7989 for branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views

Timestamp:

06/12/12 10:31:56 (12 years ago)

Author:

mkommend

Message:

#1081: Improved performance of time series prognosis.

Location:

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4

Files:

• HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj (modified) (1 diff)
• Solution Views/TimeSeriesPrognosisSolutionView.cs (modified) (2 diffs)
• TimeSeriesPrognosis/TimeSeriesPrognosisSolutionLineChartView.cs (modified) (7 diffs)
• TimeSeriesPrognosis/TimeSeriesPrognosisSolutionPrognosedValuesView.cs (modified) (1 diff)

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj

@@ -312 +312 @@
     </BootstrapperPackage>
   </ItemGroup>
-  <ItemGroup>
-    <Folder Include="obj\" />
-  </ItemGroup>
+  <ItemGroup />
   <ItemGroup>
     <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis\3.4\HeuristicLab.Problems.DataAnalysis-3.4.csproj">

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.cs

@@ -22 +22 @@
 using System.Windows.Forms;
 using HeuristicLab.Core;
-using HeuristicLab.Data;
 using HeuristicLab.MainForm;
 
 namespace HeuristicLab.Problems.DataAnalysis.Views {
   [View("TimeSeriesPrognosisnSolution View")]
-  [Content(typeof(TimeSeriesPrognosisSolutionBase), false)]
+  [Content(typeof(TimeSeriesPrognosisSolution), false)]
   public partial class TimeSeriesPrognosisSolutionView : DataAnalysisSolutionView {
     public TimeSeriesPrognosisSolutionView() {
@@ -33 +32 @@
     }
 
-    public new TimeSeriesPrognosisSolutionBase Content {
-      get { return (TimeSeriesPrognosisSolutionBase)base.Content; }
+    public new TimeSeriesPrognosisSolution Content {
+      get { return (TimeSeriesPrognosisSolution)base.Content; }
       set { base.Content = value; }
     }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionLineChartView.cs

@@ -58 +58 @@
     }
 
-    private void UpdateTargetVariables() {
-      // populate combobox
-      targetVariableComboBox.Items.Clear();
-      if (Content != null) {
-        if (testPrognosisStart < Content.ProblemData.TestPartition.Start || testPrognosisStart >= Content.ProblemData.TestPartition.End) {
-          testPrognosisStart = Content.ProblemData.TestPartition.Start;
-        }
-        foreach (var targetVariable in Content.ProblemData.TargetVariables)
-          targetVariableComboBox.Items.Add(targetVariable);
-
-        targetVariableComboBox.SelectedIndex = 0;
-      }
-    }
-
-
-
     private void RedrawChart() {
       this.chart.Series.Clear();
@@ -79 +63 @@
         this.chart.ChartAreas[0].AxisX.Minimum = 0;
         this.chart.ChartAreas[0].AxisX.Maximum = Content.ProblemData.Dataset.Rows - 1;
-        string targetVariable = (string)targetVariableComboBox.SelectedItem;
-        int varIndex = Content.ProblemData.TargetVariables.ToList().IndexOf(targetVariable);
+        string targetVariable = Content.ProblemData.TargetVariable;
 
         this.chart.Series.Add(TARGETVARIABLE_SERIES_NAME);
@@ -94 +77 @@
           this.chart.Series[PROGNOSEDVALUES_TRAINING_SERIES_NAME].Points
             .DataBindXY(Content.ProblemData.TrainingIndizes.ToArray(),
-                        Content.PrognosedTrainingValues.SelectMany(x => x).Skip(varIndex).TakeEvery(Content.ProblemData.TargetVariables.Count()).ToArray());
+                        Content.GetPrognosedValues(Content.ProblemData.TrainingIndizes.Take(1), Content.ProblemData.TrainingPartition.Size).SelectMany(x => x).ToArray());
         } else {
           this.chart.Series[PROGNOSEDVALUES_TRAINING_SERIES_NAME].Points
             .DataBindXY(Content.ProblemData.TrainingIndizes.ToArray(),
-                        Content.GetPrognosedValues(Content.ProblemData.TrainingIndizes, 1).SelectMany(x => x.Single()).Skip(varIndex).TakeEvery(Content.ProblemData.TargetVariables.Count()).ToArray());
+                        Content.GetPrognosedValues(Content.ProblemData.TrainingIndizes, 1).SelectMany(x => x).ToArray());
         }
         this.chart.Series[PROGNOSEDVALUES_TRAINING_SERIES_NAME].Tag = Content;
@@ -110 +93 @@
         this.chart.Series[PROGNOSEDVALUES_TEST_SERIES_NAME].ChartType = SeriesChartType.FastLine;
         if (prognosedValuesCheckbox.Checked) {
-          int offsetToStart = testPrognosisStart - Content.ProblemData.TestPartition.Start;
           this.chart.Series[PROGNOSEDVALUES_TEST_SERIES_NAME].Points
-            .DataBindXY(Content.ProblemData.TestIndizes.Skip(offsetToStart).ToArray(),
-                        Content.GetPrognosedValues(Enumerable.Range(testPrognosisStart, 1), Content.ProblemData.TestPartition.End - testPrognosisStart)
-                        .SelectMany(x => x.SelectMany(y => y))
-                        .Skip(varIndex)
-                        .TakeEvery(Content.ProblemData.TargetVariables.Count())
-                        .ToArray());
+            .DataBindXY(Content.ProblemData.TestIndizes.ToArray(),
+                        Content.GetPrognosedValues(Content.ProblemData.TestIndizes.Take(1), Content.ProblemData.TestPartition.Size).SelectMany(x => x).ToArray());
         } else {
           this.chart.Series[PROGNOSEDVALUES_TEST_SERIES_NAME].Points
             .DataBindXY(Content.ProblemData.TestIndizes.ToArray(),
-                        Content.GetPrognosedValues(Content.ProblemData.TestIndizes, 1)
-                        .SelectMany(x => x.Single())
-                        .Skip(varIndex)
-                        .TakeEvery(Content.ProblemData.TargetVariables.Count())
-                        .ToArray());
+                        Content.GetPrognosedValues(Content.ProblemData.TestIndizes, 1).SelectMany(x => x).ToArray());
         }
         this.chart.Series[PROGNOSEDVALUES_TEST_SERIES_NAME].Tag = Content;
@@ -159 +133 @@
     protected override void OnContentChanged() {
       base.OnContentChanged();
-      UpdateTargetVariables();
+      RedrawChart();
     }
 
     private void Content_ProblemDataChanged(object sender, EventArgs e) {
-      UpdateTargetVariables();
+      RedrawChart();
     }
     private void Content_ModelChanged(object sender, EventArgs e) {
@@ -212 +186 @@
               this.CreateAndAddStripLine("Training and Test", start, row, Color.FromArgb(40, Color.Green), Color.FromArgb(40, Color.Red), ChartHatchStyle.WideUpwardDiagonal);
               break;
-            default:
-              // should not happen
-              break;
+            default: throw new NotSupportedException();
           }
           curAttr = attr[row];
@@ -242 +214 @@
         }
       } else if (Content != null) {
-        string targetVariable = (string)targetVariableComboBox.SelectedItem;
-        int varIndex = Content.ProblemData.TargetVariables.ToList().IndexOf(targetVariable);
-
+        string targetVariable = Content.ProblemData.TargetVariable;
 
         IEnumerable<int> indizes = null;
         IEnumerable<double> predictedValues = null;
+
         switch (series.Name) {
           case PROGNOSEDVALUES_TRAINING_SERIES_NAME:
             indizes = Content.ProblemData.TrainingIndizes.ToArray();
-            predictedValues =
-              Content.PrognosedTrainingValues.SelectMany(x => x).Skip(varIndex).TakeEvery(
-                Content.ProblemData.TargetVariables.Count()).ToArray();
+            predictedValues = Content.GetPrognosedValues(Content.ProblemData.TrainingIndizes.Take(1), Content.ProblemData.TrainingPartition.Size).First();
             break;
           case PROGNOSEDVALUES_TEST_SERIES_NAME:
             testPrognosisStart = Content.ProblemData.TestPartition.Start;
             indizes = Content.ProblemData.TestIndizes.ToArray();
-            predictedValues = Content.PrognosedTestValues.SelectMany(x => x).Skip(varIndex).TakeEvery(
-              Content.ProblemData.TargetVariables.Count()).ToArray();
+            predictedValues = Content.GetPrognosedValues(Content.ProblemData.TestIndizes.Take(1), Content.ProblemData.TestPartition.Size).First();
             break;
         }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionPrognosedValuesView.cs

@@ -82 +82 @@
 
     private void UpdateEstimatedValues() {
-      if (InvokeRequired) Invoke((Action)UpdateEstimatedValues);
-      else {
-        StringMatrix matrix = null;
-        List<string> columnNames = new List<string>();
-        if (Content != null) {
-          columnNames.Add("Id");
+      if (InvokeRequired) {
+        Invoke((Action)UpdateEstimatedValues);
+        return;
+      }
+      if (Content == null) return;
 
-          string[,] values = new string[Content.ProblemData.Dataset.Rows, 1 + 3 * Content.ProblemData.TargetVariables.Count()];
-          foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows))
-            values[row, 0] = row.ToString();
+      var targetVariable = Content.ProblemData.TargetVariable;
+      StringMatrix matrix = null;
+      List<string> columnNames = new List<string>();
+      columnNames.Add("Id");
 
-          var allPrognosedTraining = Content.PrognosedTrainingValues.SelectMany(x=>x).ToArray();
-          var allPrognosedTest = Content.PrognosedTestValues.SelectMany(x => x).ToArray();
-
-          int i = 0;
-          int targetVariableIndex = 0;
-          foreach (var targetVariable in Content.ProblemData.TargetVariables) {
-            var prognosedTraining =
-              allPrognosedTraining.Skip(targetVariableIndex).TakeEvery(Content.ProblemData.TargetVariables.Count());
-            var prognosedTest =
-              allPrognosedTest.Skip(targetVariableIndex).TakeEvery(Content.ProblemData.TargetVariables.Count());
-
-            double[] target = Content.ProblemData.Dataset.GetDoubleValues(targetVariable).ToArray();
-
-            var prognosedTrainingEnumerator = prognosedTraining.GetEnumerator();
-            foreach (var row in Content.ProblemData.TrainingIndizes) {
-              prognosedTrainingEnumerator.MoveNext();
-              values[row, i + 2] = prognosedTrainingEnumerator.Current.ToString();
-            }
-
-            var prognosedTestEnumerator = prognosedTest.GetEnumerator();
-            foreach (var row in Content.ProblemData.TestIndizes) {
-              prognosedTestEnumerator.MoveNext();
-              values[row, i + 3] = prognosedTestEnumerator.Current.ToString();
-            }
-
-            foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows)) {
-              values[row, i + 1] = target[row].ToString();
-            }
-
-            columnNames.AddRange(new string[] { targetVariable + "(actual)", targetVariable + "(training)", targetVariable + "(test)" });
-            i += 3;
-            targetVariableIndex++;
-          } // foreach
+      string[,] values = new string[Content.ProblemData.Dataset.Rows, 4];
+      var prognosedTraining = Content.GetPrognosedValues(Content.ProblemData.TrainingIndizes.Take(1), Content.ProblemData.TrainingPartition.Size).First();
+      var prognosedTest = Content.GetPrognosedValues(Content.ProblemData.TrainingIndizes.Take(1), Content.ProblemData.TestPartition.Size).First();
+      double[] target = Content.ProblemData.Dataset.GetDoubleValues(targetVariable).ToArray();
 
 
-          matrix = new StringMatrix(values);
-          matrix.ColumnNames = columnNames.ToArray();
-          matrix.SortableView = true;
+      foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows)) {
+        values[row, 0] = row.ToString();
+        values[row, 1] = target[row].ToString();
+      }
 
-        } // if
-        matrixView.Content = matrix;
+      var rowsEnumerator = Content.ProblemData.TrainingIndizes.GetEnumerator();
+      var prognosisEnumerator = prognosedTraining.GetEnumerator();
+      while (rowsEnumerator.MoveNext() & prognosisEnumerator.MoveNext()) {
+        var row = rowsEnumerator.Current;
+        var prognosis = prognosisEnumerator.Current;
+        values[row, 2] = prognosis.ToString();
       }
+
+      rowsEnumerator = Content.ProblemData.TestIndizes.GetEnumerator();
+      prognosisEnumerator = prognosedTest.GetEnumerator();
+      while (rowsEnumerator.MoveNext() & prognosisEnumerator.MoveNext()) {
+        var row = rowsEnumerator.Current;
+        var prognosis = prognosisEnumerator.Current;
+        values[row, 3] = prognosis.ToString();
+      }
+
+      columnNames.AddRange(new string[] { targetVariable + "(actual)", targetVariable + "(training)", targetVariable + "(test)" });
+      matrix = new StringMatrix(values);
+      matrix.ColumnNames = columnNames.ToArray();
+      matrix.SortableView = true;
     }
     #endregion