Changeset 12586

Timestamp:

07/03/15 16:40:34 (9 years ago)

Author:

dglaser

Message:

#2388: Merged trunk into HiveStatistics branch

Location:

branches/HiveStatistics/sources

Files:

• . (modified) (1 prop)
• HeuristicLab.Clients.Hive/3.3/HiveClient.cs (modified) (1 diff)
• HeuristicLab.Optimization.Operators/3.3/MultiObjective/FastNonDominatedSort.cs (modified) (4 diffs)
• HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Views (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Views/3.4/MenuItems/ShrinkDataAnalysisRunsMenuItem.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionErrorCharacteristicsCurveView.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs (modified) (8 diffs)
• HeuristicLab.Services.WebApp.Status/3.3/WebApp/status/statusCtrl.js (modified) (1 diff)
• HeuristicLab.Services.WebApp/3.3/WebApp/helper.js (modified) (1 diff)
• HeuristicLab.Services.WebApp/3.3/WebApp/libs/cryptojs (copied) (copied from trunk/sources/HeuristicLab.Services.WebApp/3.3/WebApp/libs/cryptojs)
• HeuristicLab.Services.WebApp/3.3/WebApp/plugins/about/aboutCtrl.js (modified) (1 diff)

branches/HiveStatistics/sources/HeuristicLab.Clients.Hive/3.3/HiveClient.cs

@@ -76 +76 @@
     #endregion
 
-    private HiveClient() {
-      //this will never be deregistered
-      TaskScheduler.UnobservedTaskException += new EventHandler<UnobservedTaskExceptionEventArgs>(TaskScheduler_UnobservedTaskException);
-    }
-
-    private void TaskScheduler_UnobservedTaskException(object sender, UnobservedTaskExceptionEventArgs e) {
-      e.SetObserved(); // avoid crash of process because task crashes. first exception found is handled in Results property
-      throw new HiveException("Unobserved Exception in ConcurrentTaskDownloader", e.Exception);
-    }
+    private HiveClient() { }
 
     public void ClearHiveClient() {

branches/HiveStatistics/sources/HeuristicLab.Optimization.Operators/3.3/MultiObjective/FastNonDominatedSort.cs

@@ -68 +68 @@
     public override IOperation Apply() {
       bool dominateOnEqualQualities = DominateOnEqualQualitiesParameter.ActualValue.Value;
-      BoolArray maximization = MaximizationParameter.ActualValue;
-      ItemArray<DoubleArray> qualities = QualitiesParameter.ActualValue;
+      bool[] maximization = MaximizationParameter.ActualValue.ToArray();
+      double[][] qualities = QualitiesParameter.ActualValue.Select(x => x.ToArray()).ToArray();
       if (qualities == null) throw new InvalidOperationException(Name + ": No qualities found.");
 
@@ -82 +82 @@
       for (int pI = 0; pI < populationSize - 1; pI++) {
         IScope p = scope.SubScopes[pI];
-        if (!dominatedScopes.ContainsKey(p))
-          dominatedScopes[p] = new List<int>();
+        List<int> dominatedScopesByp;
+        if (!dominatedScopes.TryGetValue(p, out dominatedScopesByp))
+          dominatedScopes[p] = dominatedScopesByp = new List<int>();
         for (int qI = pI + 1; qI < populationSize; qI++) {
           DominationResult test = Dominates(qualities[pI], qualities[qI], maximization, dominateOnEqualQualities);
           if (test == DominationResult.Dominates) {
-            dominatedScopes[p].Add(qI);
+            dominatedScopesByp.Add(qI);
             dominationCounter[qI] += 1;
           } else if (test == DominationResult.IsDominated) {
@@ -111 +112 @@
         ScopeList nextFront = new ScopeList();
         foreach (IScope p in fronts[i]) {
-          if (dominatedScopes.ContainsKey(p)) {
-            for (int k = 0; k < dominatedScopes[p].Count; k++) {
-              int dominatedScope = dominatedScopes[p][k];
+          List<int> dominatedScopesByp;
+          if (dominatedScopes.TryGetValue(p, out dominatedScopesByp)) {
+            for (int k = 0; k < dominatedScopesByp.Count; k++) {
+              int dominatedScope = dominatedScopesByp[k];
               dominationCounter[dominatedScope] -= 1;
               if (dominationCounter[dominatedScope] == 0) {
@@ -140 +142 @@
     }
 
-    private static DominationResult Dominates(DoubleArray left, DoubleArray right, BoolArray maximizations, bool dominateOnEqualQualities) {
-      if (dominateOnEqualQualities && left.SequenceEqual(right)) return DominationResult.Dominates;
+    private static DominationResult Dominates(double[] left, double[] right, bool[] maximizations, bool dominateOnEqualQualities) {
+      //mkommend Caution: do not use LINQ.SequenceEqual for comparing the two quality arrays (left and right) due to performance reasons
+      if (dominateOnEqualQualities) {
+        var equal = true;
+        for (int i = 0; i < left.Length; i++) {
+          if (left[i] != right[i]) {
+            equal = false;
+            break;
+          }
+        }
+        if (equal) return DominationResult.Dominates;
+      }
 
       bool leftIsBetter = false, rightIsBetter = false;

branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis.Views/3.4/MenuItems/ShrinkDataAnalysisRunsMenuItem.cs

@@ -82 +82 @@
         var variableValuesMapping = new Dictionary<ValuesType, ValuesType>();
         foreach (var problemData in view.Content.GetObjectGraphObjects(excludeStaticMembers: true).OfType<IDataAnalysisProblemData>()) {
-          var originalValues = variableValuesGetter(problemData.Dataset);
+          var dataset = problemData.Dataset as Dataset;
+          if (dataset == null) continue;
+          var originalValues = variableValuesGetter(dataset);
           var matchingValues = GetEqualValues(originalValues, variableValuesMapping);
-          variableValuesSetter(problemData.Dataset, matchingValues);
+          variableValuesSetter(dataset, matchingValues);
         }
       };
@@ -116 +118 @@
     }
 
-    private static readonly Action<IDataset, Dictionary<string, IList>> variableValuesSetter;
-    private static readonly Func<IDataset, Dictionary<string, IList>> variableValuesGetter;
+    private static readonly Action<Dataset, Dictionary<string, IList>> variableValuesSetter;
+    private static readonly Func<Dataset, Dictionary<string, IList>> variableValuesGetter;
     /// <summary>
     /// The static initializer is used to create expressions for getting and setting the private variableValues field in the dataset.
@@ -123 +125 @@
     /// </summary>
     static ShrinkDataAnalysisRunsMenuItem() {
-      var dataset = Expression.Parameter(typeof(IDataset));
+      var dataset = Expression.Parameter(typeof(Dataset));
       var variableValues = Expression.Parameter(typeof(ValuesType));
       var valuesExpression = Expression.Field(dataset, "variableValues");
       var assignExpression = Expression.Assign(valuesExpression, variableValues);
 
-      var variableValuesSetExpression = Expression.Lambda<Action<IDataset, ValuesType>>(assignExpression, dataset, variableValues);
+      var variableValuesSetExpression = Expression.Lambda<Action<Dataset, ValuesType>>(assignExpression, dataset, variableValues);
       variableValuesSetter = variableValuesSetExpression.Compile();
 
-      var variableValuesGetExpression = Expression.Lambda<Func<IDataset, ValuesType>>(valuesExpression, dataset);
+      var variableValuesGetExpression = Expression.Lambda<Func<Dataset, ValuesType>>(valuesExpression, dataset);
       variableValuesGetter = variableValuesGetExpression.Compile();
     }

branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionSolutionErrorCharacteristicsCurveView.cs

@@ -125 +125 @@
 
       var maxValue = residuals.Max();
-      double scale = Math.Pow(10, Math.Floor(Math.Log10(maxValue)));
-      var maximum = scale * (1 + (int)(maxValue / scale));
-      chart.ChartAreas[0].AxisX.Maximum = maximum;
-      chart.ChartAreas[0].CursorX.Interval = residuals.Min() / 100;
+      if (maxValue >= chart.ChartAreas[0].AxisX.Maximum) {
+        double scale = Math.Pow(10, Math.Floor(Math.Log10(maxValue)));
+        var maximum = scale * (1 + (int)(maxValue / scale));
+        chart.ChartAreas[0].AxisX.Maximum = maximum;
+        chart.ChartAreas[0].CursorX.Interval = residuals.Min() / 100;
+      }
 
       UpdateSeries(residuals, solutionSeries);
@@ -211 +213 @@
           .Where(x => x > 0) // remove entries where the original value is 0
           .ToList();
-      }
-      // should never happen
-      return new List<double>();
+        default: throw new NotSupportedException();
+      }
     }
 

branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using HeuristicLab.Common;
@@ -39 +40 @@
     protected const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
     protected const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
-    protected const string TrainingMeanErrorResultName = "Mean error (training)";
-    protected const string TestMeanErrorResultName = "Mean error (test)";
+    protected const string TrainingRootMeanSquaredErrorResultName = "Root mean squared error (training)";
+    protected const string TestRootMeanSquaredErrorResultName = "Root mean squared error (test)";
+
+    // BackwardsCompatibility3.3
+    #region Backwards compatible code, remove with 3.5
+    private const string TrainingMeanErrorResultName = "Mean error (training)";
+    private const string TestMeanErrorResultName = "Mean error (test)";
+    #endregion
+
 
     protected const string TrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
@@ -52 +60 @@
     protected const string TrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
     protected const string TestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
-    protected const string TrainingMeanErrorResultDescription = "Mean of errors of the model on the training partition";
-    protected const string TestMeanErrorResultDescription = "Mean of errors of the model on the test partition";
+    protected const string TrainingRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the training partition";
+    protected const string TestRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the test partition";
 
     public new IRegressionModel Model {
@@ -111 +119 @@
       private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
     }
-    public double TrainingMeanError {
-      get { return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value; }
-      private set { ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value; }
-    }
-    public double TestMeanError {
-      get { return ((DoubleValue)this[TestMeanErrorResultName].Value).Value; }
-      private set { ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value; }
-    }
+    public double TrainingRootMeanSquaredError {
+      get { return ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value = value; }
+    }
+    public double TestRootMeanSquaredError {
+      get { return ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value = value; }
+    }
+
+    // BackwardsCompatibility3.3
+    #region Backwards compatible code, remove with 3.5
+    private double TrainingMeanError {
+      get {
+        if (!ContainsKey(TrainingMeanErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value;
+      }
+      set {
+        if (ContainsKey(TrainingMeanErrorResultName))
+          ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value;
+      }
+    }
+    private double TestMeanError {
+      get {
+        if (!ContainsKey(TestMeanErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[TestMeanErrorResultName].Value).Value;
+      }
+      set {
+        if (ContainsKey(TestMeanErrorResultName))
+          ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value;
+      }
+    }
+    #endregion
     #endregion
 
@@ -138 +170 @@
       Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, TrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
       Add(new Result(TestNormalizedMeanSquaredErrorResultName, TestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
-      Add(new Result(TrainingMeanErrorResultName, TrainingMeanErrorResultDescription, new DoubleValue()));
-      Add(new Result(TestMeanErrorResultName, TestMeanErrorResultDescription, new DoubleValue()));
+      Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
+      Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
     }
 
@@ -145 +177 @@
     private void AfterDeserialization() {
       // BackwardsCompatibility3.4
-
       #region Backwards compatible code, remove with 3.5
-
       if (!ContainsKey(TrainingMeanAbsoluteErrorResultName)) {
         OnlineCalculatorError errorState;
@@ -162 +192 @@
       }
 
-      if (!ContainsKey(TrainingMeanErrorResultName)) {
-        OnlineCalculatorError errorState;
-        Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
-        double trainingME = OnlineMeanErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
-        TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
-      }
-      if (!ContainsKey(TestMeanErrorResultName)) {
-        OnlineCalculatorError errorState;
-        Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
-        double testME = OnlineMeanErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
-        TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
+      if (!ContainsKey(TrainingRootMeanSquaredErrorResultName)) {
+        OnlineCalculatorError errorState;
+        Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
+        double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
+        TrainingRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(trainingMSE) : double.NaN;
+      }
+
+      if (!ContainsKey(TestRootMeanSquaredErrorResultName)) {
+        OnlineCalculatorError errorState;
+        Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
+        double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
+        TestRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(testMSE) : double.NaN;
       }
       #endregion
@@ -213 +244 @@
       TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
 
-      double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
-      double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
+      TrainingRootMeanSquaredError = Math.Sqrt(TrainingMeanSquaredError);
+      TestRootMeanSquaredError = Math.Sqrt(TestMeanSquaredError);
+
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.5
+      if (ContainsKey(TrainingMeanErrorResultName)) {
+        double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+        TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
+      }
+      if (ContainsKey(TestMeanErrorResultName)) {
+        double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+        TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
+      }
+      #endregion
     }
   }

branches/HiveStatistics/sources/HeuristicLab.Services.WebApp.Status/3.3/WebApp/status/statusCtrl.js

@@ -125 +125 @@
                         }
                     }
-                    cpuSeries.push([$scope.status.Timestamp, $scope.cpu.knobData]);
-
+                    
+                    cpuSeries.push([$scope.status.Timestamp, Math.round(status.CpuUtilizationStatus.TotalCpuUtilization)]);
                     coreSeries[0].push([$scope.status.Timestamp, status.CoreStatus.TotalCores]);
                     coreSeries[1].push([$scope.status.Timestamp, status.CoreStatus.UsedCores]);

branches/HiveStatistics/sources/HeuristicLab.Services.WebApp/3.3/WebApp/helper.js

@@ -62 +62 @@
 };
 
-function decryptString(s) {
+var decryptString = function(s) {
     return CryptoJS.AES.decrypt(s, "heuristiclab").toString(CryptoJS.enc.Utf8);
-}
+};

branches/HiveStatistics/sources/HeuristicLab.Services.WebApp/3.3/WebApp/plugins/about/aboutCtrl.js

@@ -1 +1 @@
 (function () {
     var module = appAboutPlugin.getAngularModule();
-    module.controller('app.about.ctrl', ['$scope', '$log', function($scope, $log) {
+    module.controller('app.about.ctrl', ['$scope', function($scope) {
         $scope.mailToSupport = function () {
             location.href = decryptString('U2FsdGVkX1/pCOITUzzsN36hx4sHh11FeVXkVyQ5b2KeZebFQ3KaNN8G9bKL3lU9');