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Ignore:
Timestamp:
07/17/11 22:51:11 (14 years ago)
Author:
abeham
Message:

#1541

  • updated to latest trunk version
Location:
branches/QAPAlgorithms
Files:
10 edited
3 copied

Legend:

Unmodified
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Removed

  • branches/QAPAlgorithms

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.3/HeuristicLabAlgorithmsDataAnalysisPlugin.cs.frame

    r6099 r6569  
    2626  /// Plugin class for HeuristicLab.Algorithms.DataAnalysis plugin.
    2727  /// </summary>
    28   [Plugin("HeuristicLab.Algorithms.DataAnalysis", "3.3.4.$WCREV$")]
     28  [Plugin("HeuristicLab.Algorithms.DataAnalysis", "3.3.5.$WCREV$")]
    2929  [PluginFile("HeuristicLab.Algorithms.DataAnalysis-3.3.dll", PluginFileType.Assembly)]
    3030  [PluginDependency("HeuristicLab.Collections", "3.3")]

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.3/Properties/AssemblyInfo.frame

    r6099 r6569  
    5353// by using the '*' as shown below:
    5454[assembly: AssemblyVersion("3.3.0.0")]
    55 [assembly: AssemblyFileVersion("3.3.4.$WCREV$")]
     55[assembly: AssemblyFileVersion("3.3.5.$WCREV$")]

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/CrossValidation.cs

    r6250 r6569  
    3434
    3535namespace HeuristicLab.Algorithms.DataAnalysis {
    36   [Item("Cross Validation", "Cross Validation wrapper for data analysis algorithms.")]
     36  [Item("Cross Validation", "Cross-validation wrapper for data analysis algorithms.")]
    3737  [Creatable("Data Analysis")]
    3838  [StorableClass]
    … …  
    363363
    364364    public void CollectResultValues(IDictionary<string, IItem> results) {
     365      var clonedResults = (ResultCollection)this.results.Clone();
     366      foreach (var result in clonedResults) {
     367        results.Add(result.Name, result.Value);
     368      }
     369    }
     370
     371    private void AggregateResultValues(IDictionary<string, IItem> results) {
    365372      Dictionary<string, List<double>> resultValues = new Dictionary<string, List<double>>();
    366373      IEnumerable<IRun> runs = clonedAlgorithms.Select(alg => alg.Runs.FirstOrDefault()).Where(run => run != null);
    … …  
    397404      List<IResult> aggregatedResults = new List<IResult>();
    398405      foreach (KeyValuePair<string, List<IRegressionSolution>> solutions in resultSolutions) {
    399         var problemDataClone = (IRegressionProblemData)Problem.ProblemData.Clone();
     406        // clone manually to correctly clone references between cloned root objects
     407        Cloner cloner = new Cloner();
     408        var problemDataClone = (IRegressionProblemData)cloner.Clone(Problem.ProblemData);
     409        // set partitions of problem data clone correctly
    400410        problemDataClone.TrainingPartition.Start = SamplesStart.Value; problemDataClone.TrainingPartition.End = SamplesEnd.Value;
    401411        problemDataClone.TestPartition.Start = SamplesStart.Value; problemDataClone.TestPartition.End = SamplesEnd.Value;
    402         var ensembleSolution = new RegressionEnsembleSolution(solutions.Value.Select(x => x.Model), problemDataClone,
    403           solutions.Value.Select(x => x.ProblemData.TrainingPartition),
    404           solutions.Value.Select(x => x.ProblemData.TestPartition));
     412        // clone models
     413        var ensembleSolution = new RegressionEnsembleSolution(
     414          solutions.Value.Select(x => cloner.Clone(x.Model)),
     415          problemDataClone,
     416          solutions.Value.Select(x => cloner.Clone(x.ProblemData.TrainingPartition)),
     417          solutions.Value.Select(x => cloner.Clone(x.ProblemData.TestPartition)));
    405418
    406419        aggregatedResults.Add(new Result(solutions.Key + " (ensemble)", ensembleSolution));
    … …  
    425438      var aggregatedResults = new List<IResult>();
    426439      foreach (KeyValuePair<string, List<IClassificationSolution>> solutions in resultSolutions) {
    427         var problemDataClone = (IClassificationProblemData)Problem.ProblemData.Clone();
     440        // clone manually to correctly clone references between cloned root objects
     441        Cloner cloner = new Cloner();
     442        var problemDataClone = (IClassificationProblemData)cloner.Clone(Problem.ProblemData);
     443        // set partitions of problem data clone correctly
    428444        problemDataClone.TrainingPartition.Start = SamplesStart.Value; problemDataClone.TrainingPartition.End = SamplesEnd.Value;
    429445        problemDataClone.TestPartition.Start = SamplesStart.Value; problemDataClone.TestPartition.End = SamplesEnd.Value;
    430         var ensembleSolution = new ClassificationEnsembleSolution(solutions.Value.Select(x => x.Model), problemDataClone,
    431           solutions.Value.Select(x => x.ProblemData.TrainingPartition),
    432           solutions.Value.Select(x => x.ProblemData.TestPartition));
     446        // clone models
     447        var ensembleSolution = new ClassificationEnsembleSolution(
     448          solutions.Value.Select(x => cloner.Clone(x.Model)),
     449          problemDataClone,
     450          solutions.Value.Select(x => cloner.Clone(x.ProblemData.TrainingPartition)),
     451          solutions.Value.Select(x => cloner.Clone(x.ProblemData.TestPartition)));
    433452
    434453        aggregatedResults.Add(new Result(solutions.Key + " (ensemble)", ensembleSolution));
    … …  
    553572      } else
    554573        SamplesEnd.Value = 0;
     574
     575      SamplesStart_ValueChanged(this, EventArgs.Empty);
     576      SamplesEnd_ValueChanged(this, EventArgs.Empty);
    555577    }
    556578
    … …  
    699721      stopPending = false;
    700722      Dictionary<string, IItem> collectedResults = new Dictionary<string, IItem>();
    701       CollectResultValues(collectedResults);
     723      AggregateResultValues(collectedResults);
    702724      results.AddRange(collectedResults.Select(x => new Result(x.Key, x.Value)).Cast<IResult>().ToArray());
    703725      runsCounter++;

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/FixedDataAnalysisAlgorithm.cs

    r5809 r6569  
    2121
    2222using System;
    23 using System.Linq;
     23using System.Threading;
     24using System.Threading.Tasks;
    2425using HeuristicLab.Common;
    25 using HeuristicLab.Core;
    26 using HeuristicLab.Data;
    2726using HeuristicLab.Optimization;
    28 using HeuristicLab.Parameters;
    2927using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    3028using HeuristicLab.Problems.DataAnalysis;
    31 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    32 using System.Collections.Generic;
    33 using HeuristicLab.Problems.DataAnalysis.Symbolic;
    34 using System.Threading.Tasks;
    35 using System.Threading;
    3629
    3730namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    5750    }
    5851    #endregion
    59    
     52
    6053    private DateTime lastUpdateTime;
    6154
    … …  
    10396    private void Run(object state) {
    10497      CancellationToken cancellationToken = (CancellationToken)state;
    105       OnStarted();
    10698      lastUpdateTime = DateTime.Now;
    10799      System.Timers.Timer timer = new System.Timers.Timer(250);

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

    r6241 r6569  
    129129      <SubType>Code</SubType>
    130130    </Compile>
     131    <Compile Include="Linear\LogitClassificationSolution.cs" />
     132    <Compile Include="Linear\LogitModel.cs" />
     133    <Compile Include="Linear\MultinomialLogitClassification.cs" />
    131134    <Compile Include="Properties\AssemblyInfo.cs" />
    132135    <Compile Include="RandomForest\RandomForestClassificationSolution.cs" />

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLabAlgorithmsDataAnalysisPlugin.cs.frame

    r5869 r6569  
    2626  /// Plugin class for HeuristicLab.Algorithms.DataAnalysis plugin.
    2727  /// </summary>
    28   [Plugin("HeuristicLab.Algorithms.DataAnalysis", "Provides wrappers for data analysis algorithms implemented in external libraries (linear regression, linear discriminant analysis, k-means clustering, support vector classification and regression)", "3.4.0.$WCREV$")]
     28  [Plugin("HeuristicLab.Algorithms.DataAnalysis", "Provides wrappers for data analysis algorithms implemented in external libraries (linear regression, linear discriminant analysis, k-means clustering, support vector classification and regression)", "3.4.1.$WCREV$")]
    2929  [PluginFile("HeuristicLab.Algorithms.DataAnalysis-3.4.dll", PluginFileType.Assembly)]
    3030  [PluginDependency("HeuristicLab.ALGLIB", "3.1.0")]

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

    r6240 r6569  
    111111
    112112      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), problemData);
     113      solution.Model.Name = "Linear Regression Model";
    113114      return solution;
    114115    }

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/Properties/AssemblyInfo.frame

    r5869 r6569  
    5353// by using the '*' as shown below:
    5454[assembly: AssemblyVersion("3.4.0.0")]
    55 [assembly: AssemblyFileVersion("3.4.0.$WCREV$")]
     55[assembly: AssemblyFileVersion("3.4.1.$WCREV$")]

  • branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineModel.cs

    r5861 r6569  
    9898      this.targetVariable = original.targetVariable;
    9999      this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
     100      foreach (var dataset in original.cachedPredictions.Keys) {
     101        this.cachedPredictions.Add(cloner.Clone(dataset), (double[])original.cachedPredictions[dataset].Clone());
     102      }
    100103      if (original.classValues != null)
    101104        this.classValues = (double[])original.classValues.Clone();
    … …  
    145148    }
    146149    #endregion
     150    // cache for predictions, which is cloned but not persisted, must be cleared when the model is changed
     151    private Dictionary<Dataset, double[]> cachedPredictions = new Dictionary<Dataset, double[]>();
    147152    private IEnumerable<double> GetEstimatedValuesHelper(Dataset dataset, IEnumerable<int> rows) {
     153      if (!cachedPredictions.ContainsKey(dataset)) {
     154        // create an array of cached predictions which is initially filled with NaNs
     155        double[] predictions = Enumerable.Repeat(double.NaN, dataset.Rows).ToArray();
     156        CalculatePredictions(dataset, rows, predictions);
     157        cachedPredictions.Add(dataset, predictions);
     158      }
     159      // get the array of predictions and select the subset of requested rows
     160      double[] p = cachedPredictions[dataset];
     161      var requestedPredictions = from r in rows
     162                                 select p[r];
     163      // check if the requested predictions contain NaNs
     164      // (this means for the request rows some predictions have not been cached)
     165      if (requestedPredictions.Any(x => double.IsNaN(x))) {
     166        // updated the predictions for currently requested rows
     167        CalculatePredictions(dataset, rows, p);
     168        cachedPredictions[dataset] = p;
     169        // now we can be sure that for the current rows all predictions are available
     170        return from r in rows
     171               select p[r];
     172      } else {
     173        // there were no NaNs => just return the cached predictions
     174        return requestedPredictions;
     175      }
     176    }
     177
     178    private void CalculatePredictions(Dataset dataset, IEnumerable<int> rows, double[] predictions) {
     179      // calculate and cache predictions for the currently requested rows
    148180      SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
    149181      SVM.Problem scaledProblem = Scaling.Scale(RangeTransform, problem);
    150182
    151       foreach (var row in Enumerable.Range(0, scaledProblem.Count)) {
    152         yield return SVM.Prediction.Predict(Model, scaledProblem.X[row]);
    153       }
    154     }
     183      // row is the index in the original dataset,
     184      // i is the index in the scaled dataset (containing only the necessary rows)
     185      int i = 0;
     186      foreach (var row in rows) {
     187        predictions[row] = SVM.Prediction.Predict(Model, scaledProblem.X[i]);
     188        i++;
     189      }
     190    }
     191
    155192    #region events
    156193    public event EventHandler Changed;
    157194    private void OnChanged(EventArgs e) {
     195      cachedPredictions.Clear();
    158196      var handlers = Changed;
    159197      if (handlers != null)
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