Package org.jamesii.simspex.spdm.evaluation

Examples of org.jamesii.simspex.spdm.evaluation.IDataSelector

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    List<PerformanceTuple> data = dataSet.getInstances();
    int trainEndIndex = (int) Math.floor(data.size() * percentage);

    // Generate predictor
    IDataSelector trainingDataSelector =
        new SimpleDataSelector(0, trainEndIndex);
    List<PerformanceTuple> trainingData = trainingDataSelector.selectData(data);
    IPerformancePredictorGenerator predGen =
        predGenFactory.createPredictorGenerator(parameters, data.get(0));
    IPerformancePredictor predictor =
        predGen.generatePredictor(trainingData, dataSet.getMetaData());

    // Evaluate predictor
    IDataSelector testDataSelector =
        new SimpleDataSelector(trainEndIndex, data.size());
    List<PerformanceTuple> testData = testDataSelector.selectData(data);
    IPredictorEvaluator sev = new FullPredictorEvaluator();

    result.add(sev.evaluate(predictor, trainingData, testData, parameters));
    return result;
  }
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   * @return the test data
   */
  private <T extends PerformanceTuple> List<T> getTestData(
      Map<Features, List<T>> featureMap, List<Features> allFeatures,
      int startIndex, int endIndex) {
    IDataSelector testDataSelector =
        new SimpleDataSelector(startIndex, endIndex);
    List<Features> testFeatures = testDataSelector.selectData(allFeatures);
    List<T> testData =
        PerformanceTuples.getTuplesForFeatures(featureMap, testFeatures);
    return testData;
  }
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   * @return the training data
   */
  private <T extends PerformanceTuple> List<T> getTrainingData(
      Map<Features, List<T>> featureMap, List<Features> allFeatures,
      int startIndex, int endIndex) {
    IDataSelector trainingDataSelector = new SimpleDataSelector(0, startIndex);
    List<Features> trainingFeatures =
        trainingDataSelector.selectData(allFeatures);

    trainingDataSelector = new SimpleDataSelector(endIndex, allFeatures.size());
    trainingFeatures.addAll(trainingDataSelector.selectData(allFeatures));
    List<T> trainingData =
        PerformanceTuples.getTuplesForFeatures(featureMap, trainingFeatures);
    return trainingData;
  }
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Related Classes of org.jamesii.simspex.spdm.evaluation.IDataSelector

  • org.jamesii.simspex.spdm.evaluation.crossvalidation.CrossValidation
  • org.jamesii.simspex.spdm.evaluation.simple.SimpleEvaluationStrategy
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