Esempio n. 1
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  @Test
  public void prune_givenValidationDataSameAsTrainingData_expect100Percent()
      throws FileNotFoundException, UnsupportedEncodingException {

    Model model = data.getMushroomModel();
    List<Sample> trainingSet = data.getAllMushroomSamples();
    List<Prediction> originalPrediction = predictor.predict(model, trainingSet);
    Double originalAccuracy = accuracy.evaluate(originalPrediction, model.getTargetAttribute());

    List<Rule> prunedRules = pruner.pruneRepeatedly(model, trainingSet);
    List<Prediction> prunedPrediction = predictor.predict(prunedRules, trainingSet);
    Double prunedAccuracy = accuracy.evaluate(prunedPrediction, model.getTargetAttribute());

    log.debug(
        "Accuracy before pruning = {}, Accuracy after pruning = {}",
        originalAccuracy,
        prunedAccuracy);

    assertThat(originalAccuracy, is(prunedAccuracy));
  }
Esempio n. 2
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  @Test
  public void prune_given25PercentValidationSet_expectNoDecreaseInAccuracy()
      throws FileNotFoundException, UnsupportedEncodingException {

    data.loadData(25.0);

    Model model = data.getMushroomModel();
    List<Sample> validationSet = data.getValidationSet();

    List<Prediction> originalPrediction = predictor.predict(model, validationSet);
    Double originalAccuracy = accuracy.evaluate(originalPrediction, model.getTargetAttribute());

    List<Rule> prunedRules = pruner.pruneRepeatedly(model, validationSet);
    List<Prediction> prunedPrediction = predictor.predict(prunedRules, validationSet);
    Double prunedAccuracy = accuracy.evaluate(prunedPrediction, model.getTargetAttribute());

    log.debug(
        "Accuracy before pruning = {}%, Accuracy after pruning = {}%",
        originalAccuracy, prunedAccuracy);

    assertThat(prunedAccuracy, Matchers.greaterThanOrEqualTo(originalAccuracy));
  }