Package edu.uci.jforestsx.sample

Examples of edu.uci.jforestsx.sample.RankingSample

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  public double[] getNDCGatAllTruncations(double[] predictions, Sample sample, TieBreaker tieBreaker) throws Exception {
    if (((RankingDataset) (sample.dataset)).maxDCG == null) {
      throw new Exception("maxDCG is not initialized for dataset.");
    }
    RankingSample rankingSample = (RankingSample) sample;

    int chunkSize = 1 + (rankingSample.numQueries / ndcgWorkers.getSize());
    int offset = 0;
    int workerCount = 0;
    for (int i = 0; i < ndcgWorkers.getSize() && offset < rankingSample.numQueries; i++) {
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    }
  }

  public double getMAP(double[] predictions, Sample sample, TieBreaker tieBreaker) throws Exception {
    RankingSample rankingSample = (RankingSample) sample;

    int chunkSize = 1 + (rankingSample.numQueries / mapWorkers.getSize());
    int offset = 0;
    int workerCount = 0;
    for (int i = 0; i < mapWorkers.getSize() && offset < rankingSample.numQueries; i++) {
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      }
    }
  }

  public double[] getPrecisions(double[] predictions, Sample sample, TieBreaker tieBreaker) throws Exception {
    RankingSample rankingSample = (RankingSample) sample;

    int chunkSize = 1 + (rankingSample.numQueries / mapWorkers.getSize());
    int offset = 0;
    int workerCount = 0;
    for (int i = 0; i < mapWorkers.getSize() && offset < rankingSample.numQueries; i++) {
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  @Override
  protected Sample getSubLearnerSample() {
    Arrays.fill(residuals, 0, curTrainSet.size, 0);
    Arrays.fill(denomWeights, 0, curTrainSet.size, 0);
    RankingSample trainSample = (RankingSample) curTrainSet;
    int chunkSize = 1 + (trainSample.numQueries / workers.getSize());
    int offset = 0;
    for (int i = 0; i < workers.getSize() && offset < trainSample.numQueries; i++) {
      int endOffset = offset + Math.min(trainSample.numQueries - offset, chunkSize);
      workers.getTask(i).init(offset, endOffset);
      BlockingThreadPoolExecutor.getInstance().execute(workers.getTask(i));
      offset += chunkSize;
    }
    BlockingThreadPoolExecutor.getInstance().await();

    trainSample = trainSample.getClone();
    trainSample.targets = residuals;
    setSubLearnerSampleWeights(trainSample);

    RankingSample zeroFilteredSample = trainSample.getClone();
    RankingSample subLearnerSample = zeroFilteredSample.getRandomSubSample(samplingRate, rnd);
    for (int i = 0; i < subLearnerSample.size; i++) {
      subLearnerSampleIndicesInTrainSet[i] = zeroFilteredSample.indicesInParentSample[subLearnerSample.indicesInParentSample[i]];
    }
    return subLearnerSample;
  }
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      double scoreDiff;
      double rho;
      double deltaWeight;
      double pairWeight;
      double queryMaxDcg;
      RankingSample trainSet = (RankingSample) curTrainSet;
      double[] targets = trainSet.targets;
      comparator.scores = trainPredictions;
      try {
        for (int query = beginIdx; query < endIdx; query++) {
          int begin = trainSet.queryBoundaries[query];
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        int qid = trainQids.get(idx);
        trainQids.remove(idx);
        validQids.add(qid);
      }
      Collections.sort(validQids);
      RankingSample newTrainSet = ((RankingSample) trainSet).getFilteredSubSample(trainQids);
      validSet = ((RankingSample) trainSet).getFilteredSubSample(validQids);
      trainSet = newTrainSet;
    }
  }
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    return super.getEvaluationMetric(name);
  }

  @Override
  protected Sample createSample(Dataset dataset, boolean trainSample) {
    RankingSample sample = new RankingSample((RankingDataset) dataset);
    RankingTrainingConfig config = (RankingTrainingConfig) trainingConfig;
    if (trainSample) {
      if (config.augmentationDocSamplingEnabled) {
        return sample.getAugmentedSampleWithDocSampling(config.augmentationDocSamplingTimes, config.augmentationDocSamplingRate, rnd);
      }
    }
    return sample;
  }
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    InputStream in = new IOUtils().getInputStream((String) options.valueOf("test-file"));
    Sample sample;
    if (options.has("ranking")) {
      RankingDataset dataset = new RankingDataset();
      RankingDatasetLoader.load(in, dataset);
      sample = new RankingSample(dataset);
    } else {
      Dataset dataset = new Dataset();
      DatasetLoader.load(in, dataset);
      sample = new Sample(dataset);
    }
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Related Classes of edu.uci.jforestsx.sample.RankingSample

  • edu.uci.jforestsx.applications.RankingApp
  • edu.uci.jforestsx.applications.Runner
  • edu.uci.jforestsx.eval.ranking.MAPEval
  • edu.uci.jforestsx.eval.ranking.NDCGEval
  • edu.uci.jforestsx.eval.ranking.PrecisionEval
  • edu.uci.jforestsx.learning.boosting.LambdaMART
  • edu.uci.jforestsx.learning.boosting.LambdaMART$LambdaWorker
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