Examples of cc.mallet.classify.Trial

cc.mallet.classify.Trial
Stores the results of classifying a collection of Instances, and provides many methods for evaluating the results. If you just need one evaluation result, you may find it easier to one of the corresponding methods in Classifier, which simply call the methods here. @see InstanceList @see Classifier @see Classification @author Andrew McCallum mccallum@cs.umass.edu

      }
    }
    
    // evaluate token classifier  
    if (testList != null) {
      Trial trial = new Trial(m_tokenClassifiers, testList);
      logger.info("Token classifier accuracy on test set = " + trial.getAccuracy());
    }
  }

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    {
      // train a classifier on the entire training set
      logger.info("Training token classifier on entire data set (size=" + trainList.size() + ")...");
      m_tokenClassifier = m_trainer.train(trainList);


      Trial t = new Trial(m_tokenClassifier, trainList);
      logger.info("Training set accuracy = " + t.getAccuracy());
      
      if (m_numCV == 0)
        return;


      // train classifiers using cross validation
      InstanceList.CrossValidationIterator cvIter = trainList.new CrossValidationIterator(m_numCV, m_randSeed);
      int f = 1;


      while (cvIter.hasNext()) {
        f++;
        InstanceList[] fold = cvIter.nextSplit();


        logger.info("Training token classifier on cv fold " + f + " / " + m_numCV + " (size=" + fold[0].size() + ")...");
        
        Classifier foldClassifier = m_trainer.train(fold[0]);
        Trial t1 = new Trial(foldClassifier, fold[0]);
        Trial t2 = new Trial(foldClassifier, fold[1]);


        logger.info("Within-fold accuracy = " + t1.getAccuracy());
        logger.info("Out-of-fold accuracy = " + t2.getAccuracy());


        /*for (int x = 0; x < t2.size(); x++) {
          logger.info("xxx pred:" + t2.getClassification(x).getLabeling().getBestLabel() + " true:" + t2.getClassification(x).getInstance().getLabeling());
        }*/

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    System.err.println("Created " + trainList.size() + " instances.");
    Classifier me = new MaxEntTrainer().train(trainList);
    ClassifyingNeighborEvaluator eval =
      new ClassifyingNeighborEvaluator(me, "YES");
                                           
    Trial trial = new Trial(me, trainList);
    System.err.println(new ConfusionMatrix(trial));
    InfoGain ig = new InfoGain(trainList);
    ig.print();


//     Clusterer clusterer = new GreedyAgglomerative(training.getInstances().getPipe(),

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    this(t, DEFAULT_NUM_BUCKETS, title, "unnamed");
  }
  
  public AccuracyCoverage(Classifier C, InstanceList ilist, String title)
  {
    this(new Trial(C, ilist), DEFAULT_NUM_BUCKETS, title, "unnamed");
  }

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    this(new Trial(C, ilist), DEFAULT_NUM_BUCKETS, title, "unnamed");
  }
  
  public AccuracyCoverage(Classifier C, InstanceList ilist, int numBuckets, String title)
  {
    this(new Trial(C, ilist), numBuckets, title, "unnamed");
  }

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          + " training instances");
      Classifier classifier = new MaxEntTrainer().train(trainingInstances);
      logger.info("InfoGain:\n");
      new InfoGain(trainingInstances).printByRank(System.out);
      logger.info("pairwise training accuracy="
          + new Trial(classifier, trainingInstances).getAccuracy());
      NeighborEvaluator neval = new PairwiseEvaluator(classifier, "YES",
          new PairwiseEvaluator.Average(), true);        
      clusterer = new GreedyAgglomerativeByDensity(
          training.get(0).getInstances().getPipe(), neval, 0.5, false,
          random);

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