Examples of EvaluateTreebank

edu.stanford.nlp.parser.lexparser.EvaluateTreebank

Examples of edu.stanford.nlp.parser.lexparser.EvaluateTreebank

        dvparser.saveModel(modelPath);
      }
    }


    if (testTreebankPath != null) {
      EvaluateTreebank evaluator = new EvaluateTreebank(dvparser.attachModelToLexicalizedParser());
      evaluator.testOnTreebank(testTreebank);
    }




    System.err.println("Successfully ran DVParser");
  }

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Examples of edu.stanford.nlp.parser.lexparser.EvaluateTreebank

      System.err.println("Loading test trees from " + testTreebankPath.first());
      Treebank testTreebank = parser.op.tlpParams.memoryTreebank();
      testTreebank.loadPath(testTreebankPath.first(), testTreebankPath.second());
      System.err.println("Loaded " + testTreebank.size() + " trees");


      EvaluateTreebank evaluator = new EvaluateTreebank(parser.op, null, parser);
      evaluator.testOnTreebank(testTreebank);


      // System.err.println("Input tree: " + tree);
      // System.err.println("Debinarized tree: " + query.getBestParse());
      // System.err.println("Parsed binarized tree: " + query.getBestBinarizedParse());
      // System.err.println("Predicted transition sequence: " + query.getBestTransitionSequence());

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Examples of edu.stanford.nlp.parser.lexparser.EvaluateTreebank

          System.err.println("Finished " + batchCount + " total batches, running evaluation cycle");
          // Time for debugging output!
          double tagF1 = 0.0;
          double labelF1 = 0.0;
          if (testTreebank != null) {
            EvaluateTreebank evaluator = new EvaluateTreebank(attachModelToLexicalizedParser());
            evaluator.testOnTreebank(testTreebank);
            labelF1 = evaluator.getLBScore();
            tagF1 = evaluator.getTagScore();
            if (labelF1 > bestLabelF1) {
              bestLabelF1 = labelF1;
            }
            System.err.println("Best label f1 on dev set so far: " + NF.format(bestLabelF1));
          }

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Examples of edu.stanford.nlp.parser.lexparser.EvaluateTreebank

      }
      testTreebank = combinedParser.getOp().tlpParams.memoryTreebank();;
      testTreebank.loadPath(testTreebankPath, testTreebankFilter);
      System.err.println("Read in " + testTreebank.size() + " trees for testing");


      EvaluateTreebank evaluator = new EvaluateTreebank(combinedParser.getOp(), null, combinedParser);
      evaluator.testOnTreebank(testTreebank);
    }
  }

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Examples of edu.stanford.nlp.parser.lexparser.EvaluateTreebank

    double[] labelResults = new double[weights.length];
    double[] tagResults = new double[weights.length];


    for (int i = 0; i < weights.length; ++i) {
      lexparser.getOp().baseParserWeight = weights[i];
      EvaluateTreebank evaluator = new EvaluateTreebank(lexparser);
      evaluator.testOnTreebank(testTreebank);
      labelResults[i] = evaluator.getLBScore();
      tagResults[i] = evaluator.getTagScore();
    }


    for (int i = 0; i < weights.length; ++i) {
      System.err.println("LexicalizedParser weight " + weights[i] + ": labeled " + labelResults[i] + " tag " + tagResults[i]);
    }

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Examples of edu.stanford.nlp.parser.lexparser.EvaluateTreebank

      outputStats();




      double labelF1 = 0.0;
      if (devTreebank != null) {
        EvaluateTreebank evaluator = new EvaluateTreebank(op, null, new ShiftReduceParser(op, this), tagger);
        evaluator.testOnTreebank(devTreebank);
        labelF1 = evaluator.getLBScore();
        System.err.println("Label F1 after " + iteration + " iterations: " + labelF1);


        if (labelF1 > bestScore) {
          System.err.println("New best dev score (previous best " + bestScore + ")");
          bestScore = labelF1;
          bestIteration = iteration;
        } else {
          System.err.println("Failed to improve for " + (iteration - bestIteration) + " iteration(s) on previous best score of " + bestScore);
          if (op.trainOptions.stalledIterationLimit > 0 && (iteration - bestIteration >= op.trainOptions.stalledIterationLimit)) {
            System.err.println("Failed to improve for too long, stopping training");
            break;
          }
        }
        System.err.println();


        if (bestModels != null) {
          bestModels.add(new ScoredObject<PerceptronModel>(new PerceptronModel(this), labelF1));
          if (bestModels.size() > op.trainOptions().averagedModels) {
            bestModels.poll();
          }
        }
      }
      if (op.trainOptions().saveIntermediateModels && serializedPath != null && op.trainOptions.debugOutputFrequency > 0) {
        String tempName = serializedPath.substring(0, serializedPath.length() - 7) + "-" + FILENAME.format(iteration) + "-" + NF.format(labelF1) + ".ser.gz";
        ShiftReduceParser temp = new ShiftReduceParser(op, this);
        temp.saveModel(tempName);
        // TODO: we could save a cutoff version of the model,
        // especially if we also get a dev set number for it, but that
        // might be overkill
      }
    }


    if (wrapper != null) {
      wrapper.join();
    }


    if (bestModels != null) {
      if (op.trainOptions().cvAveragedModels && devTreebank != null) {
        List<ScoredObject<PerceptronModel>> models = Generics.newArrayList();
        while (bestModels.size() > 0) {
          models.add(bestModels.poll());
        }
        Collections.reverse(models);
        double bestF1 = 0.0;
        int bestSize = 0;
        for (int i = 1; i <= models.size(); ++i) {
          System.err.println("Testing with " + i + " models averaged together");
          // TODO: this is kind of ugly, would prefer a separate object
          averageScoredModels(models.subList(0, i));
          ShiftReduceParser temp = new ShiftReduceParser(op, this);
          EvaluateTreebank evaluator = new EvaluateTreebank(temp.op, null, temp, tagger);
          evaluator.testOnTreebank(devTreebank);
          double labelF1 = evaluator.getLBScore();
          System.err.println("Label F1 for " + i + " models: " + labelF1);
          if (labelF1 > bestF1) {
            bestF1 = labelF1;
            bestSize = i;
          }

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