Package cc.mallet.types

Examples of cc.mallet.types.Label

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                                 Label backgroundTag)
  {
    int i = 0;
    int docidx = 0;
    while (i < tags.size ()) {
      Label thisTag = dict.lookupLabel (tags.get (i).toString ());
      int startTokenIdx = i;
      while (++i < tags.size ()) {
        Label nextTag = dict.lookupLabel (tags.get (i).toString ());
        if (isBeginTag (nextTag) || !tagsMatch (thisTag, nextTag)) break;
      }
      int endTokenIdx = i;
      Span span = createSpan (input, startTokenIdx, endTokenIdx);
      addBackgroundIfNecessary (labeled, (StringSpan) span, docidx, backgroundTag);
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      // Calc precision
      Iterator it = extracted.fieldsIterator ();
      while (it.hasNext ()) {
        Field predField = (Field) it.next ();
        Label name = predField.getName ();
        Field trueField = target.getField (name);
        int idx = name.getIndex ();

        for (int j = 0; j < predField.numValues(); j++) {
          numPred [idx]++;
          if (trueField != null && trueField.isValue (predField.value (j), comparator)) {
            numCorr [idx]++;
          } else {
            // We have an error, report if necessary (this should be moved to the per-field rather than per-filler level.)
            if (errorOutputStream != null) {
              //xxx TODO: Display name of supporting document
              errorOutputStream.println ("Error in extraction!");
              errorOutputStream.println ("Predicted "+predField);
              errorOutputStream.println ("True "+trueField);
              errorOutputStream.println ();
            }
          }

        }
      }

      // Calc true
      it = target.fieldsIterator ();
      while (it.hasNext ()) {
        Field trueField = (Field) it.next ();
        Label name = trueField.getName ();
        numTrue [name.getIndex ()] += trueField.numValues ();
      }
    }

    out.println (description+" SEGMENT counts");
    out.println ("Name\tCorrect\tPred\tTarget");
    for (int i = 0; i < numLabels; i++) {
      Label name = dict.lookupLabel (i);
      out.println (name+"\t"+numCorr[i]+"\t"+numPred[i]+"\t"+numTrue[i]);
    }
    out.println ();

    DecimalFormat f = new DecimalFormat ("0.####");

    double totalF1 = 0;
    int totalFields = 0;
    out.println (description+" per-field F1");
    out.println ("Name\tP\tR\tF1");
    for (int i = 0; i < numLabels; i++) {
      double P = (numPred[i] == 0) ? 0 : ((double)numCorr[i]) / numPred [i];
      double R = (numTrue[i] == 0) ? 1 : ((double)numCorr[i]) / numTrue [i];
      double F1 = (P + R == 0) ? 0 : (2 * P * R) / (P + R);
      if ((numPred[i] > 0) || (numTrue[i] > 0)) {
        totalF1 += F1;
        totalFields++;
      }
      Label name = dict.lookupLabel (i);
      out.println (name+"\t"+f.format(P)+"\t"+f.format(R)+"\t"+f.format(F1));
    }

    int totalCorr = MatrixOps.sum (numCorr);
    int totalPred = MatrixOps.sum (numPred);
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      int[] indices = afv1.getIndices();
      AugmentableFeatureVector afv2 = new AugmentableFeatureVector(m_dataAlphabet,
          indices, afv1.getValues(), indices.length + m_predRanks2add.length);

      for (int j = 0; j < m_predRanks2add.length; j++) {
        Label label = lv.getLabelAtRank(m_predRanks2add[j]);
        int idx = m_dataAlphabet.lookupIndex("TOK_PRED=" + label.toString() + "_@_RANK_" + m_predRanks2add[j]);

        assert(idx >= 0);
        afv2.add(idx, 1);
      }
      fva[i] = afv2;
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      // Calc precision
      Iterator it = extracted.fieldsIterator ();
      while (it.hasNext ()) {
        Field predField = (Field) it.next ();
        Label name = predField.getName ();
        Field trueField = target.getField (name);
        int idx = name.getIndex ();

        numPred [idx]++;

        if (predField.numValues() > 1)
          System.err.println ("Warning: Field "+predField+" has more than one extracted value. Picking arbitrarily...");
        if (trueField != null && trueField.isValue (predField.value (0), comparator)) {
          numCorr [idx]++;
        } else {
          // We have an error, report if necessary
          if (errorOutputStream != null) {
            //xxx TODO: Display name of supporting document
            errorOutputStream.println ("Error in extraction! Document "+extraction.getDocumentExtraction (docnum).getName ());
            errorOutputStream.println ("Predicted "+predField);
            errorOutputStream.println ("True "+trueField);
            errorOutputStream.println ();
          }
        }
      }

      // Calc true
      it = target.fieldsIterator ();
      while (it.hasNext ()) {
        Field trueField = (Field) it.next ();
        Label name = trueField.getName ();
        numTrue [name.getIndex ()]++;
      }
    }

    DecimalFormat f = new DecimalFormat ("0.####");

    double totalF1 = 0;
    int totalFields = 0;
    out.println (description+" per-document F1");
    out.println ("Name\tP\tR\tF1");
    for (int i = 0; i < numLabels; i++) {
      double P = (numPred[i] == 0) ? 0 : ((double)numCorr[i]) / numPred [i];
      double R = (numTrue[i] == 0) ? 1 : ((double)numCorr[i]) / numTrue [i];
      double F1 = (P + R == 0) ? 0 : (2 * P * R) / (P + R);
      if ((numPred[i] > 0) || (numTrue[i] > 0)) {
        totalF1 += F1;
        totalFields++;
      }
      Label name = dict.lookupLabel (i);
      out.println (name+"\t"+f.format(P)+"\t"+f.format(R)+"\t"+f.format(F1));
    }

    int totalCorr = MatrixOps.sum (numCorr);
    int totalPred = MatrixOps.sum (numPred);
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    {
      int i = 0;
      LinkedList openTags = new LinkedList();
      String[] lastTagSplit = new String [0];
      while (i < tags.size()) {
        Label thisTag = dict.lookupLabel (tags.get(i).toString());
        String[] thisTagSplit = splitTag (thisTag);
        int numToClose = compareSplitTags (thisTagSplit, lastTagSplit);

        // close all that need to be closed
        while (numToClose > 0) {
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  private void addLabeledSpan (LabeledSpans labeled, Tokenization input,
                               TagStart tagStart, int end, Label backgroundTag)
  {
    Span span = input.subspan (tagStart.start, end);
    Label splitTag = tagStart.label;
    labeled.add (new LabeledSpan (span, splitTag, splitTag == backgroundTag));
  }
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  {
    TIntArrayList vals = new TIntArrayList (maxTime () * numSlices ());
    for (int t = 0; t < lblseq.size (); t++) {
      Labels lbls = lblseq.getLabels (t);
      for (int j = 0; j < lbls.size (); j++) {
        Label lbl = lbls.get (j);
        vals.add (lbl.getIndex ());
      }
    }
    return vals.toNativeArray ();
  }
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    var2label = new THashMap ();
    for (int t = 0; t < lblseq.size (); t++) {
      Labels lbls = lblseq.getLabels (t);
      idx2var[t] = new Variable [lbls.size ()];
      for (int j = 0; j < lbls.size (); j++) {
        Label lbl = lbls.get (j);
        Variable var = new Variable (lbl.getLabelAlphabet ());
        var.setLabel ("I"+id+"_VAR[f=" + j + "][tm=" + t + "]");
        idx2var[t][j] = var;
        var2label.put (var, lbl);
      }
    }
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    DualLabeledSpans spans = intersectSpans (docExtr);
    for (int i = 0; i < spans.size(); i++) {
      LabeledSpan predSpan = spans.get (i, 0);
      LabeledSpan trueSpan = spans.get (i, 1);

      Label predLabel = predSpan.getLabel ();
      Label trueLabel = trueSpan.getLabel ();

      boolean predNonBgrnd = !predSpan.isBackground ();
      boolean trueNonBgrnd = !trueSpan.isBackground ();
      boolean isBackground = !predNonBgrnd && !trueNonBgrnd;
     
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    this.name = name;
    fieldMap = new THashMap ();
    for (int i = 0; i < spans.size(); i++) {
      LabeledSpan span = spans.getLabeledSpan (i);
      if (!span.isBackground()) {
        Label tag = span.getLabel ();
        Field field = (Field) fieldMap.get (tag);
        if (field == null) {
          field = new Field (span);
          fieldMap.put (tag, field);
        } else {
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Related Classes of cc.mallet.types.Label

  • cc.mallet.classify.RankMaxEntTrainer$MaximizableTrainer
  • cc.mallet.classify.Trial
  • cc.mallet.extract.BIOTokenizationFilter
  • cc.mallet.extract.DefaultTokenizationFilter
  • cc.mallet.extract.DocumentViewer
  • cc.mallet.extract.HierarchicalTokenizationFilter
  • cc.mallet.extract.PerDocumentF1Evaluator
  • cc.mallet.extract.PerFieldF1Evaluator
  • cc.mallet.extract.Record
  • cc.mallet.extract.test.TestDocumentExtraction
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