Package edu.msu.cme.rdp.classifier.train.validation

Examples of edu.msu.cme.rdp.classifier.train.validation.Taxonomy

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        // find all the taxa for the ancestors
        HashMap<String, Taxonomy> labeledTaxonMap = new HashMap<String, Taxonomy>();
        labeledTaxonMap.put(factory.getRoot().getTaxonomy().getHierLevel(), factory.getRoot().getTaxonomy());
        int pid = factory.getRoot().getTaxonomy().getTaxID();
        for ( int i = 1 ; i < resultFacade.getAncestors().size(); i++){
            Taxonomy tax = factory.getTaxonomy(resultFacade.getSeqName(), (String) resultFacade.getAncestors().get(i), pid, i);
            labeledTaxonMap.put(tax.getHierLevel(), tax);
            pid = tax.getTaxID();
        }


        List<ValidationClassificationResult> hitList = resultFacade.getRankAssignment();
        for ( ValidationClassificationResult curRankResult: hitList){
            String curRank = curRankResult.getBestClass().getTaxonomy().getHierLevel();
            // find the corresponding ancestor at the current rank
            Taxonomy matchingRankTaxon = labeledTaxonMap.get(curRank);
            if ( matchingRankTaxon == null) {  // no match rank found
                // System.err.println("no matching rank labeled taxon found for " + resultFacade.getSeqName() + " at " + curRank );
                continue;
            }
            HashSet<String> nodeNameSet = rankNodeMap.get(curRank);
            if ( nodeNameSet != null){
                  
                int bootstrap = (int)(curRankResult.getNumOfVotes()*100);
                //System.err.println( "rank: " + curRank + "\t" + matchingRankTaxon.getName() + "\t" + nodeNameSet.contains( matchingRankTaxon.getName()) + "\t" + bootstrap +"\t");
                if ( nodeNameSet.contains( matchingRankTaxon.getName())){  // TP or FN
                    for( int b = 0; b <= bootstrap; b++){
                        ((StatusCount)statusCountList.get(b).get(curRank)).incNumTP(1);
                    }
                    for( int b = bootstrap+1; b < statusCountList.size(); b++){
                        ((StatusCount)statusCountList.get(b).get(curRank)).incNumFN(1);
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            HierarchyTree treex = nodeMap.get((String) seqx.getAncestors().get(seqx.getAncestors().size() - 1));
            for ( int j = i+1; j < seqList.size(); j++){
                LineageSequence seqy = seqList.get(j);
                HierarchyTree treey = nodeMap.get((String) seqy.getAncestors().get(seqy.getAncestors().size() - 1));
               
                Taxonomy lowestCommonAnc = findLowestCommonAncestor(treex, treey);              
                PairwiseAlignment result = PairwiseAligner.align(seqx.getSeqString().replaceAll("U", "T"), seqy.getSeqString().replaceAll("U", "T"), scoringMatrix, mode);
                double distance = dist.getDistance(result.getAlignedSeqj().getBytes(), result.getAlignedSeqi().getBytes(), 0);

                if ( show_alignment){
                    System.out.println(">\t" + seqx.getSeqName() + "\t" + seqy.getSeqName() + "\t" + String.format("%.3f", distance) + "\t" + lowestCommonAnc.getHierLevel());
                    System.out.println(result.getAlignedSeqi() + "\n");
                    System.out.println(result.getAlignedSeqj() + "\n");
                }
       
                ArrayList<Double> distList = distanceMap.get(lowestCommonAnc);
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        while(parent != null) {
            ancestory.add(parent);
            parent = parent.getParent();
        }
       
        Taxonomy lowestCommonAnc = ancestorx.get(ancestorx.size() -1).getTaxonomy();
        for ( int i = 2; i <= ancestorx.size() ; i++){
            if (  (ancestory.size() -i ) >= 0 ){
                if ( ancestorx.get( ancestorx.size() -i).getTaxonomy().equals(ancestory.get( ancestory.size() -i).getTaxonomy()) ){
                    lowestCommonAnc = ancestorx.get( ancestorx.size() -i).getTaxonomy();
                }
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Related Classes of edu.msu.cme.rdp.classifier.train.validation.Taxonomy

  • edu.msu.cme.rdp.classifier.train.validation.crossvalidate.CrossValidate
  • edu.msu.cme.rdp.classifier.train.validation.distance.PairwiseSeqDistance
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