Examples of DecisionMaker

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

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        this.windowIndex = windowIndex;

        //for each sequence with name, and or true path
        List resultList = new ArrayList();

        DecisionMaker dm = new DecisionMaker(factory);
        HierarchyTree root = factory.getRoot();

        HashMap<String, HierarchyTree> genusNodeMap = new HashMap<String, HierarchyTree>();
        factory.getRoot().getNodeMap(testRank, genusNodeMap);

        if (genusNodeMap.isEmpty()) {
            throw new IllegalArgumentException("\nThere is no node in GENUS level!");
        }

        int i = 0;
        Iterator seqIt = seqList.iterator();
        while (seqIt.hasNext()) {
            LineageSequence pSeq = (LineageSequence) seqIt.next();

            GoodWordIterator wordIterator = getPartialSeqIteratorbyWindow(pSeq, window); // full sequence 

            if (wordIterator == null) {
                continue;
            }

            //for leave-one-out testing, we need to remove the word occurrance for
            //the current sequence. This is similiar to hiding a sequence leaf.
            HierarchyTree curTree = genusNodeMap.get((String) pSeq.getAncestors().get(pSeq.getAncestors().size() - 1));


            curTree.hideSeq(wordIterator);
            List result = dm.getBestClasspath( wordIterator, genusNodeMap, false, min_bootstrap_words);

            ValidClassificationResultFacade resultFacade = new ValidClassificationResultFacade(pSeq, result);
            resultFacade.setLabeledNode(curTree);
            compareClassificationResult(resultFacade);
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Examples of edu.msu.cme.rdp.classifier.train.validation.DecisionMaker

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        testRank = factory.getLowestRank();
        List<ValidClassificationResultFacade> resultList = new ArrayList<ValidClassificationResultFacade>();
        HashMap<String, HierarchyTree> nodeMap = new HashMap<String, HierarchyTree>();

        DecisionMaker dm = new DecisionMaker(factory);
        HierarchyTree root = factory.getRoot();
        root.getNodeMap(testRank, nodeMap);

        if (nodeMap.isEmpty()) {
            throw new IllegalArgumentException("\nThere is no node in " + testRank + " level!");
        }
      
        ArrayList<HashMap<String, StatusCount>> statusCountList = new ArrayList<HashMap<String, StatusCount>>();
        // initialize a list of statusCount, one for each bootstrap from 0 to 100
        for ( int b = 0; b <= 100; b++){
             HashMap<String, StatusCount> statusCountMap = new HashMap<String, StatusCount>();
             statusCountList.add(statusCountMap);
             for (String rank: factory.getRankSet()){
                 statusCountMap.put(rank, new StatusCount());
             }
         }       
       
        int i = 0;
        while (parser.hasNext()) {
            LineageSequence pSeq = parser.next();
            if (pSeq.getSeqString().length() == 0) {
                continue;
            }
            GoodWordIterator wordIterator = null;
            if (numGoodBases > 0) {
                wordIterator = pSeq.getPartialSeqIteratorbyGoodBases(numGoodBases);  // test partial sequences with good words only

            } else {
                wordIterator = new GoodWordIterator(pSeq.getSeqString()); // full sequence 
            }

            if (wordIterator == null || wordIterator.getNumofWords() == 0) {
                //System.err.println(pSeq.getSeqName() + " unable to find good subsequence with length " + numGoodBases);
                continue;
            }

            //for leave-one-out testing, we need to remove the word occurrence for
            //the current sequence. This is similar to hide a sequence leaf.
            HierarchyTree curTree = nodeMap.get((String) pSeq.getAncestors().get(pSeq.getAncestors().size() - 1));
            if ( !hideTaxon){
                if ( curTree.isSingleton()){
                    nodeMap.remove(curTree.getName());
                }else {
                    curTree.hideSeq(wordIterator);
                }
            }else {
                nodeMap.remove(curTree.getName());
            }
           
            List<ValidationClassificationResult> result = dm.getBestClasspath( wordIterator, nodeMap, useSeed, min_bootstrap_words);
            ValidClassificationResultFacade resultFacade = new ValidClassificationResultFacade(pSeq, result);
            resultFacade.setLabeledNode(curTree);
            compareClassificationResult(resultFacade);

            // for ROC curve
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Examples of edu.msu.cme.rdp.classifier.train.validation.DecisionMaker

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        }else {
            selectedTestSeqIDs = RdmSelectTaxon.randomSelectTaxon(tax_file, source_file, fraction, rdmSelectedRank);
        }
       
        TreeFactory factory = setup(tax_file, source_file, selectedTestSeqIDs );
        DecisionMaker dm = new DecisionMaker(factory);
         // get all the genus node list
        HashMap<String, HierarchyTree> genusNodeMap = new HashMap<String, HierarchyTree>();
        factory.getRoot().getNodeMap(Taxonomy.GENUS, genusNodeMap);
        if (genusNodeMap.isEmpty()) {
          throw new IllegalArgumentException("\nThere is no node in GENUS level!");
        }
       
        HashMap<String,HashSet> rankNodeMap = new HashMap<String,HashSet>();
        for (String rank: factory.getRankSet()){
            ArrayList<HierarchyTree> nodeList = new ArrayList<HierarchyTree>();
            factory.getRoot().getNodeList(rank, nodeList);
            HashSet<String> nodeNameSet = getnodeNameSet(nodeList);
            rankNodeMap.put(rank, nodeNameSet);
        }

        ArrayList<HashMap> statusCountList = new ArrayList<HashMap>();
        // initialize a list of statusCount, one for each bootstrap from 0 to 100
        for ( int b = 0; b <= 100; b++){
             HashMap<String, StatusCount> statusCountMap = new HashMap<String, StatusCount>();
             statusCountList.add(statusCountMap);
             for (String rank: factory.getRankSet()){
                 statusCountMap.put(rank, new StatusCount());
             }
         }

        int totalTest = 0;
        int totalSeq = 0;
        LineageSequenceParser parser = new LineageSequenceParser(source_file);
        while ( parser.hasNext()){
            totalSeq ++;
          LineageSequence pSeq = parser.next();
          if ( !selectedTestSeqIDs.contains(pSeq.getSeqName()) || pSeq.getSeqString().length() == 0){
              continue;
          }
          GoodWordIterator wordIterator = null ;
          if ( partialLength != null ){
                wordIterator = pSeq.getPartialSeqIteratorbyGoodBases(partialLength.intValue());  // test partial sequences with good words only

          }else {
                wordIterator = new GoodWordIterator(pSeq.getSeqString()); // full sequence
          }

          if (wordIterator == null || wordIterator.getNumofWords() == 0){
            //System.err.println(pSeq.getSeqName() + " unable to find good sequence");
            continue;
          }
       
          List result = dm.getBestClasspath( wordIterator, genusNodeMap, useSeed, min_bootstrap_words);

          //xxx
          ValidClassificationResultFacade resultFacade = new ValidClassificationResultFacade(pSeq, result);

          compareClassificationResult(factory, resultFacade, rankNodeMap, statusCountList);
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