Examples of BagEvent

Examples of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.BagEvent

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      //}
     
      //if(!prwf.contains(ss)) ss = stemmer.getStem(ss);
    }
    bagsToSentences.put(features, prf.tokSeq.getSourceString());
    return new BagEvent(prf.hasReact? "TRUE" : "FALSE", features);
  }
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Examples of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.BagEvent

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    if(true) {
      DecisionTree dt = new DecisionTree(bagEvents);
      dt.printTree();
      for(int i=0;i<testBagEvents.size();i++) {
        BagEvent be = testBagEvents.get(i);
        String result = dt.testBag(be.getFeatures());
        ce.logEvent(be.getClassLabel(), result);
      }
      System.out.println(ce.getAccuracy());
      System.out.println(ce.getKappa());     
      ce.pprintConfusionMatrix();
      ce.pprintPrecisionRecallEval();
      //return;
    }
   
    if(true) {
      ce = new ClassificationEvaluator();
      DecisionList dl = new DecisionList(bagEvents);
      for(int i=0;i<testBagEvents.size();i++) {
        BagEvent be = testBagEvents.get(i);
        String result = dl.testBag(be.getFeatures());
        ce.logEvent(be.getClassLabel(), result);
      }
      System.out.println(ce.getAccuracy());
      System.out.println(ce.getKappa());     
      ce.pprintConfusionMatrix();
      ce.pprintPrecisionRecallEval();
      //return;
    }
   
    ce = new ClassificationEvaluator();
    MultinomialNaiveBayes mnb = new MultinomialNaiveBayes(bagEvents);
    for(int i=0;i<testBagEvents.size();i++) {
      BagEvent be = testBagEvents.get(i);
      //Map<String,Double> results = mnb.testBag(be.getClassLabel(), be.getFeatures());
      Map<String,Double> results = mnb.testBag(be.getFeatures());
      System.out.println(be.getClassLabel() + "\t" + mnb.testBag(be.getFeatures()));
      ce.logEvent(be.getClassLabel(), mnb.bestResult(results));
    }
    System.out.println(ce.getAccuracy());
    System.out.println(ce.getKappa());     
    ce.pprintConfusionMatrix();
    ce.pprintPrecisionRecallEval();

    ce = new ClassificationEvaluator();
    List<Event> trainEvents = new ArrayList<Event>();
    List<Event> testEvents = new ArrayList<Event>();
    for(BagEvent be : bagEvents) {
      trainEvents.add(new Event(be.getClassLabel(), be.getFeatures().getSet().toArray(new String[0])));
    }
    for(BagEvent be : testBagEvents) {
      testEvents.add(new Event(be.getClassLabel(), be.getFeatures().getSet().toArray(new String[0])));
    }
    DataIndexer di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(trainEvents)), 1);
    GISModel gm = GIS.trainModel(100, di);
   
    //ClassificationEvaluator ce = new ClassificationEvaluator();
   
    for(Event event : testEvents) {
      double [] results = gm.eval(event.getContext());
      String result = results[gm.getIndex("TRUE")] > 0.5 ? "TRUE" : "FALSE";
      //String result = gm.getBestOutcome(results);
      //System.out.println(event.getOutcome() + "\t" + result + "\t" + results[gm.getIndex(event.getOutcome())] + "\t" + StringTools.arrayToList(event.getContext()));
      ce.logEvent(event.getOutcome(), result);
    }
    System.out.println(ce.getAccuracy());
    System.out.println(ce.getKappa());     
    ce.pprintConfusionMatrix();
    ce.pprintPrecisionRecallEval();
   
    if(false) {
     
      List<Bag<String>> trueBags = new ArrayList<Bag<String>>();
      List<Bag<String>> falseBags = new ArrayList<Bag<String>>();
      for(BagEvent be : bagEvents) {
        if("TRUE".equals(be.getClassLabel())) {
          trueBags.add(be.getFeatures());
        } else {
          falseBags.add(be.getFeatures());
        }
      }
      Random r = new Random(0);
      Collections.shuffle(trueBags, r);
      Collections.shuffle(falseBags, r);
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Examples of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.BagEvent

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        tokenSet.add("PROTECT:stem=" + stemmer.getStem(s));
      }
      if(hasPotentialReact) {
        Event e = new Event(hasReact ? "TRUE" : "FALSE", tokenSet.toArray(new String[0]));
        events.add(e);
        BagEvent be = new BagEvent(hasReact ? "TRUE" : "FALSE", tokenBag);
        eventBags.add(be);
      }
    }
       
    if(false) {
      ClassificationEvaluator ce = new ClassificationEvaluator();

      MultinomialNaiveBayes mnb = new MultinomialNaiveBayes(eventBags);
      for(int i=0;i<eventBags.size();i++) {
        BagEvent be = eventBags.get(i);
      //for(BagEvent be : eventBags) {
        Map<String,Double> results = mnb.testBag(be.getClassLabel(), be.getFeatures());
        System.out.println(be.getClassLabel() + "\t" + mnb.testBag(be.getFeatures()));
        ce.logEvent(be.getClassLabel(), mnb.bestResult(results));
        String rf = "MNB:" + mnb.bestResult(results);
        Event e = events.get(i);
        String [] sa = new String[e.getContext().length + 1];
        for(int j=0;j<e.getContext().length;j++) {
          sa[j] = e.getContext()[j];
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Examples of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.BagEvent

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      //}
     
      //if(!prwf.contains(ss)) ss = stemmer.getStem(ss);
    }
    bagsToSentences.put(features, prf.tokSeq.getSourceString());
    return new BagEvent(prf.isPubmed? "TRUE" : "FALSE", features);
  }
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Examples of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.BagEvent

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    if(false) {
      ce = new ClassificationEvaluator();
      DecisionTree dt = new DecisionTree(bagEvents);
      dt.printTree();
      for(int i=0;i<testBagEvents.size();i++) {
        BagEvent be = testBagEvents.get(i);
        String result = dt.testBag(be.getFeatures());
        ce.logEvent(be.getClassLabel(), result);
      }
      System.out.println(ce.getAccuracy());
      System.out.println(ce.getKappa());     
      ce.pprintConfusionMatrix();
      ce.pprintPrecisionRecallEval();
      //return;
    }
   
    if(false) {
      ce = new ClassificationEvaluator();
      DecisionList dl = new DecisionList(bagEvents);
      for(int i=0;i<testBagEvents.size();i++) {
        BagEvent be = testBagEvents.get(i);
        String result = dl.testBag(be.getFeatures());
        ce.logEvent(be.getClassLabel(), result);
      }
      System.out.println(ce.getAccuracy());
      System.out.println(ce.getKappa());     
      ce.pprintConfusionMatrix();
      ce.pprintPrecisionRecallEval();
      //return;
    }
   
    if(true) {
      ce = new ClassificationEvaluator();
      MultinomialNaiveBayes mnb = new MultinomialNaiveBayes(bagEvents);
     
      Element elem = mnb.toXML();
      Document doc = new Document(elem);
      Serializer ser = new Serializer(System.out);
      //ser.setIndent(2);
      ser.write(doc);
      mnb = new MultinomialNaiveBayes(elem);
      elem = mnb.toXML();
      doc = new Document(elem);
      ser = new Serializer(System.out);
      //ser.setIndent(2);
      ser.write(doc);
     
      for(int i=0;i<testBagEvents.size();i++) {
        BagEvent be = testBagEvents.get(i);
        //Map<String,Double> results = mnb.testBag(be.getClassLabel(), be.getFeatures());
        Map<String,Double> results = mnb.testBag(be.getFeatures());
        System.out.println(be.getClassLabel() + "\t" + mnb.testBag(be.getFeatures()));
        ce.logEvent(be.getClassLabel(), mnb.bestResult(results));
        if(!be.getClassLabel().equals(mnb.bestResult(results))) {
          System.out.println(be.getFeatures());
          System.out.println(bagsToSentences.get(be.getFeatures()));
        }
      }
      System.out.println(ce.getAccuracy());
      System.out.println(ce.getKappa());     
      ce.pprintConfusionMatrix();
      ce.pprintPrecisionRecallEval();
    }

    if(false) {
      ce = new ClassificationEvaluator();
      List<Event> trainEvents = new ArrayList<Event>();
      List<Event> testEvents = new ArrayList<Event>();
      for(BagEvent be : bagEvents) {
        trainEvents.add(new Event(be.getClassLabel(), be.getFeatures().getSet().toArray(new String[0])));
      }
      for(BagEvent be : testBagEvents) {
        testEvents.add(new Event(be.getClassLabel(), be.getFeatures().getSet().toArray(new String[0])));
      }
      DataIndexer di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(trainEvents)), 1);
      GISModel gm = GIS.trainModel(100, di);
     
      //ClassificationEvaluator ce = new ClassificationEvaluator();
     
      for(Event event : testEvents) {
        double [] results = gm.eval(event.getContext());
        String result = results[gm.getIndex("TRUE")] > 0.5 ? "TRUE" : "FALSE";
        //String result = gm.getBestOutcome(results);
        //System.out.println(event.getOutcome() + "\t" + result + "\t" + results[gm.getIndex(event.getOutcome())] + "\t" + StringTools.arrayToList(event.getContext()));
        ce.logEvent(event.getOutcome(), result);
      }
      System.out.println(ce.getAccuracy());
      System.out.println(ce.getKappa());     
      ce.pprintConfusionMatrix();
      ce.pprintPrecisionRecallEval();     
    }
   
    if(false) {
     
      List<Bag<String>> trueBags = new ArrayList<Bag<String>>();
      List<Bag<String>> falseBags = new ArrayList<Bag<String>>();
      for(BagEvent be : bagEvents) {
        if("TRUE".equals(be.getClassLabel())) {
          trueBags.add(be.getFeatures());
        } else {
          falseBags.add(be.getFeatures());
        }
      }
      Random r = new Random(0);
      Collections.shuffle(trueBags, r);
      Collections.shuffle(falseBags, r);
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