Mallet and Weka

I have been using Mallet for some time now. I have also used Weka but preferred Mallet for some reasons. However, since I sometimes tend to use Weka I created a way for me to convert a Mallet InstanceList into Weka ARFF format. This also allowed me to use the classifiers in Weka quite easily.

 package ca.uwo.csd.ai.nlp.weka;  
 import cc.mallet.types.Alphabet;  
 import cc.mallet.types.FeatureVector;  
 import cc.mallet.types.Instance;  
 import cc.mallet.types.InstanceList;  
 import java.io.IOException;  
 import java.io.StringReader;  
 import weka.core.Instances;  
 /**  
  * Converts Mallet instanceList to Weka ARFF/Instances  
  * @author Syeed Ibn Faiz  
  */  
 public class Converter {  
   /**  
    * Converts Mallet InstanceList into Weka ARFF format  
    * @param instances Mallet instances  
    * @param description a String description required by Weka  
    * @return ARFF representation of the InstanceList  
    */  
   public static String convert2ARFF(InstanceList instances, String description) {  
     Alphabet dataAlphabet = instances.getDataAlphabet();  
     Alphabet targetAlphabet = instances.getTargetAlphabet();  
     StringBuilder sb = new StringBuilder();  
     sb.append("@Relation \"").append(description).append("\"\n\n");  
     int size = dataAlphabet.size();  
     for (int i = 0; i < size; i++) {  
       sb.append("@attribute \"").append(dataAlphabet.lookupObject(i).toString().replaceAll("\\s+", "_")).append("_").append(i);  
       sb.append("\" numeric\n");  
     }  
     sb.append("@attribute target {");  
     for (int i = 0; i < targetAlphabet.size(); i++) {  
       if (i != 0) sb.append(",");  
       sb.append(targetAlphabet.lookupObject(i).toString().replace(",", ";"));  
     }  
     sb.append("}\n\n@data\n");  
     for (int i = 0; i < instances.size(); i++) {  
       Instance instance = instances.get(i);  
       sb.append("{");  
       FeatureVector fv = (FeatureVector) instance.getData();  
       int[] indices = fv.getIndices();  
       double[] values = fv.getValues();  
       boolean[] attrFlag = new boolean[size];  
       double[] attrValue = new double[size];  
       for (int j = 0; j < indices.length; j++) {  
         attrFlag[indices[j]] = true;  
         attrValue[indices[j]] = values[j];  
       }        
       for (int j = 0; j < attrFlag.length; j++) {          
         if (attrFlag[j]) {            
           //sb.append(j).append(" 1, ");            
           sb.append(j).append(" ").append(attrValue[j]).append(", ");  
         }          
       }  
       sb.append(attrFlag.length).append(" ").append(instance.getTarget().toString().replace(",", ";"));  
       sb.append("}\n");        
     }  
     return sb.toString();  
   }  
   /**  
    * Converts Mallet InstanceList into Weka Instances  
    * @param instanceList  
    * @return  
    * @throws IOException   
    */  
   public static Instances convert2WekaInstances(InstanceList instanceList) throws IOException {  
     String arff = convert2ARFF(instanceList, "DESC");  
     StringReader reader = new StringReader(arff);  
     Instances instances = new Instances(reader);  
     instances.setClassIndex(instances.numAttributes() - 1);  
     return instances;  
   }  
 }  

It is now quite straight forward to call a classifier in Weka as shown in the following example:

 public static void main(String[] args) throws IOException, Exception {  
     ArrayList<Pipe> pipes = new ArrayList<Pipe>();  
     pipes.add(new Target2Label());  
     pipes.add(new CharSequence2TokenSequence());  
     pipes.add(new TokenSequence2FeatureSequence());  
     pipes.add(new FeatureSequence2FeatureVector());  
     SerialPipes pipe = new SerialPipes(pipes);  
     //prepare training instances  
     InstanceList trainingInstanceList = new InstanceList(pipe);  
     trainingInstanceList.addThruPipe(new CsvIterator(new FileReader("webkb-train-stemmed.txt"),  
         "(.*)\t(.*)", 2, 1, -1));  
     //prepare test instances  
     InstanceList testingInstanceList = new InstanceList(pipe);  
     testingInstanceList.addThruPipe(new CsvIterator(new FileReader("webkb-test-stemmed.txt"),  
         "(.*)\t(.*)", 2, 1, -1));  
     //Using a classifier in Mallet  
     ClassifierTrainer trainer = new NaiveBayesTrainer();  
     Classifier classifier = trainer.train(trainingInstanceList);  
     System.out.println("Accuracy[Mallet]: " + classifier.getAccuracy(testingInstanceList));  
     //Getting Weka Instances  
     Instances trainingInstances = Converter.convert2WekaInstances(trainingInstanceList);  
     Instances testingInstances = Converter.convert2WekaInstances(testingInstanceList);  
     //Using a classifier in Weka  
     NaiveBayesMultinomial naiveBayesMultinomial = new NaiveBayesMultinomial();  
     naiveBayesMultinomial.buildClassifier(trainingInstances);  
     Evaluation evaluation = new Evaluation(testingInstances);  
     evaluation.evaluateModel(naiveBayesMultinomial, testingInstances);  
     System.out.println("Accuracy[Weka]: " + evaluation.correct() / testingInstanceList.size());      
   }  

Using the WebKB dataset I got the following output:

 Accuracy[Mallet]: 0.836676217765043  
 Accuracy[Weka]: 0.836676217765043  

Porting Genia POS Tagger 3.0.1 to Windows

I have just ported the latest version of Genia POS Tagger (3.0.1 ) to windows. One can use it on windows though without porting, via cygwin. In fact that was what I was doing so far. Lately, I decided for a reason to run the tagger as a server. So I wrote a Java wrapper. Today when I scanned through the code again I found that only a few adjustments can port the codes to windows. All one has to do is to change the put_stop_watch function in bidir.cpp. I replaced gettimeofday, which is not available in windows, with GetTickCount() from Windows.h. A few other minor adjustments are also required, like changing the paths to the model files etc.

You can download the windows port from here.