Running MALLET in Java

Question

I'm trying to run Mallet in Java and am getting the following error.

Couldn't open cc.mallet.util.MalletLogger resources/logging.properties file.
Perhaps the 'resources' directories weren't copied into the 'class' directory.
Continuing.

I'm trying to run the example from Mallet's website (http://mallet.cs.umass.edu/topics-devel.php). Below is my code. Any help is appreciated.

package scriptAnalyzer;

import cc.mallet.util.*;
import cc.mallet.types.*;
import cc.mallet.pipe.*;
import cc.mallet.pipe.iterator.*;
import cc.mallet.topics.*;

import java.util.*;
import java.util.regex.*;
import java.io.*;

public class Mallet {

    public static void main(String[] args) throws Exception {

        String filePath = "C:/mallet/ap.txt";
        // Begin by importing documents from text to feature sequences
        ArrayList<Pipe> pipeList = new ArrayList<Pipe>();

        // Pipes: lowercase, tokenize, remove stopwords, map to features
        pipeList.add( new CharSequenceLowercase() );
        pipeList.add( new CharSequence2TokenSequence(Pattern.compile("\\p{L}[\\p{L}\\p{P}]+\\p{L}")) );
        pipeList.add( new TokenSequenceRemoveStopwords(new File("stoplists/en.txt"), "UTF-8", false, false, false) );
        pipeList.add( new TokenSequence2FeatureSequence() );

        InstanceList instances = new InstanceList (new SerialPipes(pipeList));

        Reader fileReader = new InputStreamReader(new FileInputStream(new File(filePath)), "UTF-8");
        instances.addThruPipe(new CsvIterator (fileReader, Pattern.compile("^(\\S*)[\\s,]*(\\S*)[\\s,]*(.*)$"),
                                               3, 2, 1)); // data, label, name fields

        // Create a model with 100 topics, alpha_t = 0.01, beta_w = 0.01
        //  Note that the first parameter is passed as the sum over topics, while
        //  the second is the parameter for a single dimension of the Dirichlet prior.
        int numTopics = 5;
        ParallelTopicModel model = new ParallelTopicModel(numTopics, 1.0, 0.01);

        model.addInstances(instances);

        // Use two parallel samplers, which each look at one half the corpus and combine
        //  statistics after every iteration.
        model.setNumThreads(2);

        // Run the model for 50 iterations and stop (this is for testing only, 
        //  for real applications, use 1000 to 2000 iterations)
        model.setNumIterations(50);
        model.estimate();

        // Show the words and topics in the first instance

        // The data alphabet maps word IDs to strings
        Alphabet dataAlphabet = instances.getDataAlphabet();

        FeatureSequence tokens = (FeatureSequence) model.getData().get(0).instance.getData();
        LabelSequence topics = model.getData().get(0).topicSequence;

        Formatter out = new Formatter(new StringBuilder(), Locale.US);
        for (int position = 0; position < tokens.getLength(); position++) {
            out.format("%s-%d ", dataAlphabet.lookupObject(tokens.getIndexAtPosition(position)), topics.getIndexAtPosition(position));
        }
        System.out.println(out);

        // Estimate the topic distribution of the first instance, 
        //  given the current Gibbs state.
        double[] topicDistribution = model.getTopicProbabilities(0);

        // Get an array of sorted sets of word ID/count pairs
        ArrayList<TreeSet<IDSorter>> topicSortedWords = model.getSortedWords();

        // Show top 5 words in topics with proportions for the first document
        for (int topic = 0; topic < numTopics; topic++) {
            Iterator<IDSorter> iterator = topicSortedWords.get(topic).iterator();

            out = new Formatter(new StringBuilder(), Locale.US);
            out.format("%d\t%.3f\t", topic, topicDistribution[topic]);
            int rank = 0;
            while (iterator.hasNext() && rank < 5) {
                IDSorter idCountPair = iterator.next();
                out.format("%s (%.0f) ", dataAlphabet.lookupObject(idCountPair.getID()), idCountPair.getWeight());
                rank++;
            }
            System.out.println(out);
        }

        // Create a new instance with high probability of topic 0
        StringBuilder topicZeroText = new StringBuilder();
        Iterator<IDSorter> iterator = topicSortedWords.get(0).iterator();

        int rank = 0;
        while (iterator.hasNext() && rank < 5) {
            IDSorter idCountPair = iterator.next();
            topicZeroText.append(dataAlphabet.lookupObject(idCountPair.getID()) + " ");
            rank++;
        }

        // Create a new instance named "test instance" with empty target and source fields.
        InstanceList testing = new InstanceList(instances.getPipe());
        testing.addThruPipe(new Instance(topicZeroText.toString(), null, "test instance", null));

        TopicInferencer inferencer = model.getInferencer();
        double[] testProbabilities = inferencer.getSampledDistribution(testing.get(0), 10, 1, 5);
        System.out.println("0\t" + testProbabilities[0]);
    }

}

Answer 1

If you try to run Mallet either by downloading the version 2.0.8-SNAPSHOT (https://github.com/mimno/Mallet) or by getting the currently latest maven version (2.0.7) you will get this error.

The reason is that Mallet expects the file logging.properties inside the created target\classes\cc\mallet\util\resources folder. When you build the project with maven, this file is not created and therefore this exception occurs in MalletLogger.java.

Someone should either configure maven properly so that the logging.properties file is created in the target folder. A temporary solution would be to modify the Mallet code to set another path for logging.properties.