topics generated is inconsistent for same data

am using mallet for topic modeling of data. All things are good but the topics getting generated are inconsistent. Its generating weightage every time different on running the model on the same data I have tried to set the seed but still it's not working as expected. Apart from this, I want to get the list of documents related to each topic, so that I can check which documents if related to which topic.

package topicModelling; 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 TopicModel {

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

    // 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("D:/TCG Lucene 8/Stopwords/stopwords.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("D:/data.txt")), "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 = 10;
    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(2000);
     model.setRandomSeed(0);///setting random seed for getting the topics same as it will give inconsitent result
    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();

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

}

mimno / Mallet

topics generated is inconsistent for same data #178