KMeansClustering Example

ParagraphVectorsTextKMeansExample.java

package org.deeplearning4j.examples.paragraphvectors;

import org.apache.commons.lang.time.StopWatch;
import org.deeplearning4j.clustering.cluster.Cluster;
import org.deeplearning4j.clustering.cluster.ClusterSet;
import org.deeplearning4j.clustering.cluster.Point;
import org.deeplearning4j.clustering.cluster.PointClassification;
import org.deeplearning4j.clustering.kmeans.KMeansClustering;
import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
import org.deeplearning4j.models.paragraphvectors.ParagraphVectors;
import org.deeplearning4j.models.word2vec.wordstore.inmemory.InMemoryLookupCache;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.deeplearning4j.text.sentenceiterator.BasicLineIterator;
import org.deeplearning4j.text.documentiterator.LabelsSource;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.core.io.ClassPathResource;
import org.synthesis.java.extension.ParagraphVectorSerializer;

import java.io.File;
import java.util.ArrayList;
import java.util.List;

/**
 * This is example code for dl4j ParagraphVectors implementation. In this example we build distributed representation of all sentences present in training corpus.
 * However, you still use it for training on labelled documents, using sets of LabelledDocument and LabelAwareIterator implementation.
 *
 * *************************************************************************************************
 * PLEASE NOTE: THIS EXAMPLE REQUIRES DL4J/ND4J VERSIONS >= rc3.8 TO COMPILE SUCCESSFULLY
 * *************************************************************************************************
 *
 * @author raver119@gmail.com
 */
public class ParagraphVectorsTextKMeansExample {

    private static final Logger log = LoggerFactory.getLogger(ParagraphVectorsTextKMeansExample.class);

    public static void main(String[] args) throws Exception {
    	String datafilepath = "/raw_sentences.txt"; //This has ~99000 single-sentence paragraphs/docs
        ClassPathResource resource = new ClassPathResource(datafilepath);
        File file = resource.getFile();
        SentenceIterator iter = new BasicLineIterator(file);
        InMemoryLookupCache cache = new InMemoryLookupCache();
        TokenizerFactory t = new DefaultTokenizerFactory();
        t.setTokenPreProcessor(new CommonPreprocessor());

        StopWatch sw = new StopWatch();
        /*
             if you don't have LabelAwareIterator handy, you can use synchronized labels generator
              it will be used to label each document/sequence/line with it's own label.

              But if you have LabelAwareIterator ready, you can can provide it, for your in-house labels
        */
        LabelsSource source = new LabelsSource("DOC_");

        ParagraphVectors vec = new ParagraphVectors.Builder()
                .minWordFrequency(1)
                .iterations(3)
                .epochs(1)
                .layerSize(100)  /*length of a paragraph vector*/
                .learningRate(0.025)
                .labelsSource(source)
                .windowSize(5)
                .iterate(iter)
                .trainWordVectors(false)
                .vocabCache(cache)
                .tokenizerFactory(t)
                .sampling(0)
                .build();

        vec.fit();

        
        //1. create a kmeanscluster instance
        int maxIterationCount = 5;
        int clusterCount = 10;
        String distanceFunction = "cosinesimilarity";
        KMeansClustering kmc = KMeansClustering.setup(clusterCount, maxIterationCount, distanceFunction);

        //2. iterate over rows in the paragraphvector and create a List of paragraph vectors
        List<INDArray> vectors = new ArrayList<INDArray>();
        for (String word : vec.vocab().words()) {
        	vectors.add(vec.getWordVectorMatrix(word));
        }
        log.info(vectors.size() + " vectors extracted to create Point list");
        List<Point> pointsLst = Point.toPoints(vectors);
        log.info(pointsLst.size() + " Points created out of " + vectors.size() + " vectors");
        
        log.info("Start Clustering " + pointsLst.size() + " points/docs");
        sw.reset();
        sw.start();
        ClusterSet cs = kmc.applyTo(pointsLst);
        sw.stop();
        System.out.println("Time taken to run clustering on " + vectors.size() + " paragraphVectors: " + sw.getTime());
        vectors = null;  
        pointsLst = null;

        log.info("Finish  Clustering");

        List<Cluster> clsterLst = cs.getClusters();

		System.out.println("\nCluster Centers:");
        for(Cluster c: clsterLst) {
        	Point center = c.getCenter();
        	System.out.println(center.getId());
        }

        log.info("Trying to classify a point that was used for generating the Clusters");
        double[] nesVec = vec.getWordVector("DOC_400");
        Point newpoint = new Point("myid", "mylabel", nesVec);
        PointClassification pc = cs.classifyPoint(newpoint);
        System.out.println(pc.getCluster().getCenter().getId());
       
        System.out.println("\nEnd Test");
       
    }
}

Thanks for the example. I tried something similar with Word2Vec. I got clusters. However, I could only get IDs (random hash values?), and I could not get the text of the words themselves. I tried center.getLabel().
Clearly, I need to learn more about these objects. Where can I find documentation that is more detailed than the javadocs?
Thanks, regards
Karthik