package uk.ac.cam.ha293.tweetlabel.topics;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.regex.Pattern;
import uk.ac.cam.ha293.tweetlabel.types.Corpus;
import uk.ac.cam.ha293.tweetlabel.types.Document;
import cc.mallet.topics.ParallelTopicModel;
import cc.mallet.pipe.CharSequence2TokenSequence;
import cc.mallet.pipe.FeatureSequence2FeatureVector;
import cc.mallet.pipe.Pipe;
import cc.mallet.pipe.PrintInputAndTarget;
import cc.mallet.pipe.SerialPipes;
import cc.mallet.pipe.Target2Label;
import cc.mallet.pipe.TokenSequence2FeatureSequence;
import cc.mallet.pipe.TokenSequenceLowercase;
import cc.mallet.pipe.TokenSequenceRemoveStopwords;
import cc.mallet.types.Instance;
import cc.mallet.types.InstanceList;
public class MalletLDA implements Serializable {
private static final long serialVersionUID = 7094157759480964151L;
ParallelTopicModel LDA;
Corpus corpus;
int numTopics;
public MalletLDA(Corpus corpus, int numTopics, int numIterations, double alpha, double beta) {
this.corpus = corpus;
this.numTopics = numTopics;
LDA = new ParallelTopicModel(numTopics,alpha,beta);
LDA.setNumIterations(numIterations);
}
public MalletLDA(Corpus corpus, int numTopics, int numIterations) {
this.corpus = corpus;
this.numTopics = numTopics;
LDA = new ParallelTopicModel(numTopics);
LDA.setNumIterations(numIterations);
}
public void setCorpus(Corpus corpus) {
this.corpus = corpus;
}
public void evaluate() {
Pipe pipe = buildPipe();
InstanceList instances = new InstanceList(pipe);
for(Document document : corpus.getDocuments()) {
Instance instance = new Instance(document.getDocumentString(),null,null,document.getDocumentString());
instance.setData(document.getDocumentString());
instances.addThruPipe(instance);
}
LDA.addInstances(instances);
try {
LDA.estimate();
} catch (IOException e) {
System.err.println("Couldn't perform LDA estimation");
e.printStackTrace();
}
}
public void print() {
LDA.getTopicAlphabet().toString();
}
public void printTopWords(int topWords) {
Object[][] words = LDA.getTopWords(topWords);
for(int topicID=0; topicID<words.length; topicID++) {
System.out.print("Topic "+topicID+": ");
for(int wordID=0; wordID<words[topicID].length; wordID++) {
System.out.print((String)words[topicID][wordID]+" ");
}
System.out.println();
}
}
//From MALLET website example
//TODO: Need to do special tokenizing and parsing to account for @ and #
public Pipe buildPipe() {
ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
// Regular expression for what constitutes a token.
// This pattern includes Unicode letters, Unicode numbers,
// and the underscore character. Alternatives:
// "\\S+" (anything not whitespace)
// "\\w+" ( A-Z, a-z, 0-9, _ )
// "[\\p{L}\\p{N}_]+|[\\p{P}]+" (a group of only letters and numbers OR
// a group of only punctuation marks)
Pattern tokenPattern = Pattern.compile("[\\p{L}\\p{N}_]+|[\\p{P}]+");
// Tokenize raw strings
pipeList.add(new CharSequence2TokenSequence(tokenPattern));
// Normalize all tokens to all lowercase
pipeList.add(new TokenSequenceLowercase());
// Remove stopwords from a standard English stoplist.
// options: [case sensitive] [mark deletions]
pipeList.add(new TokenSequenceRemoveStopwords(false, false));
// Rather than storing tokens as strings, convert
// them to integers by looking them up in an alphabet.
pipeList.add(new TokenSequence2FeatureSequence());
// Do the same thing for the "target" field:
// convert a class label string to a Label object,
// which has an index in a Label alphabet.
//pipeList.add(new Target2Label());
// Now convert the sequence of features to a sparse vector,
// mapping feature IDs to counts.
//pipeList.add(new FeatureSequence2FeatureVector());
//NOTE: Commented out because LDA wanted a FeatureSequence
// Print out the features and the label
pipeList.add(new PrintInputAndTarget());
return new SerialPipes(pipeList);
}
}