public void printTopWords(int numWords, boolean useNewLines) {
class WordProb implements Comparable {
int wi;
double p;
public WordProb(int wi, double p) {
this.wi = wi;
this.p = p;
}
public final int compareTo(Object o2) {
if (p > ((WordProb) o2).p) return -1;
else if (p == ((WordProb) o2).p) return 0;
else return 1;
}
}
WordProb[] wp = new WordProb[numTypes];
for (int ti = 0; ti < numTopics; ti++) {
for (int wi = 0; wi < numTypes; wi++)
wp[wi] = new WordProb(wi, ((double) typeTopicCounts[wi][ti]) / tokensPerTopic[ti]);
Arrays.sort(wp);
if (useNewLines) {
System.out.println("\nTopic " + ti);
for (int i = 0; i < numWords; i++)
System.out.println(
ilist.getDataAlphabet().lookupObject(wp[i].wi).toString() + " " + wp[i].p);
} else {
System.out.print("Topic " + ti + ": ");
for (int i = 0; i < numWords; i++)
System.out.print(ilist.getDataAlphabet().lookupObject(wp[i].wi).toString() + " ");
System.out.println();
}
}
}
/**
* @param targetTerm
* @param sourceFile
* @param trainingAlgo
* @param outputFileClassifier
* @param outputFileResults
* @param termWindowSize
* @param pipe
* @return
*/
private static List<ClassificationResult> runTrainingAndClassification(
String targetTerm,
String sourceFile,
String trainingAlgo,
String outputFileClassifier,
String outputFileResults,
int termWindowSize,
Pipe pipe,
boolean useCollocationalVector) {
// Read in concordance file and create list of Mallet training instances
// TODO: Remove duplication of code (see execConvertToMalletFormat(...))
String vectorType = useCollocationalVector ? "coll" : "bow";
InstanceList instanceList =
readConcordanceFileToInstanceList(
targetTerm, sourceFile, termWindowSize, pipe, useCollocationalVector);
// Creating splits for training and testing
double[] proportions = {0.9, 0.1};
InstanceList[] splitLists = instanceList.split(proportions);
InstanceList trainingList = splitLists[0];
InstanceList testList = splitLists[1];
// Train the classifier
ClassifierTrainer classifierTrainer = getClassifierTrainerForAlgorithm(trainingAlgo);
Classifier classifier = classifierTrainer.train(trainingList);
if (classifier.getLabelAlphabet()
!= null) { // TODO: Make sure this is not null in RandomClassifier
System.out.println("Labels:\n" + classifier.getLabelAlphabet());
System.out.println(
"Size of data alphabet (= type count of training list): "
+ classifier.getAlphabet().size());
}
// Run tests and get results
Trial trial = new Trial(classifier, testList);
List<ClassificationResult> results = new ArrayList<ClassificationResult>();
for (int i = 0; i < classifier.getLabelAlphabet().size(); i++) {
Label label = classifier.getLabelAlphabet().lookupLabel(i);
ClassificationResult result =
new MalletClassificationResult(
trainingAlgo,
targetTerm,
vectorType,
label.toString(),
termWindowSize,
trial,
sourceFile);
results.add(result);
System.out.println(result.toString());
}
// Save classifier
saveClassifierToFile(outputFileClassifier, classifier, trainingAlgo, termWindowSize);
return results;
}
/**
* converts the sentence based instance list into a token based one This is needed for the
* ME-version of JET (JetMeClassifier)
*
* @param METrainerDummyPipe
* @param inst just the features for one sentence to be transformed
* @return
*/
public static InstanceList convertFeatsforClassifier(
final Pipe METrainerDummyPipe, final Instance inst) {
final InstanceList iList = new InstanceList(METrainerDummyPipe);
final FeatureVectorSequence fvs = (FeatureVectorSequence) inst.getData();
final LabelSequence ls = (LabelSequence) inst.getTarget();
final LabelAlphabet ldict = (LabelAlphabet) ls.getAlphabet();
final Object source = inst.getSource();
final Object name = inst.getName();
if (ls.size() != fvs.size()) {
System.err.println(
"failed making token instances: size of labelsequence != size of featue vector sequence: "
+ ls.size()
+ " - "
+ fvs.size());
System.exit(-1);
}
for (int j = 0; j < fvs.size(); j++) {
final Instance I =
new Instance(fvs.getFeatureVector(j), ldict.lookupLabel(ls.get(j)), name, source);
iList.add(I);
}
return iList;
}
public void printDocumentTopics(PrintWriter pw, double threshold, int max) {
pw.println("#doc source topic proportion ...");
int docLen;
double topicDist[] = new double[topics.length];
for (int di = 0; di < topics.length; di++) {
pw.print(di);
pw.print(' ');
if (ilist.get(di).getSource() != null) {
pw.print(ilist.get(di).getSource().toString());
} else {
pw.print("null-source");
}
pw.print(' ');
docLen = topics[di].length;
for (int ti = 0; ti < numTopics; ti++)
topicDist[ti] = (((float) docTopicCounts[di][ti]) / docLen);
if (max < 0) max = numTopics;
for (int tp = 0; tp < max; tp++) {
double maxvalue = 0;
int maxindex = -1;
for (int ti = 0; ti < numTopics; ti++)
if (topicDist[ti] > maxvalue) {
maxvalue = topicDist[ti];
maxindex = ti;
}
if (maxindex == -1 || topicDist[maxindex] < threshold) break;
pw.print(maxindex + " " + topicDist[maxindex] + " ");
topicDist[maxindex] = 0;
}
pw.println(' ');
}
}
private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException {
int featuresLength;
int version = in.readInt();
ilist = (InstanceList) in.readObject();
numTopics = in.readInt();
alpha = in.readDouble();
beta = in.readDouble();
tAlpha = in.readDouble();
vBeta = in.readDouble();
int numDocs = ilist.size();
topics = new int[numDocs][];
for (int di = 0; di < ilist.size(); di++) {
int docLen = ((FeatureSequence) ilist.get(di).getData()).getLength();
topics[di] = new int[docLen];
for (int si = 0; si < docLen; si++) topics[di][si] = in.readInt();
}
docTopicCounts = new int[numDocs][numTopics];
for (int di = 0; di < ilist.size(); di++)
for (int ti = 0; ti < numTopics; ti++) docTopicCounts[di][ti] = in.readInt();
int numTypes = ilist.getDataAlphabet().size();
typeTopicCounts = new int[numTypes][numTopics];
for (int fi = 0; fi < numTypes; fi++)
for (int ti = 0; ti < numTopics; ti++) typeTopicCounts[fi][ti] = in.readInt();
tokensPerTopic = new int[numTopics];
for (int ti = 0; ti < numTopics; ti++) tokensPerTopic[ti] = in.readInt();
}
private InstanceList readFile() throws IOException {
String NL = System.getProperty("line.separator");
Scanner scanner = new Scanner(new FileInputStream(fileName), encoding);
ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}\\p{L}+")));
pipeList.add(new TokenSequence2FeatureSequence());
InstanceList testing = new InstanceList(new SerialPipes(pipeList));
try {
while (scanner.hasNextLine()) {
String text = scanner.nextLine();
text = text.replaceAll("\\x0d", "");
Pattern patten = Pattern.compile("^(.*?),(.*?),(.*)$");
Matcher matcher = patten.matcher(text);
if (matcher.find()) {
docIds.add(matcher.group(1));
testing.addThruPipe(new Instance(matcher.group(3), null, "test instance", null));
}
}
} finally {
scanner.close();
}
return testing;
}
public void count() {
TIntIntHashMap docCounts = new TIntIntHashMap();
int index = 0;
if (instances.size() == 0) {
logger.info("Instance list is empty");
return;
}
if (instances.get(0).getData() instanceof FeatureSequence) {
for (Instance instance : instances) {
FeatureSequence features = (FeatureSequence) instance.getData();
for (int i = 0; i < features.getLength(); i++) {
docCounts.adjustOrPutValue(features.getIndexAtPosition(i), 1, 1);
}
int[] keys = docCounts.keys();
for (int i = 0; i < keys.length - 1; i++) {
int feature = keys[i];
featureCounts[feature] += docCounts.get(feature);
documentFrequencies[feature]++;
}
docCounts = new TIntIntHashMap();
index++;
if (index % 1000 == 0) {
System.err.println(index);
}
}
} else if (instances.get(0).getData() instanceof FeatureVector) {
for (Instance instance : instances) {
FeatureVector features = (FeatureVector) instance.getData();
for (int location = 0; location < features.numLocations(); location++) {
int feature = features.indexAtLocation(location);
double value = features.valueAtLocation(location);
documentFrequencies[feature]++;
featureCounts[feature] += value;
}
index++;
if (index % 1000 == 0) {
System.err.println(index);
}
}
} else {
logger.info("Unsupported data class: " + instances.get(0).getData().getClass().getName());
}
}
public SVM train(InstanceList trainingList) {
svm_problem problem = new svm_problem();
problem.l = trainingList.size();
problem.x = new svm_node[problem.l][];
problem.y = new double[problem.l];
for (int i = 0; i < trainingList.size(); i++) {
Instance instance = trainingList.get(i);
svm_node[] input = SVM.getSvmNodes(instance);
if (input == null) {
continue;
}
int labelIndex = ((Label) instance.getTarget()).getIndex();
problem.x[i] = input;
problem.y[i] = labelIndex;
}
int max_index = trainingList.getDataAlphabet().size();
if (param.gamma == 0 && max_index > 0) {
param.gamma = 1.0 / max_index;
}
// int numLabels = trainingList.getTargetAlphabet().size();
// int[] weight_label = new int[numLabels];
// double[] weight = trainingList.targetLabelDistribution().getValues();
// double minValue = Double.MAX_VALUE;
//
// for (int i = 0; i < weight.length; i++) {
// if (minValue > weight[i]) {
// minValue = weight[i];
// }
// }
//
// for (int i = 0; i < weight.length; i++) {
// weight_label[i] = i;
// weight[i] = weight[i] / minValue;
// }
//
// param.weight_label = weight_label;
// param.weight = weight;
String error_msg = svm.svm_check_parameter(problem, param);
if (error_msg != null) {
System.err.print("Error: " + error_msg + "\n");
System.exit(1);
}
svm_model model = svm.svm_train(problem, param);
classifier = new SVM(model, trainingList.getPipe());
return classifier;
}
public void estimate(
InstanceList documents,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
String outputModelFilename,
Randoms r) {
ilist = documents.shallowClone();
numTypes = ilist.getDataAlphabet().size();
int numDocs = ilist.size();
topics = new int[numDocs][];
docTopicCounts = new int[numDocs][numTopics];
typeTopicCounts = new int[numTypes][numTopics];
tokensPerTopic = new int[numTopics];
tAlpha = alpha * numTopics;
vBeta = beta * numTypes;
long startTime = System.currentTimeMillis();
// Initialize with random assignments of tokens to topics
// and finish allocating this.topics and this.tokens
int topic, seqLen;
FeatureSequence fs;
for (int di = 0; di < numDocs; di++) {
try {
fs = (FeatureSequence) ilist.get(di).getData();
} catch (ClassCastException e) {
System.err.println(
"LDA and other topic models expect FeatureSequence data, not FeatureVector data. "
+ "With text2vectors, you can obtain such data with --keep-sequence or --keep-bisequence.");
throw e;
}
seqLen = fs.getLength();
numTokens += seqLen;
topics[di] = new int[seqLen];
// Randomly assign tokens to topics
for (int si = 0; si < seqLen; si++) {
topic = r.nextInt(numTopics);
topics[di][si] = topic;
docTopicCounts[di][topic]++;
typeTopicCounts[fs.getIndexAtPosition(si)][topic]++;
tokensPerTopic[topic]++;
}
}
this.estimate(
0, numDocs, numIterations, showTopicsInterval, outputModelInterval, outputModelFilename, r);
// 124.5 seconds
// 144.8 seconds after using FeatureSequence instead of tokens[][] array
// 121.6 seconds after putting "final" on FeatureSequence.getIndexAtPosition()
// 106.3 seconds after avoiding array lookup in inner loop with a temporary variable
}
public Node(InstanceList ilist, Node parent, int minNumInsts, int[] instIndices) {
if (instIndices == null) {
instIndices = new int[ilist.size()];
for (int ii = 0; ii < instIndices.length; ii++) instIndices[ii] = ii;
}
m_gainRatio = GainRatio.createGainRatio(ilist, instIndices, minNumInsts);
m_ilist = ilist;
m_instIndices = instIndices;
m_dataDict = m_ilist.getDataAlphabet();
m_minNumInsts = minNumInsts;
m_parent = parent;
m_leftChild = m_rightChild = null;
}
public InstanceList readArray(String[] cleanTexts) {
StringArrayIterator iterator = new StringArrayIterator(cleanTexts);
// Construct a new instance list, passing it the pipe we want to use to
// process instances.
InstanceList instances = new InstanceList(pipe);
int index = 0;
for (Instance inst : instances) {
inst.setName(name_id.get(index));
inst.setTarget("english");
index++;
}
// Now process each instance provided by the iterator.
instances.addThruPipe(iterator);
return instances;
}
public FeatureCountTool(InstanceList instances) {
this.instances = instances;
numFeatures = instances.getDataAlphabet().size();
featureCounts = new double[numFeatures];
documentFrequencies = new int[numFeatures];
}
public void split() {
if (m_ilist == null) throw new IllegalStateException("Frozen. Cannot split.");
int numLeftChildren = 0;
boolean[] toLeftChild = new boolean[m_instIndices.length];
for (int i = 0; i < m_instIndices.length; i++) {
Instance instance = m_ilist.get(m_instIndices[i]);
FeatureVector fv = (FeatureVector) instance.getData();
if (fv.value(m_gainRatio.getMaxValuedIndex()) <= m_gainRatio.getMaxValuedThreshold()) {
toLeftChild[i] = true;
numLeftChildren++;
} else toLeftChild[i] = false;
}
logger.info(
"leftChild.size="
+ numLeftChildren
+ " rightChild.size="
+ (m_instIndices.length - numLeftChildren));
int[] leftIndices = new int[numLeftChildren];
int[] rightIndices = new int[m_instIndices.length - numLeftChildren];
int li = 0, ri = 0;
for (int i = 0; i < m_instIndices.length; i++) {
if (toLeftChild[i]) leftIndices[li++] = m_instIndices[i];
else rightIndices[ri++] = m_instIndices[i];
}
m_leftChild = new Node(m_ilist, this, m_minNumInsts, leftIndices);
m_rightChild = new Node(m_ilist, this, m_minNumInsts, rightIndices);
}
public boolean train(
InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval) {
assert (ilist.size() > 0);
if (emissionEstimator == null) {
emissionEstimator = new Multinomial.LaplaceEstimator[numStates()];
transitionEstimator = new Multinomial.LaplaceEstimator[numStates()];
emissionMultinomial = new Multinomial[numStates()];
transitionMultinomial = new Multinomial[numStates()];
Alphabet transitionAlphabet = new Alphabet();
for (int i = 0; i < numStates(); i++)
transitionAlphabet.lookupIndex(((State) states.get(i)).getName(), true);
for (int i = 0; i < numStates(); i++) {
emissionEstimator[i] = new Multinomial.LaplaceEstimator(inputAlphabet);
transitionEstimator[i] = new Multinomial.LaplaceEstimator(transitionAlphabet);
emissionMultinomial[i] =
new Multinomial(getUniformArray(inputAlphabet.size()), inputAlphabet);
transitionMultinomial[i] =
new Multinomial(getUniformArray(transitionAlphabet.size()), transitionAlphabet);
}
initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
}
for (Instance instance : ilist) {
FeatureSequence input = (FeatureSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
new SumLatticeDefault(this, input, output, new Incrementor());
}
initialMultinomial = initialEstimator.estimate();
for (int i = 0; i < numStates(); i++) {
emissionMultinomial[i] = emissionEstimator[i].estimate();
transitionMultinomial[i] = transitionEstimator[i].estimate();
getState(i).setInitialWeight(initialMultinomial.logProbability(getState(i).getName()));
}
return true;
}
public void generateTestInference() {
if (lda == null) {
System.out.println("Should run lda estimation first.");
System.exit(1);
return;
}
if (testTopicDistribution == null) testTopicDistribution = new double[test.size()][];
TopicInferencer infer = lda.getInferencer();
int iterations = 800;
int thinning = 5;
int burnIn = 100;
for (int ti = 0; ti < test.size(); ti++) {
testTopicDistribution[ti] =
infer.getSampledDistribution(test.get(ti), iterations, thinning, burnIn);
}
}
// in the training feature table
// Lines should be formatted as:
//
// [name] [label] [data ... ]
//
public static Classifier TrainMaxent(String trainingFilename, File modelFile) throws IOException {
// build data input pipe
ArrayList<Pipe> pipes = new ArrayList<Pipe>();
// define pipe
// the features in [data ...] should like: feature:value
pipes.add(new Target2Label());
pipes.add(new Csv2FeatureVector());
Pipe pipe = new SerialPipes(pipes);
pipe.setTargetProcessing(true);
// read data
InstanceList trainingInstances = new InstanceList(pipe);
FileReader training_file_reader = new FileReader(trainingFilename);
CsvIterator reader =
new CsvIterator(
training_file_reader,
"(\\w+)\\s+([^\\s]+)\\s+(.*)",
3,
2,
1); // (data, label, name) field indices
trainingInstances.addThruPipe(reader);
training_file_reader.close();
// calculate running time
long startTime = System.currentTimeMillis();
PrintStream temp = System.err;
System.setErr(System.out);
// train a Maxent classifier (could be other classifiers)
ClassifierTrainer trainer = new MaxEntTrainer(Gaussian_Variance);
Classifier classifier = trainer.train(trainingInstances);
System.setErr(temp);
// calculate running time
long endTime = System.currentTimeMillis();
long totalTime = endTime - startTime;
System.out.println("Total training time: " + totalTime);
// write model
ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(modelFile));
oos.writeObject(classifier);
oos.close();
return classifier;
}
public void trainClassifier(File dir, String... args) throws Exception {
InstanceListCreator instanceListCreator = new InstanceListCreator();
InstanceList instanceList = instanceListCreator.createInstanceList(getTrainingDataFile(dir));
instanceList.save(new File(dir, "training-data.ser"));
String factoryName = args[0];
Class<ClassifierTrainerFactory<?>> factoryClass = createTrainerFactory(factoryName);
if (factoryClass == null) {
String factoryName2 = "org.cleartk.ml.mallet.factory." + factoryName + "TrainerFactory";
factoryClass = createTrainerFactory(factoryName2);
}
if (factoryClass == null) {
throw new IllegalArgumentException(
String.format(
"name for classifier trainer factory is not valid: name given ='%s'. Valid classifier names include: %s, %s, %s, and %s",
factoryName,
ClassifierTrainerFactory.NAMES[0],
ClassifierTrainerFactory.NAMES[1],
ClassifierTrainerFactory.NAMES[2],
ClassifierTrainerFactory.NAMES[3]));
}
String[] factoryArgs = new String[args.length - 1];
System.arraycopy(args, 1, factoryArgs, 0, factoryArgs.length);
ClassifierTrainerFactory<?> factory = factoryClass.newInstance();
ClassifierTrainer<?> trainer = null;
try {
trainer = factory.createTrainer(factoryArgs);
} catch (Throwable t) {
throw new IllegalArgumentException(
"Unable to create trainer. Usage for "
+ factoryClass.getCanonicalName()
+ ": "
+ factory.getUsageMessage(),
t);
}
this.classifier = trainer.train(instanceList);
ObjectOutputStream oos =
new ObjectOutputStream(new FileOutputStream(new File(dir, MODEL_NAME)));
oos.writeObject(classifier);
oos.close();
}
public void train(String[] trainSections, String[] testSections) throws IOException {
pipe = defaultPipe();
InstanceList trainingInstanceList = prepareInstanceList(trainSections);
InstanceList testingInstanceList = prepareInstanceList(testSections);
// Classifier classifier = trainer.train(trainingInstanceList, testingInstanceList);
Classifier classifier = trainer.train(trainingInstanceList);
System.out.println("training size: " + trainingInstanceList.size());
System.out.println("testing size: " + testingInstanceList.size());
// showAccuracy(classifier, testingInstanceList);
// getTypeSpecificAccuracy(trainingInstanceList, testingInstanceList, true);
// showInterpolatedTCAccuracy(trainingInstanceList, testingInstanceList);
}
/**
* Create and train a CRF model from the given training data, optionally testing it on the given
* test data.
*
* @param training training data
* @param testing test data (possibly <code>null</code>)
* @param eval accuracy evaluator (possibly <code>null</code>)
* @param orders label Markov orders (main and backoff)
* @param defaultLabel default label
* @param forbidden regular expression specifying impossible label transitions <em>current</em>
* <code>,</code><em>next</em> (<code>null</code> indicates no forbidden transitions)
* @param allowed regular expression specifying allowed label transitions (<code>null</code>
* indicates everything is allowed that is not forbidden)
* @param connected whether to include even transitions not occurring in the training data.
* @param iterations number of training iterations
* @param var Gaussian prior variance
* @return the trained model
*/
public static CRF train(
InstanceList training,
InstanceList testing,
TransducerEvaluator eval,
int[] orders,
String defaultLabel,
String forbidden,
String allowed,
boolean connected,
int iterations,
double var,
CRF crf) {
Pattern forbiddenPat = Pattern.compile(forbidden);
Pattern allowedPat = Pattern.compile(allowed);
if (crf == null) {
crf = new CRF(training.getPipe(), (Pipe) null);
String startName =
crf.addOrderNStates(
training, orders, null, defaultLabel, forbiddenPat, allowedPat, connected);
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
crft.setGaussianPriorVariance(var);
for (int i = 0; i < crf.numStates(); i++)
crf.getState(i).setInitialWeight(Transducer.IMPOSSIBLE_WEIGHT);
crf.getState(startName).setInitialWeight(0.0);
}
logger.info("Training on " + training.size() + " instances");
if (testing != null) logger.info("Testing on " + testing.size() + " instances");
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
if (featureInductionOption.value) {
crft.trainWithFeatureInduction(
training, null, testing, eval, iterations, 10, 20, 500, 0.5, false, null);
} else {
boolean converged;
for (int i = 1; i <= iterations; i++) {
converged = crft.train(training, 1);
if (i % 1 == 0 && eval != null) // Change the 1 to higher integer to evaluate less often
eval.evaluate(crft);
if (viterbiOutputOption.value && i % 10 == 0)
new ViterbiWriter(
"", new InstanceList[] {training, testing}, new String[] {"training", "testing"})
.evaluate(crft);
if (converged) break;
}
}
return crf;
}
/**
* Train a classifier
*
* @param trainingInstances
* @param trainingPortion The percentage to be used for training (<=1.0), the rest is used for
* testing.
* @return
*/
public Classifier train(InstanceList trainingInstances, double trainingPortion) {
InstanceList[] instanceLists =
trainingInstances.split(
new Random(), new double[] {trainingPortion, (1 - trainingPortion)});
// InstanceList[] instanceLists =
// trainingInstances.splitInOrder(new double[]{trainingPortion, (1-trainingPortion)});
return this.train(instanceLists[0], instanceLists[1]);
}
/**
* Calculates the minimum description length of this node, i.e., the length of the binary
* encoding that describes the feature and the split value used at this node
*/
public double getMDL() {
int numClasses = m_ilist.getTargetAlphabet().size();
double mdl = getSize() * getGainRatio().getBaseEntropy();
mdl += ((numClasses - 1) * Math.log(getSize() / 2.0)) / (2 * GainRatio.log2);
double piPow = Math.pow(Math.PI, numClasses / 2.0);
double gammaVal = Maths.gamma(numClasses / 2.0);
mdl += Math.log(piPow / gammaVal) / GainRatio.log2;
return mdl;
}
// Just for testing. Recommend instead is mallet/bin/vectors2topics
public static void main(String[] args) {
InstanceList ilist = InstanceList.load(new File(args[0]));
int numIterations = args.length > 1 ? Integer.parseInt(args[1]) : 1000;
int numTopWords = args.length > 2 ? Integer.parseInt(args[2]) : 20;
System.out.println("Data loaded.");
TopicalNGrams tng = new TopicalNGrams(10);
tng.estimate(ilist, 200, 1, 0, null, new Randoms());
tng.printTopWords(60, true);
}
private InstanceList generateInstanceList() throws Exception {
ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}\\p{L}+")));
pipeList.add(new TokenSequence2FeatureSequence());
Reader fileReader = new InputStreamReader(new FileInputStream(new File(fileName)), "UTF-8");
InstanceList instances = new InstanceList(new SerialPipes(pipeList));
instances.addThruPipe(
new CsvIterator(
fileReader,
Pattern.compile("^(\\S*)[\\s,]*(\\S*)[\\s,]*(.*)$"),
3,
2,
1)); // data, label, name fields
return instances;
}
double getAccuracy(Classifier classifier, InstanceList instanceList) {
int total = instanceList.size();
int correct = 0;
for (Instance instance : instanceList) {
Classification classification = classifier.classify(instance);
if (classification.bestLabelIsCorrect()) correct++;
}
return (1.0 * correct) / total;
}
// Recommended to use mallet/bin/vectors2topics instead.
public static void main(String[] args) throws IOException {
InstanceList ilist = InstanceList.load(new File(args[0]));
int numIterations = args.length > 1 ? Integer.parseInt(args[1]) : 1000;
int numTopWords = args.length > 2 ? Integer.parseInt(args[2]) : 20;
System.out.println("Data loaded.");
LDA lda = new LDA(10);
lda.estimate(ilist, numIterations, 50, 0, null, new Randoms()); // should be 1100
lda.printTopWords(numTopWords, true);
lda.printDocumentTopics(new File(args[0] + ".lda"));
}
public DocumentStream(long ts, String rootDir, FeedSettings settings) {
this.ts = ts;
this.rootDir = rootDir + "/" + ts + "/";
// this.maxWordsPerTopic =maxWordsPerTopic;
this.settings = settings;
list = LdaModel.createInstanceList(this.ts);
testing = new InstanceList(list.getPipe());
lda = new LdaModel();
}
/**
* Prepare Instances for use with LDA.
*
* @param r
* @return
*/
public static InstanceList loadInstancesLDA(Reader r) {
ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
// Pipes: lowercase, tokenize, remove stopwords, map to features
pipeList.add(new Target2Label());
pipeList.add(new CharSequenceLowercase());
pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}[\\p{L}\\p{P}]+\\p{L}")));
pipeList.add(
new TokenSequenceRemoveStopwords(stopWords, stopWordsEncoding, false, false, false));
pipeList.add(new TokenSequence2FeatureSequence());
SerialPipes pipes = new SerialPipes(pipeList);
InstanceList instances = new InstanceList(pipes);
// create instances with: 3: data; 2: label; 1: name fields
instances.addThruPipe(new CsvIterator(r, Pattern.compile("(.*)\t(.*)\t(.*)"), 3, 2, 1));
return instances;
}
public void doInference() {
try {
ParallelTopicModel model = ParallelTopicModel.read(new File(inferencerFile));
TopicInferencer inferencer = model.getInferencer();
// TopicInferencer inferencer =
// TopicInferencer.read(new File(inferencerFile));
// InstanceList testing = readFile();
readFile();
InstanceList testing = generateInstanceList(); // readFile();
for (int i = 0; i < testing.size(); i++) {
StringBuilder probabilities = new StringBuilder();
double[] testProbabilities = inferencer.getSampledDistribution(testing.get(i), 10, 1, 5);
ArrayList probabilityList = new ArrayList();
for (int j = 0; j < testProbabilities.length; j++) {
probabilityList.add(new Pair<Integer, Double>(j, testProbabilities[j]));
}
Collections.sort(probabilityList, new CustomComparator());
for (int j = 0; j < testProbabilities.length && j < topN; j++) {
if (j > 0) probabilities.append(" ");
probabilities.append(
((Pair<Integer, Double>) probabilityList.get(j)).getFirst().toString()
+ ","
+ ((Pair<Integer, Double>) probabilityList.get(j)).getSecond().toString());
}
System.out.println(docIds.get(i) + "," + probabilities.toString());
}
} catch (Exception e) {
e.printStackTrace();
System.err.println(e.getMessage());
}
}
public static void main(String[] args) throws Exception {
CommandOption.setSummary(
FeatureCountTool.class,
"Print feature counts and instances per feature (eg document frequencies) in an instance list");
CommandOption.process(FeatureCountTool.class, args);
InstanceList instances = InstanceList.load(new File(inputFile.value));
FeatureCountTool counter = new FeatureCountTool(instances);
counter.count();
counter.printCounts();
}
public void printState(PrintWriter pw) {
Alphabet a = ilist.getDataAlphabet();
pw.println("#doc pos typeindex type topic");
for (int di = 0; di < topics.length; di++) {
FeatureSequence fs = (FeatureSequence) ilist.get(di).getData();
for (int si = 0; si < topics[di].length; si++) {
int type = fs.getIndexAtPosition(si);
pw.print(di);
pw.print(' ');
pw.print(si);
pw.print(' ');
pw.print(type);
pw.print(' ');
pw.print(a.lookupObject(type));
pw.print(' ');
pw.print(topics[di][si]);
pw.println();
}
}
}
