@Override
protected void estimateModelParameters(Dataset trainingData) {
int n = trainingData.size();
int d = trainingData.getColumnSize();
ModelParameters modelParameters = knowledgeBase.getModelParameters();
TrainingParameters trainingParameters = knowledgeBase.getTrainingParameters();
modelParameters.setN(n);
modelParameters.setD(d);
// get model parameters
int k = trainingParameters.getK(); // number of topics
Map<List<Object>, Integer> topicAssignmentOfDocumentWord =
modelParameters.getTopicAssignmentOfDocumentWord();
Map<List<Integer>, Integer> documentTopicCounts = modelParameters.getDocumentTopicCounts();
Map<List<Object>, Integer> topicWordCounts = modelParameters.getTopicWordCounts();
Map<Integer, Integer> documentWordCounts = modelParameters.getDocumentWordCounts();
Map<Integer, Integer> topicCounts = modelParameters.getTopicCounts();
// initialize topic assignments of each word randomly and update the counters
for (Record r : trainingData) {
Integer documentId = r.getId();
documentWordCounts.put(documentId, r.getX().size());
for (Map.Entry<Object, Object> entry : r.getX().entrySet()) {
Object wordPosition = entry.getKey();
Object word = entry.getValue();
// sample a topic
Integer topic = PHPfunctions.mt_rand(0, k - 1);
increase(topicCounts, topic);
topicAssignmentOfDocumentWord.put(Arrays.asList(documentId, wordPosition), topic);
increase(documentTopicCounts, Arrays.asList(documentId, topic));
increase(topicWordCounts, Arrays.asList(topic, word));
}
}
double alpha = trainingParameters.getAlpha();
double beta = trainingParameters.getBeta();
int maxIterations = trainingParameters.getMaxIterations();
int iteration = 0;
while (iteration < maxIterations) {
if (GeneralConfiguration.DEBUG) {
System.out.println("Iteration " + iteration);
}
int changedCounter = 0;
// collapsed gibbs sampler
for (Record r : trainingData) {
Integer documentId = r.getId();
AssociativeArray topicAssignments = new AssociativeArray();
for (int j = 0; j < k; ++j) {
topicAssignments.put(j, 0.0);
}
int totalWords = r.getX().size();
for (Map.Entry<Object, Object> entry : r.getX().entrySet()) {
Object wordPosition = entry.getKey();
Object word = entry.getValue();
// remove the word from the dataset
Integer topic =
topicAssignmentOfDocumentWord.get(Arrays.asList(documentId, wordPosition));
// decrease(documentWordCounts, documentId); //slow
decrease(topicCounts, topic);
decrease(documentTopicCounts, Arrays.asList(documentId, topic));
decrease(topicWordCounts, Arrays.asList(topic, word));
// int numberOfDocumentWords = r.getX().size()-1; //fast - decreased by 1
// compute the posteriors of the topics and sample from it
AssociativeArray topicProbabilities = new AssociativeArray();
for (int j = 0; j < k; ++j) {
double enumerator = 0.0;
Integer njw = topicWordCounts.get(Arrays.asList(j, word));
if (njw != null) {
enumerator = njw + beta;
} else {
enumerator = beta;
}
Integer njd = documentTopicCounts.get(Arrays.asList(documentId, j));
if (njd != null) {
enumerator *= (njd + alpha);
} else {
enumerator *= alpha;
}
double denominator = topicCounts.get((Integer) j) + beta * d;
// denominator *= numberOfDocumentWords+alpha*k; //this is not necessary because it is
// the same for all categories, so it can be omited
topicProbabilities.put(j, enumerator / denominator);
}
// normalize probabilities
Descriptives.normalize(topicProbabilities);
// sample from these probabilieis
Integer newTopic =
(Integer)
SRS.weightedProbabilitySampling(topicProbabilities, 1, true).iterator().next();
topic = newTopic; // new topic assigment
// add back the word in the dataset
topicAssignmentOfDocumentWord.put(Arrays.asList(documentId, wordPosition), topic);
// increase(documentWordCounts, documentId); //slow
increase(topicCounts, topic);
increase(documentTopicCounts, Arrays.asList(documentId, topic));
increase(topicWordCounts, Arrays.asList(topic, word));
topicAssignments.put(
topic, Dataset.toDouble(topicAssignments.get(topic)) + 1.0 / totalWords);
}
Object mainTopic = MapFunctions.selectMaxKeyValue(topicAssignments).getKey();
if (!mainTopic.equals(r.getYPredicted())) {
++changedCounter;
}
r.setYPredicted(mainTopic);
r.setYPredictedProbabilities(topicAssignments);
}
++iteration;
if (GeneralConfiguration.DEBUG) {
System.out.println("Reassigned Records " + changedCounter);
}
if (changedCounter == 0) {
break;
}
}
modelParameters.setTotalIterations(iteration);
}
