public TopicScores getDistanceFromUniform() {
int[] tokensPerTopic = model.tokensPerTopic;
TopicScores scores = new TopicScores("uniform_dist", numTopics, numTopWords);
scores.wordScoresDefined = true;
int numTypes = alphabet.size();
for (int topic = 0; topic < numTopics; topic++) {
double topicScore = 0.0;
int position = 0;
TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);
for (IDSorter info : sortedWords) {
int type = info.getID();
double count = info.getWeight();
double score =
(count / tokensPerTopic[topic]) * Math.log((count * numTypes) / tokensPerTopic[topic]);
if (position < numTopWords) {
scores.setTopicWordScore(topic, position, score);
}
topicScore += score;
position++;
}
scores.setTopicScore(topic, topicScore);
}
return scores;
}
public TopicModelDiagnostics(ParallelTopicModel model, int numTopWords) {
numTopics = model.getNumTopics();
this.numTopWords = numTopWords;
this.model = model;
alphabet = model.getAlphabet();
topicSortedWords = model.getSortedWords();
topicTopWords = new String[numTopics][numTopWords];
numRank1Documents = new int[numTopics];
numNonZeroDocuments = new int[numTopics];
numDocumentsAtProportions = new int[numTopics][DEFAULT_DOC_PROPORTIONS.length];
sumCountTimesLogCount = new double[numTopics];
diagnostics = new ArrayList<TopicScores>();
for (int topic = 0; topic < numTopics; topic++) {
int position = 0;
TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);
// How many words should we report? Some topics may have fewer than
// the default number of words with non-zero weight.
int limit = numTopWords;
if (sortedWords.size() < numTopWords) {
limit = sortedWords.size();
}
Iterator<IDSorter> iterator = sortedWords.iterator();
for (int i = 0; i < limit; i++) {
IDSorter info = iterator.next();
topicTopWords[topic][i] = (String) alphabet.lookupObject(info.getID());
}
}
collectDocumentStatistics();
diagnostics.add(getTokensPerTopic(model.tokensPerTopic));
diagnostics.add(getDocumentEntropy(model.tokensPerTopic));
diagnostics.add(getWordLengthScores());
diagnostics.add(getCoherence());
diagnostics.add(getDistanceFromUniform());
diagnostics.add(getDistanceFromCorpus());
diagnostics.add(getEffectiveNumberOfWords());
diagnostics.add(getTokenDocumentDiscrepancies());
diagnostics.add(getRank1Percent());
diagnostics.add(getDocumentPercentRatio(FIFTY_PERCENT_INDEX, TWO_PERCENT_INDEX));
diagnostics.add(getDocumentPercent(5));
diagnostics.add(getExclusivity());
}
public TopicScores getEffectiveNumberOfWords() {
int[] tokensPerTopic = model.tokensPerTopic;
TopicScores scores = new TopicScores("eff_num_words", numTopics, numTopWords);
int numTypes = alphabet.size();
for (int topic = 0; topic < numTopics; topic++) {
double sumSquaredProbabilities = 0.0;
TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);
for (IDSorter info : sortedWords) {
int type = info.getID();
double probability = info.getWeight() / tokensPerTopic[topic];
sumSquaredProbabilities += probability * probability;
}
scores.setTopicScore(topic, 1.0 / sumSquaredProbabilities);
}
return scores;
}
public void collectDocumentStatistics() {
topicCodocumentMatrices = new int[numTopics][numTopWords][numTopWords];
wordTypeCounts = new int[alphabet.size()];
numTokens = 0;
// This is an array of hash sets containing the words-of-interest for each topic,
// used for checking if the word at some position is one of those words.
IntHashSet[] topicTopWordIndices = new IntHashSet[numTopics];
// The same as the topic top words, but with int indices instead of strings,
// used for iterating over positions.
int[][] topicWordIndicesInOrder = new int[numTopics][numTopWords];
// This is an array of hash sets that will hold the words-of-interest present in a document,
// which will be cleared after every document.
IntHashSet[] docTopicWordIndices = new IntHashSet[numTopics];
int numDocs = model.getData().size();
// The count of each topic, again cleared after every document.
int[] topicCounts = new int[numTopics];
for (int topic = 0; topic < numTopics; topic++) {
IntHashSet wordIndices = new IntHashSet();
for (int i = 0; i < numTopWords; i++) {
if (topicTopWords[topic][i] != null) {
int type = alphabet.lookupIndex(topicTopWords[topic][i]);
topicWordIndicesInOrder[topic][i] = type;
wordIndices.add(type);
}
}
topicTopWordIndices[topic] = wordIndices;
docTopicWordIndices[topic] = new IntHashSet();
}
int doc = 0;
for (TopicAssignment document : model.getData()) {
FeatureSequence tokens = (FeatureSequence) document.instance.getData();
FeatureSequence topics = (FeatureSequence) document.topicSequence;
for (int position = 0; position < tokens.size(); position++) {
int type = tokens.getIndexAtPosition(position);
int topic = topics.getIndexAtPosition(position);
numTokens++;
wordTypeCounts[type]++;
topicCounts[topic]++;
if (topicTopWordIndices[topic].contains(type)) {
docTopicWordIndices[topic].add(type);
}
}
int docLength = tokens.size();
if (docLength > 0) {
int maxTopic = -1;
int maxCount = -1;
for (int topic = 0; topic < numTopics; topic++) {
if (topicCounts[topic] > 0) {
numNonZeroDocuments[topic]++;
if (topicCounts[topic] > maxCount) {
maxTopic = topic;
maxCount = topicCounts[topic];
}
sumCountTimesLogCount[topic] += topicCounts[topic] * Math.log(topicCounts[topic]);
double proportion =
(model.alpha[topic] + topicCounts[topic]) / (model.alphaSum + docLength);
for (int i = 0; i < DEFAULT_DOC_PROPORTIONS.length; i++) {
if (proportion < DEFAULT_DOC_PROPORTIONS[i]) {
break;
}
numDocumentsAtProportions[topic][i]++;
}
IntHashSet supportedWords = docTopicWordIndices[topic];
int[] indices = topicWordIndicesInOrder[topic];
for (int i = 0; i < numTopWords; i++) {
if (supportedWords.contains(indices[i])) {
for (int j = i; j < numTopWords; j++) {
if (i == j) {
// Diagonals are total number of documents with word W in topic T
topicCodocumentMatrices[topic][i][i]++;
} else if (supportedWords.contains(indices[j])) {
topicCodocumentMatrices[topic][i][j]++;
topicCodocumentMatrices[topic][j][i]++;
}
}
}
}
docTopicWordIndices[topic].clear();
topicCounts[topic] = 0;
}
}
if (maxTopic > -1) {
numRank1Documents[maxTopic]++;
}
}
doc++;
}
}