/**
* Writes the word vectors to the given path. Note that this assumes an in memory cache
*
* @param lookupTable
* @param cache
* @param path the path to write
* @throws IOException
*/
public static void writeWordVectors(
InMemoryLookupTable lookupTable, InMemoryLookupCache cache, String path) throws IOException {
BufferedWriter write = new BufferedWriter(new FileWriter(new File(path), false));
for (int i = 0; i < lookupTable.getSyn0().rows(); i++) {
String word = cache.wordAtIndex(i);
if (word == null) {
continue;
}
StringBuilder sb = new StringBuilder();
sb.append(word.replaceAll(" ", "_"));
sb.append(" ");
INDArray wordVector = lookupTable.vector(word);
for (int j = 0; j < wordVector.length(); j++) {
sb.append(wordVector.getDouble(j));
if (j < wordVector.length() - 1) {
sb.append(" ");
}
}
sb.append("\n");
write.write(sb.toString());
}
write.flush();
write.close();
}
/**
* Words nearest based on positive and negative words
*
* @param positive the positive words
* @param negative the negative words
* @param top the top n words
* @return the words nearest the mean of the words
*/
public Collection<String> wordsNearestSum(
Collection<String> positive, Collection<String> negative, int top) {
INDArray words = Nd4j.create(lookupTable().layerSize());
Set<String> union = SetUtils.union(new HashSet<>(positive), new HashSet<>(negative));
for (String s : positive) words.addi(lookupTable().vector(s));
for (String s : negative) words.addi(lookupTable.vector(s).mul(-1));
if (lookupTable() instanceof InMemoryLookupTable) {
InMemoryLookupTable l = (InMemoryLookupTable) lookupTable();
INDArray syn0 = l.getSyn0();
INDArray weights = syn0.norm2(0).rdivi(1).muli(words);
INDArray distances = syn0.mulRowVector(weights).sum(1);
INDArray[] sorted = Nd4j.sortWithIndices(distances, 0, false);
INDArray sort = sorted[0];
List<String> ret = new ArrayList<>();
if (top > sort.length()) top = sort.length();
// there will be a redundant word
int end = top;
for (int i = 0; i < end; i++) {
String word = vocab.wordAtIndex(sort.getInt(i));
if (union.contains(word)) {
end++;
if (end >= sort.length()) break;
continue;
}
String add = vocab().wordAtIndex(sort.getInt(i));
if (add == null || add.equals("UNK") || add.equals("STOP")) {
end++;
if (end >= sort.length()) break;
continue;
}
ret.add(vocab().wordAtIndex(sort.getInt(i)));
}
return ret;
}
Counter<String> distances = new Counter<>();
for (String s : vocab().words()) {
INDArray otherVec = getWordVectorMatrix(s);
double sim = Transforms.cosineSim(words, otherVec);
distances.incrementCount(s, sim);
}
distances.keepTopNKeys(top);
return distances.keySet();
}
/**
* Words nearest based on positive and negative words * @param top the top n words
*
* @return the words nearest the mean of the words
*/
@Override
public Collection<String> wordsNearest(INDArray words, int top) {
if (lookupTable instanceof InMemoryLookupTable) {
InMemoryLookupTable l = (InMemoryLookupTable) lookupTable;
INDArray syn0 = l.getSyn0();
if (!normalized) {
synchronized (this) {
if (!normalized) {
syn0.diviColumnVector(syn0.norm1(1));
normalized = true;
}
}
}
INDArray similarity = Transforms.unitVec(words).mmul(syn0.transpose());
List<Double> highToLowSimList = getTopN(similarity, top + 20);
List<WordSimilarity> result = new ArrayList<>();
for (int i = 0; i < highToLowSimList.size(); i++) {
String word = vocabCache.wordAtIndex(highToLowSimList.get(i).intValue());
if (word != null && !word.equals("UNK") && !word.equals("STOP")) {
INDArray otherVec = lookupTable.vector(word);
double sim = Transforms.cosineSim(words, otherVec);
result.add(new WordSimilarity(word, sim));
}
}
Collections.sort(result, new SimilarityComparator());
return getLabels(result, top);
}
Counter<String> distances = new Counter<>();
for (String s : vocabCache.words()) {
INDArray otherVec = lookupTable.vector(s);
double sim = Transforms.cosineSim(words, otherVec);
distances.incrementCount(s, sim);
}
distances.keepTopNKeys(top);
return distances.keySet();
}
/**
* Get the top n words most similar to the given word
*
* @param word the word to compare
* @param n the n to get
* @return the top n words
*/
public Collection<String> wordsNearestSum(String word, int n) {
INDArray vec = Transforms.unitVec(this.getWordVectorMatrix(word));
if (lookupTable() instanceof InMemoryLookupTable) {
InMemoryLookupTable l = (InMemoryLookupTable) lookupTable();
INDArray syn0 = l.getSyn0();
INDArray weights = syn0.norm2(0).rdivi(1).muli(vec);
INDArray distances = syn0.mulRowVector(weights).sum(1);
INDArray[] sorted = Nd4j.sortWithIndices(distances, 0, false);
INDArray sort = sorted[0];
List<String> ret = new ArrayList<>();
SequenceElement word2 = vocab().wordFor(word);
if (n > sort.length()) n = sort.length();
// there will be a redundant word
for (int i = 0; i < n + 1; i++) {
if (sort.getInt(i) == word2.getIndex()) continue;
String add = vocab().wordAtIndex(sort.getInt(i));
if (add == null || add.equals("UNK") || add.equals("STOP")) {
continue;
}
ret.add(vocab().wordAtIndex(sort.getInt(i)));
}
return ret;
}
if (vec == null) return new ArrayList<>();
Counter<String> distances = new Counter<>();
for (String s : vocab().words()) {
if (s.equals(word)) continue;
INDArray otherVec = getWordVectorMatrix(s);
double sim = Transforms.cosineSim(vec, otherVec);
distances.incrementCount(s, sim);
}
distances.keepTopNKeys(n);
return distances.keySet();
}
/**
* Words nearest based on positive and negative words * @param top the top n words
*
* @return the words nearest the mean of the words
*/
@Override
public Collection<String> wordsNearest(INDArray words, int top) {
if (lookupTable() instanceof InMemoryLookupTable) {
InMemoryLookupTable l = (InMemoryLookupTable) lookupTable();
INDArray syn0 = l.getSyn0();
INDArray weights = syn0.norm2(0).rdivi(1).muli(words);
INDArray distances = syn0.mulRowVector(weights).mean(1);
INDArray[] sorted = Nd4j.sortWithIndices(distances, 0, false);
INDArray sort = sorted[0];
List<String> ret = new ArrayList<>();
if (top > sort.length()) top = sort.length();
// there will be a redundant word
int end = top;
for (int i = 0; i < end; i++) {
VocabCache vocabCache = vocab();
int s = sort.getInt(0, i);
String add = vocabCache.wordAtIndex(s);
if (add == null || add.equals("UNK") || add.equals("STOP")) {
end++;
if (end >= sort.length()) break;
continue;
}
ret.add(vocabCache.wordAtIndex(s));
}
return ret;
}
Counter<String> distances = new Counter<>();
for (String s : vocab().words()) {
INDArray otherVec = getWordVectorMatrix(s);
double sim = Transforms.cosineSim(words, otherVec);
distances.incrementCount(s, sim);
}
distances.keepTopNKeys(top);
return distances.keySet();
}
/**
* Words nearest based on positive and negative words
*
* @param positive the positive words
* @param negative the negative words
* @param top the top n words
* @return the words nearest the mean of the words
*/
@Override
public Collection<String> wordsNearest(
Collection<String> positive, Collection<String> negative, int top) {
// Check every word is in the model
for (String p : SetUtils.union(new HashSet<>(positive), new HashSet<>(negative))) {
if (!vocab().containsWord(p)) {
return new ArrayList<>();
}
}
WeightLookupTable weightLookupTable = lookupTable();
INDArray words = Nd4j.create(positive.size() + negative.size(), weightLookupTable.layerSize());
int row = 0;
Set<String> union = SetUtils.union(new HashSet<>(positive), new HashSet<>(negative));
for (String s : positive) {
words.putRow(row++, weightLookupTable.vector(s));
}
for (String s : negative) {
words.putRow(row++, weightLookupTable.vector(s).mul(-1));
}
INDArray mean = words.isMatrix() ? words.mean(0) : words;
// TODO this should probably be replaced with wordsNearest(mean, top)
if (weightLookupTable instanceof InMemoryLookupTable) {
InMemoryLookupTable l = (InMemoryLookupTable) weightLookupTable;
INDArray syn0 = l.getSyn0();
syn0.diviRowVector(syn0.norm2(0));
INDArray similarity = Transforms.unitVec(mean).mmul(syn0.transpose());
// We assume that syn0 is normalized.
// Hence, the following division is not needed anymore.
// distances.diviRowVector(distances.norm2(1));
// INDArray[] sorted = Nd4j.sortWithIndices(distances,0,false);
List<Double> highToLowSimList = getTopN(similarity, top + union.size());
List<String> ret = new ArrayList<>();
for (int i = 0; i < highToLowSimList.size(); i++) {
String word = vocab().wordAtIndex(highToLowSimList.get(i).intValue());
if (word != null && !word.equals("UNK") && !word.equals("STOP") && !union.contains(word)) {
ret.add(word);
if (ret.size() >= top) {
break;
}
}
}
return ret;
}
Counter<String> distances = new Counter<>();
for (String s : vocab().words()) {
INDArray otherVec = getWordVectorMatrix(s);
double sim = Transforms.cosineSim(mean, otherVec);
distances.incrementCount(s, sim);
}
distances.keepTopNKeys(top);
return distances.keySet();
}
