public double similarity(String word, int k1, String word2, int k2) {
if (k1 > K || k2 > K) return -1;
if (word.equals(word2) && k1 == k2) return 1.0;
INDArray vector = Transforms.unitVec(getWordVectorMatrix(word, k1));
INDArray vector2 = Transforms.unitVec(getWordVectorMatrix(word2, k2));
if (vector == null || vector2 == null) return -1;
return Nd4j.getBlasWrapper().dot(vector, vector2);
}
@Test
public void testWordsNearestBasic1() throws Exception {
// WordVectors vec = WordVectorSerializer.loadTxtVectors(new
// File("/ext/Temp/Models/model.dat_trans"));
vec.setModelUtils(new BasicModelUtils<VocabWord>());
String target = "energy";
INDArray arr1 = vec.getWordVectorMatrix(target).dup();
System.out.println("[-]: " + arr1);
System.out.println("[+]: " + Transforms.unitVec(arr1));
Collection<String> list = vec.wordsNearest(target, 10);
log.info("Transpose model results:");
printWords(target, list, vec);
list = vec.wordsNearest(target, 10);
log.info("Transpose model results 2:");
printWords(target, list, vec);
list = vec.wordsNearest(target, 10);
log.info("Transpose model results 3:");
printWords(target, list, vec);
INDArray arr2 = vec.getWordVectorMatrix(target).dup();
assertEquals(arr1, arr2);
}
/**
* @return
* @throws NumberFormatException
* @throws IOException
* @throws FileNotFoundException
*/
private static Word2Vec readBinaryModel(File modelFile)
throws NumberFormatException, IOException {
InMemoryLookupTable lookupTable;
VocabCache cache;
INDArray syn0;
int words, size;
try (BufferedInputStream bis =
new BufferedInputStream(
GzipUtils.isCompressedFilename(modelFile.getName())
? new GZIPInputStream(new FileInputStream(modelFile))
: new FileInputStream(modelFile));
DataInputStream dis = new DataInputStream(bis)) {
words = Integer.parseInt(readString(dis));
size = Integer.parseInt(readString(dis));
syn0 = Nd4j.create(words, size);
cache = new InMemoryLookupCache(false);
lookupTable =
(InMemoryLookupTable)
new InMemoryLookupTable.Builder().cache(cache).vectorLength(size).build();
String word;
for (int i = 0; i < words; i++) {
word = readString(dis);
log.trace("Loading " + word + " with word " + i);
if (word.isEmpty()) {
continue;
}
float[] vector = new float[size];
for (int j = 0; j < size; j++) {
vector[j] = readFloat(dis);
}
syn0.putRow(i, Transforms.unitVec(Nd4j.create(vector)));
cache.addWordToIndex(cache.numWords(), word);
cache.addToken(new VocabWord(1, word));
cache.putVocabWord(word);
}
}
Word2Vec ret = new Word2Vec();
lookupTable.setSyn0(syn0);
ret.setVocab(cache);
ret.setLookupTable(lookupTable);
return ret;
}
/**
* 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();
}
/**
* @param modelFile
* @return
* @throws FileNotFoundException
* @throws IOException
* @throws NumberFormatException
*/
private static Word2Vec readTextModel(File modelFile) throws IOException, NumberFormatException {
InMemoryLookupTable lookupTable;
VocabCache cache;
INDArray syn0;
BufferedReader reader = new BufferedReader(new FileReader(modelFile));
String line = reader.readLine();
String[] initial = line.split(" ");
int words = Integer.parseInt(initial[0]);
int layerSize = Integer.parseInt(initial[1]);
syn0 = Nd4j.create(words, layerSize);
cache = new InMemoryLookupCache();
int currLine = 0;
while ((line = reader.readLine()) != null) {
String[] split = line.split(" ");
String word = split[0];
if (word.isEmpty()) {
continue;
}
float[] vector = new float[split.length - 1];
for (int i = 1; i < split.length; i++) {
vector[i - 1] = Float.parseFloat(split[i]);
}
syn0.putRow(currLine, Transforms.unitVec(Nd4j.create(vector)));
cache.addWordToIndex(cache.numWords(), word);
cache.addToken(new VocabWord(1, word));
cache.putVocabWord(word);
}
lookupTable =
(InMemoryLookupTable)
new InMemoryLookupTable.Builder().cache(cache).vectorLength(layerSize).build();
lookupTable.setSyn0(syn0);
Word2Vec ret = new Word2Vec();
ret.setVocab(cache);
ret.setLookupTable(lookupTable);
reader.close();
return ret;
}
/**
* 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();
}
public Collection<String> wordsNearest(String word, int k, int n) {
INDArray vector = Transforms.unitVec(getWordVectorMatrix(word, k));
INDArray similarity = vector.mmul(syn0.transpose());
List<Double> highToLowSimList = getTopN(similarity, n);
List<String> ret = new ArrayList();
for (int i = 1; i < highToLowSimList.size(); i++) {
word =
vocab.wordAtIndex(highToLowSimList.get(i).intValue() % vocab.numWords())
+ "("
+ highToLowSimList.get(i).intValue() / vocab.numWords()
+ ")";
if (word != null && !word.equals("UNK") && !word.equals("STOP")) {
ret.add(word);
if (ret.size() >= n) {
break;
}
}
}
return ret;
}
/**
* 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();
}
