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package org.deeplearning4j.models.paragraphvectors;
import com.google.common.collect.Lists;
import lombok.Getter;
import lombok.NonNull;
import lombok.Setter;
import org.deeplearning4j.models.embeddings.WeightLookupTable;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.deeplearning4j.models.embeddings.learning.ElementsLearningAlgorithm;
import org.deeplearning4j.models.embeddings.learning.SequenceLearningAlgorithm;
import org.deeplearning4j.models.embeddings.learning.impl.sequence.DM;
import org.deeplearning4j.models.embeddings.loader.VectorsConfiguration;
import org.deeplearning4j.models.embeddings.reader.ModelUtils;
import org.deeplearning4j.models.embeddings.reader.impl.BasicModelUtils;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.models.sequencevectors.interfaces.SequenceIterator;
import org.deeplearning4j.models.sequencevectors.interfaces.VectorsListener;
import org.deeplearning4j.models.sequencevectors.iterators.AbstractSequenceIterator;
import org.deeplearning4j.models.sequencevectors.sequence.Sequence;
import org.deeplearning4j.models.sequencevectors.transformers.impl.SentenceTransformer;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.Word2Vec;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.text.documentiterator.*;
import org.deeplearning4j.text.documentiterator.interoperability.DocumentIteratorConverter;
import org.deeplearning4j.text.invertedindex.InvertedIndex;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.sentenceiterator.interoperability.SentenceIteratorConverter;
import org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareSentenceIterator;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.deeplearning4j.util.ThreadUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.linalg.primitives.Counter;
import org.nd4j.linalg.primitives.Pair;
import org.threadly.concurrent.PriorityScheduler;
import org.threadly.concurrent.TaskPriority;
import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicLong;
/**
* Basic ParagraphVectors (aka Doc2Vec) implementation for DL4j, as wrapper over SequenceVectors
*
* @author [email protected]
*/
public class ParagraphVectors extends Word2Vec {
private static final long serialVersionUID = 78249242142L;
@Getter
protected LabelsSource labelsSource;
@Getter
@Setter
protected transient LabelAwareIterator labelAwareIterator;
protected INDArray labelsMatrix;
protected List labelsList = new ArrayList<>();
protected boolean normalizedLabels = false;
protected transient final Object inferenceLocker = new Object();
protected transient PriorityScheduler inferenceExecutor;
protected transient AtomicLong countSubmitted;
protected transient AtomicLong countFinished;
protected ParagraphVectors() {
super();
}
protected synchronized void initInference() {
if (countSubmitted == null || countFinished == null || inferenceExecutor == null) {
inferenceExecutor = new PriorityScheduler(
Math.max(Runtime.getRuntime().availableProcessors() - 2, 2),
TaskPriority.High, 1000,
new ThreadFactory() {
public Thread newThread(Runnable r) {
Thread t = Executors.defaultThreadFactory().newThread(r);
t.setName("ParagraphVectors inference thread");
t.setDaemon(true);
return t;
}
});
countSubmitted = new AtomicLong(0);
countFinished = new AtomicLong(0);
}
}
/**
* This method takes raw text, applies tokenizer, and returns most probable label
*
* @param rawText
* @return
*/
@Deprecated
public String predict(String rawText) {
if (tokenizerFactory == null)
throw new IllegalStateException("TokenizerFactory should be defined, prior to predict() call");
List tokens = tokenizerFactory.create(rawText).getTokens();
List document = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
document.add(vocab.wordFor(token));
}
}
return predict(document);
}
/**
* This method defines SequenceIterator instance, that will be used as training corpus source.
* Main difference with other iterators here: it allows you to pass already tokenized Sequence for training
*
* @param iterator
*/
public void setSequenceIterator(@NonNull SequenceIterator iterator) {
this.iterator = iterator;
}
/**
* This method predicts label of the document.
* Computes a similarity wrt the mean of the
* representation of words in the document
* @param document the document
* @return the word distances for each label
*/
public String predict(LabelledDocument document) {
if (document.getReferencedContent() != null)
return predict(document.getReferencedContent());
else
return predict(document.getContent());
}
public void extractLabels() {
Collection vocabWordCollection = vocab.vocabWords();
List vocabWordList = new ArrayList<>();
int[] indexArray;
//INDArray pulledArray;
//Check if word has label and build a list out of the collection
for (VocabWord vWord : vocabWordCollection) {
if (vWord.isLabel()) {
vocabWordList.add(vWord);
}
}
//Build array of indexes in the order of the vocablist
indexArray = new int[vocabWordList.size()];
int i = 0;
for (VocabWord vWord : vocabWordList) {
indexArray[i] = vWord.getIndex();
i++;
}
//pull the label rows and create new matrix
if (i > 0) {
labelsMatrix = Nd4j.pullRows(lookupTable.getWeights(), 1, indexArray);
labelsList = vocabWordList;
}
}
/**
* This method calculates inferred vector for given text
*
* @param text
* @return
*/
public INDArray inferVector(String text, double learningRate, double minLearningRate, int iterations) {
if (tokenizerFactory == null)
throw new IllegalStateException("TokenizerFactory should be defined, prior to predict() call");
if (this.vocab == null || this.vocab.numWords() == 0)
reassignExistingModel();
List tokens = tokenizerFactory.create(text).getTokens();
List document = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
document.add(vocab.wordFor(token));
}
}
if (document.isEmpty())
throw new ND4JIllegalStateException("Text passed for inference has no matches in model vocabulary.");
return inferVector(document, learningRate, minLearningRate, iterations);
}
@SuppressWarnings("unchecked")
protected synchronized void reassignExistingModel() {
if ((this.vocab == null || this.vocab.numWords() == 0) && existingModel != null) {
this.vocab = existingModel.vocab();
this.lookupTable = existingModel.lookupTable();
}
}
/**
* This method calculates inferred vector for given document
*
* @param document
* @return
*/
public INDArray inferVector(LabelledDocument document, double learningRate, double minLearningRate,
int iterations) {
if (document.getReferencedContent() != null && !document.getReferencedContent().isEmpty()) {
return inferVector(document.getReferencedContent(), learningRate, minLearningRate, iterations);
} else
return inferVector(document.getContent(), learningRate, minLearningRate, iterations);
}
/**
* This method calculates inferred vector for given document
*
* @param document
* @return
*/
public INDArray inferVector(@NonNull List document, double learningRate, double minLearningRate,
int iterations) {
if (this.vocab == null || this.vocab.numWords() == 0)
reassignExistingModel();
SequenceLearningAlgorithm learner = sequenceLearningAlgorithm;
if (learner == null) {
synchronized (this) {
if (sequenceLearningAlgorithm == null) {
log.info("Creating new PV-DM learner...");
learner = new DM<>();
learner.configure(vocab, lookupTable, configuration);
sequenceLearningAlgorithm = learner;
} else {
learner = sequenceLearningAlgorithm;
}
}
}
learner = sequenceLearningAlgorithm;
if (document.isEmpty())
throw new ND4JIllegalStateException("Impossible to apply inference to empty list of words");
Sequence sequence = new Sequence<>();
sequence.addElements(document);
sequence.setSequenceLabel(new VocabWord(1.0, String.valueOf(new Random().nextInt())));
initLearners();
INDArray inf = learner.inferSequence(sequence, seed, learningRate, minLearningRate, iterations);
return inf;
}
/**
* This method calculates inferred vector for given text, with default parameters for learning rate and iterations
*
* @param text
* @return
*/
public INDArray inferVector(String text) {
return inferVector(text, this.learningRate.get(), this.minLearningRate, this.numEpochs * this.numIterations);
}
/**
* This method calculates inferred vector for given document, with default parameters for learning rate and iterations
*
* @param document
* @return
*/
public INDArray inferVector(LabelledDocument document) {
return inferVector(document, this.learningRate.get(), this.minLearningRate,
this.numEpochs * this.numIterations);
}
/**
* This method calculates inferred vector for given list of words, with default parameters for learning rate and iterations
*
* @param document
* @return
*/
public INDArray inferVector(@NonNull List document) {
return inferVector(document, this.learningRate.get(), this.minLearningRate,
this.numEpochs * this.numIterations);
}
/**
* This method implements batched inference, based on Java Future parallelism model.
*
* PLEASE NOTE: In order to use this method, LabelledDocument being passed in should have Id field defined.
*
* @param document
* @return
*/
public Future> inferVectorBatched(@NonNull LabelledDocument document) {
if (countSubmitted == null)
initInference();
if (this.vocab == null || this.vocab.numWords() == 0)
reassignExistingModel();
// we block execution until queued amount of documents gets below acceptable level, to avoid memory exhaust
while (countSubmitted.get() - countFinished.get() > 1024) {
ThreadUtils.uncheckedSleep(50);
}
InferenceCallable callable = new InferenceCallable(vocab, tokenizerFactory, document);
Future> future = inferenceExecutor.submit(callable);
countSubmitted.incrementAndGet();
return future;
}
/**
* This method implements batched inference, based on Java Future parallelism model.
*
* PLEASE NOTE: This method will return you Future<INDArray>, so tracking relation between document and INDArray will be your responsibility
*
* @param document
* @return
*/
public Future inferVectorBatched(@NonNull String document) {
if (countSubmitted == null)
initInference();
if (this.vocab == null || this.vocab.numWords() == 0)
reassignExistingModel();
// we block execution until queued amount of documents gets below acceptable level, to avoid memory exhaust
while (countSubmitted.get() - countFinished.get() > 1024) {
ThreadUtils.uncheckedSleep(50);
}
BlindInferenceCallable callable = new BlindInferenceCallable(vocab, tokenizerFactory, document);
Future future = inferenceExecutor.submit(callable);
countSubmitted.incrementAndGet();
return future;
}
/**
* This method does inference on a given List<String>
* @param documents
* @return INDArrays in the same order as input texts
*/
public List inferVectorBatched(@NonNull List documents) {
if (countSubmitted == null)
initInference();
if (this.vocab == null || this.vocab.numWords() == 0)
reassignExistingModel();
List> futuresList = new ArrayList<>();
List results = new ArrayList<>();
final AtomicLong flag = new AtomicLong(0);
for (int i = 0; i < documents.size(); i++) {
BlindInferenceCallable callable =
new BlindInferenceCallable(vocab, tokenizerFactory, documents.get(i), flag);
futuresList.add(inferenceExecutor.submit(callable));
}
for (int i = 0; i < documents.size(); i++) {
Future future = futuresList.get(i);
try {
results.add(future.get());
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException(e);
} catch (ExecutionException e) {
throw new RuntimeException(e);
}
}
return results;
}
/**
* This method predicts label of the document.
* Computes a similarity wrt the mean of the
* representation of words in the document
* @param document the document
* @return the word distances for each label
*/
public String predict(List document) {
/*
This code was transferred from original ParagraphVectors DL4j implementation, and yet to be tested
*/
if (document.isEmpty())
throw new IllegalStateException("Document has no words inside");
/*
INDArray arr = Nd4j.create(document.size(), this.layerSize);
for (int i = 0; i < document.size(); i++) {
arr.putRow(i, getWordVectorMatrix(document.get(i).getWord()));
}*/
INDArray docMean = inferVector(document); //arr.mean(0);
Counter distances = new Counter<>();
for (String s : labelsSource.getLabels()) {
INDArray otherVec = getWordVectorMatrix(s);
double sim = Transforms.cosineSim(docMean, otherVec);
distances.incrementCount(s, (float) sim);
}
return distances.argMax();
}
/**
* Predict several labels based on the document.
* Computes a similarity wrt the mean of the
* representation of words in the document
* @param document raw text of the document
* @return possible labels in descending order
*/
public Collection predictSeveral(@NonNull LabelledDocument document, int limit) {
if (document.getReferencedContent() != null) {
return predictSeveral(document.getReferencedContent(), limit);
} else
return predictSeveral(document.getContent(), limit);
}
/**
* Predict several labels based on the document.
* Computes a similarity wrt the mean of the
* representation of words in the document
* @param rawText raw text of the document
* @return possible labels in descending order
*/
public Collection predictSeveral(String rawText, int limit) {
if (tokenizerFactory == null)
throw new IllegalStateException("TokenizerFactory should be defined, prior to predict() call");
List tokens = tokenizerFactory.create(rawText).getTokens();
List document = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
document.add(vocab.wordFor(token));
}
}
return predictSeveral(document, limit);
}
/**
* Predict several labels based on the document.
* Computes a similarity wrt the mean of the
* representation of words in the document
* @param document the document
* @return possible labels in descending order
*/
public Collection predictSeveral(List document, int limit) {
/*
This code was transferred from original ParagraphVectors DL4j implementation, and yet to be tested
*/
if (document.isEmpty())
throw new IllegalStateException("Document has no words inside");
/*
INDArray arr = Nd4j.create(document.size(), this.layerSize);
for (int i = 0; i < document.size(); i++) {
arr.putRow(i, getWordVectorMatrix(document.get(i).getWord()));
}
*/
INDArray docMean = inferVector(document); //arr.mean(0);
Counter distances = new Counter<>();
for (String s : labelsSource.getLabels()) {
INDArray otherVec = getWordVectorMatrix(s);
double sim = Transforms.cosineSim(docMean, otherVec);
log.debug("Similarity inside: [" + s + "] -> " + sim);
distances.incrementCount(s, (float) sim);
}
return distances.keySetSorted().subList(0, limit);
}
/**
* This method returns top N labels nearest to specified document
*
* @param document
* @param topN
* @return
*/
public Collection nearestLabels(LabelledDocument document, int topN) {
if (document.getReferencedContent() != null) {
return nearestLabels(document.getReferencedContent(), topN);
} else
return nearestLabels(document.getContent(), topN);
}
/**
* This method returns top N labels nearest to specified text
*
* @param rawText
* @param topN
* @return
*/
public Collection nearestLabels(@NonNull String rawText, int topN) {
List tokens = tokenizerFactory.create(rawText).getTokens();
List document = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
document.add(vocab.wordFor(token));
}
}
// we're returning empty collection for empty document
if (document.isEmpty()) {
log.info("Document passed to nearestLabels() has no matches in model vocabulary");
return new ArrayList<>();
}
return nearestLabels(document, topN);
}
/**
* This method returns top N labels nearest to specified set of vocab words
*
* @param document
* @param topN
* @return
*/
public Collection nearestLabels(@NonNull Collection document, int topN) {
if (document.isEmpty())
throw new ND4JIllegalStateException("Impossible to get nearestLabels for empty list of words");
INDArray vector = inferVector(new ArrayList(document));
return nearestLabels(vector, topN);
}
/**
* This method returns top N labels nearest to specified features vector
*
* @param labelVector
* @param topN
* @return
*/
public Collection nearestLabels(INDArray labelVector, int topN) {
if (labelsMatrix == null || labelsList == null || labelsList.isEmpty())
extractLabels();
List result = new ArrayList<>();
// if list still empty - return empty collection
if (labelsMatrix == null || labelsList == null || labelsList.isEmpty()) {
log.warn("Labels list is empty!");
return new ArrayList<>();
}
if (!normalizedLabels) {
synchronized (this) {
if (!normalizedLabels) {
labelsMatrix.diviColumnVector(labelsMatrix.norm1(1));
normalizedLabels = true;
}
}
}
INDArray similarity = Transforms.unitVec(labelVector).mmul(labelsMatrix.transpose());
List highToLowSimList = getTopN(similarity, topN + 20);
for (int i = 0; i < highToLowSimList.size(); i++) {
String word = labelsList.get(highToLowSimList.get(i).intValue()).getLabel();
if (word != null && !word.equals("UNK") && !word.equals("STOP")) {
INDArray otherVec = lookupTable.vector(word);
double sim = Transforms.cosineSim(labelVector, otherVec);
result.add(new BasicModelUtils.WordSimilarity(word, sim));
}
}
Collections.sort(result, new BasicModelUtils.SimilarityComparator());
return BasicModelUtils.getLabels(result, topN);
}
/**
* Get top N elements
*
* @param vec the vec to extract the top elements from
* @param N the number of elements to extract
* @return the indices and the sorted top N elements
*/
private List getTopN(INDArray vec, int N) {
BasicModelUtils.ArrayComparator comparator = new BasicModelUtils.ArrayComparator();
PriorityQueue queue = new PriorityQueue<>(vec.rows(), comparator);
for (int j = 0; j < vec.length(); j++) {
final Double[] pair = new Double[] {vec.getDouble(j), (double) j};
if (queue.size() < N) {
queue.add(pair);
} else {
Double[] head = queue.peek();
if (comparator.compare(pair, head) > 0) {
queue.poll();
queue.add(pair);
}
}
}
List lowToHighSimLst = new ArrayList<>();
while (!queue.isEmpty()) {
double ind = queue.poll()[1];
lowToHighSimLst.add(ind);
}
return Lists.reverse(lowToHighSimLst);
}
/**
* This method returns similarity of the document to specific label, based on mean value
*
* @param rawText
* @param label
* @return
*/
@Deprecated
public double similarityToLabel(String rawText, String label) {
if (tokenizerFactory == null)
throw new IllegalStateException("TokenizerFactory should be defined, prior to predict() call");
List tokens = tokenizerFactory.create(rawText).getTokens();
List document = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
document.add(vocab.wordFor(token));
}
}
return similarityToLabel(document, label);
}
@Override
public void fit() {
super.fit();
extractLabels();
}
/**
* This method returns similarity of the document to specific label, based on mean value
*
* @param document
* @param label
* @return
*/
public double similarityToLabel(LabelledDocument document, String label) {
if (document.getReferencedContent() != null) {
return similarityToLabel(document.getReferencedContent(), label);
} else
return similarityToLabel(document.getContent(), label);
}
/**
* This method returns similarity of the document to specific label, based on mean value
*
* @param document
* @param label
* @return
*/
public double similarityToLabel(List document, String label) {
if (document.isEmpty())
throw new IllegalStateException("Document has no words inside");
/*
INDArray arr = Nd4j.create(document.size(), this.layerSize);
for (int i = 0; i < document.size(); i++) {
arr.putRow(i, getWordVectorMatrix(document.get(i).getWord()));
}*/
INDArray docMean = inferVector(document); //arr.mean(0);
INDArray otherVec = getWordVectorMatrix(label);
double sim = Transforms.cosineSim(docMean, otherVec);
return sim;
}
public static class Builder extends Word2Vec.Builder {
protected LabelAwareIterator labelAwareIterator;
protected LabelsSource labelsSource;
protected DocumentIterator docIter;
/**
* This method allows you to use pre-built WordVectors model (Word2Vec or GloVe) for ParagraphVectors.
* Existing model will be transferred into new model before training starts.
*
* PLEASE NOTE: Non-normalized model is recommended to use here.
*
* @param vec existing WordVectors model
* @return
*/
@Override
@SuppressWarnings("unchecked")
public Builder useExistingWordVectors(@NonNull WordVectors vec) {
if (((InMemoryLookupTable) vec.lookupTable()).getSyn1() == null
&& ((InMemoryLookupTable) vec.lookupTable()).getSyn1Neg() == null)
throw new ND4JIllegalStateException("Model being passed as existing has no syn1/syn1Neg available");
this.existingVectors = vec;
return this;
}
/**
* This method defines, if words representations should be build together with documents representations.
*
* @param trainElements
* @return
*/
public Builder trainWordVectors(boolean trainElements) {
this.trainElementsRepresentation(trainElements);
return this;
}
/**
* This method attaches pre-defined labels source to ParagraphVectors
*
* @param source
* @return
*/
public Builder labelsSource(@NonNull LabelsSource source) {
this.labelsSource = source;
return this;
}
/**
* This method builds new LabelSource instance from labels.
*
* PLEASE NOTE: Order synchro between labels and input documents delegated to end-user.
* PLEASE NOTE: Due to order issues it's recommended to use label aware iterators instead.
*
* @param labels
* @return
*/
@Deprecated
public Builder labels(@NonNull List labels) {
this.labelsSource = new LabelsSource(labels);
return this;
}
/**
* This method used to feed LabelAwareDocumentIterator, that contains training corpus, into ParagraphVectors
*
* @param iterator
* @return
*/
public Builder iterate(@NonNull LabelAwareDocumentIterator iterator) {
this.docIter = iterator;
return this;
}
/**
* This method used to feed LabelAwareSentenceIterator, that contains training corpus, into ParagraphVectors
*
* @param iterator
* @return
*/
public Builder iterate(@NonNull LabelAwareSentenceIterator iterator) {
this.sentenceIterator = iterator;
return this;
}
/**
* This method used to feed LabelAwareIterator, that contains training corpus, into ParagraphVectors
*
* @param iterator
* @return
*/
public Builder iterate(@NonNull LabelAwareIterator iterator) {
this.labelAwareIterator = iterator;
return this;
}
/**
* This method used to feed DocumentIterator, that contains training corpus, into ParagraphVectors
*
* @param iterator
* @return
*/
@Override
public Builder iterate(@NonNull DocumentIterator iterator) {
this.docIter = iterator;
return this;
}
/**
* This method used to feed SentenceIterator, that contains training corpus, into ParagraphVectors
*
* @param iterator
* @return
*/
@Override
public Builder iterate(@NonNull SentenceIterator iterator) {
this.sentenceIterator = iterator;
return this;
}
/**
* Sets ModelUtils that gonna be used as provider for utility methods: similarity(), wordsNearest(), accuracy(), etc
*
* @param modelUtils model utils to be used
* @return
*/
@Override
public Builder modelUtils(@NonNull ModelUtils modelUtils) {
super.modelUtils(modelUtils);
return this;
}
/**
* This method sets vocabulary limit during construction.
*
* Default value: 0. Means no limit
*
* @param limit
* @return
*/
@Override
public Builder limitVocabularySize(int limit) {
super.limitVocabularySize(limit);
return this;
}
/**
* This method allows you to specify SequenceElement that will be used as UNK element, if UNK is used
*
* @param element
* @return
*/
@Override
public Builder unknownElement(VocabWord element) {
super.unknownElement(element);
return this;
}
/**
* This method enables/disables parallel tokenization.
*
* Default value: TRUE
* @param allow
* @return
*/
@Override
public Builder allowParallelTokenization(boolean allow) {
super.allowParallelTokenization(allow);
return this;
}
/**
* This method allows you to specify, if UNK word should be used internally
*
* @param reallyUse
* @return
*/
@Override
public Builder useUnknown(boolean reallyUse) {
super.useUnknown(reallyUse);
if (this.unknownElement == null) {
this.unknownElement(new VocabWord(1.0, ParagraphVectors.DEFAULT_UNK));
}
return this;
}
/**
* This method ebables/disables periodical vocab truncation during construction
*
* Default value: disabled
*
* @param reallyEnable
* @return
*/
@Override
public Builder enableScavenger(boolean reallyEnable) {
super.enableScavenger(reallyEnable);
return this;
}
@Override
public ParagraphVectors build() {
presetTables();
ParagraphVectors ret = new ParagraphVectors();
if (this.existingVectors != null) {
trainWordVectors(false);
trainElementsRepresentation(false);
this.elementsLearningAlgorithm = null;
// this.lookupTable = this.existingVectors.lookupTable();
// this.vocabCache = this.existingVectors.vocab();
}
if (this.labelsSource == null)
this.labelsSource = new LabelsSource();
if (docIter != null) {
/*
we're going to work with DocumentIterator.
First, we have to assume that user can provide LabelAwareIterator. In this case we'll use them, as provided source, and collec labels provided there
Otherwise we'll go for own labels via LabelsSource
*/
if (docIter instanceof LabelAwareDocumentIterator)
this.labelAwareIterator =
new DocumentIteratorConverter((LabelAwareDocumentIterator) docIter, labelsSource);
else
this.labelAwareIterator = new DocumentIteratorConverter(docIter, labelsSource);
} else if (sentenceIterator != null) {
// we have SentenceIterator. Mechanics will be the same, as above
if (sentenceIterator instanceof LabelAwareSentenceIterator)
this.labelAwareIterator = new SentenceIteratorConverter(
(LabelAwareSentenceIterator) sentenceIterator, labelsSource);
else
this.labelAwareIterator = new SentenceIteratorConverter(sentenceIterator, labelsSource);
} else if (labelAwareIterator != null) {
// if we have LabelAwareIterator defined, we have to be sure that LabelsSource is propagated properly
this.labelsSource = labelAwareIterator.getLabelsSource();
} else {
// we have nothing, probably that's restored model building. ignore iterator for now.
// probably there's few reasons to move iterator initialization code into ParagraphVectors methods. Like protected setLabelAwareIterator method.
}
if (labelAwareIterator != null) {
SentenceTransformer transformer = new SentenceTransformer.Builder().iterator(labelAwareIterator)
.tokenizerFactory(tokenizerFactory).allowMultithreading(allowParallelTokenization)
.build();
this.iterator = new AbstractSequenceIterator.Builder<>(transformer).build();
}
ret.numEpochs = this.numEpochs;
ret.numIterations = this.iterations;
ret.vocab = this.vocabCache;
ret.minWordFrequency = this.minWordFrequency;
ret.learningRate.set(this.learningRate);
ret.minLearningRate = this.minLearningRate;
ret.sampling = this.sampling;
ret.negative = this.negative;
ret.layerSize = this.layerSize;
ret.batchSize = this.batchSize;
ret.learningRateDecayWords = this.learningRateDecayWords;
ret.window = this.window;
ret.resetModel = this.resetModel;
ret.useAdeGrad = this.useAdaGrad;
ret.stopWords = this.stopWords;
ret.workers = this.workers;
ret.useUnknown = this.useUnknown;
ret.unknownElement = this.unknownElement;
ret.seed = this.seed;
ret.enableScavenger = this.enableScavenger;
ret.vocabLimit = this.vocabLimit;
ret.trainElementsVectors = this.trainElementsVectors;
ret.trainSequenceVectors = this.trainSequenceVectors;
ret.elementsLearningAlgorithm = this.elementsLearningAlgorithm;
ret.sequenceLearningAlgorithm = this.sequenceLearningAlgorithm;
ret.tokenizerFactory = this.tokenizerFactory;
ret.existingModel = this.existingVectors;
ret.lookupTable = this.lookupTable;
ret.modelUtils = this.modelUtils;
ret.eventListeners = this.vectorsListeners;
this.configuration.setLearningRate(this.learningRate);
this.configuration.setLayersSize(layerSize);
this.configuration.setHugeModelExpected(hugeModelExpected);
this.configuration.setWindow(window);
this.configuration.setMinWordFrequency(minWordFrequency);
this.configuration.setIterations(iterations);
this.configuration.setSeed(seed);
this.configuration.setBatchSize(batchSize);
this.configuration.setLearningRateDecayWords(learningRateDecayWords);
this.configuration.setMinLearningRate(minLearningRate);
this.configuration.setSampling(this.sampling);
this.configuration.setUseAdaGrad(useAdaGrad);
this.configuration.setNegative(negative);
this.configuration.setEpochs(this.numEpochs);
this.configuration.setStopList(this.stopWords);
this.configuration.setUseHierarchicSoftmax(this.useHierarchicSoftmax);
this.configuration.setTrainElementsVectors(this.trainElementsVectors);
this.configuration.setPreciseWeightInit(this.preciseWeightInit);
this.configuration
.setSequenceLearningAlgorithm(this.sequenceLearningAlgorithm.getClass().getCanonicalName());
this.configuration.setModelUtils(this.modelUtils.getClass().getCanonicalName());
this.configuration.setAllowParallelTokenization(this.allowParallelTokenization);
if (tokenizerFactory != null) {
this.configuration.setTokenizerFactory(tokenizerFactory.getClass().getCanonicalName());
if (tokenizerFactory.getTokenPreProcessor() != null)
this.configuration.setTokenPreProcessor(
tokenizerFactory.getTokenPreProcessor().getClass().getCanonicalName());
}
ret.configuration = this.configuration;
// hardcoded to TRUE, since it's ParagraphVectors wrapper
ret.trainElementsVectors = this.trainElementsVectors;
ret.trainSequenceVectors = true;
ret.labelsSource = this.labelsSource;
ret.labelAwareIterator = this.labelAwareIterator;
ret.iterator = this.iterator;
return ret;
}
public Builder() {
super();
}
public Builder(@NonNull VectorsConfiguration configuration) {
super(configuration);
}
/**
* This method defines TokenizerFactory to be used for strings tokenization during training
* PLEASE NOTE: If external VocabCache is used, the same TokenizerFactory should be used to keep derived tokens equal.
*
* @param tokenizerFactory
* @return
*/
@Override
public Builder tokenizerFactory(@NonNull TokenizerFactory tokenizerFactory) {
super.tokenizerFactory(tokenizerFactory);
return this;
}
@Override
public Builder index(@NonNull InvertedIndex index) {
super.index(index);
return this;
}
/**
* This method used to feed SequenceIterator, that contains training corpus, into ParagraphVectors
*
* @param iterator
* @return
*/
@Override
public Builder iterate(@NonNull SequenceIterator iterator) {
super.iterate(iterator);
return this;
}
/**
* This method defines mini-batch size
* @param batchSize
* @return
*/
@Override
public Builder batchSize(int batchSize) {
super.batchSize(batchSize);
return this;
}
/**
* This method defines number of iterations done for each mini-batch during training
* @param iterations
* @return
*/
@Override
public Builder iterations(int iterations) {
super.iterations(iterations);
return this;
}
/**
* This method defines number of epochs (iterations over whole training corpus) for training
* @param numEpochs
* @return
*/
@Override
public Builder epochs(int numEpochs) {
super.epochs(numEpochs);
return this;
}
/**
* This method defines number of dimensions for output vectors
* @param layerSize
* @return
*/
@Override
public Builder layerSize(int layerSize) {
super.layerSize(layerSize);
return this;
}
/**
* This method sets VectorsListeners for this SequenceVectors model
*
* @param vectorsListeners
* @return
*/
@Override
public Builder setVectorsListeners(@NonNull Collection> vectorsListeners) {
super.setVectorsListeners(vectorsListeners);
return this;
}
/**
* This method defines initial learning rate for model training
*
* @param learningRate
* @return
*/
@Override
public Builder learningRate(double learningRate) {
super.learningRate(learningRate);
return this;
}
/**
* This method defines minimal word frequency in training corpus. All words below this threshold will be removed prior model training
*
* @param minWordFrequency
* @return
*/
@Override
public Builder minWordFrequency(int minWordFrequency) {
super.minWordFrequency(minWordFrequency);
return this;
}
/**
* This method defines minimal learning rate value for training
*
* @param minLearningRate
* @return
*/
@Override
public Builder minLearningRate(double minLearningRate) {
super.minLearningRate(minLearningRate);
return this;
}
/**
* This method defines whether model should be totally wiped out prior building, or not
*
* @param reallyReset
* @return
*/
@Override
public Builder resetModel(boolean reallyReset) {
super.resetModel(reallyReset);
return this;
}
/**
* This method allows to define external VocabCache to be used
*
* @param vocabCache
* @return
*/
@Override
public Builder vocabCache(@NonNull VocabCache vocabCache) {
super.vocabCache(vocabCache);
return this;
}
/**
* This method allows to define external WeightLookupTable to be used
*
* @param lookupTable
* @return
*/
@Override
public Builder lookupTable(@NonNull WeightLookupTable lookupTable) {
super.lookupTable(lookupTable);
return this;
}
/**
* This method defines whether subsampling should be used or not
*
* @param sampling set > 0 to subsampling argument, or 0 to disable
* @return
*/
@Override
public Builder sampling(double sampling) {
super.sampling(sampling);
return this;
}
/**
* This method defines whether adaptive gradients should be used or not
*
* @param reallyUse
* @return
*/
@Override
public Builder useAdaGrad(boolean reallyUse) {
super.useAdaGrad(reallyUse);
return this;
}
/**
* This method defines whether negative sampling should be used or not
*
* PLEASE NOTE: If you're going to use negative sampling, you might want to disable HierarchicSoftmax, which is enabled by default
*
* Default value: 0
*
* @param negative set > 0 as negative sampling argument, or 0 to disable
* @return
*/
@Override
public Builder negativeSample(double negative) {
super.negativeSample(negative);
return this;
}
/**
* This method defines stop words that should be ignored during training
* @param stopList
* @return
*/
@Override
public Builder stopWords(@NonNull List stopList) {
super.stopWords(stopList);
return this;
}
/**
* This method defines, if words representation should be build together with documents representations.
*
* @param trainElements
* @return
*/
@Override
public Builder trainElementsRepresentation(boolean trainElements) {
this.trainElementsVectors = trainElements;
return this;
}
/**
* This method is hardcoded to TRUE, since that's whole point of ParagraphVectors
*
* @param trainSequences
* @return
*/
@Override
public Builder trainSequencesRepresentation(boolean trainSequences) {
this.trainSequenceVectors = trainSequences;
return this;
}
/**
* This method defines stop words that should be ignored during training
*
* @param stopList
* @return
*/
@Override
public Builder stopWords(@NonNull Collection stopList) {
super.stopWords(stopList);
return this;
}
/**
* This method defines context window size
*
* @param windowSize
* @return
*/
@Override
public Builder windowSize(int windowSize) {
super.windowSize(windowSize);
return this;
}
/**
* This method defines maximum number of concurrent threads available for training
*
* @param numWorkers
* @return
*/
@Override
public Builder workers(int numWorkers) {
super.workers(numWorkers);
return this;
}
@Override
public Builder sequenceLearningAlgorithm(SequenceLearningAlgorithm algorithm) {
super.sequenceLearningAlgorithm(algorithm);
return this;
}
@Override
public Builder sequenceLearningAlgorithm(String algorithm) {
super.sequenceLearningAlgorithm(algorithm);
return this;
}
/**
* This method enables/disables Hierarchic softmax
*
* Default value: enabled
*
* @param reallyUse
* @return
*/
@Override
public Builder useHierarchicSoftmax(boolean reallyUse) {
super.useHierarchicSoftmax(reallyUse);
return this;
}
/**
* This method has no effect for ParagraphVectors
*
* @param windows
* @return
*/
@Override
public Builder useVariableWindow(int... windows) {
// no-op
return this;
}
@Override
public Builder elementsLearningAlgorithm(ElementsLearningAlgorithm algorithm) {
super.elementsLearningAlgorithm(algorithm);
return this;
}
@Override
public Builder elementsLearningAlgorithm(String algorithm) {
super.elementsLearningAlgorithm(algorithm);
return this;
}
@Override
public Builder usePreciseWeightInit(boolean reallyUse) {
super.usePreciseWeightInit(reallyUse);
return this;
}
/**
* This method defines random seed for random numbers generator
* @param randomSeed
* @return
*/
@Override
public Builder seed(long randomSeed) {
super.seed(randomSeed);
return this;
}
}
public class InferenceCallable implements Callable> {
private final TokenizerFactory tokenizerFactory;
private final VocabCache vocab;
private final LabelledDocument document;
private AtomicLong flag;
public InferenceCallable(@NonNull VocabCache vocabCache, @NonNull TokenizerFactory tokenizerFactory,
@NonNull LabelledDocument document) {
this.tokenizerFactory = tokenizerFactory;
this.vocab = vocabCache;
this.document = document;
}
public InferenceCallable(@NonNull VocabCache vocabCache, @NonNull TokenizerFactory tokenizerFactory,
@NonNull LabelledDocument document, @NonNull AtomicLong flag) {
this(vocabCache, tokenizerFactory, document);
this.flag = flag;
}
@Override
public Pair call() throws Exception {
// first part of this callable will be actually run in parallel
List tokens = tokenizerFactory.create(document.getContent()).getTokens();
List documentAsWords = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
documentAsWords.add(vocab.wordFor(token));
}
}
if (documentAsWords.isEmpty())
throw new ND4JIllegalStateException("Text passed for inference has no matches in model vocabulary.");
// inference will be single-threaded in java, and parallel in native
Pair result = Pair.makePair(document.getId(), inferVector(documentAsWords));
countFinished.incrementAndGet();
if (flag != null)
flag.incrementAndGet();
return result;
}
}
public class BlindInferenceCallable implements Callable {
private final TokenizerFactory tokenizerFactory;
private final VocabCache vocab;
private final String document;
private AtomicLong flag;
public BlindInferenceCallable(@NonNull VocabCache vocabCache,
@NonNull TokenizerFactory tokenizerFactory, @NonNull String document) {
this.tokenizerFactory = tokenizerFactory;
this.vocab = vocabCache;
this.document = document;
}
public BlindInferenceCallable(@NonNull VocabCache vocabCache,
@NonNull TokenizerFactory tokenizerFactory, @NonNull String document,
@NonNull AtomicLong flag) {
this(vocabCache, tokenizerFactory, document);
this.flag = flag;
}
@Override
public INDArray call() throws Exception {
// first part of this callable will be actually run in parallel
List tokens = tokenizerFactory.create(document).getTokens();
List documentAsWords = new ArrayList<>();
for (String token : tokens) {
if (vocab.containsWord(token)) {
documentAsWords.add(vocab.wordFor(token));
}
}
if (documentAsWords.isEmpty())
throw new ND4JIllegalStateException("Text passed for inference has no matches in model vocabulary.");
// inference will be single-threaded in java, and parallel in native
INDArray result = inferVector(documentAsWords);
countFinished.incrementAndGet();
if (flag != null)
flag.incrementAndGet();
return result;
}
}
}
