org.deeplearning4j.models.paragraphvectors

Class ParagraphVectors

java.lang.Object

org.deeplearning4j.models.embeddings.wordvectors.WordVectorsImpl<T>

org.deeplearning4j.models.sequencevectors.SequenceVectors<VocabWord>

org.deeplearning4j.models.word2vec.Word2Vec

org.deeplearning4j.models.paragraphvectors.ParagraphVectors

All Implemented Interfaces:

java.io.Serializable, WordVectors

public class ParagraphVectors
extends Word2Vec

Basic ParagraphVectors (aka Doc2Vec) implementation for DL4j, as wrapper over SequenceVectors

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	ParagraphVectors.BlindInferenceCallable
static class	ParagraphVectors.Builder
class	ParagraphVectors.InferenceCallable

Nested classes/interfaces inherited from class org.deeplearning4j.models.sequencevectors.SequenceVectors

SequenceVectors.AsyncSequencer

Field Summary

Fields

Modifier and Type	Field and Description
protected java.util.concurrent.atomic.AtomicLong	countFinished
protected java.util.concurrent.atomic.AtomicLong	countSubmitted
protected java.util.concurrent.ExecutorService	inferenceExecutor
protected java.lang.Object	inferenceLocker
protected LabelAwareIterator	labelAwareIterator
protected java.util.List<VocabWord>	labelsList
protected org.nd4j.linalg.api.ndarray.INDArray	labelsMatrix
protected LabelsSource	labelsSource
protected boolean	normalizedLabels

Fields inherited from class org.deeplearning4j.models.word2vec.Word2Vec

sentenceIter, tokenizerFactory

Fields inherited from class org.deeplearning4j.models.sequencevectors.SequenceVectors

configuration, configured, elementsLearningAlgorithm, enableScavenger, eventListeners, existingModel, iterator, log, scoreElements, scoreSequences, sequenceLearningAlgorithm, unknownElement

Fields inherited from class org.deeplearning4j.models.embeddings.wordvectors.WordVectorsImpl

batchSize, DEFAULT_UNK, layerSize, learningRate, learningRateDecayWords, lookupTable, minLearningRate, minWordFrequency, modelUtils, negative, numEpochs, numIterations, resetModel, sampling, seed, stopWords, trainElementsVectors, trainSequenceVectors, useAdeGrad, useUnknown, variableWindows, vocab, window, workers

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	ParagraphVectors()

Method Summary

Modifier and Type	Method and Description
void	extractLabels()
void	fit() Starts training over
org.nd4j.linalg.api.ndarray.INDArray	inferVector(LabelledDocument document) This method calculates inferred vector for given document, with default parameters for learning rate and iterations
org.nd4j.linalg.api.ndarray.INDArray	inferVector(LabelledDocument document, double learningRate, double minLearningRate, int iterations) This method calculates inferred vector for given document
org.nd4j.linalg.api.ndarray.INDArray	inferVector(java.util.List<VocabWord> document) This method calculates inferred vector for given list of words, with default parameters for learning rate and iterations
org.nd4j.linalg.api.ndarray.INDArray	inferVector(java.util.List<VocabWord> document, double learningRate, double minLearningRate, int iterations) This method calculates inferred vector for given document
org.nd4j.linalg.api.ndarray.INDArray	inferVector(java.lang.String text) This method calculates inferred vector for given text, with default parameters for learning rate and iterations
org.nd4j.linalg.api.ndarray.INDArray	inferVector(java.lang.String text, double learningRate, double minLearningRate, int iterations) This method calculates inferred vector for given text
java.util.concurrent.Future<Pair<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray>>	inferVectorBatched(LabelledDocument document) This method implements batched inference, based on Java Future parallelism model.
java.util.List<org.nd4j.linalg.api.ndarray.INDArray>	inferVectorBatched(java.util.List<java.lang.String> documents) This method does inference on a given List<String>
java.util.concurrent.Future<org.nd4j.linalg.api.ndarray.INDArray>	inferVectorBatched(java.lang.String document) This method implements batched inference, based on Java Future parallelism model.
protected void	initInference()
java.util.Collection<java.lang.String>	nearestLabels(java.util.Collection<VocabWord> document, int topN) This method returns top N labels nearest to specified set of vocab words
java.util.Collection<java.lang.String>	nearestLabels(org.nd4j.linalg.api.ndarray.INDArray labelVector, int topN) This method returns top N labels nearest to specified features vector
java.util.Collection<java.lang.String>	nearestLabels(LabelledDocument document, int topN) This method returns top N labels nearest to specified document
java.util.Collection<java.lang.String>	nearestLabels(java.lang.String rawText, int topN) This method returns top N labels nearest to specified text
java.lang.String	predict(LabelledDocument document) Deprecated.
java.lang.String	predict(java.util.List<VocabWord> document) Deprecated.
java.lang.String	predict(java.lang.String rawText) Deprecated.
java.util.Collection<java.lang.String>	predictSeveral(LabelledDocument document, int limit) Deprecated.
java.util.Collection<java.lang.String>	predictSeveral(java.util.List<VocabWord> document, int limit) Deprecated.
java.util.Collection<java.lang.String>	predictSeveral(java.lang.String rawText, int limit) Deprecated.
protected void	reassignExistingModel()
void	setSequenceIterator(SequenceIterator<VocabWord> iterator) This method defines SequenceIterator instance, that will be used as training corpus source.
double	similarityToLabel(LabelledDocument document, java.lang.String label) Deprecated.
double	similarityToLabel(java.util.List<VocabWord> document, java.lang.String label) Deprecated.
double	similarityToLabel(java.lang.String rawText, java.lang.String label) Deprecated.

Methods inherited from class org.deeplearning4j.models.word2vec.Word2Vec

setSentenceIterator, setTokenizerFactory

Methods inherited from class org.deeplearning4j.models.sequencevectors.SequenceVectors

buildVocab, getElementsScore, getSequencesScore, getUNK, getWordVectorMatrix, initLearners, trainSequence

Methods inherited from class org.deeplearning4j.models.embeddings.wordvectors.WordVectorsImpl

accuracy, getLayerSize, getWordVector, getWordVectorMatrixNormalized, getWordVectors, getWordVectorsMean, hasWord, indexOf, lookupTable, setLookupTable, setModelUtils, setVocab, similarity, similarWordsInVocabTo, update, update, vocab, wordsNearest, wordsNearest, wordsNearest, wordsNearestSum, wordsNearestSum, wordsNearestSum

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.deeplearning4j.models.embeddings.wordvectors.WordVectors

accuracy, getWordVector, getWordVectorMatrixNormalized, getWordVectors, getWordVectorsMean, hasWord, indexOf, lookupTable, setModelUtils, setUNK, similarity, similarWordsInVocabTo, vocab, wordsNearest, wordsNearest, wordsNearest, wordsNearestSum, wordsNearestSum, wordsNearestSum

Field Detail

labelsSource

protected LabelsSource labelsSource

labelAwareIterator

protected transient LabelAwareIterator labelAwareIterator

labelsMatrix

protected org.nd4j.linalg.api.ndarray.INDArray labelsMatrix

labelsList

protected java.util.List<VocabWord> labelsList

normalizedLabels

protected boolean normalizedLabels

inferenceLocker

protected final transient java.lang.Object inferenceLocker

inferenceExecutor

protected transient java.util.concurrent.ExecutorService inferenceExecutor

countSubmitted

protected transient java.util.concurrent.atomic.AtomicLong countSubmitted

countFinished

protected transient java.util.concurrent.atomic.AtomicLong countFinished

Constructor Detail

ParagraphVectors

protected ParagraphVectors()

Method Detail

initInference

protected void initInference()

predict

@Deprecated
public java.lang.String predict(java.lang.String rawText)

Deprecated.

This method takes raw text, applies tokenizer, and returns most probable label

Parameters:

rawText -

Returns:

setSequenceIterator

public void setSequenceIterator(@NonNull
                                SequenceIterator<VocabWord> iterator)

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

Overrides:

setSequenceIterator in class Word2Vec

Parameters:

iterator -

predict

@Deprecated
public java.lang.String predict(LabelledDocument document)

Deprecated.

This method predicts label of the document. Computes a similarity wrt the mean of the representation of words in the document

Parameters:

document - the document

Returns:

the word distances for each label

extractLabels

public void extractLabels()

inferVector

public org.nd4j.linalg.api.ndarray.INDArray inferVector(java.lang.String text,
                                                        double learningRate,
                                                        double minLearningRate,
                                                        int iterations)

This method calculates inferred vector for given text

Parameters:

text -

Returns:

reassignExistingModel

protected void reassignExistingModel()

inferVector

public org.nd4j.linalg.api.ndarray.INDArray inferVector(LabelledDocument document,
                                                        double learningRate,
                                                        double minLearningRate,
                                                        int iterations)

This method calculates inferred vector for given document

Parameters:

document -

Returns:

inferVector

public org.nd4j.linalg.api.ndarray.INDArray inferVector(@NonNull
                                                        java.util.List<VocabWord> document,
                                                        double learningRate,
                                                        double minLearningRate,
                                                        int iterations)

This method calculates inferred vector for given document

Parameters:

document -

Returns:

inferVector

public org.nd4j.linalg.api.ndarray.INDArray inferVector(java.lang.String text)

This method calculates inferred vector for given text, with default parameters for learning rate and iterations

Parameters:

text -

Returns:

inferVector

public org.nd4j.linalg.api.ndarray.INDArray inferVector(LabelledDocument document)

This method calculates inferred vector for given document, with default parameters for learning rate and iterations

Parameters:

document -

Returns:

inferVector

public org.nd4j.linalg.api.ndarray.INDArray inferVector(@NonNull
                                                        java.util.List<VocabWord> document)

This method calculates inferred vector for given list of words, with default parameters for learning rate and iterations

Parameters:

document -

Returns:

inferVectorBatched

public java.util.concurrent.Future<Pair<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray>> inferVectorBatched(@NonNull
                                                                                                                   LabelledDocument document)

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.

Parameters:

document -

Returns:

inferVectorBatched

public java.util.concurrent.Future<org.nd4j.linalg.api.ndarray.INDArray> inferVectorBatched(@NonNull
                                                                                            java.lang.String document)

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

Parameters:

document -

Returns:

inferVectorBatched

public java.util.List<org.nd4j.linalg.api.ndarray.INDArray> inferVectorBatched(@NonNull
                                                                               java.util.List<java.lang.String> documents)

This method does inference on a given List<String>

Parameters:

documents -

Returns:

INDArrays in the same order as input texts

predict

@Deprecated
public java.lang.String predict(java.util.List<VocabWord> document)

Deprecated.

This method predicts label of the document. Computes a similarity wrt the mean of the representation of words in the document

Parameters:

document - the document

Returns:

the word distances for each label

predictSeveral

@Deprecated
public java.util.Collection<java.lang.String> predictSeveral(@NonNull
                                                                         LabelledDocument document,
                                                                         int limit)

Deprecated.

Predict several labels based on the document. Computes a similarity wrt the mean of the representation of words in the document

Parameters:

document - raw text of the document

Returns:

possible labels in descending order

predictSeveral

@Deprecated
public java.util.Collection<java.lang.String> predictSeveral(java.lang.String rawText,
                                                                         int limit)

Deprecated.

Predict several labels based on the document. Computes a similarity wrt the mean of the representation of words in the document

Parameters:

rawText - raw text of the document

Returns:

possible labels in descending order

predictSeveral

@Deprecated
public java.util.Collection<java.lang.String> predictSeveral(java.util.List<VocabWord> document,
                                                                         int limit)

Deprecated.

Predict several labels based on the document. Computes a similarity wrt the mean of the representation of words in the document

Parameters:

document - the document

Returns:

possible labels in descending order

nearestLabels

public java.util.Collection<java.lang.String> nearestLabels(LabelledDocument document,
                                                            int topN)

This method returns top N labels nearest to specified document

Parameters:

document -

topN -

Returns:

nearestLabels

public java.util.Collection<java.lang.String> nearestLabels(@NonNull
                                                            java.lang.String rawText,
                                                            int topN)

This method returns top N labels nearest to specified text

Parameters:

rawText -

topN -

Returns:

nearestLabels

public java.util.Collection<java.lang.String> nearestLabels(@NonNull
                                                            java.util.Collection<VocabWord> document,
                                                            int topN)

This method returns top N labels nearest to specified set of vocab words

Parameters:

document -

topN -

Returns:

nearestLabels

public java.util.Collection<java.lang.String> nearestLabels(org.nd4j.linalg.api.ndarray.INDArray labelVector,
                                                            int topN)

This method returns top N labels nearest to specified features vector

Parameters:

labelVector -

topN -

Returns:

similarityToLabel

@Deprecated
public double similarityToLabel(java.lang.String rawText,
                                            java.lang.String label)

Deprecated.

This method returns similarity of the document to specific label, based on mean value

Parameters:

rawText -

label -

Returns:

fit

public void fit()

Description copied from class: SequenceVectors

Starts training over

Overrides:

fit in class SequenceVectors<VocabWord>

similarityToLabel

@Deprecated
public double similarityToLabel(LabelledDocument document,
                                            java.lang.String label)

Deprecated.

This method returns similarity of the document to specific label, based on mean value

Parameters:

document -

label -

Returns:

similarityToLabel

@Deprecated
public double similarityToLabel(java.util.List<VocabWord> document,
                                            java.lang.String label)

Deprecated.

This method returns similarity of the document to specific label, based on mean value

Parameters:

document -

label -

Returns: