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Java example source code file (Word2Vec.java)

This example Java source code file (Word2Vec.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

builder, collection, defaulttokenizerfactory, getter, illegalstateexception, modelutils, override, sentenceiterator, sentencetransformer, sequenceiterator, sequencevectors, tokenizerfactory, util, word2vec, you

The Word2Vec.java Java example source code

package org.deeplearning4j.models.word2vec;

import lombok.Getter;
import lombok.NonNull;
import lombok.Setter;
import org.deeplearning4j.models.embeddings.learning.ElementsLearningAlgorithm;
import org.deeplearning4j.models.embeddings.reader.ModelUtils;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectorsImpl;
import org.deeplearning4j.models.sequencevectors.SequenceVectors;
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.transformers.impl.SentenceTransformer;
import org.deeplearning4j.models.embeddings.WeightLookupTable;
import org.deeplearning4j.models.embeddings.loader.VectorsConfiguration;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.text.documentiterator.DocumentIterator;
import org.deeplearning4j.text.invertedindex.InvertedIndex;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.sentenceiterator.StreamLineIterator;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;

import java.util.Collection;
import java.util.List;

/**
 * This is Word2Vec implementation based on SequenceVectors
 *
 * @author raver119@gmail.com
 */
public class Word2Vec extends SequenceVectors<VocabWord> {
    @Getter protected transient SentenceIterator sentenceIter;
    @Getter protected transient TokenizerFactory tokenizerFactory;

    /**
     * This method defines TokenizerFactory instance to be using during model building
     *
     * @param tokenizerFactory TokenizerFactory instance
     */
    public void setTokenizerFactory(@NonNull TokenizerFactory tokenizerFactory) {
        this.tokenizerFactory = tokenizerFactory;

        if (sentenceIter != null) {
            SentenceTransformer transformer = new SentenceTransformer.Builder()
                    .iterator(sentenceIter)
                    .tokenizerFactory(this.tokenizerFactory)
                    .build();
            this.iterator = new AbstractSequenceIterator.Builder<VocabWord>(transformer).build();
        }
    }

    /**
     * This method defines SentenceIterator instance, that will be used as training corpus source
     *
     * @param iterator SentenceIterator instance
     */
    public void setSentenceIter(@NonNull SentenceIterator iterator) {
        //if (tokenizerFactory == null) throw new IllegalStateException("Please call setTokenizerFactory() prior to setSentenceIter() call.");

        if (tokenizerFactory != null) {
            SentenceTransformer transformer = new SentenceTransformer.Builder()
                    .iterator(iterator)
                    .tokenizerFactory(tokenizerFactory)
                    .build();
            this.iterator = new AbstractSequenceIterator.Builder<VocabWord>(transformer).build();
        }
    }

    public static class Builder extends SequenceVectors.Builder<VocabWord> {
        protected SentenceIterator sentenceIterator;
        protected TokenizerFactory tokenizerFactory;


        public Builder() {

        }

        /**
         * This method has no effect for Word2Vec
         *
         * @param vec existing WordVectors model
         * @return
         */
        @Override
        protected Builder useExistingWordVectors(@NonNull WordVectors vec) {
            return this;
        }

        public Builder(@NonNull VectorsConfiguration configuration) {
            super(configuration);
        }

        public Builder iterate(@NonNull DocumentIterator iterator) {
            this.sentenceIterator = new StreamLineIterator.Builder(iterator)
                    .setFetchSize(100)
                    .build();
            return this;
        }

        /**
         * This method used to feed SentenceIterator, that contains training corpus, into ParagraphVectors
         *
         * @param iterator
         * @return
         */
        public Builder iterate(@NonNull SentenceIterator iterator) {
            this.sentenceIterator = iterator;
            return this;
        }

        /**
         * 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
         */
        public Builder tokenizerFactory(@NonNull TokenizerFactory tokenizerFactory) {
            this.tokenizerFactory = tokenizerFactory;
            return this;
        }

        @Deprecated
        public Builder index(@NonNull InvertedIndex<VocabWord> index) {
            return this;
        }

        /**
         * This method used to feed SequenceIterator, that contains training corpus, into ParagraphVectors
         *
         * @param iterator
         * @return
         */
        @Override
        public Builder iterate(@NonNull SequenceIterator<VocabWord> 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 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<VocabWord> 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<VocabWord> 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
         *
         * @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<String> stopList) {
            super.stopWords(stopList);
            return this;
        }

        /**
         * This method is hardcoded to TRUE, since that's whole point of Word2Vec
         *
         * @param trainElements
         * @return
         */
        @Override
        public Builder trainElementsRepresentation(boolean trainElements) {
            throw new IllegalStateException("You can't change this option for Word2Vec");
        }

        /**
         * This method is hardcoded to FALSE, since that's whole point of Word2Vec
         *
         * @param trainSequences
         * @return
         */
        @Override
        public Builder trainSequencesRepresentation(boolean trainSequences) {
            throw new IllegalStateException("You can't change this option for Word2Vec");
        }

        /**
         * This method defines stop words that should be ignored during training
         *
         * @param stopList
         * @return
         */
        @Override
        public Builder stopWords(@NonNull Collection<VocabWord> 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 random seed for random numbers generator
         * @param randomSeed
         * @return
         */
        @Override
        public Builder seed(long randomSeed) {
            super.seed(randomSeed);
            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;
        }

        /**
         * 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<VocabWord> modelUtils) {
            super.modelUtils(modelUtils);
            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 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, Word2Vec.DEFAULT_UNK));
            }
            return this;
        }

        /**
         * This method sets VectorsListeners for this SequenceVectors model
         *
         * @param vectorsListeners
         * @return
         */
        @Override
        public Builder setVectorsListeners(@NonNull Collection<VectorsListener vectorsListeners) {
            super.setVectorsListeners(vectorsListeners);
            return this;
        }

        @Override
        public Builder elementsLearningAlgorithm(@NonNull String algorithm) {
            super.elementsLearningAlgorithm(algorithm);
            return this;
        }

        @Override
        public Builder elementsLearningAlgorithm(@NonNull ElementsLearningAlgorithm<VocabWord> algorithm) {
            super.elementsLearningAlgorithm(algorithm);
            return this;
        }

        public Word2Vec build() {
            presetTables();

            Word2Vec ret = new Word2Vec();

            if (sentenceIterator != null) {
                if (tokenizerFactory == null) tokenizerFactory = new DefaultTokenizerFactory();

                SentenceTransformer transformer = new SentenceTransformer.Builder()
                        .iterator(sentenceIterator)
                        .tokenizerFactory(tokenizerFactory)
                        .build();
                this.iterator = new AbstractSequenceIterator.Builder<VocabWord>(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.iterator = this.iterator;
            ret.lookupTable = this.lookupTable;
            ret.tokenizerFactory = this.tokenizerFactory;
            ret.modelUtils = this.modelUtils;

            ret.elementsLearningAlgorithm = this.elementsLearningAlgorithm;
            ret.sequenceLearningAlgorithm = this.sequenceLearningAlgorithm;

            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);

            ret.configuration = this.configuration;

            // we hardcode
            ret.trainSequenceVectors = false;
            ret.trainElementsVectors = true;

            ret.eventListeners = this.vectorsListeners;

            return ret;
        }
    }
}

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