Examples with Word2Vec org.deeplearning4j.models.word2vec.Word2Vec used on opensource projects

Example 6 with Word2Vec

use of org.deeplearning4j.models.word2vec.Word2Vec in project deeplearning4j by deeplearning4j.

the class WordVectorSerializer method readWord2VecModel.

/**
     * This method
     * 1) Binary model, either compressed or not. Like well-known Google Model
     * 2) Popular CSV word2vec text format
     * 3) DL4j compressed format
     *
     * Please note: if extended data isn't available, only weights will be loaded instead.
     *
     * @param file
     * @param extendedModel if TRUE, we'll try to load HS states & Huffman tree info, if FALSE, only weights will be loaded
     * @return
     */
public static Word2Vec readWord2VecModel(@NonNull File file, boolean extendedModel) {
    InMemoryLookupTable<VocabWord> lookupTable = new InMemoryLookupTable<>();
    AbstractCache<VocabWord> vocabCache = new AbstractCache<>();
    Word2Vec vec;
    INDArray syn0 = null;
    VectorsConfiguration configuration = new VectorsConfiguration();
    if (!file.exists() || !file.isFile())
        throw new ND4JIllegalStateException("File [" + file.getAbsolutePath() + "] doesn't exist");
    int originalFreq = Nd4j.getMemoryManager().getOccasionalGcFrequency();
    boolean originalPeriodic = Nd4j.getMemoryManager().isPeriodicGcActive();
    if (originalPeriodic)
        Nd4j.getMemoryManager().togglePeriodicGc(false);
    Nd4j.getMemoryManager().setOccasionalGcFrequency(50000);
    // try to load zip format
    try {
        if (extendedModel) {
            log.debug("Trying full model restoration...");
            if (originalPeriodic)
                Nd4j.getMemoryManager().togglePeriodicGc(true);
            Nd4j.getMemoryManager().setOccasionalGcFrequency(originalFreq);
            return readWord2Vec(file);
        } else {
            log.debug("Trying simplified model restoration...");
            File tmpFileSyn0 = File.createTempFile("word2vec", "syn");
            File tmpFileConfig = File.createTempFile("word2vec", "config");
            // we don't need full model, so we go directly to syn0 file
            ZipFile zipFile = new ZipFile(file);
            ZipEntry syn = zipFile.getEntry("syn0.txt");
            InputStream stream = zipFile.getInputStream(syn);
            Files.copy(stream, Paths.get(tmpFileSyn0.getAbsolutePath()), StandardCopyOption.REPLACE_EXISTING);
            // now we're restoring configuration saved earlier
            ZipEntry config = zipFile.getEntry("config.json");
            if (config != null) {
                stream = zipFile.getInputStream(config);
                StringBuilder builder = new StringBuilder();
                try (BufferedReader reader = new BufferedReader(new InputStreamReader(stream))) {
                    String line;
                    while ((line = reader.readLine()) != null) {
                        builder.append(line);
                    }
                }
                configuration = VectorsConfiguration.fromJson(builder.toString().trim());
            }
            ZipEntry ve = zipFile.getEntry("frequencies.txt");
            if (ve != null) {
                stream = zipFile.getInputStream(ve);
                AtomicInteger cnt = new AtomicInteger(0);
                try (BufferedReader reader = new BufferedReader(new InputStreamReader(stream))) {
                    String line;
                    while ((line = reader.readLine()) != null) {
                        String[] split = line.split(" ");
                        VocabWord word = new VocabWord(Double.valueOf(split[1]), decodeB64(split[0]));
                        word.setIndex(cnt.getAndIncrement());
                        word.incrementSequencesCount(Long.valueOf(split[2]));
                        vocabCache.addToken(word);
                        vocabCache.addWordToIndex(word.getIndex(), word.getLabel());
                        Nd4j.getMemoryManager().invokeGcOccasionally();
                    }
                }
            }
            List<INDArray> rows = new ArrayList<>();
            // basically read up everything, call vstacl and then return model
            try (Reader reader = new CSVReader(tmpFileSyn0)) {
                AtomicInteger cnt = new AtomicInteger(0);
                while (reader.hasNext()) {
                    Pair<VocabWord, float[]> pair = reader.next();
                    VocabWord word = pair.getFirst();
                    INDArray vector = Nd4j.create(pair.getSecond());
                    if (ve != null) {
                        if (syn0 == null)
                            syn0 = Nd4j.create(vocabCache.numWords(), vector.length());
                        syn0.getRow(cnt.getAndIncrement()).assign(vector);
                    } else {
                        rows.add(vector);
                        vocabCache.addToken(word);
                        vocabCache.addWordToIndex(word.getIndex(), word.getLabel());
                    }
                    Nd4j.getMemoryManager().invokeGcOccasionally();
                }
            } catch (Exception e) {
                throw new RuntimeException(e);
            } finally {
                if (originalPeriodic)
                    Nd4j.getMemoryManager().togglePeriodicGc(true);
                Nd4j.getMemoryManager().setOccasionalGcFrequency(originalFreq);
            }
            if (syn0 == null && vocabCache.numWords() > 0)
                syn0 = Nd4j.vstack(rows);
            if (syn0 == null) {
                log.error("Can't build syn0 table");
                throw new DL4JInvalidInputException("Can't build syn0 table");
            }
            lookupTable = new InMemoryLookupTable.Builder<VocabWord>().cache(vocabCache).vectorLength(syn0.columns()).useHierarchicSoftmax(false).useAdaGrad(false).build();
            lookupTable.setSyn0(syn0);
            try {
                tmpFileSyn0.delete();
                tmpFileConfig.delete();
            } catch (Exception e) {
            //
            }
        }
    } catch (Exception e) {
        // let's try to load this file as csv file
        try {
            log.debug("Trying CSV model restoration...");
            Pair<InMemoryLookupTable, VocabCache> pair = loadTxt(file);
            lookupTable = pair.getFirst();
            vocabCache = (AbstractCache<VocabWord>) pair.getSecond();
        } catch (Exception ex) {
            // we fallback to trying binary model instead
            try {
                log.debug("Trying binary model restoration...");
                if (originalPeriodic)
                    Nd4j.getMemoryManager().togglePeriodicGc(true);
                Nd4j.getMemoryManager().setOccasionalGcFrequency(originalFreq);
                vec = loadGoogleModel(file, true, true);
                return vec;
            } catch (Exception ey) {
                // try to load without linebreaks
                try {
                    if (originalPeriodic)
                        Nd4j.getMemoryManager().togglePeriodicGc(true);
                    Nd4j.getMemoryManager().setOccasionalGcFrequency(originalFreq);
                    vec = loadGoogleModel(file, true, false);
                    return vec;
                } catch (Exception ez) {
                    throw new RuntimeException("Unable to guess input file format. Please use corresponding loader directly");
                }
            }
        }
    }
    Word2Vec.Builder builder = new Word2Vec.Builder(configuration).lookupTable(lookupTable).useAdaGrad(false).vocabCache(vocabCache).layerSize(lookupTable.layerSize()).useHierarchicSoftmax(false).resetModel(false);
    /*
            Trying to restore TokenizerFactory & TokenPreProcessor
         */
    TokenizerFactory factory = getTokenizerFactory(configuration);
    if (factory != null)
        builder.tokenizerFactory(factory);
    vec = builder.build();
    return vec;
}

Example 7 with Word2Vec

use of org.deeplearning4j.models.word2vec.Word2Vec in project deeplearning4j by deeplearning4j.

the class WordVectorSerializer method readParagraphVectors.

/**
     * This method restores ParagraphVectors model previously saved with writeParagraphVectors()
     *
     * @return
     */
public static ParagraphVectors readParagraphVectors(File file) throws IOException {
    File tmpFileL = File.createTempFile("paravec", "l");
    tmpFileL.deleteOnExit();
    Word2Vec w2v = readWord2Vec(file);
    // and "convert" it to ParaVec model + optionally trying to restore labels information
    ParagraphVectors vectors = new ParagraphVectors.Builder(w2v.getConfiguration()).vocabCache(w2v.getVocab()).lookupTable(w2v.getLookupTable()).resetModel(false).build();
    ZipFile zipFile = new ZipFile(file);
    // now we try to restore labels information
    ZipEntry labels = zipFile.getEntry("labels.txt");
    if (labels != null) {
        InputStream stream = zipFile.getInputStream(labels);
        Files.copy(stream, Paths.get(tmpFileL.getAbsolutePath()), StandardCopyOption.REPLACE_EXISTING);
        try (BufferedReader reader = new BufferedReader(new FileReader(tmpFileL))) {
            String line;
            while ((line = reader.readLine()) != null) {
                VocabWord word = vectors.getVocab().tokenFor(decodeB64(line.trim()));
                if (word != null) {
                    word.markAsLabel(true);
                }
            }
        }
    }
    vectors.extractLabels();
    return vectors;
}

Example 8 with Word2Vec

use of org.deeplearning4j.models.word2vec.Word2Vec in project deeplearning4j by deeplearning4j.

the class UITest method testPosting.

@Test
public void testPosting() throws Exception {
    //        File inputFile = new ClassPathResource("/big/raw_sentences.txt").getFile();
    File inputFile = new ClassPathResource("/basic/word2vec_advance.txt").getFile();
    SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
    // Split on white spaces in the line to get words
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    Word2Vec vec = new Word2Vec.Builder().minWordFrequency(1).iterations(1).epochs(1).layerSize(20).stopWords(new ArrayList<String>()).useAdaGrad(false).negativeSample(5).seed(42).windowSize(5).iterate(iter).tokenizerFactory(t).build();
    vec.fit();
    File tempFile = File.createTempFile("temp", "w2v");
    tempFile.deleteOnExit();
    WordVectorSerializer.writeWordVectors(vec, tempFile);
    WordVectors vectors = WordVectorSerializer.loadTxtVectors(tempFile);
    //Initialize
    UIServer.getInstance();
    UiConnectionInfo uiConnectionInfo = new UiConnectionInfo.Builder().setAddress("localhost").setPort(9000).build();
    BarnesHutTsne tsne = new BarnesHutTsne.Builder().normalize(false).setFinalMomentum(0.8f).numDimension(2).setMaxIter(10).build();
    vectors.lookupTable().plotVocab(tsne, vectors.lookupTable().getVocabCache().numWords(), uiConnectionInfo);
    Thread.sleep(100000);
}

Example 9 with Word2Vec

use of org.deeplearning4j.models.word2vec.Word2Vec in project deeplearning4j by deeplearning4j.

the class VectorsConfigurationTest method testFromW2V.

@Test
public void testFromW2V() throws Exception {
    VectorsConfiguration configuration = new VectorsConfiguration();
    configuration.setHugeModelExpected(true);
    configuration.setWindow(5);
    configuration.setIterations(3);
    configuration.setLayersSize(200);
    configuration.setLearningRate(1.4d);
    configuration.setSampling(0.0005d);
    configuration.setMinLearningRate(0.25d);
    configuration.setEpochs(1);
    File inputFile = new ClassPathResource("/big/raw_sentences.txt").getFile();
    SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
    Word2Vec vec = new Word2Vec.Builder(configuration).iterate(iter).build();
    VectorsConfiguration configuration2 = vec.getConfiguration();
    assertEquals(configuration, configuration2);
}

Example 10 with Word2Vec

use of org.deeplearning4j.models.word2vec.Word2Vec in project deeplearning4j by deeplearning4j.

the class WordVectorSerializerTest method testIndexPersistence.

@Test
public void testIndexPersistence() throws Exception {
    File inputFile = new ClassPathResource("/big/raw_sentences.txt").getFile();
    SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
    // Split on white spaces in the line to get words
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    Word2Vec vec = new Word2Vec.Builder().minWordFrequency(5).iterations(1).epochs(1).layerSize(100).stopWords(new ArrayList<String>()).useAdaGrad(false).negativeSample(5).seed(42).windowSize(5).iterate(iter).tokenizerFactory(t).build();
    vec.fit();
    VocabCache orig = vec.getVocab();
    File tempFile = File.createTempFile("temp", "w2v");
    tempFile.deleteOnExit();
    WordVectorSerializer.writeWordVectors(vec, tempFile);
    WordVectors vec2 = WordVectorSerializer.loadTxtVectors(tempFile);
    VocabCache rest = vec2.vocab();
    assertEquals(orig.totalNumberOfDocs(), rest.totalNumberOfDocs());
    for (VocabWord word : vec.getVocab().vocabWords()) {
        INDArray array1 = vec.getWordVectorMatrix(word.getLabel());
        INDArray array2 = vec2.getWordVectorMatrix(word.getLabel());
        assertEquals(array1, array2);
    }
}