Examples with CommonPreprocessor org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor used on opensource projects

Example 26 with CommonPreprocessor

use of org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor in project deeplearning4j by deeplearning4j.

the class AbstractCoOccurrencesTest method testFit1.

@Test
public void testFit1() throws Exception {
    ClassPathResource resource = new ClassPathResource("other/oneline.txt");
    File file = resource.getFile();
    AbstractCache<VocabWord> vocabCache = new AbstractCache.Builder<VocabWord>().build();
    BasicLineIterator underlyingIterator = new BasicLineIterator(file);
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    SentenceTransformer transformer = new SentenceTransformer.Builder().iterator(underlyingIterator).tokenizerFactory(t).build();
    AbstractSequenceIterator<VocabWord> sequenceIterator = new AbstractSequenceIterator.Builder<>(transformer).build();
    VocabConstructor<VocabWord> constructor = new VocabConstructor.Builder<VocabWord>().addSource(sequenceIterator, 1).setTargetVocabCache(vocabCache).build();
    constructor.buildJointVocabulary(false, true);
    AbstractCoOccurrences<VocabWord> coOccurrences = new AbstractCoOccurrences.Builder<VocabWord>().iterate(sequenceIterator).vocabCache(vocabCache).symmetric(false).windowSize(15).build();
    coOccurrences.fit();
    //List<Pair<VocabWord, VocabWord>> list = coOccurrences.i();
    Iterator<Pair<Pair<VocabWord, VocabWord>, Double>> iterator = coOccurrences.iterator();
    assertNotEquals(null, iterator);
    int cnt = 0;
    List<Pair<VocabWord, VocabWord>> list = new ArrayList<>();
    while (iterator.hasNext()) {
        Pair<Pair<VocabWord, VocabWord>, Double> pair = iterator.next();
        list.add(pair.getFirst());
        cnt++;
    }
    log.info("CoOccurrences: " + list);
    assertEquals(16, list.size());
    assertEquals(16, cnt);
}

Example 27 with CommonPreprocessor

use of org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor in project deeplearning4j by deeplearning4j.

the class Word2VecDataSetIteratorTest method testIterator1.

/**
     * Basically all we want from this test - being able to finish without exceptions.
     */
@Test
public void testIterator1() throws Exception {
    File inputFile = new ClassPathResource("/big/raw_sentences.txt").getFile();
    SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    Word2Vec vec = // we make sure we'll have some missing words
    new Word2Vec.Builder().minWordFrequency(10).iterations(1).learningRate(0.025).layerSize(150).seed(42).sampling(0).negativeSample(0).useHierarchicSoftmax(true).windowSize(5).modelUtils(new BasicModelUtils<VocabWord>()).useAdaGrad(false).iterate(iter).workers(8).tokenizerFactory(t).elementsLearningAlgorithm(new CBOW<VocabWord>()).build();
    vec.fit();
    List<String> labels = new ArrayList<>();
    labels.add("positive");
    labels.add("negative");
    Word2VecDataSetIterator iterator = new Word2VecDataSetIterator(vec, getLASI(iter, labels), labels, 1);
    INDArray array = iterator.next().getFeatures();
    while (iterator.hasNext()) {
        DataSet ds = iterator.next();
        assertArrayEquals(array.shape(), ds.getFeatureMatrix().shape());
    }
}

Example 28 with CommonPreprocessor

use of org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor in project deeplearning4j by deeplearning4j.

the class SequenceVectorsTest method testAbstractW2VModel.

@Test
public void testAbstractW2VModel() throws Exception {
    ClassPathResource resource = new ClassPathResource("big/raw_sentences.txt");
    File file = resource.getFile();
    logger.info("dtype: {}", Nd4j.dataType());
    AbstractCache<VocabWord> vocabCache = new AbstractCache.Builder<VocabWord>().build();
    /*
            First we build line iterator
         */
    BasicLineIterator underlyingIterator = new BasicLineIterator(file);
    /*
            Now we need the way to convert lines into Sequences of VocabWords.
            In this example that's SentenceTransformer
         */
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    SentenceTransformer transformer = new SentenceTransformer.Builder().iterator(underlyingIterator).tokenizerFactory(t).build();
    /*
            And we pack that transformer into AbstractSequenceIterator
         */
    AbstractSequenceIterator<VocabWord> sequenceIterator = new AbstractSequenceIterator.Builder<>(transformer).build();
    /*
            Now we should build vocabulary out of sequence iterator.
            We can skip this phase, and just set SequenceVectors.resetModel(TRUE), and vocabulary will be mastered internally
        */
    VocabConstructor<VocabWord> constructor = new VocabConstructor.Builder<VocabWord>().addSource(sequenceIterator, 5).setTargetVocabCache(vocabCache).build();
    constructor.buildJointVocabulary(false, true);
    assertEquals(242, vocabCache.numWords());
    assertEquals(634303, vocabCache.totalWordOccurrences());
    VocabWord wordz = vocabCache.wordFor("day");
    logger.info("Wordz: " + wordz);
    /*
            Time to build WeightLookupTable instance for our new model
        */
    WeightLookupTable<VocabWord> lookupTable = new InMemoryLookupTable.Builder<VocabWord>().lr(0.025).vectorLength(150).useAdaGrad(false).cache(vocabCache).build();
    /*
            reset model is viable only if you're setting SequenceVectors.resetModel() to false
            if set to True - it will be called internally
        */
    lookupTable.resetWeights(true);
    /*
            Now we can build SequenceVectors model, that suits our needs
         */
    SequenceVectors<VocabWord> vectors = new SequenceVectors.Builder<VocabWord>(new VectorsConfiguration()).minWordFrequency(5).lookupTable(lookupTable).iterate(sequenceIterator).vocabCache(vocabCache).batchSize(250).iterations(1).epochs(1).resetModel(false).trainElementsRepresentation(true).trainSequencesRepresentation(false).build();
    /*
            Now, after all options are set, we just call fit()
         */
    logger.info("Starting training...");
    vectors.fit();
    logger.info("Model saved...");
    /*
            As soon as fit() exits, model considered built, and we can test it.
            Please note: all similarity context is handled via SequenceElement's labels, so if you're using SequenceVectors to build models for complex
            objects/relations please take care of Labels uniqueness and meaning for yourself.
         */
    double sim = vectors.similarity("day", "night");
    logger.info("Day/night similarity: " + sim);
    assertTrue(sim > 0.6d);
    Collection<String> labels = vectors.wordsNearest("day", 10);
    logger.info("Nearest labels to 'day': " + labels);
}

Example 29 with CommonPreprocessor

use of org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor in project deeplearning4j by deeplearning4j.

the class SequenceVectorsTest method testGlove1.

@Ignore
@Test
public void testGlove1() throws Exception {
    logger.info("Max available memory: " + Runtime.getRuntime().maxMemory());
    ClassPathResource resource = new ClassPathResource("big/raw_sentences.txt");
    File file = resource.getFile();
    BasicLineIterator underlyingIterator = new BasicLineIterator(file);
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    SentenceTransformer transformer = new SentenceTransformer.Builder().iterator(underlyingIterator).tokenizerFactory(t).build();
    AbstractSequenceIterator<VocabWord> sequenceIterator = new AbstractSequenceIterator.Builder<>(transformer).build();
    VectorsConfiguration configuration = new VectorsConfiguration();
    configuration.setWindow(5);
    configuration.setLearningRate(0.06);
    configuration.setLayersSize(100);
    SequenceVectors<VocabWord> vectors = new SequenceVectors.Builder<VocabWord>(configuration).iterate(sequenceIterator).iterations(1).epochs(45).elementsLearningAlgorithm(new GloVe.Builder<VocabWord>().shuffle(true).symmetric(true).learningRate(0.05).alpha(0.75).xMax(100.0).build()).resetModel(true).trainElementsRepresentation(true).trainSequencesRepresentation(false).build();
    vectors.fit();
    double sim = vectors.similarity("day", "night");
    logger.info("Day/night similarity: " + sim);
    sim = vectors.similarity("day", "another");
    logger.info("Day/another similarity: " + sim);
    sim = vectors.similarity("night", "year");
    logger.info("Night/year similarity: " + sim);
    sim = vectors.similarity("night", "me");
    logger.info("Night/me similarity: " + sim);
    sim = vectors.similarity("day", "know");
    logger.info("Day/know similarity: " + sim);
    sim = vectors.similarity("best", "police");
    logger.info("Best/police similarity: " + sim);
    Collection<String> labels = vectors.wordsNearest("day", 10);
    logger.info("Nearest labels to 'day': " + labels);
    sim = vectors.similarity("day", "night");
    assertTrue(sim > 0.6d);
}