Java Code Examples for org.deeplearning4j.models.embeddings.loader.WordVectorSerializer#loadTxtVectors()

@Test
@Ignore
public void testWriteWordVectors() throws IOException {
    WordVectors vec = WordVectorSerializer.readWord2VecModel(binaryFile);
    InMemoryLookupTable lookupTable = (InMemoryLookupTable) vec.lookupTable();
    InMemoryLookupCache lookupCache = (InMemoryLookupCache) vec.vocab();
    WordVectorSerializer.writeWordVectors(lookupTable, lookupCache, pathToWriteto);

    WordVectors wordVectors = WordVectorSerializer.loadTxtVectors(new File(pathToWriteto));
    double[] wordVector1 = wordVectors.getWordVector("Morgan_Freeman");
    double[] wordVector2 = wordVectors.getWordVector("JA_Montalbano");
    assertTrue(wordVector1.length == 300);
    assertTrue(wordVector2.length == 300);
    assertEquals(Doubles.asList(wordVector1).get(0), 0.044423, 1e-3);
    assertEquals(Doubles.asList(wordVector2).get(0), 0.051964, 1e-3);
}
 

@Test
@Ignore
public void testWriteWordVectorsFromWord2Vec() throws IOException {
    WordVectors vec = WordVectorSerializer.readWord2VecModel(binaryFile, true);
    WordVectorSerializer.writeWordVectors((Word2Vec) vec, pathToWriteto);

    WordVectors wordVectors = WordVectorSerializer.loadTxtVectors(new File(pathToWriteto));
    INDArray wordVector1 = wordVectors.getWordVectorMatrix("Morgan_Freeman");
    INDArray wordVector2 = wordVectors.getWordVectorMatrix("JA_Montalbano");
    assertEquals(vec.getWordVectorMatrix("Morgan_Freeman"), wordVector1);
    assertEquals(vec.getWordVectorMatrix("JA_Montalbano"), wordVector2);
    assertTrue(wordVector1.length() == 300);
    assertTrue(wordVector2.length() == 300);
    assertEquals(wordVector1.getDouble(0), 0.044423, 1e-3);
    assertEquals(wordVector2.getDouble(0), 0.051964, 1e-3);
}
 

/**
 * This method tests CSV file loading via unified loader
 *
 * @throws Exception
 */
@Test
public void testUnifiedLoaderText() throws Exception {
    logger.info("Executor name: {}", Nd4j.getExecutioner().getClass().getSimpleName());

    WordVectors vectorsLive = WordVectorSerializer.loadTxtVectors(textFile);
    WordVectors vectorsUnified = WordVectorSerializer.readWord2VecModel(textFile, true);

    INDArray arrayLive = vectorsLive.getWordVectorMatrix("Morgan_Freeman");
    INDArray arrayStatic = vectorsUnified.getWordVectorMatrix("Morgan_Freeman");

    assertNotEquals(null, arrayLive);
    assertEquals(arrayLive, arrayStatic);

    // we're trying EXTENDED model, but file doesn't have syn1/huffman info, so it should be silently degraded to simplified model
    assertEquals(null, ((InMemoryLookupTable) vectorsUnified.lookupTable()).getSyn1());
}
 

@Test
public void testLoadingWordVectors() throws Exception {
    String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
    if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
        skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
    }

    File modelFile = new File(pathToWriteto);
    if (!modelFile.exists()) {
        testRunWord2Vec();
    }
    WordVectors wordVectors = WordVectorSerializer.loadTxtVectors(modelFile);
    Collection<String> lst = wordVectors.wordsNearest("day", 10);
    System.out.println(Arrays.toString(lst.toArray()));
}
 

@Test
@Ignore("AB 2019/06/24 - Failing: Ignored to get to all passing baseline to prevent regressions via CI - see issue #7912")
public void testIndexPersistence() throws Exception {
    File inputFile = Resources.asFile("big/raw_sentences.txt");
    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);
    }
}
 

@Test
@Ignore
public void testLoader() throws Exception {
    WordVectors vec = WordVectorSerializer.loadTxtVectors(new File("/home/raver119/Downloads/_vectors.txt"));

    logger.info("Rewinding: " + Arrays.toString(vec.getWordVector("rewinding")));
}
 

/**
 * This method tests CSV file loading as static model
 *
 * @throws Exception
 */
@Test
@Ignore("AB 2019/06/24 - Failing: Ignored to get to all passing baseline to prevent regressions via CI - see issue #7912")
public void testStaticLoaderText() throws Exception {
    logger.info("Executor name: {}", Nd4j.getExecutioner().getClass().getSimpleName());

    WordVectors vectorsLive = WordVectorSerializer.loadTxtVectors(textFile);
    WordVectors vectorsStatic = WordVectorSerializer.loadStaticModel(textFile);

    INDArray arrayLive = vectorsLive.getWordVectorMatrix("Morgan_Freeman");
    INDArray arrayStatic = vectorsStatic.getWordVectorMatrix("Morgan_Freeman");

    assertNotEquals(null, arrayLive);
    assertEquals(arrayLive, arrayStatic);
}
 

@Test
@Ignore
public void testPortugeseW2V() throws Exception {
    WordVectors word2Vec = WordVectorSerializer.loadTxtVectors(new File("/ext/Temp/para.txt"));
    word2Vec.setModelUtils(new FlatModelUtils());

    Collection<String> portu = word2Vec.wordsNearest("carro", 10);
    printWords("carro", portu, word2Vec);

    portu = word2Vec.wordsNearest("davi", 10);
    printWords("davi", portu, word2Vec);
}
 

@Test
@Ignore("AB 2019/06/24 - Failing: Ignored to get to all passing baseline to prevent regressions via CI - see issue #7912")
public void testOutputStream() throws Exception {
    File file = File.createTempFile("tmp_ser", "ssa");
    file.deleteOnExit();

    File inputFile = Resources.asFile("big/raw_sentences.txt");
    SentenceIterator iter = new BasicLineIterator(inputFile);
    // Split on white spaces in the line to get words
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());

    InMemoryLookupCache cache = new InMemoryLookupCache(false);
    WeightLookupTable table = new InMemoryLookupTable.Builder().vectorLength(100).useAdaGrad(false).negative(5.0)
                    .cache(cache).lr(0.025f).build();

    Word2Vec vec = new Word2Vec.Builder().minWordFrequency(5).iterations(1).epochs(1).layerSize(100)
                    .lookupTable(table).stopWords(new ArrayList<String>()).useAdaGrad(false).negativeSample(5)
                    .vocabCache(cache).seed(42)
                    //                .workers(6)
                    .windowSize(5).iterate(iter).tokenizerFactory(t).build();

    assertEquals(new ArrayList<String>(), vec.getStopWords());
    vec.fit();

    INDArray day1 = vec.getWordVectorMatrix("day");

    WordVectorSerializer.writeWordVectors(vec, new FileOutputStream(file));

    WordVectors vec2 = WordVectorSerializer.loadTxtVectors(file);

    INDArray day2 = vec2.getWordVectorMatrix("day");

    assertEquals(day1, day2);

    File tempFile = File.createTempFile("tetsts", "Fdfs");
    tempFile.deleteOnExit();

    WordVectorSerializer.writeWord2VecModel(vec, tempFile);

    Word2Vec vec3 = WordVectorSerializer.readWord2VecModel(tempFile);
}
 

@Test
public void testConcepts() throws Exception {
    // These are all default values for word2vec
    SparkConf sparkConf = new SparkConf().setMaster("local[8]")
            .set("spark.driver.host", "localhost")
            .setAppName("sparktest");

    // Set SparkContext
    JavaSparkContext sc = new JavaSparkContext(sparkConf);

    // Path of data part-00000
    String dataPath = new ClassPathResource("big/raw_sentences.txt").getFile().getAbsolutePath();
    //        dataPath = "/ext/Temp/part-00000";
    //        String dataPath = new ClassPathResource("spark_word2vec_test.txt").getFile().getAbsolutePath();

    // Read in data
    JavaRDD<String> corpus = sc.textFile(dataPath);

    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());

    Word2Vec word2Vec = new Word2Vec.Builder().setNGrams(1)
                    //     .setTokenizer("org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory")
                    //     .setTokenPreprocessor("org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor")
                    //     .setRemoveStop(false)
                    .tokenizerFactory(t).seed(42L).negative(10).useAdaGrad(false).layerSize(150).windowSize(5)
                    .learningRate(0.025).minLearningRate(0.0001).iterations(1).batchSize(100).minWordFrequency(5)
                    .stopWords(Arrays.asList("three")).useUnknown(true).build();

    word2Vec.train(corpus);

    //word2Vec.setModelUtils(new FlatModelUtils());

    System.out.println("UNK: " + word2Vec.getWordVectorMatrix("UNK"));

    InMemoryLookupTable<VocabWord> table = (InMemoryLookupTable<VocabWord>) word2Vec.lookupTable();

    double sim = word2Vec.similarity("day", "night");
    System.out.println("day/night similarity: " + sim);
    /*
    System.out.println("Hornjo: " + word2Vec.getWordVectorMatrix("hornjoserbsce"));
    System.out.println("carro: " + word2Vec.getWordVectorMatrix("carro"));
    
    Collection<String> portu = word2Vec.wordsNearest("carro", 10);
    printWords("carro", portu, word2Vec);
    
    portu = word2Vec.wordsNearest("davi", 10);
    printWords("davi", portu, word2Vec);
    
    System.out.println("---------------------------------------");
    */

    Collection<String> words = word2Vec.wordsNearest("day", 10);
    printWords("day", words, word2Vec);

    assertTrue(words.contains("night"));
    assertTrue(words.contains("week"));
    assertTrue(words.contains("year"));

    sim = word2Vec.similarity("two", "four");
    System.out.println("two/four similarity: " + sim);

    words = word2Vec.wordsNearest("two", 10);
    printWords("two", words, word2Vec);

    // three should be absent due to stopWords
    assertFalse(words.contains("three"));

    assertTrue(words.contains("five"));
    assertTrue(words.contains("four"));

    sc.stop();


    // test serialization
    File tempFile = testDir.newFile("temp" + System.currentTimeMillis() + ".tmp");

    int idx1 = word2Vec.vocab().wordFor("day").getIndex();

    INDArray array1 = word2Vec.getWordVectorMatrix("day").dup();

    VocabWord word1 = word2Vec.vocab().elementAtIndex(0);

    WordVectorSerializer.writeWordVectors(word2Vec.getLookupTable(), tempFile);

    WordVectors vectors = WordVectorSerializer.loadTxtVectors(tempFile);

    VocabWord word2 = ((VocabCache<VocabWord>) vectors.vocab()).elementAtIndex(0);
    VocabWord wordIT = ((VocabCache<VocabWord>) vectors.vocab()).wordFor("it");
    int idx2 = vectors.vocab().wordFor("day").getIndex();

    INDArray array2 = vectors.getWordVectorMatrix("day").dup();

    System.out.println("word 'i': " + word2);
    System.out.println("word 'it': " + wordIT);

    assertEquals(idx1, idx2);
    assertEquals(word1, word2);
    assertEquals(array1, array2);
}
 

@Test
public void testPosting() throws Exception {

    //        File inputFile = Resources.asFile("big/raw_sentences.txt");
    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).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);

    UIServer.getInstance(); //Initialize

    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);
}