Package opennlp.model

Examples of opennlp.model.DataIndexer

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  int numSequences;

  public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;
    DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false);
    numSequences = 0;
    for (Sequence s : sequenceStream) {
      numSequences++;
    }
    outcomeList  = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new IndexHashTable<String>(predLabels, 0.7d);
     
    display("Incorporating indexed data for training...  \n");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<String,Integer>();
    for (int oli=0;oli<outcomeLabels.length;oli++) {
      omap.put(outcomeLabels[oli], oli);
    }
    outcomeList = di.getOutcomeList();

    numPreds = predLabels.length;
    numOutcomes = outcomeLabels.length;
    if (useAverage) {
      updates = new int[numPreds][numOutcomes][3];
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  int numSequences;

  public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;
    DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false);
    numSequences = 0;
    for (Sequence s : sequenceStream) {
      numSequences++;
    }
    outcomeList  = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new IndexHashTable<String>(predLabels, 0.7d);
     
    display("Incorporating indexed data for training...  \n");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<String,Integer>();
    for (int oli=0;oli<outcomeLabels.length;oli++) {
      omap.put(outcomeLabels[oli], oli);
    }
    outcomeList = di.getOutcomeList();

    numPreds = predLabels.length;
    numOutcomes = outcomeLabels.length;
    if (useAverage) {
      updates = new int[numPreds][numOutcomes][3];
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  @Test
  public void testDomainDimensionSanity() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8"); 
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    int correctDomainDimension = testDataIndexer.getPredLabels().length * testDataIndexer.getOutcomeLabels().length;
    // then
    assertEquals(correctDomainDimension, objectFunction.getDomainDimension());
  }
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  @Test
  public void testInitialSanity() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8"); 
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] initial = objectFunction.getInitialPoint();
    // then
    for (int i = 0; i < initial.length; i++) {
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  @Test
  public void testGradientSanity() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8"); 
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] initial = objectFunction.getInitialPoint();
    double[] gradientAtInitial = objectFunction.gradientAt(initial);
    // then
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  @Test
  public void testValueAtInitialPoint() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double value = objectFunction.valueAt(objectFunction.getInitialPoint());
    double expectedValue = -13.86294361;
    // then
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  @Test
  public void testValueAtNonInitialPoint01() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] nonInitialPoint = new double[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
    double value = objectFunction.valueAt(nonInitialPoint);
    double expectedValue = -0.000206886;
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  @Test
  public void testValueAtNonInitialPoint02() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] nonInitialPoint = new double[] { 3, 2, 3, 2, 3, 2, 3, 2, 3, 2 };
    double value = objectFunction.valueAt(dealignDoubleArrayForTestData(nonInitialPoint,
      testDataIndexer.getPredLabels(),
      testDataIndexer.getOutcomeLabels()));
    double expectedValue = -0.00000000285417;
    // then
    assertEquals(expectedValue, value, TOLERANCE02);
  }
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  @Test
  public void testGradientAtInitialPoint() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] gradientAtInitialPoint = objectFunction.gradientAt(objectFunction.getInitialPoint());
    double[] expectedGradient = new double[] { -9, -14, -17, 20, 8.5, 9, 14, 17, -20, -8.5 };
    // then
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  @Test
  public void testGradientAtNonInitialPoint() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] nonInitialPoint = new double[] { 0.2, 0.5, 0.2, 0.5, 0.2,
        0.5, 0.2, 0.5, 0.2, 0.5 };
    double[] gradientAtNonInitialPoint =
        objectFunction.gradientAt(dealignDoubleArrayForTestData(nonInitialPoint,
            testDataIndexer.getPredLabels(),
            testDataIndexer.getOutcomeLabels()));
    double[] expectedGradient =
            new double[] { -0.311616214, -0.211771052, -1.324041847, 0.93340278, 0.317407069,
        0.311616214, 0.211771052, 1.324041847, -0.93340278, -0.317407069 };  
    // then
    assertTrue(compareDoubleArray(expectedGradient, gradientAtNonInitialPoint, testDataIndexer, TOLERANCE01));
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Related Classes of opennlp.model.DataIndexer

  • opennlp.maxent.quasinewton.LogLikelihoodFunctionTest
  • opennlp.maxent.quasinewton.QNTrainerTest
  • opennlp.perceptron.SimplePerceptronSequenceTrainer
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