Examples with CMLCRF edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF used on opensource projects

Example 1 with CMLCRF

use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.

the class MultiLabelSynthesizer method crfSample.

public static MultiLabelClfDataSet crfSample() {
    int numData = 10000;
    int numClass = 4;
    int numFeature = 2;
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(numFeature).numClasses(numClass).numDataPoints(numData).build();
    List<MultiLabel> support = Enumerator.enumerate(numClass);
    CMLCRF cmlcrf = new CMLCRF(numClass, numFeature, support);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(0, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(1, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(0, -10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(1, -10);
    // generate features
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < numFeature; j++) {
            dataSet.setFeatureValue(i, j, Sampling.doubleUniform(-1, 1));
        }
    }
    SamplingPredictor samplingPredictor = new SamplingPredictor(cmlcrf);
    // assign labels
    for (int i = 0; i < numData; i++) {
        MultiLabel label = samplingPredictor.predict(dataSet.getRow(i));
        dataSet.setLabels(i, label);
    }
    return dataSet;
}

Example 2 with CMLCRF

use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.

the class MultiLabelSynthesizer method crfArgmaxHide.

public static MultiLabelClfDataSet crfArgmaxHide() {
    int numData = 10000;
    int numClass = 4;
    int numFeature = 2;
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(numFeature).numClasses(numClass).numDataPoints(numData).build();
    List<MultiLabel> support = Enumerator.enumerate(numClass);
    CMLCRF cmlcrf = new CMLCRF(numClass, numFeature, support);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(0, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(1, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(0, -10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(1, -10);
    // generate features
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < numFeature; j++) {
            dataSet.setFeatureValue(i, j, Sampling.doubleUniform(-1, 1));
        }
    }
    SubsetAccPredictor predictor = new SubsetAccPredictor(cmlcrf);
    // assign labels
    for (int i = 0; i < numData; i++) {
        MultiLabel label = predictor.predict(dataSet.getRow(i));
        dataSet.setLabels(i, label);
    }
    // hide one feature
    for (int i = 0; i < numData; i++) {
        dataSet.setFeatureValue(i, 0, 0);
    }
    return dataSet;
}

Example 3 with CMLCRF

use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.

the class MultiLabelSynthesizer method crfArgmaxDrop.

public static MultiLabelClfDataSet crfArgmaxDrop() {
    int numData = 1000;
    int numClass = 4;
    int numFeature = 10;
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(numFeature).numClasses(numClass).numDataPoints(numData).build();
    List<MultiLabel> support = Enumerator.enumerate(numClass);
    CMLCRF cmlcrf = new CMLCRF(numClass, numFeature, support);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(0, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(1, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(0, -10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(1, -10);
    // generate features
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < numFeature; j++) {
            dataSet.setFeatureValue(i, j, Sampling.doubleUniform(-1, 1));
        }
    }
    SubsetAccPredictor predictor = new SubsetAccPredictor(cmlcrf);
    // drop labels
    double[] alphas = { 1, 0.9, 0.8, 0.7 };
    // assign labels
    for (int i = 0; i < numData; i++) {
        // System.out.println(dataSet.getRow(i));
        MultiLabel label = predictor.predict(dataSet.getRow(i)).copy();
        for (int l = 0; l < numClass; l++) {
            if (Math.random() > alphas[l] && label.matchClass(l)) {
                // System.out.println("drop");
                label.removeLabel(l);
            }
        }
        dataSet.setLabels(i, label);
    }
    return dataSet;
}

Example 4 with CMLCRF

use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.

the class MultiLabelSynthesizer method crfArgmax.

public static MultiLabelClfDataSet crfArgmax() {
    int numData = 1000;
    int numClass = 4;
    int numFeature = 10;
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(numFeature).numClasses(numClass).numDataPoints(numData).build();
    List<MultiLabel> support = Enumerator.enumerate(numClass);
    CMLCRF cmlcrf = new CMLCRF(numClass, numFeature, support);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(0, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(1, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(0, 10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(1, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(0, -10);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(1, -10);
    // generate features
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < numFeature; j++) {
            dataSet.setFeatureValue(i, j, Sampling.doubleUniform(-1, 1));
        }
    }
    SubsetAccPredictor predictor = new SubsetAccPredictor(cmlcrf);
    // assign labels
    for (int i = 0; i < numData; i++) {
        MultiLabel label = predictor.predict(dataSet.getRow(i));
        dataSet.setLabels(i, label);
    }
    return dataSet;
}

Example 5 with CMLCRF

use of edu.neu.ccs.pyramid.multilabel_classification.crf.CMLCRF in project pyramid by cheng-li.

the class CMLCRFTest method test9.

private static void test9() {
    MultiLabelClfDataSet train = MultiLabelSynthesizer.independentNoise();
    MultiLabelClfDataSet test = MultiLabelSynthesizer.independent();
    CMLCRF cmlcrf = new CMLCRF(train);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(0, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(0).set(1, 1);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(0, 1);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(1).set(1, 1);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(0, 1);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(2).set(1, 0);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(0, 1);
    cmlcrf.getWeights().getWeightsWithoutBiasForClass(3).set(1, -1);
    CRFLoss crfLoss = new CRFLoss(cmlcrf, train, 1);
    System.out.println(cmlcrf);
    System.out.println("initial loss = " + crfLoss.getValue());
    System.out.println("training performance");
    System.out.println(new MLMeasures(cmlcrf, train));
    System.out.println("test performance");
    System.out.println(new MLMeasures(cmlcrf, test));
    LBFGS optimizer = new LBFGS(crfLoss);
    while (!optimizer.getTerminator().shouldTerminate()) {
        System.out.println("------------");
        optimizer.iterate();
        System.out.println(optimizer.getTerminator().getLastValue());
        System.out.println("training performance");
        System.out.println(new MLMeasures(cmlcrf, train));
        System.out.println("test performance");
        System.out.println(new MLMeasures(cmlcrf, test));
    }
    System.out.println(cmlcrf);
}