Examples with MultiLabelClfDataSet edu.neu.ccs.pyramid.dataset.MultiLabelClfDataSet used on opensource projects

Example 46 with MultiLabelClfDataSet

use of edu.neu.ccs.pyramid.dataset.MultiLabelClfDataSet in project pyramid by cheng-li.

the class KLLossTest method test1.

private static void test1() throws Exception {
    MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "spam/trec_data/train.trec"), DataSetType.ML_CLF_SPARSE, true);
    MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "spam/trec_data/test.trec"), DataSetType.ML_CLF_SPARSE, true);
    CMLCRF cmlcrf = new CMLCRF(dataSet);
    List<MultiLabel> support = cmlcrf.getSupportCombinations();
    double[][] targetDistribution = new double[dataSet.getNumDataPoints()][cmlcrf.getSupportCombinations().size()];
    for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
        for (int c = 0; c < support.size(); c++) {
            MultiLabel multiLabel = dataSet.getMultiLabels()[i];
            if (support.get(c).equals(multiLabel)) {
                targetDistribution[i][c] = 1;
            }
        }
    }
    System.out.println("start");
    KLLoss klLoss = new KLLoss(cmlcrf, dataSet, targetDistribution, 1);
    cmlcrf.setConsiderPair(true);
    MultiLabel[] predTrain;
    MultiLabel[] predTest;
    LBFGS optimizer = new LBFGS(klLoss);
    for (int i = 0; i < 200; i++) {
        //            System.out.print("Obj: " + optimizer.getTerminator().getLastValue());
        System.out.println("iter: " + i);
        optimizer.iterate();
        System.out.println(klLoss.getValue());
        predTrain = cmlcrf.predict(dataSet);
        predTest = cmlcrf.predict(testSet);
        System.out.print("\tTrain acc: " + Accuracy.accuracy(dataSet.getMultiLabels(), predTrain));
        System.out.print("\tTrain overlap " + Overlap.overlap(dataSet.getMultiLabels(), predTrain));
        System.out.print("\tTest acc: " + Accuracy.accuracy(testSet.getMultiLabels(), predTest));
        System.out.println("\tTest overlap " + Overlap.overlap(testSet.getMultiLabels(), predTest));
    }
}

Example 47 with MultiLabelClfDataSet

use of edu.neu.ccs.pyramid.dataset.MultiLabelClfDataSet in project pyramid by cheng-li.

the class LogRiskOptimizerTest method test3.

private static void test3() {
    MultiLabelClfDataSet train = MultiLabelSynthesizer.crfSample();
    MultiLabelClfDataSet test = MultiLabelSynthesizer.crfSample();
    CMLCRF cmlcrf = new CMLCRF(train);
    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);
    MLScorer fScorer = new FScorer();
    MLScorer accScorer = new AccScorer();
    SubsetAccPredictor plugInAcc = new SubsetAccPredictor(cmlcrf);
    LogRiskOptimizer fOptimizer = new LogRiskOptimizer(train, fScorer, cmlcrf, 1, false, false, 1, 1);
    InstanceF1Predictor plugInF1 = new InstanceF1Predictor(cmlcrf);
    System.out.println(cmlcrf);
    System.out.println("training performance acc");
    System.out.println(new MLMeasures(cmlcrf, train));
    System.out.println("test performance acc");
    System.out.println(new MLMeasures(cmlcrf, test));
    System.out.println("training performance f1");
    System.out.println(new MLMeasures(plugInF1, train));
    System.out.println("test performance f1");
    System.out.println(new MLMeasures(plugInF1, test));
    LogRiskOptimizer accOptimizer = new LogRiskOptimizer(train, accScorer, cmlcrf, 1, false, false, 1, 1);
    accOptimizer.iterate();
    System.out.println("after ML estimation");
    System.out.println("training with Acc predictor");
    System.out.println(new MLMeasures(plugInAcc, train));
    System.out.println("training with F1 predictor");
    System.out.println(new MLMeasures(plugInF1, train));
    System.out.println("test with Acc predictor");
    System.out.println(new MLMeasures(plugInAcc, test));
    System.out.println("test with F1 predictor");
    System.out.println(new MLMeasures(plugInF1, test));
    System.out.println(cmlcrf);
    System.out.println(fOptimizer.objectiveDetail());
    while (!fOptimizer.getTerminator().shouldTerminate()) {
        System.out.println("------------");
        fOptimizer.iterate();
        System.out.println(fOptimizer.objectiveDetail());
        System.out.println("training performance acc");
        System.out.println(new MLMeasures(cmlcrf, train));
        System.out.println("test performance acc");
        System.out.println(new MLMeasures(cmlcrf, test));
        System.out.println("training performance f1");
        System.out.println(new MLMeasures(plugInF1, train));
        System.out.println("test performance f1");
        System.out.println(new MLMeasures(plugInF1, test));
        System.out.println(cmlcrf);
    }
}

Example 48 with MultiLabelClfDataSet

use of edu.neu.ccs.pyramid.dataset.MultiLabelClfDataSet in project pyramid by cheng-li.

the class LogRiskOptimizerTest method test4.

private static void test4() {
    MultiLabelClfDataSet train = MultiLabelSynthesizer.crfArgmaxHide();
    MultiLabelClfDataSet test = MultiLabelSynthesizer.crfArgmaxHide();
    CMLCRF cmlcrf = new CMLCRF(train);
    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);
    MLScorer fScorer = new FScorer();
    MLScorer accScorer = new AccScorer();
    SubsetAccPredictor plugInAcc = new SubsetAccPredictor(cmlcrf);
    LogRiskOptimizer fOptimizer = new LogRiskOptimizer(train, fScorer, cmlcrf, 1, false, false, 1, 1);
    InstanceF1Predictor plugInF1 = new InstanceF1Predictor(cmlcrf);
    System.out.println(cmlcrf);
    System.out.println("training performance acc");
    System.out.println(new MLMeasures(cmlcrf, train));
    System.out.println("test performance acc");
    System.out.println(new MLMeasures(cmlcrf, test));
    System.out.println("training performance f1");
    System.out.println(new MLMeasures(plugInF1, train));
    System.out.println("test performance f1");
    System.out.println(new MLMeasures(plugInF1, test));
    System.out.println(fOptimizer.objectiveDetail());
    LogRiskOptimizer accOptimizer = new LogRiskOptimizer(train, accScorer, cmlcrf, 1, false, false, 1, 1);
    accOptimizer.iterate();
    System.out.println("after ML estimation");
    System.out.println("training with Acc predictor");
    System.out.println(new MLMeasures(plugInAcc, train));
    System.out.println("training with F1 predictor");
    System.out.println(new MLMeasures(plugInF1, train));
    System.out.println("test with Acc predictor");
    System.out.println(new MLMeasures(plugInAcc, test));
    System.out.println("test with F1 predictor");
    System.out.println(new MLMeasures(plugInF1, test));
    System.out.println(cmlcrf);
    System.out.println(fOptimizer.objectiveDetail());
    while (!fOptimizer.getTerminator().shouldTerminate()) {
        System.out.println("------------");
        fOptimizer.iterate();
        System.out.println(fOptimizer.objectiveDetail());
        System.out.println("training performance acc");
        System.out.println(new MLMeasures(cmlcrf, train));
        System.out.println("test performance acc");
        System.out.println(new MLMeasures(cmlcrf, test));
        System.out.println("training performance f1");
        System.out.println(new MLMeasures(plugInF1, train));
        System.out.println("test performance f1");
        System.out.println(new MLMeasures(plugInF1, test));
        System.out.println(cmlcrf);
    }
}