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

Example 11 with MultiLabelClfDataSet

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

the class CMLCRFTest method test6.

private static void test6() throws Exception {
    MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "medical/train"), DataSetType.ML_CLF_SPARSE, true);
    MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(new File(DATASETS, "medical/test"), DataSetType.ML_CLF_SPARSE, true);
    CMLCRF cmlcrf = new CMLCRF(dataSet);
    CRFLoss crfLoss = new CRFLoss(cmlcrf, dataSet, 1);
    MultiLabel[] predTrain;
    MultiLabel[] predTest;
    LBFGS optimizer = new LBFGS(crfLoss);
    for (int i = 0; i < 50; i++) {
        //            System.out.print("Obj: " + optimizer.getTerminator().getLastValue());
        System.out.println("iter: " + i);
        optimizer.iterate();
        System.out.println(crfLoss.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));
    //            System.out.println("crf = "+cmlcrf.getWeights());
    //            System.out.println(Arrays.toString(predTrain));
    }
}

Example 12 with MultiLabelClfDataSet

use of edu.neu.ccs.pyramid.dataset.MultiLabelClfDataSet 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);
}

Example 13 with MultiLabelClfDataSet

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

the class CMLCRFTest 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);
    CRFLoss crfLoss = new CRFLoss(cmlcrf, dataSet, 1);
    cmlcrf.setConsiderPair(true);
    MultiLabel[] predTrain;
    MultiLabel[] predTest;
    LBFGS optimizer = new LBFGS(crfLoss);
    for (int i = 0; i < 5000; i++) {
        //            System.out.print("Obj: " + optimizer.getTerminator().getLastValue());
        System.out.println("iter: " + i);
        optimizer.iterate();
        System.out.println(crfLoss.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));
    //            System.out.println("crf = "+cmlcrf.getWeights());
    //            System.out.println(Arrays.toString(predTrain));
    }
//        LBFGS optimizer = new LBFGS(crfLoss);
//        optimizer.getTerminator().setAbsoluteEpsilon(0.01);
//        optimizer.optimize();
//        predTrain = cmlcrf.predict(dataSet);
//        predTest = cmlcrf.predict(testSet);
//        System.out.print("Train 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 14 with MultiLabelClfDataSet

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

the class CMLCRFTest method test8.

private static void test8() throws Exception {
    System.out.println(config);
    MultiLabelClfDataSet trainSet = TRECFormat.loadMultiLabelClfDataSet(config.getString("input.trainData"), DataSetType.ML_CLF_SEQ_SPARSE, true);
    MultiLabelClfDataSet testSet = TRECFormat.loadMultiLabelClfDataSet(config.getString("input.testData"), DataSetType.ML_CLF_SEQ_SPARSE, true);
    // loading or save model infos.
    String output = config.getString("output");
    String modelName = config.getString("modelName");
    CMLCRF cmlcrf = new CMLCRF(trainSet);
    BlockwiseCD blockwiseCD = new BlockwiseCD(cmlcrf, trainSet, config.getDouble("l1Ratio"), config.getDouble("regularization"));
    MultiLabel[] predTrain;
    MultiLabel[] predTest;
    for (int i = 0; i < 10000; i++) {
        blockwiseCD.iterate();
        predTrain = cmlcrf.predict(trainSet);
        predTest = cmlcrf.predict(testSet);
        System.out.print("iter: " + String.format("%04d", i));
        System.out.print("\tobjective: " + String.format("%.4f", blockwiseCD.getValue()));
        System.out.print("\tTrain acc: " + String.format("%.4f", Accuracy.accuracy(trainSet.getMultiLabels(), predTrain)));
        System.out.print("\tTrain overlap " + String.format("%.4f", Overlap.overlap(trainSet.getMultiLabels(), predTrain)));
        System.out.print("\tTrain F1 " + String.format("%.4f", FMeasure.f1(trainSet.getMultiLabels(), predTrain)));
        System.out.print("\tTest acc: " + String.format("%.4f", Accuracy.accuracy(testSet.getMultiLabels(), predTest)));
        System.out.print("\tTest overlap " + String.format("%.4f", Overlap.overlap(testSet.getMultiLabels(), predTest)));
        System.out.println("\tTest F1 " + String.format("%.4f", FMeasure.f1(testSet.getMultiLabels(), predTest)));
    }
    System.out.println();
    System.out.println();
    System.out.println("--------------------------------Results-----------------------------\n");
    MLMeasures measures = new MLMeasures(cmlcrf, trainSet);
    System.out.println("========== Train ==========\n");
    System.out.println(measures);
    System.out.println("========== Test ==========\n");
    long startTimePred = System.nanoTime();
    MultiLabel[] preds = cmlcrf.predict(testSet);
    long stopTimePred = System.nanoTime();
    long predTime = stopTimePred - startTimePred;
    System.out.println("\nprediction time: " + TimeUnit.NANOSECONDS.toSeconds(predTime) + " sec.");
    System.out.println(new MLMeasures(cmlcrf, testSet));
    System.out.println("\n\n");
    InstanceF1Predictor pluginF1 = new InstanceF1Predictor(cmlcrf);
    System.out.println("Plugin F1");
    System.out.println(new MLMeasures(pluginF1, testSet));
    if (config.getBoolean("saveModel")) {
        (new File(output)).mkdirs();
        File serializeModel = new File(output, modelName);
        cmlcrf.serialize(serializeModel);
    }
}

Example 15 with MultiLabelClfDataSet

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

the class LogRiskOptimizerTest method test2.

private static void test2() {
    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);
    InstanceF1Predictor plugInF1 = new InstanceF1Predictor(cmlcrf);
    for (int i = 0; i < test.getNumDataPoints(); i++) {
        System.out.println("=============");
        System.out.println(i);
        System.out.println(plugInF1.showPredictBySupport(test.getRow(i), test.getMultiLabels()[i]));
    }
}