Examples with Model Classifier.supervised.liblinear.Model used on opensource projects

Example 1 with Model

use of Classifier.supervised.liblinear.Model in project IR_Base by Linda-sunshine.

the class ACCTM_CZLR method updateFeatureWeight.

public void updateFeatureWeight(_ParentDoc pDoc, int iter, File weightIterFolder) {
    int totalChildWordNum = 0;
    int featureLen = 0;
    ArrayList<Double> targetValList = new ArrayList<Double>();
    ArrayList<Feature[]> featureList = new ArrayList<Feature[]>();
    for (_ChildDoc cDoc : pDoc.m_childDocs) {
        for (_Word w : cDoc.getWords()) {
            double[] wordFeatures = w.getFeatures();
            double x = w.getX();
            featureLen = wordFeatures.length;
            Feature[] featureVec = new Feature[featureLen];
            for (int i = 0; i < featureLen; i++) {
                featureVec[i] = new FeatureNode(i + 1, wordFeatures[i]);
            }
            featureList.add(featureVec);
            targetValList.add(x);
        }
    }
    totalChildWordNum = featureList.size();
    double[] targetVal = new double[totalChildWordNum];
    Feature[][] featureMatrix = new Feature[totalChildWordNum][];
    for (int i = 0; i < totalChildWordNum; i++) {
        featureMatrix[i] = featureList.get(i);
    }
    for (int i = 0; i < totalChildWordNum; i++) {
        targetVal[i] = targetValList.get(i);
    }
    Problem problem = new Problem();
    problem.l = totalChildWordNum;
    // featureNum
    problem.n = featureLen + 1;
    problem.x = featureMatrix;
    problem.y = targetVal;
    SolverType solver = SolverType.L2R_LR;
    double C = 1.0;
    double eps = 0.01;
    Parameter param = new Parameter(solver, C, eps);
    Model model = Linear.train(problem, param);
    int featureNum = model.getNrFeature();
    for (int i = 0; i < featureNum; i++) pDoc.m_featureWeight[i] = model.getDecfunCoef(i, 0);
    String weightFile = pDoc.getName() + ".txt";
    File modelFile = new File(weightIterFolder, weightFile);
    try {
        // if((iter>200)&&(iter%100==0))
        model.save(modelFile);
    } catch (Exception e) {
        System.out.println(e.getMessage());
    }
}

Example 2 with Model

use of Classifier.supervised.liblinear.Model in project IR_Base by Linda-sunshine.

the class L2RMetricLearning method L2RModelTraining.

protected void L2RModelTraining() {
    // select the training pairs
    createTrainingCorpus();
    if (m_ranker == 0) {
        ArrayList<Feature[]> fvs = new ArrayList<Feature[]>();
        ArrayList<Integer> labels = new ArrayList<Integer>();
        for (_Query q : m_queries) q.extractPairs4RankSVM(fvs, labels);
        Model rankSVM = SVM.libSVMTrain(fvs, labels, RankFVSize, SolverType.L2R_L1LOSS_SVC_DUAL, m_tradeoff, -1);
        m_weights = rankSVM.getFeatureWeights();
        System.out.format("RankSVM training performance:\nMAP: %.4f\n", evaluate(OptimizationType.OT_MAP));
    } else if (m_ranker == 1) {
        // all the rest use LambdaRank with different evaluator
        LambdaRank lambdaRank;
        if (m_multithread) {
            /**
             ** multi-thread version ***
             */
            lambdaRank = new LambdaRankParallel(RankFVSize, m_tradeoff, m_queries, OptimizationType.OT_MAP, 10);
            lambdaRank.setSigns(getRankingFVSigns());
            // lambdaRank specific parameters
            lambdaRank.train(100, 100, 1.0, 0.95);
        } else {
            /**
             ** single-thread version ***
             */
            lambdaRank = new LambdaRank(RankFVSize, m_tradeoff, m_queries, OptimizationType.OT_MAP);
            lambdaRank.setSigns(getRankingFVSigns());
            // lambdaRank specific parameters
            lambdaRank.train(300, 20, 1.0, 0.98);
        }
        m_weights = lambdaRank.getWeights();
    } else if (m_ranker == 2) {
        RankNet ranknet = new RankNet(RankFVSize, 5.0);
        ArrayList<double[]> rfvs = new ArrayList<double[]>();
        for (_Query q : m_queries) q.extractPairs4RankNet(rfvs);
        ranknet.setSigns(getRankingFVSigns());
        double likelihood = ranknet.train(rfvs);
        m_weights = ranknet.getWeights();
        System.out.format("RankNet training performance:\nlog-likelihood: %.4f\t MAP: %.4f\n", likelihood, evaluate(OptimizationType.OT_MAP));
    }
    for (int i = 0; i < RankFVSize; i++) System.out.format("%.5f ", m_weights[i]);
    System.out.println();
}