Examples with structures._QUPair structures._QUPair used on opensource projects

Example 6 with structures._QUPair

use of structures._QUPair in project IR_Base by Linda-sunshine.

the class L2RMetricLearning method createTrainingCorpus.

// In this training process, we want to get the weight of all pairs of samples.
protected int createTrainingCorpus() {
    // pre-compute the similarity between labeled documents
    calcLabeledSimilarities();
    MyPriorityQueue<_RankItem> simRanker = new MyPriorityQueue<_RankItem>(m_topK);
    ArrayList<_Doc> neighbors = new ArrayList<_Doc>();
    _Query q;
    _Doc di, dj;
    int posQ = 0, negQ = 0, pairSize = 0;
    int relevant = 0, irrelevant = 0;
    for (int i = 0; i < m_trainSet.size(); i++) {
        // candidate query document
        di = m_trainSet.get(i);
        relevant = 0;
        irrelevant = 0;
        // using content similarity to construct initial ranking
        for (int j = 0; j < m_trainSet.size(); j++) {
            if (i == j)
                continue;
            dj = m_trainSet.get(j);
            simRanker.add(new _RankItem(j, m_LabeledCache[getIndex(i, j)]));
        }
        // find the top K similar documents by default similarity measure
        for (_RankItem it : simRanker) {
            dj = m_trainSet.get(it.m_index);
            neighbors.add(dj);
            if (di.getYLabel() == dj.getYLabel())
                relevant++;
            else
                irrelevant++;
        }
        // inject some random neighbors
        int j = 0;
        while (neighbors.size() < (1.0 + m_noiseRatio) * m_topK) {
            if (i != j) {
                dj = m_trainSet.get(j);
                if (Math.random() < 0.02 && !neighbors.contains(dj)) {
                    neighbors.add(dj);
                    if (di.getYLabel() == dj.getYLabel())
                        relevant++;
                    else
                        irrelevant++;
                }
            }
            // until we use up all the random budget
            j = (j + 1) % m_trainSet.size();
        }
        if (relevant == 0 || irrelevant == 0 || (di.getYLabel() == 1 && negQ < 1.1 * posQ)) {
            // clear the cache for next query
            simRanker.clear();
            neighbors.clear();
            continue;
        } else if (di.getYLabel() == 1)
            posQ++;
        else
            negQ++;
        // accept the query
        q = new _Query();
        m_queries.add(q);
        // construct features for the most similar documents with respect to the query di
        for (_Doc d : neighbors) q.addQUPair(new _QUPair(d.getYLabel() == di.getYLabel() ? 1 : 0, genRankingFV(di, d)));
        pairSize += q.createRankingPairs();
        // clear the cache for next query
        simRanker.clear();
        neighbors.clear();
    }
    // normalize the features by z-score
    normalize();
    System.out.format("Generate %d(%d:%d) ranking pairs for L2R model training...\n", pairSize, posQ, negQ);
    return pairSize;
}

Example 7 with structures._QUPair

use of structures._QUPair in project IR_Base by Linda-sunshine.

the class LambdaRank method evaluate.

protected double evaluate() {
    double obj = 0, perf = 0, total = 0, r;
    int misorder = 0;
    for (_Query query : m_queries) {
        if ((r = m_eval.eval(query)) >= 0) {
            // ranking score should already be calculated
            perf += r;
            total++;
        }
        for (_QUPair pair : query.m_docList) {
            if (pair.m_worseURLs != null) {
                for (_QUPair worseURL : pair.m_worseURLs) {
                    if ((r = Utils.logistic(pair.m_score - worseURL.m_score)) > 0)
                        obj += Math.log(r);
                    if (pair.m_score <= worseURL.m_score)
                        misorder++;
                }
            }
            if (pair.m_betterURLs != null) {
                for (_QUPair betterURL : pair.m_betterURLs) {
                    if ((r = Utils.logistic(betterURL.m_score - pair.m_score)) > 0)
                        obj += Math.log(r);
                    if (pair.m_score >= betterURL.m_score)
                        misorder++;
                }
            }
        }
    }
    perf /= total;
    // to be maximized
    obj -= 0.5 * m_lambda * Utils.L2Norm(m_weight);
    System.out.format("%d\t%.2f\t%.4f\n", misorder / 2, obj, perf);
    return perf;
}

Example 8 with structures._QUPair

use of structures._QUPair in project IR_Base by Linda-sunshine.

the class LambdaRankWorker method evaluate.

protected void evaluate() {
    double r;
    m_obj = 0;
    m_perf = 0;
    m_misorder = 0;
    for (_Query query : m_queries) {
        // calculate ranking score with latest weight
        for (_QUPair pair : query.m_docList) pair.score(m_weight);
        if (// ranking score should already be calculated
        (r = m_eval.eval(query)) >= 0)
            m_perf += r;
        for (_QUPair pair : query.m_docList) {
            if (pair.m_worseURLs != null) {
                for (_QUPair worseURL : pair.m_worseURLs) {
                    if ((r = Utils.logistic(pair.m_score - worseURL.m_score)) > 0)
                        m_obj += Math.log(r);
                    if (pair.m_score <= worseURL.m_score)
                        m_misorder++;
                }
            }
            if (pair.m_betterURLs != null) {
                for (_QUPair betterURL : pair.m_betterURLs) {
                    if ((r = Utils.logistic(betterURL.m_score - pair.m_score)) > 0)
                        m_obj += Math.log(r);
                    if (pair.m_score >= betterURL.m_score)
                        m_misorder++;
                }
            }
        }
    }
    m_misorder /= 2;
}

Example 9 with structures._QUPair

use of structures._QUPair in project IR_Base by Linda-sunshine.

the class NDCG_Evaluator method updateDeltas.

@Override
public void updateDeltas() {
    super.updateDeltas();
    // create cache for delta
    HashMap<_QUPair, Double> change;
    _QUPair qu1, qu2;
    double delta;
    for (int i = 0; i < Math.min(m_size, m_k); i++) {
        qu1 = m_query.m_docList.get(i);
        change = new HashMap<_QUPair, Double>();
        for (int j = i + 1; j < m_size; j++) {
            qu2 = m_query.m_docList.get(j);
            if (j >= m_k) {
                delta = (m_gain[qu1.m_y] - m_gain[qu2.m_y]) * m_discount[i];
            } else {
                delta = (m_gain[qu1.m_y] - m_gain[qu2.m_y]) * (m_discount[i] - m_discount[j]);
            }
            change.put(qu2, Math.abs(delta) / m_iDCG);
        }
        m_deltas.put(qu1, change);
    }
}

Example 10 with structures._QUPair

use of structures._QUPair in project IR_Base by Linda-sunshine.

the class L2RMetricLearning method evaluate.

double evaluate(OptimizationType otype) {
    Evaluator eval;
    if (otype.equals(OptimizationType.OT_MAP))
        eval = new MAP_Evaluator();
    else if (otype.equals(OptimizationType.OT_NDCG))
        eval = new NDCG_Evaluator(LambdaRank.NDCG_K);
    else
        eval = new Evaluator();
    double perf = 0;
    for (_Query q : m_queries) {
        for (_QUPair qu : q.m_docList) qu.score(m_weights);
        perf += eval.eval(q);
    }
    return perf / m_queries.size();
}