72cd1517d6
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. - added distributions to HostDeviceVector - using HostDeviceVector for labels, weights and base margings in MetaInfo - using HostDeviceVector for offset and data in SparsePage - other necessary refactoring * Added const version of HostDeviceVector API calls. - const versions added to calls that can trigger data transfers, e.g. DevicePointer() - updated the code that uses HostDeviceVector - objective functions now accept const HostDeviceVector<bst_float>& for predictions * Updated src/linear/updater_gpu_coordinate.cu. * Added read-only state for HostDeviceVector sync. - this means no copies are performed if both host and devices access the HostDeviceVector read-only * Fixed linter and test errors. - updated the lz4 plugin - added ConstDeviceSpan to HostDeviceVector - using device % dh::NVisibleDevices() for the physical device number, e.g. in calls to cudaSetDevice() * Fixed explicit template instantiation errors for HostDeviceVector. - replaced HostDeviceVector<unsigned int> with HostDeviceVector<int> * Fixed HostDeviceVector tests that require multiple GPUs. - added a mock set device handler; when set, it is called instead of cudaSetDevice()
341 lines
13 KiB
C++
341 lines
13 KiB
C++
/*!
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* Copyright 2015 by Contributors
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* \file rank.cc
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* \brief Definition of rank loss.
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* \author Tianqi Chen, Kailong Chen
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*/
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#include <dmlc/omp.h>
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#include <xgboost/logging.h>
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#include <xgboost/objective.h>
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#include <vector>
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#include <algorithm>
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#include <utility>
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#include "../common/math.h"
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#include "../common/random.h"
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namespace xgboost {
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namespace obj {
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DMLC_REGISTRY_FILE_TAG(rank_obj);
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struct LambdaRankParam : public dmlc::Parameter<LambdaRankParam> {
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int num_pairsample;
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float fix_list_weight;
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// declare parameters
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DMLC_DECLARE_PARAMETER(LambdaRankParam) {
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DMLC_DECLARE_FIELD(num_pairsample).set_lower_bound(1).set_default(1)
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.describe("Number of pair generated for each instance.");
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DMLC_DECLARE_FIELD(fix_list_weight).set_lower_bound(0.0f).set_default(0.0f)
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.describe("Normalize the weight of each list by this value,"
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" if equals 0, no effect will happen");
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}
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};
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// objective for lambda rank
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class LambdaRankObj : public ObjFunction {
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public:
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void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
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param_.InitAllowUnknown(args);
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}
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void GetGradient(const HostDeviceVector<bst_float>& preds,
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const MetaInfo& info,
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int iter,
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HostDeviceVector<GradientPair>* out_gpair) override {
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CHECK_EQ(preds.Size(), info.labels_.Size()) << "label size predict size not match";
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const auto& preds_h = preds.HostVector();
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out_gpair->Resize(preds_h.size());
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std::vector<GradientPair>& gpair = out_gpair->HostVector();
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// quick consistency when group is not available
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std::vector<unsigned> tgptr(2, 0); tgptr[1] = static_cast<unsigned>(info.labels_.Size());
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const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
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CHECK(gptr.size() != 0 && gptr.back() == info.labels_.Size())
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<< "group structure not consistent with #rows";
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const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
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#pragma omp parallel
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{
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// parall construct, declare random number generator here, so that each
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// thread use its own random number generator, seed by thread id and current iteration
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common::RandomEngine rnd(iter * 1111 + omp_get_thread_num());
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std::vector<LambdaPair> pairs;
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std::vector<ListEntry> lst;
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std::vector< std::pair<bst_float, unsigned> > rec;
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bst_float sum_weights = 0;
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for (bst_omp_uint k = 0; k < ngroup; ++k) {
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sum_weights += info.GetWeight(k);
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}
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bst_float weight_normalization_factor = ngroup/sum_weights;
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const auto& labels = info.labels_.HostVector();
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#pragma omp for schedule(static)
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for (bst_omp_uint k = 0; k < ngroup; ++k) {
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lst.clear(); pairs.clear();
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for (unsigned j = gptr[k]; j < gptr[k+1]; ++j) {
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lst.emplace_back(preds_h[j], labels[j], j);
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gpair[j] = GradientPair(0.0f, 0.0f);
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}
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std::sort(lst.begin(), lst.end(), ListEntry::CmpPred);
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rec.resize(lst.size());
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for (unsigned i = 0; i < lst.size(); ++i) {
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rec[i] = std::make_pair(lst[i].label, i);
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}
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std::sort(rec.begin(), rec.end(), common::CmpFirst);
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// enumerate buckets with same label, for each item in the lst, grab another sample randomly
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for (unsigned i = 0; i < rec.size(); ) {
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unsigned j = i + 1;
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while (j < rec.size() && rec[j].first == rec[i].first) ++j;
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// bucket in [i,j), get a sample outside bucket
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unsigned nleft = i, nright = static_cast<unsigned>(rec.size() - j);
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if (nleft + nright != 0) {
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int nsample = param_.num_pairsample;
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while (nsample --) {
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for (unsigned pid = i; pid < j; ++pid) {
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unsigned ridx = std::uniform_int_distribution<unsigned>(0, nleft + nright - 1)(rnd);
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if (ridx < nleft) {
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pairs.emplace_back(rec[ridx].second, rec[pid].second,
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info.GetWeight(k) * weight_normalization_factor);
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} else {
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pairs.emplace_back(rec[pid].second, rec[ridx+j-i].second,
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info.GetWeight(k) * weight_normalization_factor);
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}
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}
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}
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}
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i = j;
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}
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// get lambda weight for the pairs
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this->GetLambdaWeight(lst, &pairs);
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// rescale each gradient and hessian so that the lst have constant weighted
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float scale = 1.0f / param_.num_pairsample;
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if (param_.fix_list_weight != 0.0f) {
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scale *= param_.fix_list_weight / (gptr[k + 1] - gptr[k]);
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}
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for (auto & pair : pairs) {
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const ListEntry &pos = lst[pair.pos_index];
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const ListEntry &neg = lst[pair.neg_index];
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const bst_float w = pair.weight * scale;
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const float eps = 1e-16f;
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bst_float p = common::Sigmoid(pos.pred - neg.pred);
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bst_float g = p - 1.0f;
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bst_float h = std::max(p * (1.0f - p), eps);
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// accumulate gradient and hessian in both pid, and nid
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gpair[pos.rindex] += GradientPair(g * w, 2.0f*w*h);
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gpair[neg.rindex] += GradientPair(-g * w, 2.0f*w*h);
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}
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}
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}
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}
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const char* DefaultEvalMetric() const override {
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return "map";
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}
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protected:
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/*! \brief helper information in a list */
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struct ListEntry {
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/*! \brief the predict score we in the data */
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bst_float pred;
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/*! \brief the actual label of the entry */
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bst_float label;
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/*! \brief row index in the data matrix */
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unsigned rindex;
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// constructor
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ListEntry(bst_float pred, bst_float label, unsigned rindex)
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: pred(pred), label(label), rindex(rindex) {}
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// comparator by prediction
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inline static bool CmpPred(const ListEntry &a, const ListEntry &b) {
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return a.pred > b.pred;
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}
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// comparator by label
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inline static bool CmpLabel(const ListEntry &a, const ListEntry &b) {
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return a.label > b.label;
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}
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};
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/*! \brief a pair in the lambda rank */
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struct LambdaPair {
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/*! \brief positive index: this is a position in the list */
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unsigned pos_index;
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/*! \brief negative index: this is a position in the list */
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unsigned neg_index;
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/*! \brief weight to be filled in */
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bst_float weight;
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// constructor
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LambdaPair(unsigned pos_index, unsigned neg_index)
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: pos_index(pos_index), neg_index(neg_index), weight(1.0f) {}
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// constructor
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LambdaPair(unsigned pos_index, unsigned neg_index, bst_float weight)
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: pos_index(pos_index), neg_index(neg_index), weight(weight) {}
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};
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/*!
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* \brief get lambda weight for existing pairs
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* \param list a list that is sorted by pred score
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* \param io_pairs record of pairs, containing the pairs to fill in weights
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*/
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virtual void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
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std::vector<LambdaPair> *io_pairs) = 0;
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private:
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LambdaRankParam param_;
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};
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class PairwiseRankObj: public LambdaRankObj{
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protected:
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void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
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std::vector<LambdaPair> *io_pairs) override {}
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};
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// beta version: NDCG lambda rank
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class LambdaRankObjNDCG : public LambdaRankObj {
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protected:
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void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
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std::vector<LambdaPair> *io_pairs) override {
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std::vector<LambdaPair> &pairs = *io_pairs;
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float IDCG; // NOLINT
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{
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std::vector<bst_float> labels(sorted_list.size());
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for (size_t i = 0; i < sorted_list.size(); ++i) {
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labels[i] = sorted_list[i].label;
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}
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std::sort(labels.begin(), labels.end(), std::greater<bst_float>());
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IDCG = CalcDCG(labels);
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}
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if (IDCG == 0.0) {
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for (auto & pair : pairs) {
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pair.weight = 0.0f;
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}
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} else {
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IDCG = 1.0f / IDCG;
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for (auto & pair : pairs) {
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unsigned pos_idx = pair.pos_index;
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unsigned neg_idx = pair.neg_index;
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float pos_loginv = 1.0f / std::log2(pos_idx + 2.0f);
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float neg_loginv = 1.0f / std::log2(neg_idx + 2.0f);
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auto pos_label = static_cast<int>(sorted_list[pos_idx].label);
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auto neg_label = static_cast<int>(sorted_list[neg_idx].label);
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bst_float original =
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((1 << pos_label) - 1) * pos_loginv + ((1 << neg_label) - 1) * neg_loginv;
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float changed =
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((1 << neg_label) - 1) * pos_loginv + ((1 << pos_label) - 1) * neg_loginv;
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bst_float delta = (original - changed) * IDCG;
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if (delta < 0.0f) delta = - delta;
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pair.weight *= delta;
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}
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}
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}
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inline static bst_float CalcDCG(const std::vector<bst_float> &labels) {
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double sumdcg = 0.0;
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for (size_t i = 0; i < labels.size(); ++i) {
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const auto rel = static_cast<unsigned>(labels[i]);
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if (rel != 0) {
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sumdcg += ((1 << rel) - 1) / std::log2(static_cast<bst_float>(i + 2));
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}
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}
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return static_cast<bst_float>(sumdcg);
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}
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};
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class LambdaRankObjMAP : public LambdaRankObj {
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protected:
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struct MAPStats {
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/*! \brief the accumulated precision */
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float ap_acc;
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/*!
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* \brief the accumulated precision,
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* assuming a positive instance is missing
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*/
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float ap_acc_miss;
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/*!
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* \brief the accumulated precision,
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* assuming that one more positive instance is inserted ahead
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*/
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float ap_acc_add;
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/* \brief the accumulated positive instance count */
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float hits;
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MAPStats() = default;
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MAPStats(float ap_acc, float ap_acc_miss, float ap_acc_add, float hits)
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: ap_acc(ap_acc), ap_acc_miss(ap_acc_miss), ap_acc_add(ap_acc_add), hits(hits) {}
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};
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/*!
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* \brief Obtain the delta MAP if trying to switch the positions of instances in index1 or index2
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* in sorted triples
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* \param sorted_list the list containing entry information
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* \param index1,index2 the instances switched
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* \param map_stats a vector containing the accumulated precisions for each position in a list
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*/
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inline bst_float GetLambdaMAP(const std::vector<ListEntry> &sorted_list,
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int index1, int index2,
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std::vector<MAPStats> *p_map_stats) {
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std::vector<MAPStats> &map_stats = *p_map_stats;
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if (index1 == index2 || map_stats[map_stats.size() - 1].hits == 0) {
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return 0.0f;
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}
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if (index1 > index2) std::swap(index1, index2);
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bst_float original = map_stats[index2].ap_acc;
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if (index1 != 0) original -= map_stats[index1 - 1].ap_acc;
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bst_float changed = 0;
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bst_float label1 = sorted_list[index1].label > 0.0f ? 1.0f : 0.0f;
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bst_float label2 = sorted_list[index2].label > 0.0f ? 1.0f : 0.0f;
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if (label1 == label2) {
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return 0.0;
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} else if (label1 < label2) {
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changed += map_stats[index2 - 1].ap_acc_add - map_stats[index1].ap_acc_add;
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changed += (map_stats[index1].hits + 1.0f) / (index1 + 1);
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} else {
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changed += map_stats[index2 - 1].ap_acc_miss - map_stats[index1].ap_acc_miss;
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changed += map_stats[index2].hits / (index2 + 1);
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}
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bst_float ans = (changed - original) / (map_stats[map_stats.size() - 1].hits);
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if (ans < 0) ans = -ans;
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return ans;
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}
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/*
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* \brief obtain preprocessing results for calculating delta MAP
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* \param sorted_list the list containing entry information
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* \param map_stats a vector containing the accumulated precisions for each position in a list
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*/
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inline void GetMAPStats(const std::vector<ListEntry> &sorted_list,
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std::vector<MAPStats> *p_map_acc) {
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std::vector<MAPStats> &map_acc = *p_map_acc;
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map_acc.resize(sorted_list.size());
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bst_float hit = 0, acc1 = 0, acc2 = 0, acc3 = 0;
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for (size_t i = 1; i <= sorted_list.size(); ++i) {
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if (sorted_list[i - 1].label > 0.0f) {
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hit++;
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acc1 += hit / i;
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acc2 += (hit - 1) / i;
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acc3 += (hit + 1) / i;
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}
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map_acc[i - 1] = MAPStats(acc1, acc2, acc3, hit);
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}
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}
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void GetLambdaWeight(const std::vector<ListEntry> &sorted_list,
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std::vector<LambdaPair> *io_pairs) override {
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std::vector<LambdaPair> &pairs = *io_pairs;
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std::vector<MAPStats> map_stats;
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GetMAPStats(sorted_list, &map_stats);
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for (auto & pair : pairs) {
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pair.weight *=
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GetLambdaMAP(sorted_list, pair.pos_index,
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pair.neg_index, &map_stats);
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}
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}
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};
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// register the objective functions
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DMLC_REGISTER_PARAMETER(LambdaRankParam);
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XGBOOST_REGISTER_OBJECTIVE(PairwiseRankObj, "rank:pairwise")
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.describe("Pairwise rank objective.")
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.set_body([]() { return new PairwiseRankObj(); });
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XGBOOST_REGISTER_OBJECTIVE(LambdaRankNDCG, "rank:ndcg")
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.describe("LambdaRank with NDCG as objective.")
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.set_body([]() { return new LambdaRankObjNDCG(); });
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XGBOOST_REGISTER_OBJECTIVE(LambdaRankObjMAP, "rank:map")
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.describe("LambdaRank with MAP as objective.")
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.set_body([]() { return new LambdaRankObjMAP(); });
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} // namespace obj
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} // namespace xgboost
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