everything is ready, except for propose
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@ -68,7 +68,7 @@ class TreeModel {
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}
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};
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/*! \brief tree node */
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class Node{
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class Node {
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public:
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/*! \brief index of left child */
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inline int cleft(void) const {
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@ -29,7 +29,7 @@ class HistMaker: public IUpdater {
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param.learning_rate = lr / trees.size();
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// build tree
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for (size_t i = 0; i < trees.size(); ++i) {
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// TODO
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this->Update(gpair, p_fmat, info, trees[i]);
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}
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param.learning_rate = lr;
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}
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@ -80,25 +80,16 @@ class HistMaker: public IUpdater {
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// per thread histset
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std::vector<HistSet> hset;
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// initialize the hist set
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inline void Init(const TrainParam ¶m) {
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int nthread;
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#pragma omp parallel
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{
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nthread = omp_get_num_threads();
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}
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inline void Init(const TrainParam ¶m, int nthread) {
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hset.resize(nthread);
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// cleanup statistics
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#pragma omp parallel
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{
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int tid = omp_get_thread_num();
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for (int tid = 0; tid < nthread; ++tid) {
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for (size_t i = 0; i < hset[tid].data.size(); ++i) {
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hset[tid].data[i].Clear();
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}
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}
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for (int i = 0; i < nthread; ++i) {
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hset[i].rptr = BeginPtr(rptr);
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hset[i].cut = BeginPtr(cut);
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hset[i].data.resize(cut.size(), TStats(param));
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hset[tid].rptr = BeginPtr(rptr);
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hset[tid].cut = BeginPtr(cut);
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hset[tid].data.resize(cut.size(), TStats(param));
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}
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}
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// aggregate all statistics to hset[0]
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@ -119,7 +110,7 @@ class HistMaker: public IUpdater {
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inline size_t Size(void) const {
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return rptr.size() - 1;
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}
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};
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};
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// training parameter
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TrainParam param;
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// workspace of thread
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@ -132,30 +123,116 @@ class HistMaker: public IUpdater {
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std::vector<int> node2workindex;
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// reducer for histogram
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sync::Reducer<TStats> histred;
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// helper function to get to next level of the tree
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// must work on non-leaf node
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inline static int NextLevel(const SparseBatch::Inst &inst, const RegTree &tree, int nid) {
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const RegTree::Node &n = tree[nid];
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bst_uint findex = n.split_index();
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for (unsigned i = 0; i < inst.length; ++i) {
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if (findex == inst[i].index) {
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if (inst[i].fvalue < n.split_cond()) {
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return n.cleft();
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} else {
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return n.cright();
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}
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}
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}
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return n.cdefault();
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}
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private:
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virtual void Update(const std::vector<bst_gpair> &gpair,
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IFMatrix *p_fmat,
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const BoosterInfo &info,
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RegTree *p_tree) {
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//this->InitData(gpair, *p_fmat, info.root_index, *p_tree);
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//this->InitNewNode(qexpand_, gpair, *p_fmat, info, *p_tree);
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for (int depth = 0; depth < param.max_depth; ++depth) {
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this->InitData(gpair, *p_fmat, info.root_index, *p_tree);
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this->UpdateNode2WorkIndex(*p_tree);
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for (int depth = 0; depth < param.max_depth; ++depth) {
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this->FindSplit(depth, gpair, p_fmat, info, p_tree);
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//this->ResetPosition(qexpand_, p_fmat, *p_tree);
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//this->UpdateQueueExpand(*p_tree, &qexpand_);
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//this->InitNewNode(qexpand_, gpair, *p_fmat, info, *p_tree);
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this->UpdateQueueExpand(*p_tree);
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this->UpdateNode2WorkIndex(*p_tree);
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// if nothing left to be expand, break
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if (qexpand.size() == 0) break;
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}
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}
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// initialize temp data structure
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inline void InitData(const std::vector<bst_gpair> &gpair,
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const IFMatrix &fmat,
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const std::vector<unsigned> &root_index, const RegTree &tree) {
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utils::Assert(tree.param.num_nodes == tree.param.num_roots, "HistMaker: can only grow new tree");
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{// setup position
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position.resize(gpair.size());
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if (root_index.size() == 0) {
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std::fill(position.begin(), position.end(), 0);
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} else {
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for (size_t i = 0; i < position.size(); ++i) {
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position[i] = root_index[i];
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utils::Assert(root_index[i] < (unsigned)tree.param.num_roots,
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"root index exceed setting");
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}
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}
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// mark delete for the deleted datas
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for (size_t i = 0; i < position.size(); ++i) {
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if (gpair[i].hess < 0.0f) position[i] = ~position[i];
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}
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// mark subsample
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if (param.subsample < 1.0f) {
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for (size_t i = 0; i < position.size(); ++i) {
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if (gpair[i].hess < 0.0f) continue;
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if (random::SampleBinary(param.subsample) == 0) position[i] = ~position[i];
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}
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}
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}
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{// expand query
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qexpand.reserve(256); qexpand.clear();
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for (int i = 0; i < tree.param.num_roots; ++i) {
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qexpand.push_back(i);
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}
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}
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}
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/*! \brief update queue expand add in new leaves */
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inline void UpdateQueueExpand(const RegTree &tree) {
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std::vector<int> newnodes;
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for (size_t i = 0; i < qexpand.size(); ++i) {
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const int nid = qexpand[i];
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if (!tree[nid].is_leaf()) {
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newnodes.push_back(tree[nid].cleft());
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newnodes.push_back(tree[nid].cright());
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}
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}
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// use new nodes for qexpand
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qexpand = newnodes;
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}
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inline void UpdateNode2WorkIndex(const RegTree &tree) {
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// update the node2workindex
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std::fill(node2workindex.begin(), node2workindex.end(), -1);
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node2workindex.resize(tree.param.num_nodes);
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for (size_t i = 0; i < qexpand.size(); ++i) {
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node2workindex[qexpand[i]] = static_cast<int>(i);
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}
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}
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// this function does two jobs
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// (1) reset the position in array position, to be the latest leaf id
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// (2) propose a set of candidate cuts and set wspace.rptr wspace.cut correctly
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virtual void ResetPosAndPropose(IFMatrix *p_fmat,
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const BoosterInfo &info,
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const RegTree &tree) {
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}
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// create histogram for a setup histset
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inline void CreateHist(const std::vector<bst_gpair> &gpair,
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IFMatrix *p_fmat,
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const BoosterInfo &info,
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const RegTree &tree) {
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bst_uint num_feature = tree.param.num_feature;
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int nthread;
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#pragma omp parallel
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{
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nthread = omp_get_num_threads();
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}
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// intialize work space
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wspace.Init(param);
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wspace.Init(param, nthread);
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// start accumulating statistics
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utils::IIterator<RowBatch> *iter = p_fmat->RowIterator();
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iter->BeforeFirst();
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@ -225,6 +302,8 @@ class HistMaker: public IUpdater {
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const BoosterInfo &info,
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RegTree *p_tree) {
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const bst_uint num_feature = p_tree->param.num_feature;
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// reset and propose candidate split
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this->ResetPosAndPropose(p_fmat, info, *p_tree);
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// create histogram
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this->CreateHist(gpair, p_fmat, info, *p_tree);
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// get the best split condition for each node
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@ -265,6 +344,8 @@ class HistMaker: public IUpdater {
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}
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}
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};
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} // namespace tree
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} // namespace xgboost
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#endif // XGBOOST_TREE_UPDATER_HISTMAKER_INL_HPP_
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