f0064c07ab
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
97 lines
2.7 KiB
C++
97 lines
2.7 KiB
C++
/*!
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* Copyright 2014 by Contributors
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* \file updater_prune.cc
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* \brief prune a tree given the statistics
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* \author Tianqi Chen
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*/
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#include <rabit/rabit.h>
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#include <xgboost/tree_updater.h>
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#include <string>
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#include <memory>
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#include "./param.h"
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#include "../common/io.h"
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namespace xgboost {
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namespace tree {
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DMLC_REGISTRY_FILE_TAG(updater_prune);
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/*! \brief pruner that prunes a tree after growing finishes */
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class TreePruner: public TreeUpdater {
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public:
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TreePruner() {
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syncher_.reset(TreeUpdater::Create("sync", tparam_));
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}
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char const* Name() const override {
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return "prune";
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}
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// set training parameter
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void Configure(const Args& args) override {
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param_.InitAllowUnknown(args);
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syncher_->Configure(args);
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}
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// update the tree, do pruning
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void Update(HostDeviceVector<GradientPair> *gpair,
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DMatrix *p_fmat,
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const std::vector<RegTree*> &trees) override {
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// rescale learning rate according to size of trees
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float lr = param_.learning_rate;
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param_.learning_rate = lr / trees.size();
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for (auto tree : trees) {
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this->DoPrune(*tree);
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}
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param_.learning_rate = lr;
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syncher_->Update(gpair, p_fmat, trees);
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}
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private:
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// try to prune off current leaf
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inline int TryPruneLeaf(RegTree &tree, int nid, int depth, int npruned) { // NOLINT(*)
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if (tree[nid].IsRoot()) return npruned;
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int pid = tree[nid].Parent();
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RTreeNodeStat &s = tree.Stat(pid);
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++s.leaf_child_cnt;
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if (s.leaf_child_cnt >= 2 && param_.NeedPrune(s.loss_chg, depth - 1)) {
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// need to be pruned
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tree.ChangeToLeaf(pid, param_.learning_rate * s.base_weight);
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// tail recursion
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return this->TryPruneLeaf(tree, pid, depth - 1, npruned + 2);
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} else {
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return npruned;
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}
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}
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/*! \brief do pruning of a tree */
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inline void DoPrune(RegTree &tree) { // NOLINT(*)
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int npruned = 0;
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// initialize auxiliary statistics
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for (int nid = 0; nid < tree.param.num_nodes; ++nid) {
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tree.Stat(nid).leaf_child_cnt = 0;
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}
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for (int nid = 0; nid < tree.param.num_nodes; ++nid) {
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if (tree[nid].IsLeaf()) {
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npruned = this->TryPruneLeaf(tree, nid, tree.GetDepth(nid), npruned);
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}
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}
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LOG(INFO) << "tree pruning end, " << tree.param.num_roots << " roots, "
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<< tree.NumExtraNodes() << " extra nodes, " << npruned
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<< " pruned nodes, max_depth=" << tree.MaxDepth();
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}
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private:
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// synchronizer
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std::unique_ptr<TreeUpdater> syncher_;
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// training parameter
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TrainParam param_;
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};
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XGBOOST_REGISTER_TREE_UPDATER(TreePruner, "prune")
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.describe("Pruner that prune the tree according to statistics.")
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.set_body([]() {
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return new TreePruner();
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});
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} // namespace tree
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} // namespace xgboost
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