125 lines
3.7 KiB
C++
125 lines
3.7 KiB
C++
/*!
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* Copyright 2017-2019 by Contributors
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* \file gbtree_model.h
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*/
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#ifndef XGBOOST_GBM_GBTREE_MODEL_H_
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#define XGBOOST_GBM_GBTREE_MODEL_H_
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#include <memory>
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#include <utility>
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#include <string>
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#include <vector>
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#include <dmlc/parameter.h>
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#include <dmlc/io.h>
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#include <xgboost/model.h>
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#include <xgboost/tree_model.h>
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#include <xgboost/parameter.h>
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#include <xgboost/learner.h>
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namespace xgboost {
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class Json;
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namespace gbm {
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/*! \brief model parameters */
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struct GBTreeModelParam : public dmlc::Parameter<GBTreeModelParam> {
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public:
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/*! \brief number of trees */
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int32_t num_trees;
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/*! \brief (Deprecated) number of roots */
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int32_t deprecated_num_roots;
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/*! \brief number of features to be used by trees */
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int32_t deprecated_num_feature;
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/*! \brief pad this space, for backward compatibility reason.*/
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int32_t pad_32bit;
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/*! \brief deprecated padding space. */
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int64_t deprecated_num_pbuffer;
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// deprecated. use learner_model_param_->num_output_group.
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int32_t deprecated_num_output_group;
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/*! \brief size of leaf vector needed in tree */
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int32_t size_leaf_vector;
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/*! \brief reserved parameters */
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int32_t reserved[32];
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/*! \brief constructor */
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GBTreeModelParam() {
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std::memset(this, 0, sizeof(GBTreeModelParam)); // FIXME(trivialfis): Why?
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static_assert(sizeof(GBTreeModelParam) == (4 + 2 + 2 + 32) * sizeof(int32_t),
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"64/32 bit compatibility issue");
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deprecated_num_roots = 1;
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}
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// declare parameters, only declare those that need to be set.
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DMLC_DECLARE_PARAMETER(GBTreeModelParam) {
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DMLC_DECLARE_FIELD(num_trees)
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.set_lower_bound(0)
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.set_default(0)
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.describe("Number of features used for training and prediction.");
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DMLC_DECLARE_FIELD(size_leaf_vector)
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.set_lower_bound(0)
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.set_default(0)
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.describe("Reserved option for vector tree.");
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}
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};
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struct GBTreeModel : public Model {
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public:
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explicit GBTreeModel(LearnerModelParam const* learner_model) :
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learner_model_param{learner_model} {}
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void Configure(const Args& cfg) {
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// initialize model parameters if not yet been initialized.
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if (trees.size() == 0) {
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param.UpdateAllowUnknown(cfg);
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}
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}
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void InitTreesToUpdate() {
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if (trees_to_update.size() == 0u) {
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for (auto & tree : trees) {
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trees_to_update.push_back(std::move(tree));
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}
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trees.clear();
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param.num_trees = 0;
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tree_info.clear();
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}
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}
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void Load(dmlc::Stream* fi);
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void Save(dmlc::Stream* fo) const;
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void SaveModel(Json* p_out) const override;
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void LoadModel(Json const& p_out) override;
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std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
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std::string format) const {
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std::vector<std::string> dump;
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for (const auto & tree : trees) {
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dump.push_back(tree->DumpModel(fmap, with_stats, format));
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}
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return dump;
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}
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void CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
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int bst_group) {
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for (auto & new_tree : new_trees) {
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trees.push_back(std::move(new_tree));
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tree_info.push_back(bst_group);
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}
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param.num_trees += static_cast<int>(new_trees.size());
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}
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// base margin
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LearnerModelParam const* learner_model_param;
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// model parameter
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GBTreeModelParam param;
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/*! \brief vector of trees stored in the model */
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std::vector<std::unique_ptr<RegTree> > trees;
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/*! \brief for the update process, a place to keep the initial trees */
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std::vector<std::unique_ptr<RegTree> > trees_to_update;
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/*! \brief some information indicator of the tree, reserved */
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std::vector<int> tree_info;
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};
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} // namespace gbm
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} // namespace xgboost
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#endif // XGBOOST_GBM_GBTREE_MODEL_H_
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