0110754a76
Move this function into gbtree, and uses only updater for doing so. As now the predictor knows exactly how many trees to predict, there's no need for it to update the prediction cache.
286 lines
9.5 KiB
C++
286 lines
9.5 KiB
C++
/*!
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* Copyright 2014-2019 by Contributors
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* \file gbtree.cc
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* \brief gradient boosted tree implementation.
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* \author Tianqi Chen
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*/
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#ifndef XGBOOST_GBM_GBTREE_H_
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#define XGBOOST_GBM_GBTREE_H_
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#include <dmlc/omp.h>
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#include <vector>
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#include <map>
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#include <memory>
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#include <utility>
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#include <string>
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#include <unordered_map>
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#include "xgboost/data.h"
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#include "xgboost/logging.h"
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#include "xgboost/gbm.h"
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#include "xgboost/predictor.h"
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#include "xgboost/tree_updater.h"
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#include "xgboost/parameter.h"
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#include "xgboost/json.h"
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#include "xgboost/host_device_vector.h"
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#include "gbtree_model.h"
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#include "../common/common.h"
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#include "../common/timer.h"
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namespace xgboost {
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enum class TreeMethod : int {
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kAuto = 0, kApprox = 1, kExact = 2, kHist = 3,
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kGPUHist = 5
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};
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// boosting process types
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enum class TreeProcessType : int {
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kDefault = 0,
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kUpdate = 1
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};
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enum class PredictorType : int {
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kAuto = 0,
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kCPUPredictor,
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kGPUPredictor
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};
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} // namespace xgboost
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DECLARE_FIELD_ENUM_CLASS(xgboost::TreeMethod);
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DECLARE_FIELD_ENUM_CLASS(xgboost::TreeProcessType);
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DECLARE_FIELD_ENUM_CLASS(xgboost::PredictorType);
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namespace xgboost {
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namespace gbm {
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/*! \brief training parameters */
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struct GBTreeTrainParam : public XGBoostParameter<GBTreeTrainParam> {
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/*!
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* \brief number of parallel trees constructed each iteration
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* use this option to support boosted random forest
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*/
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int num_parallel_tree;
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/*! \brief tree updater sequence */
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std::string updater_seq;
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/*! \brief type of boosting process to run */
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TreeProcessType process_type;
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// predictor type
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PredictorType predictor;
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// tree construction method
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TreeMethod tree_method;
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// declare parameters
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DMLC_DECLARE_PARAMETER(GBTreeTrainParam) {
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DMLC_DECLARE_FIELD(num_parallel_tree)
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.set_default(1)
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.set_lower_bound(1)
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.describe("Number of parallel trees constructed during each iteration."\
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" This option is used to support boosted random forest.");
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DMLC_DECLARE_FIELD(updater_seq)
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.set_default("grow_colmaker,prune")
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.describe("Tree updater sequence.");
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DMLC_DECLARE_FIELD(process_type)
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.set_default(TreeProcessType::kDefault)
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.add_enum("default", TreeProcessType::kDefault)
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.add_enum("update", TreeProcessType::kUpdate)
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.describe("Whether to run the normal boosting process that creates new trees,"\
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" or to update the trees in an existing model.");
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DMLC_DECLARE_ALIAS(updater_seq, updater);
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DMLC_DECLARE_FIELD(predictor)
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.set_default(PredictorType::kAuto)
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.add_enum("auto", PredictorType::kAuto)
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.add_enum("cpu_predictor", PredictorType::kCPUPredictor)
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.add_enum("gpu_predictor", PredictorType::kGPUPredictor)
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.describe("Predictor algorithm type");
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DMLC_DECLARE_FIELD(tree_method)
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.set_default(TreeMethod::kAuto)
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.add_enum("auto", TreeMethod::kAuto)
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.add_enum("approx", TreeMethod::kApprox)
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.add_enum("exact", TreeMethod::kExact)
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.add_enum("hist", TreeMethod::kHist)
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.add_enum("gpu_hist", TreeMethod::kGPUHist)
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.describe("Choice of tree construction method.");
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}
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};
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/*! \brief training parameters */
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struct DartTrainParam : public XGBoostParameter<DartTrainParam> {
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/*! \brief type of sampling algorithm */
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int sample_type;
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/*! \brief type of normalization algorithm */
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int normalize_type;
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/*! \brief fraction of trees to drop during the dropout */
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float rate_drop;
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/*! \brief whether at least one tree should always be dropped during the dropout */
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bool one_drop;
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/*! \brief probability of skipping the dropout during an iteration */
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float skip_drop;
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/*! \brief learning step size for a time */
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float learning_rate;
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// declare parameters
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DMLC_DECLARE_PARAMETER(DartTrainParam) {
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DMLC_DECLARE_FIELD(sample_type)
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.set_default(0)
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.add_enum("uniform", 0)
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.add_enum("weighted", 1)
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.describe("Different types of sampling algorithm.");
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DMLC_DECLARE_FIELD(normalize_type)
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.set_default(0)
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.add_enum("tree", 0)
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.add_enum("forest", 1)
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.describe("Different types of normalization algorithm.");
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DMLC_DECLARE_FIELD(rate_drop)
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.set_range(0.0f, 1.0f)
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.set_default(0.0f)
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.describe("Fraction of trees to drop during the dropout.");
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DMLC_DECLARE_FIELD(one_drop)
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.set_default(false)
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.describe("Whether at least one tree should always be dropped during the dropout.");
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DMLC_DECLARE_FIELD(skip_drop)
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.set_range(0.0f, 1.0f)
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.set_default(0.0f)
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.describe("Probability of skipping the dropout during a boosting iteration.");
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DMLC_DECLARE_FIELD(learning_rate)
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.set_lower_bound(0.0f)
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.set_default(0.3f)
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.describe("Learning rate(step size) of update.");
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DMLC_DECLARE_ALIAS(learning_rate, eta);
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}
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};
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// gradient boosted trees
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class GBTree : public GradientBooster {
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public:
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explicit GBTree(LearnerModelParam const* booster_config) :
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model_(booster_config) {}
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void Configure(const Args& cfg) override;
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// Revise `tree_method` and `updater` parameters after seeing the training
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// data matrix, only useful when tree_method is auto.
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void PerformTreeMethodHeuristic(DMatrix* fmat);
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/*! \brief Map `tree_method` parameter to `updater` parameter */
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void ConfigureUpdaters();
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void ConfigureWithKnownData(Args const& cfg, DMatrix* fmat);
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/*! \brief Carry out one iteration of boosting */
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void DoBoost(DMatrix* p_fmat,
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HostDeviceVector<GradientPair>* in_gpair,
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PredictionCacheEntry* predt) override;
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bool UseGPU() const override {
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return
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tparam_.predictor == PredictorType::kGPUPredictor ||
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tparam_.tree_method == TreeMethod::kGPUHist;
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}
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GBTreeTrainParam const& GetTrainParam() const {
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return tparam_;
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}
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void Load(dmlc::Stream* fi) override {
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model_.Load(fi);
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this->cfg_.clear();
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}
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void Save(dmlc::Stream* fo) const override {
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model_.Save(fo);
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}
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void LoadConfig(Json const& in) override;
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void SaveConfig(Json* p_out) const override;
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void SaveModel(Json* p_out) const override;
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void LoadModel(Json const& in) override;
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bool AllowLazyCheckPoint() const override {
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return model_.learner_model_param_->num_output_group == 1 ||
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tparam_.updater_seq.find("distcol") != std::string::npos;
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}
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void PredictBatch(DMatrix* p_fmat,
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PredictionCacheEntry* out_preds,
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bool training,
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unsigned ntree_limit) override;
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void PredictInstance(const SparsePage::Inst& inst,
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std::vector<bst_float>* out_preds,
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unsigned ntree_limit) override {
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CHECK(configured_);
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cpu_predictor_->PredictInstance(inst, out_preds, model_,
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ntree_limit);
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}
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void PredictLeaf(DMatrix* p_fmat,
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std::vector<bst_float>* out_preds,
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unsigned ntree_limit) override {
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CHECK(configured_);
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cpu_predictor_->PredictLeaf(p_fmat, out_preds, model_, ntree_limit);
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}
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void PredictContribution(DMatrix* p_fmat,
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std::vector<bst_float>* out_contribs,
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unsigned ntree_limit, bool approximate, int condition,
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unsigned condition_feature) override {
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CHECK(configured_);
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cpu_predictor_->PredictContribution(p_fmat, out_contribs, model_,
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ntree_limit, nullptr, approximate);
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}
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void PredictInteractionContributions(DMatrix* p_fmat,
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std::vector<bst_float>* out_contribs,
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unsigned ntree_limit, bool approximate) override {
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CHECK(configured_);
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cpu_predictor_->PredictInteractionContributions(p_fmat, out_contribs, model_,
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ntree_limit, nullptr, approximate);
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}
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std::vector<std::string> DumpModel(const FeatureMap& fmap,
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bool with_stats,
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std::string format) const override {
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return model_.DumpModel(fmap, with_stats, format);
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}
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protected:
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// initialize updater before using them
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void InitUpdater(Args const& cfg);
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// do group specific group
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void BoostNewTrees(HostDeviceVector<GradientPair>* gpair,
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DMatrix *p_fmat,
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int bst_group,
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std::vector<std::unique_ptr<RegTree> >* ret);
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std::unique_ptr<Predictor> const& GetPredictor(HostDeviceVector<float> const* out_pred = nullptr,
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DMatrix* f_dmat = nullptr) const;
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// commit new trees all at once
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virtual void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
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DMatrix* m,
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PredictionCacheEntry* predts);
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// --- data structure ---
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GBTreeModel model_;
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// training parameter
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GBTreeTrainParam tparam_;
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// ----training fields----
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bool showed_updater_warning_ {false};
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bool specified_updater_ {false};
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bool configured_ {false};
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// configurations for tree
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Args cfg_;
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// the updaters that can be applied to each of tree
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std::vector<std::unique_ptr<TreeUpdater>> updaters_;
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// Predictors
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std::unique_ptr<Predictor> cpu_predictor_;
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#if defined(XGBOOST_USE_CUDA)
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std::unique_ptr<Predictor> gpu_predictor_;
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#endif // defined(XGBOOST_USE_CUDA)
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common::Monitor monitor_;
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};
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} // namespace gbm
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} // namespace xgboost
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#endif // XGBOOST_GBM_GBTREE_H_
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