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learner.cc
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/
learner.cc
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/*!
* Copyright 2014-2022 by Contributors
* \file learner.cc
* \brief Implementation of learning algorithm.
* \author Tianqi Chen
*/
#include "xgboost/learner.h"
#include <dmlc/any.h>
#include <dmlc/io.h>
#include <dmlc/parameter.h>
#include <dmlc/thread_local.h>
#include <algorithm>
#include <array>
#include <atomic>
#include <iomanip>
#include <limits> // std::numeric_limits
#include <memory>
#include <mutex>
#include <sstream>
#include <stack>
#include <string>
#include <utility>
#include <vector>
#include "collective/communicator-inl.h"
#include "common/charconv.h"
#include "common/common.h"
#include "common/io.h"
#include "common/observer.h"
#include "common/random.h"
#include "common/threading_utils.h"
#include "common/timer.h"
#include "common/version.h"
#include "xgboost/base.h"
#include "xgboost/c_api.h"
#include "xgboost/data.h"
#include "xgboost/feature_map.h"
#include "xgboost/gbm.h"
#include "xgboost/generic_parameters.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/json.h"
#include "xgboost/logging.h"
#include "xgboost/metric.h"
#include "xgboost/model.h"
#include "xgboost/objective.h"
#include "xgboost/parameter.h"
#include "xgboost/predictor.h"
namespace {
const char* kMaxDeltaStepDefaultValue = "0.7";
} // anonymous namespace
namespace xgboost {
enum class DataSplitMode : int {
kAuto = 0, kCol = 1, kRow = 2
};
} // namespace xgboost
DECLARE_FIELD_ENUM_CLASS(xgboost::DataSplitMode);
namespace xgboost {
Learner::~Learner() = default;
namespace {
StringView ModelNotFitted() { return "Model is not yet initialized (not fitted)."; }
template <typename T>
T& UsePtr(T& ptr) { // NOLINT
CHECK(ptr);
return ptr;
}
} // anonymous namespace
/*! \brief training parameter for regression
*
* Should be deprecated, but still used for being compatible with binary IO.
* Once it's gone, `LearnerModelParam` should handle transforming `base_margin`
* with objective by itself.
*/
struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy> {
/* \brief global bias */
bst_float base_score;
/* \brief number of features */
bst_feature_t num_feature;
/* \brief number of classes, if it is multi-class classification */
std::int32_t num_class;
/*! \brief Model contain additional properties */
int32_t contain_extra_attrs;
/*! \brief Model contain eval metrics */
int32_t contain_eval_metrics;
/*! \brief the version of XGBoost. */
std::uint32_t major_version;
std::uint32_t minor_version;
uint32_t num_target{1};
/**
* \brief Whether we should calculate the base score from training data.
*
* This is a private parameter as we can't expose it as boolean due to binary model
* format. Exposing it as integer creates inconsistency with other parameters.
*
* Automatically disabled when base_score is specifed by user. int32 is used instead
* of bool for the ease of serialization.
*/
std::int32_t boost_from_average{true};
/*! \brief reserved field */
int reserved[25];
/*! \brief constructor */
LearnerModelParamLegacy() {
std::memset(this, 0, sizeof(LearnerModelParamLegacy));
base_score = ObjFunction::DefaultBaseScore();
num_target = 1;
major_version = std::get<0>(Version::Self());
minor_version = std::get<1>(Version::Self());
boost_from_average = true;
static_assert(sizeof(LearnerModelParamLegacy) == 136,
"Do not change the size of this struct, as it will break binary IO.");
}
// Skip other legacy fields.
Json ToJson() const {
Json obj{Object{}};
char floats[NumericLimits<float>::kToCharsSize];
auto ret = to_chars(floats, floats + NumericLimits<float>::kToCharsSize, base_score);
CHECK(ret.ec == std::errc{});
obj["base_score"] = std::string{floats, static_cast<size_t>(std::distance(floats, ret.ptr))};
char integers[NumericLimits<int64_t>::kToCharsSize];
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_feature));
CHECK(ret.ec == std::errc());
obj["num_feature"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_class));
CHECK(ret.ec == std::errc());
obj["num_class"] = std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_target));
obj["num_target"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
ret = to_chars(integers, integers + NumericLimits<std::int64_t>::kToCharsSize,
static_cast<std::int64_t>(boost_from_average));
obj["boost_from_average"] =
std::string{integers, static_cast<std::size_t>(std::distance(integers, ret.ptr))};
return obj;
}
void FromJson(Json const& obj) {
auto const& j_param = get<Object const>(obj);
std::map<std::string, std::string> m;
m["num_feature"] = get<String const>(j_param.at("num_feature"));
m["num_class"] = get<String const>(j_param.at("num_class"));
auto n_targets_it = j_param.find("num_target");
if (n_targets_it != j_param.cend()) {
m["num_target"] = get<String const>(n_targets_it->second);
}
auto bse_it = j_param.find("boost_from_average");
if (bse_it != j_param.cend()) {
m["boost_from_average"] = get<String const>(bse_it->second);
}
this->Init(m);
std::string str = get<String const>(j_param.at("base_score"));
from_chars(str.c_str(), str.c_str() + str.size(), base_score);
}
LearnerModelParamLegacy ByteSwap() const {
LearnerModelParamLegacy x = *this;
dmlc::ByteSwap(&x.base_score, sizeof(x.base_score), 1);
dmlc::ByteSwap(&x.num_feature, sizeof(x.num_feature), 1);
dmlc::ByteSwap(&x.num_class, sizeof(x.num_class), 1);
dmlc::ByteSwap(&x.contain_extra_attrs, sizeof(x.contain_extra_attrs), 1);
dmlc::ByteSwap(&x.contain_eval_metrics, sizeof(x.contain_eval_metrics), 1);
dmlc::ByteSwap(&x.major_version, sizeof(x.major_version), 1);
dmlc::ByteSwap(&x.minor_version, sizeof(x.minor_version), 1);
dmlc::ByteSwap(&x.num_target, sizeof(x.num_target), 1);
dmlc::ByteSwap(&x.boost_from_average, sizeof(x.boost_from_average), 1);
dmlc::ByteSwap(x.reserved, sizeof(x.reserved[0]), sizeof(x.reserved) / sizeof(x.reserved[0]));
return x;
}
template <typename Container>
Args UpdateAllowUnknown(Container const& kwargs) {
// Detect whether user has made their own base score.
auto find_key = [&kwargs](char const* key) {
return std::find_if(kwargs.cbegin(), kwargs.cend(),
[key](auto const& kv) { return kv.first == key; });
};
auto it = find_key("base_score");
if (it != kwargs.cend()) {
boost_from_average = false;
}
return dmlc::Parameter<LearnerModelParamLegacy>::UpdateAllowUnknown(kwargs);
}
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerModelParamLegacy) {
DMLC_DECLARE_FIELD(base_score)
.set_default(ObjFunction::DefaultBaseScore())
.describe("Global bias of the model.");
DMLC_DECLARE_FIELD(num_feature)
.set_default(0)
.describe(
"Number of features in training data,"
" this parameter will be automatically detected by learner.");
DMLC_DECLARE_FIELD(num_class).set_default(0).set_lower_bound(0).describe(
"Number of class option for multi-class classifier. "
" By default equals 0 and corresponds to binary classifier.");
DMLC_DECLARE_FIELD(num_target)
.set_default(1)
.set_lower_bound(1)
.describe("Number of target for multi-target regression.");
DMLC_DECLARE_FIELD(boost_from_average)
.set_default(true)
.describe("Whether we should calculate the base score from training data.");
}
};
LearnerModelParam::LearnerModelParam(LearnerModelParamLegacy const& user_param, ObjInfo t)
: num_feature{user_param.num_feature}, task{t} {
auto n_classes = std::max(static_cast<uint32_t>(user_param.num_class), 1u);
auto n_targets = user_param.num_target;
num_output_group = std::max(n_classes, n_targets);
// For version < 1.6, n_targets == 0
CHECK(n_classes <= 1 || n_targets <= 1)
<< "Multi-class multi-output is not yet supported. n_classes:" << n_classes
<< ", n_targets:" << n_targets;
}
LearnerModelParam::LearnerModelParam(Context const* ctx, LearnerModelParamLegacy const& user_param,
linalg::Tensor<float, 1> base_margin, ObjInfo t)
: LearnerModelParam{user_param, t} {
std::swap(base_score_, base_margin);
// Make sure read access everywhere for thread-safe prediction.
common::AsConst(base_score_).HostView();
if (!ctx->IsCPU()) {
common::AsConst(base_score_).View(ctx->gpu_id);
}
CHECK(common::AsConst(base_score_).Data()->HostCanRead());
}
linalg::TensorView<float const, 1> LearnerModelParam::BaseScore(int32_t device) const {
// multi-class is not yet supported.
CHECK_EQ(base_score_.Size(), 1) << ModelNotFitted();
if (device == Context::kCpuId) {
// Make sure that we won't run into race condition.
CHECK(base_score_.Data()->HostCanRead());
return base_score_.HostView();
}
// Make sure that we won't run into race condition.
CHECK(base_score_.Data()->DeviceCanRead());
auto v = base_score_.View(device);
CHECK(base_score_.Data()->HostCanRead()); // make sure read access is not removed.
return v;
}
linalg::TensorView<float const, 1> LearnerModelParam::BaseScore(Context const* ctx) const {
return this->BaseScore(ctx->gpu_id);
}
void LearnerModelParam::Copy(LearnerModelParam const& that) {
base_score_.Reshape(that.base_score_.Shape());
base_score_.Data()->SetDevice(that.base_score_.DeviceIdx());
base_score_.Data()->Copy(*that.base_score_.Data());
common::AsConst(base_score_).HostView();
if (that.base_score_.DeviceIdx() != Context::kCpuId) {
common::AsConst(base_score_).View(that.base_score_.DeviceIdx());
}
CHECK_EQ(base_score_.Data()->DeviceCanRead(), that.base_score_.Data()->DeviceCanRead());
CHECK(base_score_.Data()->HostCanRead());
num_feature = that.num_feature;
num_output_group = that.num_output_group;
task = that.task;
}
struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// data split mode, can be row, col, or none.
DataSplitMode dsplit {DataSplitMode::kAuto};
// flag to disable default metric
bool disable_default_eval_metric {false};
// FIXME(trivialfis): The following parameters belong to model itself, but can be
// specified by users. Move them to model parameter once we can get rid of binary IO.
std::string booster;
std::string objective;
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
DMLC_DECLARE_FIELD(dsplit)
.set_default(DataSplitMode::kAuto)
.add_enum("auto", DataSplitMode::kAuto)
.add_enum("col", DataSplitMode::kCol)
.add_enum("row", DataSplitMode::kRow)
.describe("Data split mode for distributed training.");
DMLC_DECLARE_FIELD(disable_default_eval_metric)
.set_default(false)
.describe("Flag to disable default metric. Set to >0 to disable");
DMLC_DECLARE_FIELD(booster)
.set_default("gbtree")
.describe("Gradient booster used for training.");
DMLC_DECLARE_FIELD(objective)
.set_default("reg:squarederror")
.describe("Objective function used for obtaining gradient.");
}
};
DMLC_REGISTER_PARAMETER(LearnerModelParamLegacy);
DMLC_REGISTER_PARAMETER(LearnerTrainParam);
DMLC_REGISTER_PARAMETER(GenericParameter);
int constexpr GenericParameter::kCpuId;
int64_t constexpr GenericParameter::kDefaultSeed;
GenericParameter::GenericParameter() : cfs_cpu_count_{common::GetCfsCPUCount()} {}
void GenericParameter::ConfigureGpuId(bool require_gpu) {
#if defined(XGBOOST_USE_CUDA)
if (gpu_id == kCpuId) { // 0. User didn't specify the `gpu_id'
if (require_gpu) { // 1. `tree_method' or `predictor' or both are using
// GPU.
// 2. Use device 0 as default.
this->UpdateAllowUnknown(Args{{"gpu_id", "0"}});
}
}
// 3. When booster is loaded from a memory image (Python pickle or R
// raw model), number of available GPUs could be different. Wrap around it.
int32_t n_gpus = common::AllVisibleGPUs();
if (n_gpus == 0) {
if (gpu_id != kCpuId) {
LOG(WARNING) << "No visible GPU is found, setting `gpu_id` to -1";
}
this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
} else if (fail_on_invalid_gpu_id) {
CHECK(gpu_id == kCpuId || gpu_id < n_gpus)
<< "Only " << n_gpus << " GPUs are visible, gpu_id "
<< gpu_id << " is invalid.";
} else if (gpu_id != kCpuId && gpu_id >= n_gpus) {
LOG(WARNING) << "Only " << n_gpus
<< " GPUs are visible, setting `gpu_id` to " << gpu_id % n_gpus;
this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(gpu_id % n_gpus)}});
}
#else
// Just set it to CPU, don't think about it.
this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
#endif // defined(XGBOOST_USE_CUDA)
common::SetDevice(this->gpu_id);
}
int32_t GenericParameter::Threads() const {
auto n_threads = common::OmpGetNumThreads(nthread);
if (cfs_cpu_count_ > 0) {
n_threads = std::min(n_threads, cfs_cpu_count_);
}
return n_threads;
}
using LearnerAPIThreadLocalStore =
dmlc::ThreadLocalStore<std::map<Learner const *, XGBAPIThreadLocalEntry>>;
using ThreadLocalPredictionCache =
dmlc::ThreadLocalStore<std::map<Learner const *, PredictionContainer>>;
namespace {
StringView ModelMsg() {
return StringView{
R"doc(
If you are loading a serialized model (like pickle in Python, RDS in R) generated by
older XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. See:
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for more details about differences between saving model and serializing.
)doc"};
}
} // anonymous namespace
class LearnerConfiguration : public Learner {
private:
std::mutex config_lock_;
protected:
static std::string const kEvalMetric; // NOLINT
protected:
std::atomic<bool> need_configuration_;
std::map<std::string, std::string> cfg_;
// Stores information like best-iteration for early stopping.
std::map<std::string, std::string> attributes_;
// Name of each feature, usually set from DMatrix.
std::vector<std::string> feature_names_;
// Type of each feature, usually set from DMatrix.
std::vector<std::string> feature_types_;
common::Monitor monitor_;
LearnerModelParamLegacy mparam_;
LearnerModelParam learner_model_param_;
LearnerTrainParam tparam_;
// Initial prediction.
std::vector<std::string> metric_names_;
void ConfigureModelParamWithoutBaseScore() {
// Convert mparam to learner_model_param
this->ConfigureTargets();
auto task = UsePtr(obj_)->Task();
linalg::Tensor<float, 1> base_score({1}, Ctx()->gpu_id);
auto h_base_score = base_score.HostView();
// transform to margin
h_base_score(0) = obj_->ProbToMargin(mparam_.base_score);
// move it to model param, which is shared with all other components.
learner_model_param_ = LearnerModelParam(Ctx(), mparam_, std::move(base_score), task);
CHECK(learner_model_param_.Initialized());
CHECK_NE(learner_model_param_.BaseScore(Ctx()).Size(), 0);
}
/**
* \brief Calculate the `base_score` based on input data.
*
* \param p_fmat The training DMatrix used to estimate the base score.
*/
void InitBaseScore(DMatrix const* p_fmat) {
// Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
// After 1.0.0 we save the value provided by user and keep it immutable instead. To
// keep the stability, we initialize it in binary LoadModel instead of configuration.
// Under what condition should we omit the transformation:
//
// - base_score is loaded from old binary model.
//
// What are the other possible conditions:
//
// - model loaded from new binary or JSON.
// - model is created from scratch.
// - model is configured second time due to change of parameter
if (!learner_model_param_.Initialized()) {
this->ConfigureModelParamWithoutBaseScore();
}
if (mparam_.boost_from_average && !UsePtr(gbm_)->ModelFitted()) {
if (p_fmat) {
auto const& info = p_fmat->Info();
info.Validate(Ctx()->gpu_id);
// We estimate it from input data.
linalg::Tensor<float, 1> base_score;
UsePtr(obj_)->InitEstimation(info, &base_score);
mparam_.base_score = base_score(0);
CHECK(!std::isnan(mparam_.base_score));
}
// Update the shared model parameter
this->ConfigureModelParamWithoutBaseScore();
}
CHECK(!std::isnan(mparam_.base_score));
CHECK(!std::isinf(mparam_.base_score));
}
public:
explicit LearnerConfiguration(std::vector<std::shared_ptr<DMatrix> > cache)
: need_configuration_{true} {
monitor_.Init("Learner");
auto& local_cache = (*ThreadLocalPredictionCache::Get())[this];
for (std::shared_ptr<DMatrix> const& d : cache) {
local_cache.Cache(d, GenericParameter::kCpuId);
}
}
~LearnerConfiguration() override {
auto local_cache = ThreadLocalPredictionCache::Get();
if (local_cache->find(this) != local_cache->cend()) {
local_cache->erase(this);
}
}
// Configuration before data is known.
void Configure() override {
// Varient of double checked lock
if (!this->need_configuration_) {
return;
}
std::lock_guard<std::mutex> guard(config_lock_);
if (!this->need_configuration_) {
return;
}
monitor_.Start("Configure");
auto old_tparam = tparam_;
Args args = {cfg_.cbegin(), cfg_.cend()};
tparam_.UpdateAllowUnknown(args);
mparam_.UpdateAllowUnknown(args);
auto initialized = ctx_.GetInitialised();
auto old_seed = ctx_.seed;
ctx_.UpdateAllowUnknown(args);
ConsoleLogger::Configure(args);
// add additional parameters
// These are cosntraints that need to be satisfied.
if (tparam_.dsplit == DataSplitMode::kAuto && collective::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
// set seed only before the model is initialized
if (!initialized || ctx_.seed != old_seed) {
common::GlobalRandom().seed(ctx_.seed);
}
// must precede configure gbm since num_features is required for gbm
this->ConfigureNumFeatures();
args = {cfg_.cbegin(), cfg_.cend()}; // renew
this->ConfigureObjective(old_tparam, &args);
learner_model_param_.task = obj_->Task(); // required by gbm configuration.
this->ConfigureGBM(old_tparam, args);
ctx_.ConfigureGpuId(this->gbm_->UseGPU());
this->ConfigureModelParamWithoutBaseScore();
this->ConfigureMetrics(args);
this->need_configuration_ = false;
if (ctx_.validate_parameters) {
this->ValidateParameters();
}
cfg_.clear();
monitor_.Stop("Configure");
}
void CheckModelInitialized() const {
CHECK(learner_model_param_.Initialized()) << ModelNotFitted();
CHECK_NE(learner_model_param_.BaseScore(this->Ctx()).Size(), 0) << ModelNotFitted();
}
virtual PredictionContainer* GetPredictionCache() const {
return &((*ThreadLocalPredictionCache::Get())[this]);
}
void LoadConfig(Json const& in) override {
// If configuration is loaded, ensure that the model came from the same version
CHECK(IsA<Object>(in));
auto origin_version = Version::Load(in);
if (!Version::Same(origin_version)) {
LOG(WARNING) << ModelMsg();
return; // skip configuration if version is not matched
}
auto const& learner_parameters = get<Object>(in["learner"]);
FromJson(learner_parameters.at("learner_train_param"), &tparam_);
auto const& gradient_booster = learner_parameters.at("gradient_booster");
auto const& objective_fn = learner_parameters.at("objective");
if (!obj_) {
obj_.reset(ObjFunction::Create(tparam_.objective, &ctx_));
}
obj_->LoadConfig(objective_fn);
learner_model_param_.task = obj_->Task();
tparam_.booster = get<String>(gradient_booster["name"]);
if (!gbm_) {
gbm_.reset(GradientBooster::Create(tparam_.booster,
&ctx_, &learner_model_param_));
}
gbm_->LoadConfig(gradient_booster);
auto const& j_metrics = learner_parameters.at("metrics");
auto n_metrics = get<Array const>(j_metrics).size();
metric_names_.resize(n_metrics);
metrics_.resize(n_metrics);
for (size_t i = 0; i < n_metrics; ++i) {
auto old_serialization = IsA<String>(j_metrics[i]);
if (old_serialization) {
LOG(WARNING) << ModelMsg();
metric_names_[i] = get<String>(j_metrics[i]);
} else {
metric_names_[i] = get<String>(j_metrics[i]["name"]);
}
metrics_[i] = std::unique_ptr<Metric>(Metric::Create(metric_names_[i], &ctx_));
if (!old_serialization) {
metrics_[i]->LoadConfig(j_metrics[i]);
}
}
FromJson(learner_parameters.at("generic_param"), &ctx_);
// make sure the GPU ID is valid in new environment before start running configure.
ctx_.ConfigureGpuId(false);
this->need_configuration_ = true;
}
void SaveConfig(Json* p_out) const override {
CHECK(!this->need_configuration_) << "Call Configure before saving model.";
Version::Save(p_out);
Json& out { *p_out };
// parameters
out["learner"] = Object();
auto& learner_parameters = out["learner"];
learner_parameters["learner_train_param"] = ToJson(tparam_);
learner_parameters["learner_model_param"] = mparam_.ToJson();
learner_parameters["gradient_booster"] = Object();
auto& gradient_booster = learner_parameters["gradient_booster"];
gbm_->SaveConfig(&gradient_booster);
learner_parameters["objective"] = Object();
auto& objective_fn = learner_parameters["objective"];
obj_->SaveConfig(&objective_fn);
std::vector<Json> metrics(metrics_.size(), Json{Object{}});
for (size_t i = 0; i < metrics_.size(); ++i) {
metrics_[i]->SaveConfig(&metrics[i]);
}
learner_parameters["metrics"] = Array(std::move(metrics));
learner_parameters["generic_param"] = ToJson(ctx_);
}
void SetParam(const std::string& key, const std::string& value) override {
this->need_configuration_ = true;
if (key == kEvalMetric) {
if (std::find(metric_names_.cbegin(), metric_names_.cend(),
value) == metric_names_.cend()) {
metric_names_.emplace_back(value);
}
} else {
cfg_[key] = value;
}
}
// Short hand for setting multiple parameters
void SetParams(std::vector<std::pair<std::string, std::string>> const& args) override {
for (auto const& kv : args) {
this->SetParam(kv.first, kv.second);
}
}
uint32_t GetNumFeature() const override {
return learner_model_param_.num_feature;
}
void SetAttr(const std::string& key, const std::string& value) override {
attributes_[key] = value;
mparam_.contain_extra_attrs = 1;
}
bool GetAttr(const std::string& key, std::string* out) const override {
auto it = attributes_.find(key);
if (it == attributes_.end()) return false;
*out = it->second;
return true;
}
bool DelAttr(const std::string& key) override {
auto it = attributes_.find(key);
if (it == attributes_.end()) { return false; }
attributes_.erase(it);
return true;
}
void SetFeatureNames(std::vector<std::string> const& fn) override {
feature_names_ = fn;
}
void GetFeatureNames(std::vector<std::string>* fn) const override {
*fn = feature_names_;
}
void SetFeatureTypes(std::vector<std::string> const& ft) override {
this->feature_types_ = ft;
}
void GetFeatureTypes(std::vector<std::string>* p_ft) const override {
auto& ft = *p_ft;
ft = this->feature_types_;
}
std::vector<std::string> GetAttrNames() const override {
std::vector<std::string> out;
for (auto const& kv : attributes_) {
out.emplace_back(kv.first);
}
return out;
}
const std::map<std::string, std::string>& GetConfigurationArguments() const override {
return cfg_;
}
Context const* Ctx() const override { return &ctx_; }
private:
void ValidateParameters() {
Json config { Object() };
this->SaveConfig(&config);
std::stack<Json> stack;
stack.push(config);
std::string const postfix{"_param"};
auto is_parameter = [&postfix](std::string const &key) {
return key.size() > postfix.size() &&
std::equal(postfix.rbegin(), postfix.rend(), key.rbegin());
};
// Extract all parameters
std::vector<std::string> keys;
// First global parameters
Json const global_config{ToJson(*GlobalConfigThreadLocalStore::Get())};
for (auto const& items : get<Object const>(global_config)) {
keys.emplace_back(items.first);
}
// Parameters in various xgboost components.
while (!stack.empty()) {
auto j_obj = stack.top();
stack.pop();
auto const &obj = get<Object const>(j_obj);
for (auto const &kv : obj) {
if (is_parameter(kv.first)) {
auto parameter = get<Object const>(kv.second);
std::transform(parameter.begin(), parameter.end(), std::back_inserter(keys),
[](std::pair<std::string const&, Json const&> const& kv) {
return kv.first;
});
} else if (IsA<Object>(kv.second)) {
stack.push(kv.second);
}
}
}
// FIXME(trivialfis): Make eval_metric a training parameter.
keys.emplace_back(kEvalMetric);
keys.emplace_back("num_output_group");
std::sort(keys.begin(), keys.end());
std::vector<std::string> provided;
for (auto const &kv : cfg_) {
if (std::any_of(kv.first.cbegin(), kv.first.cend(),
[](char ch) { return std::isspace(ch); })) {
LOG(FATAL) << "Invalid parameter \"" << kv.first << "\" contains whitespace.";
}
provided.push_back(kv.first);
}
std::sort(provided.begin(), provided.end());
std::vector<std::string> diff;
std::set_difference(provided.begin(), provided.end(), keys.begin(),
keys.end(), std::back_inserter(diff));
if (diff.size() != 0) {
std::stringstream ss;
ss << "\nParameters: { ";
for (size_t i = 0; i < diff.size() - 1; ++i) {
ss << "\"" << diff[i] << "\", ";
}
ss << "\"" << diff.back() << "\"";
ss << R"W( } are not used.
)W";
LOG(WARNING) << ss.str();
}
}
void ConfigureNumFeatures() {
// Compute number of global features if parameter not already set
if (mparam_.num_feature == 0) {
// TODO(hcho3): Change num_feature to 64-bit integer
unsigned num_feature = 0;
auto local_cache = this->GetPredictionCache();
for (auto& matrix : local_cache->Container()) {
CHECK(matrix.first);
CHECK(!matrix.second.ref.expired());
const uint64_t num_col = matrix.first->Info().num_col_;
CHECK_LE(num_col, static_cast<uint64_t>(std::numeric_limits<unsigned>::max()))
<< "Unfortunately, XGBoost does not support data matrices with "
<< std::numeric_limits<unsigned>::max() << " features or greater";
num_feature = std::max(num_feature, static_cast<uint32_t>(num_col));
}
collective::Allreduce<collective::Operation::kMax>(&num_feature, 1);
if (num_feature > mparam_.num_feature) {
mparam_.num_feature = num_feature;
}
}
CHECK_NE(mparam_.num_feature, 0)
<< "0 feature is supplied. Are you using raw Booster interface?";
// Remove these once binary IO is gone.
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
cfg_["num_class"] = common::ToString(mparam_.num_class);
}
void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
if (gbm_ == nullptr || old.booster != tparam_.booster) {
gbm_.reset(GradientBooster::Create(tparam_.booster, &ctx_,
&learner_model_param_));
}
gbm_->Configure(args);
}
void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {
// Once binary IO is gone, NONE of these config is useful.
if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0" &&
tparam_.objective != "multi:softprob") {
cfg_["num_output_group"] = cfg_["num_class"];
if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
tparam_.objective = "multi:softmax";
}
}
if (cfg_.find("max_delta_step") == cfg_.cend() &&
cfg_.find("objective") != cfg_.cend() &&
tparam_.objective == "count:poisson") {
// max_delta_step is a duplicated parameter in Poisson regression and tree param.
// Rename one of them once binary IO is gone.
cfg_["max_delta_step"] = kMaxDeltaStepDefaultValue;
}
if (obj_ == nullptr || tparam_.objective != old.objective) {
obj_.reset(ObjFunction::Create(tparam_.objective, &ctx_));
}
auto& args = *p_args;
args = {cfg_.cbegin(), cfg_.cend()}; // renew
obj_->Configure(args);
}
void ConfigureMetrics(Args const& args) {
for (auto const& name : metric_names_) {
auto DupCheck = [&name](std::unique_ptr<Metric> const& m) {
return m->Name() != name;
};
if (std::all_of(metrics_.begin(), metrics_.end(), DupCheck)) {
metrics_.emplace_back(std::unique_ptr<Metric>(Metric::Create(name, &ctx_)));
mparam_.contain_eval_metrics = 1;
}
}
for (auto& p_metric : metrics_) {
p_metric->Configure(args);
}
}
/**
* Get number of targets from objective function.
*/
void ConfigureTargets() {
CHECK(this->obj_);
auto const& cache = this->GetPredictionCache()->Container();
size_t n_targets = 1;
for (auto const& d : cache) {
if (n_targets == 1) {
n_targets = this->obj_->Targets(d.first->Info());
} else {
auto t = this->obj_->Targets(d.first->Info());
CHECK(n_targets == t || 1 == t) << "Inconsistent labels.";
}
}
if (mparam_.num_target != 1) {
CHECK(n_targets == 1 || n_targets == mparam_.num_target)
<< "Inconsistent configuration of num_target. Configuration result from input data:"
<< n_targets << ", configuration from parameter:" << mparam_.num_target;
} else {
mparam_.num_target = n_targets;
}
}
};
std::string const LearnerConfiguration::kEvalMetric {"eval_metric"}; // NOLINT
class LearnerIO : public LearnerConfiguration {
private:
std::set<std::string> saved_configs_ = {"num_round"};
// Used to identify the offset of JSON string when
// Will be removed once JSON takes over. Right now we still loads some RDS files from R.
std::string const serialisation_header_ { u8"CONFIG-offset:" };
public:
explicit LearnerIO(std::vector<std::shared_ptr<DMatrix>> cache) : LearnerConfiguration{cache} {}
void LoadModel(Json const& in) override {
CHECK(IsA<Object>(in));
auto version = Version::Load(in);
if (std::get<0>(version) == 1 && std::get<1>(version) < 6) {
LOG(WARNING)
<< "Found JSON model saved before XGBoost 1.6, please save the model using current "
"version again. The support for old JSON model will be discontinued in XGBoost 2.3.";
}
auto const& learner = get<Object>(in["learner"]);
mparam_.FromJson(learner.at("learner_model_param"));
auto const& objective_fn = learner.at("objective");
std::string name = get<String>(objective_fn["name"]);
tparam_.UpdateAllowUnknown(Args{{"objective", name}});
obj_.reset(ObjFunction::Create(name, &ctx_));
obj_->LoadConfig(objective_fn);
auto const& gradient_booster = learner.at("gradient_booster");
name = get<String>(gradient_booster["name"]);
tparam_.UpdateAllowUnknown(Args{{"booster", name}});
gbm_.reset(
GradientBooster::Create(tparam_.booster, &ctx_, &learner_model_param_));
gbm_->LoadModel(gradient_booster);
auto const& j_attributes = get<Object const>(learner.at("attributes"));
attributes_.clear();
for (auto const& kv : j_attributes) {
attributes_[kv.first] = get<String const>(kv.second);
}
// feature names and types are saved in xgboost 1.4
auto it = learner.find("feature_names");
if (it != learner.cend()) {
auto const& feature_names = get<Array const>(it->second);
feature_names_.resize(feature_names.size());
std::transform(feature_names.cbegin(), feature_names.cend(), feature_names_.begin(),
[](Json const& fn) { return get<String const>(fn); });
}
it = learner.find("feature_types");
if (it != learner.cend()) {
auto const& feature_types = get<Array const>(it->second);
feature_types_.resize(feature_types.size());
std::transform(feature_types.cbegin(), feature_types.cend(), feature_types_.begin(),
[](Json const& fn) { return get<String const>(fn); });
}
this->need_configuration_ = true;
}
void SaveModel(Json* p_out) const override {
CHECK(!this->need_configuration_) << "Call Configure before saving model.";
this->CheckModelInitialized();
Version::Save(p_out);
Json& out { *p_out };
out["learner"] = Object();
auto& learner = out["learner"];
learner["learner_model_param"] = mparam_.ToJson();
learner["gradient_booster"] = Object();
auto& gradient_booster = learner["gradient_booster"];
gbm_->SaveModel(&gradient_booster);
learner["objective"] = Object();
auto& objective_fn = learner["objective"];
obj_->SaveConfig(&objective_fn);
learner["attributes"] = Object();
for (auto const& kv : attributes_) {
learner["attributes"][kv.first] = String(kv.second);
}
learner["feature_names"] = Array();
auto& feature_names = get<Array>(learner["feature_names"]);
for (auto const& name : feature_names_) {
feature_names.emplace_back(name);
}
learner["feature_types"] = Array();
auto& feature_types = get<Array>(learner["feature_types"]);
for (auto const& type : feature_types_) {
feature_types.emplace_back(type);
}
}
// About to be deprecated by JSON format
void LoadModel(dmlc::Stream* fi) override {
ctx_.UpdateAllowUnknown(Args{});
tparam_.Init(std::vector<std::pair<std::string, std::string>>{});
// TODO(tqchen) mark deprecation of old format.
common::PeekableInStream fp(fi);
// backward compatible header check.
std::string header;
header.resize(4);
if (fp.PeekRead(&header[0], 4) == 4) {
CHECK_NE(header, "bs64")
<< "Base64 format is no longer supported in brick.";
if (header == "binf") {
CHECK_EQ(fp.Read(&header[0], 4), 4U);
}
}
// FIXME(jiamingy): Move this out of learner after the old binary model is remove.
auto first_non_space = [&](std::string::const_iterator beg, std::string::const_iterator end) {
for (auto i = beg; i != end; ++i) {
if (!std::isspace(*i)) {
return i;
}
}
return end;
};
if (header[0] == '{') { // Dispatch to JSON
auto buffer = common::ReadAll(fi, &fp);