Split up LearnerImpl. (#5350)
This commit is contained in:
Jiaming Yuan
GitHub
parent
3ad4333b0e
commit
45a97ddf32
604
src/learner.cc
604
src/learner.cc
@ -20,6 +20,7 @@
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#include "xgboost/base.h"
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#include "xgboost/data.h"
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#include "xgboost/model.h"
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#include "xgboost/predictor.h"
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#include "xgboost/feature_map.h"
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#include "xgboost/gbm.h"
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@ -196,26 +197,34 @@ void GenericParameter::ConfigureGpuId(bool require_gpu) {
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using XGBAPIThreadLocalStore =
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dmlc::ThreadLocalStore<std::map<Learner const *, XGBAPIThreadLocalEntry>>;
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/*!
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* \brief learner that performs gradient boosting for a specific objective
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* function. It does training and prediction.
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*/
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class LearnerImpl : public Learner {
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class LearnerConfiguration : public Learner {
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protected:
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static std::string const kEvalMetric; // NOLINT
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protected:
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PredictionContainer cache_;
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protected:
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bool need_configuration_;
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std::map<std::string, std::string> cfg_;
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// Stores information like best-iteration for early stopping.
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std::map<std::string, std::string> attributes_;
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common::Monitor monitor_;
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LearnerModelParamLegacy mparam_;
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LearnerModelParam learner_model_param_;
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LearnerTrainParam tparam_;
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std::vector<std::string> metric_names_;
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public:
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explicit LearnerImpl(std::vector<std::shared_ptr<DMatrix> > cache)
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explicit LearnerConfiguration(std::vector<std::shared_ptr<DMatrix> > cache)
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: need_configuration_{true} {
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monitor_.Init("Learner");
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for (std::shared_ptr<DMatrix> const& d : cache) {
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cache_.Cache(d, GenericParameter::kCpuId);
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}
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}
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~LearnerImpl() override {
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auto local_map = XGBAPIThreadLocalStore::Get();
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if (local_map->find(this) != local_map->cend()) {
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local_map->erase(this);
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}
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}
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// Configuration before data is known.
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void Configure() override {
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if (!this->need_configuration_) { return; }
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@ -279,137 +288,6 @@ class LearnerImpl : public Learner {
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monitor_.Stop("Configure");
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}
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void ValidateParameters() {
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Json config { Object() };
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this->SaveConfig(&config);
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std::stack<Json> stack;
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stack.push(config);
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std::string const postfix{"_param"};
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auto is_parameter = [&postfix](std::string const &key) {
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return key.size() > postfix.size() &&
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std::equal(postfix.rbegin(), postfix.rend(), key.rbegin());
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};
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// Extract all parameters
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std::vector<std::string> keys;
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while (!stack.empty()) {
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auto j_obj = stack.top();
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stack.pop();
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auto const &obj = get<Object const>(j_obj);
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for (auto const &kv : obj) {
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if (is_parameter(kv.first)) {
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auto parameter = get<Object const>(kv.second);
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std::transform(parameter.begin(), parameter.end(), std::back_inserter(keys),
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[](std::pair<std::string const&, Json const&> const& kv) {
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return kv.first;
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});
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} else if (IsA<Object>(kv.second)) {
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stack.push(kv.second);
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}
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}
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}
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keys.emplace_back(kEvalMetric);
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keys.emplace_back("verbosity");
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keys.emplace_back("num_output_group");
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std::sort(keys.begin(), keys.end());
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std::vector<std::string> provided;
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for (auto const &kv : cfg_) {
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// FIXME(trivialfis): Make eval_metric a training parameter.
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provided.push_back(kv.first);
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}
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std::sort(provided.begin(), provided.end());
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std::vector<std::string> diff;
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std::set_difference(provided.begin(), provided.end(), keys.begin(),
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keys.end(), std::back_inserter(diff));
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if (diff.size() != 0) {
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std::stringstream ss;
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ss << "\nParameters: { ";
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for (size_t i = 0; i < diff.size() - 1; ++i) {
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ss << diff[i] << ", ";
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}
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ss << diff.back();
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ss << R"W( } might not be used.
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This may not be accurate due to some parameters are only used in language bindings but
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passed down to XGBoost core. Or some parameters are not used but slip through this
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verification. Please open an issue if you find above cases.
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)W";
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LOG(WARNING) << ss.str();
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}
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}
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void CheckDataSplitMode() {
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if (rabit::IsDistributed()) {
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CHECK(tparam_.dsplit != DataSplitMode::kAuto)
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<< "Precondition violated; dsplit cannot be 'auto' in distributed mode";
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if (tparam_.dsplit == DataSplitMode::kCol) {
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// 'distcol' updater hidden until it becomes functional again
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// See discussion at https://github.com/dmlc/xgboost/issues/1832
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LOG(FATAL) << "Column-wise data split is currently not supported.";
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}
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}
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}
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void LoadModel(Json const& in) override {
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CHECK(IsA<Object>(in));
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Version::Load(in, false);
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auto const& learner = get<Object>(in["learner"]);
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mparam_.FromJson(learner.at("learner_model_param"));
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auto const& objective_fn = learner.at("objective");
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std::string name = get<String>(objective_fn["name"]);
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tparam_.UpdateAllowUnknown(Args{{"objective", name}});
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obj_.reset(ObjFunction::Create(name, &generic_parameters_));
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obj_->LoadConfig(objective_fn);
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auto const& gradient_booster = learner.at("gradient_booster");
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name = get<String>(gradient_booster["name"]);
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tparam_.UpdateAllowUnknown(Args{{"booster", name}});
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gbm_.reset(GradientBooster::Create(tparam_.booster,
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&generic_parameters_, &learner_model_param_));
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gbm_->LoadModel(gradient_booster);
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auto const& j_attributes = get<Object const>(learner.at("attributes"));
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attributes_.clear();
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for (auto const& kv : j_attributes) {
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attributes_[kv.first] = get<String const>(kv.second);
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}
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this->need_configuration_ = true;
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}
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void SaveModel(Json* p_out) const override {
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CHECK(!this->need_configuration_) << "Call Configure before saving model.";
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Version::Save(p_out);
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Json& out { *p_out };
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out["learner"] = Object();
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auto& learner = out["learner"];
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learner["learner_model_param"] = mparam_.ToJson();
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learner["gradient_booster"] = Object();
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auto& gradient_booster = learner["gradient_booster"];
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gbm_->SaveModel(&gradient_booster);
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learner["objective"] = Object();
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auto& objective_fn = learner["objective"];
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obj_->SaveConfig(&objective_fn);
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learner["attributes"] = Object();
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for (auto const& kv : attributes_) {
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learner["attributes"][kv.first] = String(kv.second);
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}
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}
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void LoadConfig(Json const& in) override {
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CHECK(IsA<Object>(in));
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Version::Load(in, true);
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@ -476,6 +354,266 @@ class LearnerImpl : public Learner {
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learner_parameters["generic_param"] = toJson(generic_parameters_);
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}
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void SetParam(const std::string& key, const std::string& value) override {
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this->need_configuration_ = true;
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if (key == kEvalMetric) {
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if (std::find(metric_names_.cbegin(), metric_names_.cend(),
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value) == metric_names_.cend()) {
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metric_names_.emplace_back(value);
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}
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} else {
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cfg_[key] = value;
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}
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}
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// Short hand for setting multiple parameters
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void SetParams(std::vector<std::pair<std::string, std::string>> const& args) override {
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for (auto const& kv : args) {
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this->SetParam(kv.first, kv.second);
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}
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}
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void SetAttr(const std::string& key, const std::string& value) override {
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attributes_[key] = value;
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mparam_.contain_extra_attrs = 1;
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}
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bool GetAttr(const std::string& key, std::string* out) const override {
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auto it = attributes_.find(key);
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if (it == attributes_.end()) return false;
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*out = it->second;
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return true;
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}
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bool DelAttr(const std::string& key) override {
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auto it = attributes_.find(key);
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if (it == attributes_.end()) { return false; }
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attributes_.erase(it);
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return true;
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}
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std::vector<std::string> GetAttrNames() const override {
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std::vector<std::string> out;
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for (auto const& kv : attributes_) {
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out.emplace_back(kv.first);
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}
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return out;
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}
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const std::map<std::string, std::string>& GetConfigurationArguments() const override {
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return cfg_;
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}
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GenericParameter const& GetGenericParameter() const override {
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return generic_parameters_;
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}
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private:
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void ValidateParameters() {
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Json config { Object() };
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this->SaveConfig(&config);
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std::stack<Json> stack;
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stack.push(config);
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std::string const postfix{"_param"};
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auto is_parameter = [&postfix](std::string const &key) {
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return key.size() > postfix.size() &&
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std::equal(postfix.rbegin(), postfix.rend(), key.rbegin());
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};
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// Extract all parameters
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std::vector<std::string> keys;
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while (!stack.empty()) {
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auto j_obj = stack.top();
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stack.pop();
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auto const &obj = get<Object const>(j_obj);
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for (auto const &kv : obj) {
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if (is_parameter(kv.first)) {
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auto parameter = get<Object const>(kv.second);
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std::transform(parameter.begin(), parameter.end(), std::back_inserter(keys),
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[](std::pair<std::string const&, Json const&> const& kv) {
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return kv.first;
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});
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} else if (IsA<Object>(kv.second)) {
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stack.push(kv.second);
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}
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}
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}
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keys.emplace_back(kEvalMetric);
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keys.emplace_back("verbosity");
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keys.emplace_back("num_output_group");
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std::sort(keys.begin(), keys.end());
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std::vector<std::string> provided;
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for (auto const &kv : cfg_) {
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// FIXME(trivialfis): Make eval_metric a training parameter.
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provided.push_back(kv.first);
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}
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std::sort(provided.begin(), provided.end());
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std::vector<std::string> diff;
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std::set_difference(provided.begin(), provided.end(), keys.begin(),
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keys.end(), std::back_inserter(diff));
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if (diff.size() != 0) {
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std::stringstream ss;
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ss << "\nParameters: { ";
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for (size_t i = 0; i < diff.size() - 1; ++i) {
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ss << diff[i] << ", ";
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}
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ss << diff.back();
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ss << R"W( } might not be used.
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This may not be accurate due to some parameters are only used in language bindings but
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passed down to XGBoost core. Or some parameters are not used but slip through this
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verification. Please open an issue if you find above cases.
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)W";
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LOG(WARNING) << ss.str();
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}
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}
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void ConfigureNumFeatures() {
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// estimate feature bound
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// TODO(hcho3): Change num_feature to 64-bit integer
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unsigned num_feature = 0;
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for (auto & matrix : cache_.Container()) {
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CHECK(matrix.first);
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CHECK(!matrix.second.ref.expired());
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const uint64_t num_col = matrix.first->Info().num_col_;
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CHECK_LE(num_col, static_cast<uint64_t>(std::numeric_limits<unsigned>::max()))
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<< "Unfortunately, XGBoost does not support data matrices with "
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<< std::numeric_limits<unsigned>::max() << " features or greater";
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num_feature = std::max(num_feature, static_cast<uint32_t>(num_col));
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}
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// run allreduce on num_feature to find the maximum value
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rabit::Allreduce<rabit::op::Max>(&num_feature, 1, nullptr, nullptr, "num_feature");
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if (num_feature > mparam_.num_feature) {
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mparam_.num_feature = num_feature;
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}
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CHECK_NE(mparam_.num_feature, 0)
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<< "0 feature is supplied. Are you using raw Booster interface?";
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// Remove these once binary IO is gone.
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cfg_["num_feature"] = common::ToString(mparam_.num_feature);
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cfg_["num_class"] = common::ToString(mparam_.num_class);
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}
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void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
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if (gbm_ == nullptr || old.booster != tparam_.booster) {
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gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
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&learner_model_param_));
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}
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gbm_->Configure(args);
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}
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void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {
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// Once binary IO is gone, NONE of these config is useful.
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if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0" &&
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tparam_.objective != "multi:softprob") {
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cfg_["num_output_group"] = cfg_["num_class"];
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if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
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tparam_.objective = "multi:softmax";
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}
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}
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if (cfg_.find("max_delta_step") == cfg_.cend() &&
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cfg_.find("objective") != cfg_.cend() &&
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tparam_.objective == "count:poisson") {
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// max_delta_step is a duplicated parameter in Poisson regression and tree param.
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// Rename one of them once binary IO is gone.
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cfg_["max_delta_step"] = kMaxDeltaStepDefaultValue;
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}
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if (obj_ == nullptr || tparam_.objective != old.objective) {
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obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
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}
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auto& args = *p_args;
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args = {cfg_.cbegin(), cfg_.cend()}; // renew
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obj_->Configure(args);
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}
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void ConfigureMetrics(Args const& args) {
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for (auto const& name : metric_names_) {
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auto DupCheck = [&name](std::unique_ptr<Metric> const& m) {
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return m->Name() != name;
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};
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if (std::all_of(metrics_.begin(), metrics_.end(), DupCheck)) {
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metrics_.emplace_back(std::unique_ptr<Metric>(Metric::Create(name, &generic_parameters_)));
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mparam_.contain_eval_metrics = 1;
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}
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}
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for (auto& p_metric : metrics_) {
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p_metric->Configure(args);
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}
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}
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};
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std::string const LearnerConfiguration::kEvalMetric {"eval_metric"}; // NOLINT
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class LearnerIO : public LearnerConfiguration {
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private:
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std::set<std::string> saved_configs_ = {"num_round"};
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// Used to identify the offset of JSON string when
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// `enable_experimental_json_serialization' is set to false. Will be removed once JSON
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// takes over.
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std::string const serialisation_header_ { u8"CONFIG-offset:" };
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public:
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explicit LearnerIO(std::vector<std::shared_ptr<DMatrix> > cache) :
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LearnerConfiguration{cache} {}
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void LoadModel(Json const& in) override {
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CHECK(IsA<Object>(in));
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Version::Load(in, false);
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auto const& learner = get<Object>(in["learner"]);
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mparam_.FromJson(learner.at("learner_model_param"));
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auto const& objective_fn = learner.at("objective");
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std::string name = get<String>(objective_fn["name"]);
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tparam_.UpdateAllowUnknown(Args{{"objective", name}});
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obj_.reset(ObjFunction::Create(name, &generic_parameters_));
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obj_->LoadConfig(objective_fn);
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auto const& gradient_booster = learner.at("gradient_booster");
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name = get<String>(gradient_booster["name"]);
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tparam_.UpdateAllowUnknown(Args{{"booster", name}});
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gbm_.reset(GradientBooster::Create(tparam_.booster,
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&generic_parameters_, &learner_model_param_));
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gbm_->LoadModel(gradient_booster);
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auto const& j_attributes = get<Object const>(learner.at("attributes"));
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attributes_.clear();
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for (auto const& kv : j_attributes) {
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attributes_[kv.first] = get<String const>(kv.second);
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}
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this->need_configuration_ = true;
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}
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void SaveModel(Json* p_out) const override {
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CHECK(!this->need_configuration_) << "Call Configure before saving model.";
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Version::Save(p_out);
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Json& out { *p_out };
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out["learner"] = Object();
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auto& learner = out["learner"];
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learner["learner_model_param"] = mparam_.ToJson();
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learner["gradient_booster"] = Object();
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auto& gradient_booster = learner["gradient_booster"];
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gbm_->SaveModel(&gradient_booster);
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learner["objective"] = Object();
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auto& objective_fn = learner["objective"];
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obj_->SaveConfig(&objective_fn);
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learner["attributes"] = Object();
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for (auto const& kv : attributes_) {
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learner["attributes"][kv.first] = String(kv.second);
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}
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}
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// About to be deprecated by JSON format
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void LoadModel(dmlc::Stream* fi) override {
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generic_parameters_.UpdateAllowUnknown(Args{});
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@ -721,6 +859,34 @@ class LearnerImpl : public Learner {
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this->LoadConfig(config);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/*!
|
||||
* \brief learner that performs gradient boosting for a specific objective
|
||||
* function. It does training and prediction.
|
||||
*/
|
||||
class LearnerImpl : public LearnerIO {
|
||||
public:
|
||||
explicit LearnerImpl(std::vector<std::shared_ptr<DMatrix> > cache)
|
||||
: LearnerIO{cache} {}
|
||||
~LearnerImpl() override {
|
||||
auto local_map = XGBAPIThreadLocalStore::Get();
|
||||
if (local_map->find(this) != local_map->cend()) {
|
||||
local_map->erase(this);
|
||||
}
|
||||
}
|
||||
// Configuration before data is known.
|
||||
void CheckDataSplitMode() {
|
||||
if (rabit::IsDistributed()) {
|
||||
CHECK(tparam_.dsplit != DataSplitMode::kAuto)
|
||||
<< "Precondition violated; dsplit cannot be 'auto' in distributed mode";
|
||||
if (tparam_.dsplit == DataSplitMode::kCol) {
|
||||
// 'distcol' updater hidden until it becomes functional again
|
||||
// See discussion at https://github.com/dmlc/xgboost/issues/1832
|
||||
LOG(FATAL) << "Column-wise data split is currently not supported.";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<std::string> DumpModel(const FeatureMap& fmap,
|
||||
bool with_stats,
|
||||
@ -804,55 +970,6 @@ class LearnerImpl : public Learner {
|
||||
return os.str();
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
std::vector<std::string> GetAttrNames() const override {
|
||||
std::vector<std::string> out;
|
||||
for (auto const& kv : attributes_) {
|
||||
out.emplace_back(kv.first);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
GenericParameter const& GetGenericParameter() const override {
|
||||
return generic_parameters_;
|
||||
}
|
||||
|
||||
void Predict(std::shared_ptr<DMatrix> data, bool output_margin,
|
||||
HostDeviceVector<bst_float>* out_preds, unsigned ntree_limit,
|
||||
bool training,
|
||||
@ -907,80 +1024,6 @@ class LearnerImpl : public Learner {
|
||||
gbm_->PredictBatch(data, out_preds, training, ntree_limit);
|
||||
}
|
||||
|
||||
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, &generic_parameters_));
|
||||
}
|
||||
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, &generic_parameters_)));
|
||||
mparam_.contain_eval_metrics = 1;
|
||||
}
|
||||
}
|
||||
for (auto& p_metric : metrics_) {
|
||||
p_metric->Configure(args);
|
||||
}
|
||||
}
|
||||
|
||||
void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
|
||||
if (gbm_ == nullptr || old.booster != tparam_.booster) {
|
||||
gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
|
||||
&learner_model_param_));
|
||||
}
|
||||
gbm_->Configure(args);
|
||||
}
|
||||
|
||||
// set number of features correctly.
|
||||
void ConfigureNumFeatures() {
|
||||
// estimate feature bound
|
||||
// TODO(hcho3): Change num_feature to 64-bit integer
|
||||
unsigned num_feature = 0;
|
||||
for (auto & matrix : 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));
|
||||
}
|
||||
// run allreduce on num_feature to find the maximum value
|
||||
rabit::Allreduce<rabit::op::Max>(&num_feature, 1, nullptr, nullptr, "num_feature");
|
||||
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 ValidateDMatrix(DMatrix* p_fmat) const {
|
||||
MetaInfo const& info = p_fmat->Info();
|
||||
auto const& weights = info.weights_;
|
||||
@ -1013,41 +1056,16 @@ class LearnerImpl : public Learner {
|
||||
}
|
||||
}
|
||||
|
||||
// model parameter
|
||||
LearnerModelParamLegacy mparam_;
|
||||
LearnerModelParam learner_model_param_;
|
||||
LearnerTrainParam tparam_;
|
||||
// Used to identify the offset of JSON string when
|
||||
// `enable_experimental_json_serialization' is set to false. Will be removed once JSON
|
||||
// takes over.
|
||||
std::string const serialisation_header_ { u8"CONFIG-offset:" };
|
||||
// User provided configurations
|
||||
std::map<std::string, std::string> cfg_;
|
||||
// Stores information like best-iteration for early stopping.
|
||||
std::map<std::string, std::string> attributes_;
|
||||
std::vector<std::string> metric_names_;
|
||||
static std::string const kEvalMetric; // NOLINT
|
||||
// gradient pairs
|
||||
HostDeviceVector<GradientPair> gpair_;
|
||||
bool need_configuration_;
|
||||
|
||||
private:
|
||||
/*! \brief random number transformation seed. */
|
||||
static int32_t constexpr kRandSeedMagic = 127;
|
||||
// internal cached dmatrix for prediction.
|
||||
PredictionContainer cache_;
|
||||
// gradient pairs
|
||||
HostDeviceVector<GradientPair> gpair_;
|
||||
/*! \brief Temporary storage to prediction. Useful for storing data transformed by
|
||||
* objective function */
|
||||
PredictionContainer output_predictions_;
|
||||
|
||||
common::Monitor monitor_;
|
||||
|
||||
/*! \brief (Deprecated) saved config keys used to restore failed worker */
|
||||
std::set<std::string> saved_configs_ = {"num_round"};
|
||||
};
|
||||
|
||||
std::string const LearnerImpl::kEvalMetric {"eval_metric"}; // NOLINT
|
||||
|
||||
constexpr int32_t LearnerImpl::kRandSeedMagic;
|
||||
|
||||
Learner* Learner::Create(
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user