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Implement slope for Pseduo-Huber. (#7727)

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* Add objective and metric.
* Some refactoring for CPU/GPU dispatching using linalg module.
This commit is contained in:
Jiaming Yuan
2022-03-14 21:42:38 +08:00
committed by GitHub
parent 4dafb5fac8
commit 98d6faefd6
28 changed files with 456 additions and 290 deletions
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49
src/learner.cc
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@@ -277,6 +277,21 @@ using LearnerAPIThreadLocalStore =
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_;
@@ -375,7 +390,6 @@ class LearnerConfiguration : public Learner {
this->ConfigureGBM(old_tparam, args);
generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
this->ConfigureMetrics(args);
this->need_configuration_ = false;
@@ -418,9 +432,17 @@ class LearnerConfiguration : public Learner {
metric_names_.resize(n_metrics);
metrics_.resize(n_metrics);
for (size_t i = 0; i < n_metrics; ++i) {
metric_names_[i]= get<String>(j_metrics[i]);
metrics_[i] = std::unique_ptr<Metric>(
Metric::Create(metric_names_[i], &generic_parameters_));
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], &generic_parameters_));
if (!old_serialization) {
metrics_[i]->LoadConfig(j_metrics[i]);
}
}
FromJson(learner_parameters.at("generic_param"), &generic_parameters_);
@@ -448,9 +470,9 @@ class LearnerConfiguration : public Learner {
auto& objective_fn = learner_parameters["objective"];
obj_->SaveConfig(&objective_fn);
std::vector<Json> metrics(metrics_.size());
std::vector<Json> metrics(metrics_.size(), Json{Object{}});
for (size_t i = 0; i < metrics_.size(); ++i) {
metrics[i] = String(metrics_[i]->Name());
metrics_[i]->SaveConfig(&metrics[i]);
}
learner_parameters["metrics"] = Array(std::move(metrics));
@@ -709,21 +731,6 @@ class LearnerConfiguration : public Learner {
std::string const LearnerConfiguration::kEvalMetric {"eval_metric"}; // NOLINT
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 LearnerIO : public LearnerConfiguration {
private:
std::set<std::string> saved_configs_ = {"num_round"};