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Initial support for multi-target tree. (#8616)

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* Implement multi-target for hist.

- Add new hist tree builder.
- Move data fetchers for tests.
- Dispatch function calls in gbm base on the tree type.
This commit is contained in:
Jiaming Yuan
2023-03-22 23:49:56 +08:00
committed by GitHub
parent ea04d4c46c
commit 151882dd26
34 changed files with 856 additions and 389 deletions
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12
src/learner.cc
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@@ -326,7 +326,7 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
std::string booster;
std::string objective;
// This is a training parameter and is not saved (nor loaded) in the model.
MultiStrategy multi_strategy{MultiStrategy::kComposite};
MultiStrategy multi_strategy{MultiStrategy::kOneOutputPerTree};
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
@@ -339,12 +339,12 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
.set_default("reg:squarederror")
.describe("Objective function used for obtaining gradient.");
DMLC_DECLARE_FIELD(multi_strategy)
.add_enum("composite", MultiStrategy::kComposite)
.add_enum("monolithic", MultiStrategy::kMonolithic)
.set_default(MultiStrategy::kComposite)
.add_enum("one_output_per_tree", MultiStrategy::kOneOutputPerTree)
.add_enum("multi_output_tree", MultiStrategy::kMultiOutputTree)
.set_default(MultiStrategy::kOneOutputPerTree)
.describe(
"Strategy used for training multi-target models. `monolithic` means building one "
"single tree for all targets.");
"Strategy used for training multi-target models. `multi_output_tree` means building "
"one single tree for all targets.");
}
};