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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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2
tests/cpp/gbm/test_gbtree.cc
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@@ -412,7 +412,7 @@ std::pair<Json, Json> TestModelSlice(std::string booster) {
j++;
}
// CHECK sliced model doesn't have dependency on old one
// CHECK sliced model doesn't have dependency on the old one
learner.reset();
CHECK_EQ(sliced->GetNumFeature(), kCols);

2
tests/cpp/helpers.h
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@@ -473,7 +473,7 @@ inline LearnerModelParam MakeMP(bst_feature_t n_features, float base_score, uint
int32_t device = Context::kCpuId) {
size_t shape[1]{1};
LearnerModelParam mparam(n_features, linalg::Tensor<float, 1>{{base_score}, shape, device},
n_groups, 1, MultiStrategy::kComposite);
n_groups, 1, MultiStrategy::kOneOutputPerTree);
return mparam;
}

2
tests/cpp/predictor/test_predictor.cc
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@@ -428,7 +428,7 @@ void TestVectorLeafPrediction(Context const *ctx) {
LearnerModelParam mparam{static_cast<bst_feature_t>(kCols),
linalg::Vector<float>{{0.5}, {1}, Context::kCpuId}, 1, 3,
MultiStrategy::kMonolithic};
MultiStrategy::kMultiOutputTree};
std::vector<std::unique_ptr<RegTree>> trees;
trees.emplace_back(new RegTree{mparam.LeafLength(), mparam.num_feature});

4
tests/cpp/test_multi_target.cc
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@@ -124,11 +124,11 @@ TEST(MultiStrategy, Configure) {
auto p_fmat = RandomDataGenerator{12ul, 3ul, 0.0}.GenerateDMatrix();
p_fmat->Info().labels.Reshape(p_fmat->Info().num_row_, 2);
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParams(Args{{"multi_strategy", "monolithic"}, {"num_target", "2"}});
learner->SetParams(Args{{"multi_strategy", "multi_output_tree"}, {"num_target", "2"}});
learner->Configure();
ASSERT_EQ(learner->Groups(), 2);
learner->SetParams(Args{{"multi_strategy", "monolithic"}, {"num_target", "0"}});
learner->SetParams(Args{{"multi_strategy", "multi_output_tree"}, {"num_target", "0"}});
ASSERT_THROW({ learner->Configure(); }, dmlc::Error);
}
} // namespace xgboost