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Make objectives work with vertical distributed and federated learning (#9002)

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This commit is contained in:
Rong Ou
2023-04-03 02:07:42 -07:00
committed by GitHub
parent 720a8c3273
commit 15e073ca9d
7 changed files with 199 additions and 111 deletions
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83
tests/cpp/test_learner.cc
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@@ -608,31 +608,74 @@ TEST_F(InitBaseScore, InitWithPredict) { this->TestInitWithPredt(); }
TEST_F(InitBaseScore, UpdateProcess) { this->TestUpdateProcess(); }
void TestColumnSplitBaseScore(std::shared_ptr<DMatrix> Xy_, float expected_base_score) {
void TestColumnSplit(std::shared_ptr<DMatrix> dmat, std::vector<float> const& expected_base_scores,
std::vector<Json> const& expected_models) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> sliced{Xy_->SliceCol(world_size, rank)};
std::unique_ptr<Learner> learner{Learner::Create({sliced})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", "binary:logistic");
learner->UpdateOneIter(0, sliced);
Json config{Object{}};
learner->SaveConfig(&config);
auto base_score = GetBaseScore(config);
ASSERT_EQ(base_score, expected_base_score);
std::shared_ptr<DMatrix> sliced{dmat->SliceCol(world_size, rank)};
auto i = 0;
for (auto const* entry : ::dmlc::Registry<::xgboost::ObjFunctionReg>::List()) {
std::unique_ptr<Learner> learner{Learner::Create({sliced})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", entry->name);
if (entry->name.find("quantile") != std::string::npos) {
learner->SetParam("quantile_alpha", "0.5");
}
if (entry->name.find("multi") != std::string::npos) {
learner->SetParam("num_class", "3");
}
learner->UpdateOneIter(0, sliced);
Json config{Object{}};
learner->SaveConfig(&config);
auto base_score = GetBaseScore(config);
ASSERT_EQ(base_score, expected_base_scores[i]);
Json model{Object{}};
learner->SaveModel(&model);
ASSERT_EQ(model, expected_models[i]);
i++;
}
}
TEST_F(InitBaseScore, ColumnSplit) {
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", "binary:logistic");
learner->UpdateOneIter(0, Xy_);
Json config{Object{}};
learner->SaveConfig(&config);
auto base_score = GetBaseScore(config);
ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
TEST(ColumnSplit, Objectives) {
auto constexpr kRows = 10, kCols = 10;
std::shared_ptr<DMatrix> dmat{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
auto& h_upper = dmat->Info().labels_upper_bound_.HostVector();
auto& h_lower = dmat->Info().labels_lower_bound_.HostVector();
h_lower.resize(kRows);
h_upper.resize(kRows);
for (size_t i = 0; i < kRows; ++i) {
h_lower[i] = 1;
h_upper[i] = 10;
}
std::vector<float> base_scores;
std::vector<Json> models;
for (auto const* entry : ::dmlc::Registry<::xgboost::ObjFunctionReg>::List()) {
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", entry->name);
if (entry->name.find("quantile") != std::string::npos) {
learner->SetParam("quantile_alpha", "0.5");
}
if (entry->name.find("multi") != std::string::npos) {
learner->SetParam("num_class", "3");
}
learner->UpdateOneIter(0, dmat);
Json config{Object{}};
learner->SaveConfig(&config);
base_scores.emplace_back(GetBaseScore(config));
Json model{Object{}};
learner->SaveModel(&model);
models.emplace_back(model);
}
auto constexpr kWorldSize{3};
RunWithInMemoryCommunicator(kWorldSize, &TestColumnSplitBaseScore, Xy_, base_score);
RunWithInMemoryCommunicator(kWorldSize, &TestColumnSplit, dmat, base_scores, models);
}
} // namespace xgboost