fe9dff339c
* Convert federated learner test into test suite. - Add specialization to learning to rank.
121 lines
3.8 KiB
C++
121 lines
3.8 KiB
C++
/*!
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* Copyright 2023 XGBoost contributors
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*/
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#include <dmlc/parameter.h>
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#include <gtest/gtest.h>
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#include <xgboost/data.h>
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#include <xgboost/objective.h>
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#include "../../../plugin/federated/federated_server.h"
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#include "../../../src/collective/communicator-inl.h"
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#include "../../../src/common/linalg_op.h"
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#include "../helpers.h"
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#include "../objective_helpers.h" // for MakeObjNamesForTest, ObjTestNameGenerator
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#include "helpers.h"
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namespace xgboost {
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namespace {
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auto MakeModel(std::string objective, std::shared_ptr<DMatrix> dmat) {
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std::unique_ptr<Learner> learner{Learner::Create({dmat})};
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learner->SetParam("tree_method", "approx");
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learner->SetParam("objective", objective);
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if (objective.find("quantile") != std::string::npos) {
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learner->SetParam("quantile_alpha", "0.5");
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}
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if (objective.find("multi") != std::string::npos) {
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learner->SetParam("num_class", "3");
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}
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learner->UpdateOneIter(0, dmat);
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Json config{Object{}};
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learner->SaveConfig(&config);
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Json model{Object{}};
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learner->SaveModel(&model);
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return model;
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}
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void VerifyObjective(size_t rows, size_t cols, float expected_base_score, Json expected_model,
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std::string objective) {
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auto const world_size = collective::GetWorldSize();
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auto const rank = collective::GetRank();
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std::shared_ptr<DMatrix> dmat{RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(rank == 0)};
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if (rank == 0) {
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auto &h_upper = dmat->Info().labels_upper_bound_.HostVector();
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auto &h_lower = dmat->Info().labels_lower_bound_.HostVector();
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h_lower.resize(rows);
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h_upper.resize(rows);
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for (size_t i = 0; i < rows; ++i) {
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h_lower[i] = 1;
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h_upper[i] = 10;
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}
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if (objective.find("rank:") != std::string::npos) {
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auto h_label = dmat->Info().labels.HostView();
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std::size_t k = 0;
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for (auto &v : h_label) {
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v = k % 2 == 0;
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++k;
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}
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}
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}
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std::shared_ptr<DMatrix> sliced{dmat->SliceCol(world_size, rank)};
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auto model = MakeModel(objective, sliced);
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auto base_score = GetBaseScore(model);
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ASSERT_EQ(base_score, expected_base_score);
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ASSERT_EQ(model, expected_model);
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}
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} // namespace
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class FederatedLearnerTest : public ::testing::TestWithParam<std::string> {
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std::unique_ptr<ServerForTest> server_;
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static int const kWorldSize{3};
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protected:
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void SetUp() override { server_ = std::make_unique<ServerForTest>(kWorldSize); }
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void TearDown() override { server_.reset(nullptr); }
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void Run(std::string objective) {
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static auto constexpr kRows{16};
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static auto constexpr kCols{16};
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std::shared_ptr<DMatrix> dmat{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
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auto &h_upper = dmat->Info().labels_upper_bound_.HostVector();
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auto &h_lower = dmat->Info().labels_lower_bound_.HostVector();
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h_lower.resize(kRows);
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h_upper.resize(kRows);
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for (size_t i = 0; i < kRows; ++i) {
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h_lower[i] = 1;
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h_upper[i] = 10;
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}
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if (objective.find("rank:") != std::string::npos) {
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auto h_label = dmat->Info().labels.HostView();
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std::size_t k = 0;
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for (auto &v : h_label) {
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v = k % 2 == 0;
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++k;
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}
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}
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auto model = MakeModel(objective, dmat);
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auto score = GetBaseScore(model);
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RunWithFederatedCommunicator(kWorldSize, server_->Address(), &VerifyObjective, kRows, kCols,
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score, model, objective);
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}
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};
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TEST_P(FederatedLearnerTest, Objective) {
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std::string objective = GetParam();
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this->Run(objective);
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}
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INSTANTIATE_TEST_SUITE_P(FederatedLearnerObjective, FederatedLearnerTest,
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::testing::ValuesIn(MakeObjNamesForTest()),
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[](const ::testing::TestParamInfo<FederatedLearnerTest::ParamType> &info) {
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return ObjTestNameGenerator(info);
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});
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} // namespace xgboost
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