a5a58102e5
This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features: - Federated learning for both CPU and GPU. - NCCL. - More data types. - A unified interface for all the underlying implementations. - Improved timeout handling for both tracker and workers. - Exhausted tests with metrics (fixed a couple of bugs along the way). - A reusable tracker for Python and JVM packages.
38 lines
1.1 KiB
C++
38 lines
1.1 KiB
C++
/**
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* Copyright 2023-2024, XGBoost contributors
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*/
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#include "objective_helpers.h"
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#include "../../src/common/linalg_op.h" // for begin, end
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#include "helpers.h" // for RandomDataGenerator
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namespace xgboost {
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void MakeLabelForObjTest(std::shared_ptr<DMatrix> p_fmat, std::string const& obj) {
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auto& h_upper = p_fmat->Info().labels_upper_bound_.HostVector();
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auto& h_lower = p_fmat->Info().labels_lower_bound_.HostVector();
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h_lower.resize(p_fmat->Info().num_row_);
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h_upper.resize(p_fmat->Info().num_row_);
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for (size_t i = 0; i < p_fmat->Info().num_row_; ++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 (obj.find("rank:") != std::string::npos) {
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auto h_label = p_fmat->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> MakeFmatForObjTest(std::string const& obj, bst_idx_t n_samples,
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bst_feature_t n_features) {
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auto p_fmat = RandomDataGenerator{n_samples, n_features, 0}.GenerateDMatrix(true);
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MakeLabelForObjTest(p_fmat, obj);
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return p_fmat;
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};
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} // namespace xgboost
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