972730cde0
- Use the `linalg::Matrix` for storing gradients. - New API for the custom objective. - Custom objective for multi-class/multi-target is now required to return the correct shape. - Custom objective for Python can accept arrays with any strides. (row-major, column-major)
57 lines
1.5 KiB
C++
57 lines
1.5 KiB
C++
/**
|
|
* Copyright 2022-2023, XGBoost Contributors
|
|
*/
|
|
#include <gtest/gtest.h>
|
|
#include <xgboost/linalg.h>
|
|
|
|
#include "../../src/common/linalg_op.h"
|
|
#include "../../src/tree/fit_stump.h"
|
|
#include "../helpers.h"
|
|
|
|
namespace xgboost::tree {
|
|
namespace {
|
|
void TestFitStump(Context const *ctx, DataSplitMode split = DataSplitMode::kRow) {
|
|
std::size_t constexpr kRows = 16, kTargets = 2;
|
|
linalg::Matrix<GradientPair> gpair;
|
|
gpair.SetDevice(ctx->Device());
|
|
gpair.Reshape(kRows, kTargets);
|
|
auto h_gpair = gpair.HostView();
|
|
for (std::size_t i = 0; i < kRows; ++i) {
|
|
for (std::size_t t = 0; t < kTargets; ++t) {
|
|
h_gpair(i, t) = GradientPair{static_cast<float>(i), 1};
|
|
}
|
|
}
|
|
linalg::Vector<float> out;
|
|
MetaInfo info;
|
|
info.data_split_mode = split;
|
|
FitStump(ctx, info, gpair, kTargets, &out);
|
|
auto h_out = out.HostView();
|
|
for (auto it = linalg::cbegin(h_out); it != linalg::cend(h_out); ++it) {
|
|
// sum_hess == kRows
|
|
auto n = static_cast<float>(kRows);
|
|
auto sum_grad = n * (n - 1) / 2;
|
|
ASSERT_EQ(static_cast<float>(-sum_grad / n), *it);
|
|
}
|
|
}
|
|
} // anonymous namespace
|
|
|
|
TEST(InitEstimation, FitStump) {
|
|
Context ctx;
|
|
TestFitStump(&ctx);
|
|
}
|
|
|
|
#if defined(XGBOOST_USE_CUDA)
|
|
TEST(InitEstimation, GPUFitStump) {
|
|
Context ctx;
|
|
ctx.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
|
|
TestFitStump(&ctx);
|
|
}
|
|
#endif // defined(XGBOOST_USE_CUDA)
|
|
|
|
TEST(InitEstimation, FitStumpColumnSplit) {
|
|
Context ctx;
|
|
auto constexpr kWorldSize{3};
|
|
RunWithInMemoryCommunicator(kWorldSize, &TestFitStump, &ctx, DataSplitMode::kCol);
|
|
}
|
|
} // namespace xgboost::tree
|