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Support exporting cut values (#9356)

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This commit is contained in:
Jiaming Yuan
2023-07-08 15:32:41 +08:00
committed by GitHub
parent c3124813e8
commit 20c52f07d2
28 changed files with 722 additions and 101 deletions
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222
tests/cpp/c_api/test_c_api.cc
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@@ -8,6 +8,7 @@
#include <xgboost/learner.h>
#include <xgboost/version_config.h>
#include <array> // for array
#include <cstddef> // std::size_t
#include <limits> // std::numeric_limits
#include <string> // std::string
@@ -15,6 +16,11 @@
#include "../../../src/c_api/c_api_error.h"
#include "../../../src/common/io.h"
#include "../../../src/data/adapter.h" // for ArrayAdapter
#include "../../../src/data/array_interface.h" // for ArrayInterface
#include "../../../src/data/gradient_index.h" // for GHistIndexMatrix
#include "../../../src/data/iterative_dmatrix.h" // for IterativeDMatrix
#include "../../../src/data/sparse_page_dmatrix.h" // for SparsePageDMatrix
#include "../helpers.h"
TEST(CAPI, XGDMatrixCreateFromMatDT) {
@@ -137,9 +143,9 @@ TEST(CAPI, ConfigIO) {
BoosterHandle handle = learner.get();
learner->UpdateOneIter(0, p_dmat);
char const* out[1];
std::array<char const* , 1> out;
bst_ulong len {0};
XGBoosterSaveJsonConfig(handle, &len, out);
XGBoosterSaveJsonConfig(handle, &len, out.data());
std::string config_str_0 { out[0] };
auto config_0 = Json::Load({config_str_0.c_str(), config_str_0.size()});
@@ -147,7 +153,7 @@ TEST(CAPI, ConfigIO) {
bst_ulong len_1 {0};
std::string config_str_1 { out[0] };
XGBoosterSaveJsonConfig(handle, &len_1, out);
XGBoosterSaveJsonConfig(handle, &len_1, out.data());
auto config_1 = Json::Load({config_str_1.c_str(), config_str_1.size()});
ASSERT_EQ(config_0, config_1);
@@ -266,9 +272,9 @@ TEST(CAPI, DMatrixSetFeatureName) {
ASSERT_EQ(std::to_string(i), c_out_features[i]);
}
char const* feat_types [] {"i", "q"};
std::array<char const *, 2> feat_types{"i", "q"};
static_assert(sizeof(feat_types) / sizeof(feat_types[0]) == kCols);
XGDMatrixSetStrFeatureInfo(handle, "feature_type", feat_types, kCols);
XGDMatrixSetStrFeatureInfo(handle, "feature_type", feat_types.data(), kCols);
char const **c_out_types;
XGDMatrixGetStrFeatureInfo(handle, u8"feature_type", &out_len,
&c_out_types);
@@ -405,4 +411,210 @@ TEST(CAPI, JArgs) {
ASSERT_THROW({ RequiredArg<String>(args, "null", __func__); }, dmlc::Error);
}
}
namespace {
void MakeLabelForTest(std::shared_ptr<DMatrix> Xy, DMatrixHandle cxy) {
auto n_samples = Xy->Info().num_row_;
std::vector<float> y(n_samples);
for (std::size_t i = 0; i < y.size(); ++i) {
y[i] = static_cast<float>(i);
}
Xy->Info().labels.Reshape(n_samples);
Xy->Info().labels.Data()->HostVector() = y;
auto y_int = GetArrayInterface(Xy->Info().labels.Data(), n_samples, 1);
std::string s_y_int;
Json::Dump(y_int, &s_y_int);
XGDMatrixSetInfoFromInterface(cxy, "label", s_y_int.c_str());
}
auto MakeSimpleDMatrixForTest(bst_row_t n_samples, bst_feature_t n_features, Json dconfig) {
HostDeviceVector<float> storage;
auto arr_int = RandomDataGenerator{n_samples, n_features, 0.5f}.GenerateArrayInterface(&storage);
data::ArrayAdapter adapter{StringView{arr_int}};
std::shared_ptr<DMatrix> Xy{
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), Context{}.Threads())};
DMatrixHandle p_fmat;
std::string s_dconfig;
Json::Dump(dconfig, &s_dconfig);
CHECK_EQ(XGDMatrixCreateFromDense(arr_int.c_str(), s_dconfig.c_str(), &p_fmat), 0);
MakeLabelForTest(Xy, p_fmat);
return std::pair{p_fmat, Xy};
}
auto MakeQDMForTest(Context const *ctx, bst_row_t n_samples, bst_feature_t n_features,
Json dconfig) {
bst_bin_t n_bins{16};
dconfig["max_bin"] = Integer{n_bins};
std::size_t n_batches{4};
std::unique_ptr<ArrayIterForTest> iter_0;
if (ctx->IsCUDA()) {
iter_0 = std::make_unique<CudaArrayIterForTest>(0.0f, n_samples, n_features, n_batches);
} else {
iter_0 = std::make_unique<NumpyArrayIterForTest>(0.0f, n_samples, n_features, n_batches);
}
std::string s_dconfig;
Json::Dump(dconfig, &s_dconfig);
DMatrixHandle p_fmat;
CHECK_EQ(XGQuantileDMatrixCreateFromCallback(static_cast<DataIterHandle>(iter_0.get()),
iter_0->Proxy(), nullptr, Reset, Next,
s_dconfig.c_str(), &p_fmat),
0);
std::unique_ptr<ArrayIterForTest> iter_1;
if (ctx->IsCUDA()) {
iter_1 = std::make_unique<CudaArrayIterForTest>(0.0f, n_samples, n_features, n_batches);
} else {
iter_1 = std::make_unique<NumpyArrayIterForTest>(0.0f, n_samples, n_features, n_batches);
}
auto Xy =
std::make_shared<data::IterativeDMatrix>(iter_1.get(), iter_1->Proxy(), nullptr, Reset, Next,
std::numeric_limits<float>::quiet_NaN(), 0, n_bins);
return std::pair{p_fmat, Xy};
}
auto MakeExtMemForTest(bst_row_t n_samples, bst_feature_t n_features, Json dconfig) {
std::size_t n_batches{4};
NumpyArrayIterForTest iter_0{0.0f, n_samples, n_features, n_batches};
std::string s_dconfig;
dconfig["cache_prefix"] = String{"cache"};
Json::Dump(dconfig, &s_dconfig);
DMatrixHandle p_fmat;
CHECK_EQ(XGDMatrixCreateFromCallback(static_cast<DataIterHandle>(&iter_0), iter_0.Proxy(), Reset,
Next, s_dconfig.c_str(), &p_fmat),
0);
NumpyArrayIterForTest iter_1{0.0f, n_samples, n_features, n_batches};
auto Xy = std::make_shared<data::SparsePageDMatrix>(
&iter_1, iter_1.Proxy(), Reset, Next, std::numeric_limits<float>::quiet_NaN(), 0, "");
MakeLabelForTest(Xy, p_fmat);
return std::pair{p_fmat, Xy};
}
template <typename Page>
void CheckResult(Context const *ctx, bst_feature_t n_features, std::shared_ptr<DMatrix> Xy,
float const *out_data, std::uint64_t const *out_indptr) {
for (auto const &page : Xy->GetBatches<Page>(ctx, BatchParam{16, 0.2})) {
auto const &cut = page.Cuts();
auto const &ptrs = cut.Ptrs();
auto const &vals = cut.Values();
auto const &mins = cut.MinValues();
for (bst_feature_t f = 0; f < Xy->Info().num_col_; ++f) {
ASSERT_EQ(ptrs[f] + f, out_indptr[f]);
ASSERT_EQ(mins[f], out_data[out_indptr[f]]);
auto beg = out_indptr[f];
auto end = out_indptr[f + 1];
auto val_beg = ptrs[f];
for (std::uint64_t i = beg + 1, j = val_beg; i < end; ++i, ++j) {
ASSERT_EQ(vals[j], out_data[i]);
}
}
ASSERT_EQ(ptrs[n_features] + n_features, out_indptr[n_features]);
}
}
void TestXGDMatrixGetQuantileCut(Context const *ctx) {
bst_row_t n_samples{1024};
bst_feature_t n_features{16};
Json dconfig{Object{}};
dconfig["ntread"] = Integer{Context{}.Threads()};
dconfig["missing"] = Number{std::numeric_limits<float>::quiet_NaN()};
auto check_result = [n_features, &ctx](std::shared_ptr<DMatrix> Xy, StringView s_out_data,
StringView s_out_indptr) {
auto i_out_data = ArrayInterface<1, false>{s_out_data};
ASSERT_EQ(i_out_data.type, ArrayInterfaceHandler::kF4);
auto out_data = static_cast<float const *>(i_out_data.data);
ASSERT_TRUE(out_data);
auto i_out_indptr = ArrayInterface<1, false>{s_out_indptr};
ASSERT_EQ(i_out_indptr.type, ArrayInterfaceHandler::kU8);
auto out_indptr = static_cast<std::uint64_t const *>(i_out_indptr.data);
ASSERT_TRUE(out_data);
if (ctx->IsCPU()) {
CheckResult<GHistIndexMatrix>(ctx, n_features, Xy, out_data, out_indptr);
} else {
CheckResult<EllpackPage>(ctx, n_features, Xy, out_data, out_indptr);
}
};
Json config{Null{}};
std::string s_config;
Json::Dump(config, &s_config);
char const *out_indptr;
char const *out_data;
{
// SimpleDMatrix
auto [p_fmat, Xy] = MakeSimpleDMatrixForTest(n_samples, n_features, dconfig);
// assert fail, we don't have the quantile yet.
ASSERT_EQ(XGDMatrixGetQuantileCut(p_fmat, s_config.c_str(), &out_indptr, &out_data), -1);
std::array<DMatrixHandle, 1> mats{p_fmat};
BoosterHandle booster;
ASSERT_EQ(XGBoosterCreate(mats.data(), 1, &booster), 0);
ASSERT_EQ(XGBoosterSetParam(booster, "max_bin", "16"), 0);
if (ctx->IsCUDA()) {
ASSERT_EQ(XGBoosterSetParam(booster, "tree_method", "gpu_hist"), 0);
}
ASSERT_EQ(XGBoosterUpdateOneIter(booster, 0, p_fmat), 0);
ASSERT_EQ(XGDMatrixGetQuantileCut(p_fmat, s_config.c_str(), &out_indptr, &out_data), 0);
check_result(Xy, out_data, out_indptr);
XGDMatrixFree(p_fmat);
XGBoosterFree(booster);
}
{
// IterativeDMatrix
auto [p_fmat, Xy] = MakeQDMForTest(ctx, n_samples, n_features, dconfig);
ASSERT_EQ(XGDMatrixGetQuantileCut(p_fmat, s_config.c_str(), &out_indptr, &out_data), 0);
check_result(Xy, out_data, out_indptr);
XGDMatrixFree(p_fmat);
}
{
// SparsePageDMatrix
auto [p_fmat, Xy] = MakeExtMemForTest(n_samples, n_features, dconfig);
// assert fail, we don't have the quantile yet.
ASSERT_EQ(XGDMatrixGetQuantileCut(p_fmat, s_config.c_str(), &out_indptr, &out_data), -1);
std::array<DMatrixHandle, 1> mats{p_fmat};
BoosterHandle booster;
ASSERT_EQ(XGBoosterCreate(mats.data(), 1, &booster), 0);
ASSERT_EQ(XGBoosterSetParam(booster, "max_bin", "16"), 0);
if (ctx->IsCUDA()) {
ASSERT_EQ(XGBoosterSetParam(booster, "tree_method", "gpu_hist"), 0);
}
ASSERT_EQ(XGBoosterUpdateOneIter(booster, 0, p_fmat), 0);
ASSERT_EQ(XGDMatrixGetQuantileCut(p_fmat, s_config.c_str(), &out_indptr, &out_data), 0);
XGDMatrixFree(p_fmat);
XGBoosterFree(booster);
}
}
} // namespace
TEST(CAPI, XGDMatrixGetQuantileCut) {
Context ctx;
TestXGDMatrixGetQuantileCut(&ctx);
}
#if defined(XGBOOST_USE_CUDA)
TEST(CAPI, GPUXGDMatrixGetQuantileCut) {
auto ctx = MakeCUDACtx(0);
TestXGDMatrixGetQuantileCut(&ctx);
}
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost