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Improve test coverage with predictor configuration. (#9354)

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* Improve test coverage with predictor configuration.

- Test with ext memory.
- Test with QDM.
- Test with dart.
This commit is contained in:
Jiaming Yuan
2023-07-05 15:17:22 +08:00
committed by GitHub
parent 6c9c8a9001
commit 645037e376
17 changed files with 280 additions and 79 deletions
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65
tests/cpp/helpers.cc
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@@ -210,6 +210,16 @@ SimpleLCG::StateType SimpleLCG::Max() const { return max(); }
// Make sure it's compile time constant.
static_assert(SimpleLCG::max() - SimpleLCG::min());
void RandomDataGenerator::GenerateLabels(std::shared_ptr<DMatrix> p_fmat) const {
RandomDataGenerator{p_fmat->Info().num_row_, this->n_targets_, 0.0f}.GenerateDense(
p_fmat->Info().labels.Data());
CHECK_EQ(p_fmat->Info().labels.Size(), this->rows_ * this->n_targets_);
p_fmat->Info().labels.Reshape(this->rows_, this->n_targets_);
if (device_ != Context::kCpuId) {
p_fmat->Info().labels.SetDevice(device_);
}
}
void RandomDataGenerator::GenerateDense(HostDeviceVector<float> *out) const {
xgboost::SimpleRealUniformDistribution<bst_float> dist(lower_, upper_);
CHECK(out);
@@ -363,8 +373,9 @@ void RandomDataGenerator::GenerateCSR(
CHECK_EQ(columns->Size(), value->Size());
}
std::shared_ptr<DMatrix> RandomDataGenerator::GenerateDMatrix(bool with_label, bool float_label,
size_t classes) const {
[[nodiscard]] std::shared_ptr<DMatrix> RandomDataGenerator::GenerateDMatrix(bool with_label,
bool float_label,
size_t classes) const {
HostDeviceVector<float> data;
HostDeviceVector<bst_row_t> rptrs;
HostDeviceVector<bst_feature_t> columns;
@@ -406,10 +417,58 @@ std::shared_ptr<DMatrix> RandomDataGenerator::GenerateDMatrix(bool with_label, b
return out;
}
std::shared_ptr<DMatrix> RandomDataGenerator::GenerateQuantileDMatrix() {
[[nodiscard]] std::shared_ptr<DMatrix> RandomDataGenerator::GenerateSparsePageDMatrix(
std::string prefix, bool with_label) const {
CHECK_GE(this->rows_, this->n_batches_);
CHECK_GE(this->n_batches_, 1)
<< "Must set the n_batches before generating an external memory DMatrix.";
std::unique_ptr<ArrayIterForTest> iter;
if (device_ == Context::kCpuId) {
iter = std::make_unique<NumpyArrayIterForTest>(this->sparsity_, rows_, cols_, n_batches_);
} else {
#if defined(XGBOOST_USE_CUDA)
iter = std::make_unique<CudaArrayIterForTest>(this->sparsity_, rows_, cols_, n_batches_);
#else
CHECK(iter);
#endif // defined(XGBOOST_USE_CUDA)
}
std::unique_ptr<DMatrix> dmat{
DMatrix::Create(static_cast<DataIterHandle>(iter.get()), iter->Proxy(), Reset, Next,
std::numeric_limits<float>::quiet_NaN(), Context{}.Threads(), prefix)};
auto row_page_path =
data::MakeId(prefix, dynamic_cast<data::SparsePageDMatrix*>(dmat.get())) + ".row.page";
EXPECT_TRUE(FileExists(row_page_path)) << row_page_path;
// Loop over the batches and count the number of pages
std::size_t batch_count = 0;
bst_row_t row_count = 0;
for (const auto& batch : dmat->GetBatches<xgboost::SparsePage>()) {
batch_count++;
row_count += batch.Size();
CHECK_NE(batch.data.Size(), 0);
}
EXPECT_EQ(batch_count, n_batches_);
EXPECT_EQ(row_count, dmat->Info().num_row_);
if (with_label) {
RandomDataGenerator{dmat->Info().num_row_, this->n_targets_, 0.0f}.GenerateDense(
dmat->Info().labels.Data());
CHECK_EQ(dmat->Info().labels.Size(), this->rows_ * this->n_targets_);
dmat->Info().labels.Reshape(this->rows_, this->n_targets_);
}
return dmat;
}
std::shared_ptr<DMatrix> RandomDataGenerator::GenerateQuantileDMatrix(bool with_label) {
NumpyArrayIterForTest iter{this->sparsity_, this->rows_, this->cols_, 1};
auto m = std::make_shared<data::IterativeDMatrix>(
&iter, iter.Proxy(), nullptr, Reset, Next, std::numeric_limits<float>::quiet_NaN(), 0, bins_);
if (with_label) {
this->GenerateLabels(m);
}
return m;
}