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/**
* Copyright 2018-2023, XGBoost Contributors
*/
#include "../../../src/common/random.h"
#include "../helpers.h"
#include "gtest/gtest.h"
#include "xgboost/context.h" // for Context
namespace xgboost::common {
namespace {
void TestBasic(Context const* ctx) {
int n = 128;
ColumnSampler cs{1u};
std::vector<float> feature_weights;
// No node sampling
cs.Init(ctx, n, feature_weights, 1.0f, 0.5f, 0.5f);
auto set0 = cs.GetFeatureSet(0);
ASSERT_EQ(set0->Size(), 32);
auto set1 = cs.GetFeatureSet(0);
ASSERT_EQ(set0->HostVector(), set1->HostVector());
auto set2 = cs.GetFeatureSet(1);
ASSERT_NE(set1->HostVector(), set2->HostVector());
ASSERT_EQ(set2->Size(), 32);
// Node sampling
cs.Init(ctx, n, feature_weights, 0.5f, 1.0f, 0.5f);
auto set3 = cs.GetFeatureSet(0);
ASSERT_EQ(set3->Size(), 32);
auto set4 = cs.GetFeatureSet(0);
ASSERT_NE(set3->HostVector(), set4->HostVector());
ASSERT_EQ(set4->Size(), 32);
// No level or node sampling, should be the same at different depth
cs.Init(ctx, n, feature_weights, 1.0f, 1.0f, 0.5f);
ASSERT_EQ(cs.GetFeatureSet(0)->HostVector(), cs.GetFeatureSet(1)->HostVector());
cs.Init(ctx, n, feature_weights, 1.0f, 1.0f, 1.0f);
auto set5 = cs.GetFeatureSet(0);
ASSERT_EQ(set5->Size(), n);
cs.Init(ctx, n, feature_weights, 1.0f, 1.0f, 1.0f);
auto set6 = cs.GetFeatureSet(0);
ASSERT_EQ(set5->HostVector(), set6->HostVector());
// Should always be a minimum of one feature
cs.Init(ctx, n, feature_weights, 1e-16f, 1e-16f, 1e-16f);
ASSERT_EQ(cs.GetFeatureSet(0)->Size(), 1);
}
} // namespace
TEST(ColumnSampler, Test) {
Context ctx;
TestBasic(&ctx);
}
#if defined(XGBOOST_USE_CUDA)
TEST(ColumnSampler, GPUTest) {
auto ctx = MakeCUDACtx(0);
TestBasic(&ctx);
}
#endif // defined(XGBOOST_USE_CUDA)
// Test if different threads using the same seed produce the same result
TEST(ColumnSampler, ThreadSynchronisation) {
Context ctx;
// NOLINTBEGIN(clang-analyzer-deadcode.DeadStores)
#if defined(__linux__)
std::int64_t const n_threads = std::thread::hardware_concurrency() * 128;
#else
std::int64_t const n_threads = std::thread::hardware_concurrency();
#endif
// NOLINTEND(clang-analyzer-deadcode.DeadStores)
int n = 128;
size_t iterations = 10;
size_t levels = 5;
std::vector<bst_feature_t> reference_result;
std::vector<float> feature_weights;
bool success = true; // Cannot use google test asserts in multithreaded region
#pragma omp parallel num_threads(n_threads)
{
for (auto j = 0ull; j < iterations; j++) {
ColumnSampler cs(j);
cs.Init(&ctx, n, feature_weights, 0.5f, 0.5f, 0.5f);
for (auto level = 0ull; level < levels; level++) {
auto result = cs.GetFeatureSet(level)->ConstHostVector();
#pragma omp single
{ reference_result = result; }
if (result != reference_result) {
success = false;
}
#pragma omp barrier
}
}
}
ASSERT_TRUE(success);
}
namespace {
void TestWeightedSampling(Context const* ctx) {
auto test_basic = [ctx](int first) {
std::vector<float> feature_weights(2);
feature_weights[0] = std::abs(first - 1.0f);
feature_weights[1] = first - 0.0f;
ColumnSampler cs{0};
cs.Init(ctx, 2, feature_weights, 1.0, 1.0, 0.5);
auto feature_sets = cs.GetFeatureSet(0);
auto const& h_feat_set = feature_sets->HostVector();
ASSERT_EQ(h_feat_set.size(), 1);
ASSERT_EQ(h_feat_set[0], first - 0);
};
test_basic(0);
test_basic(1);
size_t constexpr kCols = 64;
std::vector<float> feature_weights(kCols);
SimpleLCG rng;
SimpleRealUniformDistribution<float> dist(.0f, 12.0f);
std::generate(feature_weights.begin(), feature_weights.end(), [&]() { return dist(&rng); });
ColumnSampler cs{0};
cs.Init(ctx, kCols, feature_weights, 0.5f, 1.0f, 1.0f);
std::vector<bst_feature_t> features(kCols);
std::iota(features.begin(), features.end(), 0);
std::vector<float> freq(kCols, 0);
for (size_t i = 0; i < 1024; ++i) {
auto fset = cs.GetFeatureSet(0);
ASSERT_EQ(kCols * 0.5, fset->Size());
auto const& h_fset = fset->HostVector();
for (auto f : h_fset) {
freq[f] += 1.0f;
}
}
auto norm = std::accumulate(freq.cbegin(), freq.cend(), .0f);
for (auto& f : freq) {
f /= norm;
}
norm = std::accumulate(feature_weights.cbegin(), feature_weights.cend(), .0f);
for (auto& f : feature_weights) {
f /= norm;
}
for (size_t i = 0; i < feature_weights.size(); ++i) {
EXPECT_NEAR(freq[i], feature_weights[i], 1e-2);
}
}
} // namespace
TEST(ColumnSampler, WeightedSampling) {
Context ctx;
TestWeightedSampling(&ctx);
}
#if defined(XGBOOST_USE_CUDA)
TEST(ColumnSampler, GPUWeightedSampling) {
auto ctx = MakeCUDACtx(0);
TestWeightedSampling(&ctx);
}
#endif // defined(XGBOOST_USE_CUDA)
namespace {
void TestWeightedMultiSampling(Context const* ctx) {
size_t constexpr kCols = 32;
std::vector<float> feature_weights(kCols, 0);
for (size_t i = 0; i < feature_weights.size(); ++i) {
feature_weights[i] = i;
}
ColumnSampler cs{0};
float bytree{0.5}, bylevel{0.5}, bynode{0.5};
cs.Init(ctx, feature_weights.size(), feature_weights, bytree, bylevel, bynode);
auto feature_set = cs.GetFeatureSet(0);
size_t n_sampled = kCols * bytree * bylevel * bynode;
ASSERT_EQ(feature_set->Size(), n_sampled);
feature_set = cs.GetFeatureSet(1);
ASSERT_EQ(feature_set->Size(), n_sampled);
}
} // namespace
TEST(ColumnSampler, WeightedMultiSampling) {
Context ctx;
TestWeightedMultiSampling(&ctx);
}
#if defined(XGBOOST_USE_CUDA)
TEST(ColumnSampler, GPUWeightedMultiSampling) {
auto ctx = MakeCUDACtx(0);
TestWeightedMultiSampling(&ctx);
}
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost::common
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