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/*!
* Copyright 2021 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/base.h>
#include <xgboost/tree_model.h>
#include "../helpers.h"
namespace xgboost {
class TestGrowPolicy : public ::testing::Test {
protected:
std::shared_ptr<DMatrix> Xy_;
size_t n_samples_ = 4096, n_features_ = 13;
float sparsity_ = 0.5;
protected:
void SetUp() override {
Xy_ =
RandomDataGenerator{n_samples_, n_features_, sparsity_}.GenerateDMatrix(
true);
}
std::unique_ptr<Learner> TrainOneIter(Context const* ctx, std::string tree_method,
std::string policy, int32_t max_leaves, int32_t max_depth) {
std::unique_ptr<Learner> learner{Learner::Create({this->Xy_})};
learner->SetParam("tree_method", tree_method);
learner->SetParam("device", ctx->DeviceName());
if (max_leaves >= 0) {
learner->SetParam("max_leaves", std::to_string(max_leaves));
}
if (max_depth >= 0) {
learner->SetParam("max_depth", std::to_string(max_depth));
}
learner->SetParam("grow_policy", policy);
auto check_max_leave = [&]() {
Json model{Object{}};
learner->SaveModel(&model);
auto j_tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
RegTree tree;
tree.LoadModel(j_tree);
CHECK_LE(tree.GetNumLeaves(), max_leaves);
};
auto check_max_depth = [&](int32_t sol) {
Json model{Object{}};
learner->SaveModel(&model);
auto j_tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
RegTree tree;
tree.LoadModel(j_tree);
bst_node_t depth = 0;
tree.WalkTree([&](bst_node_t nidx) {
depth = std::max(tree.GetDepth(nidx), depth);
return true;
});
if (sol > -1) {
CHECK_EQ(depth, sol);
} else {
CHECK_EQ(depth, max_depth) << "tree method: " << tree_method << " policy: " << policy
<< " leaves:" << max_leaves << ", depth:" << max_depth;
}
};
if (max_leaves == 0 && max_depth == 0) {
// unconstrainted
if (ctx->IsCPU()) {
// GPU pre-allocates for all nodes.
learner->UpdateOneIter(0, Xy_);
}
} else if (max_leaves > 0 && max_depth == 0) {
learner->UpdateOneIter(0, Xy_);
check_max_leave();
} else if (max_leaves == 0 && max_depth > 0) {
learner->UpdateOneIter(0, Xy_);
check_max_depth(-1);
} else if (max_leaves > 0 && max_depth > 0) {
learner->UpdateOneIter(0, Xy_);
check_max_leave();
check_max_depth(2);
} else if (max_leaves == -1 && max_depth == 0) {
// default max_leaves is 0, so both of them are now 0
} else {
// default parameters
learner->UpdateOneIter(0, Xy_);
}
return learner;
}
void TestCombination(Context const* ctx, std::string tree_method) {
for (auto policy : {"depthwise", "lossguide"}) {
// -1 means default
for (auto leaves : {-1, 0, 3}) {
for (auto depth : {-1, 0, 3}) {
this->TrainOneIter(ctx, tree_method, policy, leaves, depth);
}
}
}
}
void TestTreeGrowPolicy(Context const* ctx, std::string tree_method, std::string policy) {
{
/**
* max_leaves
*/
auto learner = this->TrainOneIter(ctx, tree_method, policy, 16, -1);
Json model{Object{}};
learner->SaveModel(&model);
auto j_tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
RegTree tree;
tree.LoadModel(j_tree);
ASSERT_EQ(tree.GetNumLeaves(), 16);
}
{
/**
* max_depth
*/
auto learner = this->TrainOneIter(ctx, tree_method, policy, -1, 3);
Json model{Object{}};
learner->SaveModel(&model);
auto j_tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
RegTree tree;
tree.LoadModel(j_tree);
bst_node_t depth = 0;
tree.WalkTree([&](bst_node_t nidx) {
depth = std::max(tree.GetDepth(nidx), depth);
return true;
});
ASSERT_EQ(depth, 3);
}
}
};
TEST_F(TestGrowPolicy, Approx) {
Context ctx;
this->TestTreeGrowPolicy(&ctx, "approx", "depthwise");
this->TestTreeGrowPolicy(&ctx, "approx", "lossguide");
this->TestCombination(&ctx, "approx");
}
TEST_F(TestGrowPolicy, Hist) {
Context ctx;
this->TestTreeGrowPolicy(&ctx, "hist", "depthwise");
this->TestTreeGrowPolicy(&ctx, "hist", "lossguide");
this->TestCombination(&ctx, "hist");
}
#if defined(XGBOOST_USE_CUDA)
TEST_F(TestGrowPolicy, GpuHist) {
auto ctx = MakeCUDACtx(0);
this->TestTreeGrowPolicy(&ctx, "hist", "depthwise");
this->TestTreeGrowPolicy(&ctx, "hist", "lossguide");
this->TestCombination(&ctx, "hist");
}
TEST_F(TestGrowPolicy, GpuApprox) {
auto ctx = MakeCUDACtx(0);
this->TestTreeGrowPolicy(&ctx, "approx", "depthwise");
this->TestTreeGrowPolicy(&ctx, "approx", "lossguide");
this->TestCombination(&ctx, "approx");
}
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost
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