forked from dmlc/xgboost
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test_gbtree.cc
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test_gbtree.cc
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/*!
* Copyright 2019-2021 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <dmlc/filesystem.h>
#include <xgboost/generic_parameters.h>
#include "xgboost/base.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/learner.h"
#include "../helpers.h"
#include "../../../src/gbm/gbtree.h"
#include "../../../src/data/adapter.h"
#include "xgboost/predictor.h"
namespace xgboost {
TEST(GBTree, SelectTreeMethod) {
size_t constexpr kCols = 10;
GenericParameter generic_param;
generic_param.UpdateAllowUnknown(Args{});
LearnerModelParam mparam;
mparam.base_score = 0.5;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
std::unique_ptr<GradientBooster> p_gbm {
GradientBooster::Create("gbtree", &generic_param, &mparam)};
auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
// Test if `tree_method` can be set
Args args {{"tree_method", "approx"}};
gbtree.Configure({args.cbegin(), args.cend()});
gbtree.Configure(args);
auto const& tparam = gbtree.GetTrainParam();
gbtree.Configure({{"tree_method", "approx"}});
ASSERT_EQ(tparam.updater_seq, "grow_histmaker,prune");
gbtree.Configure({{"tree_method", "exact"}});
ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
gbtree.Configure({{"tree_method", "hist"}});
ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
gbtree.Configure({{"booster", "dart"}, {"tree_method", "hist"}});
ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
#ifdef XGBOOST_USE_CUDA
generic_param.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
gbtree.Configure({{"tree_method", "gpu_hist"}});
ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
gbtree.Configure({{"booster", "dart"}, {"tree_method", "gpu_hist"}});
ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
#endif // XGBOOST_USE_CUDA
}
TEST(GBTree, PredictionCache) {
size_t constexpr kRows = 100, kCols = 10;
GenericParameter generic_param;
generic_param.UpdateAllowUnknown(Args{});
LearnerModelParam mparam;
mparam.base_score = 0.5;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
std::unique_ptr<GradientBooster> p_gbm {
GradientBooster::Create("gbtree", &generic_param, &mparam)};
auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
gbtree.Configure({{"tree_method", "hist"}});
auto p_m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
auto gpair = GenerateRandomGradients(kRows);
PredictionCacheEntry out_predictions;
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions);
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
ASSERT_EQ(1, out_predictions.version);
std::vector<float> first_iter = out_predictions.predictions.HostVector();
// Add 1 more boosted round
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions);
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
ASSERT_EQ(2, out_predictions.version);
// Update the cache for all rounds
out_predictions.version = 0;
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
ASSERT_EQ(2, out_predictions.version);
gbtree.DoBoost(p_m.get(), &gpair, &out_predictions);
// drop the cache.
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 1, 2);
ASSERT_EQ(0, out_predictions.version);
// half open set [1, 3)
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 1, 3);
ASSERT_EQ(0, out_predictions.version);
// iteration end
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 2);
ASSERT_EQ(2, out_predictions.version);
// restart the cache when end iteration is smaller than cache version
gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 1);
ASSERT_EQ(1, out_predictions.version);
ASSERT_EQ(out_predictions.predictions.HostVector(), first_iter);
}
TEST(GBTree, WrongUpdater) {
size_t constexpr kRows = 17;
size_t constexpr kCols = 15;
auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
p_dmat->Info().labels_.Resize(kRows);
auto learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
// Hist can not be used for updating tree.
learner->SetParams(Args{{"tree_method", "hist"}, {"process_type", "update"}});
ASSERT_THROW(learner->UpdateOneIter(0, p_dmat), dmlc::Error);
// Prune can not be used for learning new tree.
learner->SetParams(
Args{{"tree_method", "prune"}, {"process_type", "default"}});
ASSERT_THROW(learner->UpdateOneIter(0, p_dmat), dmlc::Error);
}
#ifdef XGBOOST_USE_CUDA
TEST(GBTree, ChoosePredictor) {
// The test ensures data don't get pulled into device.
size_t constexpr kRows = 17;
size_t constexpr kCols = 15;
auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
auto& data = (*(p_dmat->GetBatches<SparsePage>().begin())).data;
p_dmat->Info().labels_.Resize(kRows);
auto learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
for (size_t i = 0; i < 4; ++i) {
learner->UpdateOneIter(i, p_dmat);
}
ASSERT_TRUE(data.HostCanWrite());
dmlc::TemporaryDirectory tempdir;
const std::string fname = tempdir.path + "/model_param.bst";
{
std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
learner->Save(fo.get());
}
// a new learner
learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
{
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
learner->Load(fi.get());
}
learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
for (size_t i = 0; i < 4; ++i) {
learner->UpdateOneIter(i, p_dmat);
}
ASSERT_TRUE(data.HostCanWrite());
// pull data into device.
data.HostVector();
data.SetDevice(0);
data.DeviceSpan();
ASSERT_FALSE(data.HostCanWrite());
// another new learner
learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
for (size_t i = 0; i < 4; ++i) {
learner->UpdateOneIter(i, p_dmat);
}
// data is not pulled back into host
ASSERT_FALSE(data.HostCanWrite());
}
#endif // XGBOOST_USE_CUDA
// Some other parts of test are in `Tree.JsonIO'.
TEST(GBTree, JsonIO) {
size_t constexpr kRows = 16, kCols = 16;
LearnerModelParam mparam;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
mparam.base_score = 0.5;
GenericParameter gparam;
gparam.Init(Args{});
std::unique_ptr<GradientBooster> gbm {
CreateTrainedGBM("gbtree", Args{}, kRows, kCols, &mparam, &gparam) };
Json model {Object()};
model["model"] = Object();
auto& j_model = model["model"];
model["config"] = Object();
auto& j_param = model["config"];
gbm->SaveModel(&j_model);
gbm->SaveConfig(&j_param);
std::string model_str;
Json::Dump(model, &model_str);
model = Json::Load({model_str.c_str(), model_str.size()});
ASSERT_EQ(get<String>(model["model"]["name"]), "gbtree");
auto const& gbtree_model = model["model"]["model"];
ASSERT_EQ(get<Array>(gbtree_model["trees"]).size(), 1ul);
ASSERT_EQ(get<Integer>(get<Object>(get<Array>(gbtree_model["trees"]).front()).at("id")), 0);
ASSERT_EQ(get<Array>(gbtree_model["tree_info"]).size(), 1ul);
auto j_train_param = model["config"]["gbtree_train_param"];
ASSERT_EQ(get<String>(j_train_param["num_parallel_tree"]), "1");
}
TEST(Dart, JsonIO) {
size_t constexpr kRows = 16, kCols = 16;
LearnerModelParam mparam;
mparam.num_feature = kCols;
mparam.base_score = 0.5;
mparam.num_output_group = 1;
GenericParameter gparam;
gparam.Init(Args{});
std::unique_ptr<GradientBooster> gbm {
CreateTrainedGBM("dart", Args{}, kRows, kCols, &mparam, &gparam) };
Json model {Object()};
model["model"] = Object();
auto& j_model = model["model"];
model["config"] = Object();
auto& j_param = model["config"];
gbm->SaveModel(&j_model);
gbm->SaveConfig(&j_param);
std::string model_str;
Json::Dump(model, &model_str);
model = Json::Load({model_str.c_str(), model_str.size()});
ASSERT_EQ(get<String>(model["model"]["name"]), "dart") << model;
ASSERT_EQ(get<String>(model["config"]["name"]), "dart");
ASSERT_TRUE(IsA<Object>(model["model"]["gbtree"]));
ASSERT_NE(get<Array>(model["model"]["weight_drop"]).size(), 0ul);
}
TEST(Dart, Prediction) {
size_t constexpr kRows = 16, kCols = 10;
HostDeviceVector<float> data;
auto array_str = RandomDataGenerator(kRows, kCols, 0).GenerateArrayInterface(&data);
auto p_mat = GetDMatrixFromData(data.HostVector(), kRows, kCols);
std::vector<bst_float> labels (kRows);
for (size_t i = 0; i < kRows; ++i) {
labels[i] = i % 2;
}
p_mat->Info().SetInfo("label", labels.data(), DataType::kFloat32, kRows);
auto learner = std::unique_ptr<Learner>(Learner::Create({p_mat}));
learner->SetParam("booster", "dart");
learner->SetParam("rate_drop", "0.5");
learner->Configure();
for (size_t i = 0; i < 16; ++i) {
learner->UpdateOneIter(i, p_mat);
}
HostDeviceVector<float> predts_training;
learner->Predict(p_mat, false, &predts_training, 0, 0, true);
HostDeviceVector<float>* inplace_predts;
auto adapter = std::shared_ptr<data::ArrayAdapter>(new data::ArrayAdapter{StringView{array_str}});
learner->InplacePredict(adapter, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(),
&inplace_predts, 0, 0);
CHECK(inplace_predts);
HostDeviceVector<float> predts_inference;
learner->Predict(p_mat, false, &predts_inference, 0, 0, false);
auto const& h_predts_training = predts_training.ConstHostVector();
auto const& h_predts_inference = predts_inference.ConstHostVector();
auto const& h_inplace_predts = inplace_predts->HostVector();
ASSERT_EQ(h_predts_training.size(), h_predts_inference.size());
ASSERT_EQ(h_inplace_predts.size(), h_predts_inference.size());
for (size_t i = 0; i < predts_inference.Size(); ++i) {
// Inference doesn't drop tree.
ASSERT_GT(std::abs(h_predts_training[i] - h_predts_inference[i]), kRtEps * 10);
// Inplace prediction is inference.
ASSERT_LT(h_inplace_predts[i] - h_predts_inference[i], kRtEps / 10);
}
}
std::pair<Json, Json> TestModelSlice(std::string booster) {
size_t constexpr kRows = 1000, kCols = 100, kForest = 2, kClasses = 3;
auto m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true, false, kClasses);
int32_t kIters = 10;
std::unique_ptr<Learner> learner {
Learner::Create({m})
};
learner->SetParams(Args{{"booster", booster},
{"tree_method", "hist"},
{"num_parallel_tree", std::to_string(kForest)},
{"num_class", std::to_string(kClasses)},
{"subsample", "0.5"},
{"max_depth", "2"}});
for (auto i = 0; i < kIters; ++i) {
learner->UpdateOneIter(i, m);
}
Json model{Object()};
Json config{Object()};
learner->SaveModel(&model);
learner->SaveConfig(&config);
bool out_of_bound = false;
size_t constexpr kSliceStart = 2, kSliceEnd = 8, kStep = 3;
std::unique_ptr<Learner> sliced {learner->Slice(kSliceStart, kSliceEnd, kStep, &out_of_bound)};
Json sliced_model{Object()};
sliced->SaveModel(&sliced_model);
auto get_shape = [&](Json const& model) {
if (booster == "gbtree") {
return get<Object const>(model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]);
} else {
return get<Object const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["gbtree_model_param"]);
}
};
auto const& model_shape = get_shape(sliced_model);
CHECK_EQ(get<String const>(model_shape.at("num_trees")), std::to_string(2 * kClasses * kForest));
Json sliced_config {Object()};
sliced->SaveConfig(&sliced_config);
CHECK_EQ(sliced_config, config);
auto get_trees = [&](Json const& model) {
if (booster == "gbtree") {
return get<Array const>(model["learner"]["gradient_booster"]["model"]["trees"]);
} else {
return get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["trees"]);
}
};
auto get_info = [&](Json const& model) {
if (booster == "gbtree") {
return get<Array const>(model["learner"]["gradient_booster"]["model"]["tree_info"]);
} else {
return get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["tree_info"]);
}
};
auto const &sliced_trees = get_trees(sliced_model);
CHECK_EQ(sliced_trees.size(), 2 * kClasses * kForest);
auto constexpr kLayerSize = kClasses * kForest;
auto const &sliced_info = get_info(sliced_model);
for (size_t layer = 0; layer < 2; ++layer) {
for (size_t j = 0; j < kClasses; ++j) {
for (size_t k = 0; k < kForest; ++k) {
auto idx = layer * kLayerSize + j * kForest + k;
auto const &group = get<Integer const>(sliced_info.at(idx));
CHECK_EQ(static_cast<size_t>(group), j);
}
}
}
auto const& trees = get_trees(model);
// Sliced layers are [2, 5]
auto begin = kLayerSize * kSliceStart;
auto end = begin + kLayerSize;
auto j = 0;
for (size_t i = begin; i < end; ++i) {
Json tree = trees[i];
tree["id"] = Integer(0); // id is different, we set it to 0 to allow comparison.
auto sliced_tree = sliced_trees[j];
sliced_tree["id"] = Integer(0);
CHECK_EQ(tree, sliced_tree);
j++;
}
begin = kLayerSize * (kSliceStart + kStep);
end = begin + kLayerSize;
for (size_t i = begin; i < end; ++i) {
Json tree = trees[i];
tree["id"] = Integer(0);
auto sliced_tree = sliced_trees[j];
sliced_tree["id"] = Integer(0);
CHECK_EQ(tree, sliced_tree);
j++;
}
// CHECK sliced model doesn't have dependency on old one
learner.reset();
CHECK_EQ(sliced->GetNumFeature(), kCols);
return std::make_pair(model, sliced_model);
}
TEST(GBTree, Slice) {
TestModelSlice("gbtree");
}
TEST(Dart, Slice) {
Json model, sliced_model;
std::tie(model, sliced_model) = TestModelSlice("dart");
auto const& weights = get<Array const>(model["learner"]["gradient_booster"]["weight_drop"]);
auto const& trees = get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["trees"]);
ASSERT_EQ(weights.size(), trees.size());
}
TEST(GBTree, FeatureScore) {
size_t n_samples = 1000, n_features = 10, n_classes = 4;
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
std::unique_ptr<Learner> learner{ Learner::Create({m}) };
learner->SetParam("num_class", std::to_string(n_classes));
learner->Configure();
for (size_t i = 0; i < 2; ++i) {
learner->UpdateOneIter(i, m);
}
std::vector<bst_feature_t> features_weight;
std::vector<float> scores_weight;
learner->CalcFeatureScore("weight", {}, &features_weight, &scores_weight);
ASSERT_EQ(features_weight.size(), scores_weight.size());
ASSERT_LE(features_weight.size(), learner->GetNumFeature());
ASSERT_TRUE(std::is_sorted(features_weight.begin(), features_weight.end()));
auto test_eq = [&learner, &scores_weight](std::string type) {
std::vector<bst_feature_t> features;
std::vector<float> scores;
learner->CalcFeatureScore(type, {}, &features, &scores);
std::vector<bst_feature_t> features_total;
std::vector<float> scores_total;
learner->CalcFeatureScore("total_" + type, {}, &features_total, &scores_total);
for (size_t i = 0; i < scores_weight.size(); ++i) {
ASSERT_LE(RelError(scores_total[i] / scores[i], scores_weight[i]), kRtEps);
}
};
test_eq("gain");
test_eq("cover");
}
TEST(GBTree, PredictRange) {
size_t n_samples = 1000, n_features = 10, n_classes = 4;
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
std::unique_ptr<Learner> learner{Learner::Create({m})};
learner->SetParam("num_class", std::to_string(n_classes));
learner->Configure();
for (size_t i = 0; i < 2; ++i) {
learner->UpdateOneIter(i, m);
}
HostDeviceVector<float> out_predt;
ASSERT_THROW(learner->Predict(m, false, &out_predt, 0, 3), dmlc::Error);
auto m_1 =
RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
HostDeviceVector<float> out_predt_full;
learner->Predict(m_1, false, &out_predt_full, 0, 0);
ASSERT_TRUE(std::equal(out_predt.HostVector().begin(), out_predt.HostVector().end(),
out_predt_full.HostVector().begin()));
{
// inplace predict
HostDeviceVector<float> raw_storage;
auto raw = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateArrayInterface(&raw_storage);
std::shared_ptr<data::ArrayAdapter> x{new data::ArrayAdapter{StringView{raw}}};
HostDeviceVector<float>* out_predt;
learner->InplacePredict(x, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 2);
auto h_out_predt = out_predt->HostVector();
learner->InplacePredict(x, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 0);
auto h_out_predt_full = out_predt->HostVector();
ASSERT_TRUE(std::equal(h_out_predt.begin(), h_out_predt.end(), h_out_predt_full.begin()));
ASSERT_THROW(learner->InplacePredict(x, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 3),
dmlc::Error);
}
}
} // namespace xgboost