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Model IO in JSON. (#5110)

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This commit is contained in:
Jiaming Yuan
2019-12-11 11:20:40 +08:00
committed by GitHub
parent c7cc657a4d
commit 208ab3b1ff
25 changed files with 667 additions and 165 deletions
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110
tests/cpp/test_learner.cc
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@@ -6,6 +6,8 @@
#include <xgboost/learner.h>
#include <xgboost/version_config.h>
#include "xgboost/json.h"
#include "../../src/common/io.h"
namespace xgboost {
@@ -112,83 +114,54 @@ TEST(Learner, Configuration) {
}
}
TEST(Learner, ObjectiveParameter) {
using Arg = std::pair<std::string, std::string>;
size_t constexpr kRows = 10;
TEST(Learner, Json_ModelIO) {
// Test of comparing JSON object directly.
size_t constexpr kRows = 8;
int32_t constexpr kIters = 4;
auto pp_dmat = CreateDMatrix(kRows, 10, 0);
auto p_dmat = *pp_dmat;
std::vector<bst_float> labels(kRows);
for (size_t i = 0; i < labels.size(); ++i) {
labels[i] = i;
}
p_dmat->Info().labels_.HostVector() = labels;
std::vector<std::shared_ptr<DMatrix>> mat {p_dmat};
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "auto"},
Arg{"objective", "multi:softprob"},
Arg{"num_class", "10"}});
learner->UpdateOneIter(0, p_dmat.get());
auto attr_names = learner->GetConfigurationArguments();
ASSERT_EQ(attr_names.at("objective"), "multi:softprob");
dmlc::TemporaryDirectory tempdir;
const std::string fname = tempdir.path + "/model_para.bst";
std::shared_ptr<DMatrix> p_dmat {*pp_dmat};
p_dmat->Info().labels_.Resize(kRows);
{
// Create a scope to close the stream before next read.
std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
learner->Save(fo.get());
std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
learner->Configure();
Json out { Object() };
learner->SaveModel(&out);
learner->LoadModel(out);
learner->Configure();
Json new_in { Object() };
learner->SaveModel(&new_in);
ASSERT_EQ(new_in, out);
}
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
std::unique_ptr<Learner> learner1 {Learner::Create(mat)};
learner1->Load(fi.get());
auto attr_names1 = learner1->GetConfigurationArguments();
ASSERT_EQ(attr_names1.at("objective"), "multi:softprob");
{
std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
learner->SetParam("verbosity", "3");
for (int32_t iter = 0; iter < kIters; ++iter) {
learner->UpdateOneIter(iter, p_dmat.get());
}
learner->SetAttr("bset_score", "15.2");
Json out { Object() };
learner->SaveModel(&out);
learner->LoadModel(out);
Json new_in { Object() };
learner->Configure();
learner->SaveModel(&new_in);
ASSERT_TRUE(IsA<Object>(out["Learner"]["attributes"]));
ASSERT_EQ(get<Object>(out["Learner"]["attributes"]).size(), 1);
ASSERT_EQ(out, new_in);
}
delete pp_dmat;
}
#if defined(XGBOOST_USE_CUDA)
TEST(Learner, IO) {
using Arg = std::pair<std::string, std::string>;
size_t constexpr kRows = 10;
auto pp_dmat = CreateDMatrix(kRows, 10, 0);
auto p_dmat = *pp_dmat;
std::vector<bst_float> labels(kRows);
for (size_t i = 0; i < labels.size(); ++i) {
labels[i] = i;
}
p_dmat->Info().labels_.HostVector() = labels;
std::vector<std::shared_ptr<DMatrix>> mat {p_dmat};
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "auto"},
Arg{"predictor", "gpu_predictor"},
Arg{"gpu_id", "0"}});
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
dmlc::TemporaryDirectory tempdir;
const std::string fname = tempdir.path + "/model.bst";
{
// Create a scope to close the stream before next read.
std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
learner->Save(fo.get());
}
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
learner->Load(fi.get());
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
delete pp_dmat;
}
// Tests for automatic GPU configuration.
TEST(Learner, GPUConfiguration) {
using Arg = std::pair<std::string, std::string>;
@@ -242,6 +215,5 @@ TEST(Learner, GPUConfiguration) {
delete pp_dmat;
}
#endif // XGBOOST_USE_CUDA
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost