fdf533f2b9
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction. For l1 error, the optimal value is the median (50 percentile). This is marked as experimental support for the following reasons: - The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors. - Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
506 lines
17 KiB
C++
506 lines
17 KiB
C++
/*!
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* Copyright 2019-2022 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <dmlc/filesystem.h>
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#include <xgboost/generic_parameters.h>
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#include "xgboost/base.h"
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#include "xgboost/host_device_vector.h"
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#include "xgboost/learner.h"
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#include "../helpers.h"
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#include "../../../src/gbm/gbtree.h"
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#include "../../../src/data/adapter.h"
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#include "xgboost/predictor.h"
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namespace xgboost {
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TEST(GBTree, SelectTreeMethod) {
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size_t constexpr kCols = 10;
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GenericParameter generic_param;
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generic_param.UpdateAllowUnknown(Args{});
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LearnerModelParam mparam;
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mparam.base_score = 0.5;
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mparam.num_feature = kCols;
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mparam.num_output_group = 1;
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std::unique_ptr<GradientBooster> p_gbm {
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GradientBooster::Create("gbtree", &generic_param, &mparam)};
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auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
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// Test if `tree_method` can be set
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Args args {{"tree_method", "approx"}};
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gbtree.Configure({args.cbegin(), args.cend()});
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gbtree.Configure(args);
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auto const& tparam = gbtree.GetTrainParam();
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gbtree.Configure({{"tree_method", "approx"}});
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ASSERT_EQ(tparam.updater_seq, "grow_histmaker");
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gbtree.Configure({{"tree_method", "exact"}});
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ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
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gbtree.Configure({{"tree_method", "hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
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gbtree.Configure({{"booster", "dart"}, {"tree_method", "hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
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#ifdef XGBOOST_USE_CUDA
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generic_param.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
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gbtree.Configure({{"tree_method", "gpu_hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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gbtree.Configure({{"booster", "dart"}, {"tree_method", "gpu_hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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#endif // XGBOOST_USE_CUDA
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}
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TEST(GBTree, PredictionCache) {
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size_t constexpr kRows = 100, kCols = 10;
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GenericParameter generic_param;
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generic_param.UpdateAllowUnknown(Args{});
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LearnerModelParam mparam;
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mparam.base_score = 0.5;
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mparam.num_feature = kCols;
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mparam.num_output_group = 1;
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std::unique_ptr<GradientBooster> p_gbm {
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GradientBooster::Create("gbtree", &generic_param, &mparam)};
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auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
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gbtree.Configure({{"tree_method", "hist"}});
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auto p_m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
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auto gpair = GenerateRandomGradients(kRows);
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PredictionCacheEntry out_predictions;
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gbtree.DoBoost(p_m.get(), &gpair, &out_predictions, nullptr);
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
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ASSERT_EQ(1, out_predictions.version);
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std::vector<float> first_iter = out_predictions.predictions.HostVector();
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// Add 1 more boosted round
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gbtree.DoBoost(p_m.get(), &gpair, &out_predictions, nullptr);
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
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ASSERT_EQ(2, out_predictions.version);
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// Update the cache for all rounds
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out_predictions.version = 0;
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 0);
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ASSERT_EQ(2, out_predictions.version);
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gbtree.DoBoost(p_m.get(), &gpair, &out_predictions, nullptr);
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// drop the cache.
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 1, 2);
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ASSERT_EQ(0, out_predictions.version);
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// half open set [1, 3)
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 1, 3);
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ASSERT_EQ(0, out_predictions.version);
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// iteration end
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 2);
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ASSERT_EQ(2, out_predictions.version);
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// restart the cache when end iteration is smaller than cache version
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gbtree.PredictBatch(p_m.get(), &out_predictions, false, 0, 1);
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ASSERT_EQ(1, out_predictions.version);
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ASSERT_EQ(out_predictions.predictions.HostVector(), first_iter);
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}
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TEST(GBTree, WrongUpdater) {
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size_t constexpr kRows = 17;
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size_t constexpr kCols = 15;
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auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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p_dmat->Info().labels.Reshape(kRows);
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auto learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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// Hist can not be used for updating tree.
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learner->SetParams(Args{{"tree_method", "hist"}, {"process_type", "update"}});
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ASSERT_THROW(learner->UpdateOneIter(0, p_dmat), dmlc::Error);
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// Prune can not be used for learning new tree.
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learner->SetParams(
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Args{{"tree_method", "prune"}, {"process_type", "default"}});
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ASSERT_THROW(learner->UpdateOneIter(0, p_dmat), dmlc::Error);
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}
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#ifdef XGBOOST_USE_CUDA
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TEST(GBTree, ChoosePredictor) {
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// The test ensures data don't get pulled into device.
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size_t constexpr kRows = 17;
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size_t constexpr kCols = 15;
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auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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auto& data = (*(p_dmat->GetBatches<SparsePage>().begin())).data;
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p_dmat->Info().labels.Reshape(kRows);
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auto learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_dmat);
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}
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ASSERT_TRUE(data.HostCanWrite());
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dmlc::TemporaryDirectory tempdir;
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const std::string fname = tempdir.path + "/model_param.bst";
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{
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std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
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learner->Save(fo.get());
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}
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// a new learner
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learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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{
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std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
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learner->Load(fi.get());
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}
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learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_dmat);
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}
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ASSERT_TRUE(data.HostCanWrite());
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// pull data into device.
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data.HostVector();
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data.SetDevice(0);
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data.DeviceSpan();
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ASSERT_FALSE(data.HostCanWrite());
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// another new learner
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learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_dmat);
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}
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// data is not pulled back into host
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ASSERT_FALSE(data.HostCanWrite());
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}
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#endif // XGBOOST_USE_CUDA
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// Some other parts of test are in `Tree.JsonIO'.
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TEST(GBTree, JsonIO) {
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size_t constexpr kRows = 16, kCols = 16;
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LearnerModelParam mparam;
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mparam.num_feature = kCols;
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mparam.num_output_group = 1;
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mparam.base_score = 0.5;
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GenericParameter gparam;
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gparam.Init(Args{});
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std::unique_ptr<GradientBooster> gbm {
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CreateTrainedGBM("gbtree", Args{}, kRows, kCols, &mparam, &gparam) };
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Json model {Object()};
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model["model"] = Object();
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auto& j_model = model["model"];
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model["config"] = Object();
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auto& j_param = model["config"];
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gbm->SaveModel(&j_model);
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gbm->SaveConfig(&j_param);
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std::string model_str;
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Json::Dump(model, &model_str);
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model = Json::Load({model_str.c_str(), model_str.size()});
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ASSERT_EQ(get<String>(model["model"]["name"]), "gbtree");
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auto const& gbtree_model = model["model"]["model"];
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ASSERT_EQ(get<Array>(gbtree_model["trees"]).size(), 1ul);
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ASSERT_EQ(get<Integer>(get<Object>(get<Array>(gbtree_model["trees"]).front()).at("id")), 0);
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ASSERT_EQ(get<Array>(gbtree_model["tree_info"]).size(), 1ul);
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auto j_train_param = model["config"]["gbtree_model_param"];
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ASSERT_EQ(get<String>(j_train_param["num_parallel_tree"]), "1");
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}
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TEST(Dart, JsonIO) {
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size_t constexpr kRows = 16, kCols = 16;
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LearnerModelParam mparam;
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mparam.num_feature = kCols;
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mparam.base_score = 0.5;
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mparam.num_output_group = 1;
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GenericParameter gparam;
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gparam.Init(Args{});
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std::unique_ptr<GradientBooster> gbm {
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CreateTrainedGBM("dart", Args{}, kRows, kCols, &mparam, &gparam) };
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Json model {Object()};
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model["model"] = Object();
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auto& j_model = model["model"];
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model["config"] = Object();
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auto& j_param = model["config"];
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gbm->SaveModel(&j_model);
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gbm->SaveConfig(&j_param);
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std::string model_str;
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Json::Dump(model, &model_str);
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model = Json::Load({model_str.c_str(), model_str.size()});
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ASSERT_EQ(get<String>(model["model"]["name"]), "dart") << model;
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ASSERT_EQ(get<String>(model["config"]["name"]), "dart");
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ASSERT_TRUE(IsA<Object>(model["model"]["gbtree"]));
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ASSERT_NE(get<Array>(model["model"]["weight_drop"]).size(), 0ul);
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}
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TEST(Dart, Prediction) {
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size_t constexpr kRows = 16, kCols = 10;
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HostDeviceVector<float> data;
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auto array_str = RandomDataGenerator(kRows, kCols, 0).GenerateArrayInterface(&data);
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auto p_mat = GetDMatrixFromData(data.HostVector(), kRows, kCols);
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std::vector<bst_float> labels (kRows);
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for (size_t i = 0; i < kRows; ++i) {
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labels[i] = i % 2;
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}
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p_mat->SetInfo("label", labels.data(), DataType::kFloat32, kRows);
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auto learner = std::unique_ptr<Learner>(Learner::Create({p_mat}));
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learner->SetParam("booster", "dart");
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learner->SetParam("rate_drop", "0.5");
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learner->Configure();
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for (size_t i = 0; i < 16; ++i) {
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learner->UpdateOneIter(i, p_mat);
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}
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HostDeviceVector<float> predts_training;
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learner->Predict(p_mat, false, &predts_training, 0, 0, true);
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HostDeviceVector<float>* inplace_predts;
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auto adapter = std::shared_ptr<data::ArrayAdapter>(new data::ArrayAdapter{StringView{array_str}});
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learner->InplacePredict(adapter, nullptr, PredictionType::kValue,
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std::numeric_limits<float>::quiet_NaN(),
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&inplace_predts, 0, 0);
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CHECK(inplace_predts);
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HostDeviceVector<float> predts_inference;
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learner->Predict(p_mat, false, &predts_inference, 0, 0, false);
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auto const& h_predts_training = predts_training.ConstHostVector();
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auto const& h_predts_inference = predts_inference.ConstHostVector();
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auto const& h_inplace_predts = inplace_predts->HostVector();
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ASSERT_EQ(h_predts_training.size(), h_predts_inference.size());
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ASSERT_EQ(h_inplace_predts.size(), h_predts_inference.size());
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for (size_t i = 0; i < predts_inference.Size(); ++i) {
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// Inference doesn't drop tree.
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ASSERT_GT(std::abs(h_predts_training[i] - h_predts_inference[i]), kRtEps * 10);
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// Inplace prediction is inference.
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ASSERT_LT(h_inplace_predts[i] - h_predts_inference[i], kRtEps / 10);
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}
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}
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std::pair<Json, Json> TestModelSlice(std::string booster) {
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size_t constexpr kRows = 1000, kCols = 100, kForest = 2, kClasses = 3;
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auto m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true, false, kClasses);
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int32_t kIters = 10;
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std::unique_ptr<Learner> learner {
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Learner::Create({m})
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};
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learner->SetParams(Args{{"booster", booster},
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{"tree_method", "hist"},
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{"num_parallel_tree", std::to_string(kForest)},
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{"num_class", std::to_string(kClasses)},
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{"subsample", "0.5"},
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{"max_depth", "2"}});
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for (auto i = 0; i < kIters; ++i) {
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learner->UpdateOneIter(i, m);
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}
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Json model{Object()};
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Json config{Object()};
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learner->SaveModel(&model);
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learner->SaveConfig(&config);
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bool out_of_bound = false;
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size_t constexpr kSliceStart = 2, kSliceEnd = 8, kStep = 3;
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std::unique_ptr<Learner> sliced {learner->Slice(kSliceStart, kSliceEnd, kStep, &out_of_bound)};
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Json sliced_model{Object()};
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sliced->SaveModel(&sliced_model);
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auto get_shape = [&](Json const& model) {
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if (booster == "gbtree") {
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return get<Object const>(model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]);
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} else {
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return get<Object const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["gbtree_model_param"]);
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}
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};
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auto const& model_shape = get_shape(sliced_model);
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CHECK_EQ(get<String const>(model_shape.at("num_trees")), std::to_string(2 * kClasses * kForest));
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Json sliced_config {Object()};
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sliced->SaveConfig(&sliced_config);
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// Only num trees is changed
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if (booster == "gbtree") {
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sliced_config["learner"]["gradient_booster"]["gbtree_model_param"]["num_trees"] = String("60");
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} else {
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sliced_config["learner"]["gradient_booster"]["gbtree"]["gbtree_model_param"]["num_trees"] =
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String("60");
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}
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CHECK_EQ(sliced_config, config);
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auto get_trees = [&](Json const& model) {
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if (booster == "gbtree") {
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return get<Array const>(model["learner"]["gradient_booster"]["model"]["trees"]);
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} else {
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return get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["trees"]);
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}
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};
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auto get_info = [&](Json const& model) {
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if (booster == "gbtree") {
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return get<Array const>(model["learner"]["gradient_booster"]["model"]["tree_info"]);
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} else {
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return get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["tree_info"]);
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}
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};
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auto const &sliced_trees = get_trees(sliced_model);
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CHECK_EQ(sliced_trees.size(), 2 * kClasses * kForest);
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auto constexpr kLayerSize = kClasses * kForest;
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auto const &sliced_info = get_info(sliced_model);
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for (size_t layer = 0; layer < 2; ++layer) {
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for (size_t j = 0; j < kClasses; ++j) {
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for (size_t k = 0; k < kForest; ++k) {
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auto idx = layer * kLayerSize + j * kForest + k;
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auto const &group = get<Integer const>(sliced_info.at(idx));
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CHECK_EQ(static_cast<size_t>(group), j);
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}
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}
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}
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auto const& trees = get_trees(model);
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// Sliced layers are [2, 5]
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auto begin = kLayerSize * kSliceStart;
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auto end = begin + kLayerSize;
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auto j = 0;
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for (size_t i = begin; i < end; ++i) {
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Json tree = trees[i];
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tree["id"] = Integer(0); // id is different, we set it to 0 to allow comparison.
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auto sliced_tree = sliced_trees[j];
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sliced_tree["id"] = Integer(0);
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CHECK_EQ(tree, sliced_tree);
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j++;
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}
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begin = kLayerSize * (kSliceStart + kStep);
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end = begin + kLayerSize;
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for (size_t i = begin; i < end; ++i) {
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Json tree = trees[i];
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tree["id"] = Integer(0);
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auto sliced_tree = sliced_trees[j];
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sliced_tree["id"] = Integer(0);
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CHECK_EQ(tree, sliced_tree);
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j++;
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}
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// CHECK sliced model doesn't have dependency on old one
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learner.reset();
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CHECK_EQ(sliced->GetNumFeature(), kCols);
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return std::make_pair(model, sliced_model);
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}
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TEST(GBTree, Slice) {
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TestModelSlice("gbtree");
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}
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TEST(Dart, Slice) {
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Json model, sliced_model;
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std::tie(model, sliced_model) = TestModelSlice("dart");
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auto const& weights = get<Array const>(model["learner"]["gradient_booster"]["weight_drop"]);
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auto const& trees = get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["trees"]);
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ASSERT_EQ(weights.size(), trees.size());
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}
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TEST(GBTree, FeatureScore) {
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size_t n_samples = 1000, n_features = 10, n_classes = 4;
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auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
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std::unique_ptr<Learner> learner{ Learner::Create({m}) };
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learner->SetParam("num_class", std::to_string(n_classes));
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learner->Configure();
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for (size_t i = 0; i < 2; ++i) {
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learner->UpdateOneIter(i, m);
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}
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std::vector<bst_feature_t> features_weight;
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std::vector<float> scores_weight;
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learner->CalcFeatureScore("weight", {}, &features_weight, &scores_weight);
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ASSERT_EQ(features_weight.size(), scores_weight.size());
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ASSERT_LE(features_weight.size(), learner->GetNumFeature());
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ASSERT_TRUE(std::is_sorted(features_weight.begin(), features_weight.end()));
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auto test_eq = [&learner, &scores_weight](std::string type) {
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std::vector<bst_feature_t> features;
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std::vector<float> scores;
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learner->CalcFeatureScore(type, {}, &features, &scores);
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std::vector<bst_feature_t> features_total;
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std::vector<float> scores_total;
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learner->CalcFeatureScore("total_" + type, {}, &features_total, &scores_total);
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for (size_t i = 0; i < scores_weight.size(); ++i) {
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ASSERT_LE(RelError(scores_total[i] / scores[i], scores_weight[i]), kRtEps);
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}
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};
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test_eq("gain");
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test_eq("cover");
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}
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TEST(GBTree, PredictRange) {
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size_t n_samples = 1000, n_features = 10, n_classes = 4;
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auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
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std::unique_ptr<Learner> learner{Learner::Create({m})};
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learner->SetParam("num_class", std::to_string(n_classes));
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learner->Configure();
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for (size_t i = 0; i < 2; ++i) {
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learner->UpdateOneIter(i, m);
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}
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HostDeviceVector<float> out_predt;
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ASSERT_THROW(learner->Predict(m, false, &out_predt, 0, 3), dmlc::Error);
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auto m_1 =
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RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
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HostDeviceVector<float> out_predt_full;
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learner->Predict(m_1, false, &out_predt_full, 0, 0);
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ASSERT_TRUE(std::equal(out_predt.HostVector().begin(), out_predt.HostVector().end(),
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out_predt_full.HostVector().begin()));
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{
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// inplace predict
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HostDeviceVector<float> raw_storage;
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auto raw = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateArrayInterface(&raw_storage);
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std::shared_ptr<data::ArrayAdapter> x{new data::ArrayAdapter{StringView{raw}}};
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HostDeviceVector<float>* out_predt;
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learner->InplacePredict(x, nullptr, PredictionType::kValue,
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std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 2);
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auto h_out_predt = out_predt->HostVector();
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learner->InplacePredict(x, nullptr, PredictionType::kValue,
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std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 0);
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auto h_out_predt_full = out_predt->HostVector();
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ASSERT_TRUE(std::equal(h_out_predt.begin(), h_out_predt.end(), h_out_predt_full.begin()));
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ASSERT_THROW(learner->InplacePredict(x, nullptr, PredictionType::kValue,
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std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 3),
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dmlc::Error);
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}
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}
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} // namespace xgboost
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