8fa32fdda2
* Add CPU implementation. * Update GPUTreeSHAP. * Add GPU implementation by defining custom split condition.
359 lines
12 KiB
C++
359 lines
12 KiB
C++
/*!
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* Copyright 2020-2021 by Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/predictor.h>
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#include <xgboost/data.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/generic_parameters.h>
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#include "test_predictor.h"
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#include "../helpers.h"
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#include "../../../src/common/io.h"
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#include "../../../src/common/categorical.h"
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#include "../../../src/common/bitfield.h"
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namespace xgboost {
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TEST(Predictor, PredictionCache) {
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size_t constexpr kRows = 16, kCols = 4;
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PredictionContainer container;
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DMatrix* m;
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// Add a cache that is immediately expired.
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auto add_cache = [&]() {
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auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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container.Cache(p_dmat, GenericParameter::kCpuId);
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m = p_dmat.get();
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};
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add_cache();
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ASSERT_EQ(container.Container().size(), 0ul);
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add_cache();
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EXPECT_ANY_THROW(container.Entry(m));
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}
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void TestTrainingPrediction(size_t rows, size_t bins,
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std::string tree_method,
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std::shared_ptr<DMatrix> p_full,
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std::shared_ptr<DMatrix> p_hist) {
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size_t constexpr kCols = 16;
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size_t constexpr kClasses = 3;
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size_t constexpr kIters = 3;
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std::unique_ptr<Learner> learner;
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auto train = [&](std::string predictor, HostDeviceVector<float> *out) {
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auto &h_label = p_hist->Info().labels_.HostVector();
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h_label.resize(rows);
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for (size_t i = 0; i < rows; ++i) {
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h_label[i] = i % kClasses;
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}
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learner.reset(Learner::Create({}));
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learner->SetParam("tree_method", tree_method);
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learner->SetParam("objective", "multi:softprob");
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learner->SetParam("num_feature", std::to_string(kCols));
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learner->SetParam("num_class", std::to_string(kClasses));
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learner->SetParam("max_bin", std::to_string(bins));
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learner->Configure();
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for (size_t i = 0; i < kIters; ++i) {
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learner->UpdateOneIter(i, p_hist);
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}
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HostDeviceVector<float> from_full;
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learner->Predict(p_full, false, &from_full, 0, 0);
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HostDeviceVector<float> from_hist;
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learner->Predict(p_hist, false, &from_hist, 0, 0);
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for (size_t i = 0; i < rows; ++i) {
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EXPECT_NEAR(from_hist.ConstHostVector()[i],
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from_full.ConstHostVector()[i], kRtEps);
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}
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};
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HostDeviceVector<float> predictions_0;
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train("cpu_predictor", &predictions_0);
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HostDeviceVector<float> predictions_1;
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train("gpu_predictor", &predictions_1);
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}
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void TestInplacePrediction(dmlc::any x, std::string predictor,
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bst_row_t rows, bst_feature_t cols,
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int32_t device) {
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size_t constexpr kClasses { 4 };
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auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(device);
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std::shared_ptr<DMatrix> m = gen.GenerateDMatrix(true, false, kClasses);
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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->SetParam("num_parallel_tree", "4");
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learner->SetParam("num_class", std::to_string(kClasses));
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learner->SetParam("seed", "0");
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learner->SetParam("subsample", "0.5");
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learner->SetParam("gpu_id", std::to_string(device));
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learner->SetParam("predictor", predictor);
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for (int32_t it = 0; it < 4; ++it) {
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learner->UpdateOneIter(it, m);
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}
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HostDeviceVector<float> *p_out_predictions_0{nullptr};
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learner->InplacePredict(x, nullptr, PredictionType::kMargin,
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std::numeric_limits<float>::quiet_NaN(),
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&p_out_predictions_0, 0, 2);
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CHECK(p_out_predictions_0);
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HostDeviceVector<float> predict_0 (p_out_predictions_0->Size());
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predict_0.Copy(*p_out_predictions_0);
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HostDeviceVector<float> *p_out_predictions_1{nullptr};
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learner->InplacePredict(x, nullptr, PredictionType::kMargin,
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std::numeric_limits<float>::quiet_NaN(),
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&p_out_predictions_1, 2, 4);
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CHECK(p_out_predictions_1);
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HostDeviceVector<float> predict_1 (p_out_predictions_1->Size());
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predict_1.Copy(*p_out_predictions_1);
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HostDeviceVector<float>* p_out_predictions{nullptr};
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learner->InplacePredict(x, nullptr, PredictionType::kMargin,
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std::numeric_limits<float>::quiet_NaN(),
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&p_out_predictions, 0, 4);
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auto& h_pred = p_out_predictions->HostVector();
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auto& h_pred_0 = predict_0.HostVector();
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auto& h_pred_1 = predict_1.HostVector();
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ASSERT_EQ(h_pred.size(), rows * kClasses);
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ASSERT_EQ(h_pred.size(), h_pred_0.size());
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ASSERT_EQ(h_pred.size(), h_pred_1.size());
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for (size_t i = 0; i < h_pred.size(); ++i) {
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// Need to remove the global bias here.
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ASSERT_NEAR(h_pred[i], h_pred_0[i] + h_pred_1[i] - 0.5f, kRtEps);
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}
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learner->SetParam("gpu_id", "-1");
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learner->Configure();
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}
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void TestPredictionWithLesserFeatures(std::string predictor_name) {
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size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
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auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
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auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
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std::unique_ptr<Learner> learner{Learner::Create({m_train})};
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for (size_t i = 0; i < kIters; ++i) {
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learner->UpdateOneIter(i, m_train);
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}
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HostDeviceVector<float> prediction;
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learner->SetParam("predictor", predictor_name);
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learner->Configure();
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Json config{Object()};
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learner->SaveConfig(&config);
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ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]), predictor_name);
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learner->Predict(m_test, false, &prediction, 0, 0);
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ASSERT_EQ(prediction.Size(), kRows);
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auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
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ASSERT_THROW({learner->Predict(m_invalid, false, &prediction, 0, 0);}, dmlc::Error);
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#if defined(XGBOOST_USE_CUDA)
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HostDeviceVector<float> from_cpu;
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learner->SetParam("predictor", "cpu_predictor");
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learner->Predict(m_test, false, &from_cpu, 0, 0);
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HostDeviceVector<float> from_cuda;
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learner->SetParam("predictor", "gpu_predictor");
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learner->Predict(m_test, false, &from_cuda, 0, 0);
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auto const& h_cpu = from_cpu.ConstHostVector();
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auto const& h_gpu = from_cuda.ConstHostVector();
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for (size_t i = 0; i < h_cpu.size(); ++i) {
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ASSERT_NEAR(h_cpu[i], h_gpu[i], kRtEps);
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}
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#endif // defined(XGBOOST_USE_CUDA)
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}
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void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
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bst_cat_t split_cat, float left_weight,
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float right_weight) {
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PredictionCacheEntry out_predictions;
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std::vector<std::unique_ptr<RegTree>> trees;
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trees.push_back(std::unique_ptr<RegTree>(new RegTree));
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auto& p_tree = trees.front();
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std::vector<uint32_t> split_cats(LBitField32::ComputeStorageSize(split_cat));
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LBitField32 cats_bits(split_cats);
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cats_bits.Set(split_cat);
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p_tree->ExpandCategorical(0, split_ind, split_cats, true, 1.5f,
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left_weight, right_weight,
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3.0f, 2.2f, 7.0f, 9.0f);
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model->CommitModel(std::move(trees), 0);
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}
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void TestCategoricalPrediction(std::string name) {
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size_t constexpr kCols = 10;
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PredictionCacheEntry out_predictions;
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LearnerModelParam param;
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param.num_feature = kCols;
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param.num_output_group = 1;
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param.base_score = 0.5;
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uint32_t split_ind = 3;
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bst_cat_t split_cat = 4;
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float left_weight = 1.3f;
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float right_weight = 1.7f;
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gbm::GBTreeModel model(¶m);
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GBTreeModelForTest(&model, split_ind, split_cat, left_weight, right_weight);
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GenericParameter runtime;
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runtime.gpu_id = 0;
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std::unique_ptr<Predictor> predictor{
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Predictor::Create(name.c_str(), &runtime)};
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std::vector<float> row(kCols);
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row[split_ind] = split_cat;
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auto m = GetDMatrixFromData(row, 1, kCols);
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std::vector<FeatureType> types(10, FeatureType::kCategorical);
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m->Info().feature_types.HostVector() = types;
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predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
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predictor->PredictBatch(m.get(), &out_predictions, model, 0);
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ASSERT_EQ(out_predictions.predictions.Size(), 1ul);
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ASSERT_EQ(out_predictions.predictions.HostVector()[0],
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right_weight + param.base_score); // go to right for matching cat
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row[split_ind] = split_cat + 1;
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m = GetDMatrixFromData(row, 1, kCols);
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out_predictions.version = 0;
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predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
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predictor->PredictBatch(m.get(), &out_predictions, model, 0);
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ASSERT_EQ(out_predictions.predictions.HostVector()[0],
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left_weight + param.base_score);
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}
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void TestCategoricalPredictLeaf(StringView name) {
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size_t constexpr kCols = 10;
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PredictionCacheEntry out_predictions;
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LearnerModelParam param;
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param.num_feature = kCols;
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param.num_output_group = 1;
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param.base_score = 0.5;
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uint32_t split_ind = 3;
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bst_cat_t split_cat = 4;
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float left_weight = 1.3f;
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float right_weight = 1.7f;
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gbm::GBTreeModel model(¶m);
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GBTreeModelForTest(&model, split_ind, split_cat, left_weight, right_weight);
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GenericParameter runtime;
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runtime.gpu_id = 0;
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std::unique_ptr<Predictor> predictor{
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Predictor::Create(name.c_str(), &runtime)};
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std::vector<float> row(kCols);
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row[split_ind] = split_cat;
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auto m = GetDMatrixFromData(row, 1, kCols);
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predictor->PredictLeaf(m.get(), &out_predictions.predictions, model);
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CHECK_EQ(out_predictions.predictions.Size(), 1);
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// go to left if it doesn't match the category, otherwise right.
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ASSERT_EQ(out_predictions.predictions.HostVector()[0], 2);
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row[split_ind] = split_cat + 1;
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m = GetDMatrixFromData(row, 1, kCols);
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out_predictions.version = 0;
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predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
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predictor->PredictLeaf(m.get(), &out_predictions.predictions, model);
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ASSERT_EQ(out_predictions.predictions.HostVector()[0], 1);
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}
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void TestIterationRange(std::string name) {
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size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3;
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auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
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std::unique_ptr<Learner> learner{Learner::Create({dmat})};
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learner->SetParams(Args{{"num_parallel_tree", std::to_string(kForest)},
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{"predictor", name}});
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size_t kIters = 10;
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for (size_t i = 0; i < kIters; ++i) {
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learner->UpdateOneIter(i, dmat);
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}
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bool bound = false;
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std::unique_ptr<Learner> sliced {learner->Slice(0, 3, 1, &bound)};
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ASSERT_FALSE(bound);
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HostDeviceVector<float> out_predt_sliced;
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HostDeviceVector<float> out_predt_ranged;
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// margin
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{
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sliced->Predict(dmat, true, &out_predt_sliced, 0, 0, false, false, false,
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false, false);
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learner->Predict(dmat, true, &out_predt_ranged, 0, 3, false, false, false,
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false, false);
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auto const &h_sliced = out_predt_sliced.HostVector();
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auto const &h_range = out_predt_ranged.HostVector();
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ASSERT_EQ(h_sliced.size(), h_range.size());
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ASSERT_EQ(h_sliced, h_range);
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}
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// SHAP
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{
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sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, false,
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true, false, false);
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learner->Predict(dmat, false, &out_predt_ranged, 0, 3, false, false, true,
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false, false);
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auto const &h_sliced = out_predt_sliced.HostVector();
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auto const &h_range = out_predt_ranged.HostVector();
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ASSERT_EQ(h_sliced.size(), h_range.size());
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ASSERT_EQ(h_sliced, h_range);
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}
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// SHAP interaction
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{
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sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, false,
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false, false, true);
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learner->Predict(dmat, false, &out_predt_ranged, 0, 3, false, false, false,
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false, true);
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auto const &h_sliced = out_predt_sliced.HostVector();
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auto const &h_range = out_predt_ranged.HostVector();
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ASSERT_EQ(h_sliced.size(), h_range.size());
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ASSERT_EQ(h_sliced, h_range);
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}
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// Leaf
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{
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sliced->Predict(dmat, false, &out_predt_sliced, 0, 0, false, true,
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false, false, false);
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learner->Predict(dmat, false, &out_predt_ranged, 0, 3, false, true, false,
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false, false);
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auto const &h_sliced = out_predt_sliced.HostVector();
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auto const &h_range = out_predt_ranged.HostVector();
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ASSERT_EQ(h_sliced.size(), h_range.size());
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ASSERT_EQ(h_sliced, h_range);
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}
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}
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} // namespace xgboost
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