acc110c251
- Fix prediction range. - Support prediction cache in mt-hist. - Support model slicing. - Make the booster a Python iterable by defining `__iter__`. - Cleanup removed/deprecated parameters. - A new field in the output model `iteration_indptr` for pointing to the ranges of trees for each iteration.
519 lines
19 KiB
C++
519 lines
19 KiB
C++
/**
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* Copyright 2020-2023 by XGBoost Contributors
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*/
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#include "test_predictor.h"
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#include <gtest/gtest.h>
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#include <xgboost/context.h> // for Context
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#include <xgboost/data.h> // for DMatrix, BatchIterator, BatchSet, MetaInfo
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#include <xgboost/host_device_vector.h> // for HostDeviceVector
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#include <xgboost/predictor.h> // for PredictionCacheEntry, Predictor, Predic...
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#include <algorithm> // for max
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#include <limits> // for numeric_limits
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#include <unordered_map> // for unordered_map
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#include "../../../src/common/bitfield.h" // for LBitField32
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#include "../../../src/data/iterative_dmatrix.h" // for IterativeDMatrix
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#include "../../../src/data/proxy_dmatrix.h" // for DMatrixProxy
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#include "../helpers.h" // for GetDMatrixFromData, RandomDataGenerator
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#include "xgboost/json.h" // for Json, Object, get, String
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#include "xgboost/linalg.h" // for MakeVec, Tensor, TensorView, Vector
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#include "xgboost/logging.h" // for CHECK
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#include "xgboost/span.h" // for operator!=, SpanIterator, Span
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#include "xgboost/tree_model.h" // for RegTree
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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, Context::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) {
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p_hist->Info().labels.Reshape(rows, 1);
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auto &h_label = p_hist->Info().labels.Data()->HostVector();
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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->SetParam("predictor", predictor);
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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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Json model{Object{}};
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learner->SaveModel(&model);
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learner.reset(Learner::Create({}));
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learner->LoadModel(model);
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learner->SetParam("predictor", predictor);
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learner->Configure();
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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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if (tree_method == "gpu_hist") {
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train("gpu_predictor");
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} else {
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train("cpu_predictor");
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}
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}
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void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bst_row_t rows,
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bst_feature_t cols, 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, PredictionType::kMargin, 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, PredictionType::kMargin, 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, PredictionType::kMargin, 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->CommitModelGroup(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 mparam{MakeMP(kCols, .5, 1)};
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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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Context ctx;
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ctx.UpdateAllowUnknown(Args{});
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gbm::GBTreeModel model(&mparam, &ctx);
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GBTreeModelForTest(&model, split_ind, split_cat, left_weight, right_weight);
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ctx.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
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std::unique_ptr<Predictor> predictor{Predictor::Create(name.c_str(), &ctx)};
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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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auto score = mparam.BaseScore(Context::kCpuId)(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 + 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], left_weight + 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 mparam{MakeMP(kCols, .5, 1)};
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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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Context ctx;
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ctx.UpdateAllowUnknown(Args{});
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gbm::GBTreeModel model(&mparam, &ctx);
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GBTreeModelForTest(&model, split_ind, split_cat, left_weight, right_weight);
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ctx.gpu_id = 0;
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std::unique_ptr<Predictor> predictor{Predictor::Create(name.c_str(), &ctx)};
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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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void TestSparsePrediction(float sparsity, std::string predictor) {
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size_t constexpr kRows = 512, kCols = 128;
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auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
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std::unique_ptr<Learner> learner{Learner::Create({Xy})};
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learner->Configure();
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, Xy);
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}
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HostDeviceVector<float> sparse_predt;
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Json model{Object{}};
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learner->SaveModel(&model);
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learner.reset(Learner::Create({Xy}));
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learner->LoadModel(model);
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learner->SetParam("predictor", predictor);
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learner->Predict(Xy, false, &sparse_predt, 0, 0);
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HostDeviceVector<float> with_nan(kRows * kCols, std::numeric_limits<float>::quiet_NaN());
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auto& h_with_nan = with_nan.HostVector();
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for (auto const &page : Xy->GetBatches<SparsePage>()) {
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auto batch = page.GetView();
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for (size_t i = 0; i < batch.Size(); ++i) {
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auto row = batch[i];
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for (auto e : row) {
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h_with_nan[i * kCols + e.index] = e.fvalue;
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}
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}
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}
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learner->SetParam("predictor", "cpu_predictor");
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// Xcode_12.4 doesn't compile with `std::make_shared`.
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auto dense = std::shared_ptr<DMatrix>(new data::DMatrixProxy{});
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auto array_interface = GetArrayInterface(&with_nan, kRows, kCols);
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std::string arr_str;
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Json::Dump(array_interface, &arr_str);
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dynamic_cast<data::DMatrixProxy *>(dense.get())->SetArrayData(arr_str.data());
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HostDeviceVector<float> *p_dense_predt;
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learner->InplacePredict(dense, PredictionType::kValue, std::numeric_limits<float>::quiet_NaN(),
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&p_dense_predt, 0, 0);
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auto const& dense_predt = *p_dense_predt;
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if (predictor == "cpu_predictor") {
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ASSERT_EQ(dense_predt.HostVector(), sparse_predt.HostVector());
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} else {
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auto const &h_dense = dense_predt.HostVector();
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auto const &h_sparse = sparse_predt.HostVector();
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ASSERT_EQ(h_dense.size(), h_sparse.size());
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for (size_t i = 0; i < h_dense.size(); ++i) {
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ASSERT_FLOAT_EQ(h_dense[i], h_sparse[i]);
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}
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}
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}
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void TestVectorLeafPrediction(Context const *ctx) {
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std::unique_ptr<Predictor> cpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", ctx));
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size_t constexpr kRows = 5;
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size_t constexpr kCols = 5;
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LearnerModelParam mparam{static_cast<bst_feature_t>(kCols),
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linalg::Vector<float>{{0.5}, {1}, Context::kCpuId}, 1, 3,
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MultiStrategy::kMultiOutputTree};
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std::vector<std::unique_ptr<RegTree>> trees;
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trees.emplace_back(new RegTree{mparam.LeafLength(), mparam.num_feature});
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std::vector<float> p_w(mparam.LeafLength(), 0.0f);
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std::vector<float> l_w(mparam.LeafLength(), 1.0f);
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std::vector<float> r_w(mparam.LeafLength(), 2.0f);
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auto &tree = trees.front();
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tree->ExpandNode(0, static_cast<bst_feature_t>(1), 2.0, true,
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linalg::MakeVec(p_w.data(), p_w.size()), linalg::MakeVec(l_w.data(), l_w.size()),
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linalg::MakeVec(r_w.data(), r_w.size()));
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ASSERT_TRUE(tree->IsMultiTarget());
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ASSERT_TRUE(mparam.IsVectorLeaf());
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gbm::GBTreeModel model{&mparam, ctx};
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model.CommitModelGroup(std::move(trees), 0);
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auto run_test = [&](float expected, HostDeviceVector<float> *p_data) {
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{
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auto p_fmat = GetDMatrixFromData(p_data->ConstHostVector(), kRows, kCols);
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PredictionCacheEntry predt_cache;
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cpu_predictor->InitOutPredictions(p_fmat->Info(), &predt_cache.predictions, model);
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ASSERT_EQ(predt_cache.predictions.Size(), kRows * mparam.LeafLength());
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cpu_predictor->PredictBatch(p_fmat.get(), &predt_cache, model, 0, 1);
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auto const &h_predt = predt_cache.predictions.HostVector();
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for (auto v : h_predt) {
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ASSERT_EQ(v, expected);
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}
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}
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{
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// inplace
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PredictionCacheEntry predt_cache;
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auto p_fmat = GetDMatrixFromData(p_data->ConstHostVector(), kRows, kCols);
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cpu_predictor->InitOutPredictions(p_fmat->Info(), &predt_cache.predictions, model);
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auto arr = GetArrayInterface(p_data, kRows, kCols);
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std::string str;
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Json::Dump(arr, &str);
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auto proxy = std::shared_ptr<DMatrix>(new data::DMatrixProxy{});
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dynamic_cast<data::DMatrixProxy *>(proxy.get())->SetArrayData(str.data());
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cpu_predictor->InplacePredict(proxy, model, std::numeric_limits<float>::quiet_NaN(),
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&predt_cache, 0, 1);
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auto const &h_predt = predt_cache.predictions.HostVector();
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for (auto v : h_predt) {
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ASSERT_EQ(v, expected);
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}
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}
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{
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// ghist
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PredictionCacheEntry predt_cache;
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auto &h_data = p_data->HostVector();
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// give it at least two bins, otherwise the histogram cuts only have min and max values.
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for (std::size_t i = 0; i < 5; ++i) {
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h_data[i] = 1.0;
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}
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auto p_fmat = GetDMatrixFromData(p_data->ConstHostVector(), kRows, kCols);
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cpu_predictor->InitOutPredictions(p_fmat->Info(), &predt_cache.predictions, model);
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auto iter = NumpyArrayIterForTest{ctx, *p_data, kRows, static_cast<bst_feature_t>(kCols),
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static_cast<std::size_t>(1)};
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p_fmat =
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std::make_shared<data::IterativeDMatrix>(&iter, iter.Proxy(), nullptr, Reset, Next,
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std::numeric_limits<float>::quiet_NaN(), 0, 256);
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cpu_predictor->InitOutPredictions(p_fmat->Info(), &predt_cache.predictions, model);
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cpu_predictor->PredictBatch(p_fmat.get(), &predt_cache, model, 0, 1);
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auto const &h_predt = predt_cache.predictions.HostVector();
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// the smallest v uses the min_value from histogram cuts, which leads to a left leaf
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// during prediction.
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for (std::size_t i = 5; i < h_predt.size(); ++i) {
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ASSERT_EQ(h_predt[i], expected) << i;
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}
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}
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};
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// go to right
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HostDeviceVector<float> data(kRows * kCols, model.trees.front()->SplitCond(RegTree::kRoot) + 1.0);
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run_test(2.5, &data);
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// go to left
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data.HostVector().assign(data.Size(), model.trees.front()->SplitCond(RegTree::kRoot) - 1.0);
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run_test(1.5, &data);
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}
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} // namespace xgboost
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