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[EM] Support SHAP contribution with QDM. (#10724)

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- Add GPU support.
- Add external memory support.
- Update the GPU tree shap.
This commit is contained in:
Jiaming Yuan
2024-08-22 05:25:10 +08:00
committed by GitHub
parent cb54374550
commit 142bdc73ec
13 changed files with 274 additions and 159 deletions
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10
tests/cpp/gbm/test_gbtree.cc
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@@ -1,5 +1,5 @@
/**
* Copyright 2019-2023, XGBoost contributors
* Copyright 2019-2024, XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
@@ -463,7 +463,7 @@ INSTANTIATE_TEST_SUITE_P(PredictorTypes, Dart, testing::Values("CPU"));
std::pair<Json, Json> TestModelSlice(std::string booster) {
size_t constexpr kRows = 1000, kCols = 100, kForest = 2, kClasses = 3;
auto m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true, false, kClasses);
auto m = RandomDataGenerator{kRows, kCols, 0}.Classes(kClasses).GenerateDMatrix(true);
int32_t kIters = 10;
std::unique_ptr<Learner> learner {
@@ -592,7 +592,7 @@ TEST(Dart, Slice) {
TEST(GBTree, FeatureScore) {
size_t n_samples = 1000, n_features = 10, n_classes = 4;
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.Classes(n_classes).GenerateDMatrix(true);
std::unique_ptr<Learner> learner{ Learner::Create({m}) };
learner->SetParam("num_class", std::to_string(n_classes));
@@ -629,7 +629,7 @@ TEST(GBTree, FeatureScore) {
TEST(GBTree, PredictRange) {
size_t n_samples = 1000, n_features = 10, n_classes = 4;
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.Classes(n_classes).GenerateDMatrix(true);
std::unique_ptr<Learner> learner{Learner::Create({m})};
learner->SetParam("num_class", std::to_string(n_classes));
@@ -642,7 +642,7 @@ TEST(GBTree, PredictRange) {
ASSERT_THROW(learner->Predict(m, false, &out_predt, 0, 3), dmlc::Error);
auto m_1 =
RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
RandomDataGenerator{n_samples, n_features, 0.5}.Classes(n_classes).GenerateDMatrix(true);
HostDeviceVector<float> out_predt_full;
learner->Predict(m_1, false, &out_predt_full, 0, 0);
ASSERT_TRUE(std::equal(out_predt.HostVector().begin(), out_predt.HostVector().end(),