* Upgrade gtest for clang-tidy. * Use CMake to install GTest instead of mv. * Don't enforce clang-tidy to return 0 due to errors in thrust. * Add a small test for tidy itself. * Reformat.
63 lines
1.9 KiB
C++
63 lines
1.9 KiB
C++
// Copyright by Contributors
|
|
#include <gtest/gtest.h>
|
|
#include <xgboost/predictor.h>
|
|
#include "../helpers.h"
|
|
|
|
namespace xgboost {
|
|
TEST(cpu_predictor, Test) {
|
|
std::unique_ptr<Predictor> cpu_predictor =
|
|
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor"));
|
|
|
|
std::vector<std::unique_ptr<RegTree>> trees;
|
|
trees.push_back(std::unique_ptr<RegTree>(new RegTree));
|
|
(*trees.back())[0].SetLeaf(1.5f);
|
|
(*trees.back()).Stat(0).sum_hess = 1.0f;
|
|
gbm::GBTreeModel model(0.5);
|
|
model.CommitModel(std::move(trees), 0);
|
|
model.param.num_output_group = 1;
|
|
model.base_margin = 0;
|
|
|
|
int n_row = 5;
|
|
int n_col = 5;
|
|
|
|
auto dmat = CreateDMatrix(n_row, n_col, 0);
|
|
|
|
// Test predict batch
|
|
HostDeviceVector<float> out_predictions;
|
|
cpu_predictor->PredictBatch((*dmat).get(), &out_predictions, model, 0);
|
|
std::vector<float>& out_predictions_h = out_predictions.HostVector();
|
|
for (int i = 0; i < out_predictions.Size(); i++) {
|
|
ASSERT_EQ(out_predictions_h[i], 1.5);
|
|
}
|
|
|
|
// Test predict instance
|
|
auto &batch = *(*dmat)->GetRowBatches().begin();
|
|
for (int i = 0; i < batch.Size(); i++) {
|
|
std::vector<float> instance_out_predictions;
|
|
cpu_predictor->PredictInstance(batch[i], &instance_out_predictions, model);
|
|
ASSERT_EQ(instance_out_predictions[0], 1.5);
|
|
}
|
|
|
|
// Test predict leaf
|
|
std::vector<float> leaf_out_predictions;
|
|
cpu_predictor->PredictLeaf((*dmat).get(), &leaf_out_predictions, model);
|
|
for (auto v : leaf_out_predictions) {
|
|
ASSERT_EQ(v, 0);
|
|
}
|
|
|
|
// Test predict contribution
|
|
std::vector<float> out_contribution;
|
|
cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model);
|
|
for (auto const& contri : out_contribution) {
|
|
ASSERT_EQ(contri, 1.5);
|
|
}
|
|
// Test predict contribution (approximate method)
|
|
cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model, true);
|
|
for (auto const& contri : out_contribution) {
|
|
ASSERT_EQ(contri, 1.5);
|
|
}
|
|
|
|
delete dmat;
|
|
}
|
|
} // namespace xgboost
|