xgboost/tests/cpp/predictor/test_cpu_predictor.cc
Jiaming Yuan 7b9043cf71
Fix clang-tidy warnings. (#4149)
* 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.
2019-03-13 02:25:51 +08:00

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