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Add travis sanitizers tests. (#3557)

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* Add travis sanitizers tests.

* Add gcc-7 in Travis.
* Add SANITIZER_PATH for CMake.
* Enable sanitizer tests in Travis.

* Fix memory leaks in tests.

* Fix all memory leaks reported by Address Sanitizer.
* tests/cpp/helpers.h/CreateDMatrix now returns raw pointer.
This commit is contained in:
trivialfis
2018-08-19 12:40:30 +08:00
committed by Rory Mitchell
parent 983cb0b374
commit cf2d86a4f6
30 changed files with 221 additions and 76 deletions
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19
tests/cpp/c_api/test_c_api.cc
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@@ -13,14 +13,14 @@ TEST(c_api, XGDMatrixCreateFromMatDT) {
DMatrixHandle handle;
XGDMatrixCreateFromDT(data.data(), types.data(), 3, 2, &handle,
0);
std::shared_ptr<xgboost::DMatrix> dmat =
*static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
xgboost::MetaInfo &info = dmat->Info();
std::shared_ptr<xgboost::DMatrix> *dmat =
static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
xgboost::MetaInfo &info = (*dmat)->Info();
ASSERT_EQ(info.num_col_, 2);
ASSERT_EQ(info.num_row_, 3);
ASSERT_EQ(info.num_nonzero_, 6);
auto iter = dmat->RowIterator();
auto iter = (*dmat)->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
@@ -29,6 +29,8 @@ TEST(c_api, XGDMatrixCreateFromMatDT) {
ASSERT_EQ(batch[2][0].fvalue, 3.0f);
ASSERT_EQ(batch[2][1].fvalue, 0.0f);
}
delete dmat;
}
TEST(c_api, XGDMatrixCreateFromMat_omp) {
@@ -46,14 +48,14 @@ TEST(c_api, XGDMatrixCreateFromMat_omp) {
std::numeric_limits<float>::quiet_NaN(), &handle,
0);
std::shared_ptr<xgboost::DMatrix> dmat =
*static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
xgboost::MetaInfo &info = dmat->Info();
std::shared_ptr<xgboost::DMatrix> *dmat =
static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
xgboost::MetaInfo &info = (*dmat)->Info();
ASSERT_EQ(info.num_col_, num_cols);
ASSERT_EQ(info.num_row_, row);
ASSERT_EQ(info.num_nonzero_, num_cols * row - num_missing);
auto iter = dmat->RowIterator();
auto iter = (*dmat)->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
@@ -64,5 +66,6 @@ TEST(c_api, XGDMatrixCreateFromMat_omp) {
}
}
}
delete dmat;
}
}

39
tests/cpp/common/test_column_matrix.cc
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@@ -7,45 +7,48 @@ namespace common {
TEST(DenseColumn, Test) {
auto dmat = CreateDMatrix(100, 10, 0.0);
GHistIndexMatrix gmat;
gmat.Init(dmat.get(), 256);
gmat.Init((*dmat).get(), 256);
ColumnMatrix column_matrix;
column_matrix.Init(gmat, 0.2);
for (auto i = 0ull; i < dmat->Info().num_row_; i++) {
for (auto j = 0ull; j < dmat->Info().num_col_; j++) {
for (auto i = 0ull; i < (*dmat)->Info().num_row_; i++) {
for (auto j = 0ull; j < (*dmat)->Info().num_col_; j++) {
auto col = column_matrix.GetColumn(j);
EXPECT_EQ(gmat.index[i * dmat->Info().num_col_ + j],
EXPECT_EQ(gmat.index[i * (*dmat)->Info().num_col_ + j],
col.GetGlobalBinIdx(i));
}
}
delete dmat;
}
TEST(SparseColumn, Test) {
auto dmat = CreateDMatrix(100, 1, 0.85);
GHistIndexMatrix gmat;
gmat.Init(dmat.get(), 256);
gmat.Init((*dmat).get(), 256);
ColumnMatrix column_matrix;
column_matrix.Init(gmat, 0.5);
auto col = column_matrix.GetColumn(0);
ASSERT_EQ(col.Size(), gmat.index.size());
for (auto i = 0ull; i < col.Size(); i++) {
EXPECT_EQ(gmat.index[gmat.row_ptr[col.GetRowIdx(i)]],
col.GetGlobalBinIdx(i));
}
auto col = column_matrix.GetColumn(0);
ASSERT_EQ(col.Size(), gmat.index.size());
for (auto i = 0ull; i < col.Size(); i++) {
EXPECT_EQ(gmat.index[gmat.row_ptr[col.GetRowIdx(i)]],
col.GetGlobalBinIdx(i));
}
delete dmat;
}
TEST(DenseColumnWithMissing, Test) {
auto dmat = CreateDMatrix(100, 1, 0.5);
GHistIndexMatrix gmat;
gmat.Init(dmat.get(), 256);
gmat.Init((*dmat).get(), 256);
ColumnMatrix column_matrix;
column_matrix.Init(gmat, 0.2);
auto col = column_matrix.GetColumn(0);
for (auto i = 0ull; i < col.Size(); i++) {
if (col.IsMissing(i)) continue;
EXPECT_EQ(gmat.index[gmat.row_ptr[col.GetRowIdx(i)]],
col.GetGlobalBinIdx(i));
}
auto col = column_matrix.GetColumn(0);
for (auto i = 0ull; i < col.Size(); i++) {
if (col.IsMissing(i)) continue;
EXPECT_EQ(gmat.index[gmat.row_ptr[col.GetRowIdx(i)]],
col.GetGlobalBinIdx(i));
}
delete dmat;
}
} // namespace common
} // namespace xgboost

10
tests/cpp/common/test_gpu_hist_util.cu
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@@ -22,7 +22,7 @@ TEST(gpu_hist_util, TestDeviceSketch) {
DMatrixHandle dmat_handle;
XGDMatrixCreateFromMat(test_data.data(), nrows, 1, -1,
&dmat_handle);
auto dmat = *static_cast<std::shared_ptr<xgboost::DMatrix> *>(dmat_handle);
auto dmat = static_cast<std::shared_ptr<xgboost::DMatrix> *>(dmat_handle);
// parameters for finding quantiles
tree::TrainParam p;
@@ -34,15 +34,15 @@ TEST(gpu_hist_util, TestDeviceSketch) {
// find quantiles on the CPU
HistCutMatrix hmat_cpu;
hmat_cpu.Init(dmat.get(), p.max_bin);
hmat_cpu.Init((*dmat).get(), p.max_bin);
// find the cuts on the GPU
dmlc::DataIter<SparsePage>* iter = dmat->RowIterator();
dmlc::DataIter<SparsePage>* iter = (*dmat)->RowIterator();
iter->BeforeFirst();
CHECK(iter->Next());
const SparsePage& batch = iter->Value();
HistCutMatrix hmat_gpu;
DeviceSketch(batch, dmat->Info(), p, &hmat_gpu);
DeviceSketch(batch, (*dmat)->Info(), p, &hmat_gpu);
CHECK(!iter->Next());
// compare the cuts
@@ -54,6 +54,8 @@ TEST(gpu_hist_util, TestDeviceSketch) {
for (int i = 0; i < hmat_gpu.cut.size(); ++i) {
ASSERT_LT(fabs(hmat_cpu.cut[i] - hmat_gpu.cut[i]), eps * nrows);
}
delete dmat;
}
} // namespace common

2
tests/cpp/data/test_metainfo.cc
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@@ -64,6 +64,8 @@ TEST(MetaInfo, SaveLoadBinary) {
EXPECT_EQ(inforead.num_row_, info.num_row_);
std::remove(tmp_file.c_str());
delete fs;
}
TEST(MetaInfo, LoadQid) {

3
tests/cpp/data/test_simple_csr_source.cc
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@@ -29,4 +29,7 @@ TEST(SimpleCSRSource, SaveLoadBinary) {
EXPECT_EQ(first_row[2].index, first_row_read[2].index);
EXPECT_EQ(first_row[2].fvalue, first_row_read[2].fvalue);
row_iter = nullptr; row_iter_read = nullptr;
delete dmat;
delete dmat_read;
}

6
tests/cpp/data/test_simple_dmatrix.cc
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@@ -14,6 +14,8 @@ TEST(SimpleDMatrix, MetaInfo) {
EXPECT_EQ(dmat->Info().num_col_, 5);
EXPECT_EQ(dmat->Info().num_nonzero_, 6);
EXPECT_EQ(dmat->Info().labels_.size(), dmat->Info().num_row_);
delete dmat;
}
TEST(SimpleDMatrix, RowAccess) {
@@ -35,6 +37,8 @@ TEST(SimpleDMatrix, RowAccess) {
EXPECT_EQ(first_row[2].index, 2);
EXPECT_EQ(first_row[2].fvalue, 20);
row_iter = nullptr;
delete dmat;
}
TEST(SimpleDMatrix, ColAccessWithoutBatches) {
@@ -76,4 +80,6 @@ TEST(SimpleDMatrix, ColAccessWithoutBatches) {
}
EXPECT_EQ(num_col_batch, 1) << "Expected number of batches to be 1";
col_iter = nullptr;
delete dmat;
}

6
tests/cpp/data/test_sparse_page_dmatrix.cc
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@@ -21,6 +21,8 @@ TEST(SparsePageDMatrix, MetaInfo) {
// Clean up of external memory files
std::remove((tmp_file + ".cache").c_str());
std::remove((tmp_file + ".cache.row.page").c_str());
delete dmat;
}
TEST(SparsePageDMatrix, RowAccess) {
@@ -48,6 +50,8 @@ TEST(SparsePageDMatrix, RowAccess) {
// Clean up of external memory files
std::remove((tmp_file + ".cache").c_str());
std::remove((tmp_file + ".cache.row.page").c_str());
delete dmat;
}
TEST(SparsePageDMatrix, ColAcess) {
@@ -84,4 +88,6 @@ TEST(SparsePageDMatrix, ColAcess) {
std::remove((tmp_file + ".cache").c_str());
std::remove((tmp_file + ".cache.col.page").c_str());
std::remove((tmp_file + ".cache.row.page").c_str());
delete dmat;
}

6
tests/cpp/helpers.cc
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@@ -107,8 +107,8 @@ xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
return metric->Eval(preds, info, false);
}
std::shared_ptr<xgboost::DMatrix> CreateDMatrix(int rows, int columns,
float sparsity, int seed) {
std::shared_ptr<xgboost::DMatrix>* CreateDMatrix(int rows, int columns,
float sparsity, int seed) {
const float missing_value = -1;
std::vector<float> test_data(rows * columns);
std::mt19937 gen(seed);
@@ -124,5 +124,5 @@ std::shared_ptr<xgboost::DMatrix> CreateDMatrix(int rows, int columns,
DMatrixHandle handle;
XGDMatrixCreateFromMat(test_data.data(), rows, columns, missing_value,
&handle);
return *static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
return static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
}

4
tests/cpp/helpers.h
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@@ -59,6 +59,6 @@ xgboost::bst_float GetMetricEval(
* \return The new d matrix.
*/
std::shared_ptr<xgboost::DMatrix> CreateDMatrix(int rows, int columns,
float sparsity, int seed = 0);
std::shared_ptr<xgboost::DMatrix> *CreateDMatrix(int rows, int columns,
float sparsity, int seed = 0);
#endif

26
tests/cpp/linear/test_linear.cc
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@@ -8,37 +8,41 @@ typedef std::pair<std::string, std::string> arg;
TEST(Linear, shotgun) {
typedef std::pair<std::string, std::string> arg;
auto mat = CreateDMatrix(10, 10, 0);
std::vector<bool> enabled(mat->Info().num_col_, true);
mat->InitColAccess(1 << 16, false);
std::vector<bool> enabled((*mat)->Info().num_col_, true);
(*mat)->InitColAccess(1 << 16, false);
auto updater = std::unique_ptr<xgboost::LinearUpdater>(
xgboost::LinearUpdater::Create("shotgun"));
updater->Init({{"eta", "1."}});
xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
mat->Info().num_row_, xgboost::GradientPair(-5, 1.0));
(*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
xgboost::gbm::GBLinearModel model;
model.param.num_feature = mat->Info().num_col_;
model.param.num_feature = (*mat)->Info().num_col_;
model.param.num_output_group = 1;
model.LazyInitModel();
updater->Update(&gpair, mat.get(), &model, gpair.Size());
updater->Update(&gpair, (*mat).get(), &model, gpair.Size());
ASSERT_EQ(model.bias()[0], 5.0f);
delete mat;
}
TEST(Linear, coordinate) {
typedef std::pair<std::string, std::string> arg;
auto mat = CreateDMatrix(10, 10, 0);
std::vector<bool> enabled(mat->Info().num_col_, true);
mat->InitColAccess(1 << 16, false);
std::vector<bool> enabled((*mat)->Info().num_col_, true);
(*mat)->InitColAccess(1 << 16, false);
auto updater = std::unique_ptr<xgboost::LinearUpdater>(
xgboost::LinearUpdater::Create("coord_descent"));
updater->Init({{"eta", "1."}});
xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
mat->Info().num_row_, xgboost::GradientPair(-5, 1.0));
(*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
xgboost::gbm::GBLinearModel model;
model.param.num_feature = mat->Info().num_col_;
model.param.num_feature = (*mat)->Info().num_col_;
model.param.num_output_group = 1;
model.LazyInitModel();
updater->Update(&gpair, mat.get(), &model, gpair.Size());
updater->Update(&gpair, (*mat).get(), &model, gpair.Size());
ASSERT_EQ(model.bias()[0], 5.0f);
}
delete mat;
}

5
tests/cpp/metric/test_elementwise_metric.cc
View File

@@ -11,6 +11,7 @@ TEST(Metric, RMSE) {
{0.1f, 0.9f, 0.1f, 0.9f},
{ 0, 0, 1, 1}),
0.6403f, 0.001f);
delete metric;
}
TEST(Metric, MAE) {
@@ -21,6 +22,7 @@ TEST(Metric, MAE) {
{0.1f, 0.9f, 0.1f, 0.9f},
{ 0, 0, 1, 1}),
0.5f, 0.001f);
delete metric;
}
TEST(Metric, LogLoss) {
@@ -31,6 +33,7 @@ TEST(Metric, LogLoss) {
{0.1f, 0.9f, 0.1f, 0.9f},
{ 0, 0, 1, 1}),
1.2039f, 0.001f);
delete metric;
}
TEST(Metric, Error) {
@@ -56,6 +59,7 @@ TEST(Metric, Error) {
{0.1f, 0.2f, 0.1f, 0.2f},
{ 0, 0, 1, 1}),
0.5f, 0.001f);
delete metric;
}
TEST(Metric, PoissionNegLogLik) {
@@ -66,4 +70,5 @@ TEST(Metric, PoissionNegLogLik) {
{0.1f, 0.2f, 0.1f, 0.2f},
{ 0, 0, 1, 1}),
1.1280f, 0.001f);
delete metric;
}

11
tests/cpp/metric/test_metric.cc
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@@ -4,8 +4,11 @@
#include "../helpers.h"
TEST(Metric, UnknownMetric) {
EXPECT_ANY_THROW(xgboost::Metric::Create("unknown_name"));
EXPECT_NO_THROW(xgboost::Metric::Create("rmse"));
EXPECT_ANY_THROW(xgboost::Metric::Create("unknown_name@1"));
EXPECT_NO_THROW(xgboost::Metric::Create("error@0.5f"));
xgboost::Metric * metric;
EXPECT_ANY_THROW(metric = xgboost::Metric::Create("unknown_name"));
EXPECT_NO_THROW(metric = xgboost::Metric::Create("rmse"));
delete metric;
EXPECT_ANY_THROW(metric = xgboost::Metric::Create("unknown_name@1"));
EXPECT_NO_THROW(metric = xgboost::Metric::Create("error@0.5f"));
delete metric;
}

4
tests/cpp/metric/test_multiclass_metric.cc
View File

@@ -13,6 +13,8 @@ TEST(Metric, MultiClassError) {
{0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f},
{0, 1, 2}),
0.666f, 0.001f);
delete metric;
}
TEST(Metric, MultiClassLogLoss) {
@@ -25,4 +27,6 @@ TEST(Metric, MultiClassLogLoss) {
{0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f},
{0, 1, 2}),
2.302f, 0.001f);
delete metric;
}

11
tests/cpp/metric/test_rank_metric.cc
View File

@@ -17,6 +17,8 @@ TEST(Metric, AMS) {
metric = xgboost::Metric::Create("ams@0");
ASSERT_STREQ(metric->Name(), "ams@0");
EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}), 0.311f, 0.001f);
delete metric;
}
TEST(Metric, AUC) {
@@ -29,6 +31,8 @@ TEST(Metric, AUC) {
0.5f, 0.001f);
EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}));
EXPECT_ANY_THROW(GetMetricEval(metric, {0, 0}, {0, 0}));
delete metric;
}
TEST(Metric, AUCPR) {
@@ -50,6 +54,8 @@ TEST(Metric, AUCPR) {
0.2769199f, 0.001f);
EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}));
EXPECT_ANY_THROW(GetMetricEval(metric, {0, 0}, {0, 0}));
delete metric;
}
TEST(Metric, Precision) {
@@ -74,6 +80,8 @@ TEST(Metric, Precision) {
0.5f, 0.001f);
EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}));
delete metric;
}
TEST(Metric, NDCG) {
@@ -114,6 +122,8 @@ TEST(Metric, NDCG) {
{0.1f, 0.9f, 0.1f, 0.9f},
{ 0, 0, 1, 1}),
0.3868f, 0.001f);
delete metric;
}
TEST(Metric, MAP) {
@@ -139,4 +149,5 @@ TEST(Metric, MAP) {
{0.1f, 0.9f, 0.1f, 0.9f},
{ 0, 0, 1, 1}),
0.25f, 0.001f);
delete metric;
}

2
tests/cpp/objective/test_hinge.cc
View File

@@ -17,4 +17,6 @@ TEST(Objective, HingeObj) {
{ eps, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, eps });
ASSERT_NO_THROW(obj->DefaultEvalMetric());
delete obj;
}

6
tests/cpp/objective/test_objective.cc
View File

@@ -4,6 +4,8 @@
#include "../helpers.h"
TEST(Objective, UnknownFunction) {
EXPECT_ANY_THROW(xgboost::ObjFunction::Create("unknown_name"));
EXPECT_NO_THROW(xgboost::ObjFunction::Create("reg:linear"));
xgboost::ObjFunction* obj;
EXPECT_ANY_THROW(obj = xgboost::ObjFunction::Create("unknown_name"));
EXPECT_NO_THROW(obj = xgboost::ObjFunction::Create("reg:linear"));
delete obj;
}

4
tests/cpp/objective/test_ranking_obj.cc
View File

@@ -25,4 +25,6 @@ TEST(Objective, PairwiseRankingGPair) {
{0.9975f, 0.9975f, 0.9975f, 0.9975f});
ASSERT_NO_THROW(obj->DefaultEvalMetric());
}
delete obj;
}

22
tests/cpp/objective/test_regression_obj.cc
View File

@@ -15,6 +15,8 @@ TEST(Objective, LinearRegressionGPair) {
{1, 1, 1, 1, 1, 1, 1, 1});
ASSERT_NO_THROW(obj->DefaultEvalMetric());
delete obj;
}
TEST(Objective, LogisticRegressionGPair) {
@@ -27,6 +29,8 @@ TEST(Objective, LogisticRegressionGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
delete obj;
}
TEST(Objective, LogisticRegressionBasic) {
@@ -53,6 +57,8 @@ TEST(Objective, LogisticRegressionBasic) {
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
delete obj;
}
TEST(Objective, LogisticRawGPair) {
@@ -65,6 +71,8 @@ TEST(Objective, LogisticRawGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
delete obj;
}
TEST(Objective, PoissonRegressionGPair) {
@@ -78,6 +86,8 @@ TEST(Objective, PoissonRegressionGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
delete obj;
}
TEST(Objective, PoissonRegressionBasic) {
@@ -102,6 +112,8 @@ TEST(Objective, PoissonRegressionBasic) {
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
delete obj;
}
TEST(Objective, GammaRegressionGPair) {
@@ -114,6 +126,8 @@ TEST(Objective, GammaRegressionGPair) {
{1, 1, 1, 1, 1, 1, 1, 1},
{1, 1, 1, 1, 0, 0.09f, 0.59f, 0.63f},
{0, 0, 0, 0, 1, 0.90f, 0.40f, 0.36f});
delete obj;
}
TEST(Objective, GammaRegressionBasic) {
@@ -138,6 +152,8 @@ TEST(Objective, GammaRegressionBasic) {
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
delete obj;
}
TEST(Objective, TweedieRegressionGPair) {
@@ -151,6 +167,8 @@ TEST(Objective, TweedieRegressionGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
delete obj;
}
TEST(Objective, TweedieRegressionBasic) {
@@ -175,6 +193,8 @@ TEST(Objective, TweedieRegressionBasic) {
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
delete obj;
}
TEST(Objective, CoxRegressionGPair) {
@@ -187,4 +207,6 @@ TEST(Objective, CoxRegressionGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
{ 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
delete obj;
}

8
tests/cpp/objective/test_regression_obj_gpu.cu
View File

@@ -17,6 +17,8 @@ TEST(Objective, GPULinearRegressionGPair) {
{1, 1, 1, 1, 1, 1, 1, 1});
ASSERT_NO_THROW(obj->DefaultEvalMetric());
delete obj;
}
TEST(Objective, GPULogisticRegressionGPair) {
@@ -29,6 +31,8 @@ TEST(Objective, GPULogisticRegressionGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
delete obj;
}
TEST(Objective, GPULogisticRegressionBasic) {
@@ -55,6 +59,8 @@ TEST(Objective, GPULogisticRegressionBasic) {
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
delete obj;
}
TEST(Objective, GPULogisticRawGPair) {
@@ -67,4 +73,6 @@ TEST(Objective, GPULogisticRawGPair) {
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
delete obj;
}

12
tests/cpp/predictor/test_cpu_predictor.cc
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@@ -25,14 +25,14 @@ TEST(cpu_predictor, Test) {
// Test predict batch
HostDeviceVector<float> out_predictions;
cpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
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->RowIterator()->Value();
auto batch = (*dmat)->RowIterator()->Value();
for (int i = 0; i < batch.Size(); i++) {
std::vector<float> instance_out_predictions;
cpu_predictor->PredictInstance(batch[i], &instance_out_predictions, model);
@@ -41,22 +41,24 @@ TEST(cpu_predictor, Test) {
// Test predict leaf
std::vector<float> leaf_out_predictions;
cpu_predictor->PredictLeaf(dmat.get(), &leaf_out_predictions, model);
cpu_predictor->PredictLeaf((*dmat).get(), &leaf_out_predictions, model);
for (int i = 0; i < leaf_out_predictions.size(); i++) {
ASSERT_EQ(leaf_out_predictions[i], 0);
}
// Test predict contribution
std::vector<float> out_contribution;
cpu_predictor->PredictContribution(dmat.get(), &out_contribution, model);
cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model);
for (int i = 0; i < out_contribution.size(); i++) {
ASSERT_EQ(out_contribution[i], 1.5);
}
// Test predict contribution (approximate method)
cpu_predictor->PredictContribution(dmat.get(), &out_contribution, model, true);
cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model, true);
for (int i = 0; i < out_contribution.size(); i++) {
ASSERT_EQ(out_contribution[i], 1.5);
}
delete dmat;
}
} // namespace xgboost

16
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -35,8 +35,8 @@ TEST(gpu_predictor, Test) {
// Test predict batch
HostDeviceVector<float> gpu_out_predictions;
HostDeviceVector<float> cpu_out_predictions;
gpu_predictor->PredictBatch(dmat.get(), &gpu_out_predictions, model, 0);
cpu_predictor->PredictBatch(dmat.get(), &cpu_out_predictions, model, 0);
gpu_predictor->PredictBatch((*dmat).get(), &gpu_out_predictions, model, 0);
cpu_predictor->PredictBatch((*dmat).get(), &cpu_out_predictions, model, 0);
std::vector<float>& gpu_out_predictions_h = gpu_out_predictions.HostVector();
std::vector<float>& cpu_out_predictions_h = cpu_out_predictions.HostVector();
float abs_tolerance = 0.001;
@@ -45,7 +45,7 @@ TEST(gpu_predictor, Test) {
abs_tolerance);
}
// Test predict instance
auto batch = dmat->RowIterator()->Value();
auto batch = (*dmat)->RowIterator()->Value();
for (int i = 0; i < batch.Size(); i++) {
std::vector<float> gpu_instance_out_predictions;
std::vector<float> cpu_instance_out_predictions;
@@ -59,8 +59,8 @@ TEST(gpu_predictor, Test) {
// Test predict leaf
std::vector<float> gpu_leaf_out_predictions;
std::vector<float> cpu_leaf_out_predictions;
cpu_predictor->PredictLeaf(dmat.get(), &cpu_leaf_out_predictions, model);
gpu_predictor->PredictLeaf(dmat.get(), &gpu_leaf_out_predictions, model);
cpu_predictor->PredictLeaf((*dmat).get(), &cpu_leaf_out_predictions, model);
gpu_predictor->PredictLeaf((*dmat).get(), &gpu_leaf_out_predictions, model);
for (int i = 0; i < gpu_leaf_out_predictions.size(); i++) {
ASSERT_EQ(gpu_leaf_out_predictions[i], cpu_leaf_out_predictions[i]);
}
@@ -68,11 +68,13 @@ TEST(gpu_predictor, Test) {
// Test predict contribution
std::vector<float> gpu_out_contribution;
std::vector<float> cpu_out_contribution;
cpu_predictor->PredictContribution(dmat.get(), &cpu_out_contribution, model);
gpu_predictor->PredictContribution(dmat.get(), &gpu_out_contribution, model);
cpu_predictor->PredictContribution((*dmat).get(), &cpu_out_contribution, model);
gpu_predictor->PredictContribution((*dmat).get(), &gpu_out_contribution, model);
for (int i = 0; i < gpu_out_contribution.size(); i++) {
ASSERT_EQ(gpu_out_contribution[i], cpu_out_contribution[i]);
}
delete dmat;
}
} // namespace predictor
} // namespace xgboost

8
tests/cpp/test_learner.cc
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@@ -1,5 +1,6 @@
// Copyright by Contributors
#include <gtest/gtest.h>
#include <vector>
#include "helpers.h"
#include "xgboost/learner.h"
@@ -7,8 +8,11 @@ namespace xgboost {
TEST(learner, Test) {
typedef std::pair<std::string, std::string> arg;
auto args = {arg("tree_method", "exact")};
auto mat = {CreateDMatrix(10, 10, 0)};
auto mat_ptr = CreateDMatrix(10, 10, 0);
std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
learner->Configure(args);
delete mat_ptr;
}
} // namespace xgboost
} // namespace xgboost

12
tests/cpp/tree/test_gpu_hist.cu
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@@ -19,11 +19,11 @@ TEST(gpu_hist_experimental, TestSparseShard) {
int max_bins = 4;
auto dmat = CreateDMatrix(rows, columns, 0.9f);
common::GHistIndexMatrix gmat;
gmat.Init(dmat.get(),max_bins);
gmat.Init((*dmat).get(),max_bins);
TrainParam p;
p.max_depth = 6;
dmlc::DataIter<SparsePage>* iter = dmat->RowIterator();
dmlc::DataIter<SparsePage>* iter = (*dmat)->RowIterator();
iter->BeforeFirst();
CHECK(iter->Next());
const SparsePage& batch = iter->Value();
@@ -50,6 +50,8 @@ TEST(gpu_hist_experimental, TestSparseShard) {
ASSERT_EQ(gidx[i * shard.row_stride + row_offset], shard.null_gidx_value);
}
}
delete dmat;
}
TEST(gpu_hist_experimental, TestDenseShard) {
@@ -58,11 +60,11 @@ TEST(gpu_hist_experimental, TestDenseShard) {
int max_bins = 4;
auto dmat = CreateDMatrix(rows, columns, 0);
common::GHistIndexMatrix gmat;
gmat.Init(dmat.get(),max_bins);
gmat.Init((*dmat).get(),max_bins);
TrainParam p;
p.max_depth = 6;
dmlc::DataIter<SparsePage>* iter = dmat->RowIterator();
dmlc::DataIter<SparsePage>* iter = (*dmat)->RowIterator();
iter->BeforeFirst();
CHECK(iter->Next());
const SparsePage& batch = iter->Value();
@@ -82,6 +84,8 @@ TEST(gpu_hist_experimental, TestDenseShard) {
for (int i = 0; i < gmat.index.size(); i++) {
ASSERT_EQ(gidx[i], gmat.index[i]);
}
delete dmat;
}
} // namespace tree