Skip to content

Commit

Permalink
#6778 Reverted indentation to 4 spaces
Browse files Browse the repository at this point in the history
  • Loading branch information
@AndreyOrb
AndreyOrb committed Jan 20, 2025
1 parent e9836f4 commit 8d3261d
Showing 1 changed file with 155 additions and 155 deletions.
310 changes: 155 additions & 155 deletions tests/cpp_tests/test_single_row.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -13,175 +13,175 @@
using LightGBM::TestUtils;

void test_predict_type(int predict_type, int num_predicts) {
// Load some test data
int result;

DatasetHandle train_dataset;
result = TestUtils::LoadDatasetFromExamples("binary_classification/binary.train", "max_bin=15", &train_dataset);
EXPECT_EQ(0, result) << "LoadDatasetFromExamples train result code: " << result;

BoosterHandle booster_handle;
result = LGBM_BoosterCreate(train_dataset, "app=binary metric=auc num_leaves=31 verbose=0", &booster_handle);
EXPECT_EQ(0, result) << "LGBM_BoosterCreate result code: " << result;

for (int i = 0; i < 51; i++) {
int is_finished;
result = LGBM_BoosterUpdateOneIter(
booster_handle,
&is_finished);
EXPECT_EQ(0, result) << "LGBM_BoosterUpdateOneIter result code: " << result;
}

int n_features;
result = LGBM_BoosterGetNumFeature(
booster_handle,
&n_features);
EXPECT_EQ(0, result) << "LGBM_BoosterGetNumFeature result code: " << result;
EXPECT_EQ(28, n_features) << "LGBM_BoosterGetNumFeature number of features: " << n_features;

// Run a single row prediction and compare with regular Mat prediction:
int64_t output_size;
result = LGBM_BoosterCalcNumPredict(
booster_handle,
1,
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
&output_size);
EXPECT_EQ(0, result) << "LGBM_BoosterCalcNumPredict result code: " << result;
EXPECT_EQ(num_predicts, output_size) << "LGBM_BoosterCalcNumPredict output size: " << output_size;

std::ifstream test_file("examples/binary_classification/binary.test");
std::vector<double> test;
double x;
int test_set_size = 0;
while (test_file >> x) {
if (test_set_size % (n_features + 1) == 0) {
// Drop the result from the dataset, we only care about checking that prediction results are equal
// in both cases
test_file >> x;
test_set_size++;
// Load some test data
int result;

DatasetHandle train_dataset;
result = TestUtils::LoadDatasetFromExamples("binary_classification/binary.train", "max_bin=15", &train_dataset);
EXPECT_EQ(0, result) << "LoadDatasetFromExamples train result code: " << result;

BoosterHandle booster_handle;
result = LGBM_BoosterCreate(train_dataset, "app=binary metric=auc num_leaves=31 verbose=0", &booster_handle);
EXPECT_EQ(0, result) << "LGBM_BoosterCreate result code: " << result;

for (int i = 0; i < 51; i++) {
int is_finished;
result = LGBM_BoosterUpdateOneIter(
booster_handle,
&is_finished);
EXPECT_EQ(0, result) << "LGBM_BoosterUpdateOneIter result code: " << result;
}

int n_features;
result = LGBM_BoosterGetNumFeature(
booster_handle,
&n_features);
EXPECT_EQ(0, result) << "LGBM_BoosterGetNumFeature result code: " << result;
EXPECT_EQ(28, n_features) << "LGBM_BoosterGetNumFeature number of features: " << n_features;

// Run a single row prediction and compare with regular Mat prediction:
int64_t output_size;
result = LGBM_BoosterCalcNumPredict(
booster_handle,
1,
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
&output_size);
EXPECT_EQ(0, result) << "LGBM_BoosterCalcNumPredict result code: " << result;
EXPECT_EQ(num_predicts, output_size) << "LGBM_BoosterCalcNumPredict output size: " << output_size;

std::ifstream test_file("examples/binary_classification/binary.test");
std::vector<double> test;
double x;
int test_set_size = 0;
while (test_file >> x) {
if (test_set_size % (n_features + 1) == 0) {
// Drop the result from the dataset, we only care about checking that prediction results are equal
// in both cases
test_file >> x;
test_set_size++;
}
test.push_back(x);
test_set_size++;
}
EXPECT_EQ(test_set_size % (n_features + 1), 0) << "Test size mismatch with dataset size (%)";
test_set_size /= (n_features + 1);
EXPECT_EQ(test_set_size, 500) << "Improperly parsed test file (test_set_size)";
EXPECT_EQ(test.size(), test_set_size * n_features) << "Improperly parsed test file (test len)";

std::vector<double> mat_output(output_size * test_set_size, -1);
int64_t written;
result = LGBM_BoosterPredictForMat(
booster_handle,
&test[0],
C_API_DTYPE_FLOAT64,
test_set_size, // nrow
n_features, // ncol
1, // is_row_major
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
"",
&written,
&mat_output[0]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMat result code: " << result;

// Test LGBM_BoosterPredictForMat in multi-threaded mode
const int kNThreads = 10;
const int numIterations = 5;
std::vector<std::thread> predict_for_mat_threads(kNThreads);
for (int i = 0; i < kNThreads; i++) {
predict_for_mat_threads[i] = std::thread(
[
i, test_set_size, output_size, n_features,
test = &test[0], booster_handle, predict_type, numIterations
]() {
for (int j = 0; j < numIterations; j++) {
int result;
std::vector<double> mat_output(output_size * test_set_size, -1);
int64_t written;
result = LGBM_BoosterPredictForMat(
booster_handle,
&test[0],
C_API_DTYPE_FLOAT64,
test_set_size, // nrow
n_features, // ncol
1, // is_row_major
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
"",
&written,
&mat_output[0]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMat result code: " << result;
}
});
}
for (std::thread& t : predict_for_mat_threads) {
t.join();
}
test.push_back(x);
test_set_size++;
}
EXPECT_EQ(test_set_size % (n_features + 1), 0) << "Test size mismatch with dataset size (%)";
test_set_size /= (n_features + 1);
EXPECT_EQ(test_set_size, 500) << "Improperly parsed test file (test_set_size)";
EXPECT_EQ(test.size(), test_set_size * n_features) << "Improperly parsed test file (test len)";

std::vector<double> mat_output(output_size * test_set_size, -1);
int64_t written;
result = LGBM_BoosterPredictForMat(
booster_handle,
&test[0],
C_API_DTYPE_FLOAT64,
test_set_size, // nrow
n_features, // ncol
1, // is_row_major
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
"",
&written,
&mat_output[0]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMat result code: " << result;

// Test LGBM_BoosterPredictForMat in multi-threaded mode
const int kNThreads = 10;
const int numIterations = 5;
std::vector<std::thread> predict_for_mat_threads(kNThreads);
for (int i = 0; i < kNThreads; i++) {
predict_for_mat_threads[i] = std::thread(
[
i, test_set_size, output_size, n_features,
test = &test[0], booster_handle, predict_type, numIterations
]() {
for (int j = 0; j < numIterations; j++) {
int result;
std::vector<double> mat_output(output_size * test_set_size, -1);
int64_t written;
result = LGBM_BoosterPredictForMat(

// Now let's run with the single row fast prediction API:
FastConfigHandle fast_configs[kNThreads];
for (int i = 0; i < kNThreads; i++) {
result = LGBM_BoosterPredictForMatSingleRowFastInit(
booster_handle,
&test[0],
C_API_DTYPE_FLOAT64,
test_set_size, // nrow
n_features, // ncol
1, // is_row_major
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
C_API_DTYPE_FLOAT64,
n_features,
"",
&written,
&mat_output[0]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMat result code: " << result;
}
});
}
for (std::thread& t : predict_for_mat_threads) {
t.join();
}

// Now let's run with the single row fast prediction API:
FastConfigHandle fast_configs[kNThreads];
for (int i = 0; i < kNThreads; i++) {
result = LGBM_BoosterPredictForMatSingleRowFastInit(
booster_handle,
predict_type, // predict_type
0, // start_iteration
-1, // num_iteration
C_API_DTYPE_FLOAT64,
n_features,
"",
&fast_configs[i]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMatSingleRowFastInit result code: " << result;
}

std::vector<double> single_row_output(output_size * test_set_size, -1);
std::vector<std::thread> single_row_threads(kNThreads);
int batch_size = (test_set_size + kNThreads - 1) / kNThreads; // round up
for (int i = 0; i < kNThreads; i++) {
single_row_threads[i] = std::thread(
[
i, batch_size, test_set_size, output_size, n_features,
test = &test[0], fast_configs = &fast_configs[0], single_row_output = &single_row_output[0]
]() {
int result;
int64_t written;
for (int j = i * batch_size; j < std::min((i + 1) * batch_size, test_set_size); j++) {
result = LGBM_BoosterPredictForMatSingleRowFast(
fast_configs[i],
&test[j * n_features],
&written,
&single_row_output[j * output_size]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMatSingleRowFast result code: " << result;
EXPECT_EQ(written, output_size) << "LGBM_BoosterPredictForMatSingleRowFast unexpected written output size";
}
});
}
for (std::thread& t : single_row_threads) {
t.join();
}
&fast_configs[i]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMatSingleRowFastInit result code: " << result;
}

std::vector<double> single_row_output(output_size * test_set_size, -1);
std::vector<std::thread> single_row_threads(kNThreads);
int batch_size = (test_set_size + kNThreads - 1) / kNThreads; // round up
for (int i = 0; i < kNThreads; i++) {
single_row_threads[i] = std::thread(
[
i, batch_size, test_set_size, output_size, n_features,
test = &test[0], fast_configs = &fast_configs[0], single_row_output = &single_row_output[0]
]() {
int result;
int64_t written;
for (int j = i * batch_size; j < std::min((i + 1) * batch_size, test_set_size); j++) {
result = LGBM_BoosterPredictForMatSingleRowFast(
fast_configs[i],
&test[j * n_features],
&written,
&single_row_output[j * output_size]);
EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMatSingleRowFast result code: " << result;
EXPECT_EQ(written, output_size) << "LGBM_BoosterPredictForMatSingleRowFast unexpected written output size";
}
});
}
for (std::thread& t : single_row_threads) {
t.join();
}

EXPECT_EQ(single_row_output, mat_output) << "LGBM_BoosterPredictForMatSingleRowFast output mismatch with LGBM_BoosterPredictForMat";
EXPECT_EQ(single_row_output, mat_output) << "LGBM_BoosterPredictForMatSingleRowFast output mismatch with LGBM_BoosterPredictForMat";

// Free all:
for (int i = 0; i < kNThreads; i++) {
result = LGBM_FastConfigFree(fast_configs[i]);
EXPECT_EQ(0, result) << "LGBM_FastConfigFree result code: " << result;
}
// Free all:
for (int i = 0; i < kNThreads; i++) {
result = LGBM_FastConfigFree(fast_configs[i]);
EXPECT_EQ(0, result) << "LGBM_FastConfigFree result code: " << result;
}

result = LGBM_BoosterFree(booster_handle);
EXPECT_EQ(0, result) << "LGBM_BoosterFree result code: " << result;
result = LGBM_BoosterFree(booster_handle);
EXPECT_EQ(0, result) << "LGBM_BoosterFree result code: " << result;

result = LGBM_DatasetFree(train_dataset);
EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result;
result = LGBM_DatasetFree(train_dataset);
EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result;
}

TEST(SingleRow, Normal) {
test_predict_type(C_API_PREDICT_NORMAL, 1);
test_predict_type(C_API_PREDICT_NORMAL, 1);
}

TEST(SingleRow, Contrib) {
test_predict_type(C_API_PREDICT_CONTRIB, 29);
test_predict_type(C_API_PREDICT_CONTRIB, 29);
}

0 comments on commit 8d3261d

Please sign in to comment.