rmontanana/BayesNet
rmontanana/BayesNet
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Add thread control to vectors predict

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This commit is contained in:
Ricardo Montañana Gómez 2024-06-23 13:02:40 +02:00
parent 59c1cf5b3b
commit 9a14133be5
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
3 changed files with 32 additions and 13 deletions
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39
bayesnet/network/Network.cc
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@ -292,17 +292,18 @@ namespace bayesnet {
std::vector<int> predictions(tsamples[0].size(), 0); std::vector<int> predictions(tsamples[0].size(), 0);
std::vector<int> sample; std::vector<int> sample;
std::vector<std::thread> threads; std::vector<std::thread> threads;
std::mutex mtx;
auto& semaphore = CountingSemaphore::getInstance(); auto& semaphore = CountingSemaphore::getInstance();
auto worker = [&](const std::vector<int>& sample, const int row, std::vector<int>& predictions) { auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
semaphore.acquire(); std::string threadName = "(V)PWorker-" + std::to_string(row);
#if defined(__linux__)
pthread_setname_np(pthread_self(), threadName.c_str());
#else
pthread_setname_np(threadName.c_str());
#endif
auto classProbabilities = predict_sample(sample); auto classProbabilities = predict_sample(sample);
auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end()); auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
int predictedClass = distance(classProbabilities.begin(), maxElem); int predictedClass = distance(classProbabilities.begin(), maxElem);
{ prediction = predictedClass;
std::lock_guard<std::mutex> lock(mtx);
predictions[row] = predictedClass;
}
semaphore.release(); semaphore.release();
}; };
for (int row = 0; row < tsamples[0].size(); ++row) { for (int row = 0; row < tsamples[0].size(); ++row) {
@ -310,7 +311,8 @@ namespace bayesnet {
for (int col = 0; col < tsamples.size(); ++col) { for (int col = 0; col < tsamples.size(); ++col) {
sample.push_back(tsamples[col][row]); sample.push_back(tsamples[col][row]);
} }
threads.emplace_back(worker, sample, row, std::ref(predictions)); semaphore.acquire();
threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
} }
for (auto& thread : threads) { for (auto& thread : threads) {
thread.join(); thread.join();
@ -329,14 +331,31 @@ namespace bayesnet {
throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) + throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
") does not match the number of features (" + std::to_string(features.size() - 1) + ")"); ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
} }
std::vector<std::vector<double>> predictions; std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
std::vector<int> sample; std::vector<int> sample;
std::vector<std::thread> threads;
auto& semaphore = CountingSemaphore::getInstance();
auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
std::string threadName = "(V)PWorker-" + std::to_string(row);
#if defined(__linux__)
pthread_setname_np(pthread_self(), threadName.c_str());
#else
pthread_setname_np(threadName.c_str());
#endif
std::vector<double> classProbabilities = predict_sample(sample);
predictions = classProbabilities;
semaphore.release();
};
for (int row = 0; row < tsamples[0].size(); ++row) { for (int row = 0; row < tsamples[0].size(); ++row) {
sample.clear(); sample.clear();
for (int col = 0; col < tsamples.size(); ++col) { for (int col = 0; col < tsamples.size(); ++col) {
sample.push_back(tsamples[col][row]); sample.push_back(tsamples[col][row]);
} }
predictions.push_back(predict_sample(sample)); semaphore.acquire();
threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
}
for (auto& thread : threads) {
thread.join();
} }
return predictions; return predictions;
} }

2
sample/CMakeLists.txt
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@ -8,7 +8,7 @@ find_package(Torch REQUIRED)
find_library(BayesNet NAMES BayesNet.a libBayesNet.a REQUIRED) find_library(BayesNet NAMES BayesNet.a libBayesNet.a REQUIRED)
include_directories( include_directories(
lib/Files ../tests/lib/Files
lib/mdlp lib/mdlp
lib/json/include lib/json/include
/usr/local/include /usr/local/include

4
sample/sample.cc
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@ -60,9 +60,9 @@ int main(int argc, char* argv[])
auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
std::cout << "Library version: " << clf.getVersion() << std::endl; std::cout << "Library version: " << clf.getVersion() << std::endl;
tie(X, y, features, className, states) = loadDataset(file_name, true); tie(X, y, features, className, states) = loadDataset(file_name, true);
clf.fit(X, y, features, className, states); clf.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
auto score = clf.score(X, y); auto score = clf.score(X, y);
std::cout << "File: " << file_name << " score: " << score << std::endl; std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
return 0; return 0;
} }