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Refactor CountingSemaphore as singleton

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-21 09:30:24 +02:00
parent 716748e18c
commit 02bcab01be
5 changed files with 79 additions and 91 deletions
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51
bayesnet/ensembles/Ensemble.cc
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@@ -3,14 +3,13 @@
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "Ensemble.h"
#include "bayesnet/utils/CountingSemaphore.h"
namespace bayesnet {
Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
{
};
const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
@@ -85,17 +84,9 @@ namespace bayesnet {
{
auto n_states = models[0]->getClassNumStates();
torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
auto threads{ std::vector<std::thread>() };
std::mutex mtx;
for (auto i = 0; i < n_models; ++i) {
threads.push_back(std::thread([&, i]() {
auto ypredict = models[i]->predict_proba(X);
std::lock_guard<std::mutex> lock(mtx);
y_pred += ypredict * significanceModels[i];
}));
}
for (auto& thread : threads) {
thread.join();
auto ypredict = models[i]->predict_proba(X);
y_pred += ypredict * significanceModels[i];
}
auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
y_pred /= sum;
@@ -105,23 +96,15 @@ namespace bayesnet {
{
auto n_states = models[0]->getClassNumStates();
std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
auto threads{ std::vector<std::thread>() };
std::mutex mtx;
for (auto i = 0; i < n_models; ++i) {
threads.push_back(std::thread([&, i]() {
auto ypredict = models[i]->predict_proba(X);
assert(ypredict.size() == y_pred.size());
assert(ypredict[0].size() == y_pred[0].size());
std::lock_guard<std::mutex> lock(mtx);
// Multiply each prediction by the significance of the model and then add it to the final prediction
for (auto j = 0; j < ypredict.size(); ++j) {
std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
[significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
}
}));
}
for (auto& thread : threads) {
thread.join();
auto ypredict = models[i]->predict_proba(X);
assert(ypredict.size() == y_pred.size());
assert(ypredict[0].size() == y_pred[0].size());
// Multiply each prediction by the significance of the model and then add it to the final prediction
for (auto j = 0; j < ypredict.size(); ++j) {
std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
[significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
}
}
auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
//Divide each element of the prediction by the sum of the significances
@@ -141,17 +124,9 @@ namespace bayesnet {
{
// Build a m x n_models tensor with the predictions of each model
torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
auto threads{ std::vector<std::thread>() };
std::mutex mtx;
for (auto i = 0; i < n_models; ++i) {
threads.push_back(std::thread([&, i]() {
auto ypredict = models[i]->predict(X);
std::lock_guard<std::mutex> lock(mtx);
y_pred.index_put_({ "...", i }, ypredict);
}));
}
for (auto& thread : threads) {
thread.join();
auto ypredict = models[i]->predict(X);
y_pred.index_put_({ "...", i }, ypredict);
}
return voting(y_pred);
}