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Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Add Cuda iniitialization in Classifier

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This commit is contained in:
Ricardo Montañana Gómez
2024-09-18 12:13:11 +02:00
parent d0955d9369
commit baa631dd66
5 changed files with 36 additions and 14 deletions
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22
sample/sample.cc
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@@ -7,6 +7,7 @@
#include <ArffFiles.hpp>
#include <CPPFImdlp.h>
#include <bayesnet/ensembles/BoostAODE.h>
#include <torch/torch.h>
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
@@ -19,7 +20,8 @@ std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, m
}
return Xd;
}
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, torch::Device device)
{
auto handler = ArffFiles();
handler.load(name, class_last);
@@ -34,16 +36,16 @@ tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<s
torch::Tensor Xd;
auto states = map<std::string, std::vector<int>>();
auto Xr = discretizeDataset(X, y);
Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32).to(device);
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32).to(device));
}
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states.at(className)), end(states.at(className)), 0);
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
return { Xd, torch::tensor(y, torch::kInt32).to(device), features, className, states };
}
int main(int argc, char* argv[])
@@ -53,16 +55,22 @@ int main(int argc, char* argv[])
return 1;
}
std::string file_name = argv[1];
torch::Device device(torch::kCPU);
if (torch::cuda::is_available()) {
device = torch::Device(torch::kCUDA);
std::cout << "CUDA is available! Using GPU." << std::endl;
} else {
std::cout << "CUDA is not available. Using CPU." << std::endl;
}
torch::Tensor X, y;
std::vector<std::string> features;
std::string className;
map<std::string, std::vector<int>> states;
auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
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, device);
clf.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
auto score = clf.score(X, y);
std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
return 0;
}
}