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Ricardo Montañana Gómez
2025-04-30 11:11:49 +02:00
commit e144d65e11
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sample/CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 3.20)
project(bayesnet_sample)
set(CMAKE_CXX_STANDARD 17)
find_package(Torch CONFIG REQUIRED)
find_package(bayesnet CONFIG REQUIRED)
find_package(fimdlp CONFIG REQUIRED)
find_package(folding CONFIG REQUIRED)
find_package(arff-files CONFIG REQUIRED)
find_package(nlohman_json CONFIG REQUIRED)
add_executable(bayesnet_sample sample.cc)
target_link_libraries(bayesnet_sample PRIVATE
fimdlp::fimdlp
arff-files::arff-files
"${TORCH_LIBRARIES}"
bayesnet::bayesnet
nlohmann_json::nlohmann_json
folding::folding
)

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sample/sample.cc Normal file
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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <ArffFiles/ArffFiles.hpp>
#include <fimdlp/CPPFImdlp.h>
#include <bayesnet/ensembles/XBAODE.h>
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
std::vector<mdlp::labels_t> Xd;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
mdlp::labels_t& xd = fimdlp.transform(X[i]);
Xd.push_back(xd);
}
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)
{
auto handler = ArffFiles();
handler.load(name, class_last);
// Get Dataset X, y
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
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);
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));
}
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 };
}
int main(int argc, char* argv[])
{
if (argc < 2) {
std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
return 1;
}
std::string file_name = argv[1];
torch::Tensor X, y;
std::vector<std::string> features;
std::string className;
map<std::string, std::vector<int>> states;
auto clf = bayesnet::XBAODE(); // 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);
torch::Tensor weights = torch::full({ X.size(1) }, 15, torch::kDouble);
torch::Tensor dataset;
try {
auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
dataset = torch::cat({ X, yresized }, 0);
}
catch (const std::exception& e) {
std::stringstream oss;
oss << "* Error in X and y dimensions *\n";
oss << "X dimensions: " << dataset.sizes() << "\n";
oss << "y dimensions: " << y.sizes();
throw std::runtime_error(oss.str());
}
clf.fit(dataset, features, className, states, weights, bayesnet::Smoothing_t::LAPLACE);
auto score = clf.score(X, y);
std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
return 0;
}

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sample/sample_xspode.cc Normal file
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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <ArffFiles/ArffFiles.hpp>
#include <fimdlp/CPPFImdlp.h>
#include <bayesnet/ensembles/BoostAODE.h>
#include <bayesnet/classifiers/XSPODE.h>
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
std::vector<mdlp::labels_t> Xd;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
mdlp::labels_t& xd = fimdlp.transform(X[i]);
Xd.push_back(xd);
}
return Xd;
}
tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(name, class_last);
// Get Dataset X, y
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<std::string, std::vector<int>>();
auto Xr = discretizeDataset(X, y);
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);
}
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states.at(className)), end(states.at(className)), 0);
return { Xr, y, features, className, states };
}
int main(int argc, char* argv[])
{
if (argc < 2) {
std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
return 1;
}
std::string file_name = argv[1];
bayesnet::BaseClassifier* clf = new bayesnet::XSpode(0);
std::cout << "Library version: " << clf->getVersion() << std::endl;
auto [X, y, features, className, states] = loadDataset(file_name, true);
torch::Tensor weights = torch::full({ static_cast<long>(X[0].size()) }, 1.0 / X[0].size(), torch::kDouble);
clf->fit(X, y, features, className, states, bayesnet::Smoothing_t::ORIGINAL);
auto score = clf->score(X, y);
std::cout << "File: " << file_name << " Model: XSpode(0) score: " << score << std::endl;
delete clf;
return 0;
}

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sample/vcpkg-configuration.json Normal file
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{
"registries": [
{
"kind": "git",
"repository": "https://github.com/rmontanana/vcpkg-stash",
"baseline": "393efa4e74e053b6f02c4ab03738c8fe796b28e5",
"packages": [
"folding",
"bayesnet",
"arff-files",
"fimdlp",
"libtorch-bin"
]
}
],
"default-registry": {
"kind": "git",
"repository": "https://github.com/microsoft/vcpkg",
"baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605"
}
}

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sample/vcpkg.json Normal file
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{
"name": "sample-project",
"version-string": "0.1.0",
"dependencies": [
"bayesnet",
"folding",
"arff-files",
"fimdlp",
"nlohmann-json",
"libtorch-bin"
]
}