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Add sample_xspode

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Ricardo Montañana Gómez
2025-03-10 21:37:14 +01:00
parent 5919fbfd34
commit e681099360
3 changed files with 89 additions and 2 deletions
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10
Makefile
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@@ -99,7 +99,15 @@ sample: ## Build sample
@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample
sample/build/bayesnet_sample $(fname)
@echo ">>> Done";
@echo ">>> Done";
fname = "tests/data/iris.arff"
sample2: ## Build sample2
@echo ">>> Building Sample...";
@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample_xspode
sample/build/bayesnet_sample_xspode $(fname)
@echo ">>> Done";
opt = ""
test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section

4
sample/CMakeLists.txt
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@@ -22,4 +22,6 @@ include_directories(
)
add_executable(bayesnet_sample sample.cc)
target_link_libraries(bayesnet_sample ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
target_link_libraries(bayesnet_sample ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
add_executable(bayesnet_sample_xspode sample_xspode.cc)
target_link_libraries(bayesnet_sample_xspode ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")

77
sample/sample_xspode.cc Normal file
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@@ -0,0 +1,77 @@
// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <ArffFiles.hpp>
#include <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 };
// 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];
// auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
bayesnet::BaseClassifier* clf = new bayesnet::XSpode(0); // false for not using voting in predict
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);
auto score = clf->score(X, y);
std::cout << "File: " << file_name << " Model: XSpode(0) score: " << score << std::endl;
delete clf;
return 0;
}