82 lines
3.3 KiB
C++
82 lines
3.3 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <ArffFiles/ArffFiles.hpp>
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#include <fimdlp/CPPFImdlp.h>
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#include <bayesnet/ensembles/XBAODE.h>
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std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
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{
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std::vector<mdlp::labels_t> Xd;
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auto fimdlp = mdlp::CPPFImdlp();
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for (int i = 0; i < X.size(); i++) {
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fimdlp.fit(X[i], y);
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mdlp::labels_t& xd = fimdlp.transform(X[i]);
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Xd.push_back(xd);
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}
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return Xd;
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}
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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)
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{
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auto handler = ArffFiles();
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handler.load(name, class_last);
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// Get Dataset X, y
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std::vector<mdlp::samples_t>& X = handler.getX();
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mdlp::labels_t& y = handler.getY();
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// Get className & Features
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auto className = handler.getClassName();
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std::vector<std::string> features;
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auto attributes = handler.getAttributes();
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transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
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torch::Tensor Xd;
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auto states = map<std::string, std::vector<int>>();
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auto Xr = discretizeDataset(X, y);
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Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
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for (int i = 0; i < features.size(); ++i) {
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states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
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auto item = states.at(features[i]);
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iota(begin(item), end(item), 0);
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Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
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}
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states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
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iota(begin(states.at(className)), end(states.at(className)), 0);
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return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
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}
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int main(int argc, char* argv[])
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{
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if (argc < 2) {
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std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
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return 1;
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}
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std::string file_name = argv[1];
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torch::Tensor X, y;
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std::vector<std::string> features;
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std::string className;
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map<std::string, std::vector<int>> states;
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auto clf = bayesnet::XBAODE(); // false for not using voting in predict
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std::cout << "Library version: " << clf.getVersion() << std::endl;
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tie(X, y, features, className, states) = loadDataset(file_name, true);
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torch::Tensor weights = torch::full({ X.size(1) }, 15, torch::kDouble);
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torch::Tensor dataset;
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try {
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auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
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dataset = torch::cat({ X, yresized }, 0);
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}
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catch (const std::exception& e) {
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std::stringstream oss;
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oss << "* Error in X and y dimensions *\n";
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oss << "X dimensions: " << dataset.sizes() << "\n";
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oss << "y dimensions: " << y.sizes();
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throw std::runtime_error(oss.str());
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}
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clf.fit(dataset, features, className, states, weights, bayesnet::Smoothing_t::LAPLACE);
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auto score = clf.score(X, y);
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std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
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return 0;
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}
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