105 lines
4.0 KiB
C++
105 lines
4.0 KiB
C++
#include "TestUtils.h"
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class Paths {
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public:
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static std::string datasets()
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{
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return "../../data/";
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}
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};
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pair<std::vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<std::string> features)
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{
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std::vector<mdlp::labels_t> Xd;
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map<std::string, int> maxes;
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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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maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
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Xd.push_back(xd);
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}
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return { Xd, maxes };
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}
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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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bool file_exists(const std::std::string& name)
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{
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if (FILE* file = fopen(name.c_str(), "r")) {
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fclose(file);
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return true;
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} else {
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return false;
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}
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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::std::string& name, bool class_last, bool discretize_dataset)
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{
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auto handler = ArffFiles();
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handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff", 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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if (discretize_dataset) {
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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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} else {
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Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
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for (int i = 0; i < features.size(); ++i) {
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Xd.index_put_({ i, "..." }, torch::tensor(X[i]));
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}
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}
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return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
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}
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tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::std::string& name)
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{
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auto handler = ArffFiles();
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handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff");
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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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// Discretize Dataset
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std::vector<mdlp::labels_t> Xd;
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map<std::string, int> maxes;
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tie(Xd, maxes) = discretize(X, y, features);
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maxes[className] = *max_element(y.begin(), y.end()) + 1;
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map<std::string, std::vector<int>> states;
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for (auto feature : features) {
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states[feature] = std::vector<int>(maxes[feature]);
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}
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states[className] = std::vector<int>(maxes[className]);
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return { Xd, y, features, className, states };
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}
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