Remove old Files library
This commit is contained in:
@@ -12,4 +12,4 @@ include_directories(
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${Bayesnet_INCLUDE_DIRS}
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)
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add_executable(PlatformSample sample.cpp ${Platform_SOURCE_DIR}/src/main/Models.cpp)
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target_link_libraries(PlatformSample "${PyClassifiers}" "${BayesNet}" ArffFiles mdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
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target_link_libraries(PlatformSample "${PyClassifiers}" "${BayesNet}" mdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
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@@ -5,7 +5,7 @@
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#include <torch/torch.h>
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#include <argparse/argparse.hpp>
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#include <nlohmann/json.hpp>
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#include <ArffFiles.h>
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#include <ArffFiles.hpp>
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#include <CPPFImdlp.h>
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#include <folding.hpp>
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#include <bayesnet/utils/BayesMetrics.h>
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@@ -79,11 +79,11 @@ int main(int argc, char** argv)
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}
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throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
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}
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);
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);
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program.add_argument("-p", "--path")
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.help(" folder where the data files are located, default")
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.default_value(std::string{ PATH }
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);
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);
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program.add_argument("-m", "--model")
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.help("Model to use " + platform::Models::instance()->toString())
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.action([](const std::string& value) {
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@@ -93,7 +93,7 @@ int main(int argc, char** argv)
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}
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throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
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}
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);
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);
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program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
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program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
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program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
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@@ -112,129 +112,129 @@ int main(int argc, char** argv)
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catch (...) {
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throw runtime_error("Number of folds must be an integer");
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}});
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program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
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bool class_last, stratified, tensors, dump_cpt;
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std::string model_name, file_name, path, complete_file_name;
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int nFolds, seed;
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try {
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program.parse_args(argc, argv);
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file_name = program.get<std::string>("dataset");
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path = program.get<std::string>("path");
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model_name = program.get<std::string>("model");
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complete_file_name = path + file_name + ".arff";
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stratified = program.get<bool>("stratified");
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tensors = program.get<bool>("tensors");
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nFolds = program.get<int>("folds");
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seed = program.get<int>("seed");
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dump_cpt = program.get<bool>("dumpcpt");
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class_last = datasets[file_name];
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if (!file_exists(complete_file_name)) {
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throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
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program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
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bool class_last, stratified, tensors, dump_cpt;
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std::string model_name, file_name, path, complete_file_name;
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int nFolds, seed;
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try {
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program.parse_args(argc, argv);
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file_name = program.get<std::string>("dataset");
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path = program.get<std::string>("path");
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model_name = program.get<std::string>("model");
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complete_file_name = path + file_name + ".arff";
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stratified = program.get<bool>("stratified");
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tensors = program.get<bool>("tensors");
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nFolds = program.get<int>("folds");
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seed = program.get<int>("seed");
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dump_cpt = program.get<bool>("dumpcpt");
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class_last = datasets[file_name];
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if (!file_exists(complete_file_name)) {
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throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
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}
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}
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catch (const exception& err) {
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cerr << err.what() << std::endl;
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cerr << program;
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exit(1);
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}
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}
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catch (const exception& err) {
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cerr << err.what() << std::endl;
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cerr << program;
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exit(1);
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}
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/*
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* Begin Processing
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*/
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auto handler = ArffFiles();
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handler.load(complete_file_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),
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[](const pair<std::string, std::string>& item) { return item.first; });
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// Discretize Dataset
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auto [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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auto clf = platform::Models::instance()->create(model_name);
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clf->fit(Xd, y, features, className, states);
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if (dump_cpt) {
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std::cout << "--- CPT Tables ---" << std::endl;
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clf->dump_cpt();
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}
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auto lines = clf->show();
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for (auto line : lines) {
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std::cout << line << std::endl;
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}
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std::cout << "--- Topological Order ---" << std::endl;
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auto order = clf->topological_order();
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for (auto name : order) {
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std::cout << name << ", ";
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}
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std::cout << "end." << std::endl;
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auto score = clf->score(Xd, y);
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std::cout << "Score: " << score << std::endl;
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auto graph = clf->graph();
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auto dot_file = model_name + "_" + file_name;
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ofstream file(dot_file + ".dot");
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file << graph;
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file.close();
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std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
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std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
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std::string stratified_string = stratified ? " Stratified" : "";
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std::cout << nFolds << " Folds" << stratified_string << " Cross validation" << std::endl;
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std::cout << "==========================================" << std::endl;
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torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
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torch::Tensor yt = torch::tensor(y, torch::kInt32);
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for (int i = 0; i < features.size(); ++i) {
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Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
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}
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float total_score = 0, total_score_train = 0, score_train, score_test;
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folding::Fold* fold;
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double nodes = 0.0;
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if (stratified)
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fold = new folding::StratifiedKFold(nFolds, y, seed);
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else
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fold = new folding::KFold(nFolds, y.size(), seed);
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for (auto i = 0; i < nFolds; ++i) {
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auto [train, test] = fold->getFold(i);
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std::cout << "Fold: " << i + 1 << std::endl;
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if (tensors) {
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auto ttrain = torch::tensor(train, torch::kInt64);
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auto ttest = torch::tensor(test, torch::kInt64);
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torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
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torch::Tensor ytraint = yt.index({ ttrain });
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torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
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torch::Tensor ytestt = yt.index({ ttest });
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clf->fit(Xtraint, ytraint, features, className, states);
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auto temp = clf->predict(Xtraint);
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score_train = clf->score(Xtraint, ytraint);
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score_test = clf->score(Xtestt, ytestt);
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} else {
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auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
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auto [Xtest, ytest] = extract_indices(test, Xd, y);
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clf->fit(Xtrain, ytrain, features, className, states);
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std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
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nodes += clf->getNumberOfNodes();
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score_train = clf->score(Xtrain, ytrain);
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score_test = clf->score(Xtest, ytest);
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/*
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* Begin Processing
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*/
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auto handler = ArffFiles();
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handler.load(complete_file_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),
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[](const pair<std::string, std::string>& item) { return item.first; });
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// Discretize Dataset
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auto [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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auto clf = platform::Models::instance()->create(model_name);
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clf->fit(Xd, y, features, className, states);
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if (dump_cpt) {
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std::cout << "--- CPT Tables ---" << std::endl;
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clf->dump_cpt();
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}
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total_score_train += score_train;
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total_score += score_test;
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std::cout << "Score Train: " << score_train << std::endl;
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std::cout << "Score Test : " << score_test << std::endl;
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std::cout << "-------------------------------------------------------------------------------" << std::endl;
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}
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std::cout << "Nodes: " << nodes / nFolds << std::endl;
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std::cout << "**********************************************************************************" << std::endl;
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std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
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std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
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auto lines = clf->show();
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for (auto line : lines) {
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std::cout << line << std::endl;
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}
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std::cout << "--- Topological Order ---" << std::endl;
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auto order = clf->topological_order();
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for (auto name : order) {
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std::cout << name << ", ";
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}
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std::cout << "end." << std::endl;
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auto score = clf->score(Xd, y);
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std::cout << "Score: " << score << std::endl;
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auto graph = clf->graph();
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auto dot_file = model_name + "_" + file_name;
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ofstream file(dot_file + ".dot");
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file << graph;
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file.close();
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std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
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std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
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std::string stratified_string = stratified ? " Stratified" : "";
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std::cout << nFolds << " Folds" << stratified_string << " Cross validation" << std::endl;
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std::cout << "==========================================" << std::endl;
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torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
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torch::Tensor yt = torch::tensor(y, torch::kInt32);
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for (int i = 0; i < features.size(); ++i) {
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Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
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}
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float total_score = 0, total_score_train = 0, score_train, score_test;
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folding::Fold* fold;
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double nodes = 0.0;
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if (stratified)
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fold = new folding::StratifiedKFold(nFolds, y, seed);
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else
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fold = new folding::KFold(nFolds, y.size(), seed);
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for (auto i = 0; i < nFolds; ++i) {
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auto [train, test] = fold->getFold(i);
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std::cout << "Fold: " << i + 1 << std::endl;
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if (tensors) {
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auto ttrain = torch::tensor(train, torch::kInt64);
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auto ttest = torch::tensor(test, torch::kInt64);
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torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
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torch::Tensor ytraint = yt.index({ ttrain });
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torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
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torch::Tensor ytestt = yt.index({ ttest });
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clf->fit(Xtraint, ytraint, features, className, states);
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auto temp = clf->predict(Xtraint);
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score_train = clf->score(Xtraint, ytraint);
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score_test = clf->score(Xtestt, ytestt);
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} else {
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auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
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auto [Xtest, ytest] = extract_indices(test, Xd, y);
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clf->fit(Xtrain, ytrain, features, className, states);
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std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
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nodes += clf->getNumberOfNodes();
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score_train = clf->score(Xtrain, ytrain);
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score_test = clf->score(Xtest, ytest);
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}
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if (dump_cpt) {
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std::cout << "--- CPT Tables ---" << std::endl;
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clf->dump_cpt();
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}
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total_score_train += score_train;
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total_score += score_test;
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std::cout << "Score Train: " << score_train << std::endl;
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std::cout << "Score Test : " << score_test << std::endl;
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std::cout << "-------------------------------------------------------------------------------" << std::endl;
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}
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std::cout << "Nodes: " << nodes / nFolds << std::endl;
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std::cout << "**********************************************************************************" << std::endl;
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std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
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std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
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}
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