323 lines
15 KiB
C++
323 lines
15 KiB
C++
#include "common/Datasets.h"
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#include "reports/ReportConsole.h"
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#include "common/Paths.h"
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#include "Models.h"
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#include "Scores.h"
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#include "Experiment.h"
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namespace platform {
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using json = nlohmann::ordered_json;
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void Experiment::saveResult()
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{
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result.setSchemaVersion("1.0");
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result.check();
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result.save();
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std::cout << "Result saved in " << Paths::results() << result.getFilename() << std::endl;
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}
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void Experiment::report()
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{
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ReportConsole report(result.getJson());
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report.show();
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if (filesToTest.size() == 1) {
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std::cout << report.showClassificationReport(Colors::BLUE());
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}
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}
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void Experiment::show()
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{
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std::cout << result.getJson().dump(4) << std::endl;
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}
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void Experiment::saveGraph()
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{
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std::cout << "Saving graphs..." << std::endl;
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auto data = result.getJson();
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for (const auto& item : data["results"]) {
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auto graphs = item["graph"];
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int i = 0;
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for (const auto& graph : graphs) {
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i++;
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auto fileName = Paths::graphs() + result.getFilename() + "_graph_" + item["dataset"].get<std::string>() + "_" + std::to_string(i) + ".dot";
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auto file = std::ofstream(fileName);
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file << graph.get<std::string>();
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file.close();
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std::cout << "Graph saved in " << fileName << std::endl;
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}
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}
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}
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Experiment& Experiment::setSmoothSrategy(const std::string& smooth_strategy)
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{
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this->smooth_strategy = smooth_strategy;
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this->result.setSmoothStrategy(smooth_strategy);
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if (smooth_strategy == "ORIGINAL")
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smooth_type = bayesnet::Smoothing_t::ORIGINAL;
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else if (smooth_strategy == "LAPLACE")
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smooth_type = bayesnet::Smoothing_t::LAPLACE;
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else if (smooth_strategy == "CESTNIK")
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smooth_type = bayesnet::Smoothing_t::CESTNIK;
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else {
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std::cerr << "Experiment: Unknown smoothing strategy: " << smooth_strategy << std::endl;
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exit(1);
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}
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return *this;
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}
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void Experiment::go()
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{
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for (auto fileName : filesToTest) {
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if (fileName.size() > max_name)
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max_name = fileName.size();
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}
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std::cout << Colors::MAGENTA() << "*** Starting experiment: " << result.getTitle() << " ***" << Colors::RESET() << std::endl << std::endl;
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auto clf = Models::instance()->create(result.getModel());
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auto version = clf->getVersion();
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std::cout << Colors::BLUE() << " Using " << result.getModel() << " ver. " << version << std::endl << std::endl;
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if (!quiet) {
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std::cout << Colors::GREEN() << " Status Meaning" << std::endl;
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std::cout << " ------ --------------------------------" << Colors::RESET() << std::endl;
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std::cout << " ( " << Colors::GREEN() << "a" << Colors::RESET() << " ) Fitting model with train dataset" << std::endl;
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std::cout << " ( " << Colors::GREEN() << "b" << Colors::RESET() << " ) Scoring train dataset" << std::endl;
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std::cout << " ( " << Colors::GREEN() << "c" << Colors::RESET() << " ) Scoring test dataset" << std::endl << std::endl;
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std::cout << Colors::YELLOW() << "Note: fold number in this color means fitting had issues such as not using all features in BoostAODE classifier" << std::endl << std::endl;
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int nc = 4 + 3 * nfolds + (nfolds >= 10 ? nfolds - 10 + 1 : 0);
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std::cout << Colors::GREEN() << left << " # " << setw(max_name) << "Dataset" << " #Samp #Feat Seed Status" << string(nc - 6, ' ') << setw(11) << " Time" << " Score" << std::endl;
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std::cout << " --- " << string(max_name, '-') << " ----- ----- ---- " << string(nc, '-') << " ----------" << " ---------";
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std::cout << Colors::RESET() << std::endl;
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}
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int num = 0;
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for (auto fileName : filesToTest) {
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if (!quiet)
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std::cout << " " << setw(3) << right << num++ << " " << setw(max_name) << left << fileName << right << flush;
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cross_validation(fileName);
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if (!quiet)
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std::cout << std::endl;
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}
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if (!quiet)
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std::cout << std::endl;
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}
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std::string getColor(bayesnet::status_t status)
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{
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switch (status) {
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case bayesnet::NORMAL:
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return Colors::GREEN();
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case bayesnet::WARNING:
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return Colors::YELLOW();
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case bayesnet::ERROR:
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return Colors::RED();
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default:
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return Colors::RESET();
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}
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}
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score_t Experiment::parse_score() const
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{
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if (result.getScoreName() == "accuracy")
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return score_t::ACCURACY;
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if (result.getScoreName() == "roc-auc-ovr")
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return score_t::ROC_AUC_OVR;
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throw std::runtime_error("Unknown score: " + result.getScoreName());
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}
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void showProgress(int fold, const std::string& color, const std::string& phase)
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{
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int nc = fold >= 10 ? 5 : 4;
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std::string prefix = phase == "-" ? "" : std::string(nc, '\b');
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std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
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}
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void generate_files(const std::string& fileName, bool discretize, bool stratified, int seed, int nfold, torch::Tensor X_train, torch::Tensor y_train, torch::Tensor X_test, torch::Tensor y_test, std::vector<int>& train, std::vector<int>& test)
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{
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std::string file_name = Paths::experiment_file(fileName, discretize, stratified, seed, nfold);
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auto file = std::ofstream(file_name);
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json output;
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output["seed"] = seed;
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output["nfold"] = nfold;
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output["X_train"] = json::array();
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auto n = X_train.size(1);
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for (int i = 0; i < X_train.size(0); i++) {
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if (X_train.dtype() == torch::kFloat32) {
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auto xvf_ptr = X_train.index({ i }).data_ptr<float>();
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auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
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output["X_train"].push_back(feature);
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} else {
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auto feature = std::vector<int>(X_train.index({ i }).data_ptr<int>(), X_train.index({ i }).data_ptr<int>() + n);
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output["X_train"].push_back(feature);
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}
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}
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output["y_train"] = std::vector<int>(y_train.data_ptr<int>(), y_train.data_ptr<int>() + n);
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output["X_test"] = json::array();
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n = X_test.size(1);
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for (int i = 0; i < X_test.size(0); i++) {
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if (X_train.dtype() == torch::kFloat32) {
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auto xvf_ptr = X_test.index({ i }).data_ptr<float>();
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auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
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output["X_test"].push_back(feature);
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} else {
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auto feature = std::vector<int>(X_test.index({ i }).data_ptr<int>(), X_test.index({ i }).data_ptr<int>() + n);
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output["X_test"].push_back(feature);
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}
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}
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output["y_test"] = std::vector<int>(y_test.data_ptr<int>(), y_test.data_ptr<int>() + n);
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output["train"] = train;
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output["test"] = test;
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file << output.dump(4);
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file.close();
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}
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void Experiment::cross_validation(const std::string& fileName)
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{
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//
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// Load dataset and prepare data
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//
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auto datasets = Datasets(discretized, Paths::datasets(), discretization_algo);
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auto& dataset = datasets.getDataset(fileName);
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dataset.load();
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auto [X, y] = dataset.getTensors(); // Only need y for folding
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auto features = dataset.getFeatures();
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auto n_features = dataset.getNFeatures();
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auto n_samples = dataset.getNSamples();
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auto className = dataset.getClassName();
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auto labels = dataset.getLabels();
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int num_classes = dataset.getNClasses();
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if (!quiet) {
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std::cout << " " << setw(5) << n_samples << " " << setw(5) << n_features << flush;
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}
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//
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// Prepare Result
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//
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auto partial_result = PartialResult();
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partial_result.setSamples(n_samples).setFeatures(n_features).setClasses(num_classes);
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partial_result.setHyperparameters(hyperparameters.get(fileName));
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//
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// Initialize results std::vectors
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//
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int nResults = nfolds * static_cast<int>(randomSeeds.size());
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auto score_test = torch::zeros({ nResults }, torch::kFloat64);
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auto score_train = torch::zeros({ nResults }, torch::kFloat64);
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auto train_time = torch::zeros({ nResults }, torch::kFloat64);
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auto test_time = torch::zeros({ nResults }, torch::kFloat64);
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auto nodes = torch::zeros({ nResults }, torch::kFloat64);
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auto edges = torch::zeros({ nResults }, torch::kFloat64);
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auto num_states = torch::zeros({ nResults }, torch::kFloat64);
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json confusion_matrices = json::array();
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json confusion_matrices_train = json::array();
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std::vector<std::string> notes;
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std::vector<std::string> graphs;
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Timer train_timer, test_timer, seed_timer;
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int item = 0;
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bool first_seed = true;
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//
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// Loop over random seeds
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//
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auto score = parse_score();
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for (auto seed : randomSeeds) {
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seed_timer.start();
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if (!quiet) {
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string prefix = " ";
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if (!first_seed) {
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prefix = "\n" + string(18 + max_name, ' ');
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}
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std::cout << prefix << setw(4) << right << seed << " " << flush;
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first_seed = false;
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}
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folding::Fold* fold;
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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, n_samples, seed);
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//
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// Loop over folds
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//
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for (int nfold = 0; nfold < nfolds; nfold++) {
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auto clf = Models::instance()->create(result.getModel());
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if (!quiet)
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showProgress(nfold + 1, getColor(clf->getStatus()), "-");
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setModelVersion(clf->getVersion());
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auto valid = clf->getValidHyperparameters();
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hyperparameters.check(valid, fileName);
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clf->setHyperparameters(hyperparameters.get(fileName));
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//
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// Split train - test dataset
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//
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train_timer.start();
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auto [train, test] = fold->getFold(nfold);
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auto [X_train, X_test, y_train, y_test] = dataset.getTrainTestTensors(train, test);
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auto states = dataset.getStates(); // Get the states of the features Once they are discretized
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if (generate_fold_files)
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generate_files(fileName, discretized, stratified, seed, nfold, X_train, y_train, X_test, y_test, train, test);
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if (!quiet)
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showProgress(nfold + 1, getColor(clf->getStatus()), "a");
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//
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// Train model
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//
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clf->fit(X_train, y_train, features, className, states, smooth_type);
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if (!quiet)
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showProgress(nfold + 1, getColor(clf->getStatus()), "b");
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auto clf_notes = clf->getNotes();
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std::transform(clf_notes.begin(), clf_notes.end(), std::back_inserter(notes), [nfold](const std::string& note)
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{ return "Fold " + std::to_string(nfold) + ": " + note; });
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nodes[item] = clf->getNumberOfNodes();
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edges[item] = clf->getNumberOfEdges();
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num_states[item] = clf->getNumberOfStates();
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train_time[item] = train_timer.getDuration();
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double score_train_value = 0.0;
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//
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// Score train
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//
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if (!no_train_score) {
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auto y_proba_train = clf->predict_proba(X_train);
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Scores scores(y_train, y_proba_train, num_classes, labels);
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score_train_value = score == score_t::ACCURACY ? scores.accuracy() : scores.auc();
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confusion_matrices_train.push_back(scores.get_confusion_matrix_json(true));
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}
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//
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// Test model
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//
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if (!quiet)
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showProgress(nfold + 1, getColor(clf->getStatus()), "c");
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test_timer.start();
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// auto y_predict = clf->predict(X_test);
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auto y_proba_test = clf->predict_proba(X_test);
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Scores scores(y_test, y_proba_test, num_classes, labels);
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auto score_test_value = score == score_t::ACCURACY ? scores.accuracy() : scores.auc();
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test_time[item] = test_timer.getDuration();
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score_train[item] = score_train_value;
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score_test[item] = score_test_value;
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confusion_matrices.push_back(scores.get_confusion_matrix_json(true));
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if (!quiet)
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std::cout << "\b\b\b, " << flush;
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//
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// Store results and times in std::vector
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//
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partial_result.addScoreTrain(score_train_value);
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partial_result.addScoreTest(score_test_value);
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partial_result.addTimeTrain(train_time[item].item<double>());
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partial_result.addTimeTest(test_time[item].item<double>());
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item++;
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if (graph) {
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std::string result = "";
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for (const auto& line : clf->graph()) {
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result += line + "\n";
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}
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graphs.push_back(result);
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}
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}
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if (!quiet) {
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seed_timer.stop();
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std::cout << "end. " << std::setw(10) << std::right << seed_timer.getDurationString();
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}
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delete fold;
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}
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// Show Results
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if (!quiet)
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std::cout << " " << setw(9) << right << std::fixed << std::setprecision(7) << torch::mean(score_test).item<double>();
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//
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// Store result totals in Result
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//
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partial_result.setGraph(graphs);
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partial_result.setScoreTest(torch::mean(score_test).item<double>()).setScoreTrain(torch::mean(score_train).item<double>());
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partial_result.setScoreTestStd(torch::std(score_test).item<double>()).setScoreTrainStd(torch::std(score_train).item<double>());
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partial_result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
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partial_result.setTestTimeStd(torch::std(test_time).item<double>()).setTrainTimeStd(torch::std(train_time).item<double>());
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partial_result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());
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partial_result.setDataset(fileName).setNotes(notes);
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partial_result.setConfusionMatrices(confusion_matrices);
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if (!no_train_score)
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partial_result.setConfusionMatricesTrain(confusion_matrices_train);
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addResult(partial_result);
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}
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} |