Begin CFS initialization
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@ -11,30 +11,7 @@ namespace bayesnet {
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void BoostAODE::buildModel(const torch::Tensor& weights)
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{
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// Models shall be built in trainModel
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}
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void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
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{
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// Check if hyperparameters are valid
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const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence" };
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checkHyperparameters(validKeys, hyperparameters);
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if (hyperparameters.contains("repeatSparent")) {
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repeatSparent = hyperparameters["repeatSparent"];
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}
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if (hyperparameters.contains("maxModels")) {
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maxModels = hyperparameters["maxModels"];
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}
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if (hyperparameters.contains("ascending")) {
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ascending = hyperparameters["ascending"];
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}
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if (hyperparameters.contains("convergence")) {
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convergence = hyperparameters["convergence"];
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}
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if (hyperparameters.contains("cfs")) {
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cfs = hyperparameters["cfs"];
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}
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}
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void BoostAODE::validationInit()
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{
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// Prepare the validation dataset
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auto y_ = dataset.index({ -1, "..." });
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if (convergence) {
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// Prepare train & validation sets from train data
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@ -60,12 +37,43 @@ namespace bayesnet {
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X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
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y_train = y_;
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}
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if (cfs != "") {
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initializeModels();
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}
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}
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void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
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{
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// Check if hyperparameters are valid
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const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence", "cfs" };
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checkHyperparameters(validKeys, hyperparameters);
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if (hyperparameters.contains("repeatSparent")) {
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repeatSparent = hyperparameters["repeatSparent"];
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}
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if (hyperparameters.contains("maxModels")) {
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maxModels = hyperparameters["maxModels"];
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}
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if (hyperparameters.contains("ascending")) {
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ascending = hyperparameters["ascending"];
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}
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if (hyperparameters.contains("convergence")) {
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convergence = hyperparameters["convergence"];
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}
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if (hyperparameters.contains("cfs")) {
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cfs = hyperparameters["cfs"];
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}
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}
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void BoostAODE::initializeModels()
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{
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ifstream file(cfs + ".json");
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if (file.is_open()) {
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nlohmann::json data;
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file >> data;
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file.close();
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auto model = "iris"; // has to come in when building object
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auto features = data[model];
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cout << "features: " << features.dump() << endl;
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} else {
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throw runtime_error("File " + cfs + ".json not found");
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}
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}
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void BoostAODE::trainModel(const torch::Tensor& weights)
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@ -74,11 +82,7 @@ namespace bayesnet {
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n_models = 0;
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if (maxModels == 0)
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maxModels = .1 * n > 10 ? .1 * n : n;
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validationInit();
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Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
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if (cfs != "") {
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initializeModels();
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}
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bool exitCondition = false;
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unordered_set<int> featuresUsed;
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// Variables to control the accuracy finish condition
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@ -15,7 +15,6 @@ namespace bayesnet {
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private:
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torch::Tensor dataset_;
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torch::Tensor X_train, y_train, X_test, y_test;
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void validationInit();
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void initializeModels();
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// Hyperparameters
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bool repeatSparent = false; // if true, a feature can be selected more than once
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@ -53,13 +53,7 @@ namespace platform {
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const string status = compareResult(r["dataset"].get<string>(), r["score"].get<double>());
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cout << status;
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cout << setw(12) << right << setprecision(6) << fixed << r["time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["time_std"].get<double>() << " ";
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try {
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cout << r["hyperparameters"].get<string>();
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}
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catch (...) {
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//cout << r["hyperparameters"];
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cout << "Arrggggghhhh!" << endl;
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}
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cout << r["hyperparameters"].dump();
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cout << endl;
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cout << flush;
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lastResult = r;
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@ -12,7 +12,7 @@
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using namespace std;
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using json = nlohmann::json;
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argparse::ArgumentParser manageArguments(int argc, char** argv)
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argparse::ArgumentParser manageArguments()
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{
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auto env = platform::DotEnv();
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argparse::ArgumentParser program("main");
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@ -48,43 +48,40 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
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}});
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auto seed_values = env.getSeeds();
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program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
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return program;
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}
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int main(int argc, char** argv)
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{
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string file_name, model_name, title;
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json hyperparameters_json;
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bool discretize_dataset, stratified, saveResults;
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vector<int> seeds;
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vector<string> filesToTest;
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int n_folds;
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auto program = manageArguments();
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try {
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program.parse_args(argc, argv);
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auto file_name = program.get<string>("dataset");
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auto model_name = program.get<string>("model");
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auto discretize_dataset = program.get<bool>("discretize");
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auto stratified = program.get<bool>("stratified");
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auto n_folds = program.get<int>("folds");
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auto seeds = program.get<vector<int>>("seeds");
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auto title = program.get<string>("title");
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file_name = program.get<string>("dataset");
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model_name = program.get<string>("model");
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discretize_dataset = program.get<bool>("discretize");
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stratified = program.get<bool>("stratified");
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n_folds = program.get<int>("folds");
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seeds = program.get<vector<int>>("seeds");
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auto hyperparameters = program.get<string>("hyperparameters");
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auto saveResults = program.get<bool>("save");
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hyperparameters_json = json::parse(hyperparameters);
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title = program.get<string>("title");
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if (title == "" && file_name == "") {
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throw runtime_error("title is mandatory if dataset is not provided");
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}
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saveResults = program.get<bool>("save");
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}
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catch (const exception& err) {
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cerr << err.what() << endl;
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cerr << program;
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exit(1);
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}
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return program;
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}
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int main(int argc, char** argv)
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{
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auto program = manageArguments(argc, argv);
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auto file_name = program.get<string>("dataset");
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auto model_name = program.get<string>("model");
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auto discretize_dataset = program.get<bool>("discretize");
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auto stratified = program.get<bool>("stratified");
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auto n_folds = program.get<int>("folds");
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auto seeds = program.get<vector<int>>("seeds");
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auto hyperparameters = program.get<string>("hyperparameters");
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vector<string> filesToTest;
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auto datasets = platform::Datasets(discretize_dataset, platform::Paths::datasets());
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auto title = program.get<string>("title");
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auto saveResults = program.get<bool>("save");
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if (file_name != "") {
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if (!datasets.isDataset(file_name)) {
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cerr << "Dataset " << file_name << " not found" << endl;
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@ -106,7 +103,7 @@ int main(int argc, char** argv)
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experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("14.0.3");
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experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
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experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
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experiment.setHyperparameters(json::parse(hyperparameters));
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experiment.setHyperparameters(hyperparameters_json);
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for (auto seed : seeds) {
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experiment.addRandomSeed(seed);
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}
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