Add generate-fold-files to b_main

src/commands/b_main.cpp

@@ -47,6 +47,7 @@ void manageArguments(argparse::ArgumentParser& program)
         );
     program.add_argument("--title").default_value("").help("Experiment title");
     program.add_argument("--discretize").help("Discretize input dataset").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
+    program.add_argument("--generate-fold-files").help("generate fold information in datasets_experiment folder").default_value(false).implicit_value(true);
     program.add_argument("--no-train-score").help("Don't compute train score").default_value(false).implicit_value(true);
     program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
     program.add_argument("--save").help("Save result (always save if no dataset is supplied)").default_value(false).implicit_value(true);
@@ -75,7 +76,7 @@ int main(int argc, char** argv)
     manageArguments(program);
     std::string file_name, model_name, title, hyperparameters_file, datasets_file;
     json hyperparameters_json;
-    bool discretize_dataset, stratified, saveResults, quiet, no_train_score;
+    bool discretize_dataset, stratified, saveResults, quiet, no_train_score, generate_fold_files;
     std::vector<int> seeds;
     std::vector<std::string> file_names;
     std::vector<std::string> filesToTest;
@@ -95,6 +96,7 @@ int main(int argc, char** argv)
         hyperparameters_json = json::parse(hyperparameters);
         hyperparameters_file = program.get<std::string>("hyper-file");
         no_train_score = program.get<bool>("no-train-score");
+        generate_fold_files = program.get<bool>("generate-fold-files");
         if (hyperparameters_file != "" && hyperparameters != "{}") {
             throw runtime_error("hyperparameters and hyper_file are mutually exclusive");
         }
@@ -184,7 +186,7 @@ int main(int argc, char** argv)
     }
     platform::Timer timer;
     timer.start();
-    experiment.go(filesToTest, quiet, no_train_score);
+    experiment.go(filesToTest, quiet, no_train_score, generate_fold_files);
     experiment.setDuration(timer.getDuration());
     if (saveResults) {
         experiment.saveResult();

src/common/Paths.h

@@ -15,6 +15,12 @@ namespace platform {
             auto env = platform::DotEnv();
             return env.get("source_data");
         }
+        static std::string experiment_file(const std::string& fileName, bool discretize, bool stratified, int seed, int nfold)
+        {
+            std::string disc = discretize ? "_disc_" : "_ndisc_";
+            std::string strat = stratified ? "strat_" : "nstrat_";
+            return "datasets_experiment/" + fileName + disc + strat + std::to_string(seed) + "_" + std::to_string(nfold) + ".json";
+        }
         static void createPath(const std::string& path)
         {
             // Create directory if it does not exist

src/main/Experiment.cpp

@@ -23,7 +23,7 @@ namespace platform {
     {
         std::cout << result.getJson().dump(4) << std::endl;
     }
-    void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score)
+    void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score, bool generate_fold_files)
     {
         for (auto fileName : filesToProcess) {
             if (fileName.size() > max_name)
@@ -47,7 +47,7 @@ namespace platform {
         for (auto fileName : filesToProcess) {
             if (!quiet)
                 std::cout << " " << setw(3) << right << num++ << " " << setw(max_name) << left << fileName << right << flush;
-            cross_validation(fileName, quiet, no_train_score);
+            cross_validation(fileName, quiet, no_train_score, generate_fold_files);
             if (!quiet)
                 std::cout << std::endl;
         }
@@ -74,7 +74,45 @@ namespace platform {
         std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
 
     }
-    void Experiment::cross_validation(const std::string& fileName, bool quiet, bool no_train_score)
+    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)
+    {
+        std::string file_name = Paths::experiment_file(fileName, discretize, stratified, seed, nfold);
+        auto file = std::ofstream(file_name);
+        json output;
+        output["seed"] = seed;
+        output["nfold"] = nfold;
+        output["X_train"] = json::array();
+        auto n = X_train.size(1);
+        for (int i = 0; i < X_train.size(0); i++) {
+            if (X_train.dtype() == torch::kFloat32) {
+                auto xvf_ptr = X_train.index({ i }).data_ptr<float>();
+                auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
+                output["X_train"].push_back(feature);
+            } else {
+                auto feature = std::vector<int>(X_train.index({ i }).data_ptr<int>(), X_train.index({ i }).data_ptr<int>() + n);
+                output["X_train"].push_back(feature);
+            }
+        }
+        output["y_train"] = std::vector<int>(y_train.data_ptr<int>(), y_train.data_ptr<int>() + n);
+        output["X_test"] = json::array();
+        n = X_test.size(1);
+        for (int i = 0; i < X_test.size(0); i++) {
+            if (X_train.dtype() == torch::kFloat32) {
+                auto xvf_ptr = X_test.index({ i }).data_ptr<float>();
+                auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
+                output["X_test"].push_back(feature);
+            } else {
+                auto feature = std::vector<int>(X_test.index({ i }).data_ptr<int>(), X_test.index({ i }).data_ptr<int>() + n);
+                output["X_test"].push_back(feature);
+            }
+        }
+        output["y_test"] = std::vector<int>(y_test.data_ptr<int>(), y_test.data_ptr<int>() + n);
+        output["train"] = train;
+        output["test"] = test;
+        file << output.dump(4);
+        file.close();
+    }
+    void Experiment::cross_validation(const std::string& fileName, bool quiet, bool no_train_score, bool generate_fold_files)
     {
         auto datasets = Datasets(discretized, Paths::datasets());
         // Get dataset
@@ -137,6 +175,8 @@ namespace platform {
                 auto y_train = y.index({ train_t });
                 auto X_test = X.index({ "...", test_t });
                 auto y_test = y.index({ test_t });
+                if (generate_fold_files)
+                    generate_files(fileName, discretized, stratified, seed, nfold, X_train, y_train, X_test, y_test, train, test);
                 if (!quiet)
                     showProgress(nfold + 1, getColor(clf->getStatus()), "a");
                 // Train model

src/main/Experiment.h

@@ -28,8 +28,8 @@ namespace platform {
         Experiment& addRandomSeed(int randomSeed) { randomSeeds.push_back(randomSeed); result.addSeed(randomSeed); return *this; }
         Experiment& setDuration(float duration) { this->result.setDuration(duration); return *this; }
         Experiment& setHyperparameters(const HyperParameters& hyperparameters_) { this->hyperparameters = hyperparameters_; return *this; }
-        void cross_validation(const std::string& fileName, bool quiet, bool no_train_score);
+        void cross_validation(const std::string& fileName, bool quiet, bool no_train_score, bool generate_fold_files);
-        void go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score);
+        void go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score, bool generate_fold_files);
         void saveResult();
         void show();
         void report(bool classification_report = false);

src/results/Result.cpp

@@ -64,7 +64,7 @@ namespace platform {
 
     void Result::save()
     {
-        std::ofstream file(Paths::results() + "/" + getFilename());
+        std::ofstream file(Paths::results() + getFilename());
         file << data;
         file.close();
     }