Begin CFS initialization

src/BayesNet/BoostAODE.cc

@@ -11,30 +11,7 @@ namespace bayesnet {
     void BoostAODE::buildModel(const torch::Tensor& weights)
     {
         // Models shall be built in trainModel
-    }
+        // Prepare the validation dataset
-    void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
-    {
-        // Check if hyperparameters are valid
-        const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence" };
-        checkHyperparameters(validKeys, hyperparameters);
-        if (hyperparameters.contains("repeatSparent")) {
-            repeatSparent = hyperparameters["repeatSparent"];
-        }
-        if (hyperparameters.contains("maxModels")) {
-            maxModels = hyperparameters["maxModels"];
-        }
-        if (hyperparameters.contains("ascending")) {
-            ascending = hyperparameters["ascending"];
-        }
-        if (hyperparameters.contains("convergence")) {
-            convergence = hyperparameters["convergence"];
-        }
-        if (hyperparameters.contains("cfs")) {
-            cfs = hyperparameters["cfs"];
-        }
-    }
-    void BoostAODE::validationInit()
-    {
         auto y_ = dataset.index({ -1, "..." });
         if (convergence) {
             // Prepare train & validation sets from train data
@@ -60,12 +37,43 @@ namespace bayesnet {
             X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
             y_train = y_;
         }
-
+        if (cfs != "") {
+            initializeModels();
+        }
+    }
+    void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        // Check if hyperparameters are valid
+        const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence", "cfs" };
+        checkHyperparameters(validKeys, hyperparameters);
+        if (hyperparameters.contains("repeatSparent")) {
+            repeatSparent = hyperparameters["repeatSparent"];
+        }
+        if (hyperparameters.contains("maxModels")) {
+            maxModels = hyperparameters["maxModels"];
+        }
+        if (hyperparameters.contains("ascending")) {
+            ascending = hyperparameters["ascending"];
+        }
+        if (hyperparameters.contains("convergence")) {
+            convergence = hyperparameters["convergence"];
+        }
+        if (hyperparameters.contains("cfs")) {
+            cfs = hyperparameters["cfs"];
+        }
     }
     void BoostAODE::initializeModels()
     {
         ifstream file(cfs + ".json");
         if (file.is_open()) {
+            nlohmann::json data;
+            file >> data;
+            file.close();
+            auto model = "iris"; // has to come in when building object
+            auto features = data[model];
+            cout << "features: " << features.dump() << endl;
+        } else {
+            throw runtime_error("File " + cfs + ".json not found");
         }
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
@@ -74,11 +82,7 @@ namespace bayesnet {
         n_models = 0;
         if (maxModels == 0)
             maxModels = .1 * n > 10 ? .1 * n : n;
-        validationInit();
         Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-        if (cfs != "") {
-            initializeModels();
-        }
         bool exitCondition = false;
         unordered_set<int> featuresUsed;
         // Variables to control the accuracy finish condition

src/BayesNet/BoostAODE.h

@@ -15,7 +15,6 @@ namespace bayesnet {
     private:
         torch::Tensor dataset_;
         torch::Tensor X_train, y_train, X_test, y_test;
-        void validationInit();
         void initializeModels();
         // Hyperparameters
         bool repeatSparent = false; // if true, a feature can be selected more than once

src/Platform/ReportConsole.cc

@@ -53,13 +53,7 @@ namespace platform {
             const string status = compareResult(r["dataset"].get<string>(), r["score"].get<double>());
             cout << status;
             cout << setw(12) << right << setprecision(6) << fixed << r["time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["time_std"].get<double>() << " ";
-            try {
+            cout << r["hyperparameters"].dump();
-                cout << r["hyperparameters"].get<string>();
-            }
-            catch (...) {
-                //cout << r["hyperparameters"];
-                cout << "Arrggggghhhh!" << endl;
-            }
             cout << endl;
             cout << flush;
             lastResult = r;

src/Platform/main.cc

@@ -12,7 +12,7 @@
 using namespace std;
 using json = nlohmann::json;
 
-argparse::ArgumentParser manageArguments(int argc, char** argv)
+argparse::ArgumentParser manageArguments()
 {
     auto env = platform::DotEnv();
     argparse::ArgumentParser program("main");
@@ -48,43 +48,40 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
         }});
     auto seed_values = env.getSeeds();
     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);
+    return program;
+}
+
+int main(int argc, char** argv)
+{
+    string file_name, model_name, title;
+    json hyperparameters_json;
+    bool discretize_dataset, stratified, saveResults;
+    vector<int> seeds;
+    vector<string> filesToTest;
+    int n_folds;
+    auto program = manageArguments();
     try {
         program.parse_args(argc, argv);
-        auto file_name = program.get<string>("dataset");
+        file_name = program.get<string>("dataset");
-        auto model_name = program.get<string>("model");
+        model_name = program.get<string>("model");
-        auto discretize_dataset = program.get<bool>("discretize");
+        discretize_dataset = program.get<bool>("discretize");
-        auto stratified = program.get<bool>("stratified");
+        stratified = program.get<bool>("stratified");
-        auto n_folds = program.get<int>("folds");
+        n_folds = program.get<int>("folds");
-        auto seeds = program.get<vector<int>>("seeds");
+        seeds = program.get<vector<int>>("seeds");
-        auto title = program.get<string>("title");
         auto hyperparameters = program.get<string>("hyperparameters");
-        auto saveResults = program.get<bool>("save");
+        hyperparameters_json = json::parse(hyperparameters);
+        title = program.get<string>("title");
         if (title == "" && file_name == "") {
             throw runtime_error("title is mandatory if dataset is not provided");
         }
+        saveResults = program.get<bool>("save");
     }
     catch (const exception& err) {
         cerr << err.what() << endl;
         cerr << program;
         exit(1);
     }
-    return program;
-}
-
-int main(int argc, char** argv)
-{
-    auto program = manageArguments(argc, argv);
-    auto file_name = program.get<string>("dataset");
-    auto model_name = program.get<string>("model");
-    auto discretize_dataset = program.get<bool>("discretize");
-    auto stratified = program.get<bool>("stratified");
-    auto n_folds = program.get<int>("folds");
-    auto seeds = program.get<vector<int>>("seeds");
-    auto hyperparameters = program.get<string>("hyperparameters");
-    vector<string> filesToTest;
     auto datasets = platform::Datasets(discretize_dataset, platform::Paths::datasets());
-    auto title = program.get<string>("title");
-    auto saveResults = program.get<bool>("save");
     if (file_name != "") {
         if (!datasets.isDataset(file_name)) {
             cerr << "Dataset " << file_name << " not found" << endl;
@@ -106,7 +103,7 @@ int main(int argc, char** argv)
     experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("14.0.3");
     experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
     experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
-    experiment.setHyperparameters(json::parse(hyperparameters));
+    experiment.setHyperparameters(hyperparameters_json);
     for (auto seed : seeds) {
         experiment.addRandomSeed(seed);
     }