First cfs working version

src/BayesNet/BoostAODE.cc

@@ -13,10 +13,10 @@ namespace bayesnet {
     void BoostAODE::buildModel(const torch::Tensor& weights)
     {
         // Models shall be built in trainModel
+        models.clear();
+        n_models = 0;
         // Prepare the validation dataset
         auto y_ = dataset.index({ -1, "..." });
-        int nSamples = dataset.size(1);
-        int nFeatures = dataset.size(0) - 1;
         if (convergence) {
             // Prepare train & validation sets from train data
             auto fold = platform::StratifiedKFold(5, y_, 271);
@@ -41,8 +41,8 @@ namespace bayesnet {
             X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
             y_train = y_;
         }
-        if (cfs != "") {
-            initializeModels(nSamples, nFeatures);
+        if (cfs) {
+            initializeModels();
         }
     }
     void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
@@ -82,18 +82,18 @@ namespace bayesnet {
         EVP_MD_CTX_free(mdctx);
         stringstream oss;
         for (unsigned int i = 0; i < hash_len; i++) {
-            oss << hex << (int)hash[i];
+            oss << hex << setfill('0') << setw(2) << (int)hash[i];
         }
         return oss.str();
     }
 
-    void BoostAODE::initializeModels(int nSamples, int nFeatures)
+    void BoostAODE::initializeModels()
     {
         // Read the CFS features
         string output = "[", prefix = "";
         bool first = true;
         for (const auto& feature : features) {
-            output += prefix + feature;
+            output += prefix + "'" + feature + "'";
             if (first) {
                 prefix = ", ";
                 first = false;
@@ -103,21 +103,30 @@ namespace bayesnet {
         // std::size_t str_hash = std::hash<std::string>{}(output);
         string str_hash = sha256(output);
         stringstream oss;
-        oss << "cfs/" << str_hash << ".json";
+        oss << platform::Paths::cfs() << str_hash << ".json";
         string name = oss.str();
         ifstream file(name);
+        Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         if (file.is_open()) {
-            nlohmann::json features = nlohmann::json::parse(file);
+            nlohmann::json cfsFeatures = nlohmann::json::parse(file);
             file.close();
-            cout << "features: " << features.dump() << endl;
+            for (const string& feature : cfsFeatures) {
+                // cout << "Feature: [" << feature << "]" << endl;
+                auto pos = find(features.begin(), features.end(), feature);
+                if (pos == features.end())
+                    throw runtime_error("Feature " + feature + " not found in dataset");
+                int numFeature = pos - features.begin();
+                cout << "Feature: [" << feature << "] " << numFeature << endl;
+                models.push_back(std::make_unique<SPODE>(numFeature));
+                models.back()->fit(dataset, features, className, states, weights_);
+                n_models++;
+            }
         } else {
             throw runtime_error("File " + name + " not found");
         }
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
-        models.clear();
-        n_models = 0;
         if (maxModels == 0)
             maxModels = .1 * n > 10 ? .1 * n : n;
         Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);

src/BayesNet/BoostAODE.h

@@ -15,13 +15,13 @@ namespace bayesnet {
     private:
         torch::Tensor dataset_;
         torch::Tensor X_train, y_train, X_test, y_test;
-        void initializeModels(int nSamples, int nFeatures);
+        void initializeModels();
         // Hyperparameters
         bool repeatSparent = false; // if true, a feature can be selected more than once
         int maxModels = 0;
         bool ascending = false; //Process KBest features ascending or descending order
         bool convergence = false; //if true, stop when the model does not improve
-        string cfs = ""; // if not empty, use CFS to select features
+        bool cfs = false; // if true use CFS to select features stored in cfs folder with sha256(features) file_name
     };
 }
 #endif

src/Platform/Paths.h

@@ -7,6 +7,7 @@ namespace platform {
     public:
         static std::string results() { return "results/"; }
         static std::string excel() { return "excel/"; }
+        static std::string cfs() { return "cfs/"; }
         static std::string datasets()
         {
             auto env = platform::DotEnv();