Complete first working cfs

CMakeLists.txt

@@ -65,7 +65,8 @@ endif (ENABLE_CLANG_TIDY)
 add_git_submodule("lib/mdlp")
 add_git_submodule("lib/argparse")
 add_git_submodule("lib/json")
-find_library(XLSXWRITER_LIB libxlsxwriter.dylib PATHS /usr/local/lib)
+
+find_library(XLSXWRITER_LIB libxlsxwriter.dylib PATHS /usr/local/lib ${HOME}/lib/usr/local/lib)
 
 # Subdirectories
 # --------------

src/BayesNet/BoostAODE.cc

@@ -41,9 +41,6 @@ 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)
     {
@@ -87,8 +84,9 @@ namespace bayesnet {
         return oss.str();
     }
 
-    void BoostAODE::initializeModels()
+    unordered_set<int> BoostAODE::initializeModels()
     {
+        unordered_set<int> featuresUsed;
         // Read the CFS features
         string output = "[", prefix = "";
         bool first = true;
@@ -110,28 +108,30 @@ namespace bayesnet {
         if (file.is_open()) {
             nlohmann::json cfsFeatures = nlohmann::json::parse(file);
             file.close();
-            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_);
+            for (const int& feature : cfsFeatures) {
+                // cout << "Feature: [" << feature << "] " << feature << " " << features.at(feature) << endl;
+                featuresUsed.insert(feature);
+                unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
+                model->fit(dataset, features, className, states, weights_);
+                models.push_back(std::move(model));
+                significanceModels.push_back(1.0);
                 n_models++;
             }
         } else {
             throw runtime_error("File " + name + " not found");
         }
+        return featuresUsed;
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
+        unordered_set<int> featuresUsed;
+        if (cfs) {
+            featuresUsed = initializeModels();
+        }
         if (maxModels == 0)
             maxModels = .1 * n > 10 ? .1 * n : n;
         Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         bool exitCondition = false;
-        unordered_set<int> featuresUsed;
         // Variables to control the accuracy finish condition
         double priorAccuracy = 0.0;
         double delta = 1.0;
@@ -150,16 +150,16 @@ namespace bayesnet {
             unique_ptr<Classifier> model;
             auto feature = featureSelection[0];
             if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
-                bool found = false;
-                for (auto feat : featureSelection) {
+                bool used = true;
+                for (const auto& feat : featureSelection) {
                     if (find(featuresUsed.begin(), featuresUsed.end(), feat) != featuresUsed.end()) {
                         continue;
                     }
-                    found = true;
+                    used = false;
                     feature = feat;
                     break;
                 }
-                if (!found) {
+                if (used) {
                     exitCondition = true;
                     continue;
                 }
@@ -199,7 +199,7 @@ namespace bayesnet {
                     count++;
                 }
             }
-            exitCondition = n_models == maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
+            exitCondition = n_models >= maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
         }
         if (featuresUsed.size() != features.size()) {
             status = WARNING;

src/BayesNet/BoostAODE.h

@@ -1,6 +1,7 @@
 #ifndef BOOSTAODE_H
 #define BOOSTAODE_H
 #include "Ensemble.h"
+#include <map>
 #include "SPODE.h"
 namespace bayesnet {
     class BoostAODE : public Ensemble {
@@ -15,7 +16,7 @@ namespace bayesnet {
     private:
         torch::Tensor dataset_;
         torch::Tensor X_train, y_train, X_test, y_test;
-        void initializeModels();
+        unordered_set<int> initializeModels();
         // Hyperparameters
         bool repeatSparent = false; // if true, a feature can be selected more than once
         int maxModels = 0;