Fixed normal classifiers

.vscode/launch.json

@@ -25,7 +25,8 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "AODELd",
+                "AODE",
+                "--discretize",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",

src/BayesNet/AODELd.h

@@ -8,7 +8,7 @@ namespace bayesnet {
     using namespace std;
     class AODELd : public Ensemble, public Proposal {
     private:
-        void trainModel();
+        void trainModel() override;
         void buildModel() override;
     public:
         AODELd();

src/BayesNet/Classifier.cc

@@ -10,26 +10,35 @@ namespace bayesnet {
         this->features = features;
         this->className = className;
         this->states = states;
+        m = dataset.size(1);
+        n = dataset.size(0) - 1;
         checkFitParameters();
         auto n_classes = states[className].size();
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
         buildModel();
-        m = dataset.size(1);
-        n = dataset.size(0);
         trainModel();
         fitted = true;
         return *this;
     }
+
+    void Classifier::buildDataset(Tensor& ytmp)
+    {
+        try {
+            auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
+            dataset = torch::cat({ dataset, yresized }, 0);
+        }
+        catch (const std::exception& e) {
+            std::cerr << e.what() << '\n';
+            cout << "X dimensions: " << dataset.sizes() << "\n";
+            cout << "y dimensions: " << ytmp.sizes() << "\n";
+            exit(1);
+        }
+    }
     void Classifier::trainModel()
     {
         model.fit(dataset, features, className);
     }
-    void Classifier::buildDataset(Tensor& ytmp)
-    {
-        ytmp = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
-        dataset = torch::cat({ dataset, ytmp }, 0);
-    }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
@@ -56,7 +65,7 @@ namespace bayesnet {
     void Classifier::checkFitParameters()
     {
         if (n != features.size()) {
-            throw invalid_argument("X and features must have the same number of features");
+            throw invalid_argument("X " + to_string(n) + " and features " + to_string(features.size()) + " must have the same number of features");
         }
         if (states.find(className) == states.end()) {
             throw invalid_argument("className not found in states");

src/BayesNet/Classifier.h

@@ -23,7 +23,7 @@ namespace bayesnet {
         map<string, vector<int>> states;
         void checkFitParameters();
         virtual void buildModel() = 0;
-        void trainModel();
+        virtual void trainModel();
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;

src/BayesNet/Ensemble.h

@@ -14,7 +14,7 @@ namespace bayesnet {
     protected:
         unsigned n_models;
         vector<unique_ptr<Classifier>> models;
-        void trainModel();
+        void trainModel() override;
         vector<int> voting(Tensor& y_pred);
     public:
         Ensemble();