Add weigths as parameter

.vscode/launch.json

@@ -25,13 +25,12 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "SPODE",
+                "TANLd",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
-                "--discretize",
                 "-d",
-                "letter"
+                "iris"
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },

src/BayesNet/AODE.cc

@@ -2,7 +2,7 @@
 
 namespace bayesnet {
     AODE::AODE() : Ensemble() {}
-    void AODE::buildModel()
+    void AODE::buildModel(const torch::Tensor& weights)
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {

src/BayesNet/AODE.h

@@ -5,7 +5,7 @@
 namespace bayesnet {
     class AODE : public Ensemble {
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODE();
         virtual ~AODE() {};

src/BayesNet/AODELd.cc

@@ -19,7 +19,7 @@ namespace bayesnet {
         return *this;
 
     }
-    void AODELd::buildModel()
+    void AODELd::buildModel(const torch::Tensor& weights)
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
@@ -27,7 +27,7 @@ namespace bayesnet {
         }
         n_models = models.size();
     }
-    void AODELd::trainModel()
+    void AODELd::trainModel(const torch::Tensor& weights)
     {
         for (const auto& model : models) {
             model->fit(Xf, y, features, className, states);

src/BayesNet/AODELd.h

@@ -8,8 +8,8 @@ namespace bayesnet {
     using namespace std;
     class AODELd : public Ensemble, public Proposal {
     protected:
-        void trainModel() override;
+        void trainModel(const torch::Tensor& weights) override;
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODELd();
         AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) override;

src/BayesNet/BaseClassifier.h

@@ -6,7 +6,7 @@ namespace bayesnet {
     using namespace std;
     class BaseClassifier {
     protected:
-        virtual void trainModel() = 0;
+        virtual void trainModel(const torch::Tensor& weights) = 0;
     public:
         // X is nxm vector, y is nx1 vector
         virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;

src/BayesNet/BayesMetrics.cc

@@ -52,7 +52,8 @@ namespace bayesnet {
                 auto mask = samples.index({ -1, "..." }) == value;
                 auto first_dataset = samples.index({ index_first, mask });
                 auto second_dataset = samples.index({ index_second, mask });
-                auto mi = mutualInformation(first_dataset, second_dataset, weights);
+                auto weights_dataset = weights.index({ mask });
+                auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset);
                 auto pb = margin[value].item<float>();
                 accumulated += pb * mi;
             }

src/BayesNet/Classifier.cc

@@ -16,8 +16,10 @@ namespace bayesnet {
         auto n_classes = states[className].size();
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
-        buildModel();
+        // TODO weights can't be ones
-        trainModel();
+        const torch::Tensor weights = torch::ones({ m }, torch::kFloat);
+        buildModel(weights);
+        trainModel(weights);
         fitted = true;
         return *this;
     }
@@ -35,9 +37,8 @@ namespace bayesnet {
             exit(1);
         }
     }
-    void Classifier::trainModel()
+    void Classifier::trainModel(const torch::Tensor& weights)
     {
-        const torch::Tensor weights = torch::ones({ m });
         model.fit(dataset, weights, features, className, states);
     }
     // X is nxm where n is the number of features and m the number of samples

src/BayesNet/Classifier.h

@@ -21,10 +21,9 @@ namespace bayesnet {
         string className;
         map<string, vector<int>> states;
         Tensor dataset; // (n+1)xm tensor
-        Tensor weights;
         void checkFitParameters();
-        virtual void buildModel() = 0;
+        virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel() override;
+        void trainModel(const torch::Tensor& weights) override;
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;

src/BayesNet/Ensemble.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
 
     Ensemble::Ensemble() : Classifier(Network()) {}
 
-    void Ensemble::trainModel()
+    void Ensemble::trainModel(const torch::Tensor& weights)
     {
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {

src/BayesNet/Ensemble.h

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

src/BayesNet/KDB.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace torch;
 
     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
-    void KDB::buildModel()
+    void KDB::buildModel(const torch::Tensor& weights)
     {
         /*
         1. For each feature Xi, compute mutual information, I(X;C),

src/BayesNet/KDB.h

@@ -1,5 +1,6 @@
 #ifndef KDB_H
 #define KDB_H
+#include <torch/torch.h>
 #include "Classifier.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
@@ -11,7 +12,7 @@ namespace bayesnet {
         float theta;
         void add_m_edges(int idx, vector<int>& S, Tensor& weights);
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};

src/BayesNet/Network.cc

@@ -107,7 +107,7 @@ namespace bayesnet {
     void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states, const torch::Tensor& weights)
     {
         if (weights.size(0) != n_samples) {
-            throw invalid_argument("Weights must have the same number of elements as samples in Network::fit");
+            throw invalid_argument("Weights (" + to_string(weights.size(0)) + ") must have the same number of elements as samples (" + to_string(n_samples) + ") in Network::fit");
         }
         if (n_samples != n_samples_y) {
             throw invalid_argument("X and y must have the same number of samples in Network::fit (" + to_string(n_samples) + " != " + to_string(n_samples_y) + ")");

src/BayesNet/Proposal.cc

@@ -65,6 +65,7 @@ namespace bayesnet {
                 //Update new states of the feature/node
                 states[pFeatures[index]] = xStates;
             }
+            // TODO weights can't be ones
             const torch::Tensor weights = torch::ones({ pDataset.size(1) }, torch::kFloat);
             model.fit(pDataset, weights, pFeatures, pClassName, states);
         }

src/BayesNet/SPODE.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
 
     SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
 
-    void SPODE::buildModel()
+    void SPODE::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();

src/BayesNet/SPODE.h

@@ -7,7 +7,7 @@ namespace bayesnet {
     private:
         int root;
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit SPODE(int root);
         virtual ~SPODE() {};

src/BayesNet/TAN.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
 
     TAN::TAN() : Classifier(Network()) {}
 
-    void TAN::buildModel()
+    void TAN::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();

src/BayesNet/TAN.h

@@ -7,7 +7,7 @@ namespace bayesnet {
     class TAN : public Classifier {
     private:
     protected:
-        void buildModel() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         TAN();
         virtual ~TAN() {};

src/Platform/Report.h

@@ -6,7 +6,7 @@
 #include "Colors.h"
 
 using json = nlohmann::json;
-const int MAXL = 121;
+const int MAXL = 122;
 namespace platform {
     using namespace std;
     class Report {

src/Platform/main.cc

@@ -103,7 +103,7 @@ int main(int argc, char** argv)
     */
     auto env = platform::DotEnv();
     auto experiment = platform::Experiment();
-    experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("1.0.0");
+    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");
     for (auto seed : seeds) {