Refactor BaseClassifier and begin TAN impl.

src/BaseClassifier.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
 
-    BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0) {}
+    BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0), metrics(Metrics()) {}
     BaseClassifier& BaseClassifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
 
@@ -13,6 +13,8 @@ namespace bayesnet {
         this->className = className;
         this->states = states;
         checkFitParameters();
+        auto n_classes = states[className].size();
+        metrics = Metrics(dataset, features, className, n_classes);
         train();
         return *this;
     }
@@ -51,6 +53,14 @@ namespace bayesnet {
             }
         }
     }
+    vector<int> BaseClassifier::argsort(vector<float>& nums)
+    {
+        int n = nums.size();
+        vector<int> indices(n);
+        iota(indices.begin(), indices.end(), 0);
+        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
+        return indices;
+    }
     vector<vector<int>> tensorToVector(const torch::Tensor& tensor)
     {
         // convert mxn tensor to nxm vector
@@ -86,8 +96,16 @@ namespace bayesnet {
         Tensor y_pred = predict(X);
         return (y_pred == y).sum().item<float>() / y.size(0);
     }
-    void BaseClassifier::show()
+    vector<string> BaseClassifier::show()
     {
-        model.show();
+        return model.show();
+    }
+    void BaseClassifier::addNodes()
+    {
+        // Add all nodes to the network
+        for (auto feature : features) {
+            model.addNode(feature, states[feature].size());
+        }
+        model.addNode(className, states[className].size());
     }
 }

src/BaseClassifier.h

@@ -1,6 +1,7 @@
 #ifndef CLASSIFIERS_H
 #include <torch/torch.h>
 #include "Network.h"
+#include "Metrics.hpp"
 using namespace std;
 using namespace torch;
 
@@ -14,6 +15,7 @@ namespace bayesnet {
         Tensor X;
         Tensor y;
         Tensor dataset;
+        Metrics metrics;
         vector<string> features;
         string className;
         map<string, vector<int>> states;
@@ -21,14 +23,13 @@ namespace bayesnet {
         virtual void train() = 0;
     public:
         BaseClassifier(Network model);
-        Tensor& getX();
-        vector<string>& getFeatures();
-        string& getClassName();
         BaseClassifier& fit(Tensor& X, Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states);
         BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states);
+        void addNodes();
         Tensor predict(Tensor& X);
         float score(Tensor& X, Tensor& y);
-        void show();
+        vector<string> show();
+        vector<int> argsort(vector<float>& nums);
     };
 }
 #endif

src/CMakeLists.txt

@@ -1,2 +1,2 @@
-add_library(BayesNet Network.cc Node.cc Metrics.cc BaseClassifier.cc KDB.cc)
+add_library(BayesNet Network.cc Node.cc Metrics.cc BaseClassifier.cc KDB.cc TAN.cc)
 target_link_libraries(BayesNet "${TORCH_LIBRARIES}")

src/KDB.cc

@@ -1,17 +1,9 @@
 #include "KDB.h"
-#include "Metrics.hpp"
 
 namespace bayesnet {
     using namespace std;
     using namespace torch;
-    vector<int> argsort(vector<float>& nums)
+
-    {
-        int n = nums.size();
-        vector<int> indices(n);
-        iota(indices.begin(), indices.end(), 0);
-        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
-        return indices;
-    }
     KDB::KDB(int k, float theta) : BaseClassifier(Network()), k(k), theta(theta) {}
     void KDB::train()
     {
@@ -36,31 +28,23 @@ namespace bayesnet {
         */
         // 1. For each feature Xi, compute mutual information, I(X;C),
         // where C is the class.
-        cout << "Computing mutual information between features and class" << endl;
-        auto n_classes = states[className].size();
-        auto metrics = Metrics(dataset, features, className, n_classes);
         vector <float> mi;
         for (auto i = 0; i < features.size(); i++) {
             Tensor firstFeature = X.index({ "...", i });
             mi.push_back(metrics.mutualInformation(firstFeature, y));
-            cout << "Mutual information between " << features[i] << " and " << className << " is " << mi[i] << endl;
         }
         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
         auto conditionalEdgeWeights = metrics.conditionalEdge();
-        cout << "Conditional edge weights" << endl;
-        cout << conditionalEdgeWeights << endl;
         // 3. Let the used variable list, S, be empty.
         vector<int> S;
         // 4. Let the DAG network being constructed, BN, begin with a single
         // class node, C.
         model.addNode(className, states[className].size());
-        cout << "Adding node " << className << " to the network" << endl;
         // 5. Repeat until S includes all domain features
         // 5.1. Select feature Xmax which is not in S and has the largest value
         // I(Xmax;C).
         auto order = argsort(mi);
         for (auto idx : order) {
-            cout << idx << " " << mi[idx] << endl;
             // 5.2. Add a node to BN representing Xmax.
             model.addNode(features[idx], states[features[idx]].size());
             // 5.3. Add an arc from C to Xmax in BN.
@@ -76,8 +60,6 @@ namespace bayesnet {
     {
         auto n_edges = min(k, static_cast<int>(S.size()));
         auto cond_w = clone(weights);
-        cout << "Conditional edge weights cloned for idx " << idx << endl;
-        cout << cond_w << endl;
         bool exit_cond = k == 0;
         int num = 0;
         while (!exit_cond) {
@@ -93,22 +75,9 @@ namespace bayesnet {
                 }
             }
             cond_w.index_put_({ idx, max_minfo }, -1);
-            cout << "Conditional edge weights cloned for idx " << idx << " After -1" << endl;
-            cout << cond_w << endl;
-            cout << "cond_w.index({ idx, '...'})" << endl;
-            cout << cond_w.index({ idx, "..." }) << endl;
             auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);
             auto candidates = candidates_mask.nonzero();
-            cout << "Candidates mask" << endl;
-            cout << candidates_mask << endl;
-            cout << "Candidates: " << endl;
-            cout << candidates << endl;
-            cout << "Candidates size: " << candidates.size(0) << endl;
             exit_cond = num == n_edges || candidates.size(0) == 0;
         }
     }
-    vector<string> KDB::show()
-    {
-        return model.show();
-    }
 }

src/KDB.h

@@ -13,7 +13,6 @@ namespace bayesnet {
         void train() override;
     public:
         KDB(int k, float theta = 0.03);
-        vector<string> show();
     };
 }
 #endif

src/Metrics.cc

@@ -116,4 +116,12 @@ namespace bayesnet {
     {
         return entropy(firstFeature) - conditionalEntropy(firstFeature, secondFeature);
     }
+    vector<pair<int, int>> Metrics::maximumSpanningTree(Tensor& weights)
+    {
+        auto result = vector<pair<int, int>>();
+        // Compute the maximum spanning tree considering the weights as distances
+        // and the indices of the weights as nodes of this square matrix
+
+        return result;
+    }
 }

src/Metrics.hpp

@@ -3,23 +3,26 @@
 #include <torch/torch.h>
 #include <vector>
 #include <string>
-using namespace std;
 namespace bayesnet {
+    using namespace std;
+    using namespace torch;
     class Metrics {
     private:
-        torch::Tensor samples;
+        Tensor samples;
         vector<string> features;
         string className;
         int classNumStates;
-        vector<pair<string, string>> doCombinations(const vector<string>&);
-        double entropy(torch::Tensor&);
-        double conditionalEntropy(torch::Tensor&, torch::Tensor&);
     public:
-        double mutualInformation(torch::Tensor&, torch::Tensor&);
+        Metrics() = default;
-        Metrics(torch::Tensor&, vector<string>&, string&, int);
+        Metrics(Tensor&, vector<string>&, string&, int);
         Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
+        double entropy(Tensor&);
+        double conditionalEntropy(Tensor&, Tensor&);
+        double mutualInformation(Tensor&, Tensor&);
         vector<float> conditionalEdgeWeights();
-        torch::Tensor conditionalEdge();
+        Tensor conditionalEdge();
+        vector<pair<string, string>> doCombinations(const vector<string>&);
+        vector<pair<int, int>> maximumSpanningTree(Tensor& weights);
     };
 }
 #endif

src/TAN.cc

@@ -0,0 +1,25 @@
+#include "TAN.h"
+
+namespace bayesnet {
+    using namespace std;
+    using namespace torch;
+
+    TAN::TAN() : BaseClassifier(Network()) {}
+
+    void TAN::train()
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        // 1. Compute mutual information between each feature and the class
+        auto weights = metrics.conditionalEdge();
+        // 2. Compute the maximum spanning tree
+        auto mst = metrics.maximumSpanningTree(weights);
+        // 3. Add edges from the maximum spanning tree to the model
+        for (auto i = 0; i < mst.size(); ++i) {
+            auto [from, to] = mst[i];
+            model.addEdge(features[from], features[to]);
+        }
+
+    }
+
+}

src/TAN.h

@@ -0,0 +1,15 @@
+#ifndef TAN_H
+#define TAN_H
+#include "BaseClassifier.h"
+namespace bayesnet {
+    using namespace std;
+    using namespace torch;
+    class TAN : public BaseClassifier {
+    private:
+    protected:
+        void train() override;
+    public:
+        TAN();
+    };
+}
+#endif