Refactor library structure

bayesnet/classifiers/KDB.cc

@@ -0,0 +1,101 @@
+#include "KDB.h"
+
+namespace bayesnet {
+    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)
+    {
+        validHyperparameters = { "k", "theta" };
+
+    }
+    void KDB::setHyperparameters(const nlohmann::json& hyperparameters)
+    {
+        if (hyperparameters.contains("k")) {
+            k = hyperparameters["k"];
+        }
+        if (hyperparameters.contains("theta")) {
+            theta = hyperparameters["theta"];
+        }
+    }
+    void KDB::buildModel(const torch::Tensor& weights)
+    {
+        /*
+        1. For each feature Xi, compute mutual information, I(X;C),
+        where C is the class.
+        2. Compute class conditional mutual information I(Xi;XjIC), f or each
+        pair of features Xi and Xj, where i#j.
+        3. Let the used variable list, S, be empty.
+        4. Let the DAG network being constructed, BN, begin with a single
+        class node, C.
+        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).
+        5.2. Add a node to BN representing Xmax.
+        5.3. Add an arc from C to Xmax in BN.
+        5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
+        the highest value for I(Xmax;X,jC).
+        5.5. Add Xmax to S.
+        Compute the conditional probabilility infered by the structure of BN by
+        using counts from DB, and output BN.
+        */
+        // 1. For each feature Xi, compute mutual information, I(X;C),
+        // where C is the class.
+        addNodes();
+        const torch::Tensor& y = dataset.index({ -1, "..." });
+        std::vector<double> mi;
+        for (auto i = 0; i < features.size(); i++) {
+            torch::Tensor firstFeature = dataset.index({ i, "..." });
+            mi.push_back(metrics.mutualInformation(firstFeature, y, weights));
+        }
+        // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
+        auto conditionalEdgeWeights = metrics.conditionalEdge(weights);
+        // 3. Let the used variable list, S, be empty.
+        std::vector<int> S;
+        // 4. Let the DAG network being constructed, BN, begin with a single
+        // class node, C.
+        // 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) {
+            // 5.2. Add a node to BN representing Xmax.
+            // 5.3. Add an arc from C to Xmax in BN.
+            model.addEdge(className, features[idx]);
+            // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
+            // the highest value for I(Xmax;X,jC).
+            add_m_edges(idx, S, conditionalEdgeWeights);
+            // 5.5. Add Xmax to S.
+            S.push_back(idx);
+        }
+    }
+    void KDB::add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights)
+    {
+        auto n_edges = std::min(k, static_cast<int>(S.size()));
+        auto cond_w = clone(weights);
+        bool exit_cond = k == 0;
+        int num = 0;
+        while (!exit_cond) {
+            auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>();
+            auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();
+            if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) {
+                try {
+                    model.addEdge(features[max_minfo], features[idx]);
+                    num++;
+                }
+                catch (const std::invalid_argument& e) {
+                    // Loops are not allowed
+                }
+            }
+            cond_w.index_put_({ idx, max_minfo }, -1);
+            auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);
+            auto candidates = candidates_mask.nonzero();
+            exit_cond = num == n_edges || candidates.size(0) == 0;
+        }
+    }
+    std::vector<std::string> KDB::graph(const std::string& title) const
+    {
+        std::string header{ title };
+        if (title == "KDB") {
+            header += " (k=" + std::to_string(k) + ", theta=" + std::to_string(theta) + ")";
+        }
+        return model.graph(header);
+    }
+}