Remove catch2 as submodule

Add link to pdf coverage report

bayesnet/ensembles/BoostAODE.h

@@ -11,19 +11,19 @@
 #include "bayesnet/feature_selection/FeatureSelect.h"
 #include "Ensemble.h"
 namespace bayesnet {
-    struct {
+    const struct {
         std::string CFS = "CFS";
         std::string FCBF = "FCBF";
         std::string IWSS = "IWSS";
     }SelectFeatures;
-    struct {
+    const struct {
         std::string ASC = "asc";
         std::string DESC = "desc";
         std::string RAND = "rand";
     }Orders;
     class BoostAODE : public Ensemble {
     public:
-        BoostAODE(bool predict_voting = false);
+        explicit BoostAODE(bool predict_voting = false);
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
         void setHyperparameters(const nlohmann::json& hyperparameters_) override;

bayesnet/utils/BayesMetrics.cc

@@ -10,17 +10,17 @@ namespace bayesnet {
     //samples is n+1xm tensor used to fit the model
     Metrics::Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates)
         : samples(samples)
-        , features(features)
         , className(className)
+        , features(features)
         , classNumStates(classNumStates)
     {
     }
     //samples is n+1xm std::vector used to fit the model
     Metrics::Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates)
-        : features(features)
+        : samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
         , className(className)
+        , features(features)
         , classNumStates(classNumStates)
-        , samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
     {
         for (int i = 0; i < vsamples.size(); ++i) {
             samples.index_put_({ i,  "..." }, torch::tensor(vsamples[i], torch::kInt32));
@@ -105,14 +105,6 @@ namespace bayesnet {
         }
         return matrix;
     }
-    // To use in Python
-    std::vector<float> Metrics::conditionalEdgeWeights(std::vector<float>& weights_)
-    {
-        const torch::Tensor weights = torch::tensor(weights_);
-        auto matrix = conditionalEdge(weights);
-        std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
-        return v;
-    }
     double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
         torch::Tensor counts = feature.bincount(weights);

bayesnet/utils/BayesMetrics.h

@@ -18,7 +18,6 @@ namespace bayesnet {
         std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
         std::vector<double> getScoresKBest() const;
         double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
-        std::vector<float> conditionalEdgeWeights(std::vector<float>& weights); // To use in Python
         torch::Tensor conditionalEdge(const torch::Tensor& weights);
         std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
     protected: