Refactor Library renaming Base classes

.vscode/settings.json

@@ -99,7 +99,8 @@
         "typeindex": "cpp",
         "shared_mutex": "cpp",
         "*.ipp": "cpp",
-        "cassert": "cpp"
+        "cassert": "cpp",
+        "charconv": "cpp"
     },
     "cmake.configureOnOpen": false,
     "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools"

sample/sample.cc

@@ -2,7 +2,9 @@
 #include <string>
 #include <torch/torch.h>
 #include <thread>
+#include <map>
 #include <argparse/argparse.hpp>
+#include "BaseClassifier.h"
 #include "ArffFiles.h"
 #include "Network.h"
 #include "BayesMetrics.h"
@@ -143,38 +145,12 @@ int main(int argc, char** argv)
     states[className] = vector<int>(
         maxes[className]);
     double score;
-    vector<string> lines;
-    vector<string> graph;
-    auto kdb = bayesnet::KDB(2);
-    auto aode = bayesnet::AODE();
-    auto spode = bayesnet::SPODE(2);
-    auto tan = bayesnet::TAN();
-    switch (hash_conv(model_name)) {
-        case "AODE"_sh:
-            aode.fit(Xd, y, features, className, states);
-            lines = aode.show();
-            score = aode.score(Xd, y);
-            graph = aode.graph();
-            break;
-        case "KDB"_sh:
-            kdb.fit(Xd, y, features, className, states);
-            lines = kdb.show();
-            score = kdb.score(Xd, y);
-            graph = kdb.graph();
-            break;
-        case "SPODE"_sh:
-            spode.fit(Xd, y, features, className, states);
-            lines = spode.show();
-            score = spode.score(Xd, y);
-            graph = spode.graph();
-            break;
-        case "TAN"_sh:
-            tan.fit(Xd, y, features, className, states);
-            lines = tan.show();
-            score = tan.score(Xd, y);
-            graph = tan.graph();
-            break;
-    }
+    auto classifiers = map<string, bayesnet::BaseClassifier*>({ { "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) }, { "SPODE",  new bayesnet::SPODE(2) }, { "TAN",  new bayesnet::TAN() } });
+    bayesnet::BaseClassifier* clf = classifiers[model_name];
+    clf->fit(Xd, y, features, className, states);
+    score = clf->score(Xd, y);
+    auto lines = clf->show();
+    auto graph = clf->graph();
     for (auto line : lines) {
         cout << line << endl;
     }

src/BayesNet/AODE.h

@@ -8,7 +8,7 @@ namespace bayesnet {
         void train() override;
     public:
         AODE();
-        vector<string> graph(string title = "AODE");
+        vector<string> graph(string title = "AODE") override;
     };
 }
 #endif

src/BayesNet/BaseClassifier.h

@@ -1,48 +1,17 @@
-#ifndef CLASSIFIERS_H
-#define CLASSIFIERS_H
+#ifndef BASE_H
+#define BASE_H
 #include <torch/torch.h>
-#include "Network.h"
-#include "BayesMetrics.h"
-using namespace std;
-using namespace torch;
-
+#include <vector>
 namespace bayesnet {
+    using namespace std;
     class BaseClassifier {
-    private:
-        bool fitted;
-        BaseClassifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
-    protected:
-        Network model;
-        int m, n; // m: number of samples, n: number of features
-        Tensor X;
-        vector<vector<int>> Xv;
-        Tensor y;
-        vector<int> yv;
-        Tensor dataset;
-        Metrics metrics;
-        vector<string> features;
-        string className;
-        map<string, vector<int>> states;
-        void checkFitParameters();
-        virtual void train() = 0;
     public:
-        BaseClassifier(Network model);
+        virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        vector<int> virtual predict(vector<vector<int>>& X) = 0;
+        float virtual score(vector<vector<int>>& X, vector<int>& y) = 0;
+        vector<string> virtual show() = 0;
+        vector<string> virtual graph(string title = "") = 0;
         virtual ~BaseClassifier() = default;
-        BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states);
-        void addNodes();
-        int getNumberOfNodes();
-        int getNumberOfEdges();
-        Tensor predict(Tensor& X);
-        vector<int> predict(vector<vector<int>>& X);
-        float score(Tensor& X, Tensor& y);
-        float score(vector<vector<int>>& X, vector<int>& y);
-        vector<string> show();
-        virtual vector<string> graph(string title) = 0;
     };
 }
-#endif
-
-
-
-
-
+#endif

src/BayesNet/CMakeLists.txt

@@ -1,2 +1,2 @@
-add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc BaseClassifier.cc KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc Mst.cc)
+add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc Mst.cc)
 target_link_libraries(BayesNet "${TORCH_LIBRARIES}")

src/BayesNet/Classifier.cc

@@ -1,12 +1,12 @@
-#include "BaseClassifier.h"
+#include "Classifier.h"
 #include "bayesnetUtils.h"
 
 namespace bayesnet {
     using namespace std;
     using namespace torch;
 
-    BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    BaseClassifier& BaseClassifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
+    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
         dataset = torch::cat({ X, y.view({y.size(0), 1}) }, 1);
         this->features = features;
@@ -20,7 +20,7 @@ namespace bayesnet {
         fitted = true;
         return *this;
     }
-    BaseClassifier& BaseClassifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
         this->X = torch::zeros({ static_cast<int64_t>(X[0].size()), static_cast<int64_t>(X.size()) }, kInt64);
         Xv = X;
@@ -31,7 +31,7 @@ namespace bayesnet {
         yv = y;
         return build(features, className, states);
     }
-    void BaseClassifier::checkFitParameters()
+    void Classifier::checkFitParameters()
     {
         auto sizes = X.sizes();
         m = sizes[0];
@@ -52,7 +52,7 @@ namespace bayesnet {
         }
     }
 
-    Tensor BaseClassifier::predict(Tensor& X)
+    Tensor Classifier::predict(Tensor& X)
     {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
@@ -68,7 +68,7 @@ namespace bayesnet {
         auto ypred = torch::tensor(yp, torch::kInt64);
         return ypred;
     }
-    vector<int> BaseClassifier::predict(vector<vector<int>>& X)
+    vector<int> Classifier::predict(vector<vector<int>>& X)
     {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
@@ -82,7 +82,7 @@ namespace bayesnet {
         auto yp = model.predict(Xd);
         return yp;
     }
-    float BaseClassifier::score(Tensor& X, Tensor& y)
+    float Classifier::score(Tensor& X, Tensor& y)
     {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
@@ -90,7 +90,7 @@ namespace bayesnet {
         Tensor y_pred = predict(X);
         return (y_pred == y).sum().item<float>() / y.size(0);
     }
-    float BaseClassifier::score(vector<vector<int>>& X, vector<int>& y)
+    float Classifier::score(vector<vector<int>>& X, vector<int>& y)
     {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
@@ -103,11 +103,11 @@ namespace bayesnet {
         }
         return model.score(Xd, y);
     }
-    vector<string> BaseClassifier::show()
+    vector<string> Classifier::show()
     {
         return model.show();
     }
-    void BaseClassifier::addNodes()
+    void Classifier::addNodes()
     {
         // Add all nodes to the network
         for (auto feature : features) {
@@ -115,12 +115,12 @@ namespace bayesnet {
         }
         model.addNode(className, states[className].size());
     }
-    int BaseClassifier::getNumberOfNodes()
+    int Classifier::getNumberOfNodes()
     {
         // Features does not include class
         return fitted ? model.getFeatures().size() + 1 : 0;
     }
-    int BaseClassifier::getNumberOfEdges()
+    int Classifier::getNumberOfEdges()
     {
         return fitted ? model.getEdges().size() : 0;
     }

src/BayesNet/Classifier.h

@@ -0,0 +1,48 @@
+#ifndef CLASSIFIER_H
+#define CLASSIFIER_H
+#include <torch/torch.h>
+#include "BaseClassifier.h"
+#include "Network.h"
+#include "BayesMetrics.h"
+using namespace std;
+using namespace torch;
+
+namespace bayesnet {
+    class Classifier : public BaseClassifier {
+    private:
+        bool fitted;
+        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
+    protected:
+        Network model;
+        int m, n; // m: number of samples, n: number of features
+        Tensor X;
+        vector<vector<int>> Xv;
+        Tensor y;
+        vector<int> yv;
+        Tensor dataset;
+        Metrics metrics;
+        vector<string> features;
+        string className;
+        map<string, vector<int>> states;
+        void checkFitParameters();
+        virtual void train() = 0;
+    public:
+        Classifier(Network model);
+        virtual ~Classifier() = default;
+        Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states);
+        void addNodes();
+        int getNumberOfNodes();
+        int getNumberOfEdges();
+        Tensor predict(Tensor& X);
+        vector<int> predict(vector<vector<int>>& X);
+        float score(Tensor& X, Tensor& y);
+        float score(vector<vector<int>>& X, vector<int>& y);
+        vector<string> show();
+    };
+}
+#endif
+
+
+
+
+

src/BayesNet/Ensemble.h

@@ -1,20 +1,20 @@
 #ifndef ENSEMBLE_H
 #define ENSEMBLE_H
 #include <torch/torch.h>
-#include "BaseClassifier.h"
+#include "Classifier.h"
 #include "BayesMetrics.h"
 #include "bayesnetUtils.h"
 using namespace std;
 using namespace torch;
 
 namespace bayesnet {
-    class Ensemble {
+    class Ensemble : public BaseClassifier {
     private:
         bool fitted;
         long n_models;
         Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
-        vector<unique_ptr<BaseClassifier>> models;
+        vector<unique_ptr<Classifier>> models;
         int m, n; // m: number of samples, n: number of features
         Tensor X;
         vector<vector<int>> Xv;
@@ -30,13 +30,13 @@ namespace bayesnet {
     public:
         Ensemble();
         virtual ~Ensemble() = default;
-        Ensemble& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states);
+        Ensemble& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Tensor predict(Tensor& X);
-        vector<int> predict(vector<vector<int>>& X);
+        vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y);
-        float score(vector<vector<int>>& X, vector<int>& y);
-        vector<string> show();
-        vector<string> graph(string title);
+        float score(vector<vector<int>>& X, vector<int>& y) override;
+        vector<string> show() override;
+        vector<string> graph(string title) override;
     };
 }
 #endif

src/BayesNet/KDB.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
 
-    KDB::KDB(int k, float theta) : BaseClassifier(Network()), k(k), theta(theta) {}
+    KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
     void KDB::train()
     {
         /*

src/BayesNet/KDB.h

@@ -1,11 +1,11 @@
 #ifndef KDB_H
 #define KDB_H
-#include "BaseClassifier.h"
+#include "Classifier.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
     using namespace std;
     using namespace torch;
-    class KDB : public BaseClassifier {
+    class KDB : public Classifier {
     private:
         int k;
         float theta;

src/BayesNet/SPODE.cc

@@ -2,7 +2,7 @@
 
 namespace bayesnet {
 
-    SPODE::SPODE(int root) : BaseClassifier(Network()), root(root) {}
+    SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
 
     void SPODE::train()
     {

src/BayesNet/SPODE.h

@@ -1,8 +1,8 @@
 #ifndef SPODE_H
 #define SPODE_H
-#include "BaseClassifier.h"
+#include "Classifier.h"
 namespace bayesnet {
-    class SPODE : public BaseClassifier {
+    class SPODE : public Classifier {
     private:
         int root;
     protected:

src/BayesNet/TAN.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
 
-    TAN::TAN() : BaseClassifier(Network()) {}
+    TAN::TAN() : Classifier(Network()) {}
 
     void TAN::train()
     {

src/BayesNet/TAN.h

@@ -1,10 +1,10 @@
 #ifndef TAN_H
 #define TAN_H
-#include "BaseClassifier.h"
+#include "Classifier.h"
 namespace bayesnet {
     using namespace std;
     using namespace torch;
-    class TAN : public BaseClassifier {
+    class TAN : public Classifier {
     private:
     protected:
         void train() override;

src/Platform/Experiment.cc

@@ -11,12 +11,11 @@
 #include "SPODE.h"
 #include "AODE.h"
 #include "TAN.h"
+#include "platformUtils.h"
 
 
 using namespace std;
 
-const string PATH = "../../data/";
-
 inline constexpr auto hash_conv(const std::string_view sv)
 {
     unsigned long hash{ 5381 };
@@ -31,31 +30,6 @@ inline constexpr auto operator"" _sh(const char* str, size_t len)
     return hash_conv(std::string_view{ str, len });
 }
 
-pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
-{
-    vector<mdlp::labels_t>Xd;
-    map<string, int> maxes;
-
-    auto fimdlp = mdlp::CPPFImdlp();
-    for (int i = 0; i < X.size(); i++) {
-        fimdlp.fit(X[i], y);
-        mdlp::labels_t& xd = fimdlp.transform(X[i]);
-        maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
-        Xd.push_back(xd);
-    }
-    return { Xd, maxes };
-}
-
-bool file_exists(const std::string& name)
-{
-    if (FILE* file = fopen(name.c_str(), "r")) {
-        fclose(file);
-        return true;
-    } else {
-        return false;
-    }
-}
-
 int main(int argc, char** argv)
 {
     map<string, bool> datasets = {

src/Platform/Folding.cc

@@ -63,7 +63,7 @@ StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed) :
             class_indices[label].erase(class_indices[label].begin(), it);
         }
         while (remainder_samples_to_take > 0) {
-            int fold = (rand() % static_cast<int>(k));
+            int fold = (arc4random() % static_cast<int>(k));
             if (stratified_indices[fold].size() == fold_size) {
                 continue;
             }