Add XGBoost & RandomForest

src/CMakeLists.txt

@@ -2,7 +2,7 @@ include_directories(${PyWrap_SOURCE_DIR}/lib/Files)
 include_directories(${Python3_INCLUDE_DIRS})
 include_directories(${TORCH_INCLUDE_DIRS})
 
-add_executable(main main.cc STree.cc SVC.cc PyClassifier.cc PyWrap.cc)
+add_executable(main main.cc STree.cc SVC.cc RandomForest.cc PyClassifier.cc PyWrap.cc)
 add_executable(example example.cpp PyWrap.cc)
 
 target_link_libraries(main ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)

src/PyHelper.hpp

@@ -23,8 +23,6 @@ namespace pywrap {
             Py_Finalize();
         }
     };
-
-
     class CPyObject {
     private:
         PyObject* p;
@@ -36,23 +34,18 @@ namespace pywrap {
         CPyObject(PyObject* _p) : p(_p)
         {
         }
-
-
         ~CPyObject()
         {
             Release();
         }
-
         PyObject* getObject()
         {
             return p;
         }
-
         PyObject* setObject(PyObject* _p)
         {
             return (p = _p);
         }
-
         PyObject* AddRef()
         {
             if (p) {
@@ -60,7 +53,6 @@ namespace pywrap {
             }
             return p;
         }
-
         void Release()
         {
             if (p) {
@@ -69,33 +61,27 @@ namespace pywrap {
 
             p = NULL;
         }
-
         PyObject* operator ->()
         {
             return p;
         }
-
         bool is()
         {
             return p ? true : false;
         }
-
         operator PyObject* ()
         {
             return p;
         }
-
         PyObject* operator = (PyObject* pp)
         {
             p = pp;
             return p;
         }
-
         operator bool()
         {
             return p ? true : false;
         }
     };
-
 } /* namespace pywrap */
 #endif

src/PyWrap.cc

@@ -143,6 +143,7 @@ namespace pywrap {
             RemoveInstance();
             exit(1);
         }
+        Py_INCREF(result);
         return result; // Caller must free this object
     }
     double PyWrap::score(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y)

src/RandomForest.cc

@@ -0,0 +1,8 @@
+#include "RandomForest.h"
+
+namespace pywrap {
+    std::string RandomForest::version()
+    {
+        return callMethodString("1.0");
+    }
+} /* namespace pywrap */

src/RandomForest.h

@@ -0,0 +1,13 @@
+#ifndef RANDOMFOREST_H
+#define RANDOMFOREST_H
+#include "PyClassifier.h"
+
+namespace pywrap {
+    class RandomForest : public PyClassifier {
+    public:
+        RandomForest() : PyClassifier("sklearn.ensemble", "RandomForestClassifier") {};
+        ~RandomForest() = default;
+        std::string version();
+    };
+} /* namespace pywrap */
+#endif /* RANDOMFOREST_H */

src/SVC.cc

@@ -3,6 +3,6 @@
 namespace pywrap {
     std::string SVC::version()
     {
-        return callMethodString("_repr_html_");
+        return callMethodString("1.0");
     }
 } /* namespace pywrap */

src/XGBoost.cc

@@ -0,0 +1,8 @@
+#include "XGBoost.h"
+
+namespace pywrap {
+    std::string XGBoost::version()
+    {
+        return callMethodString("1.0");
+    }
+} /* namespace pywrap */

src/XGBoost.h

@@ -0,0 +1,13 @@
+#ifndef XGBOOST_H
+#define XGBOOST_H
+#include "PyClassifier.h"
+
+namespace pywrap {
+    class XGBoost : public PyClassifier {
+    public:
+        XGBoost() : PyClassifier("xgboost", "XGBClassifier") {};
+        ~XGBoost() = default;
+        std::string version();
+    };
+} /* namespace pywrap */
+#endif /* XGBOOST_H */

src/main.cc

@@ -7,6 +7,7 @@
 #include <tuple>
 #include "STree.h"
 #include "SVC.h"
+#include "RandomForest.h"
 
 using namespace std;
 using namespace torch;
@@ -44,25 +45,32 @@ int main(int argc, char* argv[])
 {
     cout << "* Begin." << endl;
     {
-        auto [X, y, features, className, states] = loadDataset("wine", false);
+        auto datasetName = "iris";
+        bool class_last = true;
+        auto [X, y, features, className, states] = loadDataset(datasetName, class_last);
+        cout << "Dataset: " << datasetName << endl;
         cout << "X: " << X.sizes() << endl;
         cout << "y: " << y.sizes() << endl;
         auto clf = pywrap::STree();
         cout << "STree Version: " << clf.version() << endl;
-        if (true) {
+        auto svc = pywrap::SVC();
-            auto svc = pywrap::SVC();
+        svc.fit(X, y, features, className, states);
-            svc.fit(X, y, features, className, states);
-            cout << "SVC Score: " << svc.score(X, y) << endl;
-        }
         cout << "Graph: " << endl << clf.graph() << endl;
         clf.fit(X, y, features, className, states);
-        cout << "STree Score: " << clf.score(X, y) << endl;
         auto prediction = clf.predict(X);
         cout << "Prediction: " << endl << "{";
         for (int i = 0; i < prediction.size(0); ++i) {
             cout << prediction[i].item<int>() << ", ";
         }
         cout << "}" << endl;
+        auto rf = pywrap::RandomForest();
+        rf.fit(X, y, features, className, states);
+        auto xg = pywrap::RandomForest();
+        xg.fit(X, y, features, className, states);
+        cout << "STree Score ......: " << clf.score(X, y) << endl;
+        cout << "RandomForest Score: " << rf.score(X, y) << endl;
+        cout << "SVC Score ........: " << svc.score(X, y) << endl;
+        cout << "XGBoost Score ....: " << xg.score(X, y) << endl;
     }
     cout << "* End." << endl;
 }