refactor folders

CMakeLists.txt

@@ -40,4 +40,5 @@ add_subdirectory(lib/Files)
 
 # Include directories
 # -------------------
-add_subdirectory(src)
+add_subdirectory(src)
+add_subdirectory(example)

example/CMakeLists.txt

@@ -0,0 +1,8 @@
+include_directories(${PyWrap_SOURCE_DIR}/lib/Files)
+include_directories(${PyWrap_SOURCE_DIR}/lib/json/include)
+include_directories(${Python3_INCLUDE_DIRS})
+include_directories(${PyWrap_SOURCE_DIR}/src)
+include_directories(${TORCH_INCLUDE_DIRS})
+
+add_executable(example example.cc)
+target_link_libraries(example PyWrap)

example/example.cc

@@ -0,0 +1,97 @@
+#include <torch/torch.h>
+#include "ArffFiles.h"
+#include <vector>
+#include <string>
+#include <iostream>
+#include <map>
+#include <tuple>
+#include "STree.h"
+#include "SVC.h"
+#include "RandomForest.h"
+#include "XGBoost.h"
+
+using namespace std;
+using namespace torch;
+
+class Paths {
+public:
+    static string datasets()
+    {
+        return "../discretizbench/datasets/";
+    }
+};
+
+tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
+{
+    auto handler = ArffFiles();
+    handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
+    // Get Dataset X, y
+    vector<vector<float>> X = handler.getX();
+    vector<int> y = handler.getY();
+    // Get className & Features
+    auto className = handler.getClassName();
+    vector<string> features;
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
+    Tensor Xd;
+    auto states = map<string, vector<int>>();
+    Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
+    for (int i = 0; i < features.size(); ++i) {
+        Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
+    }
+    return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
+}
+
+int main(int argc, char* argv[])
+{
+    using json = nlohmann::json;
+    cout << "* Begin." << endl;
+    {
+        using namespace torch::indexing;
+        auto datasetName = "iris";
+        bool class_last = true;
+        auto [X, y, features, className, states] = loadDataset(datasetName, class_last);
+        auto m = y.size(0);
+        int train_split = m * .7;
+        auto Xtrain = X.index({ "...", Slice(0, train_split) });
+        auto ytrain = y.index({ Slice(0, train_split) });
+        auto Xtest = X.index({ "...", Slice(train_split, m) });
+        auto ytest = y.index({ Slice(train_split, m) });
+        cout << "Dataset: " << datasetName << endl;
+        cout << "X: " << X.sizes() << endl;
+        cout << "y: " << y.sizes() << endl;
+        // auto clf = pywrap::STree();
+        // auto stree = pywrap::STree();
+        // auto hyperparameters = json::parse("{\"C\": 0.7, \"max_iter\": 10000, \"kernel\": \"rbf\", \"random_state\": 17}");
+        // stree.setHyperparameters(hyperparameters);
+        // cout << "STree Version: " << clf.version() << endl;
+        // auto svc = pywrap::SVC();
+        // cout << "SVC with hyperparameters" << endl;
+        // svc.fit(X, y, features, className, states);
+        // cout << "Graph: " << endl << clf.graph() << endl;
+        // double clf_score = clf.fit(X, y, features, className, states).score(X, y);
+        // double stree_score = stree.fit(X, y, features, className, states).score(X, y);
+        // auto prediction = clf.predict(X);
+        // cout << "Prediction: " << endl << "{";
+        // for (int i = 0; i < prediction.size(0); ++i) {
+        //     cout << prediction[i].item<int>() << ", ";
+        // }
+        // cout << "}" << endl;
+        // cout << "Building Random Forest" << endl;
+        // auto rf = pywrap::RandomForest();
+        // rf.fit(X, y, features, className, states);
+        cout << "Building XGBoost" << endl;
+        auto xg = pywrap::XGBoost();
+        cout << "Fitting XGBoost" << endl;
+        xg.fit(Xtrain, ytrain, features, className, states);
+        cout << "Scoring dataset" << endl;
+        double xg_score = xg.score(Xtest, ytest);
+        // cout << "Scores:" << endl;
+        // cout << "STree Score ......: " << clf_score << endl;
+        // cout << "STree hyper score : " << stree_score << endl;
+        // cout << "RandomForest Score: " << rf.score(X, y) << endl;
+        // cout << "SVC Score ........: " << svc.score(X, y) << endl;
+        cout << "XGBoost Score ....: " << xg_score << endl;
+    }
+    cout << "* End." << endl;
+}

pspp.jnl

@@ -1,3 +0,0 @@
-GET FILE="/home/rmontanana/Code/covbench/data/covid_v9_20220630.sav".
-SHOW SYSTEM.
-SHOW SYSTEM.

src/CMakeLists.txt

@@ -3,9 +3,5 @@ include_directories(${PyWrap_SOURCE_DIR}/lib/json/include)
 include_directories(${Python3_INCLUDE_DIRS})
 include_directories(${TORCH_INCLUDE_DIRS})
 
-
 add_library(PyWrap SHARED PyWrap.cc STree.cc SVC.cc RandomForest.cc PyClassifier.cc)
-target_link_libraries(PyWrap ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)
+target_link_libraries(PyWrap ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)
-
-add_executable(example example.cc)
-target_link_libraries(example PyWrap)

src/PyClassifier.cc

@@ -61,8 +61,8 @@ namespace pywrap {
             throw std::runtime_error("Error creating object for predict in " + module + " and class " + className);
         }
         int* data = reinterpret_cast<int*>(prediction.get_data());
-        std::vector<int> v1(data, data + prediction.shape(0));
+        std::vector<int> vPrediction(data, data + prediction.shape(0));
-        auto resultTensor = torch::tensor(v1, torch::kInt32);
+        auto resultTensor = torch::tensor(vPrediction, torch::kInt32);
         Py_XDECREF(incoming);
         return resultTensor;
     }

src/PyWrap.cc

@@ -142,24 +142,22 @@ namespace pywrap {
     {
         PyObject* instance = getClass(id);
         CPyObject result;
-        std::string method = "fit";
+        CPyObject method = PyUnicode_FromString("fit");
         try {
-            if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), y.getObject(), NULL)))
+            if (!(result = PyObject_CallMethodObjArgs(instance, method.getObject(), X.getObject(), y.getObject(), NULL)))
                 errorAbort("Couldn't call method fit");
         }
         catch (const std::exception& e) {
             errorAbort(e.what());
         }
-        // Py_XDECREF(result);
     }
-
     PyObject* PyWrap::predict(const clfId_t id, CPyObject& X)
     {
         PyObject* instance = getClass(id);
         PyObject* result;
-        std::string method = "predict";
+        CPyObject method = PyUnicode_FromString("predict");
         try {
-            if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), NULL)))
+            if (!(result = PyObject_CallMethodObjArgs(instance, method.getObject(), X.getObject(), NULL)))
                 errorAbort("Couldn't call method predict");
         }
         catch (const std::exception& e) {
@@ -172,16 +170,15 @@ namespace pywrap {
     {
         PyObject* instance = getClass(id);
         CPyObject result;
-        std::string method = "score";
+        CPyObject method = PyUnicode_FromString("score");
         try {
-            if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), y.getObject(), NULL)))
+            if (!(result = PyObject_CallMethodObjArgs(instance, method.getObject(), X.getObject(), y.getObject(), NULL)))
                 errorAbort("Couldn't call method score");
         }
         catch (const std::exception& e) {
             errorAbort(e.what());
         }
         double resultValue = PyFloat_AsDouble(result);
-        // Py_XDECREF(result);
         return resultValue;
     }
 }

src/TypeId.h

@@ -1,4 +1,6 @@
 #ifndef TYPEDEF_H
 #define TYPEDEF_H
-typedef uint64_t clfId_t;
+namespace pywrap {
+    typedef uint64_t clfId_t;
+}
 #endif /* TYPEDEF_H */

src/example.cc

@@ -1,84 +0,0 @@
-#include <torch/torch.h>
-#include "ArffFiles.h"
-#include <vector>
-#include <string>
-#include <iostream>
-#include <map>
-#include <tuple>
-#include "STree.h"
-#include "SVC.h"
-#include "RandomForest.h"
-#include "XGBoost.h"
-
-using namespace std;
-using namespace torch;
-
-class Paths {
-public:
-    static string datasets()
-    {
-        return "../discretizbench/datasets/";
-    }
-};
-
-tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
-{
-    auto handler = ArffFiles();
-    handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
-    // Get Dataset X, y
-    vector<vector<float>> X = handler.getX();
-    vector<int> y = handler.getY();
-    // Get className & Features
-    auto className = handler.getClassName();
-    vector<string> features;
-    auto attributes = handler.getAttributes();
-    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
-    Tensor Xd;
-    auto states = map<string, vector<int>>();
-    Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
-    for (int i = 0; i < features.size(); ++i) {
-        Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
-    }
-    return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
-}
-
-int main(int argc, char* argv[])
-{
-    using json = nlohmann::json;
-    cout << "* Begin." << endl;
-    {
-        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();
-        auto stree = pywrap::STree();
-        auto hyperparameters = json::parse("{\"C\": 0.7, \"max_iter\": 10000, \"kernel\": \"rbf\", \"random_state\": 17}");
-        stree.setHyperparameters(hyperparameters);
-        cout << "STree Version: " << clf.version() << endl;
-        auto svc = pywrap::SVC();
-        cout << "SVC with hyperparameters" << endl;
-        svc.fit(X, y, features, className, states);
-        cout << "Graph: " << endl << clf.graph() << endl;
-        clf.fit(X, y, features, className, states);
-        stree.fit(X, y, features, className, states);
-        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::XGBoost();
-        xg.fit(X, y, features, className, states);
-        cout << "STree Score ......: " << clf.score(X, y) << endl;
-        cout << "STree hyper score : " << stree.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;
-}