Fix ExcelFile warning

CMakeLists.txt

@@ -13,6 +13,7 @@ set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED            ON)
 set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
+set(CMAKE_POSITION_INDEPENDENT_CODE        ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")

conanfile.py

@@ -15,12 +15,12 @@ class PlatformConan(ConanFile):
     def requirements(self):
         # Core dependencies from vcpkg.json
         self.requires("argparse/3.2")
-        self.requires("libtorch/2.7.0")
+        self.requires("libtorch/2.7.1")
         self.requires("nlohmann_json/3.11.3")
-        self.requires("folding/1.1.1")
+        self.requires("folding/1.1.2")
-        self.requires("fimdlp/2.1.0")
+        self.requires("fimdlp/2.1.1")
-        self.requires("arff-files/1.2.0")
+        self.requires("arff-files/1.2.1")
-        self.requires("bayesnet/1.2.0")
+        self.requires("bayesnet/1.2.1")
         self.requires("pyclassifiers/1.0.3")
         self.requires("libxlsxwriter/1.2.2")
         

src/common/TensorUtils.h

@@ -1,18 +0,0 @@
-#ifndef TENSOR_UTILS_H
-#define TENSOR_UTILS_H
-
-#include <torch/torch.h>
-#include <vector>
-
-namespace platform {
-    template <typename T>
-    std::vector<T> tensorToVector(const torch::Tensor& tensor)
-    {
-        torch::Tensor contig_tensor = tensor.contiguous();
-        auto num_elements = contig_tensor.numel();
-        const T* tensor_data = contig_tensor.data_ptr<T>();
-        std::vector<T> result(tensor_data, tensor_data + num_elements);
-        return result;
-    }
-}
-#endif

src/common/TensorUtils.hpp

@@ -5,6 +5,15 @@
 namespace platform {
     class TensorUtils {
     public:
+        template <typename T>
+        static std::vector<T> tensorToVector(const torch::Tensor& tensor)
+        {
+            torch::Tensor contig_tensor = tensor.contiguous();
+            auto num_elements = contig_tensor.numel();
+            const T* tensor_data = contig_tensor.data_ptr<T>();
+            std::vector<T> result(tensor_data, tensor_data + num_elements);
+            return result;
+        }
         static std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
         {
             // Ensure tensor is contiguous in memory
@@ -53,7 +62,7 @@ namespace platform {
             torch::Tensor tensor = torch::empty({ static_cast<long>(rows), static_cast<long>(cols) }, torch::kInt64);
             for (size_t i = 0; i < rows; ++i) {
                 for (size_t j = 0; j < cols; ++j) {
-                    tensor.index_put_({ static_cast<long>(i), static_cast<long>(j) }, data[i][j]);
+                    tensor.index_put_({static_cast<int64_t>(i), static_cast<int64_t>(j)}, torch::scalar_tensor(data[i][j]));
                 }
             }
             return tensor;

src/experimental_clfs/AdaBoost.cpp

@@ -11,7 +11,7 @@
 #include <numeric>
 #include <sstream>
 #include <iomanip>
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 // Conditional debug macro for performance-critical sections
 #define DEBUG_LOG(condition, ...) \

src/experimental_clfs/AdaBoost.h

@@ -38,7 +38,7 @@ namespace bayesnet {
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         torch::Tensor predict_proba(torch::Tensor& X) override;
-        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X);
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
         void setDebug(bool debug) { this->debug = debug; }
 
     protected:

src/experimental_clfs/DecisionTree.cpp

@@ -10,7 +10,7 @@
 #include <sstream>
 #include <iomanip>
 #include <limits>
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace bayesnet {
 

src/experimental_clfs/DecisionTree.h

@@ -40,7 +40,7 @@ namespace bayesnet {
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         torch::Tensor predict_proba(torch::Tensor& X) override;
-        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X);
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
 
         // Make predictions for a single sample
         int predictSample(const torch::Tensor& x) const;

src/experimental_clfs/ExpClf.cpp

@@ -5,7 +5,7 @@
 // ***************************************************************
 
 #include "ExpClf.h"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace platform {
     ExpClf::ExpClf() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)

src/experimental_clfs/ExpEnsemble.cpp

@@ -5,7 +5,7 @@
 // ***************************************************************
 
 #include "ExpEnsemble.h"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace platform {
     ExpEnsemble::ExpEnsemble() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)

src/experimental_clfs/XA1DE.cpp

@@ -5,7 +5,7 @@
 // ***************************************************************
 
 #include "XA1DE.h"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace platform {
     void XA1DE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)

src/experimental_clfs/XBAODE.cpp

@@ -10,7 +10,7 @@
 #include <tuple>
 #include "XBAODE.h"
 #include "XSpode.hpp"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 #include <loguru.hpp>
 
 namespace platform {

src/main/RocAuc.cpp

@@ -3,7 +3,7 @@
 #include <numeric>
 #include <utility>
 #include "RocAuc.h"
-#include "common/TensorUtils.h" // tensorToVector
+#include "common/TensorUtils.hpp" // tensorToVector
 namespace platform {
 
     double RocAuc::compute(const torch::Tensor& y_proba, const torch::Tensor& labels)

src/main/Scores.cpp

@@ -1,6 +1,6 @@
 #include <sstream>
 #include "Scores.h"
-#include "common/TensorUtils.h" // tensorToVector
+#include "common/TensorUtils.hpp" // tensorToVector
 #include "common/Colors.h"
 namespace platform {
     Scores::Scores(torch::Tensor& y_test, torch::Tensor& y_proba, int num_classes, std::vector<std::string> labels) : num_classes(num_classes), labels(labels), y_test(y_test), y_proba(y_proba)
@@ -50,7 +50,7 @@ namespace platform {
         auto nClasses = num_classes;
         if (num_classes == 2)
             nClasses = 1;
-        auto y_testv = tensorToVector<int>(y_test);
+        auto y_testv = TensorUtils::tensorToVector<int>(y_test);
         std::vector<double> aucScores(nClasses, 0.0);
         std::vector<std::pair<double, int>> scoresAndLabels;
         for (size_t classIdx = 0; classIdx < nClasses; ++classIdx) {

src/reports/ExcelFile.cpp

@@ -54,10 +54,8 @@ namespace platform {
     }
     void ExcelFile::setProperties(std::string title)
     {
-        char line[title.size() + 1];
-        strcpy(line, title.c_str());
         lxw_doc_properties properties = {
-            .title = line,
+            .title = title.c_str(),
             .subject = (char*)"Machine learning results",
             .author = (char*)"Ricardo Montañana Gómez",
             .manager = (char*)"Dr. J. A. Gámez, Dr. J. M. Puerta",

tests/TestAdaBoost.cpp

@@ -13,7 +13,7 @@
 #include <stdexcept>
 #include "experimental_clfs/AdaBoost.h"
 #include "experimental_clfs/DecisionTree.h"
-#include "experimental_clfs/TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 #include "TestUtils.h"
 
 using namespace bayesnet;