Begin conan integration

2025-07-03 01:40:30 +02:00
parent 1ef7ca6180
commit 3d814a79c6
16 changed files with 350 additions and 127 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -43,3 +43,5 @@ diagrams/html/**
 diagrams/latex/**
 .cache
 vcpkg_installed
+.claude/settings.local.json
+CMakeUserPresets.json
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -0,0 +1,93 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+### Changed
+- **BREAKING**: Migrated dependency management from vcpkg to Conan
+- Updated build system to use Conan toolchain files instead of vcpkg
+- Updated `make init` command to use `conan install` instead of `vcpkg install`
+- Modified CMakeLists.txt to use Conan's find_package mechanism
+- Updated documentation in CLAUDE.md to reflect Conan usage
+
+### Added
+- `conanfile.py` - Conan recipe for dependency management with all required dependencies
+- CMakeUserPresets.json (generated by Conan)
+- Support for Conan build profiles (Release/Debug)
+
+### Removed
+- `vcpkg.json` - vcpkg manifest file
+- `vcpkg-configuration.json` - vcpkg registry configuration
+- vcpkg toolchain dependency in build system
+
+### Notes
+- The migration maintains compatibility with existing make targets and workflow
+- All dependencies now managed through Conan package manager
+
+## [1.1.0] - 2025-07-02
+
+### Added
+- **AdaBoost Implementation**: Complete multi-class SAMME AdaBoost classifier with optimization
+  - Optimized AdaBoostPredict with 100 estimators as default
+  - Enhanced predictProbaSample functionality
+  - Full predict_proba support for probabilistic predictions
+- **Decision Tree Classifier**: New base classifier implementation with comprehensive tests
+- **XA1DE Model Family**: Extended Averaged One-Dependence Estimators
+  - XA1DE, XBAODE, XSPODE variants with threading support
+  - Complete integration with memory optimization
+  - Prior probability computation in prediction
+- **Wilcoxon Statistical Test**: Statistical significance testing for model comparison
+- **Folder Management**: Enhanced file organization with folder parameter support across tools
+  - Added folder parameter to b_best, b_grid, b_main, and b_manage
+- **vcpkg Integration**: Package management system integration (now migrated to Conan)
+
+### Enhanced
+- **Grid Search System**: Complete refactoring with MPI parallelization
+  - Grid experiment functionality with conditional result saving
+  - Fixed smoothing problems and dataset ordering
+  - Enhanced reporting and summary generation
+- **Excel Reporting**: Advanced Excel export capabilities
+  - ReportExcelCompared class for side-by-side result comparison
+  - Enhanced formatting with colors and fixed headers
+  - Automatic file opening after generation
+- **Results Management**: Comprehensive result handling and validation
+  - JSON schema validation for result format integrity
+  - Improved console reporting with classification reports
+  - Pagination support for large result sets
+- **Statistical Analysis**: Enhanced statistical testing and reporting
+  - AUC (Area Under Curve) computation and reporting
+  - Confusion matrix generation and visualization
+  - Classification reports with color coding
+
+### Performance Improvements
+- Optimized AdaBoost training and prediction algorithms
+- Enhanced memory management in XA1DE implementations
+- Improved discretization algorithms with MDLP integration
+- Faster ROC-AUC computation for binary classification problems
+
+### Developer Experience
+- **Testing Framework**: Comprehensive test suite with Catch2
+- **Build System**: Streamlined CMake configuration with dependency management
+- **Documentation**: Enhanced project documentation and build instructions
+- **Code Quality**: Refactored codebase with improved error handling and logging
+
+### Bug Fixes
+- Fixed predict_proba implementations across multiple classifiers
+- Resolved grid search dataset ordering issues  
+- Fixed Excel report formatting and column width problems
+- Corrected time output formatting in various tools
+- Fixed memory leaks and stability issues in model implementations
+
+## [1.0.0] - 2024-01-09
+
+### Initial Release
+- **Core Framework**: Machine learning experimentation platform for Bayesian Networks
+- **Basic Classifiers**: Initial set of Bayesian network classifiers
+- **Experiment Management**: Basic experiment orchestration and result storage
+- **Dataset Support**: ARFF file format support with discretization
+- **Build System**: CMake-based build system with external library integration
+- **Command Line Tools**: Initial versions of b_main, b_best, b_list utilities
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -0,0 +1,139 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Platform is a C++ machine learning framework for running experiments with Bayesian Networks and other classifiers. It supports both research-focused experimental classifiers and production-ready models through a unified interface.
+
+## Build System
+
+The project uses CMake with Make as the primary build system:
+
+- **Release build**: `make release` (creates `build_Release/` directory)
+- **Debug build**: `make debug` (creates `build_Debug/` directory with testing and coverage enabled)
+- **Install binaries**: `make install` (copies executables to `~/bin` by default)
+- **Clean project**: `make clean` (removes build directories)
+- **Initialize dependencies**: `make init` (runs conan install for both Release and Debug)
+
+### Testing
+
+- **Run tests**: `make test` (builds debug version and runs all tests)
+- **Coverage report**: `make coverage` (runs tests and generates coverage with gcovr)
+- **Single test with options**: `make test opt="-s"` (verbose) or `make test opt="-c='Test Name'"` (specific test)
+
+### Build Targets
+
+Main executables (built from `src/commands/`):
+- `b_main`: Main experiment runner
+- `b_grid`: Grid search over hyperparameters 
+- `b_best`: Best results analysis and comparison
+- `b_list`: Dataset listing and properties
+- `b_manage`: Results management interface
+- `b_results`: Results processing
+
+## Dependencies
+
+The project uses Conan for package management with these key dependencies:
+- **libtorch**: PyTorch C++ backend for tensor operations
+- **nlohmann_json**: JSON processing
+- **catch2**: Unit testing framework
+- **cli11**: Command-line argument parsing (replacement for argparse)
+
+Custom dependencies (not available in ConanCenter):
+- **fimdlp**: MDLP discretization library (needs manual integration)
+- **folding**: Cross-validation utilities (needs manual integration)
+- **arff-files**: ARFF dataset file handling (needs manual integration)
+
+External dependencies (managed separately):
+- **BayesNet**: Core Bayesian network classifiers (from `../lib/`)
+- **PyClassifiers**: Python classifier wrappers (from `../lib/`)
+- **MPI**: Message Passing Interface for parallel processing
+- **Boost**: Python integration and utilities
+
+**Note**: Some dependencies (fimdlp, folding, arff-files) are not available in ConanCenter and need to be:
+- Built as custom Conan packages, or
+- Integrated using CMake FetchContent, or
+- Built separately and found via find_package
+
+## Architecture
+
+### Core Components
+
+**Experiment Framework** (`src/main/`):
+- `Experiment.cpp/h`: Main experiment orchestration
+- `Models.cpp/h`: Classifier factory and registration system
+- `Scores.cpp/h`: Performance metrics calculation
+- `HyperParameters.cpp/h`: Parameter management
+- `ArgumentsExperiment.cpp/h`: Command-line argument handling
+
+**Data Handling** (`src/common/`):
+- `Dataset.cpp/h`: Individual dataset representation
+- `Datasets.cpp/h`: Dataset collection management
+- `Discretization.cpp/h`: Data discretization utilities
+
+**Classifiers** (`src/experimental_clfs/`):
+- `AdaBoost.cpp/h`: Multi-class SAMME AdaBoost implementation
+- `DecisionTree.cpp/h`: Decision tree base classifier
+- `XA1DE.cpp/h`: Extended AODE variants
+- Experimental implementations of Bayesian network classifiers
+
+**Grid Search** (`src/grid/`):
+- `GridSearch.cpp/h`: Hyperparameter optimization
+- `GridExperiment.cpp/h`: Grid search experiment management
+- Uses MPI for parallel hyperparameter evaluation
+
+**Results & Reporting** (`src/results/`, `src/reports/`):
+- JSON-based result storage with schema validation
+- Excel export capabilities via libxlsxwriter
+- Console and paginated result display
+
+### Model Registration System
+
+The framework uses a factory pattern with automatic registration:
+- All classifiers inherit from `bayesnet::BaseClassifier`
+- Registration happens in `src/main/modelRegister.h`
+- Factory creates instances by string name via `Models::create()`
+
+## Configuration
+
+**Environment Configuration** (`.env` file):
+- `experiment`: Experiment name/type
+- `n_folds`: Cross-validation folds (default: 5)
+- `seeds`: Random seeds for reproducibility
+- `model`: Default classifier name
+- `score`: Primary evaluation metric
+- `platform`: System identifier for results
+
+**Grid Search Configuration**:
+- `grid_<model_name>_input.json`: Hyperparameter search space
+- `grid_<model_name>_output.json`: Search results
+
+## Data Format
+
+**Dataset Requirements**:
+- ARFF format files in `datasets/` directory
+- `all.txt` file listing datasets: `<name>,<class_name>,<real_features>`
+- Supports both discrete and continuous features
+- Automatic discretization available via MDLP
+
+**Experimental Data**:
+- Results stored in JSON format with versioned schemas
+- Test data in `tests/data/` for unit testing
+- Sample datasets: iris, diabetes, ecoli, glass, etc.
+
+## Development Workflow
+
+1. **Setup**: Run `make init` to install dependencies via Conan
+2. **Development**: Use `make debug` for development builds with testing
+3. **Testing**: Run `make test` after changes
+4. **Release**: Use `make release` for optimized builds
+5. **Experiments**: Use `.env` configuration and run `b_main` with appropriate flags
+
+## Key Features
+
+- **Multi-threaded**: Uses MPI for parallel grid search and experiments
+- **Cross-platform**: Supports Linux and macOS via vcpkg
+- **Extensible**: Easy classifier registration and integration
+- **Research-focused**: Designed for machine learning experimentation
+- **Visualization**: DOT graph generation for decision trees and networks
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -15,7 +15,7 @@ set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
-set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
 set(CMAKE_CXX_FLAGS_DEBUG " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")

 # Options
@@ -43,8 +43,6 @@ find_package(Boost 1.66.0 REQUIRED COMPONENTS python3 numpy3)

 # # Python
 find_package(Python3 REQUIRED COMPONENTS Development)
-# # target_include_directories(MyTarget SYSTEM PRIVATE ${Python3_INCLUDE_DIRS})
-# message("Python_LIBRARIES=${Python_LIBRARIES}")

 # # Boost Python
 # find_package(boost_python${Python3_VERSION_MAJOR}${Python3_VERSION_MINOR} CONFIG REQUIRED COMPONENTS python${Python3_VERSION_MAJOR}${Python3_VERSION_MINOR})
@@ -63,28 +61,23 @@ endif()
 # External libraries - dependencies of Platform
 # ---------------------------------------------

-find_library(XLSXWRITER_LIB NAMES libxlsxwriter.dylib libxlsxwriter.so PATHS ${Platform_SOURCE_DIR}/lib/libxlsxwriter/lib)
+# find_library(XLSXWRITER_LIB NAMES libxlsxwriter.dylib libxlsxwriter.so PATHS ${Platform_SOURCE_DIR}/lib/libxlsxwriter/lib)
 # find_path(XLSXWRITER_INCLUDE_DIR xlsxwriter.h)
 # find_library(XLSXWRITER_LIBRARY xlsxwriter)
 # message("XLSXWRITER_INCLUDE_DIR=${XLSXWRITER_INCLUDE_DIR}")
 # message("XLSXWRITER_LIBRARY=${XLSXWRITER_LIBRARY}")
+# Conan dependencies
+find_package(nlohmann_json CONFIG REQUIRED)
+find_package(Catch2 CONFIG REQUIRED)
+find_package(argparse CONFIG REQUIRED)
 find_package(Torch CONFIG REQUIRED)
+find_package(arff-files CONFIG REQUIRED)
 find_package(fimdlp CONFIG REQUIRED)
 find_package(folding CONFIG REQUIRED)
-find_package(argparse CONFIG REQUIRED)
-find_package(nlohmann_json CONFIG REQUIRED)
-find_package(Boost REQUIRED COMPONENTS python)
-find_package(arff-files CONFIG REQUIRED)
+find_package(bayesnet CONFIG REQUIRED)
+find_package(libxlsxwriter CONFIG REQUIRED)

-# BayesNet
-find_library(bayesnet NAMES libbayesnet bayesnet libbayesnet.a PATHS ${Platform_SOURCE_DIR}/../lib/lib REQUIRED)
-find_path(Bayesnet_INCLUDE_DIRS REQUIRED NAMES bayesnet PATHS ${Platform_SOURCE_DIR}/../lib/include)
-add_library(bayesnet::bayesnet UNKNOWN IMPORTED)
-set_target_properties(bayesnet::bayesnet PROPERTIES
-  IMPORTED_LOCATION ${bayesnet}
-  INTERFACE_INCLUDE_DIRECTORIES ${Bayesnet_INCLUDE_DIRS})
-message(STATUS "BayesNet=${bayesnet}")
-message(STATUS "BayesNet_INCLUDE_DIRS=${Bayesnet_INCLUDE_DIRS}")
+find_package(Boost REQUIRED COMPONENTS python)

 # PyClassifiers
 find_library(PyClassifiers NAMES libPyClassifiers PyClassifiers libPyClassifiers.a PATHS ${Platform_SOURCE_DIR}/../lib/lib REQUIRED)
@@ -106,8 +99,9 @@ file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${Platform_SOURCE_DIR}/src/*.cpp)
 # -------
 if (ENABLE_TESTING)
  enable_testing()
+  set(CODE_COVERAGE ON)
  MESSAGE("Testing enabled")
-  find_package(Catch2 CONFIG REQUIRED)
+  # Catch2 is already found above via Conan
  include(CTest)
  add_subdirectory(tests)
 endif (ENABLE_TESTING)
--- a/52
+++ b/52
@@ -20,15 +20,33 @@ define ClearTests
 	fi ; 
 endef

+define build_target
+	@echo ">>> Building the project for $(1)..."
+	@if [ -d $(2) ]; then rm -fr $(2); fi
+	@conan install . --build=missing -of $(2) -s build_type=$(1)
+	@cmake -S . -B $(2) -DCMAKE_TOOLCHAIN_FILE=$(2)/build/$(1)/generators/conan_toolchain.cmake -DCMAKE_BUILD_TYPE=$(1) -D$(3)
+endef
+
+define compile_target
+	@echo ">>> Compiling for $(1)..."
+		if [ "$(3)" != "" ]; then \
+		target="-t$(3)"; \
+	else \
+		target=""; \
+	fi
+	@cmake --build $(2) --config $(1) --parallel $(target)
+endef
+
 init: ## Initialize the project installing dependencies
-	@echo ">>> Installing dependencies"
-	@vcpkg install
+	@echo ">>> Installing dependencies with Conan"
+	@conan install . --output-folder=build --build=missing -s build_type=Release
+	@conan install . --output-folder=build_debug --build=missing -s build_type=Debug
 	@echo ">>> Done";

 clean: ## Clean the project
 	@echo ">>> Cleaning the project..."
 	@if test -f CMakeCache.txt ; then echo "- Deleting CMakeCache.txt"; rm -f CMakeCache.txt; fi
-	@for folder in $(f_release) $(f_debug) vpcpkg_installed install_test ; do \
+	@for folder in $(f_release) $(f_debug) build build_debug install_test ; do \
 	if test -d "$$folder" ; then \
 		echo "- Deleting $$folder folder" ; \
 		rm -rf "$$folder"; \
@@ -45,11 +63,6 @@ setup: ## Install dependencies for tests and coverage
 		pip install gcovr; \
 	fi

-dest ?= ${HOME}/bin
-main: ## Build only the b_main target
-	@cmake --build $(f_release) -t b_main --parallel
-	@cp $(f_release)/src/b_main $(dest)
-
 dest ?= ${HOME}/bin
 install: ## Copy binary files to bin folder
 	@echo "Destination folder: $(dest)"
@@ -70,34 +83,27 @@ dependency: ## Create a dependency graph diagram of the project (build/dependenc
 	cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png

 buildd: ## Build the debug targets
-	@cmake --build $(f_debug) -t $(app_targets) PlatformSample --parallel 
+	@$(call compile_target,"Debug","$(f_debug)")

 buildr: ## Build the release targets
-	@cmake --build $(f_release) -t $(app_targets) --parallel
+	@$(call compile_target,"Release","$(f_release)")

 clang-uml: ## Create uml class and sequence diagrams
 	clang-uml -p --add-compile-flag -I /usr/lib/gcc/x86_64-redhat-linux/8/include/

-debug: ## Build a debug version of the project with BayesNet from vcpkg
-	@echo ">>> Building Debug Platform...";
-	@if [ -d ./$(f_debug) ]; then rm -rf ./$(f_debug); fi
-	@mkdir $(f_debug); 
-	@cmake -S . -B $(f_debug) -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON -D CMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake
-	@echo ">>> Done";
+debug: ## Build a debug version of the project with Conan
+	@$(call build_target,"Debug","$(f_debug)", "ENABLE_TESTING=ON")
+
+release: ## Build a Release version of the project with Conan
+	@$(call build_target,"Release","$(f_release)", "ENABLE_TESTING=OFF")

-release: ## Build a Release version of the project with BayesNet from vcpkg
-	@echo ">>> Building Release Platform...";
-	@if [ -d ./$(f_release) ]; then rm -rf ./$(f_release); fi
-	@mkdir $(f_release); 
-	@cmake -S . -B $(f_release) -D CMAKE_BUILD_TYPE=Release -D CMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake
-	@echo ">>> Done";

 opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
 	@echo ">>> Running Platform tests...";
 	@$(MAKE) clean
 	@$(MAKE) debug
-	@cmake --build $(f_debug) -t $(test_targets) --parallel
+	@$(call "Compile_target", "Debug", "$(f_debug)", $(test_targets))
 	@for t in $(test_targets); do \
 		if [ -f $(f_debug)/tests/$$t ]; then \
 			cd $(f_debug)/tests ; \
--- a/conanfile.py
+++ b/conanfile.py
@@ -0,0 +1,41 @@
+from conan import ConanFile
+from conan.tools.cmake import CMakeToolchain, CMakeDeps, cmake_layout
+
+
+class PlatformConan(ConanFile):
+    name = "platform"
+    version = "1.1.0"
+    
+    # Binary configuration
+    settings = "os", "compiler", "build_type", "arch"
+    
+    # Sources are located in the same place as this recipe, copy them to the recipe
+    exports_sources = "CMakeLists.txt", "src/*", "tests/*", "config/*", "cmake/*"
+    
+    def requirements(self):
+        # Core dependencies from vcpkg.json
+        self.requires("argparse/3.2")
+        self.requires("libtorch/2.7.0")
+        self.requires("nlohmann_json/3.11.3")
+        self.requires("folding/1.1.1")
+        self.requires("fimdlp/2.1.0")
+        self.requires("arff-files/1.2.0")
+        self.requires("bayesnet/1.2.0")
+        self.requires("libxlsxwriter/1.2.2")
+        
+    def build_requirements(self):
+        self.tool_requires("cmake/[>=3.30]")
+        self.test_requires("catch2/3.8.1")
+    
+    def layout(self):
+        cmake_layout(self)
+    
+    def generate(self):
+        deps = CMakeDeps(self)
+        deps.generate()
+        tc = CMakeToolchain(self)
+        tc.generate()
+    
+    def configure(self):
+        # C++20 requirement
+        self.settings.compiler.cppstd = "20"
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -2,12 +2,10 @@ include_directories(
    ## Libs
    ${Python3_INCLUDE_DIRS}
    ${MPI_CXX_INCLUDE_DIRS}
-    ${TORCH_INCLUDE_DIRS}
    ${CMAKE_BINARY_DIR}/configured_files/include
    ${PyClassifiers_INCLUDE_DIRS}
    ## Platform
    ${Platform_SOURCE_DIR}/src
-    ${Platform_SOURCE_DIR}/results
 )

 # b_best
@@ -23,7 +21,7 @@ add_executable(
    experimental_clfs/DecisionTree.cpp
    experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_best Boost::boost "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy "${XLSXWRITER_LIB}")
+target_link_libraries(b_best Boost::boost "${PyClassifiers}" bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy "${XLSXWRITER_LIB}")

 # b_grid
 set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp GridBase.cpp )
@@ -38,7 +36,7 @@ add_executable(b_grid commands/b_grid.cpp ${grid_sources}
    experimental_clfs/DecisionTree.cpp
    experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy)
+target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy)

 # b_list
 add_executable(b_list commands/b_list.cpp
@@ -51,7 +49,7 @@ add_executable(b_list commands/b_list.cpp
    experimental_clfs/DecisionTree.cpp
    experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_list "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy "${XLSXWRITER_LIB}")
+target_link_libraries(b_list "${PyClassifiers}" bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy "${XLSXWRITER_LIB}")

 # b_main
 set(main_sources Experiment.cpp Models.cpp HyperParameters.cpp Scores.cpp ArgumentsExperiment.cpp)
@@ -66,7 +64,7 @@ add_executable(b_main commands/b_main.cpp ${main_sources}
    experimental_clfs/DecisionTree.cpp
    experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_main PRIVATE nlohmann_json::nlohmann_json "${PyClassifiers}" bayesnet::bayesnet fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::python Boost::numpy)
+target_link_libraries(b_main PRIVATE nlohmann_json::nlohmann_json "${PyClassifiers}" bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy)

 # b_manage
 set(manage_sources ManageScreen.cpp OptionsMenu.cpp ResultsManager.cpp)
@@ -78,7 +76,8 @@ add_executable(
    results/Result.cpp results/ResultsDataset.cpp results/ResultsDatasetConsole.cpp
    main/Scores.cpp
 )
-target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" fimdlp bayesnet::bayesnet)
+target_link_libraries(b_manage torch::torch "${XLSXWRITER_LIB}" fimdlp::fimdlp bayesnet::bayesnet argparse::argparse)

 # b_results
 add_executable(b_results commands/b_results.cpp)
+target_link_libraries(b_results torch::torch "${XLSXWRITER_LIB}" fimdlp::fimdlp bayesnet::bayesnet argparse::argparse)
--- a/src/commands/b_results.cpp
+++ b/src/commands/b_results.cpp
@@ -2,8 +2,8 @@
 #include <filesystem>
 #include <fstream>
 #include <vector>
-#include <argparse/argparse.hpp>
-#include <nlohmann/json.hpp>
+#include "nlohmann/json.hpp"
+#include "argparse/argparse.hpp"
 #include "common/Paths.h"
 #include "results/JsonValidator.h"
 #include "results/SchemaV1_0.h"
--- a/src/common/Dataset.cpp
+++ b/src/common/Dataset.cpp
@@ -1,4 +1,4 @@
-#include <ArffFiles/ArffFiles.hpp>
+#include <ArffFiles.hpp>
 #include <fstream>
 #include "Dataset.h"
 namespace platform {
--- a/src/common/TensorUtils.h
+++ b/src/common/TensorUtils.h
@@ -0,0 +1,18 @@
+#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
--- a/src/common/Utils.h
+++ b/src/common/Utils.h
@@ -6,17 +6,11 @@
 #include <string>
 #include <vector>
 #include <algorithm>
-#include <torch/torch.h>
+#include <cstdlib>
+
+extern char **environ;
+
 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;
-    }
    static std::string trim(const std::string& str)
    {
        std::string result = str;
--- a/src/main/RocAuc.cpp
+++ b/src/main/RocAuc.cpp
@@ -3,6 +3,7 @@
 #include <numeric>
 #include <utility>
 #include "RocAuc.h"
+#include "common/TensorUtils.h" // tensorToVector
 namespace platform {

    double RocAuc::compute(const torch::Tensor& y_proba, const torch::Tensor& labels)
--- a/src/main/Scores.cpp
+++ b/src/main/Scores.cpp
@@ -1,6 +1,6 @@
 #include <sstream>
 #include "Scores.h"
-#include "common/Utils.h" // tensorToVector
+#include "common/TensorUtils.h" // 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)
--- a/tests/TestPlatform.cpp
+++ b/tests/TestPlatform.cpp
@@ -7,7 +7,7 @@
 #include <string>
 #include "TestUtils.h"
 #include "folding.hpp"
-#include <ArffFiles/ArffFiles.hpp>
+#include <ArffFiles.hpp>
 #include <bayesnet/classifiers/TAN.h>
 #include "config_platform.h"

--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -1,21 +0,0 @@
-{
-  "default-registry": {
-    "kind": "git",
-    "baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605",
-    "repository": "https://github.com/microsoft/vcpkg"
-  },
-  "registries": [
-    {
-      "kind": "git",
-      "repository": "https://github.com/rmontanana/vcpkg-stash",
-      "baseline": "1ea69243c0e8b0de77c9d1dd6e1d7593ae7f3627",
-      "packages": [
-        "arff-files",
-        "bayesnet",
-        "fimdlp",
-        "folding",
-        "libtorch-bin"
-      ]
-    }
-  ]
-}
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,43 +0,0 @@
-  {
-    "name": "platform",
-    "version-string": "1.1.0",
-    "dependencies": [
-      "arff-files",
-      "nlohmann-json",
-      "fimdlp",
-      "libtorch-bin",
-      "folding",
-      "catch2",
-      "argparse"
-    ],
-    "overrides": [
-      {
-        "name": "arff-files",
-        "version": "1.1.0"
-      },
-      {
-        "name": "fimdlp",
-        "version": "2.0.1"
-      },
-      {
-        "name": "libtorch-bin",
-        "version": "2.7.0"
-      },
-      {
-        "name": "folding",
-        "version": "1.1.1"
-      },
-      {
-        "name": "argparse",
-        "version": "3.2"
-      },
-      {
-        "name": "catch2",
-        "version": "3.8.1"
-      },
-      {
-        "name": "nlohmann-json",
-        "version": "3.11.3"
-      }
-    ]
-  }