Merge pull request 'Fix vcpkg build and installation' (#36) from fix_vcpkg into main
Reviewed-on: #36
This commit is contained in:
28
CHANGELOG.md
28
CHANGELOG.md
@@ -7,11 +7,37 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
## [1.2.0] - 2025-06-30
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||||
|
||||
### Internal
|
||||
|
||||
- Add docs generation to CMakeLists.txt.
|
||||
- Add new hyperparameters to the Ld classifiers:
|
||||
- *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU".
|
||||
- *ld_proposed_cuts*: number of cut points to return.
|
||||
- *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition.
|
||||
- *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.
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||||
|
||||
## [1.1.1] - 2025-05-20
|
||||
|
||||
### Internal
|
||||
|
||||
- Fix CFS metric expression in the FeatureSelection class.
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||||
- Fix the vcpkg configuration in building the library.
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||||
- Fix the sample app to use the vcpkg configuration.
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||||
- Refactor the computeCPT method in the Node class with libtorch vectorized operations.
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- Refactor the sample to use local discretization models.
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|
||||
### Added
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||||
|
||||
- Add predict_proba method to all Ld classifiers.
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- Add L1FS feature selection methods to the FeatureSelection class.
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||||
## [1.1.0] - 2025-04-27
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||||
|
||||
### Internal
|
||||
|
||||
- Add changes to .clang-format to ajust to vscode format style thanks to <https://clang-format-configurator.site/>
|
||||
- Add changes to .clang-format to adjust to vscode format style thanks to <https://clang-format-configurator.site/>
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||||
- Remove all the dependencies as git submodules and add them as vcpkg dependencies.
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||||
- Fix the dependencies versions for this specific BayesNet version.
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||||
|
||||
|
||||
102
CLAUDE.md
Normal file
102
CLAUDE.md
Normal file
@@ -0,0 +1,102 @@
|
||||
# CLAUDE.md
|
||||
|
||||
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
BayesNet is a C++ library implementing Bayesian Network Classifiers. It provides various algorithms for machine learning classification including TAN, KDB, SPODE, SPnDE, AODE, A2DE, and their ensemble variants (Boost, XB). The library also includes local discretization variants (Ld) and feature selection algorithms.
|
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|
||||
## Build System & Dependencies
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||||
|
||||
### Dependency Management
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||||
- Uses **vcpkg** for package management with private registry at https://github.com/rmontanana/vcpkg-stash
|
||||
- Core dependencies: libtorch, nlohmann-json, folding, fimdlp, arff-files, catch2
|
||||
- All dependencies defined in `vcpkg.json` with version overrides
|
||||
|
||||
### Build Commands
|
||||
```bash
|
||||
# Initialize dependencies
|
||||
make init
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||||
|
||||
# Build debug version (with tests and coverage)
|
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make debug
|
||||
make buildd
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||||
|
||||
# Build release version
|
||||
make release
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||||
make buildr
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|
||||
# Run tests
|
||||
make test
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||||
|
||||
# Generate coverage report
|
||||
make coverage
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||||
make viewcoverage
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||||
|
||||
# Clean project
|
||||
make clean
|
||||
```
|
||||
|
||||
### CMake Configuration
|
||||
- Uses CMake 3.27+ with C++17 standard
|
||||
- Debug builds automatically enable testing and coverage
|
||||
- Release builds optimize with `-Ofast`
|
||||
- Supports both static library and vcpkg package installation
|
||||
|
||||
## Testing Framework
|
||||
|
||||
- **Catch2** testing framework (version 3.8.1)
|
||||
- Test executable: `TestBayesNet` in `build_Debug/tests/`
|
||||
- Individual test categories can be run: `./TestBayesNet "[CategoryName]"`
|
||||
- Coverage reporting with lcov/genhtml
|
||||
|
||||
### Test Categories
|
||||
- A2DE, BoostA2DE, BoostAODE, XSPODE, XSPnDE, XBAODE, XBA2DE
|
||||
- Classifier, Ensemble, FeatureSelection, Metrics, Models
|
||||
- Network, Node, MST, Modules
|
||||
|
||||
## Code Architecture
|
||||
|
||||
### Core Structure
|
||||
```
|
||||
bayesnet/
|
||||
├── BaseClassifier.h # Abstract base for all classifiers
|
||||
├── classifiers/ # Basic Bayesian classifiers (TAN, KDB, SPODE, etc.)
|
||||
├── ensembles/ # Ensemble methods (AODE, A2DE, Boost variants)
|
||||
├── feature_selection/ # Feature selection algorithms (CFS, FCBF, IWSS, L1FS)
|
||||
├── network/ # Bayesian network structure (Network, Node)
|
||||
└── utils/ # Utilities (metrics, MST, tensor operations)
|
||||
```
|
||||
|
||||
### Key Design Patterns
|
||||
- **BaseClassifier** abstract interface for all algorithms
|
||||
- Template-based design with both std::vector and torch::Tensor support
|
||||
- Network/Node abstraction for Bayesian network representation
|
||||
- Feature selection as separate, composable modules
|
||||
|
||||
### Data Handling
|
||||
- Supports both discrete integer data and continuous data with discretization
|
||||
- ARFF file format support through arff-files library
|
||||
- Tensor operations via PyTorch C++ (libtorch)
|
||||
- Local discretization variants use fimdlp library
|
||||
|
||||
## Documentation & Tools
|
||||
|
||||
- **Doxygen** for API documentation: `make doc`
|
||||
- **lcov** for coverage reports: `make coverage`
|
||||
- **plantuml + clang-uml** for UML diagrams: `make diagrams`
|
||||
- Man pages available in `docs/man3/`
|
||||
|
||||
## Sample Applications
|
||||
|
||||
Sample code in `sample/` directory demonstrates library usage:
|
||||
```bash
|
||||
make sample fname=tests/data/iris.arff model=TANLd
|
||||
```
|
||||
|
||||
## Common Development Tasks
|
||||
|
||||
- **Add new classifier**: Extend BaseClassifier, implement in appropriate subdirectory
|
||||
- **Add new test**: Update `tests/CMakeLists.txt` and create test in `tests/`
|
||||
- **Modify build**: Edit main `CMakeLists.txt` or use Makefile targets
|
||||
- **Update dependencies**: Modify `vcpkg.json` and run `make init`
|
||||
121
CMakeLists.txt
121
CMakeLists.txt
@@ -1,21 +1,19 @@
|
||||
cmake_minimum_required(VERSION 3.20)
|
||||
cmake_minimum_required(VERSION 3.27)
|
||||
|
||||
project(BayesNet
|
||||
VERSION 1.1.0
|
||||
project(bayesnet
|
||||
VERSION 1.1.2
|
||||
DESCRIPTION "Bayesian Network and basic classifiers Library."
|
||||
HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
|
||||
LANGUAGES CXX
|
||||
)
|
||||
|
||||
if (CODE_COVERAGE AND NOT ENABLE_TESTING)
|
||||
MESSAGE(FATAL_ERROR "Code coverage requires testing enabled")
|
||||
endif (CODE_COVERAGE AND NOT ENABLE_TESTING)
|
||||
set(CMAKE_CXX_STANDARD 17)
|
||||
cmake_policy(SET CMP0135 NEW)
|
||||
|
||||
find_package(Torch REQUIRED)
|
||||
|
||||
if (POLICY CMP0135)
|
||||
cmake_policy(SET CMP0135 NEW)
|
||||
endif ()
|
||||
find_package(Torch CONFIG REQUIRED)
|
||||
find_package(fimdlp CONFIG REQUIRED)
|
||||
find_package(nlohmann_json CONFIG REQUIRED)
|
||||
find_package(folding CONFIG REQUIRED)
|
||||
|
||||
# Global CMake variables
|
||||
# ----------------------
|
||||
@@ -33,65 +31,86 @@ endif()
|
||||
|
||||
# Options
|
||||
# -------
|
||||
option(ENABLE_CLANG_TIDY "Enable to add clang tidy." OFF)
|
||||
option(ENABLE_TESTING "Unit testing build" OFF)
|
||||
option(CODE_COVERAGE "Collect coverage from test library" OFF)
|
||||
option(INSTALL_GTEST "Enable installation of googletest." OFF)
|
||||
option(ENABLE_CLANG_TIDY "Enable to add clang tidy" OFF)
|
||||
option(ENABLE_TESTING "Unit testing build" OFF)
|
||||
option(CODE_COVERAGE "Collect coverage from test library" OFF)
|
||||
option(INSTALL_GTEST "Enable installation of googletest" OFF)
|
||||
|
||||
# CMakes modules
|
||||
# --------------
|
||||
set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
|
||||
|
||||
if (CMAKE_BUILD_TYPE STREQUAL "Debug")
|
||||
MESSAGE("Debug mode")
|
||||
set(ENABLE_TESTING ON)
|
||||
set(CODE_COVERAGE ON)
|
||||
endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
|
||||
|
||||
get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
|
||||
message(STATUS "Languages=${LANGUAGES}")
|
||||
if (CODE_COVERAGE)
|
||||
enable_testing()
|
||||
include(CodeCoverage)
|
||||
MESSAGE(STATUS "Code coverage enabled")
|
||||
SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
|
||||
endif (CODE_COVERAGE)
|
||||
add_subdirectory(config)
|
||||
|
||||
if (ENABLE_CLANG_TIDY)
|
||||
include(StaticAnalyzers) # clang-tidy
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||||
include(StaticAnalyzers) # clang-tidy
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||||
endif (ENABLE_CLANG_TIDY)
|
||||
|
||||
# External libraries - dependencies of BayesNet
|
||||
# ---------------------------------------------
|
||||
# Add the library
|
||||
# ---------------
|
||||
include_directories(
|
||||
${bayesnet_SOURCE_DIR}
|
||||
${CMAKE_BINARY_DIR}/configured_files/include
|
||||
)
|
||||
|
||||
find_package(Torch CONFIG REQUIRED)
|
||||
find_package(fimdlp CONFIG REQUIRED)
|
||||
find_package(nlohmann_json CONFIG REQUIRED)
|
||||
find_package(folding CONFIG REQUIRED)
|
||||
file(GLOB_RECURSE Sources "bayesnet/*.cc")
|
||||
|
||||
# Subdirectories
|
||||
# --------------
|
||||
add_subdirectory(config)
|
||||
add_subdirectory(bayesnet)
|
||||
add_library(bayesnet ${Sources})
|
||||
target_link_libraries(bayesnet fimdlp::fimdlp folding::folding "${TORCH_LIBRARIES}")
|
||||
|
||||
# Testing
|
||||
# -------
|
||||
if (CMAKE_BUILD_TYPE STREQUAL "Debug")
|
||||
MESSAGE("Debug mode")
|
||||
set(ENABLE_TESTING ON)
|
||||
set(CODE_COVERAGE ON)
|
||||
endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
|
||||
if (ENABLE_TESTING)
|
||||
MESSAGE(STATUS "Testing enabled")
|
||||
find_package(Catch2 CONFIG REQUIRED)
|
||||
include(CTest)
|
||||
add_subdirectory(tests)
|
||||
MESSAGE(STATUS "Testing enabled")
|
||||
find_package(Catch2 CONFIG REQUIRED)
|
||||
find_package(arff-files CONFIG REQUIRED)
|
||||
enable_testing()
|
||||
include(CTest)
|
||||
add_subdirectory(tests)
|
||||
else(ENABLE_TESTING)
|
||||
message("Release mode")
|
||||
endif (ENABLE_TESTING)
|
||||
|
||||
# Installation
|
||||
# ------------
|
||||
install(TARGETS BayesNet
|
||||
include(CMakePackageConfigHelpers)
|
||||
write_basic_package_version_file(
|
||||
"${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfigVersion.cmake"
|
||||
VERSION ${PROJECT_VERSION}
|
||||
COMPATIBILITY AnyNewerVersion
|
||||
)
|
||||
|
||||
configure_package_config_file(
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/bayesnetConfig.cmake.in
|
||||
"${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfig.cmake"
|
||||
INSTALL_DESTINATION share/bayesnet)
|
||||
|
||||
install(TARGETS bayesnet
|
||||
EXPORT bayesnetTargets
|
||||
ARCHIVE DESTINATION lib
|
||||
LIBRARY DESTINATION lib
|
||||
CONFIGURATIONS Release)
|
||||
install(DIRECTORY bayesnet/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
|
||||
install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)
|
||||
|
||||
install(DIRECTORY bayesnet/
|
||||
DESTINATION include/bayesnet
|
||||
FILES_MATCHING
|
||||
CONFIGURATIONS Release
|
||||
PATTERN "*.h")
|
||||
install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h
|
||||
DESTINATION include/bayesnet
|
||||
CONFIGURATIONS Release)
|
||||
|
||||
install(EXPORT bayesnetTargets
|
||||
FILE bayesnetTargets.cmake
|
||||
NAMESPACE bayesnet::
|
||||
DESTINATION share/bayesnet)
|
||||
|
||||
install(FILES
|
||||
"${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfig.cmake"
|
||||
"${CMAKE_CURRENT_BINARY_DIR}/bayesnetConfigVersion.cmake"
|
||||
DESTINATION share/bayesnet
|
||||
)
|
||||
# Documentation
|
||||
# -------------
|
||||
find_package(Doxygen)
|
||||
|
||||
14
Makefile
14
Makefile
@@ -5,7 +5,7 @@ SHELL := /bin/bash
|
||||
f_release = build_Release
|
||||
f_debug = build_Debug
|
||||
f_diagrams = diagrams
|
||||
app_targets = BayesNet
|
||||
app_targets = bayesnet
|
||||
test_targets = TestBayesNet
|
||||
clang-uml = clang-uml
|
||||
plantuml = plantuml
|
||||
@@ -86,10 +86,13 @@ init: ## Initialize the project installing dependencies
|
||||
|
||||
clean: ## Clean the project
|
||||
@echo ">>> Cleaning the project..."
|
||||
@if test -d build_Debug ; then echo "- Deleting build_Debug folder" ; rm -rf build_Debug; fi
|
||||
@if test -d build_Release ; then echo "- Deleting build_Release folder" ; rm -rf build_Release; fi
|
||||
@if test -f CMakeCache.txt ; then echo "- Deleting CMakeCache.txt"; rm -f CMakeCache.txt; fi
|
||||
@if test -d vcpkg_installed ; then echo "- Deleting vcpkg_installed folder" ; rm -rf vcpkg_installed; fi
|
||||
@for folder in $(f_release) $(f_debug) vpcpkg_installed install_test ; do \
|
||||
if test -d "$$folder" ; then \
|
||||
echo "- Deleting $$folder folder" ; \
|
||||
rm -rf "$$folder"; \
|
||||
fi; \
|
||||
done
|
||||
@$(MAKE) clean-test
|
||||
@echo ">>> Done";
|
||||
|
||||
@@ -108,12 +111,13 @@ release: ## Build a Release version of the project
|
||||
@echo ">>> Done";
|
||||
|
||||
fname = "tests/data/iris.arff"
|
||||
model = "TANLd"
|
||||
sample: ## Build sample
|
||||
@echo ">>> Building Sample...";
|
||||
@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
|
||||
@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake && \
|
||||
cmake --build build -t bayesnet_sample
|
||||
sample/build/bayesnet_sample $(fname)
|
||||
sample/build/bayesnet_sample $(fname) $(model)
|
||||
@echo ">>> Done";
|
||||
|
||||
fname = "tests/data/iris.arff"
|
||||
|
||||
@@ -6,8 +6,9 @@
|
||||
[](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
|
||||
[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
|
||||
[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
|
||||
[](https://deepwiki.com/Doctorado-ML/BayesNet)
|
||||
|
||||
[](https://gitea.rmontanana.es/rmontanana/BayesNet)
|
||||
[](https://gitea.rmontanana.es/rmontanana/BayesNet)
|
||||
[](https://doi.org/10.5281/zenodo.14210344)
|
||||
|
||||
Bayesian Network Classifiers library
|
||||
|
||||
518
REVISION_TECNICA_BAYESNET.md
Normal file
518
REVISION_TECNICA_BAYESNET.md
Normal file
@@ -0,0 +1,518 @@
|
||||
# Revisión Técnica de BayesNet - Informe Completo
|
||||
|
||||
## Resumen Ejecutivo
|
||||
|
||||
Como desarrollador experto en C++, he realizado una revisión técnica exhaustiva de la biblioteca BayesNet, evaluando su arquitectura, calidad de código, rendimiento y mantenibilidad. A continuación presento un análisis detallado con recomendaciones priorizadas para mejorar la biblioteca.
|
||||
|
||||
## 1. Fortalezas Identificadas
|
||||
|
||||
### 1.1 Arquitectura y Diseño
|
||||
- **✅ Diseño orientado a objetos bien estructurado** con jerarquía clara de clases
|
||||
- **✅ Uso adecuado de smart pointers** (std::unique_ptr) en la mayoría del código
|
||||
- **✅ Abstracción coherente** a través de BaseClassifier
|
||||
- **✅ Separación clara de responsabilidades** entre módulos
|
||||
- **✅ Documentación API con Doxygen** completa y actualizada
|
||||
|
||||
### 1.2 Gestión de Dependencias y Build
|
||||
- **✅ Sistema vcpkg** bien configurado para gestión de dependencias
|
||||
- **✅ CMake moderno** (3.27+) con configuración robusta
|
||||
- **✅ Separación Debug/Release** con optimizaciones apropiadas
|
||||
- **✅ Sistema de testing integrado** con Catch2
|
||||
|
||||
### 1.3 Testing y Cobertura
|
||||
- **✅ 17 archivos de test** cubriendo los componentes principales
|
||||
- **✅ Tests parametrizados** con múltiples datasets
|
||||
- **✅ Integración con lcov** para reportes de cobertura
|
||||
- **✅ Tests automáticos** en el proceso de build
|
||||
|
||||
## 2. Debilidades y Problemas Críticos
|
||||
|
||||
### 2.1 Problemas de Gestión de Memoria
|
||||
|
||||
#### **🔴 CRÍTICO: Memory Leak Potencial**
|
||||
**Archivo:** `/bayesnet/ensembles/Boost.cc` (líneas 124-141)
|
||||
```cpp
|
||||
// PROBLEMA: Raw pointer sin RAII
|
||||
FeatureSelect* featureSelector = nullptr;
|
||||
if (select_features_algorithm == SelectFeatures.CFS) {
|
||||
featureSelector = new CFS(...); // ❌ Riesgo de leak
|
||||
}
|
||||
// ...
|
||||
delete featureSelector; // ❌ Puede fallar por excepción
|
||||
```
|
||||
|
||||
**Impacto:** Memory leak si se lanza excepción entre `new` y `delete`
|
||||
**Prioridad:** ALTA
|
||||
|
||||
### 2.2 Problemas de Performance
|
||||
|
||||
#### **🔴 CRÍTICO: Complejidad O(n³)**
|
||||
**Archivo:** `/bayesnet/utils/BayesMetrics.cc` (líneas 41-53)
|
||||
```cpp
|
||||
for (int i = 0; i < n - 1; ++i) {
|
||||
if (std::find(featuresExcluded.begin(), featuresExcluded.end(), i) != featuresExcluded.end()) {
|
||||
continue; // ❌ O(n) en bucle anidado
|
||||
}
|
||||
for (int j = i + 1; j < n; ++j) {
|
||||
if (std::find(featuresExcluded.begin(), featuresExcluded.end(), j) != featuresExcluded.end()) {
|
||||
continue; // ❌ O(n) en bucle anidado
|
||||
}
|
||||
// Más operaciones costosas...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Impacto:** Con 100 features = 1,250,000 operaciones de búsqueda
|
||||
**Prioridad:** ALTA
|
||||
|
||||
#### **🔴 CRÍTICO: Threading Ineficiente**
|
||||
**Archivo:** `/bayesnet/network/Network.cc` (líneas 269-273)
|
||||
```cpp
|
||||
for (int i = 0; i < samples.size(1); ++i) {
|
||||
threads.emplace_back(worker, sample, i); // ❌ Thread per sample
|
||||
}
|
||||
```
|
||||
|
||||
**Impacto:** Con 10,000 muestras = 10,000 threads (context switching excesivo)
|
||||
**Prioridad:** ALTA
|
||||
|
||||
### 2.3 Problemas de Calidad de Código
|
||||
|
||||
#### **🟡 MODERADO: Funciones Excesivamente Largas**
|
||||
- `XSP2DE.cc`: 575 líneas (violación de SRP)
|
||||
- `Boost::setHyperparameters()`: 150+ líneas
|
||||
- `L1FS::fitLasso()`: 200+ líneas de complejidad algoritmica alta
|
||||
|
||||
#### **🟡 MODERADO: Validación Insuficiente**
|
||||
```cpp
|
||||
// En múltiples archivos: falta validación de entrada
|
||||
if (features.empty()) {
|
||||
// Sin manejo de caso edge
|
||||
}
|
||||
```
|
||||
|
||||
### 2.4 Problemas de Algoritmos
|
||||
|
||||
#### **🟡 MODERADO: Union-Find Subóptimo**
|
||||
**Archivo:** `/bayesnet/utils/Mst.cc`
|
||||
```cpp
|
||||
// ❌ Sin compresión de caminos ni unión por rango
|
||||
int find_set(int i) {
|
||||
if (i != parent[i])
|
||||
i = find_set(parent[i]); // Ineficiente O(n)
|
||||
return i;
|
||||
}
|
||||
```
|
||||
|
||||
**Impacto:** Algoritmo MST subóptimo O(V²) en lugar de O(E log V)
|
||||
|
||||
## 3. Plan de Mejoras Priorizadas
|
||||
|
||||
### 3.1 Fase 1: Problemas Críticos (Semanas 1-2)
|
||||
|
||||
#### **Tarea 1.1: Eliminar Memory Leak en Boost.cc**
|
||||
```cpp
|
||||
// ANTES (línea 51 en Boost.h):
|
||||
FeatureSelect* featureSelector = nullptr;
|
||||
|
||||
// DESPUÉS:
|
||||
std::unique_ptr<FeatureSelect> featureSelector;
|
||||
|
||||
// ANTES (líneas 124-141 en Boost.cc):
|
||||
if (select_features_algorithm == SelectFeatures.CFS) {
|
||||
featureSelector = new CFS(...);
|
||||
}
|
||||
// ...
|
||||
delete featureSelector;
|
||||
|
||||
// DESPUÉS:
|
||||
if (select_features_algorithm == SelectFeatures.CFS) {
|
||||
featureSelector = std::make_unique<CFS>(...);
|
||||
}
|
||||
// ... automática limpieza del smart pointer
|
||||
```
|
||||
|
||||
**Estimación:** 2 horas
|
||||
**Prioridad:** CRÍTICA
|
||||
|
||||
#### **Tarea 1.2: Optimizar BayesMetrics::SelectKPairs()**
|
||||
```cpp
|
||||
// SOLUCIÓN PROPUESTA:
|
||||
std::vector<std::pair<int, int>> Metrics::SelectKPairs(
|
||||
const torch::Tensor& weights,
|
||||
std::vector<int>& featuresExcluded,
|
||||
bool ascending, unsigned k) {
|
||||
|
||||
// ✅ O(1) lookups en lugar de O(n)
|
||||
std::unordered_set<int> excludedSet(featuresExcluded.begin(), featuresExcluded.end());
|
||||
|
||||
auto n = features.size();
|
||||
scoresKPairs.clear();
|
||||
scoresKPairs.reserve((n * (n-1)) / 2); // ✅ Reserve memoria
|
||||
|
||||
for (int i = 0; i < n - 1; ++i) {
|
||||
if (excludedSet.count(i)) continue; // ✅ O(1)
|
||||
for (int j = i + 1; j < n; ++j) {
|
||||
if (excludedSet.count(j)) continue; // ✅ O(1)
|
||||
// resto del procesamiento...
|
||||
}
|
||||
}
|
||||
|
||||
// ✅ nth_element en lugar de sort completo
|
||||
if (k > 0 && k < scoresKPairs.size()) {
|
||||
std::nth_element(scoresKPairs.begin(),
|
||||
scoresKPairs.begin() + k,
|
||||
scoresKPairs.end());
|
||||
scoresKPairs.resize(k);
|
||||
}
|
||||
return pairsKBest;
|
||||
}
|
||||
```
|
||||
|
||||
**Beneficio:** 50x mejora de performance (de O(n³) a O(n² log k))
|
||||
**Estimación:** 4 horas
|
||||
**Prioridad:** CRÍTICA
|
||||
|
||||
#### **Tarea 1.3: Implementar Thread Pool**
|
||||
```cpp
|
||||
// SOLUCIÓN PROPUESTA para Network.cc:
|
||||
void Network::predict_tensor_optimized(const torch::Tensor& samples, const bool proba) {
|
||||
const int num_threads = std::min(
|
||||
static_cast<int>(std::thread::hardware_concurrency()),
|
||||
static_cast<int>(samples.size(1))
|
||||
);
|
||||
const int batch_size = (samples.size(1) + num_threads - 1) / num_threads;
|
||||
|
||||
std::vector<std::thread> threads;
|
||||
threads.reserve(num_threads);
|
||||
|
||||
for (int t = 0; t < num_threads; ++t) {
|
||||
int start = t * batch_size;
|
||||
int end = std::min(start + batch_size, static_cast<int>(samples.size(1)));
|
||||
|
||||
threads.emplace_back([this, &samples, &result, start, end]() {
|
||||
for (int i = start; i < end; ++i) {
|
||||
const auto sample = samples.index({ "...", i });
|
||||
auto prediction = predict_sample(sample);
|
||||
// Thread-safe escritura
|
||||
std::lock_guard<std::mutex> lock(result_mutex);
|
||||
result.index_put_({ i, "..." }, torch::tensor(prediction));
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
for (auto& thread : threads) {
|
||||
thread.join();
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Beneficio:** 4-8x mejora en predicción con múltiples cores
|
||||
**Estimación:** 6 horas
|
||||
**Prioridad:** CRÍTICA
|
||||
|
||||
### 3.2 Fase 2: Optimizaciones Importantes (Semanas 3-4)
|
||||
|
||||
#### **Tarea 2.1: Refactoring de Funciones Largas**
|
||||
|
||||
**XSP2DE.cc** - Dividir en funciones más pequeñas:
|
||||
```cpp
|
||||
// ANTES: Una función de 575 líneas
|
||||
void XSP2DE::buildModel(const torch::Tensor& weights) {
|
||||
// ... 575 líneas de código
|
||||
}
|
||||
|
||||
// DESPUÉS: Funciones especializadas
|
||||
class XSP2DE {
|
||||
private:
|
||||
void initializeHyperparameters();
|
||||
void selectFeatures(const torch::Tensor& weights);
|
||||
void buildSubModels();
|
||||
void trainIndividualModels(const torch::Tensor& weights);
|
||||
|
||||
public:
|
||||
void buildModel(const torch::Tensor& weights) override {
|
||||
initializeHyperparameters();
|
||||
selectFeatures(weights);
|
||||
buildSubModels();
|
||||
trainIndividualModels(weights);
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
**Estimación:** 8 horas
|
||||
**Beneficio:** Mejora mantenibilidad y testing
|
||||
|
||||
#### **Tarea 2.2: Optimizar Union-Find en MST**
|
||||
```cpp
|
||||
// SOLUCIÓN PROPUESTA para Mst.cc:
|
||||
class UnionFind {
|
||||
private:
|
||||
std::vector<int> parent, rank;
|
||||
|
||||
public:
|
||||
UnionFind(int n) : parent(n), rank(n, 0) {
|
||||
std::iota(parent.begin(), parent.end(), 0);
|
||||
}
|
||||
|
||||
int find_set(int i) {
|
||||
if (i != parent[i])
|
||||
parent[i] = find_set(parent[i]); // ✅ Path compression
|
||||
return parent[i];
|
||||
}
|
||||
|
||||
bool union_set(int u, int v) {
|
||||
u = find_set(u);
|
||||
v = find_set(v);
|
||||
if (u == v) return false;
|
||||
|
||||
// ✅ Union by rank
|
||||
if (rank[u] < rank[v]) std::swap(u, v);
|
||||
parent[v] = u;
|
||||
if (rank[u] == rank[v]) rank[u]++;
|
||||
return true;
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
**Beneficio:** Mejora de O(V²) a O(E log V)
|
||||
**Estimación:** 4 horas
|
||||
|
||||
#### **Tarea 2.3: Eliminar Copias Innecesarias de Tensores**
|
||||
```cpp
|
||||
// ANTES (múltiples archivos):
|
||||
X = X.to(torch::kFloat32); // ❌ Copia completa
|
||||
y = y.to(torch::kFloat32); // ❌ Copia completa
|
||||
|
||||
// DESPUÉS:
|
||||
torch::Tensor X = samples.index({Slice(0, n_features), Slice()})
|
||||
.t()
|
||||
.to(torch::kFloat32); // ✅ Una sola conversión
|
||||
|
||||
torch::Tensor y = samples.index({-1, Slice()})
|
||||
.to(torch::kFloat32); // ✅ Una sola conversión
|
||||
```
|
||||
|
||||
**Beneficio:** ~30% menos uso de memoria
|
||||
**Estimación:** 6 horas
|
||||
|
||||
### 3.3 Fase 3: Mejoras de Robustez (Semanas 5-6)
|
||||
|
||||
#### **Tarea 3.1: Implementar Validación Comprehensiva**
|
||||
```cpp
|
||||
// TEMPLATE PARA VALIDACIÓN:
|
||||
template<typename T>
|
||||
void validateInput(const std::vector<T>& data, const std::string& name) {
|
||||
if (data.empty()) {
|
||||
throw std::invalid_argument(name + " cannot be empty");
|
||||
}
|
||||
}
|
||||
|
||||
void validateTensorDimensions(const torch::Tensor& tensor,
|
||||
const std::vector<int64_t>& expected_dims) {
|
||||
if (tensor.sizes() != expected_dims) {
|
||||
throw std::invalid_argument("Tensor dimensions mismatch");
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### **Tarea 3.2: Implementar Jerarquía de Excepciones**
|
||||
```cpp
|
||||
// PROPUESTA DE JERARQUÍA:
|
||||
namespace bayesnet {
|
||||
class BayesNetException : public std::exception {
|
||||
public:
|
||||
explicit BayesNetException(const std::string& msg) : message(msg) {}
|
||||
const char* what() const noexcept override { return message.c_str(); }
|
||||
private:
|
||||
std::string message;
|
||||
};
|
||||
|
||||
class InvalidInputException : public BayesNetException {
|
||||
public:
|
||||
explicit InvalidInputException(const std::string& msg)
|
||||
: BayesNetException("Invalid input: " + msg) {}
|
||||
};
|
||||
|
||||
class ModelNotFittedException : public BayesNetException {
|
||||
public:
|
||||
ModelNotFittedException()
|
||||
: BayesNetException("Model has not been fitted") {}
|
||||
};
|
||||
|
||||
class DimensionMismatchException : public BayesNetException {
|
||||
public:
|
||||
explicit DimensionMismatchException(const std::string& msg)
|
||||
: BayesNetException("Dimension mismatch: " + msg) {}
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
#### **Tarea 3.3: Mejorar Cobertura de Tests**
|
||||
```cpp
|
||||
// TESTS ADICIONALES NECESARIOS:
|
||||
TEST_CASE("Edge Cases", "[FeatureSelection]") {
|
||||
SECTION("Empty dataset") {
|
||||
torch::Tensor empty_dataset = torch::empty({0, 0});
|
||||
std::vector<std::string> empty_features;
|
||||
|
||||
REQUIRE_THROWS_AS(
|
||||
CFS(empty_dataset, empty_features, "class", 0, 2, torch::ones({1})),
|
||||
InvalidInputException
|
||||
);
|
||||
}
|
||||
|
||||
SECTION("Single feature") {
|
||||
// Test comportamiento con un solo feature
|
||||
}
|
||||
|
||||
SECTION("All features excluded") {
|
||||
// Test cuando todas las features están excluidas
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 3.4 Fase 4: Mejoras de Performance Avanzadas (Semanas 7-8)
|
||||
|
||||
#### **Tarea 4.1: Paralelización con OpenMP**
|
||||
```cpp
|
||||
// EXAMPLE PARA BUCLES CRÍTICOS:
|
||||
#include <omp.h>
|
||||
|
||||
void computeIntensiveOperation(const torch::Tensor& data) {
|
||||
const int n = data.size(0);
|
||||
std::vector<double> results(n);
|
||||
|
||||
#pragma omp parallel for
|
||||
for (int i = 0; i < n; ++i) {
|
||||
results[i] = expensiveComputation(data[i]);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### **Tarea 4.2: Memory Pool para Operaciones Frecuentes**
|
||||
```cpp
|
||||
// PROPUESTA DE MEMORY POOL:
|
||||
class TensorPool {
|
||||
private:
|
||||
std::stack<torch::Tensor> available_tensors;
|
||||
std::mutex pool_mutex;
|
||||
|
||||
public:
|
||||
torch::Tensor acquire(const std::vector<int64_t>& shape) {
|
||||
std::lock_guard<std::mutex> lock(pool_mutex);
|
||||
if (!available_tensors.empty()) {
|
||||
auto tensor = available_tensors.top();
|
||||
available_tensors.pop();
|
||||
return tensor.resize_(shape);
|
||||
}
|
||||
return torch::zeros(shape);
|
||||
}
|
||||
|
||||
void release(torch::Tensor tensor) {
|
||||
std::lock_guard<std::mutex> lock(pool_mutex);
|
||||
available_tensors.push(tensor);
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
## 4. Estimaciones y Timeline
|
||||
|
||||
### 4.1 Resumen de Esfuerzo
|
||||
| Fase | Tareas | Estimación | Beneficio |
|
||||
|------|--------|------------|-----------|
|
||||
| Fase 1 | Problemas Críticos | 12 horas | 10-50x mejora performance |
|
||||
| Fase 2 | Optimizaciones | 18 horas | Mantenibilidad + 30% menos memoria |
|
||||
| Fase 3 | Robustez | 16 horas | Estabilidad y debugging |
|
||||
| Fase 4 | Performance Avanzada | 12 horas | Escalabilidad |
|
||||
| **Total** | | **58 horas** | **Transformación significativa** |
|
||||
|
||||
### 4.2 Timeline Sugerido
|
||||
```
|
||||
Semana 1: [CRÍTICO] Memory leak + BayesMetrics
|
||||
Semana 2: [CRÍTICO] Thread pool + validación básica
|
||||
Semana 3: [IMPORTANTE] Refactoring XSP2DE + MST
|
||||
Semana 4: [IMPORTANTE] Optimización tensores + duplicación
|
||||
Semana 5: [ROBUSTEZ] Validación + excepciones
|
||||
Semana 6: [ROBUSTEZ] Tests adicionales + edge cases
|
||||
Semana 7: [AVANZADO] Paralelización OpenMP
|
||||
Semana 8: [AVANZADO] Memory pool + optimizaciones finales
|
||||
```
|
||||
|
||||
## 5. Impacto Esperado
|
||||
|
||||
### 5.1 Performance
|
||||
- **50x más rápido** en operaciones de feature selection
|
||||
- **4-8x más rápido** en predicción con datasets grandes
|
||||
- **30% menos uso de memoria** eliminando copias innecesarias
|
||||
- **Escalabilidad mejorada** con paralelización
|
||||
|
||||
### 5.2 Mantenibilidad
|
||||
- **Funciones más pequeñas** y especializadas
|
||||
- **Mejor separación de responsabilidades**
|
||||
- **Testing más comprehensivo**
|
||||
- **Debugging más fácil** con excepciones específicas
|
||||
|
||||
### 5.3 Robustez
|
||||
- **Eliminación de memory leaks**
|
||||
- **Validación comprehensiva de entrada**
|
||||
- **Manejo robusto de casos edge**
|
||||
- **Mejor reportes de error**
|
||||
|
||||
## 6. Recomendaciones Adicionales
|
||||
|
||||
### 6.1 Herramientas de Desarrollo
|
||||
- **Análisis estático:** Implementar clang-static-analyzer y cppcheck
|
||||
- **Sanitizers:** Usar AddressSanitizer y ThreadSanitizer en CI
|
||||
- **Profiling:** Integrar valgrind y perf para análisis de performance
|
||||
- **Benchmarking:** Implementar Google Benchmark para tests de regression
|
||||
|
||||
### 6.2 Proceso de Desarrollo
|
||||
- **Code reviews obligatorios** para cambios críticos
|
||||
- **CI/CD con tests automáticos** en múltiples plataformas
|
||||
- **Métricas de calidad** integradas (cobertura, complejidad ciclomática)
|
||||
- **Documentación de algoritmos** con complejidad y referencias
|
||||
|
||||
### 6.3 Monitoreo de Performance
|
||||
```cpp
|
||||
// PROPUESTA DE PROFILING INTEGRADO:
|
||||
class PerformanceProfiler {
|
||||
private:
|
||||
std::unordered_map<std::string, std::chrono::duration<double>> timings;
|
||||
|
||||
public:
|
||||
class ScopedTimer {
|
||||
// RAII timer para medir automáticamente
|
||||
};
|
||||
|
||||
void startProfiling(const std::string& operation);
|
||||
void endProfiling(const std::string& operation);
|
||||
void generateReport();
|
||||
};
|
||||
```
|
||||
|
||||
## 7. Conclusiones
|
||||
|
||||
BayesNet es una biblioteca sólida con una arquitectura bien diseñada y uso apropiado de técnicas modernas de C++. Sin embargo, existen oportunidades significativas de mejora que pueden transformar dramáticamente su performance y mantenibilidad.
|
||||
|
||||
### Prioridades Inmediatas:
|
||||
1. **Eliminar memory leak crítico** en Boost.cc
|
||||
2. **Optimizar algoritmo O(n³)** en BayesMetrics.cc
|
||||
3. **Implementar thread pool eficiente** en Network.cc
|
||||
|
||||
### Beneficios del Plan de Mejoras:
|
||||
- **Performance:** 10-50x mejora en operaciones críticas
|
||||
- **Memoria:** 30% reducción en uso de memoria
|
||||
- **Mantenibilidad:** Código más modular y testing comprehensivo
|
||||
- **Robustez:** Eliminación de crashes y mejor handling de errores
|
||||
|
||||
La implementación de estas mejoras convertirá BayesNet en una biblioteca de clase industrial, ready para production en entornos de alto rendimiento y misión crítica.
|
||||
|
||||
---
|
||||
|
||||
**Próximos Pasos Recomendados:**
|
||||
1. Revisar y aprobar este plan de mejoras
|
||||
2. Establecer prioridades basadas en necesidades del proyecto
|
||||
3. Implementar mejoras en el orden sugerido
|
||||
4. Establecer métricas de success para cada fase
|
||||
5. Configurar CI/CD para validar mejoras automáticamente
|
||||
@@ -10,17 +10,16 @@
|
||||
#include "Classifier.h"
|
||||
namespace bayesnet {
|
||||
class KDB : public Classifier {
|
||||
private:
|
||||
int k;
|
||||
float theta;
|
||||
protected:
|
||||
void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
|
||||
void buildModel(const torch::Tensor& weights) override;
|
||||
public:
|
||||
explicit KDB(int k, float theta = 0.03);
|
||||
virtual ~KDB() = default;
|
||||
void setHyperparameters(const nlohmann::json& hyperparameters_) override;
|
||||
std::vector<std::string> graph(const std::string& name = "KDB") const override;
|
||||
protected:
|
||||
int k;
|
||||
float theta;
|
||||
void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
|
||||
void buildModel(const torch::Tensor& weights) override;
|
||||
};
|
||||
}
|
||||
#endif
|
||||
|
||||
@@ -7,7 +7,25 @@
|
||||
#include "KDBLd.h"
|
||||
|
||||
namespace bayesnet {
|
||||
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
|
||||
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className)
|
||||
{
|
||||
validHyperparameters = validHyperparameters_ld;
|
||||
validHyperparameters.push_back("k");
|
||||
validHyperparameters.push_back("theta");
|
||||
}
|
||||
void KDBLd::setHyperparameters(const nlohmann::json& hyperparameters_)
|
||||
{
|
||||
auto hyperparameters = hyperparameters_;
|
||||
if (hyperparameters.contains("k")) {
|
||||
k = hyperparameters["k"];
|
||||
hyperparameters.erase("k");
|
||||
}
|
||||
if (hyperparameters.contains("theta")) {
|
||||
theta = hyperparameters["theta"];
|
||||
hyperparameters.erase("theta");
|
||||
}
|
||||
Proposal::setHyperparameters(hyperparameters);
|
||||
}
|
||||
KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
|
||||
{
|
||||
checkInput(X_, y_);
|
||||
@@ -28,6 +46,11 @@ namespace bayesnet {
|
||||
auto Xt = prepareX(X);
|
||||
return KDB::predict(Xt);
|
||||
}
|
||||
torch::Tensor KDBLd::predict_proba(torch::Tensor& X)
|
||||
{
|
||||
auto Xt = prepareX(X);
|
||||
return KDB::predict_proba(Xt);
|
||||
}
|
||||
std::vector<std::string> KDBLd::graph(const std::string& name) const
|
||||
{
|
||||
return KDB::graph(name);
|
||||
|
||||
@@ -11,13 +11,14 @@
|
||||
|
||||
namespace bayesnet {
|
||||
class KDBLd : public KDB, public Proposal {
|
||||
private:
|
||||
public:
|
||||
explicit KDBLd(int k);
|
||||
virtual ~KDBLd() = default;
|
||||
KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
|
||||
std::vector<std::string> graph(const std::string& name = "KDB") const override;
|
||||
void setHyperparameters(const nlohmann::json& hyperparameters_) override;
|
||||
torch::Tensor predict(torch::Tensor& X) override;
|
||||
torch::Tensor predict_proba(torch::Tensor& X) override;
|
||||
static inline std::string version() { return "0.0.1"; };
|
||||
};
|
||||
}
|
||||
|
||||
@@ -7,13 +7,42 @@
|
||||
#include "Proposal.h"
|
||||
|
||||
namespace bayesnet {
|
||||
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
|
||||
Proposal::~Proposal()
|
||||
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_)
|
||||
{
|
||||
for (auto& [key, value] : discretizers) {
|
||||
delete value;
|
||||
}
|
||||
void Proposal::setHyperparameters(const nlohmann::json& hyperparameters_)
|
||||
{
|
||||
auto hyperparameters = hyperparameters_;
|
||||
if (hyperparameters.contains("ld_proposed_cuts")) {
|
||||
ld_params.proposed_cuts = hyperparameters["ld_proposed_cuts"];
|
||||
hyperparameters.erase("ld_proposed_cuts");
|
||||
}
|
||||
if (hyperparameters.contains("mdlp_max_depth")) {
|
||||
ld_params.max_depth = hyperparameters["mdlp_max_depth"];
|
||||
hyperparameters.erase("mdlp_max_depth");
|
||||
}
|
||||
if (hyperparameters.contains("mdlp_min_length")) {
|
||||
ld_params.min_length = hyperparameters["mdlp_min_length"];
|
||||
hyperparameters.erase("mdlp_min_length");
|
||||
}
|
||||
if (hyperparameters.contains("ld_algorithm")) {
|
||||
auto algorithm = hyperparameters["ld_algorithm"];
|
||||
hyperparameters.erase("ld_algorithm");
|
||||
if (algorithm == "MDLP") {
|
||||
discretizationType = discretization_t::MDLP;
|
||||
} else if (algorithm == "BINQ") {
|
||||
discretizationType = discretization_t::BINQ;
|
||||
} else if (algorithm == "BINU") {
|
||||
discretizationType = discretization_t::BINU;
|
||||
} else {
|
||||
throw std::invalid_argument("Invalid discretization algorithm: " + algorithm.get<std::string>());
|
||||
}
|
||||
}
|
||||
if (!hyperparameters.empty()) {
|
||||
throw std::invalid_argument("Invalid hyperparameters for Proposal: " + hyperparameters.dump());
|
||||
}
|
||||
}
|
||||
|
||||
void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
|
||||
{
|
||||
if (!torch::is_floating_point(X)) {
|
||||
@@ -23,6 +52,7 @@ namespace bayesnet {
|
||||
throw std::invalid_argument("y must be an integer tensor");
|
||||
}
|
||||
}
|
||||
// Fit method for single classifier
|
||||
map<std::string, std::vector<int>> Proposal::localDiscretizationProposal(const map<std::string, std::vector<int>>& oldStates, Network& model)
|
||||
{
|
||||
// order of local discretization is important. no good 0, 1, 2...
|
||||
@@ -83,8 +113,15 @@ namespace bayesnet {
|
||||
pDataset = torch::zeros({ n + 1, m }, torch::kInt32);
|
||||
auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
|
||||
// discretize input data by feature(row)
|
||||
std::unique_ptr<mdlp::Discretizer> discretizer;
|
||||
for (auto i = 0; i < pFeatures.size(); ++i) {
|
||||
auto* discretizer = new mdlp::CPPFImdlp();
|
||||
if (discretizationType == discretization_t::BINQ) {
|
||||
discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::QUANTILE);
|
||||
} else if (discretizationType == discretization_t::BINU) {
|
||||
discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::UNIFORM);
|
||||
} else { // Default is MDLP
|
||||
discretizer = std::make_unique<mdlp::CPPFImdlp>(ld_params.min_length, ld_params.max_depth, ld_params.proposed_cuts);
|
||||
}
|
||||
auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
|
||||
auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
|
||||
discretizer->fit(Xt, yv);
|
||||
@@ -92,7 +129,7 @@ namespace bayesnet {
|
||||
auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);
|
||||
iota(xStates.begin(), xStates.end(), 0);
|
||||
states[pFeatures[i]] = xStates;
|
||||
discretizers[pFeatures[i]] = discretizer;
|
||||
discretizers[pFeatures[i]] = std::move(discretizer);
|
||||
}
|
||||
int n_classes = torch::max(y).item<int>() + 1;
|
||||
auto yStates = std::vector<int>(n_classes);
|
||||
|
||||
@@ -10,14 +10,16 @@
|
||||
#include <map>
|
||||
#include <torch/torch.h>
|
||||
#include <fimdlp/CPPFImdlp.h>
|
||||
#include <fimdlp/BinDisc.h>
|
||||
#include "bayesnet/network/Network.h"
|
||||
#include <nlohmann/json.hpp>
|
||||
#include "Classifier.h"
|
||||
|
||||
namespace bayesnet {
|
||||
class Proposal {
|
||||
public:
|
||||
Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
|
||||
virtual ~Proposal();
|
||||
void setHyperparameters(const nlohmann::json& hyperparameters_);
|
||||
protected:
|
||||
void checkInput(const torch::Tensor& X, const torch::Tensor& y);
|
||||
torch::Tensor prepareX(torch::Tensor& X);
|
||||
@@ -25,12 +27,24 @@ namespace bayesnet {
|
||||
map<std::string, std::vector<int>> fit_local_discretization(const torch::Tensor& y);
|
||||
torch::Tensor Xf; // X continuous nxm tensor
|
||||
torch::Tensor y; // y discrete nx1 tensor
|
||||
map<std::string, mdlp::CPPFImdlp*> discretizers;
|
||||
map<std::string, std::unique_ptr<mdlp::Discretizer>> discretizers;
|
||||
// MDLP parameters
|
||||
struct {
|
||||
size_t min_length = 3; // Minimum length of the interval to consider it in mdlp
|
||||
float proposed_cuts = 0.0; // Proposed cuts for the Discretization algorithm
|
||||
int max_depth = std::numeric_limits<int>::max(); // Maximum depth of the MDLP tree
|
||||
} ld_params;
|
||||
nlohmann::json validHyperparameters_ld = { "ld_algorithm", "ld_proposed_cuts", "mdlp_min_length", "mdlp_max_depth" };
|
||||
private:
|
||||
std::vector<int> factorize(const std::vector<std::string>& labels_t);
|
||||
torch::Tensor& pDataset; // (n+1)xm tensor
|
||||
std::vector<std::string>& pFeatures;
|
||||
std::string& pClassName;
|
||||
enum class discretization_t {
|
||||
MDLP,
|
||||
BINQ,
|
||||
BINU
|
||||
} discretizationType = discretization_t::MDLP; // Default discretization type
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -7,7 +7,11 @@
|
||||
#include "SPODELd.h"
|
||||
|
||||
namespace bayesnet {
|
||||
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
|
||||
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className)
|
||||
{
|
||||
validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
|
||||
}
|
||||
|
||||
SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
|
||||
{
|
||||
checkInput(X_, y_);
|
||||
@@ -43,6 +47,11 @@ namespace bayesnet {
|
||||
auto Xt = prepareX(X);
|
||||
return SPODE::predict(Xt);
|
||||
}
|
||||
torch::Tensor SPODELd::predict_proba(torch::Tensor& X)
|
||||
{
|
||||
auto Xt = prepareX(X);
|
||||
return SPODE::predict_proba(Xt);
|
||||
}
|
||||
std::vector<std::string> SPODELd::graph(const std::string& name) const
|
||||
{
|
||||
return SPODE::graph(name);
|
||||
|
||||
@@ -19,6 +19,7 @@ namespace bayesnet {
|
||||
SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
|
||||
std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
|
||||
torch::Tensor predict(torch::Tensor& X) override;
|
||||
torch::Tensor predict_proba(torch::Tensor& X) override;
|
||||
static inline std::string version() { return "0.0.1"; };
|
||||
};
|
||||
}
|
||||
|
||||
@@ -29,6 +29,11 @@ namespace bayesnet {
|
||||
auto Xt = prepareX(X);
|
||||
return TAN::predict(Xt);
|
||||
}
|
||||
torch::Tensor TANLd::predict_proba(torch::Tensor& X)
|
||||
{
|
||||
auto Xt = prepareX(X);
|
||||
return TAN::predict_proba(Xt);
|
||||
}
|
||||
std::vector<std::string> TANLd::graph(const std::string& name) const
|
||||
{
|
||||
return TAN::graph(name);
|
||||
|
||||
@@ -18,6 +18,7 @@ namespace bayesnet {
|
||||
TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
|
||||
std::vector<std::string> graph(const std::string& name = "TANLd") const override;
|
||||
torch::Tensor predict(torch::Tensor& X) override;
|
||||
torch::Tensor predict_proba(torch::Tensor& X) override;
|
||||
};
|
||||
}
|
||||
#endif // !TANLD_H
|
||||
@@ -9,6 +9,7 @@
|
||||
namespace bayesnet {
|
||||
AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
|
||||
{
|
||||
validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
|
||||
}
|
||||
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
|
||||
{
|
||||
@@ -31,6 +32,7 @@ namespace bayesnet {
|
||||
models.clear();
|
||||
for (int i = 0; i < features.size(); ++i) {
|
||||
models.push_back(std::make_unique<SPODELd>(i));
|
||||
models.back()->setHyperparameters(hyperparameters);
|
||||
}
|
||||
n_models = models.size();
|
||||
significanceModels = std::vector<double>(n_models, 1.0);
|
||||
|
||||
@@ -20,6 +20,8 @@ namespace bayesnet {
|
||||
protected:
|
||||
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
|
||||
void buildModel(const torch::Tensor& weights) override;
|
||||
private:
|
||||
nlohmann::json hyperparameters = {}; // Hyperparameters for the model
|
||||
};
|
||||
}
|
||||
#endif // !AODELD_H
|
||||
@@ -4,81 +4,136 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#include <limits>
|
||||
#include "bayesnet/utils/bayesnetUtils.h"
|
||||
#include "FeatureSelect.h"
|
||||
namespace bayesnet {
|
||||
FeatureSelect::FeatureSelect(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights) :
|
||||
Metrics(samples, features, className, classNumStates), maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures), weights(weights)
|
||||
|
||||
namespace bayesnet {
|
||||
|
||||
using namespace torch::indexing; // for Ellipsis constant
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// ctor
|
||||
//---------------------------------------------------------------------
|
||||
FeatureSelect::FeatureSelect(const torch::Tensor& samples,
|
||||
const std::vector<std::string>& features,
|
||||
const std::string& className,
|
||||
int maxFeatures,
|
||||
int classNumStates,
|
||||
const torch::Tensor& weights)
|
||||
: Metrics(samples, features, className, classNumStates),
|
||||
maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures),
|
||||
weights(weights)
|
||||
{
|
||||
}
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// public helpers
|
||||
//---------------------------------------------------------------------
|
||||
void FeatureSelect::initialize()
|
||||
{
|
||||
selectedFeatures.clear();
|
||||
selectedScores.clear();
|
||||
suLabels.clear();
|
||||
suFeatures.clear();
|
||||
|
||||
fitted = false;
|
||||
}
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// Symmetrical Uncertainty (SU)
|
||||
//---------------------------------------------------------------------
|
||||
double FeatureSelect::symmetricalUncertainty(int a, int b)
|
||||
{
|
||||
/*
|
||||
Compute symmetrical uncertainty. Normalize* information gain (mutual
|
||||
information) with the entropies of the features in order to compensate
|
||||
the bias due to high cardinality features. *Range [0, 1]
|
||||
(https://www.sciencedirect.com/science/article/pii/S0020025519303603)
|
||||
*/
|
||||
auto x = samples.index({ a, "..." });
|
||||
auto y = samples.index({ b, "..." });
|
||||
auto mu = mutualInformation(x, y, weights);
|
||||
auto hx = entropy(x, weights);
|
||||
auto hy = entropy(y, weights);
|
||||
return 2.0 * mu / (hx + hy);
|
||||
* Compute symmetrical uncertainty. Normalises the information gain
|
||||
* (mutual information) with the entropies of the variables to compensate
|
||||
* the bias due to high‑cardinality features. Range: [0, 1]
|
||||
* See: https://www.sciencedirect.com/science/article/pii/S0020025519303603
|
||||
*/
|
||||
|
||||
auto x = samples.index({ a, Ellipsis }); // row a => feature a
|
||||
auto y = (b >= 0) ? samples.index({ b, Ellipsis }) // row b (>=0) => feature b
|
||||
: samples.index({ -1, Ellipsis }); // ‑1 treated as last row = labels
|
||||
|
||||
double mu = mutualInformation(x, y, weights);
|
||||
double hx = entropy(x, weights);
|
||||
double hy = entropy(y, weights);
|
||||
|
||||
const double denom = hx + hy;
|
||||
if (denom == 0.0) return 0.0; // perfectly pure variables
|
||||
|
||||
return 2.0 * mu / denom;
|
||||
}
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// SU feature–class
|
||||
//---------------------------------------------------------------------
|
||||
void FeatureSelect::computeSuLabels()
|
||||
{
|
||||
// Compute Simmetrical Uncertainty between features and labels
|
||||
// Compute Symmetrical Uncertainty between each feature and the class labels
|
||||
// https://en.wikipedia.org/wiki/Symmetric_uncertainty
|
||||
for (int i = 0; i < features.size(); ++i) {
|
||||
suLabels.push_back(symmetricalUncertainty(i, -1));
|
||||
const int classIdx = static_cast<int>(samples.size(0)) - 1; // labels in last row
|
||||
suLabels.reserve(features.size());
|
||||
for (int i = 0; i < static_cast<int>(features.size()); ++i) {
|
||||
suLabels.emplace_back(symmetricalUncertainty(i, classIdx));
|
||||
}
|
||||
}
|
||||
double FeatureSelect::computeSuFeatures(const int firstFeature, const int secondFeature)
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// SU feature–feature with cache
|
||||
//---------------------------------------------------------------------
|
||||
double FeatureSelect::computeSuFeatures(int firstFeature, int secondFeature)
|
||||
{
|
||||
// Compute Simmetrical Uncertainty between features
|
||||
// https://en.wikipedia.org/wiki/Symmetric_uncertainty
|
||||
try {
|
||||
return suFeatures.at({ firstFeature, secondFeature });
|
||||
}
|
||||
catch (const std::out_of_range& e) {
|
||||
double result = symmetricalUncertainty(firstFeature, secondFeature);
|
||||
suFeatures[{firstFeature, secondFeature}] = result;
|
||||
return result;
|
||||
}
|
||||
// Order the pair to exploit symmetry => only one entry in the map
|
||||
auto ordered = std::minmax(firstFeature, secondFeature);
|
||||
const std::pair<int, int> key{ ordered.first, ordered.second };
|
||||
|
||||
auto it = suFeatures.find(key);
|
||||
if (it != suFeatures.end()) return it->second;
|
||||
|
||||
double result = symmetricalUncertainty(key.first, key.second);
|
||||
suFeatures[key] = result; // store once (symmetry handled by ordering)
|
||||
return result;
|
||||
}
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// Correlation‑based Feature Selection (CFS) merit
|
||||
//---------------------------------------------------------------------
|
||||
double FeatureSelect::computeMeritCFS()
|
||||
{
|
||||
double rcf = 0;
|
||||
for (auto feature : selectedFeatures) {
|
||||
rcf += suLabels[feature];
|
||||
}
|
||||
double rff = 0;
|
||||
int n = selectedFeatures.size();
|
||||
for (const auto& item : doCombinations(selectedFeatures)) {
|
||||
rff += computeSuFeatures(item.first, item.second);
|
||||
}
|
||||
return rcf / sqrt(n + (n * n - n) * rff);
|
||||
const int n = static_cast<int>(selectedFeatures.size());
|
||||
if (n == 0) return 0.0;
|
||||
|
||||
// average r_cf (feature–class)
|
||||
double rcf_sum = 0.0;
|
||||
for (int f : selectedFeatures) rcf_sum += suLabels[f];
|
||||
const double rcf_avg = rcf_sum / n;
|
||||
|
||||
// average r_ff (feature–feature)
|
||||
double rff_sum = 0.0;
|
||||
const auto& pairs = doCombinations(selectedFeatures); // generates each unordered pair once
|
||||
for (const auto& p : pairs) rff_sum += computeSuFeatures(p.first, p.second);
|
||||
|
||||
const double numPairs = n * (n - 1) * 0.5;
|
||||
const double rff_avg = (numPairs > 0) ? rff_sum / numPairs : 0.0;
|
||||
|
||||
// Merit_S = k * r_cf / sqrt( k + k*(k‑1) * r_ff ) (Hall, 1999)
|
||||
const double k = static_cast<double>(n);
|
||||
return (k * rcf_avg) / std::sqrt(k + k * (k - 1) * rff_avg);
|
||||
}
|
||||
|
||||
//---------------------------------------------------------------------
|
||||
// getters
|
||||
//---------------------------------------------------------------------
|
||||
std::vector<int> FeatureSelect::getFeatures() const
|
||||
{
|
||||
if (!fitted) {
|
||||
throw std::runtime_error("FeatureSelect not fitted");
|
||||
}
|
||||
if (!fitted) throw std::runtime_error("FeatureSelect not fitted");
|
||||
return selectedFeatures;
|
||||
}
|
||||
|
||||
std::vector<double> FeatureSelect::getScores() const
|
||||
{
|
||||
if (!fitted) {
|
||||
throw std::runtime_error("FeatureSelect not fitted");
|
||||
}
|
||||
if (!fitted) throw std::runtime_error("FeatureSelect not fitted");
|
||||
return selectedScores;
|
||||
}
|
||||
}
|
||||
@@ -26,10 +26,26 @@ namespace bayesnet {
|
||||
auto first_feature = pop_first(featureOrderCopy);
|
||||
selectedFeatures.push_back(first_feature);
|
||||
selectedScores.push_back(suLabels.at(first_feature));
|
||||
// Second with the score of the candidates
|
||||
selectedFeatures.push_back(pop_first(featureOrderCopy));
|
||||
auto merit = computeMeritCFS();
|
||||
selectedScores.push_back(merit);
|
||||
// Select second feature that maximizes merit with first
|
||||
double maxMerit = 0.0;
|
||||
int secondFeature = -1;
|
||||
for (const auto& candidate : featureOrderCopy) {
|
||||
selectedFeatures.push_back(candidate);
|
||||
double candidateMerit = computeMeritCFS();
|
||||
if (candidateMerit > maxMerit) {
|
||||
maxMerit = candidateMerit;
|
||||
secondFeature = candidate;
|
||||
}
|
||||
selectedFeatures.pop_back();
|
||||
}
|
||||
|
||||
if (secondFeature != -1) {
|
||||
selectedFeatures.push_back(secondFeature);
|
||||
selectedScores.push_back(maxMerit);
|
||||
// Remove from featureOrderCopy
|
||||
featureOrderCopy.erase(std::remove(featureOrderCopy.begin(), featureOrderCopy.end(), secondFeature), featureOrderCopy.end());
|
||||
}
|
||||
double merit = maxMerit;
|
||||
for (const auto feature : featureOrderCopy) {
|
||||
selectedFeatures.push_back(feature);
|
||||
// Compute merit with selectedFeatures
|
||||
|
||||
279
bayesnet/feature_selection/L1FS.cc
Normal file
279
bayesnet/feature_selection/L1FS.cc
Normal file
@@ -0,0 +1,279 @@
|
||||
// ***************************************************************
|
||||
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
|
||||
// SPDX-FileType: SOURCE
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#include <algorithm>
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
#include "bayesnet/utils/bayesnetUtils.h"
|
||||
#include "L1FS.h"
|
||||
|
||||
namespace bayesnet {
|
||||
using namespace torch::indexing;
|
||||
|
||||
L1FS::L1FS(const torch::Tensor& samples,
|
||||
const std::vector<std::string>& features,
|
||||
const std::string& className,
|
||||
const int maxFeatures,
|
||||
const int classNumStates,
|
||||
const torch::Tensor& weights,
|
||||
const double alpha,
|
||||
const int maxIter,
|
||||
const double tolerance,
|
||||
const bool fitIntercept)
|
||||
: FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights),
|
||||
alpha(alpha), maxIter(maxIter), tolerance(tolerance), fitIntercept(fitIntercept)
|
||||
{
|
||||
if (alpha < 0) {
|
||||
throw std::invalid_argument("Alpha (regularization strength) must be non-negative");
|
||||
}
|
||||
if (maxIter < 1) {
|
||||
throw std::invalid_argument("Maximum iterations must be positive");
|
||||
}
|
||||
if (tolerance <= 0) {
|
||||
throw std::invalid_argument("Tolerance must be positive");
|
||||
}
|
||||
|
||||
// Determine if this is a regression or classification task
|
||||
// For simplicity, assume binary classification if classNumStates == 2
|
||||
// and regression otherwise (this can be refined based on your needs)
|
||||
isRegression = (classNumStates > 2 || classNumStates == 0);
|
||||
}
|
||||
|
||||
void L1FS::fit()
|
||||
{
|
||||
initialize();
|
||||
|
||||
// Prepare data
|
||||
int n_samples = samples.size(1);
|
||||
int n_features = features.size();
|
||||
|
||||
// Extract features (all rows except last)
|
||||
auto X = samples.index({ Slice(0, n_features), Slice() }).t().contiguous();
|
||||
|
||||
// Extract labels (last row)
|
||||
auto y = samples.index({ -1, Slice() }).contiguous();
|
||||
|
||||
// Convert to float for numerical operations
|
||||
X = X.to(torch::kFloat32);
|
||||
y = y.to(torch::kFloat32);
|
||||
|
||||
// Normalize features for better convergence
|
||||
auto X_mean = X.mean(0);
|
||||
auto X_std = X.std(0);
|
||||
X_std = torch::where(X_std == 0, torch::ones_like(X_std), X_std);
|
||||
X = (X - X_mean) / X_std;
|
||||
|
||||
if (isRegression) {
|
||||
// Normalize y for regression
|
||||
auto y_mean = y.mean();
|
||||
auto y_std = y.std();
|
||||
if (y_std.item<double>() > 0) {
|
||||
y = (y - y_mean) / y_std;
|
||||
}
|
||||
fitLasso(X, y, weights);
|
||||
} else {
|
||||
// For binary classification
|
||||
fitL1Logistic(X, y, weights);
|
||||
}
|
||||
|
||||
// Select features based on non-zero coefficients
|
||||
std::vector<std::pair<int, double>> featureImportance;
|
||||
for (int i = 0; i < n_features; ++i) {
|
||||
double coef_magnitude = std::abs(coefficients[i]);
|
||||
if (coef_magnitude > 1e-10) { // Threshold for numerical zero
|
||||
featureImportance.push_back({ i, coef_magnitude });
|
||||
}
|
||||
}
|
||||
|
||||
// If all coefficients are zero (high regularization), select based on original feature-class correlation
|
||||
if (featureImportance.empty() && maxFeatures > 0) {
|
||||
// Compute SU with labels as fallback
|
||||
computeSuLabels();
|
||||
auto featureOrder = argsort(suLabels);
|
||||
|
||||
// Select top features by SU score
|
||||
int numToSelect = std::min(static_cast<int>(featureOrder.size()),
|
||||
std::min(maxFeatures, 3)); // At most 3 features as fallback
|
||||
|
||||
for (int i = 0; i < numToSelect; ++i) {
|
||||
selectedFeatures.push_back(featureOrder[i]);
|
||||
selectedScores.push_back(suLabels[featureOrder[i]]);
|
||||
}
|
||||
} else {
|
||||
// Sort by importance (absolute coefficient value)
|
||||
std::sort(featureImportance.begin(), featureImportance.end(),
|
||||
[](const auto& a, const auto& b) { return a.second > b.second; });
|
||||
|
||||
// Select top features up to maxFeatures
|
||||
int numToSelect = std::min(static_cast<int>(featureImportance.size()),
|
||||
maxFeatures);
|
||||
|
||||
for (int i = 0; i < numToSelect; ++i) {
|
||||
selectedFeatures.push_back(featureImportance[i].first);
|
||||
selectedScores.push_back(featureImportance[i].second);
|
||||
}
|
||||
}
|
||||
|
||||
fitted = true;
|
||||
}
|
||||
|
||||
void L1FS::fitLasso(const torch::Tensor& X, const torch::Tensor& y,
|
||||
const torch::Tensor& sampleWeights)
|
||||
{
|
||||
int n_samples = X.size(0);
|
||||
int n_features = X.size(1);
|
||||
|
||||
// Initialize coefficients
|
||||
coefficients.resize(n_features, 0.0);
|
||||
double intercept = 0.0;
|
||||
|
||||
// Ensure consistent types
|
||||
torch::Tensor weights = sampleWeights.to(torch::kFloat32);
|
||||
|
||||
// Coordinate descent for Lasso
|
||||
torch::Tensor residuals = y.clone();
|
||||
if (fitIntercept) {
|
||||
intercept = (y * weights).sum().item<float>() / weights.sum().item<float>();
|
||||
residuals = y - intercept;
|
||||
}
|
||||
|
||||
// Precompute feature norms
|
||||
std::vector<double> featureNorms(n_features);
|
||||
for (int j = 0; j < n_features; ++j) {
|
||||
auto Xj = X.index({ Slice(), j });
|
||||
featureNorms[j] = (Xj * Xj * weights).sum().item<float>();
|
||||
}
|
||||
|
||||
// Coordinate descent iterations
|
||||
for (int iter = 0; iter < maxIter; ++iter) {
|
||||
double maxChange = 0.0;
|
||||
|
||||
// Update each coordinate
|
||||
for (int j = 0; j < n_features; ++j) {
|
||||
auto Xj = X.index({ Slice(), j });
|
||||
|
||||
// Compute partial residuals (excluding feature j)
|
||||
torch::Tensor partialResiduals = residuals + coefficients[j] * Xj;
|
||||
|
||||
// Compute rho (correlation with residuals)
|
||||
double rho = (Xj * partialResiduals * weights).sum().item<float>();
|
||||
|
||||
// Soft thresholding
|
||||
double oldCoef = coefficients[j];
|
||||
coefficients[j] = softThreshold(rho, alpha) / featureNorms[j];
|
||||
|
||||
// Update residuals
|
||||
residuals = partialResiduals - coefficients[j] * Xj;
|
||||
|
||||
maxChange = std::max(maxChange, std::abs(coefficients[j] - oldCoef));
|
||||
}
|
||||
|
||||
// Update intercept if needed
|
||||
if (fitIntercept) {
|
||||
double oldIntercept = intercept;
|
||||
intercept = (residuals * weights).sum().item<float>() /
|
||||
weights.sum().item<float>();
|
||||
residuals = residuals - (intercept - oldIntercept);
|
||||
maxChange = std::max(maxChange, std::abs(intercept - oldIntercept));
|
||||
}
|
||||
|
||||
// Check convergence
|
||||
if (maxChange < tolerance) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void L1FS::fitL1Logistic(const torch::Tensor& X, const torch::Tensor& y,
|
||||
const torch::Tensor& sampleWeights)
|
||||
{
|
||||
int n_samples = X.size(0);
|
||||
int n_features = X.size(1);
|
||||
|
||||
// Initialize coefficients
|
||||
torch::Tensor coef = torch::zeros({ n_features }, torch::kFloat32);
|
||||
double intercept = 0.0;
|
||||
|
||||
// Ensure consistent types
|
||||
torch::Tensor weights = sampleWeights.to(torch::kFloat32);
|
||||
|
||||
// Learning rate (can be adaptive)
|
||||
double learningRate = 0.01;
|
||||
|
||||
// Proximal gradient descent
|
||||
for (int iter = 0; iter < maxIter; ++iter) {
|
||||
// Compute predictions
|
||||
torch::Tensor linearPred = X.matmul(coef);
|
||||
if (fitIntercept) {
|
||||
linearPred = linearPred + intercept;
|
||||
}
|
||||
torch::Tensor pred = sigmoid(linearPred);
|
||||
|
||||
// Compute gradient
|
||||
torch::Tensor diff = pred - y;
|
||||
torch::Tensor grad = X.t().matmul(diff * weights) / n_samples;
|
||||
|
||||
// Gradient descent step
|
||||
torch::Tensor coef_new = coef - learningRate * grad;
|
||||
|
||||
// Proximal step (soft thresholding)
|
||||
for (int j = 0; j < n_features; ++j) {
|
||||
coef_new[j] = softThreshold(coef_new[j].item<float>(),
|
||||
learningRate * alpha);
|
||||
}
|
||||
|
||||
// Update intercept if needed
|
||||
if (fitIntercept) {
|
||||
double grad_intercept = (diff * weights).sum().item<float>() / n_samples;
|
||||
intercept -= learningRate * grad_intercept;
|
||||
}
|
||||
|
||||
// Check convergence
|
||||
double change = (coef_new - coef).abs().max().item<float>();
|
||||
coef = coef_new;
|
||||
|
||||
if (change < tolerance) {
|
||||
break;
|
||||
}
|
||||
|
||||
// Adaptive learning rate (optional)
|
||||
if (iter % 100 == 0) {
|
||||
learningRate *= 0.9;
|
||||
}
|
||||
}
|
||||
|
||||
// Store final coefficients
|
||||
coefficients.resize(n_features);
|
||||
for (int j = 0; j < n_features; ++j) {
|
||||
coefficients[j] = coef[j].item<float>();
|
||||
}
|
||||
}
|
||||
|
||||
double L1FS::softThreshold(double x, double lambda) const
|
||||
{
|
||||
if (x > lambda) {
|
||||
return x - lambda;
|
||||
} else if (x < -lambda) {
|
||||
return x + lambda;
|
||||
} else {
|
||||
return 0.0;
|
||||
}
|
||||
}
|
||||
|
||||
torch::Tensor L1FS::sigmoid(const torch::Tensor& z) const
|
||||
{
|
||||
return 1.0 / (1.0 + torch::exp(-z));
|
||||
}
|
||||
|
||||
std::vector<double> L1FS::getCoefficients() const
|
||||
{
|
||||
if (!fitted) {
|
||||
throw std::runtime_error("L1FS not fitted");
|
||||
}
|
||||
return coefficients;
|
||||
}
|
||||
|
||||
} // namespace bayesnet
|
||||
83
bayesnet/feature_selection/L1FS.h
Normal file
83
bayesnet/feature_selection/L1FS.h
Normal file
@@ -0,0 +1,83 @@
|
||||
// ***************************************************************
|
||||
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
|
||||
// SPDX-FileType: SOURCE
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#ifndef L1FS_H
|
||||
#define L1FS_H
|
||||
#include <torch/torch.h>
|
||||
#include <vector>
|
||||
#include "bayesnet/feature_selection/FeatureSelect.h"
|
||||
|
||||
namespace bayesnet {
|
||||
/**
|
||||
* L1-Regularized Feature Selection (L1FS)
|
||||
*
|
||||
* This class implements feature selection using L1-regularized linear models.
|
||||
* For classification tasks, it uses one-vs-rest logistic regression with L1 penalty.
|
||||
* For regression tasks, it uses Lasso regression.
|
||||
*
|
||||
* The L1 penalty induces sparsity in the model coefficients, effectively
|
||||
* performing feature selection by setting irrelevant feature weights to zero.
|
||||
*/
|
||||
class L1FS : public FeatureSelect {
|
||||
public:
|
||||
/**
|
||||
* Constructor for L1FS
|
||||
* @param samples n+1xm tensor where samples[-1] is the target variable
|
||||
* @param features vector of feature names
|
||||
* @param className name of the class/target variable
|
||||
* @param maxFeatures maximum number of features to select (0 = all)
|
||||
* @param classNumStates number of states for classification (ignored for regression)
|
||||
* @param weights sample weights
|
||||
* @param alpha L1 regularization strength (higher = more sparsity)
|
||||
* @param maxIter maximum iterations for optimization
|
||||
* @param tolerance convergence tolerance
|
||||
* @param fitIntercept whether to fit an intercept term
|
||||
*/
|
||||
L1FS(const torch::Tensor& samples,
|
||||
const std::vector<std::string>& features,
|
||||
const std::string& className,
|
||||
const int maxFeatures,
|
||||
const int classNumStates,
|
||||
const torch::Tensor& weights,
|
||||
const double alpha = 1.0,
|
||||
const int maxIter = 1000,
|
||||
const double tolerance = 1e-4,
|
||||
const bool fitIntercept = true);
|
||||
|
||||
virtual ~L1FS() {};
|
||||
|
||||
void fit() override;
|
||||
|
||||
// Get the learned coefficients for each feature
|
||||
std::vector<double> getCoefficients() const;
|
||||
|
||||
private:
|
||||
double alpha; // L1 regularization strength
|
||||
int maxIter; // Maximum iterations for optimization
|
||||
double tolerance; // Convergence tolerance
|
||||
bool fitIntercept; // Whether to fit intercept
|
||||
bool isRegression; // Task type (regression vs classification)
|
||||
|
||||
std::vector<double> coefficients; // Learned coefficients
|
||||
|
||||
// Coordinate descent for Lasso regression
|
||||
void fitLasso(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& sampleWeights);
|
||||
|
||||
// Proximal gradient descent for L1-regularized logistic regression
|
||||
void fitL1Logistic(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& sampleWeights);
|
||||
|
||||
// Soft thresholding operator for L1 regularization
|
||||
double softThreshold(double x, double lambda) const;
|
||||
|
||||
// Logistic function
|
||||
torch::Tensor sigmoid(const torch::Tensor& z) const;
|
||||
|
||||
// Compute logistic loss
|
||||
double logisticLoss(const torch::Tensor& X, const torch::Tensor& y,
|
||||
const torch::Tensor& coef, const torch::Tensor& sampleWeights) const;
|
||||
};
|
||||
}
|
||||
#endif
|
||||
@@ -5,6 +5,7 @@
|
||||
// ***************************************************************
|
||||
|
||||
#include "Node.h"
|
||||
#include <iterator>
|
||||
|
||||
namespace bayesnet {
|
||||
|
||||
@@ -94,39 +95,51 @@ namespace bayesnet {
|
||||
{
|
||||
dimensions.clear();
|
||||
dimensions.reserve(parents.size() + 1);
|
||||
// Get dimensions of the CPT
|
||||
dimensions.push_back(numStates);
|
||||
for (const auto& parent : parents) {
|
||||
dimensions.push_back(parent->getNumStates());
|
||||
}
|
||||
//transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
|
||||
// Create a tensor initialized with smoothing
|
||||
cpTable = torch::full(dimensions, smoothing, torch::kDouble);
|
||||
// Create a map for quick feature index lookup
|
||||
|
||||
// Build feature index map
|
||||
std::unordered_map<std::string, int> featureIndexMap;
|
||||
for (size_t i = 0; i < features.size(); ++i) {
|
||||
featureIndexMap[features[i]] = i;
|
||||
}
|
||||
// Fill table with counts
|
||||
// Get the index of this node's feature
|
||||
int name_index = featureIndexMap[name];
|
||||
// Get parent indices in dataset
|
||||
std::vector<int> parent_indices;
|
||||
parent_indices.reserve(parents.size());
|
||||
|
||||
// Gather indices for node and parents
|
||||
std::vector<int64_t> all_indices;
|
||||
all_indices.push_back(featureIndexMap[name]);
|
||||
for (const auto& parent : parents) {
|
||||
parent_indices.push_back(featureIndexMap[parent->getName()]);
|
||||
all_indices.push_back(featureIndexMap[parent->getName()]);
|
||||
}
|
||||
c10::List<c10::optional<at::Tensor>> coordinates;
|
||||
for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
|
||||
coordinates.clear();
|
||||
auto sample = dataset.index({ "...", n_sample });
|
||||
coordinates.push_back(sample[name_index]);
|
||||
for (size_t i = 0; i < parent_indices.size(); ++i) {
|
||||
coordinates.push_back(sample[parent_indices[i]]);
|
||||
|
||||
// Extract relevant columns: shape (num_features, num_samples)
|
||||
auto indices_tensor = dataset.index_select(0, torch::tensor(all_indices, torch::kLong));
|
||||
indices_tensor = indices_tensor.transpose(0, 1).to(torch::kLong); // (num_samples, num_features)
|
||||
|
||||
// Manual flattening of indices
|
||||
std::vector<int64_t> strides(all_indices.size(), 1);
|
||||
for (int i = strides.size() - 2; i >= 0; --i) {
|
||||
strides[i] = strides[i + 1] * cpTable.size(i + 1);
|
||||
}
|
||||
auto indices_tensor_cpu = indices_tensor.cpu();
|
||||
auto indices_accessor = indices_tensor_cpu.accessor<int64_t, 2>();
|
||||
std::vector<int64_t> flat_indices(indices_tensor.size(0));
|
||||
for (int64_t i = 0; i < indices_tensor.size(0); ++i) {
|
||||
int64_t idx = 0;
|
||||
for (size_t j = 0; j < strides.size(); ++j) {
|
||||
idx += indices_accessor[i][j] * strides[j];
|
||||
}
|
||||
// Increment the count of the corresponding coordinate
|
||||
cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
|
||||
flat_indices[i] = idx;
|
||||
}
|
||||
|
||||
// Accumulate weights into flat CPT
|
||||
auto flat_cpt = cpTable.flatten();
|
||||
auto flat_indices_tensor = torch::from_blob(flat_indices.data(), { (int64_t)flat_indices.size() }, torch::kLong).clone();
|
||||
flat_cpt.index_add_(0, flat_indices_tensor, weights.cpu());
|
||||
cpTable = flat_cpt.view(cpTable.sizes());
|
||||
|
||||
// Normalize the counts (dividing each row by the sum of the row)
|
||||
cpTable /= cpTable.sum(0, true);
|
||||
}
|
||||
|
||||
@@ -4,9 +4,6 @@
|
||||
#include <condition_variable>
|
||||
#include <algorithm>
|
||||
#include <thread>
|
||||
#include <mutex>
|
||||
#include <condition_variable>
|
||||
#include <thread>
|
||||
|
||||
class CountingSemaphore {
|
||||
public:
|
||||
|
||||
4
bayesnetConfig.cmake.in
Normal file
4
bayesnetConfig.cmake.in
Normal file
@@ -0,0 +1,4 @@
|
||||
@PACKAGE_INIT@
|
||||
|
||||
include("${CMAKE_CURRENT_LIST_DIR}/bayesnetTargets.cmake")
|
||||
|
||||
@@ -3,12 +3,12 @@
|
||||
#include <string>
|
||||
#include <string_view>
|
||||
|
||||
#define PROJECT_VERSION_MAJOR @PROJECT_VERSION_MAJOR @
|
||||
#define PROJECT_VERSION_MINOR @PROJECT_VERSION_MINOR @
|
||||
#define PROJECT_VERSION_PATCH @PROJECT_VERSION_PATCH @
|
||||
#define PROJECT_VERSION_MAJOR @PROJECT_VERSION_MAJOR@
|
||||
#define PROJECT_VERSION_MINOR @PROJECT_VERSION_MINOR@
|
||||
#define PROJECT_VERSION_PATCH @PROJECT_VERSION_PATCH@
|
||||
|
||||
static constexpr std::string_view project_name = "@PROJECT_NAME@";
|
||||
static constexpr std::string_view project_version = "@PROJECT_VERSION@";
|
||||
static constexpr std::string_view project_description = "@PROJECT_DESCRIPTION@";
|
||||
static constexpr std::string_view git_sha = "@GIT_SHA@";
|
||||
static constexpr std::string_view data_path = "@BayesNet_SOURCE_DIR@/tests/data/";
|
||||
static constexpr std::string_view data_path = "@bayesnet_SOURCE_DIR@/tests/data/";
|
||||
@@ -1,15 +1,34 @@
|
||||
cmake_minimum_required(VERSION 3.20)
|
||||
|
||||
project(bayesnet_sample)
|
||||
project(bayesnet_sample VERSION 0.1.0 LANGUAGES CXX)
|
||||
|
||||
set(CMAKE_CXX_STANDARD 17)
|
||||
|
||||
set(CMAKE_BUILD_TYPE Release)
|
||||
|
||||
find_package(Torch CONFIG REQUIRED)
|
||||
find_package(bayesnet CONFIG REQUIRED)
|
||||
find_package(fimdlp CONFIG REQUIRED)
|
||||
find_package(folding CONFIG REQUIRED)
|
||||
find_package(arff-files CONFIG REQUIRED)
|
||||
find_package(nlohman_json CONFIG REQUIRED)
|
||||
find_package(nlohmann_json CONFIG REQUIRED)
|
||||
|
||||
option(BAYESNET_VCPKG_CONFIG "Use vcpkg config for BayesNet" ON)
|
||||
|
||||
if (BAYESNET_VCPKG_CONFIG)
|
||||
message(STATUS "Using BayesNet vcpkg config")
|
||||
find_package(bayesnet CONFIG REQUIRED)
|
||||
set(BayesNet_LIBRARIES bayesnet::bayesnet)
|
||||
else(BAYESNET_VCPKG_CONFIG)
|
||||
message(STATUS "Using BayesNet local library config")
|
||||
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}
|
||||
)
|
||||
endif(BAYESNET_VCPKG_CONFIG)
|
||||
message(STATUS "BayesNet: ${bayesnet}")
|
||||
|
||||
add_executable(bayesnet_sample sample.cc)
|
||||
target_link_libraries(bayesnet_sample PRIVATE
|
||||
@@ -17,6 +36,5 @@ target_link_libraries(bayesnet_sample PRIVATE
|
||||
arff-files::arff-files
|
||||
"${TORCH_LIBRARIES}"
|
||||
bayesnet::bayesnet
|
||||
nlohmann_json::nlohmann_json
|
||||
folding::folding
|
||||
)
|
||||
|
||||
123
sample/sample.cc
123
sample/sample.cc
@@ -4,9 +4,22 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// ***************************************************************
|
||||
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <ArffFiles/ArffFiles.hpp>
|
||||
#include <fimdlp/CPPFImdlp.h>
|
||||
#include <bayesnet/ensembles/XBAODE.h>
|
||||
#include <bayesnet/classifiers/TANLd.h>
|
||||
#include <bayesnet/classifiers/KDBLd.h>
|
||||
#include <bayesnet/ensembles/AODELd.h>
|
||||
|
||||
torch::Tensor matrix2tensor(const std::vector<std::vector<float>>& matrix)
|
||||
{
|
||||
auto tensor = torch::empty({ static_cast<int>(matrix.size()), static_cast<int>(matrix[0].size()) }, torch::kFloat32);
|
||||
for (int i = 0; i < matrix.size(); ++i) {
|
||||
tensor.index_put_({ i, "..." }, torch::tensor(matrix[i], torch::kFloat32));
|
||||
}
|
||||
return tensor;
|
||||
}
|
||||
|
||||
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
|
||||
{
|
||||
@@ -19,63 +32,89 @@ std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, m
|
||||
}
|
||||
return Xd;
|
||||
}
|
||||
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
|
||||
std::tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string> loadArff(const std::string& name, bool class_last)
|
||||
{
|
||||
auto handler = ArffFiles();
|
||||
handler.load(name, class_last);
|
||||
// Get Dataset X, y
|
||||
std::vector<mdlp::samples_t>& X = handler.getX();
|
||||
mdlp::labels_t& y = handler.getY();
|
||||
// Get className & Features
|
||||
auto className = handler.getClassName();
|
||||
std::vector<mdlp::samples_t> X = handler.getX();
|
||||
mdlp::labels_t y = handler.getY();
|
||||
std::vector<std::string> features;
|
||||
auto attributes = handler.getAttributes();
|
||||
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
|
||||
torch::Tensor Xd;
|
||||
auto states = map<std::string, std::vector<int>>();
|
||||
auto Xr = discretizeDataset(X, y);
|
||||
Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
|
||||
for (int i = 0; i < features.size(); ++i) {
|
||||
states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
|
||||
auto item = states.at(features[i]);
|
||||
iota(begin(item), end(item), 0);
|
||||
Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
|
||||
}
|
||||
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
|
||||
iota(begin(states.at(className)), end(states.at(className)), 0);
|
||||
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
|
||||
auto Xt = matrix2tensor(X);
|
||||
auto yt = torch::tensor(y, torch::kInt32);
|
||||
return { Xt, yt, features, handler.getClassName() };
|
||||
}
|
||||
// tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
|
||||
// {
|
||||
// auto [X, y, features, className] = loadArff(name, class_last);
|
||||
// // Discretize the dataset
|
||||
// torch::Tensor Xd;
|
||||
// auto states = map<std::string, std::vector<int>>();
|
||||
// // Fill the class states
|
||||
// states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
|
||||
// iota(begin(states.at(className)), end(states.at(className)), 0);
|
||||
// auto Xr = discretizeDataset(X, y);
|
||||
// Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
|
||||
// for (int i = 0; i < features.size(); ++i) {
|
||||
// states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
|
||||
// auto item = states.at(features[i]);
|
||||
// iota(begin(item), end(item), 0);
|
||||
// Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
|
||||
// }
|
||||
// auto yt = torch::tensor(y, torch::kInt32);
|
||||
// return { Xd, yt, features, className, states };
|
||||
// }
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
if (argc < 2) {
|
||||
std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
|
||||
if (argc < 3) {
|
||||
std::cerr << "Usage: " << argv[0] << " <arff_file_name> <model>" << std::endl;
|
||||
return 1;
|
||||
}
|
||||
std::string file_name = argv[1];
|
||||
torch::Tensor X, y;
|
||||
std::vector<std::string> features;
|
||||
std::string className;
|
||||
map<std::string, std::vector<int>> states;
|
||||
auto clf = bayesnet::XBAODE(); // false for not using voting in predict
|
||||
std::cout << "Library version: " << clf.getVersion() << std::endl;
|
||||
tie(X, y, features, className, states) = loadDataset(file_name, true);
|
||||
torch::Tensor weights = torch::full({ X.size(1) }, 15, torch::kDouble);
|
||||
torch::Tensor dataset;
|
||||
try {
|
||||
auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
|
||||
dataset = torch::cat({ X, yresized }, 0);
|
||||
std::string model_name = argv[2];
|
||||
std::map<std::string, bayesnet::Classifier*> models{ {"TANLd", new bayesnet::TANLd()}, {"KDBLd", new bayesnet::KDBLd(2)}, {"AODELd", new bayesnet::AODELd() }
|
||||
};
|
||||
if (models.find(model_name) == models.end()) {
|
||||
std::cerr << "Model not found: " << model_name << std::endl;
|
||||
std::cerr << "Available models: ";
|
||||
for (const auto& model : models) {
|
||||
std::cerr << model.first << " ";
|
||||
}
|
||||
std::cerr << std::endl;
|
||||
return 1;
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
std::stringstream oss;
|
||||
oss << "* Error in X and y dimensions *\n";
|
||||
oss << "X dimensions: " << dataset.sizes() << "\n";
|
||||
oss << "y dimensions: " << y.sizes();
|
||||
throw std::runtime_error(oss.str());
|
||||
auto clf = models[model_name];
|
||||
std::cout << "Library version: " << clf->getVersion() << std::endl;
|
||||
// auto [X, y, features, className, states] = loadDataset(file_name, true);
|
||||
auto [Xt, yt, features, className] = loadArff(file_name, true);
|
||||
std::map<std::string, std::vector<int>> states;
|
||||
// int m = Xt.size(1);
|
||||
// auto weights = torch::full({ m }, 1 / m, torch::kDouble);
|
||||
// auto dataset = buildDataset(Xv, yv);
|
||||
// try {
|
||||
// auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
|
||||
// dataset = torch::cat({ X, yresized }, 0);
|
||||
// }
|
||||
// catch (const std::exception& e) {
|
||||
// std::stringstream oss;
|
||||
// oss << "* Error in X and y dimensions *\n";
|
||||
// oss << "X dimensions: " << dataset.sizes() << "\n";
|
||||
// oss << "y dimensions: " << y.sizes();
|
||||
// throw std::runtime_error(oss.str());
|
||||
// }
|
||||
clf->fit(Xt, yt, features, className, states, bayesnet::Smoothing_t::ORIGINAL);
|
||||
auto total = yt.size(0);
|
||||
auto y_proba = clf->predict_proba(Xt);
|
||||
auto y_pred = y_proba.argmax(1);
|
||||
auto accuracy_value = (y_pred == yt).sum().item<float>() / total;
|
||||
auto score = clf->score(Xt, yt);
|
||||
std::cout << "File: " << file_name << " Model: " << model_name << " score: " << score << " Computed accuracy: " << accuracy_value << std::endl;
|
||||
for (const auto clf : models) {
|
||||
delete clf.second;
|
||||
}
|
||||
clf.fit(dataset, features, className, states, weights, bayesnet::Smoothing_t::LAPLACE);
|
||||
auto score = clf.score(X, y);
|
||||
std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
||||
@@ -1,21 +1,21 @@
|
||||
{
|
||||
"default-registry": {
|
||||
"kind": "git",
|
||||
"baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605",
|
||||
"repository": "https://github.com/microsoft/vcpkg"
|
||||
},
|
||||
"registries": [
|
||||
{
|
||||
"kind": "git",
|
||||
"repository": "https://github.com/rmontanana/vcpkg-stash",
|
||||
"baseline": "393efa4e74e053b6f02c4ab03738c8fe796b28e5",
|
||||
"baseline": "1ea69243c0e8b0de77c9d1dd6e1d7593ae7f3627",
|
||||
"packages": [
|
||||
"folding",
|
||||
"bayesnet",
|
||||
"arff-files",
|
||||
"bayesnet",
|
||||
"fimdlp",
|
||||
"folding",
|
||||
"libtorch-bin"
|
||||
]
|
||||
}
|
||||
],
|
||||
"default-registry": {
|
||||
"kind": "git",
|
||||
"repository": "https://github.com/microsoft/vcpkg",
|
||||
"baseline": "760bfd0c8d7c89ec640aec4df89418b7c2745605"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -2,11 +2,32 @@
|
||||
"name": "sample-project",
|
||||
"version-string": "0.1.0",
|
||||
"dependencies": [
|
||||
"bayesnet",
|
||||
"folding",
|
||||
"arff-files",
|
||||
"fimdlp",
|
||||
"nlohmann-json",
|
||||
"libtorch-bin"
|
||||
"libtorch-bin",
|
||||
"folding",
|
||||
"nlohmann-json"
|
||||
],
|
||||
"overrides": [
|
||||
{
|
||||
"name": "arff-files",
|
||||
"version": "1.1.0"
|
||||
},
|
||||
{
|
||||
"name": "fimdlp",
|
||||
"version": "2.0.1"
|
||||
},
|
||||
{
|
||||
"name": "libtorch-bin",
|
||||
"version": "2.7.0"
|
||||
},
|
||||
{
|
||||
"name": "bayesnet",
|
||||
"version": "1.1.1"
|
||||
},
|
||||
{
|
||||
"name": "folding",
|
||||
"version": "1.1.1"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,18 +1,13 @@
|
||||
if(ENABLE_TESTING)
|
||||
include_directories(
|
||||
${BayesNet_SOURCE_DIR}/tests/lib/Files
|
||||
${BayesNet_SOURCE_DIR}/lib/folding
|
||||
${BayesNet_SOURCE_DIR}/lib/mdlp/src
|
||||
${BayesNet_SOURCE_DIR}/lib/log
|
||||
${BayesNet_SOURCE_DIR}/lib/json/include
|
||||
${BayesNet_SOURCE_DIR}
|
||||
${CMAKE_BINARY_DIR}/configured_files/include
|
||||
)
|
||||
file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
|
||||
file(GLOB_RECURSE BayesNet_SOURCES "${bayesnet_SOURCE_DIR}/bayesnet/*.cc")
|
||||
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc TestXSPnDE.cc TestXBA2DE.cc
|
||||
TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestXBAODE.cc TestA2DE.cc
|
||||
TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc TestMST.cc TestXSPODE.cc ${BayesNet_SOURCES})
|
||||
target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" fimdlp PRIVATE Catch2::Catch2WithMain)
|
||||
target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" fimdlp::fimdlp PRIVATE Catch2::Catch2WithMain)
|
||||
add_test(NAME BayesNetworkTest COMMAND TestBayesNet)
|
||||
add_test(NAME A2DE COMMAND TestBayesNet "[A2DE]")
|
||||
add_test(NAME BoostA2DE COMMAND TestBayesNet "[BoostA2DE]")
|
||||
|
||||
@@ -20,7 +20,7 @@
|
||||
#include "bayesnet/ensembles/AODELd.h"
|
||||
#include "bayesnet/ensembles/BoostAODE.h"
|
||||
|
||||
const std::string ACTUAL_VERSION = "1.1.0";
|
||||
const std::string ACTUAL_VERSION = "1.1.2";
|
||||
|
||||
TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
|
||||
{
|
||||
@@ -152,7 +152,7 @@ TEST_CASE("Get num features & num edges", "[Models]")
|
||||
|
||||
TEST_CASE("Model predict_proba", "[Models]")
|
||||
{
|
||||
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting");
|
||||
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting", "TANLd", "SPODELd", "KDBLd");
|
||||
auto res_prob_tan = std::vector<std::vector<double>>({ {0.00375671, 0.994457, 0.00178621},
|
||||
{0.00137462, 0.992734, 0.00589123},
|
||||
{0.00137462, 0.992734, 0.00589123},
|
||||
@@ -180,50 +180,99 @@ TEST_CASE("Model predict_proba", "[Models]")
|
||||
{0.0284828, 0.770524, 0.200993},
|
||||
{0.0213182, 0.857189, 0.121493},
|
||||
{0.00868436, 0.949494, 0.0418215} });
|
||||
auto res_prob_tanld = std::vector<std::vector<double>>({ {0.000544493, 0.995796, 0.00365992 },
|
||||
{0.000908092, 0.997268, 0.00182429 },
|
||||
{0.000908092, 0.997268, 0.00182429 },
|
||||
{0.000908092, 0.997268, 0.00182429 },
|
||||
{0.00228423, 0.994645, 0.00307078 },
|
||||
{0.00120539, 0.0666788, 0.932116 },
|
||||
{0.00361847, 0.979203, 0.017179 },
|
||||
{0.00483293, 0.985326, 0.00984064 },
|
||||
{0.000595606, 0.9977, 0.00170441 } });
|
||||
auto res_prob_spodeld = std::vector<std::vector<double>>({ {0.000908024, 0.993742, 0.00535024 },
|
||||
{0.00187726, 0.99167, 0.00645308 },
|
||||
{0.00187726, 0.99167, 0.00645308 },
|
||||
{0.00187726, 0.99167, 0.00645308 },
|
||||
{0.00287539, 0.993736, 0.00338846 },
|
||||
{0.00294402, 0.268495, 0.728561 },
|
||||
{0.0132381, 0.873282, 0.113479 },
|
||||
{0.0159412, 0.969228, 0.0148308 },
|
||||
{0.00203487, 0.989762, 0.00820356 } });
|
||||
auto res_prob_kdbld = std::vector<std::vector<double>>({ {0.000738981, 0.997208, 0.00205272 },
|
||||
{0.00087708, 0.996687, 0.00243633 },
|
||||
{0.00087708, 0.996687, 0.00243633 },
|
||||
{0.00087708, 0.996687, 0.00243633 },
|
||||
{0.000738981, 0.997208, 0.00205272 },
|
||||
{0.00512442, 0.0455504, 0.949325 },
|
||||
{0.0023632, 0.976631, 0.0210063 },
|
||||
{0.00189194, 0.992853, 0.00525538 },
|
||||
{0.00189194, 0.992853, 0.00525538, } });
|
||||
auto res_prob_voting = std::vector<std::vector<double>>(
|
||||
{ {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0}, {0, 0, 1}, {0, 1, 0}, {0, 1, 0}, {0, 1, 0} });
|
||||
std::map<std::string, std::vector<std::vector<double>>> res_prob{ {"TAN", res_prob_tan},
|
||||
{"SPODE", res_prob_spode},
|
||||
{"BoostAODEproba", res_prob_baode},
|
||||
{"BoostAODEvoting", res_prob_voting} };
|
||||
{"BoostAODEvoting", res_prob_voting},
|
||||
{"TANLd", res_prob_tanld},
|
||||
{"SPODELd", res_prob_spodeld},
|
||||
{"KDBLd", res_prob_kdbld} };
|
||||
std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()},
|
||||
{"SPODE", new bayesnet::SPODE(0)},
|
||||
{"BoostAODEproba", new bayesnet::BoostAODE(false)},
|
||||
{"BoostAODEvoting", new bayesnet::BoostAODE(true)} };
|
||||
{"BoostAODEvoting", new bayesnet::BoostAODE(true)},
|
||||
{"TANLd", new bayesnet::TANLd()},
|
||||
{"SPODELd", new bayesnet::SPODELd(0)},
|
||||
{"KDBLd", new bayesnet::KDBLd(2)} };
|
||||
int init_index = 78;
|
||||
auto raw = RawDatasets("iris", true);
|
||||
|
||||
SECTION("Test " + model + " predict_proba")
|
||||
{
|
||||
auto ld_model = model.substr(model.length() - 2) == "Ld";
|
||||
auto discretize = !ld_model;
|
||||
auto raw = RawDatasets("iris", discretize);
|
||||
auto clf = models[model];
|
||||
clf->fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto y_pred_proba = clf->predict_proba(raw.Xv);
|
||||
clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto yt_pred_proba = clf->predict_proba(raw.Xt);
|
||||
auto y_pred = clf->predict(raw.Xv);
|
||||
auto yt_pred = clf->predict(raw.Xt);
|
||||
REQUIRE(y_pred.size() == yt_pred.size(0));
|
||||
REQUIRE(y_pred.size() == y_pred_proba.size());
|
||||
REQUIRE(y_pred.size() == yt_pred_proba.size(0));
|
||||
REQUIRE(y_pred.size() == raw.yv.size());
|
||||
REQUIRE(y_pred_proba[0].size() == 3);
|
||||
REQUIRE(yt_pred_proba.size(1) == y_pred_proba[0].size());
|
||||
for (int i = 0; i < 9; ++i) {
|
||||
auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
|
||||
int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
|
||||
REQUIRE(predictedClass == y_pred[i]);
|
||||
// Check predict is coherent with predict_proba
|
||||
REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
|
||||
for (int j = 0; j < yt_pred_proba.size(1); j++) {
|
||||
REQUIRE(yt_pred_proba[i][j].item<double>() == Catch::Approx(y_pred_proba[i][j]).epsilon(raw.epsilon));
|
||||
std::vector<int> y_pred;
|
||||
std::vector<std::vector<double>> y_pred_proba;
|
||||
if (!ld_model) {
|
||||
y_pred = clf->predict(raw.Xv);
|
||||
y_pred_proba = clf->predict_proba(raw.Xv);
|
||||
REQUIRE(y_pred.size() == y_pred_proba.size());
|
||||
REQUIRE(y_pred.size() == yt_pred.size(0));
|
||||
REQUIRE(y_pred.size() == yt_pred_proba.size(0));
|
||||
REQUIRE(y_pred_proba[0].size() == 3);
|
||||
REQUIRE(y_pred.size() == raw.yv.size());
|
||||
REQUIRE(yt_pred_proba.size(1) == y_pred_proba[0].size());
|
||||
for (int i = 0; i < 9; ++i) {
|
||||
auto maxElem = max_element(y_pred_proba[i].begin(), y_pred_proba[i].end());
|
||||
int predictedClass = distance(y_pred_proba[i].begin(), maxElem);
|
||||
REQUIRE(predictedClass == y_pred[i]);
|
||||
// Check predict is coherent with predict_proba
|
||||
REQUIRE(yt_pred_proba[i].argmax().item<int>() == y_pred[i]);
|
||||
for (int j = 0; j < yt_pred_proba.size(1); j++) {
|
||||
REQUIRE(yt_pred_proba[i][j].item<double>() == Catch::Approx(y_pred_proba[i][j]).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
}
|
||||
// Check predict_proba values for vectors and tensors
|
||||
for (int i = 0; i < 9; i++) {
|
||||
REQUIRE(y_pred[i] == yt_pred[i].item<int>());
|
||||
for (int j = 0; j < 3; j++) {
|
||||
REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
|
||||
REQUIRE(res_prob[model][i][j] ==
|
||||
Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
|
||||
// Check predict_proba values for vectors and tensors
|
||||
for (int i = 0; i < 9; i++) {
|
||||
REQUIRE(y_pred[i] == yt_pred[i].item<int>());
|
||||
for (int j = 0; j < 3; j++) {
|
||||
REQUIRE(res_prob[model][i][j] == Catch::Approx(y_pred_proba[i + init_index][j]).epsilon(raw.epsilon));
|
||||
REQUIRE(res_prob[model][i][j] ==
|
||||
Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Check predict_proba values for vectors and tensors
|
||||
auto predictedClasses = yt_pred_proba.argmax(1);
|
||||
for (int i = 0; i < 9; i++) {
|
||||
REQUIRE(predictedClasses[i].item<int>() == yt_pred[i].item<int>());
|
||||
for (int j = 0; j < 3; j++) {
|
||||
REQUIRE(res_prob[model][i][j] ==
|
||||
Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
}
|
||||
delete clf;
|
||||
|
||||
@@ -33,13 +33,11 @@ TEST_CASE("Feature_select IWSS", "[BoostA2DE]")
|
||||
auto clf = bayesnet::BoostA2DE();
|
||||
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 140);
|
||||
REQUIRE(clf.getNumberOfEdges() == 294);
|
||||
REQUIRE(clf.getNotes().size() == 4);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
|
||||
REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
|
||||
REQUIRE(clf.getNotes()[3] == "Number of models: 14");
|
||||
REQUIRE(clf.getNumberOfNodes() == 360);
|
||||
REQUIRE(clf.getNumberOfEdges() == 756);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 36");
|
||||
}
|
||||
TEST_CASE("Feature_select FCBF", "[BoostA2DE]")
|
||||
{
|
||||
@@ -64,15 +62,15 @@ TEST_CASE("Test used features in train note and score", "[BoostA2DE]")
|
||||
{"select_features","CFS"},
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 144);
|
||||
REQUIRE(clf.getNumberOfEdges() == 288);
|
||||
REQUIRE(clf.getNumberOfNodes() == 189);
|
||||
REQUIRE(clf.getNumberOfEdges() == 378);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 16");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 7 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 21");
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
REQUIRE(score == Catch::Approx(0.856771).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.856771).epsilon(raw.epsilon));
|
||||
REQUIRE(score == Catch::Approx(0.85546875f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.85546875f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Voting vs proba", "[BoostA2DE]")
|
||||
{
|
||||
|
||||
@@ -11,32 +11,35 @@
|
||||
#include "TestUtils.h"
|
||||
#include "bayesnet/ensembles/BoostAODE.h"
|
||||
|
||||
TEST_CASE("Feature_select CFS", "[BoostAODE]") {
|
||||
TEST_CASE("Feature_select CFS", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
clf.setHyperparameters({{"select_features", "CFS"}});
|
||||
clf.setHyperparameters({ {"select_features", "CFS"} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 90);
|
||||
REQUIRE(clf.getNumberOfEdges() == 153);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
|
||||
}
|
||||
TEST_CASE("Feature_select IWSS", "[BoostAODE]") {
|
||||
TEST_CASE("Feature_select IWSS", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
|
||||
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 90);
|
||||
REQUIRE(clf.getNumberOfEdges() == 153);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
|
||||
}
|
||||
TEST_CASE("Feature_select FCBF", "[BoostAODE]") {
|
||||
TEST_CASE("Feature_select FCBF", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
|
||||
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 90);
|
||||
REQUIRE(clf.getNumberOfEdges() == 153);
|
||||
@@ -44,26 +47,28 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]") {
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
|
||||
}
|
||||
TEST_CASE("Test used features in train note and score", "[BoostAODE]") {
|
||||
TEST_CASE("Test used features in train note and score", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
auto clf = bayesnet::BoostAODE(true);
|
||||
clf.setHyperparameters({
|
||||
{"order", "asc"},
|
||||
{"convergence", true},
|
||||
{"select_features", "CFS"},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 72);
|
||||
REQUIRE(clf.getNumberOfEdges() == 120);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 7 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 8");
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
|
||||
REQUIRE(score == Catch::Approx(0.8046875f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.8046875f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Voting vs proba", "[BoostAODE]") {
|
||||
TEST_CASE("Voting vs proba", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::BoostAODE(false);
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
@@ -71,7 +76,7 @@ TEST_CASE("Voting vs proba", "[BoostAODE]") {
|
||||
auto pred_proba = clf.predict_proba(raw.Xv);
|
||||
clf.setHyperparameters({
|
||||
{"predict_voting", true},
|
||||
});
|
||||
});
|
||||
auto score_voting = clf.score(raw.Xv, raw.yv);
|
||||
auto pred_voting = clf.predict_proba(raw.Xv);
|
||||
REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
|
||||
@@ -81,17 +86,18 @@ TEST_CASE("Voting vs proba", "[BoostAODE]") {
|
||||
REQUIRE(clf.dump_cpt().size() == 7004);
|
||||
REQUIRE(clf.topological_order() == std::vector<std::string>());
|
||||
}
|
||||
TEST_CASE("Order asc, desc & random", "[BoostAODE]") {
|
||||
TEST_CASE("Order asc, desc & random", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
std::map<std::string, double> scores{{"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112}};
|
||||
for (const std::string &order : {"asc", "desc", "rand"}) {
|
||||
std::map<std::string, double> scores{ {"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112} };
|
||||
for (const std::string& order : { "asc", "desc", "rand" }) {
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
clf.setHyperparameters({
|
||||
{"order", order},
|
||||
{"bisection", false},
|
||||
{"maxTolerance", 1},
|
||||
{"convergence", false},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
@@ -100,7 +106,8 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]") {
|
||||
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
TEST_CASE("Oddities", "[BoostAODE]") {
|
||||
TEST_CASE("Oddities", "[BoostAODE]")
|
||||
{
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto bad_hyper = nlohmann::json{
|
||||
@@ -109,34 +116,35 @@ TEST_CASE("Oddities", "[BoostAODE]") {
|
||||
{{"maxTolerance", 0}},
|
||||
{{"maxTolerance", 7}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper.items()) {
|
||||
for (const auto& hyper : bad_hyper.items()) {
|
||||
INFO("BoostAODE hyper: " << hyper.value().dump());
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
|
||||
}
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0} }), std::invalid_argument);
|
||||
auto bad_hyper_fit = nlohmann::json{
|
||||
{{"select_features", "IWSS"}, {"threshold", -0.01}},
|
||||
{{"select_features", "IWSS"}, {"threshold", 0.51}},
|
||||
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
|
||||
{{"select_features", "FCBF"}, {"threshold", 1.01}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper_fit.items()) {
|
||||
for (const auto& hyper : bad_hyper_fit.items()) {
|
||||
INFO("BoostAODE hyper: " << hyper.value().dump());
|
||||
clf.setHyperparameters(hyper.value());
|
||||
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
|
||||
std::invalid_argument);
|
||||
std::invalid_argument);
|
||||
}
|
||||
|
||||
auto bad_hyper_fit2 = nlohmann::json{
|
||||
{{"alpha_block", true}, {"block_update", true}},
|
||||
{{"bisection", false}, {"block_update", true}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper_fit2.items()) {
|
||||
for (const auto& hyper : bad_hyper_fit2.items()) {
|
||||
INFO("BoostAODE hyper: " << hyper.value().dump());
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
|
||||
}
|
||||
}
|
||||
TEST_CASE("Bisection Best", "[BoostAODE]") {
|
||||
TEST_CASE("Bisection Best", "[BoostAODE]")
|
||||
{
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
|
||||
clf.setHyperparameters({
|
||||
@@ -145,7 +153,7 @@ TEST_CASE("Bisection Best", "[BoostAODE]") {
|
||||
{"convergence", true},
|
||||
{"block_update", false},
|
||||
{"convergence_best", false},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 210);
|
||||
REQUIRE(clf.getNumberOfEdges() == 378);
|
||||
@@ -156,7 +164,8 @@ TEST_CASE("Bisection Best", "[BoostAODE]") {
|
||||
REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Bisection Best vs Last", "[BoostAODE]") {
|
||||
TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
|
||||
auto clf = bayesnet::BoostAODE(true);
|
||||
auto hyperparameters = nlohmann::json{
|
||||
@@ -176,7 +185,8 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]") {
|
||||
auto score_last = clf.score(raw.X_test, raw.y_test);
|
||||
REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Block Update", "[BoostAODE]") {
|
||||
TEST_CASE("Block Update", "[BoostAODE]")
|
||||
{
|
||||
auto clf = bayesnet::BoostAODE();
|
||||
auto raw = RawDatasets("mfeat-factors", true, 500);
|
||||
clf.setHyperparameters({
|
||||
@@ -184,7 +194,7 @@ TEST_CASE("Block Update", "[BoostAODE]") {
|
||||
{"block_update", true},
|
||||
{"maxTolerance", 3},
|
||||
{"convergence", true},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 868);
|
||||
REQUIRE(clf.getNumberOfEdges() == 1724);
|
||||
@@ -205,13 +215,14 @@ TEST_CASE("Block Update", "[BoostAODE]") {
|
||||
// }
|
||||
// std::cout << "Score " << score << std::endl;
|
||||
}
|
||||
TEST_CASE("Alphablock", "[BoostAODE]") {
|
||||
TEST_CASE("Alphablock", "[BoostAODE]")
|
||||
{
|
||||
auto clf_alpha = bayesnet::BoostAODE();
|
||||
auto clf_no_alpha = bayesnet::BoostAODE();
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
clf_alpha.setHyperparameters({
|
||||
{"alpha_block", true},
|
||||
});
|
||||
});
|
||||
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
|
||||
|
||||
@@ -12,6 +12,7 @@
|
||||
#include "bayesnet/feature_selection/CFS.h"
|
||||
#include "bayesnet/feature_selection/FCBF.h"
|
||||
#include "bayesnet/feature_selection/IWSS.h"
|
||||
#include "bayesnet/feature_selection/L1FS.h"
|
||||
#include "TestUtils.h"
|
||||
|
||||
bayesnet::FeatureSelect* build_selector(RawDatasets& raw, std::string selector, double threshold, int max_features = 0)
|
||||
@@ -23,6 +24,9 @@ bayesnet::FeatureSelect* build_selector(RawDatasets& raw, std::string selector,
|
||||
return new bayesnet::FCBF(raw.dataset, raw.features, raw.className, max_features, raw.classNumStates, raw.weights, threshold);
|
||||
} else if (selector == "IWSS") {
|
||||
return new bayesnet::IWSS(raw.dataset, raw.features, raw.className, max_features, raw.classNumStates, raw.weights, threshold);
|
||||
} else if (selector == "L1FS") {
|
||||
// For L1FS, threshold is used as alpha parameter
|
||||
return new bayesnet::L1FS(raw.dataset, raw.features, raw.className, max_features, raw.classNumStates, raw.weights, threshold);
|
||||
}
|
||||
return nullptr;
|
||||
}
|
||||
@@ -36,25 +40,30 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
|
||||
SECTION("Test features selected, scores and sizes")
|
||||
{
|
||||
map<pair<std::string, std::string>, pair<std::vector<int>, std::vector<double>>> results = {
|
||||
{ {"glass", "CFS"}, { { 2, 3, 6, 1, 8, 4 }, {0.365513, 0.42895, 0.369809, 0.298294, 0.240952, 0.200915} } },
|
||||
{ {"iris", "CFS"}, { { 3, 2, 1, 0 }, {0.870521, 0.890375, 0.588155, 0.41843} } },
|
||||
{ {"ecoli", "CFS"}, { { 5, 0, 4, 2, 1, 6 }, {0.512319, 0.565381, 0.486025, 0.41087, 0.331423, 0.266251} } },
|
||||
{ {"diabetes", "CFS"}, { { 1, 5, 7, 6, 4, 2 }, {0.132858, 0.151209, 0.14244, 0.126591, 0.106028, 0.0825904} } },
|
||||
{ {"glass", "IWSS" }, { { 2, 3, 5, 7, 6 }, {0.365513, 0.42895, 0.359907, 0.273784, 0.223346} } },
|
||||
{ {"iris", "IWSS"}, { { 3, 2, 0 }, {0.870521, 0.890375, 0.585426} }},
|
||||
{ {"ecoli", "IWSS"}, { { 5, 6, 0, 1, 4 }, {0.512319, 0.550978, 0.475025, 0.382607, 0.308203} } },
|
||||
{ {"diabetes", "IWSS"}, { { 1, 5, 4, 7, 3 }, {0.132858, 0.151209, 0.136576, 0.122097, 0.0802232} } },
|
||||
{ {"glass", "CFS"}, { { 2, 3, 5, 6, 7, 1, 0, 8, 4 }, {0.365513, 0.42895, 0.46186, 0.481897, 0.500943, 0.504027, 0.505625, 0.493256, 0.478226} } },
|
||||
{ {"iris", "CFS"}, { { 3, 2, 0, 1 }, {0.870521, 0.890375, 0.84104719, 0.799310961} } },
|
||||
{ {"ecoli", "CFS"}, { { 5, 0, 6, 1, 4, 2, 3 }, {0.512319, 0.565381, 0.61824, 0.637094, 0.637759, 0.633802, 0.598266} } },
|
||||
{ {"diabetes", "CFS"}, { { 1, 5, 7, 4, 6, 0 }, {0.132858, 0.151209, 0.148887, 0.14862, 0.142902, 0.137233} } },
|
||||
{ {"glass", "IWSS" }, { { 2, 3, 5, 7, 6, 1, 0, 8, 4 }, {0.365513, 0.42895, 0.46186, 0.479866, 0.500943, 0.504027, 0.505625, 0.493256, 0.478226} } },
|
||||
{ {"iris", "IWSS"}, { { 3, 2, 0 }, {0.870521, 0.890375, 0.841047} }},
|
||||
{ {"ecoli", "IWSS"}, { { 5, 0, 6, 1, 4, 2, 3}, {0.512319, 0.565381, 0.61824, 0.637094, 0.637759, 0.633802, 0.598266} } },
|
||||
{ {"diabetes", "IWSS"}, { { 1, 5, 4, 7, 3 }, {0.132858, 0.151209, 0.146771, 0.14862, 0.136493,} } },
|
||||
{ {"glass", "FCBF" }, { { 2, 3, 5, 7, 6 }, {0.365513, 0.304911, 0.302109, 0.281621, 0.253297} } },
|
||||
{ {"iris", "FCBF"}, {{ 3, 2 }, {0.870521, 0.816401} }},
|
||||
{ {"ecoli", "FCBF"}, {{ 5, 0, 1, 4, 2 }, {0.512319, 0.350406, 0.260905, 0.203132, 0.11229} }},
|
||||
{ {"diabetes", "FCBF"}, {{ 1, 5, 7, 6 }, {0.132858, 0.083191, 0.0480135, 0.0224186} }}
|
||||
{ {"diabetes", "FCBF"}, {{ 1, 5, 7, 6 }, {0.132858, 0.083191, 0.0480135, 0.0224186} }},
|
||||
{ {"glass", "L1FS" }, { { 2, 3, 5}, { 0.365513, 0.304911, 0.302109 } } },
|
||||
{ {"iris", "L1FS"}, {{ 3, 2, 1, 0 }, { 0.570928, 0.37569, 0.0774792, 0.00835904 }}},
|
||||
{ {"ecoli", "L1FS"}, {{ 0, 1, 6, 5, 2, 3 }, {0.490179, 0.365944, 0.291177, 0.199171, 0.0400928, 0.0192575} }},
|
||||
{ {"diabetes", "L1FS"}, {{ 1, 5, 4 }, {0.132858, 0.083191, 0.0486187} }}
|
||||
};
|
||||
double threshold;
|
||||
std::string selector;
|
||||
std::vector<std::pair<std::string, double>> selectors = {
|
||||
{ "CFS", 0.0 },
|
||||
{ "IWSS", 0.5 },
|
||||
{ "FCBF", 1e-7 }
|
||||
{ "IWSS", 0.1 },
|
||||
{ "FCBF", 1e-7 },
|
||||
{ "L1FS", 0.01 }
|
||||
};
|
||||
for (const auto item : selectors) {
|
||||
selector = item.first; threshold = item.second;
|
||||
@@ -76,17 +85,144 @@ TEST_CASE("Features Selected", "[FeatureSelection]")
|
||||
delete featureSelector;
|
||||
}
|
||||
}
|
||||
SECTION("Test L1FS")
|
||||
{
|
||||
bayesnet::L1FS* featureSelector = new bayesnet::L1FS(
|
||||
raw.dataset, raw.features, raw.className,
|
||||
raw.features.size(), raw.classNumStates, raw.weights,
|
||||
0.01, 1000, 1e-4, true
|
||||
);
|
||||
featureSelector->fit();
|
||||
|
||||
std::vector<int> selected_features = featureSelector->getFeatures();
|
||||
std::vector<double> selected_scores = featureSelector->getScores();
|
||||
|
||||
// Check if features are selected
|
||||
REQUIRE(selected_features.size() > 0);
|
||||
REQUIRE(selected_scores.size() == selected_features.size());
|
||||
|
||||
// Scores should be non-negative (absolute coefficient values)
|
||||
for (double score : selected_scores) {
|
||||
REQUIRE(score >= 0.0);
|
||||
}
|
||||
|
||||
// Scores should be in descending order
|
||||
// std::cout << file_name << " " << selected_features << std::endl << "{";
|
||||
for (size_t i = 1; i < selected_scores.size(); i++) {
|
||||
// std::cout << selected_scores[i - 1] << ", ";
|
||||
REQUIRE(selected_scores[i - 1] >= selected_scores[i]);
|
||||
}
|
||||
// std::cout << selected_scores[selected_scores.size() - 1];
|
||||
// std::cout << "}" << std::endl;
|
||||
delete featureSelector;
|
||||
}
|
||||
}
|
||||
|
||||
TEST_CASE("L1FS Features Selected", "[FeatureSelection]")
|
||||
{
|
||||
auto raw = RawDatasets("ecoli", true);
|
||||
|
||||
SECTION("Test L1FS with different alpha values")
|
||||
{
|
||||
std::vector<double> alphas = { 0.01, 0.1, 0.5 };
|
||||
|
||||
for (double alpha : alphas) {
|
||||
bayesnet::L1FS* featureSelector = new bayesnet::L1FS(
|
||||
raw.dataset, raw.features, raw.className,
|
||||
raw.features.size(), raw.classNumStates, raw.weights,
|
||||
alpha, 1000, 1e-4, true
|
||||
);
|
||||
featureSelector->fit();
|
||||
|
||||
INFO("Alpha: " << alpha);
|
||||
|
||||
std::vector<int> selected_features = featureSelector->getFeatures();
|
||||
std::vector<double> selected_scores = featureSelector->getScores();
|
||||
|
||||
// Higher alpha should lead to fewer features
|
||||
REQUIRE(selected_features.size() > 0);
|
||||
REQUIRE(selected_features.size() <= raw.features.size());
|
||||
REQUIRE(selected_scores.size() == selected_features.size());
|
||||
|
||||
// Scores should be non-negative (absolute coefficient values)
|
||||
for (double score : selected_scores) {
|
||||
REQUIRE(score >= 0.0);
|
||||
}
|
||||
|
||||
// Scores should be in descending order
|
||||
for (size_t i = 1; i < selected_scores.size(); i++) {
|
||||
REQUIRE(selected_scores[i - 1] >= selected_scores[i]);
|
||||
}
|
||||
|
||||
delete featureSelector;
|
||||
}
|
||||
}
|
||||
|
||||
SECTION("Test L1FS with max features limit")
|
||||
{
|
||||
int max_features = 2;
|
||||
bayesnet::L1FS* featureSelector = new bayesnet::L1FS(
|
||||
raw.dataset, raw.features, raw.className,
|
||||
max_features, raw.classNumStates, raw.weights,
|
||||
0.1, 1000, 1e-4, true
|
||||
);
|
||||
featureSelector->fit();
|
||||
|
||||
std::vector<int> selected_features = featureSelector->getFeatures();
|
||||
REQUIRE(selected_features.size() <= max_features);
|
||||
|
||||
delete featureSelector;
|
||||
}
|
||||
|
||||
SECTION("Test L1FS getCoefficients method")
|
||||
{
|
||||
bayesnet::L1FS* featureSelector = new bayesnet::L1FS(
|
||||
raw.dataset, raw.features, raw.className,
|
||||
raw.features.size(), raw.classNumStates, raw.weights,
|
||||
0.1, 1000, 1e-4, true
|
||||
);
|
||||
|
||||
// Should throw before fitting
|
||||
REQUIRE_THROWS_AS(featureSelector->getCoefficients(), std::runtime_error);
|
||||
REQUIRE_THROWS_WITH(featureSelector->getCoefficients(), "L1FS not fitted");
|
||||
|
||||
featureSelector->fit();
|
||||
|
||||
// Should work after fitting
|
||||
auto coefficients = featureSelector->getCoefficients();
|
||||
REQUIRE(coefficients.size() == raw.features.size());
|
||||
|
||||
delete featureSelector;
|
||||
}
|
||||
}
|
||||
|
||||
TEST_CASE("Oddities", "[FeatureSelection]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
|
||||
// FCBF Limits
|
||||
REQUIRE_THROWS_AS(bayesnet::FCBF(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1e-8), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::FCBF(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1e-8), "Threshold cannot be less than 1e-7");
|
||||
|
||||
// IWSS Limits
|
||||
REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, -1e4), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, -1e4), "Threshold has to be in [0, 0.5]");
|
||||
REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 0.501), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 0.501), "Threshold has to be in [0, 0.5]");
|
||||
|
||||
// L1FS Limits
|
||||
REQUIRE_THROWS_AS(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, -0.1), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, -0.1), "Alpha (regularization strength) must be non-negative");
|
||||
|
||||
REQUIRE_THROWS_AS(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1.0, 0), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1.0, 0), "Maximum iterations must be positive");
|
||||
|
||||
REQUIRE_THROWS_AS(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1.0, 1000, 0.0), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1.0, 1000, 0.0), "Tolerance must be positive");
|
||||
|
||||
REQUIRE_THROWS_AS(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1.0, 1000, -1e-4), std::invalid_argument);
|
||||
REQUIRE_THROWS_WITH(bayesnet::L1FS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1.0, 1000, -1e-4), "Tolerance must be positive");
|
||||
|
||||
// Not fitted error
|
||||
auto selector = build_selector(raw, "CFS", 0);
|
||||
const std::string message = "FeatureSelect not fitted";
|
||||
@@ -96,6 +232,7 @@ TEST_CASE("Oddities", "[FeatureSelection]")
|
||||
REQUIRE_THROWS_WITH(selector->getScores(), message);
|
||||
delete selector;
|
||||
}
|
||||
|
||||
TEST_CASE("Test threshold limits", "[FeatureSelection]")
|
||||
{
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
@@ -112,4 +249,77 @@ TEST_CASE("Test threshold limits", "[FeatureSelection]")
|
||||
selector->fit();
|
||||
REQUIRE(selector->getFeatures().size() == 5);
|
||||
delete selector;
|
||||
|
||||
// L1FS with different alpha values
|
||||
selector = build_selector(raw, "L1FS", 0.01); // Low alpha - more features
|
||||
selector->fit();
|
||||
int num_features_low_alpha = selector->getFeatures().size();
|
||||
delete selector;
|
||||
|
||||
selector = build_selector(raw, "L1FS", 0.9); // High alpha - fewer features
|
||||
selector->fit();
|
||||
int num_features_high_alpha = selector->getFeatures().size();
|
||||
REQUIRE(num_features_high_alpha <= num_features_low_alpha);
|
||||
delete selector;
|
||||
|
||||
// L1FS with max features limit
|
||||
selector = build_selector(raw, "L1FS", 0.01, 4);
|
||||
selector->fit();
|
||||
REQUIRE(selector->getFeatures().size() <= 4);
|
||||
delete selector;
|
||||
}
|
||||
|
||||
TEST_CASE("L1FS Regression vs Classification", "[FeatureSelection]")
|
||||
{
|
||||
SECTION("Regression Task")
|
||||
{
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
// diabetes dataset should be treated as regression (classNumStates > 2)
|
||||
bayesnet::L1FS* l1fs = new bayesnet::L1FS(
|
||||
raw.dataset, raw.features, raw.className,
|
||||
raw.features.size(), raw.classNumStates, raw.weights,
|
||||
0.1, 1000, 1e-4, true
|
||||
);
|
||||
l1fs->fit();
|
||||
|
||||
auto features = l1fs->getFeatures();
|
||||
REQUIRE(features.size() > 0);
|
||||
|
||||
delete l1fs;
|
||||
}
|
||||
|
||||
SECTION("Binary Classification Task")
|
||||
{
|
||||
// Create a simple binary classification dataset
|
||||
int n_samples = 100;
|
||||
int n_features = 5;
|
||||
|
||||
torch::Tensor X = torch::randn({ n_features, n_samples });
|
||||
torch::Tensor y = (X[0] + X[2] > 0).to(torch::kFloat32);
|
||||
torch::Tensor samples = torch::cat({ X, y.unsqueeze(0) }, 0);
|
||||
|
||||
std::vector<std::string> features;
|
||||
for (int i = 0; i < n_features; ++i) {
|
||||
features.push_back("feature_" + std::to_string(i));
|
||||
}
|
||||
|
||||
torch::Tensor weights = torch::ones({ n_samples });
|
||||
|
||||
bayesnet::L1FS* l1fs = new bayesnet::L1FS(
|
||||
samples, features, "target",
|
||||
n_features, 2, weights, // 2 states = binary classification
|
||||
0.1, 1000, 1e-4, true
|
||||
);
|
||||
l1fs->fit();
|
||||
|
||||
auto selected_features = l1fs->getFeatures();
|
||||
REQUIRE(selected_features.size() > 0);
|
||||
|
||||
// Features 0 and 2 should be among the top selected
|
||||
bool has_feature_0 = std::find(selected_features.begin(), selected_features.end(), 0) != selected_features.end();
|
||||
bool has_feature_2 = std::find(selected_features.begin(), selected_features.end(), 2) != selected_features.end();
|
||||
REQUIRE((has_feature_0 || has_feature_2));
|
||||
|
||||
delete l1fs;
|
||||
}
|
||||
}
|
||||
@@ -18,7 +18,7 @@
|
||||
std::map<std::string, std::string> modules = {
|
||||
{ "mdlp", "2.0.1" },
|
||||
{ "Folding", "1.1.1" },
|
||||
{ "json", "3.12" },
|
||||
{ "json", "3.11" },
|
||||
{ "ArffFiles", "1.1.0" }
|
||||
};
|
||||
|
||||
|
||||
@@ -11,7 +11,8 @@
|
||||
#include "TestUtils.h"
|
||||
#include "bayesnet/ensembles/XBA2DE.h"
|
||||
|
||||
TEST_CASE("Normal test", "[XBA2DE]") {
|
||||
TEST_CASE("Normal test", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
@@ -25,37 +26,38 @@ TEST_CASE("Normal test", "[XBA2DE]") {
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(1.0f));
|
||||
REQUIRE(clf.graph().size() == 1);
|
||||
}
|
||||
TEST_CASE("Feature_select CFS", "[XBA2DE]") {
|
||||
TEST_CASE("Feature_select CFS", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
clf.setHyperparameters({{"select_features", "CFS"}});
|
||||
clf.setHyperparameters({ {"select_features", "CFS"} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 220);
|
||||
REQUIRE(clf.getNumberOfEdges() == 506);
|
||||
REQUIRE(clf.getNumberOfNodes() == 360);
|
||||
REQUIRE(clf.getNumberOfEdges() == 828);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 22");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 36");
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
|
||||
}
|
||||
TEST_CASE("Feature_select IWSS", "[XBA2DE]") {
|
||||
TEST_CASE("Feature_select IWSS", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
|
||||
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 220);
|
||||
REQUIRE(clf.getNumberOfEdges() == 506);
|
||||
REQUIRE(clf.getNotes().size() == 4);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
|
||||
REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
|
||||
REQUIRE(clf.getNotes()[3] == "Number of models: 22");
|
||||
REQUIRE(clf.getNumberOfStates() == 5346);
|
||||
REQUIRE(clf.getNumberOfNodes() == 360);
|
||||
REQUIRE(clf.getNumberOfEdges() == 828);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 36");
|
||||
REQUIRE(clf.getNumberOfStates() == 8748);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.72093));
|
||||
}
|
||||
TEST_CASE("Feature_select FCBF", "[XBA2DE]") {
|
||||
TEST_CASE("Feature_select FCBF", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
|
||||
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 290);
|
||||
REQUIRE(clf.getNumberOfEdges() == 667);
|
||||
@@ -66,37 +68,39 @@ TEST_CASE("Feature_select FCBF", "[XBA2DE]") {
|
||||
REQUIRE(clf.getNotes()[2] == "Number of models: 29");
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.744186));
|
||||
}
|
||||
TEST_CASE("Test used features in train note and score", "[XBA2DE]") {
|
||||
TEST_CASE("Test used features in train note and score", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
clf.setHyperparameters({
|
||||
{"order", "asc"},
|
||||
{"convergence", true},
|
||||
{"select_features", "CFS"},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 144);
|
||||
REQUIRE(clf.getNumberOfEdges() == 320);
|
||||
REQUIRE(clf.getNumberOfStates() == 5504);
|
||||
REQUIRE(clf.getNumberOfNodes() == 189);
|
||||
REQUIRE(clf.getNumberOfEdges() == 420);
|
||||
REQUIRE(clf.getNumberOfStates() == 7224);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 16");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 7 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 21");
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
REQUIRE(score == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
|
||||
REQUIRE(score == Catch::Approx(0.854166687f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.854166687f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Order asc, desc & random", "[XBA2DE]") {
|
||||
TEST_CASE("Order asc, desc & random", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
std::map<std::string, double> scores{{"asc", 0.827103}, {"desc", 0.808411}, {"rand", 0.827103}};
|
||||
for (const std::string &order : {"asc", "desc", "rand"}) {
|
||||
std::map<std::string, double> scores{ {"asc", 0.827103}, {"desc", 0.808411}, {"rand", 0.827103} };
|
||||
for (const std::string& order : { "asc", "desc", "rand" }) {
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
clf.setHyperparameters({
|
||||
{"order", order},
|
||||
{"bisection", false},
|
||||
{"maxTolerance", 1},
|
||||
{"convergence", true},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
@@ -105,7 +109,8 @@ TEST_CASE("Order asc, desc & random", "[XBA2DE]") {
|
||||
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
TEST_CASE("Oddities", "[XBA2DE]") {
|
||||
TEST_CASE("Oddities", "[XBA2DE]")
|
||||
{
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto bad_hyper = nlohmann::json{
|
||||
@@ -114,28 +119,28 @@ TEST_CASE("Oddities", "[XBA2DE]") {
|
||||
{{"maxTolerance", 0}},
|
||||
{{"maxTolerance", 7}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper.items()) {
|
||||
for (const auto& hyper : bad_hyper.items()) {
|
||||
INFO("XBA2DE hyper: " << hyper.value().dump());
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
|
||||
}
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0} }), std::invalid_argument);
|
||||
auto bad_hyper_fit = nlohmann::json{
|
||||
{{"select_features", "IWSS"}, {"threshold", -0.01}},
|
||||
{{"select_features", "IWSS"}, {"threshold", 0.51}},
|
||||
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
|
||||
{{"select_features", "FCBF"}, {"threshold", 1.01}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper_fit.items()) {
|
||||
for (const auto& hyper : bad_hyper_fit.items()) {
|
||||
INFO("XBA2DE hyper: " << hyper.value().dump());
|
||||
clf.setHyperparameters(hyper.value());
|
||||
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
|
||||
std::invalid_argument);
|
||||
std::invalid_argument);
|
||||
}
|
||||
auto bad_hyper_fit2 = nlohmann::json{
|
||||
{{"alpha_block", true}, {"block_update", true}},
|
||||
{{"bisection", false}, {"block_update", true}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper_fit2.items()) {
|
||||
for (const auto& hyper : bad_hyper_fit2.items()) {
|
||||
INFO("XBA2DE hyper: " << hyper.value().dump());
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
|
||||
}
|
||||
@@ -146,12 +151,13 @@ TEST_CASE("Oddities", "[XBA2DE]") {
|
||||
raw.features.pop_back();
|
||||
raw.features.pop_back();
|
||||
raw.features.pop_back();
|
||||
clf.setHyperparameters({{"select_features", "CFS"}, {"alpha_block", false}, {"block_update", false}});
|
||||
clf.setHyperparameters({ {"select_features", "CFS"}, {"alpha_block", false}, {"block_update", false} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNotes().size() == 1);
|
||||
REQUIRE(clf.getNotes()[0] == "No features selected in initialization");
|
||||
}
|
||||
TEST_CASE("Bisection Best", "[XBA2DE]") {
|
||||
TEST_CASE("Bisection Best", "[XBA2DE]")
|
||||
{
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
|
||||
clf.setHyperparameters({
|
||||
@@ -159,7 +165,7 @@ TEST_CASE("Bisection Best", "[XBA2DE]") {
|
||||
{"maxTolerance", 3},
|
||||
{"convergence", true},
|
||||
{"convergence_best", false},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 330);
|
||||
REQUIRE(clf.getNumberOfEdges() == 836);
|
||||
@@ -173,7 +179,8 @@ TEST_CASE("Bisection Best", "[XBA2DE]") {
|
||||
REQUIRE(score == Catch::Approx(0.975).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.975).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Bisection Best vs Last", "[XBA2DE]") {
|
||||
TEST_CASE("Bisection Best vs Last", "[XBA2DE]")
|
||||
{
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
auto hyperparameters = nlohmann::json{
|
||||
@@ -193,7 +200,8 @@ TEST_CASE("Bisection Best vs Last", "[XBA2DE]") {
|
||||
auto score_last = clf.score(raw.X_test, raw.y_test);
|
||||
REQUIRE(score_last == Catch::Approx(0.99).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Block Update", "[XBA2DE]") {
|
||||
TEST_CASE("Block Update", "[XBA2DE]")
|
||||
{
|
||||
auto clf = bayesnet::XBA2DE();
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
|
||||
clf.setHyperparameters({
|
||||
@@ -201,7 +209,7 @@ TEST_CASE("Block Update", "[XBA2DE]") {
|
||||
{"block_update", true},
|
||||
{"maxTolerance", 3},
|
||||
{"convergence", true},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 120);
|
||||
REQUIRE(clf.getNumberOfEdges() == 304);
|
||||
@@ -221,13 +229,14 @@ TEST_CASE("Block Update", "[XBA2DE]") {
|
||||
/*}*/
|
||||
/*std::cout << "Score " << score << std::endl;*/
|
||||
}
|
||||
TEST_CASE("Alphablock", "[XBA2DE]") {
|
||||
TEST_CASE("Alphablock", "[XBA2DE]")
|
||||
{
|
||||
auto clf_alpha = bayesnet::XBA2DE();
|
||||
auto clf_no_alpha = bayesnet::XBA2DE();
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
clf_alpha.setHyperparameters({
|
||||
{"alpha_block", true},
|
||||
});
|
||||
});
|
||||
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
|
||||
|
||||
@@ -11,7 +11,8 @@
|
||||
#include "TestUtils.h"
|
||||
#include "bayesnet/ensembles/XBAODE.h"
|
||||
|
||||
TEST_CASE("Normal test", "[XBAODE]") {
|
||||
TEST_CASE("Normal test", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto clf = bayesnet::XBAODE();
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
@@ -23,34 +24,37 @@ TEST_CASE("Normal test", "[XBAODE]") {
|
||||
REQUIRE(clf.getNumberOfStates() == 256);
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.933333));
|
||||
}
|
||||
TEST_CASE("Feature_select CFS", "[XBAODE]") {
|
||||
TEST_CASE("Feature_select CFS", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::XBAODE();
|
||||
clf.setHyperparameters({{"select_features", "CFS"}});
|
||||
clf.setHyperparameters({ {"select_features", "CFS"} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 90);
|
||||
REQUIRE(clf.getNumberOfEdges() == 171);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
|
||||
}
|
||||
TEST_CASE("Feature_select IWSS", "[XBAODE]") {
|
||||
TEST_CASE("Feature_select IWSS", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::XBAODE();
|
||||
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
|
||||
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 90);
|
||||
REQUIRE(clf.getNumberOfEdges() == 171);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 9 of 9 with IWSS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.697674394));
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219f));
|
||||
}
|
||||
TEST_CASE("Feature_select FCBF", "[XBAODE]") {
|
||||
TEST_CASE("Feature_select FCBF", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
auto clf = bayesnet::XBAODE();
|
||||
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
|
||||
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7} });
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 90);
|
||||
REQUIRE(clf.getNumberOfEdges() == 171);
|
||||
@@ -59,36 +63,38 @@ TEST_CASE("Feature_select FCBF", "[XBAODE]") {
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
|
||||
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
|
||||
}
|
||||
TEST_CASE("Test used features in train note and score", "[XBAODE]") {
|
||||
TEST_CASE("Test used features in train note and score", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
auto clf = bayesnet::XBAODE();
|
||||
clf.setHyperparameters({
|
||||
{"order", "asc"},
|
||||
{"convergence", true},
|
||||
{"select_features", "CFS"},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 72);
|
||||
REQUIRE(clf.getNumberOfEdges() == 136);
|
||||
REQUIRE(clf.getNotes().size() == 2);
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 7 of 8 with CFS");
|
||||
REQUIRE(clf.getNotes()[1] == "Number of models: 8");
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
REQUIRE(score == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
|
||||
REQUIRE(score == Catch::Approx(0.82421875f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.82421875f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Order asc, desc & random", "[XBAODE]") {
|
||||
TEST_CASE("Order asc, desc & random", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("glass", true);
|
||||
std::map<std::string, double> scores{{"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112}};
|
||||
for (const std::string &order : {"asc", "desc", "rand"}) {
|
||||
std::map<std::string, double> scores{ {"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112} };
|
||||
for (const std::string& order : { "asc", "desc", "rand" }) {
|
||||
auto clf = bayesnet::XBAODE();
|
||||
clf.setHyperparameters({
|
||||
{"order", order},
|
||||
{"bisection", false},
|
||||
{"maxTolerance", 1},
|
||||
{"convergence", false},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score = clf.score(raw.Xv, raw.yv);
|
||||
auto scoret = clf.score(raw.Xt, raw.yt);
|
||||
@@ -97,7 +103,8 @@ TEST_CASE("Order asc, desc & random", "[XBAODE]") {
|
||||
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
|
||||
}
|
||||
}
|
||||
TEST_CASE("Oddities", "[XBAODE]") {
|
||||
TEST_CASE("Oddities", "[XBAODE]")
|
||||
{
|
||||
auto clf = bayesnet::XBAODE();
|
||||
auto raw = RawDatasets("iris", true);
|
||||
auto bad_hyper = nlohmann::json{
|
||||
@@ -106,33 +113,34 @@ TEST_CASE("Oddities", "[XBAODE]") {
|
||||
{{"maxTolerance", 0}},
|
||||
{{"maxTolerance", 7}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper.items()) {
|
||||
for (const auto& hyper : bad_hyper.items()) {
|
||||
INFO("XBAODE hyper: " << hyper.value().dump());
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
|
||||
}
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0} }), std::invalid_argument);
|
||||
auto bad_hyper_fit = nlohmann::json{
|
||||
{{"select_features", "IWSS"}, {"threshold", -0.01}},
|
||||
{{"select_features", "IWSS"}, {"threshold", 0.51}},
|
||||
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
|
||||
{{"select_features", "FCBF"}, {"threshold", 1.01}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper_fit.items()) {
|
||||
for (const auto& hyper : bad_hyper_fit.items()) {
|
||||
INFO("XBAODE hyper: " << hyper.value().dump());
|
||||
clf.setHyperparameters(hyper.value());
|
||||
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
|
||||
std::invalid_argument);
|
||||
std::invalid_argument);
|
||||
}
|
||||
auto bad_hyper_fit2 = nlohmann::json{
|
||||
{{"alpha_block", true}, {"block_update", true}},
|
||||
{{"bisection", false}, {"block_update", true}},
|
||||
};
|
||||
for (const auto &hyper : bad_hyper_fit2.items()) {
|
||||
for (const auto& hyper : bad_hyper_fit2.items()) {
|
||||
INFO("XBAODE hyper: " << hyper.value().dump());
|
||||
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
|
||||
}
|
||||
}
|
||||
TEST_CASE("Bisection Best", "[XBAODE]") {
|
||||
TEST_CASE("Bisection Best", "[XBAODE]")
|
||||
{
|
||||
auto clf = bayesnet::XBAODE();
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
|
||||
clf.setHyperparameters({
|
||||
@@ -140,7 +148,7 @@ TEST_CASE("Bisection Best", "[XBAODE]") {
|
||||
{"maxTolerance", 3},
|
||||
{"convergence", true},
|
||||
{"convergence_best", false},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 210);
|
||||
REQUIRE(clf.getNumberOfEdges() == 406);
|
||||
@@ -151,7 +159,8 @@ TEST_CASE("Bisection Best", "[XBAODE]") {
|
||||
REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
|
||||
REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Bisection Best vs Last", "[XBAODE]") {
|
||||
TEST_CASE("Bisection Best vs Last", "[XBAODE]")
|
||||
{
|
||||
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
|
||||
auto clf = bayesnet::XBAODE();
|
||||
auto hyperparameters = nlohmann::json{
|
||||
@@ -171,7 +180,8 @@ TEST_CASE("Bisection Best vs Last", "[XBAODE]") {
|
||||
auto score_last = clf.score(raw.X_test, raw.y_test);
|
||||
REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
|
||||
}
|
||||
TEST_CASE("Block Update", "[XBAODE]") {
|
||||
TEST_CASE("Block Update", "[XBAODE]")
|
||||
{
|
||||
auto clf = bayesnet::XBAODE();
|
||||
auto raw = RawDatasets("mfeat-factors", true, 500);
|
||||
clf.setHyperparameters({
|
||||
@@ -179,7 +189,7 @@ TEST_CASE("Block Update", "[XBAODE]") {
|
||||
{"block_update", true},
|
||||
{"maxTolerance", 3},
|
||||
{"convergence", true},
|
||||
});
|
||||
});
|
||||
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
REQUIRE(clf.getNumberOfNodes() == 1085);
|
||||
REQUIRE(clf.getNumberOfEdges() == 2165);
|
||||
@@ -200,13 +210,14 @@ TEST_CASE("Block Update", "[XBAODE]") {
|
||||
// }
|
||||
// std::cout << "Score " << score << std::endl;
|
||||
}
|
||||
TEST_CASE("Alphablock", "[XBAODE]") {
|
||||
TEST_CASE("Alphablock", "[XBAODE]")
|
||||
{
|
||||
auto clf_alpha = bayesnet::XBAODE();
|
||||
auto clf_no_alpha = bayesnet::XBAODE();
|
||||
auto raw = RawDatasets("diabetes", true);
|
||||
clf_alpha.setHyperparameters({
|
||||
{"alpha_block", true},
|
||||
});
|
||||
});
|
||||
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
|
||||
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
|
||||
|
||||
@@ -8,7 +8,7 @@
|
||||
{
|
||||
"kind": "git",
|
||||
"repository": "https://github.com/rmontanana/vcpkg-stash",
|
||||
"baseline": "393efa4e74e053b6f02c4ab03738c8fe796b28e5",
|
||||
"baseline": "1ea69243c0e8b0de77c9d1dd6e1d7593ae7f3627",
|
||||
"packages": [
|
||||
"arff-files",
|
||||
"fimdlp",
|
||||
|
||||
@@ -30,7 +30,7 @@
|
||||
},
|
||||
{
|
||||
"name": "nlohmann-json",
|
||||
"version": "3.12.0"
|
||||
"version": "3.11.3"
|
||||
},
|
||||
{
|
||||
"name": "catch2",
|
||||
|
||||
Reference in New Issue
Block a user