Refactor library structure

.clang-tidy

@@ -10,7 +10,7 @@ Checks: '-*,
          -modernize-use-trailing-return-type,
          -bugprone-exception-escape'
 
-HeaderFilterRegex: 'src/*'
+HeaderFilterRegex: 'bayesnet/*'
 AnalyzeTemporaryDtors: false
 WarningsAsErrors: ''
 FormatStyle: file

CHANGELOG.md

@@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [unpublished]
+## [unreleased]
 
 ### Added
 

CMakeLists.txt

@@ -66,7 +66,7 @@ add_git_submodule("lib/json")
 # --------------
 add_subdirectory(config)
 add_subdirectory(lib/Files)
-add_subdirectory(src)
+add_subdirectory(bayesnet)
 
 # Testing
 # -------
@@ -83,5 +83,5 @@ install(TARGETS BayesNet
         ARCHIVE DESTINATION lib
         LIBRARY DESTINATION lib
         CONFIGURATIONS Release)
-install(DIRECTORY src/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
+install(DIRECTORY bayesnet/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
 install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)

Makefile

@@ -69,6 +69,7 @@ release: ## Build a Release version of the project
 fname = "tests/data/iris.arff"
 sample: ## Build sample
 	@echo ">>> Building Sample...";
+	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
 	@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample
 	sample/build/bayesnet_sample $(fname)
 	@echo ">>> Done";	

bayesnet/BaseClassifier.h

@@ -1,8 +1,8 @@
 #ifndef BASE_H
 #define BASE_H
+#include <vector>
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
-#include <vector>
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {

bayesnet/CMakeLists.txt

@@ -3,7 +3,7 @@ include_directories(
     ${BayesNet_SOURCE_DIR}/lib/Files
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
-    ${BayesNet_SOURCE_DIR}/src
+    ${BayesNet_SOURCE_DIR}
     ${CMAKE_BINARY_DIR}/configured_files/include
 )
 

bayesnet/classifiers/Classifier.cc

@@ -1,5 +1,5 @@
+#include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
-#include "utils/bayesnetUtils.h"
 
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}

bayesnet/classifiers/Classifier.h

@@ -1,9 +1,9 @@
 #ifndef CLASSIFIER_H
 #define CLASSIFIER_H
 #include <torch/torch.h>
-#include "BaseClassifier.h"
+#include "bayesnet/utils/BayesMetrics.h"
-#include "bayesian_network/Network.h"
+#include "bayesnet/network/Network.h"
-#include "utils/BayesMetrics.h"
+#include "bayesnet/BaseClassifier.h"
 
 namespace bayesnet {
     class Classifier : public BaseClassifier {

bayesnet/classifiers/KDB.h

@@ -1,8 +1,8 @@
 #ifndef KDB_H
 #define KDB_H
 #include <torch/torch.h>
+#include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
-#include "utils/bayesnetUtils.h"
 namespace bayesnet {
     class KDB : public Classifier {
     private:

bayesnet/classifiers/KDBLd.h

@@ -1,7 +1,7 @@
 #ifndef KDBLD_H
 #define KDBLD_H
-#include "KDB.h"
 #include "Proposal.h"
+#include "KDB.h"
 
 namespace bayesnet {
     class KDBLd : public KDB, public Proposal {

bayesnet/classifiers/Proposal.cc

@@ -1,5 +1,5 @@
-#include "Proposal.h"
 #include <ArffFiles.h>
+#include "Proposal.h"
 
 namespace bayesnet {
     Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}

bayesnet/classifiers/Proposal.h

@@ -3,8 +3,8 @@
 #include <string>
 #include <map>
 #include <torch/torch.h>
-#include "bayesian_network/Network.h"
 #include <CPPFImdlp.h>
+#include "bayesnet/network/Network.h"
 #include "Classifier.h"
 
 namespace bayesnet {

bayesnet/ensembles/AODE.h

@@ -1,7 +1,7 @@
 #ifndef AODE_H
 #define AODE_H
+#include "bayesnet/classifiers/SPODE.h"
 #include "Ensemble.h"
-#include "classifiers/SPODE.h"
 namespace bayesnet {
     class AODE : public Ensemble {
     public:

bayesnet/ensembles/AODELd.h

@@ -1,8 +1,8 @@
 #ifndef AODELD_H
 #define AODELD_H
+#include "bayesnet/classifiers/Proposal.h"
+#include "bayesnet/classifiers/SPODELd.h"
 #include "Ensemble.h"
-#include "classifiers/Proposal.h"
-#include "classifiers/SPODELd.h"
 
 namespace bayesnet {
     class AODELd : public Ensemble, public Proposal {

bayesnet/ensembles/BoostAODE.cc

@@ -3,10 +3,10 @@
 #include <limits.h>
 #include <tuple>
 #include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
 #include "BoostAODE.h"
-#include "feature_selection/CFS.h"
-#include "feature_selection/FCBF.h"
-#include "feature_selection/IWSS.h"
 
 namespace bayesnet {
     struct {

bayesnet/ensembles/BoostAODE.h

@@ -1,9 +1,9 @@
 #ifndef BOOSTAODE_H
 #define BOOSTAODE_H
-#include "Ensemble.h"
 #include <map>
-#include "classifiers/SPODE.h"
+#include "bayesnet/classifiers/SPODE.h"
-#include "feature_selection/FeatureSelect.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
+#include "Ensemble.h"
 namespace bayesnet {
     class BoostAODE : public Ensemble {
     public:

bayesnet/ensembles/Ensemble.h

@@ -1,9 +1,9 @@
 #ifndef ENSEMBLE_H
 #define ENSEMBLE_H
 #include <torch/torch.h>
-#include "classifiers/Classifier.h"
+#include "bayesnet/utils/BayesMetrics.h"
-#include "utils/BayesMetrics.h"
+#include "bayesnet/utils/bayesnetUtils.h"
-#include "utils/bayesnetUtils.h"
+#include "bayesnet/classifiers/Classifier.h"
 
 namespace bayesnet {
     class Ensemble : public Classifier {

bayesnet/feature_selection/CFS.cc

@@ -1,6 +1,6 @@
-#include "CFS.h"
 #include <limits>
-#include "utils/bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "CFS.h"
 namespace bayesnet {
     void CFS::fit()
     {

bayesnet/feature_selection/CFS.h

@@ -2,7 +2,7 @@
 #define CFS_H
 #include <torch/torch.h>
 #include <vector>
-#include "feature_selection/FeatureSelect.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class CFS : public FeatureSelect {
     public:

bayesnet/feature_selection/FCBF.cc

@@ -1,4 +1,4 @@
-#include "utils/bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
 #include "FCBF.h"
 namespace bayesnet {
 

bayesnet/feature_selection/FCBF.h

@@ -2,7 +2,7 @@
 #define FCBF_H
 #include <torch/torch.h>
 #include <vector>
-#include "feature_selection/FeatureSelect.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
 namespace bayesnet {
     class FCBF : public FeatureSelect {
     public:

bayesnet/feature_selection/FeatureSelect.cc

@@ -1,6 +1,6 @@
-#include "FeatureSelect.h"
 #include <limits>
-#include "utils/bayesnetUtils.h"
+#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)

bayesnet/feature_selection/FeatureSelect.h

@@ -2,7 +2,7 @@
 #define FEATURE_SELECT_H
 #include <torch/torch.h>
 #include <vector>
-#include "utils/BayesMetrics.h"
+#include "bayesnet/utils/BayesMetrics.h"
 namespace bayesnet {
     class FeatureSelect : public Metrics {
     public:

bayesnet/feature_selection/IWSS.cc

@@ -1,6 +1,6 @@
-#include "IWSS.h"
 #include <limits>
-#include "utils/bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
+#include "IWSS.h"
 namespace bayesnet {
     IWSS::IWSS(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 threshold) :
         FeatureSelect(samples, features, className, maxFeatures, classNumStates, weights), threshold(threshold)

bayesnet/feature_selection/IWSS.h

@@ -1,7 +1,7 @@
 #ifndef IWSS_H
 #define IWSS_H
-#include <torch/torch.h>
 #include <vector>
+#include <torch/torch.h>
 #include "FeatureSelect.h"
 namespace bayesnet {
     class IWSS : public FeatureSelect {

bayesnet/network/Network.cc

@@ -1,7 +1,7 @@
 #include <thread>
 #include <mutex>
 #include "Network.h"
-#include "utils/bayesnetUtils.h"
+#include "bayesnet/utils/bayesnetUtils.h"
 namespace bayesnet {
     Network::Network() : features(std::vector<std::string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
     Network::Network(float maxT) : features(std::vector<std::string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}

bayesnet/network/Network.h

@@ -1,9 +1,9 @@
 #ifndef NETWORK_H
 #define NETWORK_H
-#include "Node.h"
 #include <map>
 #include <vector>
-#include "config.h"
+#include "bayesnet/config.h"
+#include "Node.h"
 
 namespace bayesnet {
     class Network {

bayesnet/network/Node.h

@@ -1,9 +1,9 @@
 #ifndef NODE_H
 #define NODE_H
-#include <torch/torch.h>
 #include <unordered_set>
 #include <vector>
 #include <string>
+#include <torch/torch.h>
 namespace bayesnet {
     class Node {
     private:

bayesnet/utils/BayesMetrics.cc

@@ -1,5 +1,5 @@
-#include "BayesMetrics.h"
 #include "Mst.h"
+#include "BayesMetrics.h"
 namespace bayesnet {
     //samples is n+1xm tensor used to fit the model
     Metrics::Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates)

bayesnet/utils/BayesMetrics.h

@@ -1,8 +1,8 @@
 #ifndef BAYESNET_METRICS_H
 #define BAYESNET_METRICS_H
-#include <torch/torch.h>
 #include <vector>
 #include <string>
+#include <torch/torch.h>
 namespace bayesnet {
     class Metrics {
     private:

bayesnet/utils/Mst.cc

@@ -1,6 +1,6 @@
-#include "Mst.h"
 #include <vector>
 #include <list>
+#include "Mst.h"
 /*
     Based on the code from https://www.softwaretestinghelp.com/minimum-spanning-tree-tutorial/
 

bayesnet/utils/Mst.h

@@ -1,8 +1,8 @@
 #ifndef MST_H
 #define MST_H
-#include <torch/torch.h>
 #include <vector>
 #include <string>
+#include <torch/torch.h>
 namespace bayesnet {
     class MST {
     private:

bayesnet/utils/bayesnetUtils.h

@@ -1,7 +1,7 @@
 #ifndef BAYESNET_UTILS_H
 #define BAYESNET_UTILS_H
-#include <torch/torch.h>
 #include <vector>
+#include <torch/torch.h>
 namespace bayesnet {
     std::vector<int> argsort(std::vector<double>& nums);
     std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor);

gcovr.cfg

@@ -1,4 +1,4 @@
-filter = src/
+filter = bayesnet/
 exclude-directories = build_debug/lib/
 print-summary = yes
 sort = uncovered-percent

sample/CMakeLists.txt

@@ -11,7 +11,7 @@ include_directories(
     lib/Files
     lib/mdlp
     lib/json/include
-    /usr/local/include/bayesnet
+    /usr/local/include/
 )
 
 add_subdirectory(lib/Files)

sample/sample.cc

@@ -1,6 +1,6 @@
 #include <ArffFiles.h>
 #include <CPPFImdlp.h>
-#include <ensembles/BoostAODE.h>
+#include <bayesnet/ensembles/BoostAODE.h>
 
 std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
 {

tests/CMakeLists.txt

@@ -1,19 +1,14 @@
 if(ENABLE_TESTING)
     set(TEST_BAYESNET "unit_tests_bayesnet")
     include_directories(
-        ${BayesNet_SOURCE_DIR}/src
-        ${BayesNet_SOURCE_DIR}/src/feature_selection
-        ${BayesNet_SOURCE_DIR}/src/bayesian_network
-        ${BayesNet_SOURCE_DIR}/src/classifiers
-        ${BayesNet_SOURCE_DIR}/src/utils
-        ${BayesNet_SOURCE_DIR}/src/ensembles
         ${BayesNet_SOURCE_DIR}/lib/Files
         ${BayesNet_SOURCE_DIR}/lib/mdlp
         ${BayesNet_SOURCE_DIR}/lib/folding
         ${BayesNet_SOURCE_DIR}/lib/json/include
+        ${BayesNet_SOURCE_DIR}
         ${CMAKE_BINARY_DIR}/configured_files/include
     )
-    file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/src/*.cc")
+    file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
     set(TEST_SOURCES_BAYESNET TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCES})
     add_executable(${TEST_BAYESNET} ${TEST_SOURCES_BAYESNET})
     target_link_libraries(${TEST_BAYESNET} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain )

tests/TestBayesMetrics.cc

@@ -1,7 +1,7 @@
 #include <catch2/catch_test_macros.hpp>
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
-#include "BayesMetrics.h"
+#include "bayesnet/utils/BayesMetrics.h"
 #include "TestUtils.h"
 
 

tests/TestBayesModels.cc

@@ -2,15 +2,15 @@
 #include <catch2/catch_test_macros.hpp>
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
-#include "KDB.h"
+#include "bayesnet/classifiers/KDB.h"
-#include "TAN.h"
+#include "bayesnet/classifiers/TAN.h"
-#include "SPODE.h"
+#include "bayesnet/classifiers/SPODE.h"
-#include "AODE.h"
+#include "bayesnet/classifiers/TANLd.h"
-#include "BoostAODE.h"
+#include "bayesnet/classifiers/KDBLd.h"
-#include "TANLd.h"
+#include "bayesnet/classifiers/SPODELd.h"
-#include "KDBLd.h"
+#include "bayesnet/ensembles/AODE.h"
-#include "SPODELd.h"
+#include "bayesnet/ensembles/AODELd.h"
-#include "AODELd.h"
+#include "bayesnet/ensembles/BoostAODE.h"
 #include "TestUtils.h"
 
 const std::string ACTUAL_VERSION = "1.0.4";

tests/TestBayesNetwork.cc

@@ -3,7 +3,7 @@
 #include <catch2/generators/catch_generators.hpp>
 #include <string>
 #include "TestUtils.h"
-#include "Network.h"
+#include "bayesnet/network/Network.h"
 
 void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::string& className)
 {

tests/TestUtils.cc

@@ -1,5 +1,5 @@
 #include "TestUtils.h"
-#include "config.h"
+#include "bayesnet/config.h"
 
 class Paths {
 public: