Merge pull request 'Add the probabilities aggregation method to compute prediction with ensembles' (#16) from baode_proba into main

Reviewed-on: #16

As only the voting method was implemented, this approach computes the classifiers prediction using a weighted average of the probabilities computed by each model.
Added the predict_proba methods to BaseClassifier - Classifier and Ensemble classes.
Add a hyperparameter to decide the type of computation for ensembles voting - probability aggregation

.clang-uml

@@ -1,31 +0,0 @@
-compilation_database_dir: build
-output_directory: puml
-diagrams:
-  BayesNet:
-    type: class
-    glob:
-      - src/BayesNet/*.cc
-      - src/Platform/*.cc
-    using_namespace: bayesnet
-    include:
-      namespaces:
-        - bayesnet
-        - platform
-    plantuml:
-      after:
-        - "note left of {{ alias(\"MyProjectMain\") }}: Main class of myproject library."
-  sequence:
-    type: sequence
-    glob:
-      - src/Platform/main.cc
-    combine_free_functions_into_file_participants: true
-    using_namespace:
-      - std
-      - bayesnet
-      - platform
-    include:
-      paths:
-        - src/BayesNet
-        - src/Platform
-    start_from:
-      - function: main(int,const char **)

.gitmodules

@@ -1,15 +1,20 @@
 [submodule "lib/mdlp"]
 	path = lib/mdlp
 	url = https://github.com/rmontanana/mdlp
+	main = main
+	update = merge
 [submodule "lib/catch2"]
 	path = lib/catch2
+    main  = v2.x
+	update = merge
 	url = https://github.com/catchorg/Catch2.git
-[submodule "lib/argparse"]
-	path = lib/argparse
-	url = https://github.com/p-ranav/argparse
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
-[submodule "lib/libxlsxwriter"]
-	path = lib/libxlsxwriter
-	url = https://github.com/jmcnamara/libxlsxwriter.git
+    master = master
+	update = merge
+[submodule "lib/folding"]
+	path = lib/folding
+	url = https://github.com/rmontanana/folding
+	main = main
+	update = merge

.vscode/launch.json

@@ -14,7 +14,7 @@
                 "-s",
                 "271",
                 "-p",
-                "/home/rmontanana/Code/discretizbench/datasets/",
+                "/Users/rmontanana/Code/discretizbench/datasets/",
             ],
             //"cwd": "${workspaceFolder}/build/sample/",
         },
@@ -33,7 +33,23 @@
                 // "--hyperparameters",
                 // "{\"repeatSparent\": true, \"maxModels\": 12}"
             ],
-            "cwd": "/home/rmontanana/Code/discretizbench",
+            "cwd": "${workspaceFolder}/../discretizbench",
+        },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "gridsearch",
+            "program": "${workspaceFolder}/build_debug/src/Platform/b_grid",
+            "args": [
+                "-m",
+                "KDB",
+                "--discretize",
+                "--continue",
+                "glass",
+                "--only",
+                "--compute"
+            ],
+            "cwd": "${workspaceFolder}/../discretizbench",
         },
         {
             "type": "lldb",
@@ -64,7 +80,7 @@
                 "accuracy",
                 "--build",
             ],
-            "cwd": "/home/rmontanana/Code/discretizbench",
+            "cwd": "${workspaceFolder}/../discretizbench",
         },
         {
             "type": "lldb",
@@ -75,7 +91,7 @@
                 "-n",
                 "20"
             ],
-            "cwd": "/home/rmontanana/Code/discretizbench",
+            "cwd": "${workspaceFolder}/../discretizbench",
         },
         {
             "type": "lldb",
@@ -84,18 +100,18 @@
             "program": "${workspaceFolder}/build_debug/src/Platform/b_list",
             "args": [],
             //"cwd": "/Users/rmontanana/Code/discretizbench",
-            "cwd": "/home/rmontanana/Code/covbench",
+            "cwd": "${workspaceFolder}/../discretizbench",
         },
         {
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/unit_tests",
+            "program": "${workspaceFolder}/build_debug/tests/unit_tests_bayesnet",
             "args": [
-                "-c=\"Metrics Test\"",
+                //"-c=\"Metrics Test\"",
                 // "-s",
             ],
-            "cwd": "${workspaceFolder}/build/tests",
+            "cwd": "${workspaceFolder}/build_debug/tests",
         },
         {
             "name": "Build & debug active file",

CHANGELOG.md

@@ -0,0 +1,41 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+### Added
+
+- Voting / probability aggregation in Ensemble classes
+- predict_proba method in Classifier
+- predict_proba method in BoostAODE
+- predict_voting parameter in BoostAODE constructor to use voting or probability to predict (default is voting)
+- hyperparameter predict_voting to AODE, AODELd and BoostAODE (Ensemble child classes)
+- tests to check predict & predict_proba coherence
+
+## [1.0.2] - 2024-02-20
+
+### Fixed
+
+- Fix bug in BoostAODE: do not include the model if epsilon sub t is greater than 0.5
+- Fix bug in BoostAODE: compare accuracy with previous accuracy instead of the first of the ensemble if convergence true
+
+## [1.0.1] - 2024-02-12
+
+### Added
+
+- Notes in Classifier class
+- BoostAODE: Add note with used features in initialization with feature selection
+- BoostAODE: Add note with the number of models
+- BoostAODE: Add note with the number of features used to create models if not all features are used
+- Test version number in TestBayesModels
+- Add tests with feature_select and notes on BoostAODE
+
+### Fixed
+
+- Network predict test
+- Network predict_proba test
+- Network score test

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 0.2.0
+  VERSION 1.0.3
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -25,27 +25,12 @@ set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
-
 # 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)
 
-# Boost Library
-set(Boost_USE_STATIC_LIBS OFF) 
-set(Boost_USE_MULTITHREADED ON)  
-set(Boost_USE_STATIC_RUNTIME OFF) 
-find_package(Boost 1.66.0 REQUIRED COMPONENTS python3 numpy3) 
-if(Boost_FOUND)
-    message("Boost_INCLUDE_DIRS=${Boost_INCLUDE_DIRS}")
-    include_directories(${Boost_INCLUDE_DIRS}) 
-endif()
-
-# Python
-find_package(Python3 3.11...3.11.9 COMPONENTS Interpreter Development REQUIRED)
-message("Python3_LIBRARIES=${Python3_LIBRARIES}")
-
 # CMakes modules
 # --------------
 set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
@@ -67,30 +52,18 @@ endif (ENABLE_CLANG_TIDY)
 # ---------------------------------------------
 # include(FetchContent)
 add_git_submodule("lib/mdlp")
-add_git_submodule("lib/argparse")
 add_git_submodule("lib/json")
 
-
-find_library(XLSXWRITER_LIB NAMES libxlsxwriter.dylib libxlsxwriter.so PATHS ${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/lib)
-message("XLSXWRITER_LIB=${XLSXWRITER_LIB}")
-
-
 # Subdirectories
 # --------------
 add_subdirectory(config)
 add_subdirectory(lib/Files)
-add_subdirectory(src/BayesNet)
-add_subdirectory(src/Platform)
-add_subdirectory(src/PyClassifiers)
-add_subdirectory(sample)
+add_subdirectory(src)
 
-file(GLOB BayesNet_HEADERS CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/BayesNet/*.h ${BayesNet_SOURCE_DIR}/BayesNet/*.h)
-file(GLOB BayesNet_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/BayesNet/*.cc ${BayesNet_SOURCE_DIR}/src/BayesNet/*.cpp)
-file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/Platform/*.cc ${BayesNet_SOURCE_DIR}/src/Platform/*.cpp)
+file(GLOB BayesNet_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/*.cc)
 
 # Testing
 # -------
-
 if (ENABLE_TESTING)
   MESSAGE("Testing enabled")
   add_git_submodule("lib/catch2")  

Makefile

@@ -1,11 +1,11 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: coverage setup help build test clean debug release
+.PHONY: coverage setup help buildr buildd test clean debug release
 
 f_release = build_release
 f_debug = build_debug
-app_targets = b_best b_list b_main b_manage b_grid
-test_targets = unit_tests_bayesnet unit_tests_platform
+app_targets = BayesNet
+test_targets = unit_tests_bayesnet 
 n_procs = -j 16
 
 define ClearTests
@@ -31,18 +31,6 @@ setup: ## Install dependencies for tests and coverage
 		pip install gcovr; \
 	fi
 
-dest ?= ${HOME}/bin
-install: ## Copy binary files to bin folder
-	@echo "Destination folder: $(dest)"
-	make buildr
-	@echo "*******************************************"
-	@echo ">>> Copying files to $(dest)"
-	@echo "*******************************************"
-	@for item in $(app_targets); do \
-		echo ">>> Copying $$item" ; \
-		cp $(f_release)/src/Platform/$$item $(dest) ; \
-	done
-
 dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
 	@echo ">>> Creating dependency graph diagram of the project...";
 	$(MAKE) debug
@@ -59,9 +47,6 @@ clean: ## Clean the tests info
 	$(call ClearTests)
 	@echo ">>> Done";
 
-clang-uml: ## Create uml class and sequence diagrams
-	clang-uml -p --add-compile-flag -I /usr/lib/gcc/x86_64-redhat-linux/8/include/
-
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet...";
 	@if [ -d ./$(f_debug) ]; then rm -rf ./$(f_debug); fi
@@ -89,27 +74,10 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
 	done
 	@echo ">>> Done";
 
-opt = ""
-testp: ## Run platform tests (opt="-s") to verbose output the tests, (opt="-c='Stratified Fold Test'") to run only that section
-	@echo ">>> Running Platform tests...";
-	@$(MAKE) clean
-	@cmake --build $(f_debug) --target unit_tests_platform $(n_procs)
-	@if [ -f $(f_debug)/tests/unit_tests_platform ]; then cd $(f_debug)/tests ; ./unit_tests_platform $(opt) ; fi ; 
-	@echo ">>> Done";
-
-opt = ""
-testb: ## Run BayesNet tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
-	@echo ">>> Running BayesNet tests...";
-	@$(MAKE) clean
-	@cmake --build $(f_debug) --target unit_tests_bayesnet $(n_procs)
-	@if [ -f $(f_debug)/tests/unit_tests_bayesnet ]; then cd $(f_debug)/tests ; ./unit_tests_bayesnet $(opt) ; fi ; 
-	@echo ">>> Done";
-
 coverage: ## Run tests and generate coverage report (build/index.html)
-	@echo ">>> Building tests with coverage...";
+	@echo ">>> Building tests with coverage..."
 	@$(MAKE) test
-	@cd $(f_debug) ; \
-	gcovr --config ../gcovr.cfg tests ;
+	@gcovr $(f_debug)/tests
 	@echo ">>> Done";	
 
 

README.md

@@ -2,64 +2,21 @@
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
-Bayesian Network Classifier with libtorch from scratch
-
-## 0. Setup
-
-Before compiling BayesNet.
-
-### boost library
-
-[Getting Started](<https://www.boost.org/doc/libs/1_83_0/more/getting_started/index.html>)
-
-The best option is install the packages that the Linux distribution have in its repository. If this is the case:
-
-```bash
-sudo dnf install boost-devel
-```
-
-If this is not possible and the compressed packaged is installed, the following environment variable has to be set pointing to the folder where it was unzipped to:
-
-```bash
-export BOOST_ROOT=/path/to/library/
-```
-
-In some cases, it is needed to build the library, to do so:
-
-```bash
-cd /path/to/library
-mkdir own
-./bootstrap.sh --prefix=/path/to/library/own
-./b2 install
-export BOOST_ROOT=/path/to/library/own/
-```
-
-Don't forget to add the export BOOST_ROOT statement to .bashrc or wherever it is meant to be.
-
-### libxlswriter
-
-```bash
-cd lib/libxlsxwriter
-make
-make install DESTDIR=/home/rmontanana/Code PREFIX=
-```
-
-Environment variable has to be set:
-
-```bash
- export LD_LIBRARY_PATH=/usr/local/lib
- ```
+Bayesian Network Classifiers using libtorch from scratch
 
 ### Release
 
 ```bash
 make release
+make buildr
 ```
 
 ### Debug & Tests
 
 ```bash
 make debug
+make test
+make coverage
 ```
 
 ## 1. Introduction

config/config.h.in

@@ -7,7 +7,8 @@
 #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_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/";

data/glass.net

@@ -1,25 +0,0 @@
-Type Si
-Type Fe
-Type RI
-Type Na
-Type Ba
-Type Ca
-Type Al
-Type K
-Type Mg
-Fe RI
-Fe Ba
-Fe Ca
-RI Na
-RI Ba
-RI Ca
-RI Al
-RI K
-RI Mg
-Ba Ca
-Ba Al
-Ca Al
-Ca K
-Ca Mg
-Al K
-K Mg

data/mfeat-factors-kdb2.net

@@ -1,645 +0,0 @@
-class att215
-class att25
-class att131
-class att95
-class att122
-class att17
-class att28
-class att5
-class att121
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-class att60
-class att168
-class att178
-class att206
-class att89
-class att77
-class att209
-class att73
-class att126
-class att16
-class att74
-class att27
-class att61
-class att20
-class att101
-class att85
-class att76
-class att137
-class att211
-class att143
-class att14
-class att40
-class att210
-class att155
-class att170
-class att160
-class att23
-class att162
-class att203
-class att164
-class att107
-class att62
-class att42
-class att71
-class att128
-class att138
-class att83
-class att171
-class att92
-class att163
-class att49
-class att161
-class att158
-class att176
-class att11
-class att145
-class att4
-class att172
-class att196
-class att58
-class att68
-class att169
-class att80
-class att32
-class att175
-class att87
-class att88
-class att159
-class att18
-class att52
-class att98
-class att136
-class att150
-class att156
-class att110
-class att100
-class att63
-class att148
-class att90
-class att167
-class att35
-class att205
-class att51
-class att21
-class att142
-class att46
-class att134
-class att39
-class att102
-class att208
-class att130
-class att149
-class att96
-class att75
-class att118
-class att78
-class att213
-class att112
-class att38
-class att174
-class att189
-class att70
-class att179
-class att59
-class att79
-class att15
-class att47
-class att124
-class att34
-class att54
-class att191
-class att86
-class att56
-class att151
-class att66
-class att173
-class att44
-class att198
-class att139
-class att216
-class att129
-class att152
-class att69
-class att81
-class att50
-class att153
-class att41
-class att204
-class att188
-class att26
-class att13
-class att117
-class att114
-class att10
-class att64
-class att200
-class att9
-class att3
-class att119
-class att45
-class att104
-class att140
-class att30
-class att183
-class att146
-class att141
-class att202
-class att194
-class att24
-class att147
-class att8
-class att212
-class att123
-class att166
-class att187
-class att127
-class att190
-class att105
-class att106
-class att184
-class att82
-class att2
-class att135
-class att154
-class att111
-class att115
-class att99
-class att22
-class att84
-class att207
-class att94
-class att177
-class att103
-class att93
-class att201
-class att43
-class att36
-class att12
-class att125
-class att165
-class att180
-class att195
-class att157
-class att48
-class att6
-class att113
-class att193
-class att91
-class att72
-class att31
-class att132
-class att33
-class att57
-class att144
-class att192
-class att185
-class att37
-class att53
-class att120
-class att186
-class att199
-class att65
-class att108
-class att133
-class att29
-class att19
-class att7
-class att97
-class att67
-class att55
-class att1
-class att109
-class att181
-att215 att25
-att215 att131
-att215 att95
-att25 att131
-att25 att121
-att25 att73
-att25 att61
-att25 att85
-att25 att169
-att25 att13
-att131 att95
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-att131 att17
-att131 att28
-att131 att121
-att131 att214
-att131 att116
-att131 att126
-att131 att143
-att95 att122
-att95 att17
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-att95 att5
-att95 att214
-att95 att116
-att95 att60
-att95 att143
-att95 att155
-att95 att71
-att122 att182
-att122 att170
-att17 att5
-att17 att197
-att17 att89
-att17 att77
-att17 att161
-att28 att206
-att28 att16
-att28 att76
-att28 att172
-att28 att124
-att28 att64
-att5 att197
-att5 att89
-att5 att209
-att121 att73
-att214 att178
-att214 att58
-att214 att142
-att197 att209
-att197 att101
-att116 att182
-att116 att60
-att116 att168
-att116 att178
-att116 att206
-att116 att126
-att116 att16
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-att116 att211
-att116 att164
-att116 att128
-att182 att27
-att182 att14
-att60 att168
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-att168 att96
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-att178 att142
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data/mfeat-factors-kdb3.net

@@ -1,859 +0,0 @@
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-

data/mfeat-factors.net

@@ -1,859 +0,0 @@
-class att215
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-class att1
-class att109
-class att181
-att215 att25
-att215 att131
-att215 att95
-att215 att17
-att215 att214
-att215 att143
-att25 att131
-att25 att95
-att25 att122
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-att25 att73
-att25 att61
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-att25 att169
-att25 att13
-att25 att157
-att131 att95
-att131 att122
-att131 att17
-att131 att28
-att131 att5
-att131 att121
-att131 att214
-att131 att116
-att131 att182
-att131 att60
-att131 att126
-att131 att16
-att131 att27
-att131 att20
-att131 att143
-att131 att155
-att95 att122
-att95 att17
-att95 att28
-att95 att5
-att95 att121
-att95 att214
-att95 att197
-att95 att116
-att95 att60
-att95 att168
-att95 att178
-att95 att143
-att95 att155
-att95 att23
-att95 att71
-att95 att167
-att122 att28
-att122 att182
-att122 att170
-att17 att5
-att17 att197
-att17 att89
-att17 att77
-att17 att209
-att17 att137
-att17 att161
-att17 att41
-att28 att206
-att28 att16
-att28 att76
-att28 att40
-att28 att210
-att28 att160
-att28 att172
-att28 att124
-att28 att64
-att5 att197
-att5 att89
-att5 att77
-att5 att209
-att5 att101
-att121 att73
-att121 att61
-att214 att116
-att214 att178
-att214 att206
-att214 att58
-att214 att142
-att214 att46
-att197 att89
-att197 att209
-att197 att101
-att116 att182
-att116 att60
-att116 att168
-att116 att178
-att116 att206
-att116 att73
-att116 att126
-att116 att16
-att116 att74
-att116 att27
-att116 att20
-att116 att211
-att116 att164
-att116 att128
-att116 att92
-att116 att176
-att116 att68
-att182 att27
-att182 att14
-att60 att168
-att60 att156
-att60 att96
-att168 att126
-att168 att156
-att168 att96
-att168 att216
-att178 att20
-att178 att211
-att178 att58
-att178 att142
-att178 att130
-att178 att166
-att206 att74
-att206 att170
-att206 att158
-att89 att77
-att89 att137
-att89 att149
-att89 att173
-att77 att137
-att77 att161
-att77 att149
-att209 att101
-att209 att41
-att73 att61
-att73 att85
-att73 att13
-att73 att157
-att126 att162
-att126 att138
-att126 att18
-att126 att150
-att16 att74
-att16 att76
-att16 att40
-att16 att4
-att16 att196
-att16 att136
-att74 att14
-att74 att62
-att27 att171
-att27 att63
-att61 att85
-att61 att169
-att20 att76
-att20 att211
-att20 att210
-att20 att170
-att20 att164
-att20 att128
-att20 att176
-att20 att80
-att101 att41
-att85 att169
-att85 att13
-att76 att14
-att76 att40
-att76 att160
-att76 att4
-att76 att52
-att137 att161
-att137 att149
-att137 att173
-att137 att125
-att211 att210
-att211 att162
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-att211 att62
-att211 att42
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-att211 att151
-att211 att43
-att143 att155
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-att143 att71
-att143 att83
-att143 att11
-att14 att98
-att40 att160
-att40 att4
-att40 att196
-att40 att88
-att40 att52
-att210 att162
-att210 att42
-att210 att114
-att155 att23
-att155 att203
-att155 att107
-att155 att11
-att170 att158
-att160 att52
-att160 att124
-att23 att203
-att23 att107
-att23 att71
-att23 att11
-att162 att138
-att162 att18
-att162 att150
-att162 att90
-att162 att102
-att162 att174
-att162 att66
-att203 att107
-att203 att49
-att203 att59
-att203 att47
-att203 att191
-att203 att119
-att164 att62
-att164 att42
-att164 att128
-att164 att171
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-att164 att158
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-att164 att145
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-att164 att32
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-att164 att156
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-att164 att21
-att164 att134
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-att164 att38
-att164 att189
-att164 att56
-att164 att44
-att164 att152
-att164 att8
-att107 att83
-att107 att49
-att107 att59
-att107 att47
-att107 att191
-att42 att138
-att42 att54
-att42 att114
-att71 att83
-att71 att167
-att71 att35
-att71 att179
-att128 att92
-att128 att112
-att138 att18
-att138 att150
-att83 att167
-att83 att35
-att171 att87
-att171 att159
-att171 att63
-att171 att51
-att171 att39
-att171 att75
-att92 att163
-att92 att145
-att92 att56
-att163 att49
-att163 att175
-att163 att87
-att163 att79
-att163 att151
-att163 att139
-att163 att187
-att163 att127
-att163 att103
-att163 att91
-att49 att37
-att161 att173
-att161 att113
-att176 att145
-att176 att172
-att176 att68
-att176 att80
-att176 att32
-att176 att175
-att176 att98
-att176 att110
-att176 att205
-att176 att21
-att176 att134
-att176 att213
-att176 att56
-att4 att196
-att4 att88
-att4 att136
-att4 att100
-att4 att148
-att4 att208
-att172 att112
-att172 att184
-att196 att88
-att196 att136
-att196 att100
-att196 att148
-att196 att208
-att58 att142
-att58 att46
-att58 att34
-att68 att32
-att80 att38
-att32 att110
-att32 att21
-att32 att44
-att32 att200
-att175 att87
-att175 att159
-att175 att79
-att175 att187
-att175 att115
-att87 att159
-att87 att63
-att87 att51
-att87 att75
-att87 att15
-att87 att99
-att159 att75
-att159 att15
-att159 att195
-att18 att90
-att18 att102
-att18 att78
-att18 att198
-att52 att124
-att52 att64
-att98 att86
-att136 att100
-att136 att208
-att150 att90
-att150 att174
-att150 att66
-att156 att205
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-att156 att216
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-att156 att24
-att156 att84
-att156 att36
-att156 att12
-att156 att108
-att100 att148
-att63 att51
-att63 att39
-att63 att3
-att63 att183
-att63 att147
-att90 att102
-att90 att78
-att167 att35
-att167 att179
-att35 att179
-att51 att39
-att51 att3
-att51 att183
-att21 att134
-att21 att213
-att21 att38
-att21 att189
-att21 att129
-att21 att81
-att21 att153
-att21 att117
-att21 att9
-att142 att46
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-att142 att70
-att142 att10
-att142 att202
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-att142 att106
-att46 att130
-att46 att118
-att46 att70
-att46 att34
-att46 att166
-att46 att82
-att134 att2
-att39 att3
-att102 att78
-att102 att174
-att102 att54
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-att130 att118
-att130 att10
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-att130 att106
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-att96 att24
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-att118 att10
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-att79 att43
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-att54 att114
-att54 att30
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-att56 att152
-att56 att50
-att56 att188
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-att56 att200
-att56 att104
-att56 att140
-att56 att146
-att56 att194
-att56 att8
-att56 att2
-att56 att133
-att56 att1
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-att66 att30
-att173 att125
-att173 att113
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-att44 att50
-att44 att188
-att44 att200
-att44 att104
-att44 att140
-att44 att194
-att44 att212
-att44 att1
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-att139 att99
-att139 att103
-att139 att43
-att139 att91
-att139 att31
-att139 att199
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-att216 att24
-att216 att84
-att216 att36
-att216 att12
-att216 att180
-att216 att108
-att129 att69
-att152 att188
-att152 att140
-att69 att153
-att69 att9
-att69 att177
-att81 att45
-att81 att105
-att153 att117
-att153 att141
-att41 att53
-att204 att12
-att204 att180
-att188 att146
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-att13 att157
-att114 att6
-att114 att186
-att10 att190
-att64 att184
-att200 att104
-att9 att45
-att9 att146
-att9 att141
-att9 att177
-att9 att37
-att9 att133
-att9 att109
-att9 att181
-att3 att183
-att3 att147
-att3 att123
-att3 att135
-att3 att111
-att45 att105
-att45 att177
-att45 att93
-att45 att201
-att45 att165
-att45 att193
-att45 att33
-att45 att37
-att45 att133
-att45 att97
-att140 att8
-att30 att6
-att30 att186
-att183 att147
-att183 att123
-att183 att135
-att146 att2
-att202 att166
-att202 att106
-att202 att82
-att24 att84
-att24 att36
-att24 att132
-att147 att123
-att147 att135
-att147 att111
-att147 att207
-att8 att212
-att166 att82
-att166 att22
-att166 att94
-att187 att127
-att187 att115
-att127 att115
-att105 att184
-att105 att93
-att105 att201
-att106 att154
-att82 att154
-att82 att22
-att135 att111
-att135 att207
-att154 att22
-att154 att94
-att111 att207
-att99 att195
-att22 att94
-att84 att48
-att177 att93
-att177 att165
-att177 att181
-att103 att195
-att103 att97
-att103 att109
-att93 att201
-att93 att165
-att93 att193
-att93 att33
-att93 att57
-att201 att33
-att201 att57
-att43 att31
-att36 att180
-att36 att48
-att36 att72
-att36 att132
-att36 att144
-att125 att113
-att125 att185
-att125 att65
-att125 att29
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-att180 att72
-att180 att192
-att180 att108
-att48 att72
-att6 att186
-att113 att185
-att113 att53
-att113 att65
-att193 att97
-att91 att31
-att91 att199
-att91 att19
-att72 att132
-att72 att144
-att72 att192
-att72 att120
-att31 att199
-att31 att7
-att31 att67
-att31 att55
-att31 att1
-att132 att144
-att132 att120
-att33 att57
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-att144 att120
-att185 att53
-att185 att65
-att185 att29
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-att199 att7
-att199 att67
-att199 att55
-att199 att109
-att65 att29
-att7 att67
-att67 att55
-att109 att181
-

gcovr.cfg

@@ -1,4 +1,4 @@
 filter = src/
-exclude-directories = build/lib/
+exclude-directories = build_debug/lib/
 print-summary = yes
 sort-percentage = yes

grid_stree.json

@@ -1,162 +0,0 @@
-{
-    "balance-scale": {
-        "C": 10000.0,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "balloons": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "breast-cancer-wisc-diag": {
-        "C": 0.2,
-        "max_iter": 10000
-    },
-    "breast-cancer-wisc-prog": {
-        "C": 0.2,
-        "max_iter": 10000
-    },
-    "breast-cancer-wisc": {},
-    "breast-cancer": {},
-    "cardiotocography-10clases": {},
-    "cardiotocography-3clases": {},
-    "conn-bench-sonar-mines-rocks": {},
-    "cylinder-bands": {},
-    "dermatology": {
-        "C": 55,
-        "max_iter": 10000
-    },
-    "echocardiogram": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_features": "auto",
-        "max_iter": 10000
-    },
-    "fertility": {
-        "C": 0.05,
-        "max_features": "auto",
-        "max_iter": 10000
-    },
-    "haberman-survival": {},
-    "heart-hungarian": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "hepatitis": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "ilpd-indian-liver": {},
-    "ionosphere": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "iris": {},
-    "led-display": {},
-    "libras": {
-        "C": 0.08,
-        "max_iter": 10000
-    },
-    "low-res-spect": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "lymphography": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "mammographic": {},
-    "molec-biol-promoter": {
-        "C": 0.05,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "musk-1": {
-        "C": 0.05,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "oocytes_merluccius_nucleus_4d": {
-        "C": 8.25,
-        "gamma": 0.1,
-        "kernel": "poly"
-    },
-    "oocytes_merluccius_states_2f": {},
-    "oocytes_trisopterus_nucleus_2f": {},
-    "oocytes_trisopterus_states_5b": {
-        "C": 0.11,
-        "max_iter": 10000
-    },
-    "parkinsons": {},
-    "pima": {},
-    "pittsburg-bridges-MATERIAL": {
-        "C": 7,
-        "gamma": 0.1,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "pittsburg-bridges-REL-L": {},
-    "pittsburg-bridges-SPAN": {
-        "C": 0.05,
-        "max_iter": 10000
-    },
-    "pittsburg-bridges-T-OR-D": {},
-    "planning": {
-        "C": 7,
-        "gamma": 10.0,
-        "kernel": "rbf",
-        "max_iter": 10000
-    },
-    "post-operative": {
-        "C": 55,
-        "degree": 5,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "seeds": {
-        "C": 10000.0,
-        "max_iter": 10000
-    },
-    "statlog-australian-credit": {
-        "C": 0.05,
-        "max_features": "auto",
-        "max_iter": 10000
-    },
-    "statlog-german-credit": {},
-    "statlog-heart": {},
-    "statlog-image": {
-        "C": 7,
-        "max_iter": 10000
-    },
-    "statlog-vehicle": {},
-    "synthetic-control": {
-        "C": 0.55,
-        "max_iter": 10000
-    },
-    "tic-tac-toe": {
-        "C": 0.2,
-        "gamma": 0.1,
-        "kernel": "poly",
-        "max_iter": 10000
-    },
-    "vertebral-column-2clases": {},
-    "wine": {
-        "C": 0.55,
-        "max_iter": 10000
-    },
-    "zoo": {
-        "C": 0.1,
-        "max_iter": 10000
-    }
-}

sample/CMakeLists.txt

@@ -1,8 +0,0 @@
-include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
-include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
-include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
-include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
-include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
-include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
-add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc) 
-target_link_libraries(BayesNetSample BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

sample/sample.cc

@@ -1,235 +0,0 @@
-#include <iostream>
-#include <torch/torch.h>
-#include <string>
-#include <map>
-#include <argparse/argparse.hpp>
-#include <nlohmann/json.hpp>
-#include "ArffFiles.h"
-#include "BayesMetrics.h"
-#include "CPPFImdlp.h"
-#include "Folding.h"
-#include "Models.h"
-#include "modelRegister.h"
-#include <fstream>
-
-const std::string PATH = "../../data/";
-
-pair<std::vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<std::string> features)
-{
-    std::vector<mdlp::labels_t>Xd;
-    map<std::string, int> maxes;
-
-    auto fimdlp = mdlp::CPPFImdlp();
-    for (int i = 0; i < X.size(); i++) {
-        fimdlp.fit(X[i], y);
-        mdlp::labels_t& xd = fimdlp.transform(X[i]);
-        maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
-        Xd.push_back(xd);
-    }
-    return { Xd, maxes };
-}
-
-bool file_exists(const std::std::std::string& name)
-{
-    if (FILE* file = fopen(name.c_str(), "r")) {
-        fclose(file);
-        return true;
-    } else {
-        return false;
-    }
-}
-pair<std::vector<std::vector<int>>, std::vector<int>> extract_indices(std::vector<int> indices, std::vector<std::vector<int>> X, std::vector<int> y)
-{
-    std::vector<std::vector<int>> Xr; // nxm
-    std::vector<int> yr;
-    for (int col = 0; col < X.size(); ++col) {
-        Xr.push_back(std::vector<int>());
-    }
-    for (auto index : indices) {
-        for (int col = 0; col < X.size(); ++col) {
-            Xr[col].push_back(X[col][index]);
-        }
-        yr.push_back(y[index]);
-    }
-    return { Xr, yr };
-}
-
-int main(int argc, char** argv)
-{
-    map<std::string, bool> datasets = {
-            {"diabetes",           true},
-            {"ecoli",              true},
-            {"glass",              true},
-            {"iris",               true},
-            {"kdd_JapaneseVowels", false},
-            {"letter",             true},
-            {"liver-disorders",    true},
-            {"mfeat-factors",      true},
-    };
-    auto valid_datasets = std::vector<std::string>();
-    transform(datasets.begin(), datasets.end(), back_inserter(valid_datasets),
-        [](const pair<std::string, bool>& pair) { return pair.first; });
-    argparse::ArgumentParser program("BayesNetSample");
-    program.add_argument("-d", "--dataset")
-        .help("Dataset file name")
-        .action([valid_datasets](const std::std::std::string& value) {
-        if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
-            return value;
-        }
-        throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
-            }
-    );
-    program.add_argument("-p", "--path")
-        .help(" folder where the data files are located, default")
-        .default_value(std::string{ PATH }
-    );
-    program.add_argument("-m", "--model")
-        .help("Model to use " + platform::Models::instance()->tostd::string())
-        .action([](const std::std::std::string& value) {
-        static const std::vector<std::string> choices = platform::Models::instance()->getNames();
-        if (find(choices.begin(), choices.end(), value) != choices.end()) {
-            return value;
-        }
-        throw runtime_error("Model must be one of " + platform::Models::instance()->tostd::string());
-            }
-    );
-    program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
-    program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
-    program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
-    program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
-    program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const std::std::string& value) {
-        try {
-            auto k = stoi(value);
-            if (k < 2) {
-                throw runtime_error("Number of folds must be greater than 1");
-            }
-            return k;
-        }
-        catch (const runtime_error& err) {
-            throw runtime_error(err.what());
-        }
-        catch (...) {
-            throw runtime_error("Number of folds must be an integer");
-        }});
-    program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
-    bool class_last, stratified, tensors, dump_cpt;
-    std::string model_name, file_name, path, complete_file_name;
-    int nFolds, seed;
-    try {
-        program.parse_args(argc, argv);
-        file_name = program.get<std::string>("dataset");
-        path = program.get<std::string>("path");
-        model_name = program.get<std::string>("model");
-        complete_file_name = path + file_name + ".arff";
-        stratified = program.get<bool>("stratified");
-        tensors = program.get<bool>("tensors");
-        nFolds = program.get<int>("folds");
-        seed = program.get<int>("seed");
-        dump_cpt = program.get<bool>("dumpcpt");
-        class_last = datasets[file_name];
-        if (!file_exists(complete_file_name)) {
-            throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
-        }
-    }
-    catch (const exception& err) {
-        cerr << err.what() << std::endl;
-        cerr << program;
-        exit(1);
-    }
-
-    /*
-    * Begin Processing
-    */
-    auto handler = ArffFiles();
-    handler.load(complete_file_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<std::string> features;
-    auto attributes = handler.getAttributes();
-    transform(attributes.begin(), attributes.end(), back_inserter(features),
-        [](const pair<std::string, std::string>& item) { return item.first; });
-    // Discretize Dataset
-    auto [Xd, maxes] = discretize(X, y, features);
-    maxes[className] = *max_element(y.begin(), y.end()) + 1;
-    map<std::string, std::vector<int>> states;
-    for (auto feature : features) {
-        states[feature] = std::vector<int>(maxes[feature]);
-    }
-    states[className] = std::vector<int>(maxes[className]);
-    auto clf = platform::Models::instance()->create(model_name);
-    clf->fit(Xd, y, features, className, states);
-    if (dump_cpt) {
-        std::cout << "--- CPT Tables ---" << std::endl;
-        clf->dump_cpt();
-    }
-    auto lines = clf->show();
-    for (auto line : lines) {
-        std::cout << line << std::endl;
-    }
-    std::cout << "--- Topological Order ---" << std::endl;
-    auto order = clf->topological_order();
-    for (auto name : order) {
-        std::cout << name << ", ";
-    }
-    std::cout << "end." << std::endl;
-    auto score = clf->score(Xd, y);
-    std::cout << "Score: " << score << std::endl;
-    auto graph = clf->graph();
-    auto dot_file = model_name + "_" + file_name;
-    ofstream file(dot_file + ".dot");
-    file << graph;
-    file.close();
-    std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
-    std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
-    std::string stratified_std::string = stratified ? " Stratified" : "";
-    std::cout << nFolds << " Folds" << stratified_std::string << " Cross validation" << std::endl;
-    std::cout << "==========================================" << std::endl;
-    torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
-    torch::Tensor yt = torch::tensor(y, torch::kInt32);
-    for (int i = 0; i < features.size(); ++i) {
-        Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
-    }
-    float total_score = 0, total_score_train = 0, score_train, score_test;
-    platform::Fold* fold;
-    if (stratified)
-        fold = new platform::StratifiedKFold(nFolds, y, seed);
-    else
-        fold = new platform::KFold(nFolds, y.size(), seed);
-    for (auto i = 0; i < nFolds; ++i) {
-        auto [train, test] = fold->getFold(i);
-        std::cout << "Fold: " << i + 1 << std::endl;
-        if (tensors) {
-            auto ttrain = torch::tensor(train, torch::kInt64);
-            auto ttest = torch::tensor(test, torch::kInt64);
-            torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
-            torch::Tensor ytraint = yt.index({ ttrain });
-            torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
-            torch::Tensor ytestt = yt.index({ ttest });
-            clf->fit(Xtraint, ytraint, features, className, states);
-            auto temp = clf->predict(Xtraint);
-            score_train = clf->score(Xtraint, ytraint);
-            score_test = clf->score(Xtestt, ytestt);
-        } else {
-            auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
-            auto [Xtest, ytest] = extract_indices(test, Xd, y);
-            clf->fit(Xtrain, ytrain, features, className, states);
-            score_train = clf->score(Xtrain, ytrain);
-            score_test = clf->score(Xtest, ytest);
-        }
-        if (dump_cpt) {
-            std::cout << "--- CPT Tables ---" << std::endl;
-            clf->dump_cpt();
-        }
-        total_score_train += score_train;
-        total_score += score_test;
-        std::cout << "Score Train: " << score_train << std::endl;
-        std::cout << "Score Test : " << score_test << std::endl;
-        std::cout << "-------------------------------------------------------------------------------" << std::endl;
-    }
-    std::cout << "**********************************************************************************" << std::endl;
-    std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
-    std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
-}

src/AODE.cc

@@ -0,0 +1,34 @@
+#include "AODE.h"
+
+namespace bayesnet {
+    AODE::AODE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "predict_voting" };
+
+    }
+    void AODE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
+    }
+    void AODE::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        significanceModels.clear();
+        for (int i = 0; i < features.size(); ++i) {
+            models.push_back(std::make_unique<SPODE>(i));
+        }
+        n_models = models.size();
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    std::vector<std::string> AODE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

src/AODE.h

@@ -4,12 +4,13 @@
 #include "SPODE.h"
 namespace bayesnet {
     class AODE : public Ensemble {
+    public:
+        AODE(bool predict_voting = true);
+        virtual ~AODE() {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& title = "AODE") const override;
     protected:
         void buildModel(const torch::Tensor& weights) override;
-    public:
-        AODE();
-        virtual ~AODE() {};
-        std::vector<std::string> graph(const std::string& title = "AODE") const override;
     };
 }
 #endif

src/AODELd.cc

@@ -1,7 +1,22 @@
 #include "AODELd.h"
 
 namespace bayesnet {
-    AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
+    AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
+    {
+        validHyperparameters = { "predict_voting" };
+
+    }
+    void AODELd::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
+    }
     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_)
     {
         checkInput(X_, y_);

src/AODELd.h

@@ -6,15 +6,15 @@
 
 namespace bayesnet {
     class AODELd : public Ensemble, public Proposal {
+    public:
+        AODELd(bool predict_voting = true);
+        virtual ~AODELd() = default;
+        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_) override;
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& name = "AODELd") const override;
     protected:
         void trainModel(const torch::Tensor& weights) override;
         void buildModel(const torch::Tensor& weights) override;
-    public:
-        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_) override;
-        virtual ~AODELd() = default;
-        std::vector<std::string> graph(const std::string& name = "AODELd") const override;
-        static inline std::string version() { return "0.0.1"; };
     };
 }
 #endif // !AODELD_H

src/BaseClassifier.h

@@ -16,16 +16,20 @@ namespace bayesnet {
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
+        torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
+        std::vector<std::vector<double>> virtual predict_proba(std::vector<std::vector<int >>& X) = 0;
         status_t virtual getStatus() const = 0;
         float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;
         float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
         int virtual getNumberOfNodes()const = 0;
         int virtual getNumberOfEdges()const = 0;
         int virtual getNumberOfStates() const = 0;
+        int virtual getClassNumStates() const = 0;
         std::vector<std::string> virtual show() const = 0;
         std::vector<std::string> virtual graph(const std::string& title = "") const = 0;
         virtual std::string getVersion() = 0;
         std::vector<std::string> virtual topological_order() = 0;
+        std::vector<std::string> virtual getNotes() const = 0;
         void virtual dump_cpt()const = 0;
         virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }

src/BayesNet/AODE.cc

@@ -1,18 +0,0 @@
-#include "AODE.h"
-
-namespace bayesnet {
-    AODE::AODE() : Ensemble() {}
-    void AODE::buildModel(const torch::Tensor& weights)
-    {
-        models.clear();
-        for (int i = 0; i < features.size(); ++i) {
-            models.push_back(std::make_unique<SPODE>(i));
-        }
-        n_models = models.size();
-        significanceModels = std::vector<double>(n_models, 1.0);
-    }
-    std::vector<std::string> AODE::graph(const std::string& title) const
-    {
-        return Ensemble::graph(title);
-    }
-}

src/BayesNet/CMakeLists.txt

@@ -1,12 +0,0 @@
-include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
-include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
-include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
-include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
-include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
-include_directories(${BayesNet_SOURCE_DIR}/src/PyClassifiers)
-include_directories(${Python3_INCLUDE_DIRS})
-
-add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
-    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc BoostAODE.cc 
-    Mst.cc Proposal.cc CFS.cc FCBF.cc IWSS.cc FeatureSelect.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
-target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

src/BayesNet/Ensemble.cc

@@ -1,141 +0,0 @@
-#include "Ensemble.h"
-
-namespace bayesnet {
-
-    Ensemble::Ensemble() : Classifier(Network()), n_models(0) {}
-
-    void Ensemble::trainModel(const torch::Tensor& weights)
-    {
-        n_models = models.size();
-        for (auto i = 0; i < n_models; ++i) {
-            // fit with std::vectors
-            models[i]->fit(dataset, features, className, states);
-        }
-    }
-    std::vector<int> Ensemble::voting(torch::Tensor& y_pred)
-    {
-        auto y_pred_ = y_pred.accessor<int, 2>();
-        std::vector<int> y_pred_final;
-        int numClasses = states.at(className).size();
-        // y_pred is m x n_models with the prediction of every model for each sample
-        for (int i = 0; i < y_pred.size(0); ++i) {
-            // votes store in each index (value of class) the significance added by each model
-            // i.e. votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
-            std::vector<double> votes(numClasses, 0.0);
-            for (int j = 0; j < n_models; ++j) {
-                votes[y_pred_[i][j]] += significanceModels.at(j);
-            }
-            // argsort in descending order
-            auto indices = argsort(votes);
-            y_pred_final.push_back(indices[0]);
-        }
-        return y_pred_final;
-    }
-    torch::Tensor Ensemble::predict(torch::Tensor& X)
-    {
-        if (!fitted) {
-            throw std::logic_error("Ensemble has not been fitted");
-        }
-        torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
-        for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
-                auto ypredict = models[i]->predict(X);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred.index_put_({ "...", i }, ypredict);
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
-        }
-        return torch::tensor(voting(y_pred));
-    }
-    std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)
-    {
-        if (!fitted) {
-            throw std::logic_error("Ensemble has not been fitted");
-        }
-        long m_ = X[0].size();
-        long n_ = X.size();
-        std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));
-        for (auto i = 0; i < n_; i++) {
-            Xd[i] = std::vector<int>(X[i].begin(), X[i].end());
-        }
-        torch::Tensor y_pred = torch::zeros({ m_, n_models }, torch::kInt32);
-        for (auto i = 0; i < n_models; ++i) {
-            y_pred.index_put_({ "...", i }, torch::tensor(models[i]->predict(Xd), torch::kInt32));
-        }
-        return voting(y_pred);
-    }
-    float Ensemble::score(torch::Tensor& X, torch::Tensor& y)
-    {
-        if (!fitted) {
-            throw std::logic_error("Ensemble has not been fitted");
-        }
-        auto y_pred = predict(X);
-        int correct = 0;
-        for (int i = 0; i < y_pred.size(0); ++i) {
-            if (y_pred[i].item<int>() == y[i].item<int>()) {
-                correct++;
-            }
-        }
-        return (double)correct / y_pred.size(0);
-    }
-    float Ensemble::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
-    {
-        if (!fitted) {
-            throw std::logic_error("Ensemble has not been fitted");
-        }
-        auto y_pred = predict(X);
-        int correct = 0;
-        for (int i = 0; i < y_pred.size(); ++i) {
-            if (y_pred[i] == y[i]) {
-                correct++;
-            }
-        }
-        return (double)correct / y_pred.size();
-    }
-    std::vector<std::string> Ensemble::show() const
-    {
-        auto result = std::vector<std::string>();
-        for (auto i = 0; i < n_models; ++i) {
-            auto res = models[i]->show();
-            result.insert(result.end(), res.begin(), res.end());
-        }
-        return result;
-    }
-    std::vector<std::string> Ensemble::graph(const std::string& title) const
-    {
-        auto result = std::vector<std::string>();
-        for (auto i = 0; i < n_models; ++i) {
-            auto res = models[i]->graph(title + "_" + std::to_string(i));
-            result.insert(result.end(), res.begin(), res.end());
-        }
-        return result;
-    }
-    int Ensemble::getNumberOfNodes() const
-    {
-        int nodes = 0;
-        for (auto i = 0; i < n_models; ++i) {
-            nodes += models[i]->getNumberOfNodes();
-        }
-        return nodes;
-    }
-    int Ensemble::getNumberOfEdges() const
-    {
-        int edges = 0;
-        for (auto i = 0; i < n_models; ++i) {
-            edges += models[i]->getNumberOfEdges();
-        }
-        return edges;
-    }
-    int Ensemble::getNumberOfStates() const
-    {
-        int nstates = 0;
-        for (auto i = 0; i < n_models; ++i) {
-            nstates += models[i]->getNumberOfStates();
-        }
-        return nstates;
-    }
-}

src/BayesNet/bayesnetUtils.cc

@@ -1,25 +0,0 @@
-
-#include "bayesnetUtils.h"
-namespace bayesnet {
-    // Return the indices in descending order
-    std::vector<int> argsort(std::vector<double>& nums)
-    {
-        int n = nums.size();
-        std::vector<int> indices(n);
-        iota(indices.begin(), indices.end(), 0);
-        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
-        return indices;
-    }
-    std::vector<std::vector<int>> tensorToVector(torch::Tensor& tensor)
-    {
-        // convert mxn tensor to nxm std::vector
-        std::vector<std::vector<int>> result;
-        // Iterate over cols
-        for (int i = 0; i < tensor.size(1); ++i) {
-            auto col_tensor = tensor.index({ "...", i });
-            auto col = std::vector<int>(col_tensor.data_ptr<int>(), col_tensor.data_ptr<int>() + tensor.size(0));
-            result.push_back(col);
-        }
-        return result;
-    }
-}

src/BoostAODE.cc

@@ -2,29 +2,28 @@
 #include <functional>
 #include <limits.h>
 #include "BoostAODE.h"
-#include "Colors.h"
-#include "Folding.h"
-#include "Paths.h"
 #include "CFS.h"
 #include "FCBF.h"
 #include "IWSS.h"
+#include "folding.hpp"
 
 namespace bayesnet {
-    BoostAODE::BoostAODE() : Ensemble()
+    BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
     {
-        validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features" };
+        validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features", "tolerance", "predict_voting" };
 
     }
     void BoostAODE::buildModel(const torch::Tensor& weights)
     {
         // Models shall be built in trainModel
         models.clear();
+        significanceModels.clear();
         n_models = 0;
         // Prepare the validation dataset
         auto y_ = dataset.index({ -1, "..." });
         if (convergence) {
             // Prepare train & validation sets from train data
-            auto fold = platform::StratifiedKFold(5, y_, 271);
+            auto fold = folding::StratifiedKFold(5, y_, 271);
             dataset_ = torch::clone(dataset);
             // save input dataset
             auto [train, test] = fold.getFold(0);
@@ -47,22 +46,36 @@ namespace bayesnet {
             y_train = y_;
         }
     }
-    void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters)
+    void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
     {
+        auto hyperparameters = hyperparameters_;
         if (hyperparameters.contains("repeatSparent")) {
             repeatSparent = hyperparameters["repeatSparent"];
+            hyperparameters.erase("repeatSparent");
         }
         if (hyperparameters.contains("maxModels")) {
             maxModels = hyperparameters["maxModels"];
+            hyperparameters.erase("maxModels");
         }
         if (hyperparameters.contains("ascending")) {
             ascending = hyperparameters["ascending"];
+            hyperparameters.erase("ascending");
         }
         if (hyperparameters.contains("convergence")) {
             convergence = hyperparameters["convergence"];
+            hyperparameters.erase("convergence");
         }
         if (hyperparameters.contains("threshold")) {
             threshold = hyperparameters["threshold"];
+            hyperparameters.erase("threshold");
+        }
+        if (hyperparameters.contains("tolerance")) {
+            tolerance = hyperparameters["tolerance"];
+            hyperparameters.erase("tolerance");
+        }
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
         }
         if (hyperparameters.contains("select_features")) {
             auto selectedAlgorithm = hyperparameters["select_features"];
@@ -72,6 +85,10 @@ namespace bayesnet {
             if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
                 throw std::invalid_argument("Invalid selectFeatures value [IWSS, FCBF, CFS]");
             }
+            hyperparameters.erase("select_features");
+        }
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
         }
     }
     std::unordered_set<int> BoostAODE::initializeModels()
@@ -103,27 +120,31 @@ namespace bayesnet {
             significanceModels.push_back(1.0);
             n_models++;
         }
+        notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + algorithm);
         delete featureSelector;
         return featuresUsed;
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
+        fitted = true;
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         std::unordered_set<int> featuresUsed;
-        int tolerance = 5; // number of times the accuracy can be lower than the threshold
         if (selectFeatures) {
             featuresUsed = initializeModels();
-            tolerance = 0; // Remove tolerance if features are selected
         }
-        if (maxModels == 0)
+        bool resetMaxModels = false;
+        if (maxModels == 0) {
             maxModels = .1 * n > 10 ? .1 * n : n;
+            resetMaxModels = true; // Flag to unset maxModels
+        }
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
         bool exitCondition = false;
         // Variables to control the accuracy finish condition
         double priorAccuracy = 0.0;
         double delta = 1.0;
-        double threshold = 1e-4;
-        int count = 0; // number of times the accuracy is lower than the threshold
-        fitted = true; // to enable predict
+        double convergence_threshold = 1e-4;
+        int count = 0; // number of times the accuracy is lower than the convergence_threshold
         // Step 0: Set the finish condition
         // if not repeatSparent a finish condition is run out of features
         // n_models == maxModels
@@ -149,7 +170,6 @@ namespace bayesnet {
                     continue;
                 }
             }
-            featuresUsed.insert(feature);
             model = std::make_unique<SPODE>(feature);
             model->fit(dataset, features, className, states, weights_);
             auto ypred = model->predict(X_train);
@@ -158,6 +178,12 @@ namespace bayesnet {
             auto mask_right = ypred == y_train;
             auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
             double epsilon_t = masked_weights.sum().item<double>();
+            if (epsilon_t > 0.5) {
+                // Inverse the weights policy (plot ln(wt))
+                // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
+                // learner is better than random guess" (Zhi-Hua Zhou, 2012)
+                break;
+            }
             double wt = (1 - epsilon_t) / epsilon_t;
             double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
             // Step 3.2: Update weights for next classifier
@@ -169,6 +195,7 @@ namespace bayesnet {
             double totalWeights = torch::sum(weights_).item<double>();
             weights_ = weights_ / totalWeights;
             // Step 3.4: Store classifier and its accuracy to weigh its future vote
+            featuresUsed.insert(feature);
             models.push_back(std::move(model));
             significanceModels.push_back(alpha_t);
             n_models++;
@@ -180,15 +207,21 @@ namespace bayesnet {
                 } else {
                     delta = accuracy - priorAccuracy;
                 }
-                if (delta < threshold) {
+                if (delta < convergence_threshold) {
                     count++;
                 }
+                priorAccuracy = accuracy;
             }
-            exitCondition = n_models >= maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
+            exitCondition = n_models >= maxModels && repeatSparent || count > tolerance;
         }
         if (featuresUsed.size() != features.size()) {
+            notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
             status = WARNING;
         }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+        if (resetMaxModels) {
+            maxModels = 0;
+        }
     }
     std::vector<std::string> BoostAODE::graph(const std::string& title) const
     {

src/BoostAODE.h

@@ -7,7 +7,7 @@
 namespace bayesnet {
     class BoostAODE : public Ensemble {
     public:
-        BoostAODE();
+        BoostAODE(bool predict_voting = true);
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
         void setHyperparameters(const nlohmann::json& hyperparameters) override;
@@ -21,6 +21,7 @@ namespace bayesnet {
         // Hyperparameters
         bool repeatSparent = false; // if true, a feature can be selected more than once
         int maxModels = 0;
+        int tolerance = 0;
         bool ascending = false; //Process KBest features ascending or descending order
         bool convergence = false; //if true, stop when the model does not improve
         bool selectFeatures = false; // if true, use feature selection

src/CMakeLists.txt

@@ -0,0 +1,13 @@
+include_directories(
+    ${BayesNet_SOURCE_DIR}/lib/mdlp
+    ${BayesNet_SOURCE_DIR}/lib/Files
+    ${BayesNet_SOURCE_DIR}/lib/folding
+    ${BayesNet_SOURCE_DIR}/lib/json/include
+    ${BayesNet_SOURCE_DIR}/src
+    ${CMAKE_BINARY_DIR}/configured_files/include
+)
+
+add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc BoostAODE.cc 
+    Mst.cc Proposal.cc CFS.cc FCBF.cc IWSS.cc FeatureSelect.cc )
+target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

src/Classifier.cc

@@ -3,6 +3,7 @@
 
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
+    const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
     Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
     {
         this->features = features;
@@ -87,14 +88,14 @@ namespace bayesnet {
     torch::Tensor Classifier::predict(torch::Tensor& X)
     {
         if (!fitted) {
-            throw std::logic_error("Classifier has not been fitted");
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
         }
         return model.predict(X);
     }
     std::vector<int> Classifier::predict(std::vector<std::vector<int>>& X)
     {
         if (!fitted) {
-            throw std::logic_error("Classifier has not been fitted");
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
         }
         auto m_ = X[0].size();
         auto n_ = X.size();
@@ -105,18 +106,37 @@ namespace bayesnet {
         auto yp = model.predict(Xd);
         return yp;
     }
-    float Classifier::score(torch::Tensor& X, torch::Tensor& y)
+    torch::Tensor Classifier::predict_proba(torch::Tensor& X)
     {
         if (!fitted) {
-            throw std::logic_error("Classifier has not been fitted");
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
         }
+        return model.predict_proba(X);
+    }
+    std::vector<std::vector<double>> Classifier::predict_proba(std::vector<std::vector<int>>& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto m_ = X[0].size();
+        auto n_ = X.size();
+        std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));
+        // Convert to nxm vector
+        for (auto i = 0; i < n_; i++) {
+            Xd[i] = std::vector<int>(X[i].begin(), X[i].end());
+        }
+        auto yp = model.predict_proba(Xd);
+        return yp;
+    }
+    float Classifier::score(torch::Tensor& X, torch::Tensor& y)
+    {
         torch::Tensor y_pred = predict(X);
         return (y_pred == y).sum().item<float>() / y.size(0);
     }
     float Classifier::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
     {
         if (!fitted) {
-            throw std::logic_error("Classifier has not been fitted");
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
         }
         return model.score(X, y);
     }
@@ -145,6 +165,10 @@ namespace bayesnet {
     {
         return fitted ? model.getStates() : 0;
     }
+    int Classifier::getClassNumStates() const
+    {
+        return fitted ? model.getClassNumStates() : 0;
+    }
     std::vector<std::string> Classifier::topological_order()
     {
         return model.topological_sort();

src/Classifier.h

@@ -7,22 +7,6 @@
 
 namespace bayesnet {
     class Classifier : public BaseClassifier {
-    private:
-        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-    protected:
-        bool fitted;
-        int m, n; // m: number of samples, n: number of features
-        Network model;
-        Metrics metrics;
-        std::vector<std::string> features;
-        std::string className;
-        std::map<std::string, std::vector<int>> states;
-        torch::Tensor dataset; // (n+1)xm tensor
-        status_t status = NORMAL;
-        void checkFitParameters();
-        virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel(const torch::Tensor& weights) override;
-        void buildDataset(torch::Tensor& y);
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
@@ -34,16 +18,37 @@ namespace bayesnet {
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
         int getNumberOfStates() const override;
+        int getClassNumStates() const override;
         torch::Tensor predict(torch::Tensor& X) override;
-        status_t getStatus() const override { return status; }
-        std::string getVersion() override { return "0.2.0"; };
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        status_t getStatus() const override { return status; }
+        std::string getVersion() override { return { project_version.begin(), project_version.end() }; };
         float score(torch::Tensor& X, torch::Tensor& y) override;
         float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
         std::vector<std::string> show() const override;
         std::vector<std::string> topological_order()  override;
+        std::vector<std::string> getNotes() const override { return notes; }
         void dump_cpt() const override;
         void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
+    protected:
+        bool fitted;
+        unsigned int m, n; // m: number of samples, n: number of features
+        Network model;
+        Metrics metrics;
+        std::vector<std::string> features;
+        std::string className;
+        std::map<std::string, std::vector<int>> states;
+        torch::Tensor dataset; // (n+1)xm tensor
+        status_t status = NORMAL;
+        std::vector<std::string> notes; // Used to store messages occurred during the fit process
+        void checkFitParameters();
+        virtual void buildModel(const torch::Tensor& weights) = 0;
+        void trainModel(const torch::Tensor& weights) override;
+        void buildDataset(torch::Tensor& y);
+    private:
+        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
     };
 }
 #endif

src/Ensemble.cc

@@ -0,0 +1,216 @@
+#include "Ensemble.h"
+
+namespace bayesnet {
+
+    Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
+    {
+
+    };
+    const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
+    void Ensemble::trainModel(const torch::Tensor& weights)
+    {
+        n_models = models.size();
+        for (auto i = 0; i < n_models; ++i) {
+            // fit with std::vectors
+            models[i]->fit(dataset, features, className, states);
+        }
+    }
+    std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
+    {
+        std::vector<int> y_pred;
+        for (auto i = 0; i < X.size(); ++i) {
+            auto max = std::max_element(X[i].begin(), X[i].end());
+            y_pred.push_back(std::distance(X[i].begin(), max));
+        }
+        return y_pred;
+    }
+    torch::Tensor Ensemble::compute_arg_max(torch::Tensor& X)
+    {
+        auto y_pred = torch::argmax(X, 1);
+        return y_pred;
+    }
+    torch::Tensor Ensemble::voting(torch::Tensor& votes)
+    {
+        // Convert m x n_models tensor to a m x n_class_states with voting probabilities
+        auto y_pred_ = votes.accessor<int, 2>();
+        std::vector<int> y_pred_final;
+        int numClasses = states.at(className).size();
+        // votes is m x n_models with the prediction of every model for each sample
+        auto result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);
+        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
+        for (int i = 0; i < votes.size(0); ++i) {
+            // n_votes store in each index (value of class) the significance added by each model
+            // i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
+            std::vector<double> n_votes(numClasses, 0.0);
+            for (int j = 0; j < n_models; ++j) {
+                n_votes[y_pred_[i][j]] += significanceModels.at(j);
+            }
+            result[i] = torch::tensor(n_votes);
+        }
+        // To only do one division and gain precision
+        result /= sum;
+        return result;
+    }
+    std::vector<std::vector<double>> Ensemble::predict_proba(std::vector<std::vector<int>>& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(ENSEMBLE_NOT_FITTED);
+        }
+        return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
+    }
+    torch::Tensor Ensemble::predict_proba(torch::Tensor& X)
+    {
+        if (!fitted) {
+            throw std::logic_error(ENSEMBLE_NOT_FITTED);
+        }
+        return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
+    }
+    std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)
+    {
+        auto res = predict_proba(X);
+        return compute_arg_max(res);
+    }
+    torch::Tensor Ensemble::predict(torch::Tensor& X)
+    {
+        auto res = predict_proba(X);
+        return compute_arg_max(res);
+    }
+    torch::Tensor Ensemble::predict_average_proba(torch::Tensor& X)
+    {
+        auto n_states = models[0]->getClassNumStates();
+        torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
+        auto threads{ std::vector<std::thread>() };
+        std::mutex mtx;
+        for (auto i = 0; i < n_models; ++i) {
+            threads.push_back(std::thread([&, i]() {
+                auto ypredict = models[i]->predict_proba(X);
+                std::lock_guard<std::mutex> lock(mtx);
+                y_pred += ypredict * significanceModels[i];
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
+        y_pred /= sum;
+        return y_pred;
+    }
+    std::vector<std::vector<double>> Ensemble::predict_average_proba(std::vector<std::vector<int>>& X)
+    {
+        auto n_states = models[0]->getClassNumStates();
+        std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
+        auto threads{ std::vector<std::thread>() };
+        std::mutex mtx;
+        for (auto i = 0; i < n_models; ++i) {
+            threads.push_back(std::thread([&, i]() {
+                auto ypredict = models[i]->predict_proba(X);
+                assert(ypredict.size() == y_pred.size());
+                assert(ypredict[0].size() == y_pred[0].size());
+                std::lock_guard<std::mutex> lock(mtx);
+                // Multiply each prediction by the significance of the model and then add it to the final prediction
+                for (auto j = 0; j < ypredict.size(); ++j) {
+                    std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
+                        [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
+                }
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
+        //Divide each element of the prediction by the sum of the significances
+        for (auto j = 0; j < y_pred.size(); ++j) {
+            std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](double x) { return x / sum; });
+        }
+        return y_pred;
+    }
+    std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)
+    {
+        torch::Tensor Xt = bayesnet::vectorToTensor(X, false);
+        auto y_pred = predict_average_voting(Xt);
+        std::vector<std::vector<double>> result = tensorToVectorDouble(y_pred);
+        return result;
+    }
+    torch::Tensor Ensemble::predict_average_voting(torch::Tensor& X)
+    {
+        // Build a m x n_models tensor with the predictions of each model
+        torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
+        auto threads{ std::vector<std::thread>() };
+        std::mutex mtx;
+        for (auto i = 0; i < n_models; ++i) {
+            threads.push_back(std::thread([&, i]() {
+                auto ypredict = models[i]->predict(X);
+                std::lock_guard<std::mutex> lock(mtx);
+                y_pred.index_put_({ "...", i }, ypredict);
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return voting(y_pred);
+    }
+    float Ensemble::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        auto y_pred = predict(X);
+        int correct = 0;
+        for (int i = 0; i < y_pred.size(0); ++i) {
+            if (y_pred[i].item<int>() == y[i].item<int>()) {
+                correct++;
+            }
+        }
+        return (double)correct / y_pred.size(0);
+    }
+    float Ensemble::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
+    {
+        auto y_pred = predict(X);
+        int correct = 0;
+        for (int i = 0; i < y_pred.size(); ++i) {
+            if (y_pred[i] == y[i]) {
+                correct++;
+            }
+        }
+        return (double)correct / y_pred.size();
+    }
+    std::vector<std::string> Ensemble::show() const
+    {
+        auto result = std::vector<std::string>();
+        for (auto i = 0; i < n_models; ++i) {
+            auto res = models[i]->show();
+            result.insert(result.end(), res.begin(), res.end());
+        }
+        return result;
+    }
+    std::vector<std::string> Ensemble::graph(const std::string& title) const
+    {
+        auto result = std::vector<std::string>();
+        for (auto i = 0; i < n_models; ++i) {
+            auto res = models[i]->graph(title + "_" + std::to_string(i));
+            result.insert(result.end(), res.begin(), res.end());
+        }
+        return result;
+    }
+    int Ensemble::getNumberOfNodes() const
+    {
+        int nodes = 0;
+        for (auto i = 0; i < n_models; ++i) {
+            nodes += models[i]->getNumberOfNodes();
+        }
+        return nodes;
+    }
+    int Ensemble::getNumberOfEdges() const
+    {
+        int edges = 0;
+        for (auto i = 0; i < n_models; ++i) {
+            edges += models[i]->getNumberOfEdges();
+        }
+        return edges;
+    }
+    int Ensemble::getNumberOfStates() const
+    {
+        int nstates = 0;
+        for (auto i = 0; i < n_models; ++i) {
+            nstates += models[i]->getNumberOfStates();
+        }
+        return nstates;
+    }
+}

src/Ensemble.h

@@ -7,19 +7,13 @@
 
 namespace bayesnet {
     class Ensemble : public Classifier {
-    private:
-        Ensemble& build(std::vector<std::string>& features, std::string className, std::map<std::string, std::vector<int>>& states);
-    protected:
-        unsigned n_models;
-        std::vector<std::unique_ptr<Classifier>> models;
-        std::vector<double> significanceModels;
-        void trainModel(const torch::Tensor& weights) override;
-        std::vector<int> voting(torch::Tensor& y_pred);
     public:
-        Ensemble();
+        Ensemble(bool predict_voting = true);
         virtual ~Ensemble() = default;
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
         float score(torch::Tensor& X, torch::Tensor& y) override;
         float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
         int getNumberOfNodes() const override;
@@ -34,6 +28,19 @@ namespace bayesnet {
         void dump_cpt() const override
         {
         }
+    protected:
+        torch::Tensor predict_average_voting(torch::Tensor& X);
+        std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
+        torch::Tensor predict_average_proba(torch::Tensor& X);
+        std::vector<std::vector<double>> predict_average_proba(std::vector<std::vector<int>>& X);
+        torch::Tensor compute_arg_max(torch::Tensor& X);
+        std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
+        torch::Tensor voting(torch::Tensor& votes);
+        unsigned n_models;
+        std::vector<std::unique_ptr<Classifier>> models;
+        std::vector<double> significanceModels;
+        void trainModel(const torch::Tensor& weights) override;
+        bool predict_voting;
     };
 }
 #endif

src/Network.cc

@@ -238,6 +238,7 @@ namespace bayesnet {
         return predictions;
     }
     // Return mxn std::vector of probabilities
+    // tsamples is nxm std::vector of samples
     std::vector<std::vector<double>> Network::predict_proba(const std::vector<std::vector<int>>& tsamples)
     {
         if (!fitted) {

src/Network.h

@@ -3,26 +3,10 @@
 #include "Node.h"
 #include <map>
 #include <vector>
+#include "config.h"
 
 namespace bayesnet {
     class Network {
-    private:
-        std::map<std::string, std::unique_ptr<Node>> nodes;
-        bool fitted;
-        float maxThreads = 0.95;
-        int classNumStates;
-        std::vector<std::string> features; // Including classname
-        std::string className;
-        double laplaceSmoothing;
-        torch::Tensor samples; // nxm tensor used to fit the model
-        bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
-        std::vector<double> predict_sample(const std::vector<int>&);
-        std::vector<double> predict_sample(const torch::Tensor&);
-        std::vector<double> exactInference(std::map<std::string, int>&);
-        double computeFactor(std::map<std::string, int>&);
-        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void setStates(const std::map<std::string, std::vector<int>>&);
     public:
         Network();
         explicit Network(float);
@@ -56,7 +40,24 @@ namespace bayesnet {
         std::vector<std::string> graph(const std::string& title) const; // Returns a std::vector of std::strings representing the graph in graphviz format
         void initialize();
         void dump_cpt() const;
-        inline std::string version() { return "0.2.0"; }
+        inline std::string version() { return  { project_version.begin(), project_version.end() }; }
+    private:
+        std::map<std::string, std::unique_ptr<Node>> nodes;
+        bool fitted;
+        float maxThreads = 0.95;
+        int classNumStates;
+        std::vector<std::string> features; // Including classname
+        std::string className;
+        double laplaceSmoothing;
+        torch::Tensor samples; // nxm tensor used to fit the model
+        bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
+        std::vector<double> predict_sample(const std::vector<int>&);
+        std::vector<double> predict_sample(const torch::Tensor&);
+        std::vector<double> exactInference(std::map<std::string, int>&);
+        double computeFactor(std::map<std::string, int>&);
+        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void setStates(const std::map<std::string, std::vector<int>>&);
     };
 }
 #endif

src/Platform/BestResults.cc

@@ -1,343 +0,0 @@
-#include <filesystem>
-#include <set>
-#include <fstream>
-#include <iostream>
-#include <sstream>
-#include <algorithm>
-#include "BestResults.h"
-#include "Result.h"
-#include "Colors.h"
-#include "Statistics.h"
-#include "BestResultsExcel.h"
-#include "CLocale.h"
-
-
-namespace fs = std::filesystem;
-// function ftime_to_std::string, Code taken from 
-// https://stackoverflow.com/a/58237530/1389271
-template <typename TP>
-std::string ftime_to_string(TP tp)
-{
-    auto sctp = std::chrono::time_point_cast<std::chrono::system_clock::duration>(tp - TP::clock::now()
-        + std::chrono::system_clock::now());
-    auto tt = std::chrono::system_clock::to_time_t(sctp);
-    std::tm* gmt = std::gmtime(&tt);
-    std::stringstream buffer;
-    buffer << std::put_time(gmt, "%Y-%m-%d %H:%M");
-    return buffer.str();
-}
-namespace platform {
-    std::string BestResults::build()
-    {
-        auto files = loadResultFiles();
-        if (files.size() == 0) {
-            std::cerr << Colors::MAGENTA() << "No result files were found!" << Colors::RESET() << std::endl;
-            exit(1);
-        }
-        json bests;
-        for (const auto& file : files) {
-            auto result = Result(path, file);
-            auto data = result.load();
-            for (auto const& item : data.at("results")) {
-                bool update = false;
-                // Check if results file contains only one dataset
-                auto datasetName = item.at("dataset").get<std::string>();
-                if (bests.contains(datasetName)) {
-                    if (item.at("score").get<double>() > bests[datasetName].at(0).get<double>()) {
-                        update = true;
-                    }
-                } else {
-                    update = true;
-                }
-                if (update) {
-                    bests[datasetName] = { item.at("score").get<double>(), item.at("hyperparameters"), file };
-                }
-            }
-        }
-        std::string bestFileName = path + bestResultFile();
-        if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
-            fclose(fileTest);
-            std::cout << Colors::MAGENTA() << "File " << bestFileName << " already exists and it shall be overwritten." << Colors::RESET() << std::endl;
-        }
-        std::ofstream file(bestFileName);
-        file << bests;
-        file.close();
-        return bestFileName;
-    }
-    std::string BestResults::bestResultFile()
-    {
-        return "best_results_" + score + "_" + model + ".json";
-    }
-    std::pair<std::string, std::string> getModelScore(std::string name)
-    {
-        // results_accuracy_BoostAODE_MacBookpro16_2023-09-06_12:27:00_1.json
-        int i = 0;
-        auto pos = name.find("_");
-        auto pos2 = name.find("_", pos + 1);
-        std::string score = name.substr(pos + 1, pos2 - pos - 1);
-        pos = name.find("_", pos2 + 1);
-        std::string model = name.substr(pos2 + 1, pos - pos2 - 1);
-        return { model, score };
-    }
-    std::vector<std::string> BestResults::loadResultFiles()
-    {
-        std::vector<std::string> files;
-        using std::filesystem::directory_iterator;
-        std::string fileModel, fileScore;
-        for (const auto& file : directory_iterator(path)) {
-            auto fileName = file.path().filename().string();
-            if (fileName.find(".json") != std::string::npos && fileName.find("results_") == 0) {
-                tie(fileModel, fileScore) = getModelScore(fileName);
-                if (score == fileScore && (model == fileModel || model == "any")) {
-                    files.push_back(fileName);
-                }
-            }
-        }
-        return files;
-    }
-    json BestResults::loadFile(const std::string& fileName)
-    {
-        std::ifstream resultData(fileName);
-        if (resultData.is_open()) {
-            json data = json::parse(resultData);
-            return data;
-        }
-        throw std::invalid_argument("Unable to open result file. [" + fileName + "]");
-    }
-    std::vector<std::string> BestResults::getModels()
-    {
-        std::set<std::string> models;
-        std::vector<std::string> result;
-        auto files = loadResultFiles();
-        if (files.size() == 0) {
-            std::cerr << Colors::MAGENTA() << "No result files were found!" << Colors::RESET() << std::endl;
-            exit(1);
-        }
-        std::string fileModel, fileScore;
-        for (const auto& file : files) {
-            // extract the model from the file name
-            tie(fileModel, fileScore) = getModelScore(file);
-            // add the model to the std::vector of models
-            models.insert(fileModel);
-        }
-        result = std::vector<std::string>(models.begin(), models.end());
-        return result;
-    }
-    std::vector<std::string> BestResults::getDatasets(json table)
-    {
-        std::vector<std::string> datasets;
-        for (const auto& dataset : table.items()) {
-            datasets.push_back(dataset.key());
-        }
-        return datasets;
-    }
-    void BestResults::buildAll()
-    {
-        auto models = getModels();
-        for (const auto& model : models) {
-            std::cout << "Building best results for model: " << model << std::endl;
-            this->model = model;
-            build();
-        }
-        model = "any";
-    }
-    void BestResults::listFile()
-    {
-        std::string bestFileName = path + bestResultFile();
-        if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
-            fclose(fileTest);
-        } else {
-            std::cerr << Colors::MAGENTA() << "File " << bestFileName << " doesn't exist." << Colors::RESET() << std::endl;
-            exit(1);
-        }
-        auto temp = ConfigLocale();
-        auto date = ftime_to_string(std::filesystem::last_write_time(bestFileName));
-        auto data = loadFile(bestFileName);
-        auto datasets = getDatasets(data);
-        int maxDatasetName = (*max_element(datasets.begin(), datasets.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
-        int maxFileName = 0;
-        int maxHyper = 15;
-        for (auto const& item : data.items()) {
-            maxHyper = std::max(maxHyper, (int)item.value().at(1).dump().size());
-            maxFileName = std::max(maxFileName, (int)item.value().at(2).get<std::string>().size());
-        }
-        std::stringstream oss;
-        oss << Colors::GREEN() << "Best results for " << model << " as of " << date << std::endl;
-        std::cout << oss.str();
-        std::cout << std::string(oss.str().size() - 8, '-') << std::endl;
-        std::cout << Colors::GREEN() << " #  " << std::setw(maxDatasetName + 1) << std::left << "Dataset" << "Score       " << std::setw(maxFileName) << "File" << " Hyperparameters" << std::endl;
-        std::cout << "=== " << std::string(maxDatasetName, '=') << " =========== " << std::string(maxFileName, '=') << " " << std::string(maxHyper, '=') << std::endl;
-        auto i = 0;
-        bool odd = true;
-        double total = 0;
-        for (auto const& item : data.items()) {
-            auto color = odd ? Colors::BLUE() : Colors::CYAN();
-            double value = item.value().at(0).get<double>();
-            std::cout << color << std::setw(3) << std::fixed << std::right << i++ << " ";
-            std::cout << std::setw(maxDatasetName) << std::left << item.key() << " ";
-            std::cout << std::setw(11) << std::setprecision(9) << std::fixed << value << " ";
-            std::cout << std::setw(maxFileName) << item.value().at(2).get<std::string>() << " ";
-            std::cout << item.value().at(1) << " ";
-            std::cout << std::endl;
-            total += value;
-            odd = !odd;
-        }
-        std::cout << Colors::GREEN() << "=== " << std::string(maxDatasetName, '=') << " ===========" << std::endl;
-        std::cout << std::setw(5 + maxDatasetName) << "Total.................. " << std::setw(11) << std::setprecision(8) << std::fixed << total << std::endl;
-    }
-    json BestResults::buildTableResults(std::vector<std::string> models)
-    {
-        json table;
-        auto maxDate = std::filesystem::file_time_type::max();
-        for (const auto& model : models) {
-            this->model = model;
-            std::string bestFileName = path + bestResultFile();
-            if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
-                fclose(fileTest);
-            } else {
-                std::cerr << Colors::MAGENTA() << "File " << bestFileName << " doesn't exist." << Colors::RESET() << std::endl;
-                exit(1);
-            }
-            auto dateWrite = std::filesystem::last_write_time(bestFileName);
-            if (dateWrite < maxDate) {
-                maxDate = dateWrite;
-            }
-            auto data = loadFile(bestFileName);
-            table[model] = data;
-        }
-        table["dateTable"] = ftime_to_string(maxDate);
-        return table;
-    }
-    void BestResults::printTableResults(std::vector<std::string> models, json table)
-    {
-        std::stringstream oss;
-        oss << Colors::GREEN() << "Best results for " << score << " as of " << table.at("dateTable").get<std::string>() << std::endl;
-        std::cout << oss.str();
-        std::cout << std::string(oss.str().size() - 8, '-') << std::endl;
-        std::cout << Colors::GREEN() << " #  " << std::setw(maxDatasetName + 1) << std::left << std::string("Dataset");
-        for (const auto& model : models) {
-            std::cout << std::setw(maxModelName) << std::left << model << " ";
-        }
-        std::cout << std::endl;
-        std::cout << "=== " << std::string(maxDatasetName, '=') << " ";
-        for (const auto& model : models) {
-            std::cout << std::string(maxModelName, '=') << " ";
-        }
-        std::cout << std::endl;
-        auto i = 0;
-        bool odd = true;
-        std::map<std::string, double> totals;
-        int nDatasets = table.begin().value().size();
-        for (const auto& model : models) {
-            totals[model] = 0.0;
-        }
-        auto datasets = getDatasets(table.begin().value());
-        for (auto const& dataset : datasets) {
-            auto color = odd ? Colors::BLUE() : Colors::CYAN();
-            std::cout << color << std::setw(3) << std::fixed << std::right << i++ << " ";
-            std::cout << std::setw(maxDatasetName) << std::left << dataset << " ";
-            double maxValue = 0;
-            // Find out the max value for this dataset
-            for (const auto& model : models) {
-                double value = table[model].at(dataset).at(0).get<double>();
-                if (value > maxValue) {
-                    maxValue = value;
-                }
-            }
-            // Print the row with red colors on max values
-            for (const auto& model : models) {
-                std::string efectiveColor = color;
-                double value = table[model].at(dataset).at(0).get<double>();
-                if (value == maxValue) {
-                    efectiveColor = Colors::RED();
-                }
-                totals[model] += value;
-                std::cout << efectiveColor << std::setw(maxModelName) << std::setprecision(maxModelName - 2) << std::fixed << value << " ";
-            }
-            std::cout << std::endl;
-            odd = !odd;
-        }
-        std::cout << Colors::GREEN() << "=== " << std::string(maxDatasetName, '=') << " ";
-        for (const auto& model : models) {
-            std::cout << std::string(maxModelName, '=') << " ";
-        }
-        std::cout << std::endl;
-        std::cout << Colors::GREEN() << std::setw(5 + maxDatasetName) << "    Totals...................";
-        double max = 0.0;
-        for (const auto& total : totals) {
-            if (total.second > max) {
-                max = total.second;
-            }
-        }
-        for (const auto& model : models) {
-            std::string efectiveColor = Colors::GREEN();
-            if (totals[model] == max) {
-                efectiveColor = Colors::RED();
-            }
-            std::cout << efectiveColor << std::right << std::setw(maxModelName) << std::setprecision(maxModelName - 4) << std::fixed << totals[model] << " ";
-        }
-        std::cout << std::endl;
-    }
-    void BestResults::reportSingle(bool excel)
-    {
-        listFile();
-        if (excel) {
-            auto models = getModels();
-            // Build the table of results
-            json table = buildTableResults(models);
-            std::vector<std::string> datasets = getDatasets(table.begin().value());
-            BestResultsExcel excel(score, datasets);
-            excel.reportSingle(model, path + bestResultFile());
-            messageExcelFile(excel.getFileName());
-        }
-    }
-    void BestResults::reportAll(bool excel)
-    {
-        auto models = getModels();
-        // Build the table of results
-        json table = buildTableResults(models);
-        std::vector<std::string> datasets = getDatasets(table.begin().value());
-        maxModelName = (*max_element(models.begin(), models.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
-        maxModelName = std::max(12, maxModelName);
-        maxDatasetName = (*max_element(datasets.begin(), datasets.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
-        maxDatasetName = std::max(25, maxDatasetName);
-        // Print the table of results
-        printTableResults(models, table);
-        // Compute the Friedman test
-        std::map<std::string, std::map<std::string, float>> ranksModels;
-        if (friedman) {
-            Statistics stats(models, datasets, table, significance);
-            auto result = stats.friedmanTest();
-            stats.postHocHolmTest(result);
-            ranksModels = stats.getRanks();
-        }
-        if (excel) {
-            BestResultsExcel excel(score, datasets);
-            excel.reportAll(models, table, ranksModels, friedman, significance);
-            if (friedman) {
-                int idx = -1;
-                double min = 2000;
-                // Find out the control model
-                auto totals = std::vector<double>(models.size(), 0.0);
-                for (const auto& dataset : datasets) {
-                    for (int i = 0; i < models.size(); ++i) {
-                        totals[i] += ranksModels[dataset][models[i]];
-                    }
-                }
-                for (int i = 0; i < models.size(); ++i) {
-                    if (totals[i] < min) {
-                        min = totals[i];
-                        idx = i;
-                    }
-                }
-                model = models.at(idx);
-                excel.reportSingle(model, path + bestResultFile());
-            }
-            messageExcelFile(excel.getFileName());
-        }
-    }
-    void BestResults::messageExcelFile(const std::string& fileName)
-    {
-        std::cout << Colors::YELLOW() << "** Excel file generated: " << fileName << Colors::RESET() << std::endl;
-    }
-}

src/Platform/BestResults.h

@@ -1,36 +0,0 @@
-#ifndef BESTRESULTS_H
-#define BESTRESULTS_H
-#include <string>
-#include <nlohmann/json.hpp>
-using json = nlohmann::json;
-namespace platform {
-    class BestResults {
-    public:
-        explicit BestResults(const std::string& path, const std::string& score, const std::string& model, bool friedman, double significance = 0.05)
-            : path(path), score(score), model(model), friedman(friedman), significance(significance)
-        {
-        }
-        std::string build();
-        void reportSingle(bool excel);
-        void reportAll(bool excel);
-        void buildAll();
-    private:
-        std::vector<std::string> getModels();
-        std::vector<std::string> getDatasets(json table);
-        std::vector<std::string> loadResultFiles();
-        void messageExcelFile(const std::string& fileName);
-        json buildTableResults(std::vector<std::string> models);
-        void printTableResults(std::vector<std::string> models, json table);
-        std::string bestResultFile();
-        json loadFile(const std::string& fileName);
-        void listFile();
-        std::string path;
-        std::string score;
-        std::string model;
-        bool friedman;
-        double significance;
-        int maxModelName = 0;
-        int maxDatasetName = 0;
-    };
-}
-#endif //BESTRESULTS_H

src/Platform/BestResultsExcel.cc

@@ -1,300 +0,0 @@
-#include <sstream>
-#include "BestResultsExcel.h"
-#include "Paths.h"
-#include <map>
-#include <nlohmann/json.hpp>
-#include "Statistics.h"
-#include "ReportExcel.h"
-
-namespace platform {
-    json loadResultData(const std::string& fileName)
-    {
-        json data;
-        std::ifstream resultData(fileName);
-        if (resultData.is_open()) {
-            data = json::parse(resultData);
-        } else {
-            throw std::invalid_argument("Unable to open result file. [" + fileName + "]");
-        }
-        return data;
-    }
-    std::string getColumnName(int colNum)
-    {
-        std::string columnName = "";
-        if (colNum == 0)
-            return "A";
-        while (colNum > 0) {
-            int modulo = colNum % 26;
-            columnName = char(65 + modulo) + columnName;
-            colNum = (int)((colNum - modulo) / 26);
-        }
-        return columnName;
-    }
-    BestResultsExcel::BestResultsExcel(const std::string& score, const std::vector<std::string>& datasets) : score(score), datasets(datasets)
-    {
-        workbook = workbook_new((Paths::excel() + fileName).c_str());
-        setProperties("Best Results");
-        int maxDatasetName = (*max_element(datasets.begin(), datasets.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
-        datasetNameSize = std::max(datasetNameSize, maxDatasetName);
-        createFormats();
-    }
-    void BestResultsExcel::reportAll(const std::vector<std::string>& models, const json& table, const std::map<std::string, std::map<std::string, float>>& ranks, bool friedman, double significance)
-    {
-        this->table = table;
-        this->models = models;
-        ranksModels = ranks;
-        this->friedman = friedman;
-        this->significance = significance;
-        worksheet = workbook_add_worksheet(workbook, "Best Results");
-        int maxModelName = (*std::max_element(models.begin(), models.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
-        modelNameSize = std::max(modelNameSize, maxModelName);
-        formatColumns();
-        build();
-    }
-    void BestResultsExcel::reportSingle(const std::string& model, const std::string& fileName)
-    {
-        worksheet = workbook_add_worksheet(workbook, "Report");
-        if (FILE* fileTest = fopen(fileName.c_str(), "r")) {
-            fclose(fileTest);
-        } else {
-            std::cerr << "File " << fileName << " doesn't exist." << std::endl;
-            exit(1);
-        }
-        json data = loadResultData(fileName);
-
-        std::string title = "Best results for " + model;
-        worksheet_merge_range(worksheet, 0, 0, 0, 4, title.c_str(), styles["headerFirst"]);
-        // Body header
-        row = 3;
-        int col = 1;
-        writeString(row, 0, "Nº", "bodyHeader");
-        writeString(row, 1, "Dataset", "bodyHeader");
-        writeString(row, 2, "Score", "bodyHeader");
-        writeString(row, 3, "File", "bodyHeader");
-        writeString(row, 4, "Hyperparameters", "bodyHeader");
-        auto i = 0;
-        std::string hyperparameters;
-        int hypSize = 22;
-        std::map<std::string, std::string> files; // map of files imported and their tabs
-        for (auto const& item : data.items()) {
-            row++;
-            writeInt(row, 0, i++, "ints");
-            writeString(row, 1, item.key().c_str(), "text");
-            writeDouble(row, 2, item.value().at(0).get<double>(), "result");
-            auto fileName = item.value().at(2).get<std::string>();
-            std::string hyperlink = "";
-            try {
-                hyperlink = files.at(fileName);
-            }
-            catch (const std::out_of_range& oor) {
-                auto tabName = "table_" + std::to_string(i);
-                auto worksheetNew = workbook_add_worksheet(workbook, tabName.c_str());
-                json data = loadResultData(Paths::results() + fileName);
-                auto report = ReportExcel(data, false, workbook, worksheetNew);
-                report.show();
-                hyperlink = "#table_" + std::to_string(i);
-                files[fileName] = hyperlink;
-            }
-            hyperlink += "!H" + std::to_string(i + 6);
-            std::string fileNameText = "=HYPERLINK(\"" + hyperlink + "\",\"" + fileName + "\")";
-            worksheet_write_formula(worksheet, row, 3, fileNameText.c_str(), efectiveStyle("text"));
-            hyperparameters = item.value().at(1).dump();
-            if (hyperparameters.size() > hypSize) {
-                hypSize = hyperparameters.size();
-            }
-            writeString(row, 4, hyperparameters, "text");
-        }
-        row++;
-        // Set Totals
-        writeString(row, 1, "Total", "bodyHeader");
-        std::stringstream oss;
-        auto colName = getColumnName(2);
-        oss << "=sum(" << colName << "5:" << colName << row << ")";
-        worksheet_write_formula(worksheet, row, 2, oss.str().c_str(), styles["bodyHeader_odd"]);
-        // Set format
-        worksheet_freeze_panes(worksheet, 4, 2);
-        std::vector<int> columns_sizes = { 5, datasetNameSize, modelNameSize, 66, hypSize + 1 };
-        for (int i = 0; i < columns_sizes.size(); ++i) {
-            worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
-        }
-    }
-    BestResultsExcel::~BestResultsExcel()
-    {
-        workbook_close(workbook);
-    }
-    void BestResultsExcel::formatColumns()
-    {
-        worksheet_freeze_panes(worksheet, 4, 2);
-        std::vector<int> columns_sizes = { 5, datasetNameSize };
-        for (int i = 0; i < models.size(); ++i) {
-            columns_sizes.push_back(modelNameSize);
-        }
-        for (int i = 0; i < columns_sizes.size(); ++i) {
-            worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
-        }
-    }
-    void BestResultsExcel::addConditionalFormat(std::string formula)
-    {
-        // Add conditional format for max/min values in scores/ranks sheets
-        lxw_format* custom_format = workbook_add_format(workbook);
-        format_set_bg_color(custom_format, 0xFFC7CE);
-        format_set_font_color(custom_format, 0x9C0006);
-        // Create a conditional format object. A static object would also work.
-        lxw_conditional_format* conditional_format = (lxw_conditional_format*)calloc(1, sizeof(lxw_conditional_format));
-        conditional_format->type = LXW_CONDITIONAL_TYPE_FORMULA;
-        std::string col = getColumnName(models.size() + 1);
-        std::stringstream oss;
-        oss << "=C5=" << formula << "($C5:$" << col << "5)";
-        auto formulaValue = oss.str();
-        conditional_format->value_string = formulaValue.c_str();
-        conditional_format->format = custom_format;
-        worksheet_conditional_format_range(worksheet, 4, 2, datasets.size() + 3, models.size() + 1, conditional_format);
-    }
-    void BestResultsExcel::build()
-    {
-        // Create Sheet with scores
-        header(false);
-        body(false);
-        // Add conditional format for max values
-        addConditionalFormat("max");
-        footer(false);
-        if (friedman) {
-            // Create Sheet with ranks
-            worksheet = workbook_add_worksheet(workbook, "Ranks");
-            formatColumns();
-            header(true);
-            body(true);
-            addConditionalFormat("min");
-            footer(true);
-            // Create Sheet with Friedman Test
-            doFriedman();
-        }
-    }
-    std::string BestResultsExcel::getFileName()
-    {
-        return Paths::excel() + fileName;
-    }
-    void BestResultsExcel::header(bool ranks)
-    {
-        row = 0;
-        std::string message = ranks ? "Ranks for score " + score : "Best results for " + score;
-        worksheet_merge_range(worksheet, 0, 0, 0, 1 + models.size(), message.c_str(), styles["headerFirst"]);
-        // Body header
-        row = 3;
-        int col = 1;
-        writeString(row, 0, "Nº", "bodyHeader");
-        writeString(row, 1, "Dataset", "bodyHeader");
-        for (const auto& model : models) {
-            writeString(row, ++col, model.c_str(), "bodyHeader");
-        }
-    }
-    void BestResultsExcel::body(bool ranks)
-    {
-        row = 4;
-        int i = 0;
-        json origin = table.begin().value();
-        for (auto const& item : origin.items()) {
-            writeInt(row, 0, i++, "ints");
-            writeString(row, 1, item.key().c_str(), "text");
-            int col = 1;
-            for (const auto& model : models) {
-                double value = ranks ? ranksModels[item.key()][model] : table[model].at(item.key()).at(0).get<double>();
-                writeDouble(row, ++col, value, "result");
-            }
-            ++row;
-        }
-    }
-    void BestResultsExcel::footer(bool ranks)
-    {
-        // Set Totals
-        writeString(row, 1, "Total", "bodyHeader");
-        int col = 1;
-        for (const auto& model : models) {
-            std::stringstream oss;
-            auto colName = getColumnName(col + 1);
-            oss << "=SUM(" << colName << "5:" << colName << row << ")";
-            worksheet_write_formula(worksheet, row, ++col, oss.str().c_str(), styles["bodyHeader_odd"]);
-        }
-        if (ranks) {
-            row++;
-            writeString(row, 1, "Average ranks", "bodyHeader");
-            int col = 1;
-            for (const auto& model : models) {
-                auto colName = getColumnName(col + 1);
-                std::stringstream oss;
-                oss << "=SUM(" << colName << "5:" << colName << row - 1 << ")/" << datasets.size();
-                worksheet_write_formula(worksheet, row, ++col, oss.str().c_str(), styles["bodyHeader_odd"]);
-            }
-        }
-    }
-    void BestResultsExcel::doFriedman()
-    {
-        worksheet = workbook_add_worksheet(workbook, "Friedman");
-        std::vector<int> columns_sizes = { 5, datasetNameSize };
-        for (int i = 0; i < models.size(); ++i) {
-            columns_sizes.push_back(modelNameSize);
-        }
-        for (int i = 0; i < columns_sizes.size(); ++i) {
-            worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
-        }
-        worksheet_merge_range(worksheet, 0, 0, 0, 1 + models.size(), "Friedman Test", styles["headerFirst"]);
-        row = 2;
-        Statistics stats(models, datasets, table, significance, false);
-        auto result = stats.friedmanTest();
-        stats.postHocHolmTest(result);
-        auto friedmanResult = stats.getFriedmanResult();
-        auto holmResult = stats.getHolmResult();
-        worksheet_merge_range(worksheet, row, 0, row, 1 + models.size(), "Null hypothesis: H0 'There is no significant differences between all the classifiers.'", styles["headerSmall"]);
-        row += 2;
-        writeString(row, 1, "Friedman Q", "bodyHeader");
-        writeDouble(row, 2, friedmanResult.statistic, "bodyHeader");
-        row++;
-        writeString(row, 1, "Critical χ2 value", "bodyHeader");
-        writeDouble(row, 2, friedmanResult.criticalValue, "bodyHeader");
-        row++;
-        writeString(row, 1, "p-value", "bodyHeader");
-        writeDouble(row, 2, friedmanResult.pvalue, "bodyHeader");
-        writeString(row, 3, friedmanResult.reject ? "<" : ">", "bodyHeader");
-        writeDouble(row, 4, significance, "bodyHeader");
-        writeString(row, 5, friedmanResult.reject ? "Reject H0" : "Accept H0", "bodyHeader");
-        row += 3;
-        worksheet_merge_range(worksheet, row, 0, row, 1 + models.size(), "Holm Test", styles["headerFirst"]);
-        row += 2;
-        worksheet_merge_range(worksheet, row, 0, row, 1 + models.size(), "Null hypothesis: H0 'There is no significant differences between the control model and the other models.'", styles["headerSmall"]);
-        row += 2;
-        std::string controlModel = "Control Model: " + holmResult.model;
-        worksheet_merge_range(worksheet, row, 1, row, 7, controlModel.c_str(), styles["bodyHeader_odd"]);
-        row++;
-        writeString(row, 1, "Model", "bodyHeader");
-        writeString(row, 2, "p-value", "bodyHeader");
-        writeString(row, 3, "Rank", "bodyHeader");
-        writeString(row, 4, "Win", "bodyHeader");
-        writeString(row, 5, "Tie", "bodyHeader");
-        writeString(row, 6, "Loss", "bodyHeader");
-        writeString(row, 7, "Reject H0", "bodyHeader");
-        row++;
-        bool first = true;
-        for (const auto& item : holmResult.holmLines) {
-            writeString(row, 1, item.model, "text");
-            if (first) {
-                // Control model info
-                first = false;
-                writeString(row, 2, "", "text");
-                writeDouble(row, 3, item.rank, "result");
-                writeString(row, 4, "", "text");
-                writeString(row, 5, "", "text");
-                writeString(row, 6, "", "text");
-                writeString(row, 7, "", "textCentered");
-            } else {
-                // Rest of the models info
-                writeDouble(row, 2, item.pvalue, "result");
-                writeDouble(row, 3, item.rank, "result");
-                writeInt(row, 4, item.wtl.win, "ints");
-                writeInt(row, 5, item.wtl.tie, "ints");
-                writeInt(row, 6, item.wtl.loss, "ints");
-                writeString(row, 7, item.reject ? "Yes" : "No", "textCentered");
-            }
-            row++;
-        }
-    }
-}

src/Platform/BestResultsExcel.h

@@ -1,39 +0,0 @@
-#ifndef BESTRESULTS_EXCEL_H
-#define BESTRESULTS_EXCEL_H
-#include "ExcelFile.h"
-#include <vector>
-#include <map>
-#include <nlohmann/json.hpp>
-
-using json = nlohmann::json;
-
-namespace platform {
-
-    class BestResultsExcel : ExcelFile {
-    public:
-        BestResultsExcel(const std::string& score, const std::vector<std::string>& datasets);
-        ~BestResultsExcel();
-        void reportAll(const std::vector<std::string>& models, const json& table, const std::map<std::string, std::map<std::string, float>>& ranks, bool friedman, double significance);
-        void reportSingle(const std::string& model, const std::string& fileName);
-        std::string getFileName();
-    private:
-        void build();
-        void header(bool ranks);
-        void body(bool ranks);
-        void footer(bool ranks);
-        void formatColumns();
-        void doFriedman();
-        void addConditionalFormat(std::string formula);
-        const std::string fileName = "BestResults.xlsx";
-        std::string score;
-        std::vector<std::string> models;
-        std::vector<std::string> datasets;
-        json table;
-        std::map<std::string, std::map<std::string, float>> ranksModels;
-        bool friedman;
-        double significance;
-        int modelNameSize = 12; // Min size of the column
-        int datasetNameSize = 25; // Min size of the column
-    };
-}
-#endif //BESTRESULTS_EXCEL_H

src/Platform/BestScore.h

@@ -1,28 +0,0 @@
-#ifndef BESTSCORE_H
-#define BESTSCORE_H
-#include <string>
-#include <map>
-#include <utility>
-#include "DotEnv.h"
-namespace platform {
-    class BestScore {
-    public:
-        static std::pair<std::string, double> getScore(const std::string& metric)
-        {
-            static std::map<std::pair<std::string, std::string>, std::pair<std::string, double>> data = {
-               {{"discretiz", "accuracy"}, {"STree_default (linear-ovo)",  22.109799}},
-               {{"odte", "accuracy"}, {"STree_default (linear-ovo)",  22.109799}},
-            };
-            auto env = platform::DotEnv();
-            std::string experiment = env.get("experiment");
-            try {
-                return data[{experiment, metric}];
-            }
-            catch (...) {
-                return { "", 0.0 };
-            }
-        }
-    };
-}
-
-#endif

src/Platform/CLocale.h

@@ -1,22 +0,0 @@
-#ifndef LOCALE_H
-#define LOCALE_H
-#include <locale>
-#include <iostream>
-#include <string>
-namespace platform {
-    struct separation : std::numpunct<char> {
-        char do_decimal_point() const { return ','; }
-        char do_thousands_sep() const { return '.'; }
-        std::string do_grouping() const { return "\03"; }
-    };
-    class ConfigLocale {
-    public:
-        explicit ConfigLocale()
-        {
-            std::locale mylocale(std::cout.getloc(), new separation);
-            std::locale::global(mylocale);
-            std::cout.imbue(mylocale);
-        }
-    };
-}
-#endif 

src/Platform/CMakeLists.txt

@@ -1,21 +0,0 @@
-include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
-include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
-include_directories(${BayesNet_SOURCE_DIR}/src/PyClassifiers)
-include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
-include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
-include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
-include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
-include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
-include_directories(${Python3_INCLUDE_DIRS})
-
-add_executable(b_best b_best.cc BestResults.cc Result.cc Statistics.cc BestResultsExcel.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
-add_executable(b_grid b_grid.cc GridSearch.cc GridData.cc HyperParameters.cc Folding.cc Datasets.cc Dataset.cc)
-add_executable(b_list b_list.cc Datasets.cc Dataset.cc)
-add_executable(b_main b_main.cc Folding.cc Experiment.cc Datasets.cc Dataset.cc Models.cc HyperParameters.cc ReportConsole.cc ReportBase.cc)
-add_executable(b_manage b_manage.cc Results.cc ManageResults.cc CommandParser.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc Dataset.cc ExcelFile.cc)
-
-target_link_libraries(b_best Boost::boost "${XLSXWRITER_LIB}" "${TORCH_LIBRARIES}" ArffFiles mdlp)
-target_link_libraries(b_grid BayesNet  PyWrap)
-target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
-target_link_libraries(b_main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}" PyWrap)
-target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)

src/Platform/Colors.h

@@ -1,14 +0,0 @@
-#ifndef COLORS_H
-#define COLORS_H
-class Colors {
-public:
-    static std::string MAGENTA() { return "\033[1;35m"; }
-    static std::string BLUE() { return "\033[1;34m"; }
-    static std::string CYAN() { return "\033[1;36m"; }
-    static std::string GREEN() { return "\033[1;32m"; }
-    static std::string YELLOW() { return "\033[1;33m"; }
-    static std::string RED() { return "\033[1;31m"; }
-    static std::string WHITE() { return "\033[1;37m"; }
-    static std::string RESET() { return "\033[0m"; }
-};
-#endif // COLORS_H

src/Platform/CommandParser.cc

@@ -1,87 +0,0 @@
-#include "CommandParser.h"
-#include <iostream>
-#include <sstream>
-#include <algorithm>
-#include "Colors.h"
-#include "Utils.h"
-
-namespace platform {
-    void CommandParser::messageError(const std::string& message)
-    {
-        std::cout << Colors::RED() << message << Colors::RESET() << std::endl;
-    }
-    std::pair<char, int> CommandParser::parse(const std::string& color, const std::vector<std::tuple<std::string, char, bool>>& options, const char defaultCommand, const int maxIndex)
-    {
-        bool finished = false;
-        while (!finished) {
-            std::stringstream oss;
-            std::string line;
-            oss << color << "Choose option (";
-            bool first = true;
-            for (auto& option : options) {
-                if (first) {
-                    first = false;
-                } else {
-                    oss << ", ";
-                }
-                oss << std::get<char>(option) << "=" << std::get<std::string>(option);
-            }
-            oss << "): ";
-            std::cout << oss.str();
-            getline(std::cin, line);
-            std::cout << Colors::RESET();
-            line = trim(line);
-            if (line.size() == 0)
-                continue;
-            if (all_of(line.begin(), line.end(), ::isdigit)) {
-                command = defaultCommand;
-                index = stoi(line);
-                if (index > maxIndex || index < 0) {
-                    messageError("Index out of range");
-                    continue;
-                }
-                finished = true;
-                break;
-            }
-            bool found = false;
-            for (auto& option : options) {
-                if (line[0] == std::get<char>(option)) {
-                    found = true;
-                    // it's a match
-                    line.erase(line.begin());
-                    line = trim(line);
-                    if (std::get<bool>(option)) {
-                        // The option requires a value
-                        if (line.size() == 0) {
-                            messageError("Option " + std::get<std::string>(option) + " requires a value");
-                            break;
-                        }
-                        try {
-                            index = stoi(line);
-                            if (index > maxIndex || index < 0) {
-                                messageError("Index out of range");
-                                break;
-                            }
-                        }
-                        catch (const std::invalid_argument& ia) {
-                            messageError("Invalid value: " + line);
-                            break;
-                        }
-                    } else {
-                        if (line.size() > 0) {
-                            messageError("option " + std::get<std::string>(option) + " doesn't accept values");
-                            break;
-                        }
-                    }
-                    command = std::get<char>(option);
-                    finished = true;
-                    break;
-                }
-            }
-            if (!found) {
-                messageError("I don't know " + line);
-            }
-        }
-        return { command, index };
-    }
-} /* namespace platform */

src/Platform/CommandParser.h

@@ -1,20 +0,0 @@
-#ifndef COMMAND_PARSER_H
-#define COMMAND_PARSER_H
-#include <string>
-#include <vector>
-#include <tuple>
-
-namespace platform {
-    class CommandParser {
-    public:
-        CommandParser() = default;
-        std::pair<char, int> parse(const std::string& color, const std::vector<std::tuple<std::string, char, bool>>& options, const char defaultCommand, const int maxIndex);
-        char getCommand() const { return command; };
-        int getIndex() const { return index; };
-    private:
-        void messageError(const std::string& message);
-        char command;
-        int index;
-    };
-} /* namespace platform */
-#endif /* COMMAND_PARSER_H */

src/Platform/Dataset.cc

@@ -1,215 +0,0 @@
-#include "Dataset.h"
-#include "ArffFiles.h"
-#include <fstream>
-namespace platform {
-    Dataset::Dataset(const Dataset& dataset) : path(dataset.path), name(dataset.name), className(dataset.className), n_samples(dataset.n_samples), n_features(dataset.n_features), features(dataset.features), states(dataset.states), loaded(dataset.loaded), discretize(dataset.discretize), X(dataset.X), y(dataset.y), Xv(dataset.Xv), Xd(dataset.Xd), yv(dataset.yv), fileType(dataset.fileType)
-    {
-    }
-    std::string Dataset::getName() const
-    {
-        return name;
-    }
-    std::string Dataset::getClassName() const
-    {
-        return className;
-    }
-    std::vector<std::string> Dataset::getFeatures() const
-    {
-        if (loaded) {
-            return features;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    int Dataset::getNFeatures() const
-    {
-        if (loaded) {
-            return n_features;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    int Dataset::getNSamples() const
-    {
-        if (loaded) {
-            return n_samples;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    std::map<std::string, std::vector<int>> Dataset::getStates() const
-    {
-        if (loaded) {
-            return states;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    pair<std::vector<std::vector<float>>&, std::vector<int>&> Dataset::getVectors()
-    {
-        if (loaded) {
-            return { Xv, yv };
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    pair<std::vector<std::vector<int>>&, std::vector<int>&> Dataset::getVectorsDiscretized()
-    {
-        if (loaded) {
-            return { Xd, yv };
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    pair<torch::Tensor&, torch::Tensor&> Dataset::getTensors()
-    {
-        if (loaded) {
-            buildTensors();
-            return { X, y };
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    void Dataset::load_csv()
-    {
-        ifstream file(path + "/" + name + ".csv");
-        if (file.is_open()) {
-            std::string line;
-            getline(file, line);
-            std::vector<std::string> tokens = split(line, ',');
-            features = std::vector<std::string>(tokens.begin(), tokens.end() - 1);
-            if (className == "-1") {
-                className = tokens.back();
-            }
-            for (auto i = 0; i < features.size(); ++i) {
-                Xv.push_back(std::vector<float>());
-            }
-            while (getline(file, line)) {
-                tokens = split(line, ',');
-                for (auto i = 0; i < features.size(); ++i) {
-                    Xv[i].push_back(stof(tokens[i]));
-                }
-                yv.push_back(stoi(tokens.back()));
-            }
-            file.close();
-        } else {
-            throw std::invalid_argument("Unable to open dataset file.");
-        }
-    }
-    void Dataset::computeStates()
-    {
-        for (int i = 0; i < features.size(); ++i) {
-            states[features[i]] = std::vector<int>(*max_element(Xd[i].begin(), Xd[i].end()) + 1);
-            auto item = states.at(features[i]);
-            iota(begin(item), end(item), 0);
-        }
-        states[className] = std::vector<int>(*max_element(yv.begin(), yv.end()) + 1);
-        iota(begin(states.at(className)), end(states.at(className)), 0);
-    }
-    void Dataset::load_arff()
-    {
-        auto arff = ArffFiles();
-        arff.load(path + "/" + name + ".arff", className);
-        // Get Dataset X, y
-        Xv = arff.getX();
-        yv = arff.getY();
-        // Get className & Features
-        className = arff.getClassName();
-        auto attributes = arff.getAttributes();
-        transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& attribute) { return attribute.first; });
-    }
-    std::vector<std::string> tokenize(std::string line)
-    {
-        std::vector<std::string> tokens;
-        for (auto i = 0; i < line.size(); ++i) {
-            if (line[i] == ' ' || line[i] == '\t' || line[i] == '\n') {
-                std::string token = line.substr(0, i);
-                tokens.push_back(token);
-                line.erase(line.begin(), line.begin() + i + 1);
-                i = 0;
-                while (line[i] == ' ' || line[i] == '\t' || line[i] == '\n')
-                    line.erase(line.begin(), line.begin() + i + 1);
-            }
-        }
-        if (line.size() > 0) {
-            tokens.push_back(line);
-        }
-        return tokens;
-    }
-    void Dataset::load_rdata()
-    {
-        ifstream file(path + "/" + name + "_R.dat");
-        if (file.is_open()) {
-            std::string line;
-            getline(file, line);
-            line = ArffFiles::trim(line);
-            std::vector<std::string> tokens = tokenize(line);
-            transform(tokens.begin(), tokens.end() - 1, back_inserter(features), [](const auto& attribute) { return ArffFiles::trim(attribute); });
-            if (className == "-1") {
-                className = ArffFiles::trim(tokens.back());
-            }
-            for (auto i = 0; i < features.size(); ++i) {
-                Xv.push_back(std::vector<float>());
-            }
-            while (getline(file, line)) {
-                tokens = tokenize(line);
-                // We have to skip the first token, which is the instance number.
-                for (auto i = 1; i < features.size() + 1; ++i) {
-                    const float value = stof(tokens[i]);
-                    Xv[i - 1].push_back(value);
-                }
-                yv.push_back(stoi(tokens.back()));
-            }
-            file.close();
-        } else {
-            throw std::invalid_argument("Unable to open dataset file.");
-        }
-    }
-    void Dataset::load()
-    {
-        if (loaded) {
-            return;
-        }
-        if (fileType == CSV) {
-            load_csv();
-        } else if (fileType == ARFF) {
-            load_arff();
-        } else if (fileType == RDATA) {
-            load_rdata();
-        }
-        if (discretize) {
-            Xd = discretizeDataset(Xv, yv);
-            computeStates();
-        }
-        n_samples = Xv[0].size();
-        n_features = Xv.size();
-        loaded = true;
-    }
-    void Dataset::buildTensors()
-    {
-        if (discretize) {
-            X = torch::zeros({ static_cast<int>(n_features), static_cast<int>(n_samples) }, torch::kInt32);
-        } else {
-            X = torch::zeros({ static_cast<int>(n_features), static_cast<int>(n_samples) }, torch::kFloat32);
-        }
-        for (int i = 0; i < features.size(); ++i) {
-            if (discretize) {
-                X.index_put_({ i,  "..." }, torch::tensor(Xd[i], torch::kInt32));
-            } else {
-                X.index_put_({ i,  "..." }, torch::tensor(Xv[i], torch::kFloat32));
-            }
-        }
-        y = torch::tensor(yv, torch::kInt32);
-    }
-    std::vector<mdlp::labels_t> Dataset::discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
-    {
-        std::vector<mdlp::labels_t> Xd;
-        auto fimdlp = mdlp::CPPFImdlp();
-        for (int i = 0; i < X.size(); i++) {
-            fimdlp.fit(X[i], y);
-            mdlp::labels_t& xd = fimdlp.transform(X[i]);
-            Xd.push_back(xd);
-        }
-        return Xd;
-    }
-}

src/Platform/Dataset.h

@@ -1,78 +0,0 @@
-#ifndef DATASET_H
-#define DATASET_H
-#include <torch/torch.h>
-#include <map>
-#include <vector>
-#include <string>
-#include "CPPFImdlp.h"
-#include "Utils.h"
-namespace platform {
-    enum fileType_t { CSV, ARFF, RDATA };
-    class SourceData {
-    public:
-        SourceData(std::string source)
-        {
-            if (source == "Surcov") {
-                path = "datasets/";
-                fileType = CSV;
-            } else if (source == "Arff") {
-                path = "datasets/";
-                fileType = ARFF;
-            } else if (source == "Tanveer") {
-                path = "data/";
-                fileType = RDATA;
-            } else {
-                throw std::invalid_argument("Unknown source.");
-            }
-        }
-        std::string getPath()
-        {
-            return path;
-        }
-        fileType_t getFileType()
-        {
-            return fileType;
-        }
-    private:
-        std::string path;
-        fileType_t fileType;
-    };
-    class Dataset {
-    private:
-        std::string path;
-        std::string name;
-        fileType_t fileType;
-        std::string className;
-        int n_samples{ 0 }, n_features{ 0 };
-        std::vector<std::string> features;
-        std::map<std::string, std::vector<int>> states;
-        bool loaded;
-        bool discretize;
-        torch::Tensor X, y;
-        std::vector<std::vector<float>> Xv;
-        std::vector<std::vector<int>> Xd;
-        std::vector<int> yv;
-        void buildTensors();
-        void load_csv();
-        void load_arff();
-        void load_rdata();
-        void computeStates();
-        std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
-    public:
-        Dataset(const std::string& path, const std::string& name, const std::string& className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
-        explicit Dataset(const Dataset&);
-        std::string getName() const;
-        std::string getClassName() const;
-        std::vector<string> getFeatures() const;
-        std::map<std::string, std::vector<int>> getStates() const;
-        std::pair<vector<std::vector<float>>&, std::vector<int>&> getVectors();
-        std::pair<vector<std::vector<int>>&, std::vector<int>&> getVectorsDiscretized();
-        std::pair<torch::Tensor&, torch::Tensor&> getTensors();
-        int getNFeatures() const;
-        int getNSamples() const;
-        void load();
-        const bool inline isLoaded() const { return loaded; };
-    };
-};
-
-#endif

src/Platform/Datasets.cc

@@ -1,129 +0,0 @@
-#include "Datasets.h"
-#include <fstream>
-namespace platform {
-    void Datasets::load()
-    {
-        auto sd = SourceData(sfileType);
-        fileType = sd.getFileType();
-        path = sd.getPath();
-        ifstream catalog(path + "all.txt");
-        if (catalog.is_open()) {
-            std::string line;
-            while (getline(catalog, line)) {
-                if (line.empty() || line[0] == '#') {
-                    continue;
-                }
-                std::vector<std::string> tokens = split(line, ',');
-                std::string name = tokens[0];
-                std::string className;
-                if (tokens.size() == 1) {
-                    className = "-1";
-                } else {
-                    className = tokens[1];
-                }
-                datasets[name] = make_unique<Dataset>(path, name, className, discretize, fileType);
-            }
-            catalog.close();
-        } else {
-            throw std::invalid_argument("Unable to open catalog file. [" + path + "all.txt" + "]");
-        }
-    }
-    std::vector<std::string> Datasets::getNames()
-    {
-        std::vector<std::string> result;
-        transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
-        return result;
-    }
-    std::vector<std::string> Datasets::getFeatures(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getFeatures();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    map<std::string, std::vector<int>> Datasets::getStates(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getStates();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    void Datasets::loadDataset(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return;
-        } else {
-            datasets.at(name)->load();
-        }
-    }
-    std::string Datasets::getClassName(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getClassName();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    int Datasets::getNSamples(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getNSamples();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    int Datasets::getNClasses(const std::string& name)
-    {
-        if (datasets.at(name)->isLoaded()) {
-            auto className = datasets.at(name)->getClassName();
-            if (discretize) {
-                auto states = getStates(name);
-                return states.at(className).size();
-            }
-            auto [Xv, yv] = getVectors(name);
-            return *std::max_element(yv.begin(), yv.end()) + 1;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    std::vector<int> Datasets::getClassesCounts(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            auto [Xv, yv] = datasets.at(name)->getVectors();
-            std::vector<int> counts(*std::max_element(yv.begin(), yv.end()) + 1);
-            for (auto y : yv) {
-                counts[y]++;
-            }
-            return counts;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    pair<std::vector<std::vector<float>>&, std::vector<int>&> Datasets::getVectors(const std::string& name)
-    {
-        if (!datasets[name]->isLoaded()) {
-            datasets[name]->load();
-        }
-        return datasets[name]->getVectors();
-    }
-    pair<std::vector<std::vector<int>>&, std::vector<int>&> Datasets::getVectorsDiscretized(const std::string& name)
-    {
-        if (!datasets[name]->isLoaded()) {
-            datasets[name]->load();
-        }
-        return datasets[name]->getVectorsDiscretized();
-    }
-    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(const std::string& name)
-    {
-        if (!datasets[name]->isLoaded()) {
-            datasets[name]->load();
-        }
-        return datasets[name]->getTensors();
-    }
-    bool Datasets::isDataset(const std::string& name) const
-    {
-        return datasets.find(name) != datasets.end();
-    }
-}

src/Platform/Datasets.h

@@ -1,30 +0,0 @@
-#ifndef DATASETS_H
-#define DATASETS_H
-#include "Dataset.h"
-namespace platform {
-    class Datasets {
-    private:
-        std::string path;
-        fileType_t fileType;
-        std::string sfileType;
-        std::map<std::string, std::unique_ptr<Dataset>> datasets;
-        bool discretize;
-        void load(); // Loads the list of datasets
-    public:
-        explicit Datasets(bool discretize, std::string sfileType) : discretize(discretize), sfileType(sfileType) { load(); };
-        std::vector<string> getNames();
-        std::vector<string> getFeatures(const std::string& name) const;
-        int getNSamples(const std::string& name) const;
-        std::string getClassName(const std::string& name) const;
-        int getNClasses(const std::string& name);
-        std::vector<int> getClassesCounts(const std::string& name) const;
-        std::map<std::string, std::vector<int>> getStates(const std::string& name) const;
-        std::pair<std::vector<std::vector<float>>&, std::vector<int>&> getVectors(const std::string& name);
-        std::pair<std::vector<std::vector<int>>&, std::vector<int>&> getVectorsDiscretized(const std::string& name);
-        std::pair<torch::Tensor&, torch::Tensor&> getTensors(const std::string& name);
-        bool isDataset(const std::string& name) const;
-        void loadDataset(const std::string& name) const;
-    };
-};
-
-#endif

src/Platform/DotEnv.h

@@ -1,55 +0,0 @@
-#ifndef DOTENV_H
-#define DOTENV_H
-#include <string>
-#include <map>
-#include <fstream>
-#include <sstream>
-#include <algorithm>
-#include <iostream>
-#include "Utils.h"
-
-//#include "Dataset.h"
-namespace platform {
-    class DotEnv {
-    private:
-        std::map<std::string, std::string> env;
-    public:
-        DotEnv()
-        {
-            std::ifstream file(".env");
-            if (!file.is_open()) {
-                std::cerr << "File .env not found" << std::endl;
-                exit(1);
-            }
-            std::string line;
-            while (std::getline(file, line)) {
-                line = trim(line);
-                if (line.empty() || line[0] == '#') {
-                    continue;
-                }
-                std::istringstream iss(line);
-                std::string key, value;
-                if (std::getline(iss, key, '=') && std::getline(iss, value)) {
-                    env[key] = value;
-                }
-            }
-        }
-        std::string get(const std::string& key)
-        {
-            return env.at(key);
-        }
-        std::vector<int> getSeeds()
-        {
-            auto seeds = std::vector<int>();
-            auto seeds_str = env["seeds"];
-            seeds_str = trim(seeds_str);
-            seeds_str = seeds_str.substr(1, seeds_str.size() - 2);
-            auto seeds_str_split = split(seeds_str, ',');
-            transform(seeds_str_split.begin(), seeds_str_split.end(), back_inserter(seeds), [](const std::string& str) {
-                return stoi(str);
-                });
-            return seeds;
-        }
-    };
-}
-#endif

src/Platform/ExcelFile.cc

@@ -1,168 +0,0 @@
-#include "ExcelFile.h"
-
-namespace platform {
-    ExcelFile::ExcelFile()
-    {
-        setDefault();
-    }
-    ExcelFile::ExcelFile(lxw_workbook* workbook) : workbook(workbook)
-    {
-        setDefault();
-    }
-    ExcelFile::ExcelFile(lxw_workbook* workbook, lxw_worksheet* worksheet) : workbook(workbook), worksheet(worksheet)
-    {
-        setDefault();
-    }
-    void ExcelFile::setDefault()
-    {
-        normalSize = 14; //font size for report body
-        row = 0;
-        colorTitle = 0xB1A0C7;
-        colorOdd = 0xDCE6F1;
-        colorEven = 0xFDE9D9;
-    }
-
-    lxw_workbook* ExcelFile::getWorkbook()
-    {
-        return workbook;
-    }
-    void ExcelFile::setProperties(std::string title)
-    {
-        char line[title.size() + 1];
-        strcpy(line, title.c_str());
-        lxw_doc_properties properties = {
-            .title = line,
-            .subject = (char*)"Machine learning results",
-            .author = (char*)"Ricardo Montañana Gómez",
-            .manager = (char*)"Dr. J. A. Gámez, Dr. J. M. Puerta",
-            .company = (char*)"UCLM",
-            .comments = (char*)"Created with libxlsxwriter and c++",
-        };
-        workbook_set_properties(workbook, &properties);
-    }
-    lxw_format* ExcelFile::efectiveStyle(const std::string& style)
-    {
-        lxw_format* efectiveStyle = NULL;
-        if (style != "") {
-            std::string suffix = row % 2 ? "_odd" : "_even";
-            try {
-                efectiveStyle = styles.at(style + suffix);
-            }
-            catch (const std::out_of_range& oor) {
-                try {
-                    efectiveStyle = styles.at(style);
-                }
-                catch (const std::out_of_range& oor) {
-                    throw std::invalid_argument("Style " + style + " not found");
-                }
-            }
-        }
-        return efectiveStyle;
-    }
-    void ExcelFile::writeString(int row, int col, const std::string& text, const std::string& style)
-    {
-        worksheet_write_string(worksheet, row, col, text.c_str(), efectiveStyle(style));
-    }
-    void ExcelFile::writeInt(int row, int col, const int number, const std::string& style)
-    {
-        worksheet_write_number(worksheet, row, col, number, efectiveStyle(style));
-    }
-    void ExcelFile::writeDouble(int row, int col, const double number, const std::string& style)
-    {
-        worksheet_write_number(worksheet, row, col, number, efectiveStyle(style));
-    }
-    void ExcelFile::addColor(lxw_format* style, bool odd)
-    {
-        uint32_t efectiveColor = odd ? colorEven : colorOdd;
-        format_set_bg_color(style, lxw_color_t(efectiveColor));
-    }
-    void ExcelFile::createStyle(const std::string& name, lxw_format* style, bool odd)
-    {
-        addColor(style, odd);
-        if (name == "textCentered") {
-            format_set_align(style, LXW_ALIGN_CENTER);
-            format_set_font_size(style, normalSize);
-            format_set_border(style, LXW_BORDER_THIN);
-        } else if (name == "text") {
-            format_set_font_size(style, normalSize);
-            format_set_border(style, LXW_BORDER_THIN);
-        } else if (name == "bodyHeader") {
-            format_set_bold(style);
-            format_set_font_size(style, normalSize);
-            format_set_align(style, LXW_ALIGN_CENTER);
-            format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
-            format_set_border(style, LXW_BORDER_THIN);
-            format_set_bg_color(style, lxw_color_t(colorTitle));
-        } else if (name == "result") {
-            format_set_font_size(style, normalSize);
-            format_set_border(style, LXW_BORDER_THIN);
-            format_set_num_format(style, "0.0000000");
-        } else if (name == "time") {
-            format_set_font_size(style, normalSize);
-            format_set_border(style, LXW_BORDER_THIN);
-            format_set_num_format(style, "#,##0.000000");
-        } else if (name == "ints") {
-            format_set_font_size(style, normalSize);
-            format_set_num_format(style, "###,##0");
-            format_set_border(style, LXW_BORDER_THIN);
-        } else if (name == "floats") {
-            format_set_border(style, LXW_BORDER_THIN);
-            format_set_font_size(style, normalSize);
-            format_set_num_format(style, "#,##0.00");
-        }
-    }
-
-    void ExcelFile::createFormats()
-    {
-        auto styleNames = { "text", "textCentered", "bodyHeader", "result", "time", "ints", "floats" };
-        lxw_format* style;
-        for (std::string name : styleNames) {
-            lxw_format* style = workbook_add_format(workbook);
-            style = workbook_add_format(workbook);
-            createStyle(name, style, true);
-            styles[name + "_odd"] = style;
-            style = workbook_add_format(workbook);
-            createStyle(name, style, false);
-            styles[name + "_even"] = style;
-        }
-
-        // Header 1st line
-        lxw_format* headerFirst = workbook_add_format(workbook);
-        format_set_bold(headerFirst);
-        format_set_font_size(headerFirst, 18);
-        format_set_align(headerFirst, LXW_ALIGN_CENTER);
-        format_set_align(headerFirst, LXW_ALIGN_VERTICAL_CENTER);
-        format_set_border(headerFirst, LXW_BORDER_THIN);
-        format_set_bg_color(headerFirst, lxw_color_t(colorTitle));
-
-        // Header rest
-        lxw_format* headerRest = workbook_add_format(workbook);
-        format_set_bold(headerRest);
-        format_set_align(headerRest, LXW_ALIGN_CENTER);
-        format_set_font_size(headerRest, 16);
-        format_set_align(headerRest, LXW_ALIGN_VERTICAL_CENTER);
-        format_set_border(headerRest, LXW_BORDER_THIN);
-        format_set_bg_color(headerRest, lxw_color_t(colorOdd));
-
-        // Header small
-        lxw_format* headerSmall = workbook_add_format(workbook);
-        format_set_bold(headerSmall);
-        format_set_align(headerSmall, LXW_ALIGN_LEFT);
-        format_set_font_size(headerSmall, 12);
-        format_set_border(headerSmall, LXW_BORDER_THIN);
-        format_set_align(headerSmall, LXW_ALIGN_VERTICAL_CENTER);
-        format_set_bg_color(headerSmall, lxw_color_t(colorOdd));
-
-        // Summary style
-        lxw_format* summaryStyle = workbook_add_format(workbook);
-        format_set_bold(summaryStyle);
-        format_set_font_size(summaryStyle, 16);
-        format_set_border(summaryStyle, LXW_BORDER_THIN);
-        format_set_align(summaryStyle, LXW_ALIGN_VERTICAL_CENTER);
-
-        styles["headerFirst"] = headerFirst;
-        styles["headerRest"] = headerRest;
-        styles["headerSmall"] = headerSmall;
-        styles["summaryStyle"] = summaryStyle;
-    }
-}

src/Platform/ExcelFile.h

@@ -1,43 +0,0 @@
-#ifndef EXCELFILE_H
-#define EXCELFILE_H
-#include <locale>
-#include <string>
-#include <map>
-#include "xlsxwriter.h"
-
-namespace platform {
-    struct separated : std::numpunct<char> {
-        char do_decimal_point() const { return ','; }
-
-        char do_thousands_sep() const { return '.'; }
-
-        std::string do_grouping() const { return "\03"; }
-    };
-    class ExcelFile {
-    public:
-        ExcelFile();
-        ExcelFile(lxw_workbook* workbook);
-        ExcelFile(lxw_workbook* workbook, lxw_worksheet* worksheet);
-        lxw_workbook* getWorkbook();
-    protected:
-        void setProperties(std::string title);
-        void writeString(int row, int col, const std::string& text, const std::string& style = "");
-        void writeInt(int row, int col, const int number, const std::string& style = "");
-        void writeDouble(int row, int col, const double number, const std::string& style = "");
-        void createFormats();
-        void createStyle(const std::string& name, lxw_format* style, bool odd);
-        void addColor(lxw_format* style, bool odd);
-        lxw_format* efectiveStyle(const std::string& name);
-        lxw_workbook* workbook;
-        lxw_worksheet* worksheet;
-        std::map<std::string, lxw_format*> styles;
-        int row;
-        int normalSize; //font size for report body
-        uint32_t colorTitle;
-        uint32_t colorOdd;
-        uint32_t colorEven;
-    private:
-        void setDefault();
-    };
-}
-#endif // !EXCELFILE_H

src/Platform/Experiment.cc

@@ -1,226 +0,0 @@
-#include <fstream>
-#include "Experiment.h"
-#include "Datasets.h"
-#include "Models.h"
-#include "ReportConsole.h"
-#include "Paths.h"
-namespace platform {
-    using json = nlohmann::json;
-    std::string get_date()
-    {
-        time_t rawtime;
-        tm* timeinfo;
-        time(&rawtime);
-        timeinfo = std::localtime(&rawtime);
-        std::ostringstream oss;
-        oss << std::put_time(timeinfo, "%Y-%m-%d");
-        return oss.str();
-    }
-    std::string get_time()
-    {
-        time_t rawtime;
-        tm* timeinfo;
-        time(&rawtime);
-        timeinfo = std::localtime(&rawtime);
-        std::ostringstream oss;
-        oss << std::put_time(timeinfo, "%H:%M:%S");
-        return oss.str();
-    }
-    std::string Experiment::get_file_name()
-    {
-        std::string result = "results_" + score_name + "_" + model + "_" + platform + "_" + get_date() + "_" + get_time() + "_" + (stratified ? "1" : "0") + ".json";
-        return result;
-    }
-
-    json Experiment::build_json()
-    {
-        json result;
-        result["title"] = title;
-        result["date"] = get_date();
-        result["time"] = get_time();
-        result["model"] = model;
-        result["version"] = model_version;
-        result["platform"] = platform;
-        result["score_name"] = score_name;
-        result["language"] = language;
-        result["language_version"] = language_version;
-        result["discretized"] = discretized;
-        result["stratified"] = stratified;
-        result["folds"] = nfolds;
-        result["seeds"] = randomSeeds;
-        result["duration"] = duration;
-        result["results"] = json::array();
-        for (const auto& r : results) {
-            json j;
-            j["dataset"] = r.getDataset();
-            j["hyperparameters"] = r.getHyperparameters();
-            j["samples"] = r.getSamples();
-            j["features"] = r.getFeatures();
-            j["classes"] = r.getClasses();
-            j["score_train"] = r.getScoreTrain();
-            j["score_test"] = r.getScoreTest();
-            j["score"] = r.getScoreTest();
-            j["score_std"] = r.getScoreTestStd();
-            j["score_train_std"] = r.getScoreTrainStd();
-            j["score_test_std"] = r.getScoreTestStd();
-            j["train_time"] = r.getTrainTime();
-            j["train_time_std"] = r.getTrainTimeStd();
-            j["test_time"] = r.getTestTime();
-            j["test_time_std"] = r.getTestTimeStd();
-            j["time"] = r.getTestTime() + r.getTrainTime();
-            j["time_std"] = r.getTestTimeStd() + r.getTrainTimeStd();
-            j["scores_train"] = r.getScoresTrain();
-            j["scores_test"] = r.getScoresTest();
-            j["times_train"] = r.getTimesTrain();
-            j["times_test"] = r.getTimesTest();
-            j["nodes"] = r.getNodes();
-            j["leaves"] = r.getLeaves();
-            j["depth"] = r.getDepth();
-            result["results"].push_back(j);
-        }
-        return result;
-    }
-    void Experiment::save(const std::string& path)
-    {
-        json data = build_json();
-        ofstream file(path + "/" + get_file_name());
-        file << data;
-        file.close();
-    }
-
-    void Experiment::report()
-    {
-        json data = build_json();
-        ReportConsole report(data);
-        report.show();
-    }
-
-    void Experiment::show()
-    {
-        json data = build_json();
-        std::cout << data.dump(4) << std::endl;
-    }
-
-    void Experiment::go(std::vector<std::string> filesToProcess, bool quiet)
-    {
-        std::cout << "*** Starting experiment: " << title << " ***" << std::endl;
-        for (auto fileName : filesToProcess) {
-            std::cout << "- " << setw(20) << left << fileName << " " << right << flush;
-            cross_validation(fileName, quiet);
-            std::cout << std::endl;
-        }
-    }
-
-    std::string getColor(bayesnet::status_t status)
-    {
-        switch (status) {
-            case bayesnet::NORMAL:
-                return Colors::GREEN();
-            case bayesnet::WARNING:
-                return Colors::YELLOW();
-            case bayesnet::ERROR:
-                return Colors::RED();
-            default:
-                return Colors::RESET();
-        }
-    }
-
-    void showProgress(int fold, const std::string& color, const std::string& phase)
-    {
-        std::string prefix = phase == "a" ? "" : "\b\b\b\b";
-        std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
-
-    }
-    void Experiment::cross_validation(const std::string& fileName, bool quiet)
-    {
-        auto datasets = Datasets(discretized, Paths::datasets());
-        // Get dataset
-        auto [X, y] = datasets.getTensors(fileName);
-        auto states = datasets.getStates(fileName);
-        auto features = datasets.getFeatures(fileName);
-        auto samples = datasets.getNSamples(fileName);
-        auto className = datasets.getClassName(fileName);
-        if (!quiet) {
-            std::cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
-        }
-        // Prepare Result
-        auto result = Result();
-        auto [values, counts] = at::_unique(y);
-        result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
-        result.setHyperparameters(hyperparameters.get(fileName));
-        // Initialize results std::vectors
-        int nResults = nfolds * static_cast<int>(randomSeeds.size());
-        auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
-        auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64);
-        auto train_time = torch::zeros({ nResults }, torch::kFloat64);
-        auto test_time = torch::zeros({ nResults }, torch::kFloat64);
-        auto nodes = torch::zeros({ nResults }, torch::kFloat64);
-        auto edges = torch::zeros({ nResults }, torch::kFloat64);
-        auto num_states = torch::zeros({ nResults }, torch::kFloat64);
-        Timer train_timer, test_timer;
-        int item = 0;
-        for (auto seed : randomSeeds) {
-            if (!quiet)
-                std::cout << "(" << seed << ") doing Fold: " << flush;
-            Fold* fold;
-            if (stratified)
-                fold = new StratifiedKFold(nfolds, y, seed);
-            else
-                fold = new KFold(nfolds, y.size(0), seed);
-            for (int nfold = 0; nfold < nfolds; nfold++) {
-                auto clf = Models::instance()->create(model);
-                setModelVersion(clf->getVersion());
-                auto valid = clf->getValidHyperparameters();
-                hyperparameters.check(valid, fileName);
-                clf->setHyperparameters(hyperparameters.get(fileName));
-                // Split train - test dataset
-                train_timer.start();
-                auto [train, test] = fold->getFold(nfold);
-                auto train_t = torch::tensor(train);
-                auto test_t = torch::tensor(test);
-                auto X_train = X.index({ "...", train_t });
-                auto y_train = y.index({ train_t });
-                auto X_test = X.index({ "...", test_t });
-                auto y_test = y.index({ test_t });
-                if (!quiet)
-                    showProgress(nfold + 1, getColor(clf->getStatus()), "a");
-                // Train model
-                clf->fit(X_train, y_train, features, className, states);
-                if (!quiet)
-                    showProgress(nfold + 1, getColor(clf->getStatus()), "b");
-                nodes[item] = clf->getNumberOfNodes();
-                edges[item] = clf->getNumberOfEdges();
-                num_states[item] = clf->getNumberOfStates();
-                train_time[item] = train_timer.getDuration();
-                // Score train
-                auto accuracy_train_value = clf->score(X_train, y_train);
-                // Test model
-                if (!quiet)
-                    showProgress(nfold + 1, getColor(clf->getStatus()), "c");
-                test_timer.start();
-                auto accuracy_test_value = clf->score(X_test, y_test);
-                test_time[item] = test_timer.getDuration();
-                accuracy_train[item] = accuracy_train_value;
-                accuracy_test[item] = accuracy_test_value;
-                if (!quiet)
-                    std::cout << "\b\b\b, " << flush;
-                // Store results and times in std::vector
-                result.addScoreTrain(accuracy_train_value);
-                result.addScoreTest(accuracy_test_value);
-                result.addTimeTrain(train_time[item].item<double>());
-                result.addTimeTest(test_time[item].item<double>());
-                item++;
-            }
-            if (!quiet)
-                std::cout << "end. " << flush;
-            delete fold;
-        }
-        result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
-        result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
-        result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
-        result.setTestTimeStd(torch::std(test_time).item<double>()).setTrainTimeStd(torch::std(train_time).item<double>());
-        result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());
-        result.setDataset(fileName);
-        addResult(result);
-    }
-}

src/Platform/Experiment.h

@@ -1,103 +0,0 @@
-#ifndef EXPERIMENT_H
-#define EXPERIMENT_H
-#include <torch/torch.h>
-#include <nlohmann/json.hpp>
-#include <string>
-#include "Folding.h"
-#include "BaseClassifier.h"
-#include "HyperParameters.h"
-#include "TAN.h"
-#include "KDB.h"
-#include "AODE.h"
-#include "Timer.h"
-
-namespace platform {
-    using json = nlohmann::json;
-    class Result {
-    private:
-        std::string dataset, model_version;
-        json hyperparameters;
-        int samples{ 0 }, features{ 0 }, classes{ 0 };
-        double score_train{ 0 }, score_test{ 0 }, score_train_std{ 0 }, score_test_std{ 0 }, train_time{ 0 }, train_time_std{ 0 }, test_time{ 0 }, test_time_std{ 0 };
-        float nodes{ 0 }, leaves{ 0 }, depth{ 0 };
-        std::vector<double> scores_train, scores_test, times_train, times_test;
-    public:
-        Result() = default;
-        Result& setDataset(const std::string& dataset) { this->dataset = dataset; return *this; }
-        Result& setHyperparameters(const json& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
-        Result& setSamples(int samples) { this->samples = samples; return *this; }
-        Result& setFeatures(int features) { this->features = features; return *this; }
-        Result& setClasses(int classes) { this->classes = classes; return *this; }
-        Result& setScoreTrain(double score) { this->score_train = score; return *this; }
-        Result& setScoreTest(double score) { this->score_test = score; return *this; }
-        Result& setScoreTrainStd(double score_std) { this->score_train_std = score_std; return *this; }
-        Result& setScoreTestStd(double score_std) { this->score_test_std = score_std; return *this; }
-        Result& setTrainTime(double train_time) { this->train_time = train_time; return *this; }
-        Result& setTrainTimeStd(double train_time_std) { this->train_time_std = train_time_std; return *this; }
-        Result& setTestTime(double test_time) { this->test_time = test_time; return *this; }
-        Result& setTestTimeStd(double test_time_std) { this->test_time_std = test_time_std; return *this; }
-        Result& setNodes(float nodes) { this->nodes = nodes; return *this; }
-        Result& setLeaves(float leaves) { this->leaves = leaves; return *this; }
-        Result& setDepth(float depth) { this->depth = depth; return *this; }
-        Result& addScoreTrain(double score) { scores_train.push_back(score); return *this; }
-        Result& addScoreTest(double score) { scores_test.push_back(score); return *this; }
-        Result& addTimeTrain(double time) { times_train.push_back(time); return *this; }
-        Result& addTimeTest(double time) { times_test.push_back(time); return *this; }
-        const float get_score_train() const { return score_train; }
-        float get_score_test() { return score_test; }
-        const std::string& getDataset() const { return dataset; }
-        const json& getHyperparameters() const { return hyperparameters; }
-        const int getSamples() const { return samples; }
-        const int getFeatures() const { return features; }
-        const int getClasses() const { return classes; }
-        const double getScoreTrain() const { return score_train; }
-        const double getScoreTest() const { return score_test; }
-        const double getScoreTrainStd() const { return score_train_std; }
-        const double getScoreTestStd() const { return score_test_std; }
-        const double getTrainTime() const { return train_time; }
-        const double getTrainTimeStd() const { return train_time_std; }
-        const double getTestTime() const { return test_time; }
-        const double getTestTimeStd() const { return test_time_std; }
-        const float getNodes() const { return nodes; }
-        const float getLeaves() const { return leaves; }
-        const float getDepth() const { return depth; }
-        const std::vector<double>& getScoresTrain() const { return scores_train; }
-        const std::vector<double>& getScoresTest() const { return scores_test; }
-        const std::vector<double>& getTimesTrain() const { return times_train; }
-        const std::vector<double>& getTimesTest() const { return times_test; }
-    };
-    class Experiment {
-    public:
-        Experiment() = default;
-        Experiment& setTitle(const std::string& title) { this->title = title; return *this; }
-        Experiment& setModel(const std::string& model) { this->model = model; return *this; }
-        Experiment& setPlatform(const std::string& platform) { this->platform = platform; return *this; }
-        Experiment& setScoreName(const std::string& score_name) { this->score_name = score_name; return *this; }
-        Experiment& setModelVersion(const std::string& model_version) { this->model_version = model_version; return *this; }
-        Experiment& setLanguage(const std::string& language) { this->language = language; return *this; }
-        Experiment& setLanguageVersion(const std::string& language_version) { this->language_version = language_version; return *this; }
-        Experiment& setDiscretized(bool discretized) { this->discretized = discretized; return *this; }
-        Experiment& setStratified(bool stratified) { this->stratified = stratified; return *this; }
-        Experiment& setNFolds(int nfolds) { this->nfolds = nfolds; return *this; }
-        Experiment& addResult(Result result) { results.push_back(result); return *this; }
-        Experiment& addRandomSeed(int randomSeed) { randomSeeds.push_back(randomSeed); return *this; }
-        Experiment& setDuration(float duration) { this->duration = duration; return *this; }
-        Experiment& setHyperparameters(const HyperParameters& hyperparameters_) { this->hyperparameters = hyperparameters_; return *this; }
-        std::string get_file_name();
-        void save(const std::string& path);
-        void cross_validation(const std::string& fileName, bool quiet);
-        void go(std::vector<std::string> filesToProcess, bool quiet);
-        void show();
-        void report();
-    private:
-        std::string title, model, platform, score_name, model_version, language_version, language;
-        bool discretized{ false }, stratified{ false };
-        std::vector<Result> results;
-        std::vector<int> randomSeeds;
-        HyperParameters hyperparameters;
-        int nfolds{ 0 };
-        float duration{ 0 };
-        json build_json();
-    };
-}
-#endif

src/Platform/Folding.cc

@@ -1,104 +0,0 @@
-#include "Folding.h"
-#include <algorithm>
-#include <map>
-namespace platform {
-    Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
-    {
-        std::random_device rd;
-        random_seed = std::default_random_engine(seed == -1 ? rd() : seed);
-        std::srand(seed == -1 ? time(0) : seed);
-    }
-    KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(std::vector<int>(n))
-    {
-        std::iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
-        shuffle(indices.begin(), indices.end(), random_seed);
-    }
-    std::pair<std::vector<int>, std::vector<int>> KFold::getFold(int nFold)
-    {
-        if (nFold >= k || nFold < 0) {
-            throw std::out_of_range("nFold (" + std::to_string(nFold) + ") must be less than k (" + std::to_string(k) + ")");
-        }
-        int nTest = n / k;
-        auto train = std::vector<int>();
-        auto test = std::vector<int>();
-        for (int i = 0; i < n; i++) {
-            if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
-                test.push_back(indices[i]);
-            } else {
-                train.push_back(indices[i]);
-            }
-        }
-        return { train, test };
-    }
-    StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
-    {
-        n = y.numel();
-        this->y = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
-        build();
-    }
-    StratifiedKFold::StratifiedKFold(int k, const std::vector<int>& y, int seed)
-        : Fold(k, y.size(), seed)
-    {
-        this->y = y;
-        n = y.size();
-        build();
-    }
-    void StratifiedKFold::build()
-    {
-        stratified_indices = std::vector<std::vector<int>>(k);
-        int fold_size = n / k;
-
-        // Compute class counts and indices
-        auto class_indices = std::map<int, std::vector<int>>();
-        std::vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
-        for (auto i = 0; i < n; ++i) {
-            class_counts[y[i]]++;
-            class_indices[y[i]].push_back(i);
-        }
-        // Shuffle class indices
-        for (auto& [cls, indices] : class_indices) {
-            shuffle(indices.begin(), indices.end(), random_seed);
-        }
-        // Assign indices to folds
-        for (auto label = 0; label < class_counts.size(); ++label) {
-            auto num_samples_to_take = class_counts.at(label) / k;
-            if (num_samples_to_take == 0) {
-                std::cerr << "Warning! The number of samples in class " << label << " (" << class_counts.at(label)
-                    << ") is less than the number of folds (" << k << ")." << std::endl;
-                faulty = true;
-                continue;
-            }
-            auto remainder_samples_to_take = class_counts[label] % k;
-            for (auto fold = 0; fold < k; ++fold) {
-                auto it = next(class_indices[label].begin(), num_samples_to_take);
-                move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold]));  // ##
-                class_indices[label].erase(class_indices[label].begin(), it);
-            }
-            auto chosen = std::vector<bool>(k, false);
-            while (remainder_samples_to_take > 0) {
-                int fold = (rand() % static_cast<int>(k));
-                if (chosen.at(fold)) {
-                    continue;
-                }
-                chosen[fold] = true;
-                auto it = next(class_indices[label].begin(), 1);
-                stratified_indices[fold].push_back(*class_indices[label].begin());
-                class_indices[label].erase(class_indices[label].begin(), it);
-                remainder_samples_to_take--;
-            }
-        }
-    }
-    std::pair<std::vector<int>, std::vector<int>> StratifiedKFold::getFold(int nFold)
-    {
-        if (nFold >= k || nFold < 0) {
-            throw std::out_of_range("nFold (" + std::to_string(nFold) + ") must be less than k (" + std::to_string(k) + ")");
-        }
-        std::vector<int> test_indices = stratified_indices[nFold];
-        std::vector<int> train_indices;
-        for (int i = 0; i < k; ++i) {
-            if (i == nFold) continue;
-            train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
-        }
-        return { train_indices, test_indices };
-    }
-}

src/Platform/Folding.h

@@ -1,39 +0,0 @@
-#ifndef FOLDING_H
-#define FOLDING_H
-#include <torch/torch.h>
-#include <vector>
-#include <random> 
-namespace platform {
-    class Fold {
-    protected:
-        int k;
-        int n;
-        int seed;
-        std::default_random_engine random_seed;
-    public:
-        Fold(int k, int n, int seed = -1);
-        virtual std::pair<std::vector<int>, std::vector<int>> getFold(int nFold) = 0;
-        virtual ~Fold() = default;
-        int getNumberOfFolds() { return k; }
-    };
-    class KFold : public Fold {
-    private:
-        std::vector<int> indices;
-    public:
-        KFold(int k, int n, int seed = -1);
-        std::pair<std::vector<int>, std::vector<int>> getFold(int nFold) override;
-    };
-    class StratifiedKFold : public Fold {
-    private:
-        std::vector<int> y;
-        std::vector<std::vector<int>> stratified_indices;
-        void build();
-        bool faulty = false; // Only true if the number of samples of any class is less than the number of folds.
-    public:
-        StratifiedKFold(int k, const std::vector<int>& y, int seed = -1);
-        StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
-        std::pair<std::vector<int>, std::vector<int>> getFold(int nFold) override;
-        bool isFaulty() { return faulty; }
-    };
-}
-#endif

src/Platform/GridData.cc

@@ -1,55 +0,0 @@
-#include "GridData.h"
-#include <fstream>
-
-namespace platform {
-    GridData::GridData(const std::string& fileName)
-    {
-        std::ifstream resultData(fileName);
-        if (resultData.is_open()) {
-            grid = json::parse(resultData);
-        } else {
-            throw std::invalid_argument("Unable to open input file. [" + fileName + "]");
-        }
-    }
-    int GridData::computeNumCombinations(const json& line)
-    {
-        int numCombinations = 1;
-        for (const auto& item : line.items()) {
-            numCombinations *= item.value().size();
-        }
-        return numCombinations;
-    }
-    int GridData::getNumCombinations()
-    {
-        int numCombinations = 0;
-        for (const auto& line : grid) {
-            numCombinations += computeNumCombinations(line);
-        }
-        return numCombinations;
-    }
-    json GridData::generateCombinations(json::iterator index, const json::iterator last, std::vector<json>& output, json currentCombination)
-    {
-        if (index == last) {
-            // If we reached the end of input, store the current combination
-            output.push_back(currentCombination);
-            return  currentCombination;
-        }
-        const auto& key = index.key();
-        const auto& values = index.value();
-        for (const auto& value : values) {
-            auto combination = currentCombination;
-            combination[key] = value;
-            json::iterator nextIndex = index;
-            generateCombinations(++nextIndex, last, output, combination);
-        }
-        return currentCombination;
-    }
-    std::vector<json> GridData::getGrid()
-    {
-        auto result = std::vector<json>();
-        for (json line : grid) {
-            generateCombinations(line.begin(), line.end(), result, json({}));
-        }
-        return result;
-    }
-} /* namespace platform */

src/Platform/GridData.h

@@ -1,22 +0,0 @@
-#ifndef GRIDDATA_H
-#define GRIDDATA_H
-#include <string>
-#include <vector>
-#include <map>
-#include <nlohmann/json.hpp>
-
-namespace platform {
-    using json = nlohmann::json;
-    class GridData {
-    public:
-        explicit GridData(const std::string& fileName);
-        ~GridData() = default;
-        std::vector<json> getGrid();
-        int getNumCombinations();
-    private:
-        json generateCombinations(json::iterator index, const json::iterator last, std::vector<json>& output, json currentCombination);
-        int computeNumCombinations(const json& line);
-        json grid;
-    };
-} /* namespace platform */
-#endif /* GRIDDATA_H */

src/Platform/GridSearch.cc

@@ -1,130 +0,0 @@
-#include <iostream>
-#include <torch/torch.h>
-#include "GridSearch.h"
-#include "Models.h"
-#include "Paths.h"
-#include "Folding.h"
-#include "Colors.h"
-
-namespace platform {
-    GridSearch::GridSearch(struct ConfigGrid& config) : config(config)
-    {
-        this->config.output_file = config.path + "grid_" + config.model + "_output.json";
-        this->config.input_file = config.path + "grid_" + config.model + "_input.json";
-    }
-    void showProgressComb(const int num, const int total, const std::string& color)
-    {
-        int spaces = int(log(total) / log(10)) + 1;
-        int magic = 37 + 2 * spaces;
-        std::string prefix = num == 1 ? "" : string(magic, '\b') + string(magic + 1, ' ') + string(magic + 1, '\b');
-        std::cout << prefix << color << "(" << setw(spaces) << num << "/" << setw(spaces) << total << ") " << Colors::RESET() << flush;
-    }
-    void showProgressFold(int fold, const std::string& color, const std::string& phase)
-    {
-        std::string prefix = phase == "a" ? "" : "\b\b\b\b";
-        std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
-    }
-    std::string getColor(bayesnet::status_t status)
-    {
-        switch (status) {
-            case bayesnet::NORMAL:
-                return Colors::GREEN();
-            case bayesnet::WARNING:
-                return Colors::YELLOW();
-            case bayesnet::ERROR:
-                return Colors::RED();
-            default:
-                return Colors::RESET();
-        }
-    }
-    double GridSearch::processFile(std::string fileName, Datasets& datasets, HyperParameters& hyperparameters)
-    {
-        // Get dataset
-        auto [X, y] = datasets.getTensors(fileName);
-        auto states = datasets.getStates(fileName);
-        auto features = datasets.getFeatures(fileName);
-        auto samples = datasets.getNSamples(fileName);
-        auto className = datasets.getClassName(fileName);
-        double totalScore = 0.0;
-        int numItems = 0;
-        for (const auto& seed : config.seeds) {
-            if (!config.quiet)
-                std::cout << "(" << seed << ") doing Fold: " << flush;
-            Fold* fold;
-            if (config.stratified)
-                fold = new StratifiedKFold(config.n_folds, y, seed);
-            else
-                fold = new KFold(config.n_folds, y.size(0), seed);
-            double bestScore = 0.0;
-            for (int nfold = 0; nfold < config.n_folds; nfold++) {
-                auto clf = Models::instance()->create(config.model);
-                clf->setHyperparameters(hyperparameters.get(fileName));
-                auto [train, test] = fold->getFold(nfold);
-                auto train_t = torch::tensor(train);
-                auto test_t = torch::tensor(test);
-                auto X_train = X.index({ "...", train_t });
-                auto y_train = y.index({ train_t });
-                auto X_test = X.index({ "...", test_t });
-                auto y_test = y.index({ test_t });
-                // Train model
-                if (!config.quiet)
-                    showProgressFold(nfold + 1, getColor(clf->getStatus()), "a");
-                clf->fit(X_train, y_train, features, className, states);
-                // Test model
-                if (!config.quiet)
-                    showProgressFold(nfold + 1, getColor(clf->getStatus()), "b");
-                totalScore += clf->score(X_test, y_test);
-                numItems++;
-                if (!config.quiet)
-                    std::cout << "\b\b\b,  \b" << flush;
-            }
-            delete fold;
-        }
-        return numItems == 0 ? 0.0 : totalScore / numItems;
-    }
-    void GridSearch::go()
-    {
-        // Load datasets
-        auto datasets = Datasets(config.discretize, Paths::datasets());
-        // Create model
-        std::cout << "***************** Starting Gridsearch *****************" << std::endl;
-        std::cout << "input file=" << config.input_file << std::endl;
-        auto grid = GridData(config.input_file);
-        auto totalComb = grid.getNumCombinations();
-        std::cout << "* Doing " << totalComb << " combinations for each dataset/seed/fold" << std::endl;
-        // Generate hyperparameters grid & run gridsearch
-        // Check each combination of hyperparameters for each dataset and each seed
-        for (const auto& dataset : datasets.getNames()) {
-            if (!config.quiet)
-                std::cout << "- " << setw(20) << left << dataset << " " << right << flush;
-            int num = 0;
-            double bestScore = 0.0;
-            json bestHyperparameters;
-            for (const auto& hyperparam_line : grid.getGrid()) {
-                if (!config.quiet)
-                    showProgressComb(++num, totalComb, Colors::CYAN());
-                auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
-                double score = processFile(dataset, datasets, hyperparameters);
-                if (score > bestScore) {
-                    bestScore = score;
-                    bestHyperparameters = hyperparam_line;
-                }
-            }
-            if (!config.quiet) {
-                std::cout << "end." << " Score: " << setw(9) << setprecision(7) << fixed
-                    << bestScore << " [" << bestHyperparameters.dump() << "]" << std::endl;
-            }
-            results[dataset]["score"] = bestScore;
-            results[dataset]["hyperparameters"] = bestHyperparameters;
-        }
-        // Save results
-        save();
-        std::cout << "***************** Ending Gridsearch *******************" << std::endl;
-    }
-    void GridSearch::save() const
-    {
-        std::ofstream file(config.output_file);
-        file << results.dump(4);
-        file.close();
-    }
-} /* namespace platform */

src/Platform/GridSearch.h

@@ -1,36 +0,0 @@
-#ifndef GRIDSEARCH_H
-#define GRIDSEARCH_H
-#include <string>
-#include <vector>
-#include <nlohmann/json.hpp>
-#include "Datasets.h"
-#include "HyperParameters.h"
-#include "GridData.h"
-
-namespace platform {
-    using json = nlohmann::json;
-    struct ConfigGrid {
-        std::string model;
-        std::string score;
-        std::string path;
-        std::string input_file;
-        std::string output_file;
-        bool quiet;
-        bool discretize;
-        bool stratified;
-        int n_folds;
-        std::vector<int> seeds;
-    };
-    class GridSearch {
-    public:
-        explicit GridSearch(struct ConfigGrid& config);
-        void go();
-        void save() const;
-        ~GridSearch() = default;
-    private:
-        double processFile(std::string fileName, Datasets& datasets, HyperParameters& hyperparameters);
-        json results;
-        struct ConfigGrid config;
-    };
-} /* namespace platform */
-#endif /* GRIDSEARCH_H */

src/Platform/HyperParameters.cc

@@ -1,55 +0,0 @@
-#include "HyperParameters.h"
-#include <fstream>
-#include <sstream>
-#include <iostream>
-
-namespace platform {
-    HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_)
-    {
-        // Initialize all datasets with the given hyperparameters
-        for (const auto& item : datasets) {
-            hyperparameters[item] = hyperparameters_;
-        }
-    }
-    // https://www.techiedelight.com/implode-a-vector-of-strings-into-a-comma-separated-string-in-cpp/
-    std::string join(std::vector<std::string> const& strings, std::string delim)
-    {
-        std::stringstream ss;
-        std::copy(strings.begin(), strings.end(),
-            std::ostream_iterator<std::string>(ss, delim.c_str()));
-        return ss.str();
-    }
-    HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file)
-    {
-        // Check if file exists
-        std::ifstream file(hyperparameters_file);
-        if (!file.is_open()) {
-            throw std::runtime_error("File " + hyperparameters_file + " not found");
-        }
-        // Check if file is a json
-        json input_hyperparameters = json::parse(file);
-        // Check if hyperparameters are valid
-        for (const auto& dataset : datasets) {
-            if (!input_hyperparameters.contains(dataset)) {
-                std::cerr << "*Warning: Dataset " << dataset << " not found in hyperparameters file" << " assuming default hyperparameters" << std::endl;
-                hyperparameters[dataset] = json({});
-                continue;
-            }
-            hyperparameters[dataset] = input_hyperparameters[dataset].get<json>();
-        }
-    }
-    void HyperParameters::check(const std::vector<std::string>& valid, const std::string& fileName)
-    {
-        json result = hyperparameters.at(fileName);
-        for (const auto& item : result.items()) {
-            if (find(valid.begin(), valid.end(), item.key()) == valid.end()) {
-                throw std::invalid_argument("Hyperparameter " + item.key() + " is not valid. Passed Hyperparameters are: "
-                    + result.dump(4) + "\n Valid hyperparameters are: {" + join(valid, ",") + "}");
-            }
-        }
-    }
-    json HyperParameters::get(const std::string& fileName)
-    {
-        return hyperparameters.at(fileName);
-    }
-} /* namespace platform */

src/Platform/HyperParameters.h

@@ -1,23 +0,0 @@
-#ifndef HYPERPARAMETERS_H
-#define HYPERPARAMETERS_H
-#include <string>
-#include <map>
-#include <vector>
-#include <nlohmann/json.hpp>
-
-namespace platform {
-    using json = nlohmann::json;
-    class HyperParameters {
-    public:
-        HyperParameters() = default;
-        explicit HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_);
-        explicit HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file);
-        ~HyperParameters() = default;
-        bool notEmpty(const std::string& key) const { return !hyperparameters.at(key).empty(); }
-        void check(const std::vector<std::string>& valid, const std::string& fileName);
-        json get(const std::string& fileName);
-    private:
-        std::map<std::string, json> hyperparameters;
-    };
-} /* namespace platform */
-#endif /* HYPERPARAMETERS_H */

src/Platform/ManageResults.cc

@@ -1,213 +0,0 @@
-#include "ManageResults.h"
-#include "CommandParser.h"
-#include <filesystem>
-#include <tuple>
-#include "Colors.h"
-#include "CLocale.h"
-#include "Paths.h"
-#include "ReportConsole.h"
-#include "ReportExcel.h"
-
-namespace platform {
-
-    ManageResults::ManageResults(int numFiles, const std::string& model, const std::string& score, bool complete, bool partial, bool compare) :
-        numFiles{ numFiles }, complete{ complete }, partial{ partial }, compare{ compare }, results(Results(Paths::results(), model, score, complete, partial))
-    {
-        indexList = true;
-        openExcel = false;
-        workbook = NULL;
-        if (numFiles == 0) {
-            this->numFiles = results.size();
-        }
-    }
-    void ManageResults::doMenu()
-    {
-        if (results.empty()) {
-            std::cout << Colors::MAGENTA() << "No results found!" << Colors::RESET() << std::endl;
-            return;
-        }
-        results.sortDate();
-        list();
-        menu();
-        if (openExcel) {
-            workbook_close(workbook);
-        }
-        std::cout << Colors::RESET() << "Done!" << std::endl;
-    }
-    void ManageResults::list()
-    {
-        auto temp = ConfigLocale();
-        std::string suffix = numFiles != results.size() ? " of " + std::to_string(results.size()) : "";
-        std::stringstream oss;
-        oss << "Results on screen: " << numFiles << suffix;
-        std::cout << Colors::GREEN() << oss.str() << std::endl;
-        std::cout << std::string(oss.str().size(), '-') << std::endl;
-        if (complete) {
-            std::cout << Colors::MAGENTA() << "Only listing complete results" << std::endl;
-        }
-        if (partial) {
-            std::cout << Colors::MAGENTA() << "Only listing partial results" << std::endl;
-        }
-        auto i = 0;
-        int maxModel = results.maxModelSize();
-        std::cout << Colors::GREEN() << " #  Date       " << std::setw(maxModel) << std::left << "Model" << " Score Name  Score       C/P Duration  Title" << std::endl;
-        std::cout << "=== ========== " << std::string(maxModel, '=') << " =========== =========== === ========= =============================================================" << std::endl;
-        bool odd = true;
-        for (auto& result : results) {
-            auto color = odd ? Colors::BLUE() : Colors::CYAN();
-            std::cout << color << std::setw(3) << std::fixed << std::right << i++ << " ";
-            std::cout << result.to_string(maxModel) << std::endl;
-            if (i == numFiles) {
-                break;
-            }
-            odd = !odd;
-        }
-    }
-    bool ManageResults::confirmAction(const std::string& intent, const std::string& fileName) const
-    {
-        std::string color;
-        if (intent == "delete") {
-            color = Colors::RED();
-        } else {
-            color = Colors::YELLOW();
-        }
-        std::string line;
-        bool finished = false;
-        while (!finished) {
-            std::cout << color << "Really want to " << intent << " " << fileName << "? (y/n): ";
-            getline(std::cin, line);
-            finished = line.size() == 1 && (tolower(line[0]) == 'y' || tolower(line[0] == 'n'));
-        }
-        if (tolower(line[0]) == 'y') {
-            return true;
-        }
-        std::cout << "Not done!" << std::endl;
-        return false;
-    }
-    void ManageResults::report(const int index, const bool excelReport)
-    {
-        std::cout << Colors::YELLOW() << "Reporting " << results.at(index).getFilename() << std::endl;
-        auto data = results.at(index).load();
-        if (excelReport) {
-            ReportExcel reporter(data, compare, workbook);
-            reporter.show();
-            openExcel = true;
-            workbook = reporter.getWorkbook();
-            std::cout << "Adding sheet to " << Paths::excel() + Paths::excelResults() << std::endl;
-        } else {
-            ReportConsole reporter(data, compare);
-            reporter.show();
-        }
-    }
-    void ManageResults::showIndex(const int index, const int idx)
-    {
-        // Show a dataset result inside a report
-        auto data = results.at(index).load();
-        std::cout << Colors::YELLOW() << "Showing " << results.at(index).getFilename() << std::endl;
-        ReportConsole reporter(data, compare, idx);
-        reporter.show();
-    }
-    void ManageResults::sortList()
-    {
-        std::cout << Colors::YELLOW() << "Choose sorting field (date='d', score='s', duration='u', model='m'): ";
-        std::string line;
-        char option;
-        getline(std::cin, line);
-        if (line.size() == 0)
-            return;
-        if (line.size() > 1) {
-            std::cout << "Invalid option" << std::endl;
-            return;
-        }
-        option = line[0];
-        switch (option) {
-            case 'd':
-                results.sortDate();
-                break;
-            case 's':
-                results.sortScore();
-                break;
-            case 'u':
-                results.sortDuration();
-                break;
-            case 'm':
-                results.sortModel();
-                break;
-            default:
-                std::cout << "Invalid option" << std::endl;
-        }
-    }
-    void ManageResults::menu()
-    {
-        char option;
-        int index, subIndex;
-        bool finished = false;
-        std::string filename;
-        // tuple<Option, digit, requires value>
-        std::vector<std::tuple<std::string, char, bool>>  mainOptions = {
-            {"quit", 'q', false},
-            {"list", 'l', false},
-            {"delete", 'd', true},
-            {"hide", 'h', true},
-            {"sort", 's', false},
-            {"report", 'r', true},
-            {"excel", 'e', true}
-        };
-        std::vector<std::tuple<std::string, char, bool>> listOptions = {
-            {"report", 'r', true},
-            {"list", 'l', false},
-            {"quit", 'q', false}
-        };
-        auto parser = CommandParser();
-        while (!finished) {
-            if (indexList) {
-                std::tie(option, index) = parser.parse(Colors::GREEN(), mainOptions, 'r', numFiles - 1);
-            } else {
-                std::tie(option, subIndex) = parser.parse(Colors::MAGENTA(), listOptions, 'r', results.at(index).load()["results"].size() - 1);
-            }
-            switch (option) {
-                case 'q':
-                    finished = true;
-                    break;
-                case 'l':
-                    list();
-                    indexList = true;
-                    break;
-                case 'd':
-                    filename = results.at(index).getFilename();
-                    if (!confirmAction("delete", filename))
-                        break;
-                    std::cout << "Deleting " << filename << std::endl;
-                    results.deleteResult(index);
-                    std::cout << "File: " + filename + " deleted!" << std::endl;
-                    list();
-                    break;
-                case 'h':
-                    filename = results.at(index).getFilename();
-                    if (!confirmAction("hide", filename))
-                        break;
-                    filename = results.at(index).getFilename();
-                    std::cout << "Hiding " << filename << std::endl;
-                    results.hideResult(index, Paths::hiddenResults());
-                    std::cout << "File: " + filename + " hidden! (moved to " << Paths::hiddenResults() << ")" << std::endl;
-                    list();
-                    break;
-                case 's':
-                    sortList();
-                    list();
-                    break;
-                case 'r':
-                    if (indexList) {
-                        report(index, false);
-                        indexList = false;
-                    } else {
-                        showIndex(index, subIndex);
-                    }
-                    break;
-                case 'e':
-                    report(index, true);
-                    break;
-            }
-        }
-    }
-} /* namespace platform */

src/Platform/ManageResults.h

@@ -1,31 +0,0 @@
-#ifndef MANAGE_RESULTS_H
-#define MANAGE_RESULTS_H
-#include "Results.h"
-#include "xlsxwriter.h"
-
-namespace platform {
-    class ManageResults {
-    public:
-        ManageResults(int numFiles, const std::string& model, const std::string& score, bool complete, bool partial, bool compare);
-        ~ManageResults() = default;
-        void doMenu();
-    private:
-        void list();
-        bool confirmAction(const std::string& intent, const std::string& fileName) const;
-        void report(const int index, const bool excelReport);
-        void showIndex(const int index, const int idx);
-        void sortList();
-        void menu();
-        int numFiles;
-        bool indexList;
-        bool openExcel;
-        bool complete;
-        bool partial;
-        bool compare;
-        Results results;
-        lxw_workbook* workbook;
-    };
-
-}
-
-#endif /* MANAGE_RESULTS_H */

src/Platform/Models.cc

@@ -1,52 +0,0 @@
-#include "Models.h"
-namespace platform {
-    // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
-    Models* Models::factory = nullptr;;
-    Models* Models::instance()
-    {
-        //manages singleton
-        if (factory == nullptr)
-            factory = new Models();
-        return factory;
-    }
-    void Models::registerFactoryFunction(const std::string& name,
-        function<bayesnet::BaseClassifier* (void)> classFactoryFunction)
-    {
-        // register the class factory function
-        functionRegistry[name] = classFactoryFunction;
-    }
-    shared_ptr<bayesnet::BaseClassifier> Models::create(const std::string& name)
-    {
-        bayesnet::BaseClassifier* instance = nullptr;
-
-        // find name in the registry and call factory method.
-        auto it = functionRegistry.find(name);
-        if (it != functionRegistry.end())
-            instance = it->second();
-        // wrap instance in a shared ptr and return
-        if (instance != nullptr)
-            return unique_ptr<bayesnet::BaseClassifier>(instance);
-        else
-            return nullptr;
-    }
-    std::vector<std::string> Models::getNames()
-    {
-        std::vector<std::string> names;
-        transform(functionRegistry.begin(), functionRegistry.end(), back_inserter(names),
-            [](const pair<std::string, function<bayesnet::BaseClassifier* (void)>>& pair) { return pair.first; });
-        return names;
-    }
-    std::string Models::tostring()
-    {
-        std::string result = "";
-        for (const auto& pair : functionRegistry) {
-            result += pair.first + ", ";
-        }
-        return "{" + result.substr(0, result.size() - 2) + "}";
-    }
-    Registrar::Registrar(const std::string& name, function<bayesnet::BaseClassifier* (void)> classFactoryFunction)
-    {
-        // register the class factory function 
-        Models::instance()->registerFactoryFunction(name, classFactoryFunction);
-    }
-}

src/Platform/Models.h

@@ -1,41 +0,0 @@
-#ifndef MODELS_H
-#define MODELS_H
-#include <map>
-#include "BaseClassifier.h"
-#include "AODE.h"
-#include "TAN.h"
-#include "KDB.h"
-#include "SPODE.h"
-#include "TANLd.h"
-#include "KDBLd.h"
-#include "SPODELd.h"
-#include "AODELd.h"
-#include "BoostAODE.h"
-#include "STree.h"
-#include "ODTE.h"
-#include "SVC.h"
-#include "RandomForest.h"
-namespace platform {
-    class Models {
-    private:
-        map<std::string, function<bayesnet::BaseClassifier* (void)>> functionRegistry;
-        static Models* factory; //singleton
-        Models() {};
-    public:
-        Models(Models&) = delete;
-        void operator=(const Models&) = delete;
-        // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
-        static Models* instance();
-        shared_ptr<bayesnet::BaseClassifier> create(const std::string& name);
-        void registerFactoryFunction(const std::string& name,
-            function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
-        std::vector<string> getNames();
-        std::string tostring();
-
-    };
-    class Registrar {
-    public:
-        Registrar(const std::string& className, function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
-    };
-}
-#endif

src/Platform/Paths.h

@@ -1,31 +0,0 @@
-#ifndef PATHS_H
-#define PATHS_H
-#include <string>
-#include <filesystem>
-#include "DotEnv.h"
-namespace platform {
-    class Paths {
-    public:
-        static std::string results() { return "results/"; }
-        static std::string hiddenResults() { return "hidden_results/"; }
-        static std::string excel() { return "excel/"; }
-        static std::string grid() { return "grid/"; }
-        static std::string datasets()
-        {
-            auto env = platform::DotEnv();
-            return env.get("source_data");
-        }
-        static void createPath(const std::string& path)
-        {
-            // Create directory if it does not exist
-            try {
-                std::filesystem::create_directory(path);
-            }
-            catch (std::exception& e) {
-                throw std::runtime_error("Could not create directory " + path);
-            }
-        }
-        static std::string excelResults() { return "some_results.xlsx"; }
-    };
-}
-#endif

src/Platform/ReportBase.cc

@@ -1,113 +0,0 @@
-#include <sstream>
-#include <locale>
-#include "Datasets.h"
-#include "ReportBase.h"
-#include "DotEnv.h"
-
-namespace platform {
-    ReportBase::ReportBase(json data_, bool compare) : data(data_), compare(compare), margin(0.1)
-    {
-        std::stringstream oss;
-        oss << "Better than ZeroR + " << std::setprecision(1) << fixed << margin * 100 << "%";
-        meaning = {
-            {Symbols::equal_best, "Equal to best"},
-            {Symbols::better_best, "Better than best"},
-            {Symbols::cross, "Less than or equal to ZeroR"},
-            {Symbols::upward_arrow, oss.str()}
-        };
-    }
-    std::string ReportBase::fromVector(const std::string& key)
-    {
-        std::stringstream oss;
-        std::string sep = "";
-        oss << "[";
-        for (auto& item : data[key]) {
-            oss << sep << item.get<double>();
-            sep = ", ";
-        }
-        oss << "]";
-        return oss.str();
-    }
-    std::string ReportBase::fVector(const std::string& title, const json& data, const int width, const int precision)
-    {
-        std::stringstream oss;
-        std::string sep = "";
-        oss << title << "[";
-        for (const auto& item : data) {
-            oss << sep << fixed << setw(width) << std::setprecision(precision) << item.get<double>();
-            sep = ", ";
-        }
-        oss << "]";
-        return oss.str();
-    }
-    void ReportBase::show()
-    {
-        header();
-        body();
-    }
-    std::string ReportBase::compareResult(const std::string& dataset, double result)
-    {
-        std::string status = " ";
-        if (compare) {
-            double best = bestResult(dataset, data["model"].get<std::string>());
-            if (result == best) {
-                status = Symbols::equal_best;
-            } else if (result > best) {
-                status = Symbols::better_best;
-            }
-        } else {
-            if (data["score_name"].get<std::string>() == "accuracy") {
-                auto dt = Datasets(false, Paths::datasets());
-                dt.loadDataset(dataset);
-                auto numClasses = dt.getNClasses(dataset);
-                if (numClasses == 2) {
-                    std::vector<int> distribution = dt.getClassesCounts(dataset);
-                    double nSamples = dt.getNSamples(dataset);
-                    std::vector<int>::iterator maxValue = max_element(distribution.begin(), distribution.end());
-                    double mark = *maxValue / nSamples * (1 + margin);
-                    if (mark > 1) {
-                        mark = 0.9995;
-                    }
-                    status = result < mark ? Symbols::cross : result > mark ? Symbols::upward_arrow : "=";
-                }
-            }
-        }
-        if (status != " ") {
-            auto item = summary.find(status);
-            if (item != summary.end()) {
-                summary[status]++;
-            } else {
-                summary[status] = 1;
-            }
-        }
-        return status;
-    }
-    double ReportBase::bestResult(const std::string& dataset, const std::string& model)
-    {
-        double value = 0.0;
-        if (bestResults.size() == 0) {
-            // try to load the best results
-            std::string score = data["score_name"];
-            replace(score.begin(), score.end(), '_', '-');
-            std::string fileName = "best_results_" + score + "_" + model + ".json";
-            ifstream resultData(Paths::results() + "/" + fileName);
-            if (resultData.is_open()) {
-                bestResults = json::parse(resultData);
-            } else {
-                existBestFile = false;
-            }
-        }
-        try {
-            value = bestResults.at(dataset).at(0);
-        }
-        catch (exception) {
-            value = 1.0;
-
-        }
-        return value;
-    }
-    bool ReportBase::getExistBestFile()
-    {
-        return existBestFile;
-    }
-}

src/Platform/ReportBase.h

@@ -1,36 +0,0 @@
-#ifndef REPORTBASE_H
-#define REPORTBASE_H
-#include <string>
-#include <iostream>
-#include "Paths.h"
-#include "Symbols.h"
-#include <nlohmann/json.hpp>
-
-using json = nlohmann::json;
-namespace platform {
-
-    class ReportBase {
-    public:
-        explicit ReportBase(json data_, bool compare);
-        virtual ~ReportBase() = default;
-        void show();
-    protected:
-        json data;
-        std::string fromVector(const std::string& key);
-        std::string fVector(const std::string& title, const json& data, const int width, const int precision);
-        bool getExistBestFile();
-        virtual void header() = 0;
-        virtual void body() = 0;
-        virtual void showSummary() = 0;
-        std::string compareResult(const std::string& dataset, double result);
-        std::map<std::string, int> summary;
-        double margin;
-        std::map<std::string, std::string> meaning;
-        bool compare;
-    private:
-        double bestResult(const std::string& dataset, const std::string& model);
-        json bestResults;
-        bool existBestFile = true;
-    };
-};
-#endif

src/Platform/ReportConsole.cc

@@ -1,114 +0,0 @@
-#include <iostream>
-#include <sstream>
-#include <locale>
-#include "ReportConsole.h"
-#include "BestScore.h"
-#include "CLocale.h"
-
-namespace platform {
-    std::string ReportConsole::headerLine(const std::string& text, int utf = 0)
-    {
-        int n = MAXL - text.length() - 3;
-        n = n < 0 ? 0 : n;
-        return "* " + text + std::string(n + utf, ' ') + "*\n";
-    }
-
-    void ReportConsole::header()
-    {
-        std::stringstream oss;
-        std::cout << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
-        std::cout << headerLine(
-            "Report " + data["model"].get<std::string>() + " ver. " + data["version"].get<std::string>()
-            + " with " + std::to_string(data["folds"].get<int>()) + " Folds cross validation and " + std::to_string(data["seeds"].size())
-            + " random seeds. " + data["date"].get<std::string>() + " " + data["time"].get<std::string>()
-        );
-        std::cout << headerLine(data["title"].get<std::string>());
-        std::cout << headerLine("Random seeds: " + fromVector("seeds") + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False"));
-        oss << "Execution took  " << std::setprecision(2) << std::fixed << data["duration"].get<float>()
-            << " seconds,   " << data["duration"].get<float>() / 3600 << " hours, on " << data["platform"].get<std::string>();
-        std::cout << headerLine(oss.str());
-        std::cout << headerLine("Score is " + data["score_name"].get<std::string>());
-        std::cout << std::string(MAXL, '*') << std::endl;
-        std::cout << std::endl;
-    }
-    void ReportConsole::body()
-    {
-        auto tmp = ConfigLocale();
-        int maxHyper = 15;
-        int maxDataset = 7;
-        for (const auto& r : data["results"]) {
-            maxHyper = std::max(maxHyper, (int)r["hyperparameters"].dump().size());
-            maxDataset = std::max(maxDataset, (int)r["dataset"].get<std::string>().size());
-
-        }
-        std::cout << Colors::GREEN() << " #  " << std::setw(maxDataset) << std::left << "Dataset" << " Sampl. Feat. Cls Nodes     Edges     States    Score           Time                Hyperparameters" << std::endl;
-        std::cout << "=== " << std::string(maxDataset, '=') << " ====== ===== === ========= ========= ========= =============== =================== " << std::string(maxHyper, '=') << std::endl;
-        json lastResult;
-        double totalScore = 0.0;
-        bool odd = true;
-        int index = 0;
-        for (const auto& r : data["results"]) {
-            if (selectedIndex != -1 && index != selectedIndex) {
-                index++;
-                continue;
-            }
-            auto color = odd ? Colors::CYAN() : Colors::BLUE();
-            std::cout << color;
-            std::cout << std::setw(3) << std::right << index++ << " ";
-            std::cout << std::setw(maxDataset) << std::left << r["dataset"].get<std::string>() << " ";
-            std::cout << std::setw(6) << std::right << r["samples"].get<int>() << " ";
-            std::cout << std::setw(5) << std::right << r["features"].get<int>() << " ";
-            std::cout << std::setw(3) << std::right << r["classes"].get<int>() << " ";
-            std::cout << std::setw(9) << std::setprecision(2) << std::fixed << r["nodes"].get<float>() << " ";
-            std::cout << std::setw(9) << std::setprecision(2) << std::fixed << r["leaves"].get<float>() << " ";
-            std::cout << std::setw(9) << std::setprecision(2) << std::fixed << r["depth"].get<float>() << " ";
-            std::cout << std::setw(8) << std::right << std::setprecision(6) << std::fixed << r["score"].get<double>() << "±" << std::setw(6) << std::setprecision(4) << std::fixed << r["score_std"].get<double>();
-            const std::string status = compareResult(r["dataset"].get<std::string>(), r["score"].get<double>());
-            std::cout << status;
-            std::cout << std::setw(12) << std::right << std::setprecision(6) << std::fixed << r["time"].get<double>() << "±" << std::setw(6) << std::setprecision(4) << std::fixed << r["time_std"].get<double>() << " ";
-            std::cout << r["hyperparameters"].dump();
-            std::cout << std::endl;
-            std::cout << std::flush;
-            lastResult = r;
-            totalScore += r["score"].get<double>();
-            odd = !odd;
-        }
-        if (data["results"].size() == 1 || selectedIndex != -1) {
-            std::cout << std::string(MAXL, '*') << std::endl;
-            std::cout << headerLine(fVector("Train scores: ", lastResult["scores_train"], 14, 12));
-            std::cout << headerLine(fVector("Test  scores: ", lastResult["scores_test"], 14, 12));
-            std::cout << headerLine(fVector("Train  times: ", lastResult["times_train"], 10, 3));
-            std::cout << headerLine(fVector("Test   times: ", lastResult["times_test"], 10, 3));
-            std::cout << std::string(MAXL, '*') << std::endl;
-        } else {
-            footer(totalScore);
-        }
-    }
-    void ReportConsole::showSummary()
-    {
-        for (const auto& item : summary) {
-            std::stringstream oss;
-            oss << std::setw(3) << std::left << item.first;
-            oss << std::setw(3) << std::right << item.second << " ";
-            oss << std::left << meaning.at(item.first);
-            std::cout << headerLine(oss.str(), 2);
-        }
-    }
-
-    void ReportConsole::footer(double totalScore)
-    {
-        std::cout << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
-        showSummary();
-        auto score = data["score_name"].get<std::string>();
-        auto best = BestScore::getScore(score);
-        if (best.first != "") {
-            std::stringstream oss;
-            oss << score << " compared to " << best.first << " .:  " << totalScore / best.second;
-            std::cout << headerLine(oss.str());
-        }
-        if (!getExistBestFile() && compare) {
-            std::cout << headerLine("*** Best Results File not found. Couldn't compare any result!");
-        }
-        std::cout << std::string(MAXL, '*') << std::endl << Colors::RESET();
-    }
-}

src/Platform/ReportConsole.h

@@ -1,22 +0,0 @@
-#ifndef REPORTCONSOLE_H
-#define REPORTCONSOLE_H
-#include <string>
-#include "ReportBase.h"
-#include "Colors.h"
-
-namespace platform {
-    const int MAXL = 133;
-    class ReportConsole : public ReportBase {
-    public:
-        explicit ReportConsole(json data_, bool compare = false, int index = -1) : ReportBase(data_, compare), selectedIndex(index) {};
-        virtual ~ReportConsole() = default;
-    private:
-        int selectedIndex;
-        std::string headerLine(const std::string& text, int utf);
-        void header() override;
-        void body() override;
-        void footer(double totalScore);
-        void showSummary() override;
-    };
-};
-#endif

src/Platform/ReportExcel.cc

@@ -1,180 +0,0 @@
-#include <sstream>
-#include <locale>
-#include "ReportExcel.h"
-#include "BestScore.h"
-
-
-namespace platform {
-
-    ReportExcel::ReportExcel(json data_, bool compare, lxw_workbook* workbook, lxw_worksheet* worksheet) : ReportBase(data_, compare), ExcelFile(workbook, worksheet)
-    {
-        createFile();
-    }
-
-    void ReportExcel::formatColumns()
-    {
-        worksheet_freeze_panes(worksheet, 6, 1);
-        std::vector<int> columns_sizes = { 22, 10, 9, 7, 12, 12, 12, 12, 12, 3, 15, 12, 23 };
-        for (int i = 0; i < columns_sizes.size(); ++i) {
-            worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
-        }
-    }
-    void ReportExcel::createWorksheet()
-    {
-        const std::string name = data["model"].get<std::string>();
-        std::string suffix = "";
-        std::string efectiveName;
-        int num = 1;
-        // Create a sheet with the name of the model
-        while (true) {
-            efectiveName = name + suffix;
-            if (workbook_get_worksheet_by_name(workbook, efectiveName.c_str())) {
-                suffix = std::to_string(++num);
-            } else {
-                worksheet = workbook_add_worksheet(workbook, efectiveName.c_str());
-                break;
-            }
-            if (num > 100) {
-                throw std::invalid_argument("Couldn't create sheet " + efectiveName);
-            }
-        }
-    }
-
-    void ReportExcel::createFile()
-    {
-        if (workbook == NULL) {
-            workbook = workbook_new((Paths::excel() + Paths::excelResults()).c_str());
-        }
-        if (worksheet == NULL) {
-            createWorksheet();
-        }
-        setProperties(data["title"].get<std::string>());
-        createFormats();
-        formatColumns();
-    }
-
-    void ReportExcel::closeFile()
-    {
-        workbook_close(workbook);
-    }
-
-    void ReportExcel::header()
-    {
-        std::locale mylocale(std::cout.getloc(), new separated);
-        std::locale::global(mylocale);
-        std::cout.imbue(mylocale);
-        std::stringstream oss;
-        std::string message = data["model"].get<std::string>() + " ver. " + data["version"].get<std::string>() + " " +
-            data["language"].get<std::string>() + " ver. " + data["language_version"].get<std::string>() +
-            " with " + std::to_string(data["folds"].get<int>()) + " Folds cross validation and " + std::to_string(data["seeds"].size()) +
-            " random seeds. " + data["date"].get<std::string>() + " " + data["time"].get<std::string>();
-        worksheet_merge_range(worksheet, 0, 0, 0, 12, message.c_str(), styles["headerFirst"]);
-        worksheet_merge_range(worksheet, 1, 0, 1, 12, data["title"].get<std::string>().c_str(), styles["headerRest"]);
-        worksheet_merge_range(worksheet, 2, 0, 3, 0, ("Score is " + data["score_name"].get<std::string>()).c_str(), styles["headerRest"]);
-        worksheet_merge_range(worksheet, 2, 1, 3, 3, "Execution time", styles["headerRest"]);
-        oss << std::setprecision(2) << std::fixed << data["duration"].get<float>() << " s";
-        worksheet_merge_range(worksheet, 2, 4, 2, 5, oss.str().c_str(), styles["headerRest"]);
-        oss.str("");
-        oss.clear();
-        oss << std::setprecision(2) << std::fixed << data["duration"].get<float>() / 3600 << " h";
-        worksheet_merge_range(worksheet, 3, 4, 3, 5, oss.str().c_str(), styles["headerRest"]);
-        worksheet_merge_range(worksheet, 2, 6, 3, 7, "Platform", styles["headerRest"]);
-        worksheet_merge_range(worksheet, 2, 8, 3, 9, data["platform"].get<std::string>().c_str(), styles["headerRest"]);
-        worksheet_merge_range(worksheet, 2, 10, 2, 12, ("Random seeds: " + fromVector("seeds")).c_str(), styles["headerSmall"]);
-        oss.str("");
-        oss.clear();
-        oss << "Stratified: " << (data["stratified"].get<bool>() ? "True" : "False");
-        worksheet_merge_range(worksheet, 3, 10, 3, 11, oss.str().c_str(), styles["headerSmall"]);
-        oss.str("");
-        oss.clear();
-        oss << "Discretized: " << (data["discretized"].get<bool>() ? "True" : "False");
-        worksheet_write_string(worksheet, 3, 12, oss.str().c_str(), styles["headerSmall"]);
-    }
-
-    void ReportExcel::body()
-    {
-        auto head = std::vector<std::string>(
-            { "Dataset", "Samples", "Features", "Classes", "Nodes", "Edges", "States", "Score", "Score Std.", "St.", "Time",
-             "Time Std.", "Hyperparameters" });
-        int col = 0;
-        for (const auto& item : head) {
-            writeString(5, col++, item, "bodyHeader");
-        }
-        row = 6;
-        col = 0;
-        int hypSize = 22;
-        json lastResult;
-        double totalScore = 0.0;
-        std::string hyperparameters;
-        for (const auto& r : data["results"]) {
-            writeString(row, col, r["dataset"].get<std::string>(), "text");
-            writeInt(row, col + 1, r["samples"].get<int>(), "ints");
-            writeInt(row, col + 2, r["features"].get<int>(), "ints");
-            writeInt(row, col + 3, r["classes"].get<int>(), "ints");
-            writeDouble(row, col + 4, r["nodes"].get<float>(), "floats");
-            writeDouble(row, col + 5, r["leaves"].get<float>(), "floats");
-            writeDouble(row, col + 6, r["depth"].get<double>(), "floats");
-            writeDouble(row, col + 7, r["score"].get<double>(), "result");
-            writeDouble(row, col + 8, r["score_std"].get<double>(), "result");
-            const std::string status = compareResult(r["dataset"].get<std::string>(), r["score"].get<double>());
-            writeString(row, col + 9, status, "textCentered");
-            writeDouble(row, col + 10, r["time"].get<double>(), "time");
-            writeDouble(row, col + 11, r["time_std"].get<double>(), "time");
-            hyperparameters = r["hyperparameters"].dump();
-            if (hyperparameters.size() > hypSize) {
-                hypSize = hyperparameters.size();
-            }
-            writeString(row, col + 12, hyperparameters, "text");
-            lastResult = r;
-            totalScore += r["score"].get<double>();
-            row++;
-        }
-        // Set the right column width of hyperparameters with the maximum length
-        worksheet_set_column(worksheet, 12, 12, hypSize + 5, NULL);
-        // Show totals if only one dataset is present in the result
-        if (data["results"].size() == 1) {
-            for (const std::string& group : { "scores_train", "scores_test", "times_train", "times_test" }) {
-                row++;
-                col = 1;
-                writeString(row, col, group, "text");
-                for (double item : lastResult[group]) {
-                    std::string style = group.find("scores") != std::string::npos ? "result" : "time";
-                    writeDouble(row, ++col, item, style);
-                }
-            }
-            // Set with of columns to show those totals completely
-            worksheet_set_column(worksheet, 1, 1, 12, NULL);
-            for (int i = 2; i < 7; ++i) {
-                // doesn't work with from col to col, so...
-                worksheet_set_column(worksheet, i, i, 15, NULL);
-            }
-        } else {
-            footer(totalScore, row);
-        }
-    }
-
-    void ReportExcel::showSummary()
-    {
-        for (const auto& item : summary) {
-            worksheet_write_string(worksheet, row + 2, 1, item.first.c_str(), styles["summaryStyle"]);
-            worksheet_write_number(worksheet, row + 2, 2, item.second, styles["summaryStyle"]);
-            worksheet_merge_range(worksheet, row + 2, 3, row + 2, 5, meaning.at(item.first).c_str(), styles["summaryStyle"]);
-            row += 1;
-        }
-    }
-
-    void ReportExcel::footer(double totalScore, int row)
-    {
-        showSummary();
-        row += 4 + summary.size();
-        auto score = data["score_name"].get<std::string>();
-        auto best = BestScore::getScore(score);
-        if (best.first != "") {
-            worksheet_merge_range(worksheet, row, 1, row, 5, (score + " compared to " + best.first + " .:").c_str(), efectiveStyle("text"));
-            writeDouble(row, 6, totalScore / best.second, "result");
-        }
-        if (!getExistBestFile() && compare) {
-            worksheet_write_string(worksheet, row + 1, 0, "*** Best Results File not found. Couldn't compare any result!", styles["summaryStyle"]);
-        }
-    }
-}

src/Platform/ReportExcel.h

@@ -1,24 +0,0 @@
-#ifndef REPORTEXCEL_H
-#define REPORTEXCEL_H
-#include<map>
-#include "xlsxwriter.h"
-#include "ReportBase.h"
-#include "ExcelFile.h"
-#include "Colors.h"
-namespace platform {
-    class ReportExcel : public ReportBase, public ExcelFile {
-    public:
-        explicit ReportExcel(json data_, bool compare, lxw_workbook* workbook, lxw_worksheet* worksheet = NULL);
-    private:
-        void formatColumns();
-        void createFile();
-        void createWorksheet();
-        void closeFile();
-        void header() override;
-        void body() override;
-        void showSummary() override;
-        void footer(double totalScore, int row);
-
-    };
-};
-#endif // !REPORTEXCEL_H

src/Platform/Result.cc

@@ -1,58 +0,0 @@
-#include "Result.h"
-#include "BestScore.h"
-#include <filesystem>
-#include <fstream>
-#include <sstream>
-#include "Colors.h"
-#include "DotEnv.h"
-#include "CLocale.h"
-
-namespace platform {
-    Result::Result(const std::string& path, const std::string& filename)
-        : path(path)
-        , filename(filename)
-    {
-        auto data = load();
-        date = data["date"];
-        score = 0;
-        for (const auto& result : data["results"]) {
-            score += result["score"].get<double>();
-        }
-        scoreName = data["score_name"];
-        auto best = BestScore::getScore(scoreName);
-        if (best.first != "") {
-            score /= best.second;
-        }
-        title = data["title"];
-        duration = data["duration"];
-        model = data["model"];
-        complete = data["results"].size() > 1;
-    }
-
-    json Result::load() const
-    {
-        std::ifstream resultData(path + "/" + filename);
-        if (resultData.is_open()) {
-            json data = json::parse(resultData);
-            return data;
-        }
-        throw std::invalid_argument("Unable to open result file. [" + path + "/" + filename + "]");
-    }
-
-    std::string Result::to_string(int maxModel) const
-    {
-        auto tmp = ConfigLocale();
-        std::stringstream oss;
-        double durationShow = duration > 3600 ? duration / 3600 : duration > 60 ? duration / 60 : duration;
-        std::string durationUnit = duration > 3600 ? "h" : duration > 60 ? "m" : "s";
-        oss << date << " ";
-        oss << std::setw(maxModel) << std::left << model << " ";
-        oss << std::setw(11) << std::left << scoreName << " ";
-        oss << std::right << std::setw(11) << std::setprecision(7) << std::fixed << score << " ";
-        auto completeString = isComplete() ? "C" : "P";
-        oss << std::setw(1) << " " << completeString << "  ";
-        oss << std::setw(7) << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit << " ";
-        oss << std::setw(50) << std::left << title << " ";
-        return  oss.str();
-    }
-}

src/Platform/Result.h

@@ -1,36 +0,0 @@
-#ifndef RESULT_H
-#define RESULT_H
-#include <map>
-#include <vector>
-#include <string>
-#include <nlohmann/json.hpp>
-namespace platform {
-    using json = nlohmann::json;
-
-    class Result {
-    public:
-        Result(const std::string& path, const std::string& filename);
-        json load() const;
-        std::string to_string(int maxModel) const;
-        std::string getFilename() const { return filename; };
-        std::string getDate() const { return date; };
-        double getScore() const { return score; };
-        std::string getTitle() const { return title; };
-        double getDuration() const { return duration; };
-        std::string getModel() const { return model; };
-        std::string getScoreName() const { return scoreName; };
-        bool isComplete() const { return complete; };
-    private:
-        std::string path;
-        std::string filename;
-        std::string date;
-        double score;
-        std::string title;
-        double duration;
-        std::string model;
-        std::string scoreName;
-        bool complete;
-    };
-};
-
-#endif

src/Platform/Results.cc

@@ -1,74 +0,0 @@
-#include "Results.h"
-#include <algorithm>
-
-namespace platform {
-    Results::Results(const std::string& path, const std::string& model, const std::string& score, bool complete, bool partial) :
-        path(path), model(model), scoreName(score), complete(complete), partial(partial)
-    {
-        load();
-        if (!files.empty()) {
-            maxModel = (*max_element(files.begin(), files.end(), [](const Result& a, const Result& b) { return a.getModel().size() < b.getModel().size(); })).getModel().size();
-        } else {
-            maxModel = 0;
-        }
-    };
-    void Results::load()
-    {
-        using std::filesystem::directory_iterator;
-        for (const auto& file : directory_iterator(path)) {
-            auto filename = file.path().filename().string();
-            if (filename.find(".json") != std::string::npos && filename.find("results_") == 0) {
-                auto result = Result(path, filename);
-                bool addResult = true;
-                if (model != "any" && result.getModel() != model || scoreName != "any" && scoreName != result.getScoreName() || complete && !result.isComplete() || partial && result.isComplete())
-                    addResult = false;
-                if (addResult)
-                    files.push_back(result);
-            }
-        }
-    }
-    void Results::hideResult(int index, const std::string& pathHidden)
-    {
-        auto filename = files.at(index).getFilename();
-        rename((path + "/" + filename).c_str(), (pathHidden + "/" + filename).c_str());
-        files.erase(files.begin() + index);
-    }
-    void Results::deleteResult(int index)
-    {
-        auto filename = files.at(index).getFilename();
-        remove((path + "/" + filename).c_str());
-        files.erase(files.begin() + index);
-    }
-    int Results::size() const
-    {
-        return files.size();
-    }
-    void Results::sortDate()
-    {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
-            return a.getDate() > b.getDate();
-            });
-    }
-    void Results::sortModel()
-    {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
-            return a.getModel() > b.getModel();
-            });
-    }
-    void Results::sortDuration()
-    {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
-            return a.getDuration() > b.getDuration();
-            });
-    }
-    void Results::sortScore()
-    {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
-            return a.getScore() > b.getScore();
-            });
-    }
-    bool Results::empty() const
-    {
-        return files.empty();
-    }
-}

src/Platform/Results.h

@@ -1,38 +0,0 @@
-#ifndef RESULTS_H
-#define RESULTS_H
-#include <map>
-#include <vector>
-#include <string>
-#include <nlohmann/json.hpp>
-#include "Result.h"
-namespace platform {
-    using json = nlohmann::json;
-
-    class Results {
-    public:
-        Results(const std::string& path, const std::string& model, const std::string& score, bool complete, bool partial);
-        void sortDate();
-        void sortScore();
-        void sortModel();
-        void sortDuration();
-        int maxModelSize() const { return maxModel; };
-        void hideResult(int index, const std::string& pathHidden);
-        void deleteResult(int index);
-        int size() const;
-        bool empty() const;
-        std::vector<Result>::iterator begin() { return files.begin(); };
-        std::vector<Result>::iterator end() { return files.end(); };
-        Result& at(int index) { return files.at(index); };
-    private:
-        std::string path;
-        std::string model;
-        std::string scoreName;
-        bool complete;
-        bool partial;
-        int maxModel;
-        std::vector<Result> files;
-        void load(); // Loads the list of results
-    };
-};
-
-#endif