rmontanana/BayesNet
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Merge pull request #1 from rmontanana/library

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Ricardo Montañana Gómez 2024-01-07 20:01:37 +01:00 committed by GitHub
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31
.clang-uml
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@ -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 **)

8
.gitmodules vendored
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@ -18,8 +18,6 @@
url = https://github.com/nlohmann/json.git url = https://github.com/nlohmann/json.git
master = master master = master
update = merge update = merge
[submodule "lib/libxlsxwriter"] [submodule "lib/folding"]
path = lib/libxlsxwriter path = lib/folding
url = https://github.com/jmcnamara/libxlsxwriter.git url = https://github.com/rmontanana/folding
main = main
update = merge

32
CMakeLists.txt
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@ -1,7 +1,7 @@
cmake_minimum_required(VERSION 3.20) cmake_minimum_required(VERSION 3.20)
project(BayesNet project(BayesNet
VERSION 0.2.0 VERSION 1.0.0
DESCRIPTION "Bayesian Network and basic classifiers Library." DESCRIPTION "Bayesian Network and basic classifiers Library."
HOMEPAGE_URL "https://github.com/rmontanana/bayesnet" HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
LANGUAGES CXX LANGUAGES CXX
@ -30,27 +30,6 @@ SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
option(ENABLE_CLANG_TIDY "Enable to add clang tidy." OFF) option(ENABLE_CLANG_TIDY "Enable to add clang tidy." OFF)
option(ENABLE_TESTING "Unit testing build" OFF) option(ENABLE_TESTING "Unit testing build" OFF)
option(CODE_COVERAGE "Collect coverage from test library" OFF) option(CODE_COVERAGE "Collect coverage from test library" OFF)
option(MPI_ENABLED "Enable MPI options" ON)
if (MPI_ENABLED)
find_package(MPI REQUIRED)
message("MPI_CXX_LIBRARIES=${MPI_CXX_LIBRARIES}")
message("MPI_CXX_INCLUDE_DIRS=${MPI_CXX_INCLUDE_DIRS}")
endif (MPI_ENABLED)
# Boost Library
set(Boost_USE_STATIC_LIBS OFF)
set(Boost_USE_MULTITHREADED ON)
set(Boost_USE_STATIC_RUNTIME OFF)
find_package(Boost 1.66.0 REQUIRED COMPONENTS python3 numpy3)
if(Boost_FOUND)
message("Boost_INCLUDE_DIRS=${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 # CMakes modules
# -------------- # --------------
@ -76,23 +55,14 @@ add_git_submodule("lib/mdlp")
add_git_submodule("lib/argparse") add_git_submodule("lib/argparse")
add_git_submodule("lib/json") 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 # Subdirectories
# -------------- # --------------
add_subdirectory(config) add_subdirectory(config)
add_subdirectory(lib/Files) add_subdirectory(lib/Files)
add_subdirectory(src/BayesNet) add_subdirectory(src/BayesNet)
add_subdirectory(src/Platform)
add_subdirectory(src/PyClassifiers)
add_subdirectory(sample)
file(GLOB BayesNet_HEADERS CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/BayesNet/*.h ${BayesNet_SOURCE_DIR}/BayesNet/*.h) 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 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)
# Testing # Testing
# ------- # -------

46
Makefile
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@ -1,11 +1,11 @@
SHELL := /bin/bash SHELL := /bin/bash
.DEFAULT_GOAL := help .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_release = build_release
f_debug = build_debug f_debug = build_debug
app_targets = b_best b_list b_main b_manage b_grid app_targets = BayesNet
test_targets = unit_tests_bayesnet unit_tests_platform test_targets = unit_tests_bayesnet
n_procs = -j 16 n_procs = -j 16
define ClearTests define ClearTests
@ -31,37 +31,22 @@ setup: ## Install dependencies for tests and coverage
pip install gcovr; \ pip install gcovr; \
fi 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) dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
@echo ">>> Creating dependency graph diagram of the project..."; @echo ">>> Creating dependency graph diagram of the project...";
$(MAKE) debug $(MAKE) debug
cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
buildd: ## Build the debug targets buildd: ## Build the debug targets
cmake --build $(f_debug) -t $(app_targets) BayesNetSample $(n_procs) cmake --build $(f_debug) -t $(app_targets) $(n_procs)
buildr: ## Build the release targets buildr: ## Build the release targets
cmake --build $(f_release) -t $(app_targets) BayesNetSample $(n_procs) cmake --build $(f_release) -t $(app_targets) $(n_procs)
clean: ## Clean the tests info clean: ## Clean the tests info
@echo ">>> Cleaning Debug BayesNet tests..."; @echo ">>> Cleaning Debug BayesNet tests...";
$(call ClearTests) $(call ClearTests)
@echo ">>> Done"; @echo ">>> Done";
clang-uml: ## Create uml class and sequence diagrams
clang-uml -p --add-compile-flag -I /usr/lib/gcc/x86_64-redhat-linux/8/include/
debug: ## Build a debug version of the project debug: ## Build a debug version of the project
@echo ">>> Building Debug BayesNet..."; @echo ">>> Building Debug BayesNet...";
@if [ -d ./$(f_debug) ]; then rm -rf ./$(f_debug); fi @if [ -d ./$(f_debug) ]; then rm -rf ./$(f_debug); fi
@ -89,27 +74,10 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
done done
@echo ">>> Done"; @echo ">>> Done";
opt = ""
testp: ## Run platform tests (opt="-s") to verbose output the tests, (opt="-c='Stratified Fold Test'") to run only that section
@echo ">>> Running Platform tests...";
@$(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 $(opt) ; fi ;
@echo ">>> Done";
coverage: ## Run tests and generate coverage report (build/index.html) coverage: ## Run tests and generate coverage report (build/index.html)
@echo ">>> Building tests with coverage..."; @echo ">>> Building tests with coverage..."
@$(MAKE) test @$(MAKE) test
@cd $(f_debug) ; \ @gcovr $(f_debug)/tests
gcovr --config ../gcovr.cfg tests ;
@echo ">>> Done"; @echo ">>> Done";

77
README.md
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@ -2,90 +2,21 @@
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
Bayesian Network Classifier with libtorch from scratch Bayesian Network Classifiers using libtorch from scratch
## 0. Setup
Before compiling BayesNet.
### Miniconda
To be able to run Python Classifiers such as STree, ODTE, SVC, etc. it is needed to install Miniconda. To do so, download the installer from [Miniconda](https://docs.conda.io/en/latest/miniconda.html) and run it. It is recommended to install it in the home folder.
In Linux sometimes the library libstdc++ is mistaken from the miniconda installation and produces the next message when running the b_xxxx executables:
```bash
libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by b_xxxx)
```
The solution is to erase the libstdc++ library from the miniconda installation:
### MPI
In Linux just install openmpi & openmpi-devel packages. Only if cmake can't find openmpi installation (like in Oracle Linux) set the following variable:
```bash
export MPI_HOME="/usr/lib64/openmpi"
```
In Mac OS X, install mpich with brew and if cmake doesn't find it, edit mpicxx wrapper to remove the ",-commons,use_dylibs" from final_ldflags
```bash
vi /opt/homebrew/bin/mpicx
```
### 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
```
### Release ### Release
```bash ```bash
make release make release
make buildr
``` ```
### Debug & Tests ### Debug & Tests
```bash ```bash
make debug make debug
make test
make coverage
``` ```
## 1. Introduction ## 1. Introduction

1
config/config.h.in
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@ -11,3 +11,4 @@ static constexpr std::string_view project_name = "@PROJECT_NAME@";
static constexpr std::string_view project_version = "@PROJECT_VERSION@"; static constexpr std::string_view project_version = "@PROJECT_VERSION@";
static constexpr std::string_view project_description = "@PROJECT_DESCRIPTION@"; static constexpr std::string_view project_description = "@PROJECT_DESCRIPTION@";
static constexpr std::string_view git_sha = "@GIT_SHA@"; static constexpr std::string_view git_sha = "@GIT_SHA@";
static constexpr std::string_view data_path = "@BayesNet_SOURCE_DIR@/tests/data/";

25
data/glass.net
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@ -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

645
data/mfeat-factors-kdb2.net
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@ -1,645 +0,0 @@
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data/mfeat-factors-kdb3.net
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@ -1,859 +0,0 @@
class att215
class att25
class att131
class att95
class att122
class att17
class att28
class att5
class att121
class att214
class att197
class att116
class att182
class att60
class att168
class att178
class att206
class att89
class att77
class att209
class att73
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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
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class att170
class att160
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class att203
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class att71
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class att138
class att83
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class att163
class att49
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859
data/mfeat-factors.net
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@ -1,859 +0,0 @@
class att215
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att118 att70
att118 att10
att118 att202
att78 att198
att213 att189
att213 att129
att213 att69
att213 att81
att38 att50
att38 att26
att174 att54
att174 att66
att174 att30
att189 att86
att189 att129
att189 att69
att189 att81
att189 att153
att189 att117
att189 att9
att189 att45
att189 att141
att189 att105
att70 att34
att70 att154
att179 att59
att59 att47
att59 att191
att59 att119
att79 att86
att79 att151
att79 att139
att79 att187
att79 att127
att79 att103
att79 att43
att79 att193
att79 att91
att79 att19
att124 att64
att54 att114
att54 att30
att54 att6
att191 att119
att86 att194
att56 att44
att56 att152
att56 att50
att56 att188
att56 att26
att56 att200
att56 att104
att56 att140
att56 att146
att56 att194
att56 att8
att56 att2
att56 att133
att56 att1
att151 att139
att66 att30
att173 att125
att173 att113
att173 att185
att44 att152
att44 att50
att44 att188
att44 att200
att44 att104
att44 att140
att44 att194
att44 att212
att44 att1
att139 att26
att139 att99
att139 att103
att139 att43
att139 att91
att139 att31
att139 att199
att139 att7
att216 att204
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
att188 att212
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
att180 att48
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
att144 att192
att144 att120
att185 att53
att185 att65
att185 att29
att199 att19
att199 att7
att199 att67
att199 att55
att199 att109
att65 att29
att7 att67
att67 att55
att109 att181

2
gcovr.cfg
View File

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

162
grid_stree.json
View File

@ -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
}
}

1
lib/folding Submodule

@ -0,0 +1 @@
Subproject commit a3a2977996223b709c0f9149772c01a5f771e391

1
lib/libxlsxwriter

@ -1 +0,0 @@
Subproject commit 29355a0887475488c7cc470ad43cc867fcfa92e2

10
sample/CMakeLists.txt
View File

@ -1,10 +0,0 @@
include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
include_directories(${BayesNet_SOURCE_DIR}/src/PyClassifiers)
include_directories(${Python3_INCLUDE_DIRS})
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}" PyWrap)

235
sample/sample.cc
View File

@ -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::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::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()->tostring())
.action([](const 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()->tostring());
}
);
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::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_string = stratified ? " Stratified" : "";
std::cout << nFolds << " Folds" << stratified_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;
}

6
src/BayesNet/BoostAODE.cc
View File

@ -2,12 +2,10 @@
#include <functional> #include <functional>
#include <limits.h> #include <limits.h>
#include "BoostAODE.h" #include "BoostAODE.h"
#include "Colors.h"
#include "Folding.h"
#include "Paths.h"
#include "CFS.h" #include "CFS.h"
#include "FCBF.h" #include "FCBF.h"
#include "IWSS.h" #include "IWSS.h"
#include "folding.hpp"
namespace bayesnet { namespace bayesnet {
BoostAODE::BoostAODE() : Ensemble() BoostAODE::BoostAODE() : Ensemble()
@ -24,7 +22,7 @@ namespace bayesnet {
auto y_ = dataset.index({ -1, "..." }); auto y_ = dataset.index({ -1, "..." });
if (convergence) { if (convergence) {
// Prepare train & validation sets from train data // 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); dataset_ = torch::clone(dataset);
// save input dataset // save input dataset
auto [train, test] = fold.getFold(0); auto [train, test] = fold.getFold(0);

7
src/BayesNet/CMakeLists.txt
View File

@ -1,14 +1,13 @@
include_directories( include_directories(
${BayesNet_SOURCE_DIR}/lib/mdlp ${BayesNet_SOURCE_DIR}/lib/mdlp
${BayesNet_SOURCE_DIR}/lib/Files ${BayesNet_SOURCE_DIR}/lib/Files
${BayesNet_SOURCE_DIR}/lib/folding
${BayesNet_SOURCE_DIR}/lib/json/include ${BayesNet_SOURCE_DIR}/lib/json/include
${BayesNet_SOURCE_DIR}/src/BayesNet ${BayesNet_SOURCE_DIR}/src/BayesNet
${BayesNet_SOURCE_DIR}/src/Platform ${CMAKE_BINARY_DIR}/configured_files/include
${BayesNet_SOURCE_DIR}/src/PyClassifiers
${Python3_INCLUDE_DIRS}
) )
add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 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 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 ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc) Mst.cc Proposal.cc CFS.cc FCBF.cc IWSS.cc FeatureSelect.cc )
target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}") target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

3
src/BayesNet/Network.h
View File

@ -3,6 +3,7 @@
#include "Node.h" #include "Node.h"
#include <map> #include <map>
#include <vector> #include <vector>
#include "config.h"
namespace bayesnet { namespace bayesnet {
class Network { class Network {
@ -56,7 +57,7 @@ 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 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 initialize();
void dump_cpt() const; void dump_cpt() const;
inline std::string version() { return "0.2.0"; } inline std::string version() { return { project_version.begin(), project_version.end() }; }
}; };
} }
#endif #endif

343
src/Platform/BestResults.cc
View File

@ -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;
}
}

36
src/Platform/BestResults.h
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#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

300
src/Platform/BestResultsExcel.cc
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#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++;
}
}
}

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src/Platform/BestResultsExcel.h
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#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

28
src/Platform/BestScore.h
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#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

22
src/Platform/CLocale.h
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#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

25
src/Platform/CMakeLists.txt
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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)
include_directories(
${BayesNet_SOURCE_DIR}/src/BayesNet
${BayesNet_SOURCE_DIR}/src/Platform
${BayesNet_SOURCE_DIR}/src/PyClassifiers
${BayesNet_SOURCE_DIR}/lib/Files
${BayesNet_SOURCE_DIR}/lib/mdlp
${BayesNet_SOURCE_DIR}/lib/argparse/include
${BayesNet_SOURCE_DIR}/lib/json/include
${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include
${Python3_INCLUDE_DIRS}
${MPI_CXX_INCLUDE_DIRS}
${CMAKE_BINARY_DIR}/configured_files/include
)
target_link_libraries(b_best Boost::boost "${XLSXWRITER_LIB}" "${TORCH_LIBRARIES}" ArffFiles mdlp)
target_link_libraries(b_grid BayesNet PyWrap ${MPI_CXX_LIBRARIES})
target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
target_link_libraries(b_main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}" PyWrap)
target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)

15
src/Platform/Colors.h
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#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 IBLUE() { return "\033[0;94m"; }
static std::string RESET() { return "\033[0m"; }
};
#endif // COLORS_H

87
src/Platform/CommandParser.cc
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#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 */

20
src/Platform/CommandParser.h
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#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 */

215
src/Platform/Dataset.cc
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#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;
}
}

78
src/Platform/Dataset.h
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#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

129
src/Platform/Datasets.cc
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#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();
}
}

30
src/Platform/Datasets.h
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#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

55
src/Platform/DotEnv.h
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#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

168
src/Platform/ExcelFile.cc
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#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;
}
}

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src/Platform/ExcelFile.h
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#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

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src/Platform/Experiment.cc
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#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);
}
}

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src/Platform/Experiment.h
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#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

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src/Platform/Folding.cc
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#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 };
}
}

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src/Platform/Folding.h
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#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

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src/Platform/GridData.cc
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#include "GridData.h"
#include <fstream>
namespace platform {
GridData::GridData(const std::string& fileName)
{
json grid_file;
std::ifstream resultData(fileName);
if (resultData.is_open()) {
grid_file = json::parse(resultData);
} else {
throw std::invalid_argument("Unable to open input file. [" + fileName + "]");
}
for (const auto& item : grid_file.items()) {
auto key = item.key();
auto value = item.value();
grid[key] = value;
}
}
int GridData::computeNumCombinations(const json& line)
{
int numCombinations = 1;
for (const auto& item : line.items()) {
numCombinations *= item.value().size();
}
return numCombinations;
}
int GridData::getNumCombinations(const std::string& dataset)
{
int numCombinations = 0;
auto selected = decide_dataset(dataset);
for (const auto& line : grid.at(selected)) {
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(const std::string& dataset)
{
auto selected = decide_dataset(dataset);
auto result = std::vector<json>();
for (json line : grid.at(selected)) {
generateCombinations(line.begin(), line.end(), result, json({}));
}
return result;
}
json& GridData::getInputGrid(const std::string& dataset)
{
auto selected = decide_dataset(dataset);
return grid.at(selected);
}
std::string GridData::decide_dataset(const std::string& dataset)
{
if (grid.find(dataset) != grid.end())
return dataset;
return ALL_DATASETS;
}
} /* namespace platform */

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src/Platform/GridData.h
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#ifndef GRIDDATA_H
#define GRIDDATA_H
#include <string>
#include <vector>
#include <map>
#include <nlohmann/json.hpp>
namespace platform {
using json = nlohmann::json;
const std::string ALL_DATASETS = "all";
class GridData {
public:
explicit GridData(const std::string& fileName);
~GridData() = default;
std::vector<json> getGrid(const std::string& dataset = ALL_DATASETS);
int getNumCombinations(const std::string& dataset = ALL_DATASETS);
json& getInputGrid(const std::string& dataset = ALL_DATASETS);
std::map<std::string, json>& getGridFile() { return grid; }
private:
std::string decide_dataset(const std::string& dataset);
json generateCombinations(json::iterator index, const json::iterator last, std::vector<json>& output, json currentCombination);
int computeNumCombinations(const json& line);
std::map<std::string, json> grid;
};
} /* namespace platform */
#endif /* GRIDDATA_H */

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src/Platform/GridSearch.cc
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#include <iostream>
#include <cstddef>
#include <torch/torch.h>
#include "GridSearch.h"
#include "Models.h"
#include "Paths.h"
#include "Folding.h"
#include "Colors.h"
namespace platform {
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 get_color_rank(int rank)
{
auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(), Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() };
return *(colors.begin() + rank % colors.size());
}
GridSearch::GridSearch(struct ConfigGrid& config) : config(config)
{
}
json GridSearch::loadResults()
{
std::ifstream file(Paths::grid_output(config.model));
if (file.is_open()) {
return json::parse(file);
}
return json();
}
std::vector<std::string> GridSearch::filterDatasets(Datasets& datasets) const
{
// Load datasets
auto datasets_names = datasets.getNames();
if (config.continue_from != NO_CONTINUE()) {
// Continue previous execution:
if (std::find(datasets_names.begin(), datasets_names.end(), config.continue_from) == datasets_names.end()) {
throw std::invalid_argument("Dataset " + config.continue_from + " not found");
}
// Remove datasets already processed
std::vector<string>::iterator it = datasets_names.begin();
while (it != datasets_names.end()) {
if (*it != config.continue_from) {
it = datasets_names.erase(it);
} else {
if (config.only)
++it;
else
break;
}
}
}
// Exclude datasets
for (const auto& name : config.excluded) {
auto dataset = name.get<std::string>();
auto it = std::find(datasets_names.begin(), datasets_names.end(), dataset);
if (it == datasets_names.end()) {
throw std::invalid_argument("Dataset " + dataset + " already excluded or doesn't exist!");
}
datasets_names.erase(it);
}
return datasets_names;
}
json GridSearch::build_tasks_mpi(int rank)
{
auto tasks = json::array();
auto grid = GridData(Paths::grid_input(config.model));
auto datasets = Datasets(false, Paths::datasets());
auto all_datasets = datasets.getNames();
auto datasets_names = filterDatasets(datasets);
for (int idx_dataset = 0; idx_dataset < datasets_names.size(); ++idx_dataset) {
auto dataset = datasets_names[idx_dataset];
for (const auto& seed : config.seeds) {
auto combinations = grid.getGrid(dataset);
for (int n_fold = 0; n_fold < config.n_folds; n_fold++) {
json task = {
{ "dataset", dataset },
{ "idx_dataset", idx_dataset},
{ "seed", seed },
{ "fold", n_fold},
};
tasks.push_back(task);
}
}
}
// Shuffle the array so heavy datasets are spread across the workers
std::mt19937 g{ 271 }; // Use fixed seed to obtain the same shuffle
std::shuffle(tasks.begin(), tasks.end(), g);
std::cout << get_color_rank(rank) << "* Number of tasks: " << tasks.size() << std::endl;
std::cout << "|";
for (int i = 0; i < tasks.size(); ++i) {
std::cout << (i + 1) % 10;
}
std::cout << "|" << std::endl << "|" << std::flush;
return tasks;
}
void process_task_mpi_consumer(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result)
{
// initialize
Timer timer;
timer.start();
json task = tasks[n_task];
auto model = config.model;
auto grid = GridData(Paths::grid_input(model));
auto dataset = task["dataset"].get<std::string>();
auto idx_dataset = task["idx_dataset"].get<int>();
auto seed = task["seed"].get<int>();
auto n_fold = task["fold"].get<int>();
bool stratified = config.stratified;
// Generate the hyperparamters combinations
auto combinations = grid.getGrid(dataset);
auto [X, y] = datasets.getTensors(dataset);
auto states = datasets.getStates(dataset);
auto features = datasets.getFeatures(dataset);
auto className = datasets.getClassName(dataset);
//
// Start working on task
//
Fold* fold;
if (stratified)
fold = new StratifiedKFold(config.n_folds, y, seed);
else
fold = new KFold(config.n_folds, y.size(0), seed);
auto [train, test] = fold->getFold(n_fold);
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 });
double best_fold_score = 0.0;
int best_idx_combination = -1;
json best_fold_hyper;
for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
auto hyperparam_line = combinations[idx_combination];
auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
Fold* nested_fold;
if (config.stratified)
nested_fold = new StratifiedKFold(config.nested, y_train, seed);
else
nested_fold = new KFold(config.nested, y_train.size(0), seed);
double score = 0.0;
for (int n_nested_fold = 0; n_nested_fold < config.nested; n_nested_fold++) {
// Nested level fold
auto [train_nested, test_nested] = nested_fold->getFold(n_nested_fold);
auto train_nested_t = torch::tensor(train_nested);
auto test_nested_t = torch::tensor(test_nested);
auto X_nested_train = X_train.index({ "...", train_nested_t });
auto y_nested_train = y_train.index({ train_nested_t });
auto X_nested_test = X_train.index({ "...", test_nested_t });
auto y_nested_test = y_train.index({ test_nested_t });
// Build Classifier with selected hyperparameters
auto clf = Models::instance()->create(config.model);
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, dataset);
clf->setHyperparameters(hyperparameters.get(dataset));
// Train model
clf->fit(X_nested_train, y_nested_train, features, className, states);
// Test model
score += clf->score(X_nested_test, y_nested_test);
}
delete nested_fold;
score /= config.nested;
if (score > best_fold_score) {
best_fold_score = score;
best_idx_combination = idx_combination;
best_fold_hyper = hyperparam_line;
}
}
delete fold;
// Build Classifier with the best hyperparameters to obtain the best score
auto hyperparameters = platform::HyperParameters(datasets.getNames(), best_fold_hyper);
auto clf = Models::instance()->create(config.model);
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, dataset);
clf->setHyperparameters(best_fold_hyper);
clf->fit(X_train, y_train, features, className, states);
best_fold_score = clf->score(X_test, y_test);
// Return the result
result->idx_dataset = task["idx_dataset"].get<int>();
result->idx_combination = best_idx_combination;
result->score = best_fold_score;
result->n_fold = n_fold;
result->time = timer.getDuration();
// Update progress bar
std::cout << get_color_rank(config_mpi.rank) << "*" << std::flush;
}
json store_result(std::vector<std::string>& names, Task_Result& result, json& results)
{
json json_result = {
{ "score", result.score },
{ "combination", result.idx_combination },
{ "fold", result.n_fold },
{ "time", result.time },
{ "dataset", result.idx_dataset }
};
auto name = names[result.idx_dataset];
if (!results.contains(name)) {
results[name] = json::array();
}
results[name].push_back(json_result);
return results;
}
json producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
{
Task_Result result;
json results;
int num_tasks = tasks.size();
//
// 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
//
for (int i = 0; i < num_tasks; ++i) {
MPI_Status status;
MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
if (status.MPI_TAG == TAG_RESULT) {
//Store result
store_result(names, result, results);
}
MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
}
//
// 2a.2 Producer will send the end message to all the consumers
//
for (int i = 0; i < config_mpi.n_procs - 1; ++i) {
MPI_Status status;
MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
if (status.MPI_TAG == TAG_RESULT) {
//Store result
store_result(names, result, results);
}
MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
}
return results;
}
void select_best_results_folds(json& results, json& all_results, std::string& model)
{
Timer timer;
auto grid = GridData(Paths::grid_input(model));
//
// Select the best result of the computed outer folds
//
for (const auto& result : all_results.items()) {
// each result has the results of all the outer folds as each one were a different task
double best_score = 0.0;
json best;
for (const auto& result_fold : result.value()) {
double score = result_fold["score"].get<double>();
if (score > best_score) {
best_score = score;
best = result_fold;
}
}
auto dataset = result.key();
auto combinations = grid.getGrid(dataset);
json json_best = {
{ "score", best_score },
{ "hyperparameters", combinations[best["combination"].get<int>()] },
{ "date", get_date() + " " + get_time() },
{ "grid", grid.getInputGrid(dataset) },
{ "duration", timer.translate2String(best["time"].get<double>()) }
};
results[dataset] = json_best;
}
}
void consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
{
Task_Result result;
//
// 2b.1 Consumers announce to the producer that they are ready to receive a task
//
MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_QUERY, MPI_COMM_WORLD);
int task;
while (true) {
MPI_Status status;
//
// 2b.2 Consumers receive the task from the producer and process it
//
MPI_Recv(&task, 1, MPI_INT, config_mpi.manager, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
if (status.MPI_TAG == TAG_END) {
break;
}
process_task_mpi_consumer(config, config_mpi, tasks, task, datasets, &result);
//
// 2b.3 Consumers send the result to the producer
//
MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_RESULT, MPI_COMM_WORLD);
}
}
void GridSearch::go(struct ConfigMPI& config_mpi)
{
/*
* Each task is a json object with the following structure:
* {
* "dataset": "dataset_name",
* "idx_dataset": idx_dataset, // used to identify the dataset in the results
* // this index is relative to the used datasets in the actual run not to the whole datasets
* "seed": # of seed to use,
* "Fold": # of fold to process
* }
*
* The overall process consists in these steps:
* 0. Create the MPI result type & tasks
* 0.1 Create the MPI result type
* 0.2 Manager creates the tasks
* 1. Manager will broadcast the tasks to all the processes
* 1.1 Broadcast the number of tasks
* 1.2 Broadcast the length of the following string
* 1.2 Broadcast the tasks as a char* string
* 2a. Producer delivers the tasks to the consumers
* 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
* 2a.2 Producer will send the end message to all the consumers
* 2b. Consumers process the tasks and send the results to the producer
* 2b.1 Consumers announce to the producer that they are ready to receive a task
* 2b.2 Consumers receive the task from the producer and process it
* 2b.3 Consumers send the result to the producer
* 3. Manager select the bests sccores for each dataset
* 3.1 Loop thru all the results obtained from each outer fold (task) and select the best
* 3.2 Save the results
*/
//
// 0.1 Create the MPI result type
//
Task_Result result;
int tasks_size;
MPI_Datatype MPI_Result;
MPI_Datatype type[5] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_INT, MPI_DOUBLE, MPI_DOUBLE };
int blocklen[5] = { 1, 1, 1, 1, 1 };
MPI_Aint disp[5];
disp[0] = offsetof(Task_Result, idx_dataset);
disp[1] = offsetof(Task_Result, idx_combination);
disp[2] = offsetof(Task_Result, n_fold);
disp[3] = offsetof(Task_Result, score);
disp[4] = offsetof(Task_Result, time);
MPI_Type_create_struct(5, blocklen, disp, type, &MPI_Result);
MPI_Type_commit(&MPI_Result);
//
// 0.2 Manager creates the tasks
//
char* msg;
json tasks;
if (config_mpi.rank == config_mpi.manager) {
timer.start();
tasks = build_tasks_mpi(config_mpi.rank);
auto tasks_str = tasks.dump();
tasks_size = tasks_str.size();
msg = new char[tasks_size + 1];
strcpy(msg, tasks_str.c_str());
}
//
// 1. Manager will broadcast the tasks to all the processes
//
MPI_Bcast(&tasks_size, 1, MPI_INT, config_mpi.manager, MPI_COMM_WORLD);
if (config_mpi.rank != config_mpi.manager) {
msg = new char[tasks_size + 1];
}
MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD);
tasks = json::parse(msg);
delete[] msg;
auto datasets = Datasets(config.discretize, Paths::datasets());
if (config_mpi.rank == config_mpi.manager) {
//
// 2a. Producer delivers the tasks to the consumers
//
auto datasets_names = filterDatasets(datasets);
json all_results = producer(datasets_names, tasks, config_mpi, MPI_Result);
std::cout << get_color_rank(config_mpi.rank) << "|" << std::endl;
//
// 3. Manager select the bests sccores for each dataset
//
auto results = initializeResults();
select_best_results_folds(results, all_results, config.model);
//
// 3.2 Save the results
//
save(results);
} else {
//
// 2b. Consumers process the tasks and send the results to the producer
//
consumer(datasets, tasks, config, config_mpi, MPI_Result);
}
}
json GridSearch::initializeResults()
{
// Load previous results if continue is set
json results;
if (config.continue_from != NO_CONTINUE()) {
if (!config.quiet)
std::cout << "* Loading previous results" << std::endl;
try {
std::ifstream file(Paths::grid_output(config.model));
if (file.is_open()) {
results = json::parse(file);
results = results["results"];
}
}
catch (const std::exception& e) {
std::cerr << "* There were no previous results" << std::endl;
std::cerr << "* Initizalizing new results" << std::endl;
results = json();
}
}
return results;
}
void GridSearch::save(json& results)
{
std::ofstream file(Paths::grid_output(config.model));
json output = {
{ "model", config.model },
{ "score", config.score },
{ "discretize", config.discretize },
{ "stratified", config.stratified },
{ "n_folds", config.n_folds },
{ "seeds", config.seeds },
{ "date", get_date() + " " + get_time()},
{ "nested", config.nested},
{ "platform", config.platform },
{ "duration", timer.getDurationString(true)},
{ "results", results }
};
file << output.dump(4);
}
} /* namespace platform */

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src/Platform/GridSearch.h
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#ifndef GRIDSEARCH_H
#define GRIDSEARCH_H
#include <string>
#include <map>
#include <mpi.h>
#include <nlohmann/json.hpp>
#include "Datasets.h"
#include "HyperParameters.h"
#include "GridData.h"
#include "Timer.h"
namespace platform {
using json = nlohmann::json;
struct ConfigGrid {
std::string model;
std::string score;
std::string continue_from;
std::string platform;
bool quiet;
bool only; // used with continue_from to only compute that dataset
bool discretize;
bool stratified;
int nested;
int n_folds;
json excluded;
std::vector<int> seeds;
};
struct ConfigMPI {
int rank;
int n_procs;
int manager;
};
typedef struct {
uint idx_dataset;
uint idx_combination;
int n_fold;
double score;
double time;
} Task_Result;
const int TAG_QUERY = 1;
const int TAG_RESULT = 2;
const int TAG_TASK = 3;
const int TAG_END = 4;
class GridSearch {
public:
explicit GridSearch(struct ConfigGrid& config);
void go(struct ConfigMPI& config_mpi);
~GridSearch() = default;
json loadResults();
static inline std::string NO_CONTINUE() { return "NO_CONTINUE"; }
private:
void save(json& results);
json initializeResults();
std::vector<std::string> filterDatasets(Datasets& datasets) const;
struct ConfigGrid config;
json build_tasks_mpi(int rank);
Timer timer; // used to measure the time of the whole process
};
} /* namespace platform */
#endif /* GRIDSEARCH_H */

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src/Platform/HyperParameters.cc
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#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]["hyperparameters"].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 */

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src/Platform/HyperParameters.h
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#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 */

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src/Platform/ManageResults.cc
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#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 */

31
src/Platform/ManageResults.h
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#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 */

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src/Platform/Models.cc
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#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);
}
}

41
src/Platform/Models.h
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#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

39
src/Platform/Paths.h
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#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"; }
static std::string grid_input(const std::string& model)
{
return grid() + "grid_" + model + "_input.json";
}
static std::string grid_output(const std::string& model)
{
return grid() + "grid_" + model + "_output.json";
}
};
}
#endif

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src/Platform/ReportBase.cc
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#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;
}
}

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src/Platform/ReportBase.h
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#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

114
src/Platform/ReportConsole.cc
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#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();
}
}

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src/Platform/ReportConsole.h
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#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

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src/Platform/ReportExcel.cc
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#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"]);
}
}
}

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src/Platform/ReportExcel.h
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#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

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src/Platform/Result.cc
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#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();
}
}

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src/Platform/Result.h
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#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

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src/Platform/Results.cc
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#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();
}
}

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src/Platform/Results.h
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#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

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src/Platform/Statistics.cc
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#include <sstream>
#include "Statistics.h"
#include "Colors.h"
#include "Symbols.h"
#include <boost/math/distributions/chi_squared.hpp>
#include <boost/math/distributions/normal.hpp>
#include "CLocale.h"
namespace platform {
Statistics::Statistics(const std::vector<std::string>& models, const std::vector<std::string>& datasets, const json& data, double significance, bool output) :
models(models), datasets(datasets), data(data), significance(significance), output(output)
{
nModels = models.size();
nDatasets = datasets.size();
auto temp = ConfigLocale();
};
void Statistics::fit()
{
if (nModels < 3 || nDatasets < 3) {
std::cerr << "nModels: " << nModels << std::endl;
std::cerr << "nDatasets: " << nDatasets << std::endl;
throw std::runtime_error("Can't make the Friedman test with less than 3 models and/or less than 3 datasets.");
}
ranksModels.clear();
computeRanks();
// Set the control model as the one with the lowest average rank
controlIdx = distance(ranks.begin(), min_element(ranks.begin(), ranks.end(), [](const auto& l, const auto& r) { return l.second < r.second; }));
computeWTL();
maxModelName = (*std::max_element(models.begin(), models.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
maxDatasetName = (*std::max_element(datasets.begin(), datasets.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
fitted = true;
}
std::map<std::string, float> assignRanks(std::vector<std::pair<std::string, double>>& ranksOrder)
{
// sort the ranksOrder std::vector by value
std::sort(ranksOrder.begin(), ranksOrder.end(), [](const std::pair<std::string, double>& a, const std::pair<std::string, double>& b) {
return a.second > b.second;
});
//Assign ranks to values and if they are the same they share the same averaged rank
std::map<std::string, float> ranks;
for (int i = 0; i < ranksOrder.size(); i++) {
ranks[ranksOrder[i].first] = i + 1.0;
}
int i = 0;
while (i < static_cast<int>(ranksOrder.size())) {
int j = i + 1;
int sumRanks = ranks[ranksOrder[i].first];
while (j < static_cast<int>(ranksOrder.size()) && ranksOrder[i].second == ranksOrder[j].second) {
sumRanks += ranks[ranksOrder[j++].first];
}
if (j > i + 1) {
float averageRank = (float)sumRanks / (j - i);
for (int k = i; k < j; k++) {
ranks[ranksOrder[k].first] = averageRank;
}
}
i = j;
}
return ranks;
}
void Statistics::computeRanks()
{
std::map<std::string, float> ranksLine;
for (const auto& dataset : datasets) {
std::vector<std::pair<std::string, double>> ranksOrder;
for (const auto& model : models) {
double value = data[model].at(dataset).at(0).get<double>();
ranksOrder.push_back({ model, value });
}
// Assign the ranks
ranksLine = assignRanks(ranksOrder);
// Store the ranks of the dataset
ranksModels[dataset] = ranksLine;
if (ranks.size() == 0) {
ranks = ranksLine;
} else {
for (const auto& rank : ranksLine) {
ranks[rank.first] += rank.second;
}
}
}
// Average the ranks
for (const auto& rank : ranks) {
ranks[rank.first] /= nDatasets;
}
}
void Statistics::computeWTL()
{
// Compute the WTL matrix
for (int i = 0; i < nModels; ++i) {
wtl[i] = { 0, 0, 0 };
}
json origin = data.begin().value();
for (auto const& item : origin.items()) {
auto controlModel = models.at(controlIdx);
double controlValue = data[controlModel].at(item.key()).at(0).get<double>();
for (int i = 0; i < nModels; ++i) {
if (i == controlIdx) {
continue;
}
double value = data[models[i]].at(item.key()).at(0).get<double>();
if (value < controlValue) {
wtl[i].win++;
} else if (value == controlValue) {
wtl[i].tie++;
} else {
wtl[i].loss++;
}
}
}
}
void Statistics::postHocHolmTest(bool friedmanResult)
{
if (!fitted) {
fit();
}
std::stringstream oss;
// Reference https://link.springer.com/article/10.1007/s44196-022-00083-8
// Post-hoc Holm test
// Calculate the p-value for the models paired with the control model
std::map<int, double> stats; // p-value of each model paired with the control model
boost::math::normal dist(0.0, 1.0);
double diff = sqrt(nModels * (nModels + 1) / (6.0 * nDatasets));
for (int i = 0; i < nModels; i++) {
if (i == controlIdx) {
stats[i] = 0.0;
continue;
}
double z = abs(ranks.at(models[controlIdx]) - ranks.at(models[i])) / diff;
double p_value = (long double)2 * (1 - cdf(dist, z));
stats[i] = p_value;
}
// Sort the models by p-value
std::vector<std::pair<int, double>> statsOrder;
for (const auto& stat : stats) {
statsOrder.push_back({ stat.first, stat.second });
}
std::sort(statsOrder.begin(), statsOrder.end(), [](const std::pair<int, double>& a, const std::pair<int, double>& b) {
return a.second < b.second;
});
// Holm adjustment
for (int i = 0; i < statsOrder.size(); ++i) {
auto item = statsOrder.at(i);
double before = i == 0 ? 0.0 : statsOrder.at(i - 1).second;
double p_value = std::min((double)1.0, item.second * (nModels - i));
p_value = std::max(before, p_value);
statsOrder[i] = { item.first, p_value };
}
holmResult.model = models.at(controlIdx);
auto color = friedmanResult ? Colors::CYAN() : Colors::YELLOW();
oss << color;
oss << " *************************************************************************************************************" << std::endl;
oss << " Post-hoc Holm test: H0: 'There is no significant differences between the control model and the other models.'" << std::endl;
oss << " Control model: " << models.at(controlIdx) << std::endl;
oss << " " << std::left << std::setw(maxModelName) << std::string("Model") << " p-value rank win tie loss Status" << std::endl;
oss << " " << std::string(maxModelName, '=') << " ============ ========= === === ==== =============" << std::endl;
// sort ranks from lowest to highest
std::vector<std::pair<std::string, float>> ranksOrder;
for (const auto& rank : ranks) {
ranksOrder.push_back({ rank.first, rank.second });
}
std::sort(ranksOrder.begin(), ranksOrder.end(), [](const std::pair<std::string, float>& a, const std::pair<std::string, float>& b) {
return a.second < b.second;
});
// Show the control model info.
oss << " " << Colors::BLUE() << std::left << std::setw(maxModelName) << ranksOrder.at(0).first << " ";
oss << std::setw(12) << " " << std::setprecision(7) << std::fixed << " " << ranksOrder.at(0).second << std::endl;
for (const auto& item : ranksOrder) {
auto idx = distance(models.begin(), find(models.begin(), models.end(), item.first));
double pvalue = 0.0;
for (const auto& stat : statsOrder) {
if (stat.first == idx) {
pvalue = stat.second;
}
}
holmResult.holmLines.push_back({ item.first, pvalue, item.second, wtl.at(idx), pvalue < significance });
if (item.first == models.at(controlIdx)) {
continue;
}
auto colorStatus = pvalue > significance ? Colors::GREEN() : Colors::MAGENTA();
auto status = pvalue > significance ? Symbols::check_mark : Symbols::cross;
auto textStatus = pvalue > significance ? " accepted H0" : " rejected H0";
oss << " " << colorStatus << std::left << std::setw(maxModelName) << item.first << " ";
oss << std::setprecision(6) << std::scientific << pvalue << std::setprecision(7) << std::fixed << " " << item.second;
oss << " " << std::right << std::setw(3) << wtl.at(idx).win << " " << std::setw(3) << wtl.at(idx).tie << " " << std::setw(4) << wtl.at(idx).loss;
oss << " " << status << textStatus << std::endl;
}
oss << color << " *************************************************************************************************************" << std::endl;
oss << Colors::RESET();
if (output) {
std::cout << oss.str();
}
}
bool Statistics::friedmanTest()
{
if (!fitted) {
fit();
}
std::stringstream oss;
// Friedman test
// Calculate the Friedman statistic
oss << Colors::BLUE() << std::endl;
oss << "***************************************************************************************************************" << std::endl;
oss << Colors::GREEN() << "Friedman test: H0: 'There is no significant differences between all the classifiers.'" << Colors::BLUE() << std::endl;
double degreesOfFreedom = nModels - 1.0;
double sumSquared = 0;
for (const auto& rank : ranks) {
sumSquared += pow(rank.second, 2);
}
// Compute the Friedman statistic as in https://link.springer.com/article/10.1007/s44196-022-00083-8
double friedmanQ = 12.0 * nDatasets / (nModels * (nModels + 1)) * (sumSquared - (nModels * pow(nModels + 1, 2)) / 4);
// Calculate the critical value
boost::math::chi_squared chiSquared(degreesOfFreedom);
long double p_value = (long double)1.0 - cdf(chiSquared, friedmanQ);
double criticalValue = quantile(chiSquared, 1 - significance);
oss << "Friedman statistic: " << friedmanQ << std::endl;
oss << "Critical χ2 Value for df=" << std::fixed << (int)degreesOfFreedom
<< " and alpha=" << std::setprecision(2) << std::fixed << significance << ": " << std::setprecision(7) << std::scientific << criticalValue << std::endl;
oss << "p-value: " << std::scientific << p_value << " is " << (p_value < significance ? "less" : "greater") << " than " << std::setprecision(2) << std::fixed << significance << std::endl;
bool result;
if (p_value < significance) {
oss << Colors::GREEN() << "The null hypothesis H0 is rejected." << std::endl;
result = true;
} else {
oss << Colors::YELLOW() << "The null hypothesis H0 is accepted. Computed p-values will not be significant." << std::endl;
result = false;
}
oss << Colors::BLUE() << "***************************************************************************************************************" << Colors::RESET() << std::endl;
if (output) {
std::cout << oss.str();
}
friedmanResult = { friedmanQ, criticalValue, p_value, result };
return result;
}
FriedmanResult& Statistics::getFriedmanResult()
{
return friedmanResult;
}
HolmResult& Statistics::getHolmResult()
{
return holmResult;
}
std::map<std::string, std::map<std::string, float>>& Statistics::getRanks()
{
return ranksModels;
}
} // namespace platform

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src/Platform/Statistics.h
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#ifndef STATISTICS_H
#define STATISTICS_H
#include <iostream>
#include <vector>
#include <map>
#include <nlohmann/json.hpp>
using json = nlohmann::json;
namespace platform {
struct WTL {
int win;
int tie;
int loss;
};
struct FriedmanResult {
double statistic;
double criticalValue;
long double pvalue;
bool reject;
};
struct HolmLine {
std::string model;
long double pvalue;
double rank;
WTL wtl;
bool reject;
};
struct HolmResult {
std::string model;
std::vector<HolmLine> holmLines;
};
class Statistics {
public:
Statistics(const std::vector<std::string>& models, const std::vector<std::string>& datasets, const json& data, double significance = 0.05, bool output = true);
bool friedmanTest();
void postHocHolmTest(bool friedmanResult);
FriedmanResult& getFriedmanResult();
HolmResult& getHolmResult();
std::map<std::string, std::map<std::string, float>>& getRanks();
private:
void fit();
void computeRanks();
void computeWTL();
const std::vector<std::string>& models;
const std::vector<std::string>& datasets;
const json& data;
double significance;
bool output;
bool fitted = false;
int nModels = 0;
int nDatasets = 0;
int controlIdx = 0;
std::map<int, WTL> wtl;
std::map<std::string, float> ranks;
int maxModelName = 0;
int maxDatasetName = 0;
FriedmanResult friedmanResult;
HolmResult holmResult;
std::map<std::string, std::map<std::string, float>> ranksModels;
};
}
#endif // !STATISTICS_H

17
src/Platform/Symbols.h
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#ifndef SYMBOLS_H
#define SYMBOLS_H
#include <string>
namespace platform {
class Symbols {
public:
inline static const std::string check_mark{ "\u2714" };
inline static const std::string exclamation{ "\u2757" };
inline static const std::string black_star{ "\u2605" };
inline static const std::string cross{ "\u2717" };
inline static const std::string upward_arrow{ "\u27B6" };
inline static const std::string down_arrow{ "\u27B4" };
inline static const std::string equal_best{ check_mark };
inline static const std::string better_best{ black_star };
};
}
#endif // !SYMBOLS_H

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src/Platform/Timer.h
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#ifndef TIMER_H
#define TIMER_H
#include <chrono>
#include <string>
#include <sstream>
namespace platform {
class Timer {
private:
std::chrono::high_resolution_clock::time_point begin;
std::chrono::high_resolution_clock::time_point end;
public:
Timer() = default;
~Timer() = default;
void start() { begin = std::chrono::high_resolution_clock::now(); }
void stop() { end = std::chrono::high_resolution_clock::now(); }
double getDuration()
{
stop();
std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (end - begin);
return time_span.count();
}
double getLapse()
{
std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (std::chrono::high_resolution_clock::now() - begin);
return time_span.count();
}
std::string getDurationString(bool lapse = false)
{
double duration = lapse ? getLapse() : getDuration();
return translate2String(duration);
}
std::string translate2String(double duration)
{
double durationShow = duration > 3600 ? duration / 3600 : duration > 60 ? duration / 60 : duration;
std::string durationUnit = duration > 3600 ? "h" : duration > 60 ? "m" : "s";
std::stringstream ss;
ss << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit;
return ss.str();
}
};
} /* namespace platform */
#endif /* TIMER_H */

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src/Platform/Utils.h
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#ifndef UTILS_H
#define UTILS_H
#include <sstream>
#include <string>
#include <vector>
namespace platform {
//static std::vector<std::string> split(const std::string& text, char delimiter);
static std::vector<std::string> split(const std::string& text, char delimiter)
{
std::vector<std::string> result;
std::stringstream ss(text);
std::string token;
while (std::getline(ss, token, delimiter)) {
result.push_back(token);
}
return result;
}
static std::string trim(const std::string& str)
{
std::string result = str;
result.erase(result.begin(), std::find_if(result.begin(), result.end(), [](int ch) {
return !std::isspace(ch);
}));
result.erase(std::find_if(result.rbegin(), result.rend(), [](int ch) {
return !std::isspace(ch);
}).base(), result.end());
return result;
}
}
#endif

85
src/Platform/b_best.cc
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#include <iostream>
#include <argparse/argparse.hpp>
#include "Paths.h"
#include "BestResults.h"
#include "Colors.h"
#include "config.h"
void manageArguments(argparse::ArgumentParser& program, int argc, char** argv)
{
program.add_argument("-m", "--model").default_value("").help("Filter results of the selected model) (any for all models)");
program.add_argument("-s", "--score").default_value("").help("Filter results of the score name supplied");
program.add_argument("--build").help("build best score results file").default_value(false).implicit_value(true);
program.add_argument("--report").help("report of best score results file").default_value(false).implicit_value(true);
program.add_argument("--friedman").help("Friedman test").default_value(false).implicit_value(true);
program.add_argument("--excel").help("Output to excel").default_value(false).implicit_value(true);
program.add_argument("--level").help("significance level").default_value(0.05).scan<'g', double>().action([](const std::string& value) {
try {
auto k = std::stod(value);
if (k < 0.01 || k > 0.15) {
throw std::runtime_error("Significance level hast to be a number in [0.01, 0.15]");
}
return k;
}
catch (const std::runtime_error& err) {
throw std::runtime_error(err.what());
}
catch (...) {
throw std::runtime_error("Number of folds must be an decimal number");
}});
}
int main(int argc, char** argv)
{
argparse::ArgumentParser program("b_best", { project_version.begin(), project_version.end() });
manageArguments(program, argc, argv);
std::string model, score;
bool build, report, friedman, excel;
double level;
try {
program.parse_args(argc, argv);
model = program.get<std::string>("model");
score = program.get<std::string>("score");
build = program.get<bool>("build");
report = program.get<bool>("report");
friedman = program.get<bool>("friedman");
excel = program.get<bool>("excel");
level = program.get<double>("level");
if (model == "" || score == "") {
throw std::runtime_error("Model and score name must be supplied");
}
if (friedman && model != "any") {
std::cerr << "Friedman test can only be used with all models" << std::endl;
std::cerr << program;
exit(1);
}
if (!report && !build) {
std::cerr << "Either build, report or both, have to be selected to do anything!" << std::endl;
std::cerr << program;
exit(1);
}
}
catch (const std::exception& err) {
std::cerr << err.what() << std::endl;
std::cerr << program;
exit(1);
}
// Generate report
auto results = platform::BestResults(platform::Paths::results(), score, model, friedman, level);
if (build) {
if (model == "any") {
results.buildAll();
} else {
std::string fileName = results.build();
std::cout << Colors::GREEN() << fileName << " created!" << Colors::RESET() << std::endl;
}
}
if (report) {
if (model == "any") {
results.reportAll(excel);
} else {
results.reportSingle(excel);
}
}
return 0;
}

232
src/Platform/b_grid.cc
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#include <iostream>
#include <argparse/argparse.hpp>
#include <map>
#include <nlohmann/json.hpp>
#include <mpi.h>
#include "DotEnv.h"
#include "Models.h"
#include "modelRegister.h"
#include "GridSearch.h"
#include "Paths.h"
#include "Timer.h"
#include "Colors.h"
#include "config.h"
using json = nlohmann::json;
const int MAXL = 133;
void manageArguments(argparse::ArgumentParser& program)
{
auto env = platform::DotEnv();
auto& group = program.add_mutually_exclusive_group(true);
program.add_argument("-m", "--model")
.help("Model to use " + platform::Models::instance()->tostring())
.action([](const 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 std::runtime_error("Model must be one of " + platform::Models::instance()->tostring());
}
);
group.add_argument("--dump").help("Show the grid combinations").default_value(false).implicit_value(true);
group.add_argument("--report").help("Report the computed hyperparameters").default_value(false).implicit_value(true);
group.add_argument("--compute").help("Perform computation of the grid output hyperparameters").default_value(false).implicit_value(true);
program.add_argument("--discretize").help("Discretize input datasets").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
program.add_argument("--continue").help("Continue computing from that dataset").default_value(platform::GridSearch::NO_CONTINUE());
program.add_argument("--only").help("Used with continue to compute that dataset only").default_value(false).implicit_value(true);
program.add_argument("--exclude").default_value("[]").help("Datasets to exclude in json format, e.g. [\"dataset1\", \"dataset2\"]");
program.add_argument("--nested").help("Set the double/nested cross validation number of folds").default_value(5).scan<'i', int>().action([](const std::string& value) {
try {
auto k = stoi(value);
if (k < 2) {
throw std::runtime_error("Number of nested folds must be greater than 1");
}
return k;
}
catch (const runtime_error& err) {
throw std::runtime_error(err.what());
}
catch (...) {
throw std::runtime_error("Number of nested folds must be an integer");
}});
program.add_argument("--score").help("Score used in gridsearch").default_value("accuracy");
program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
try {
auto k = stoi(value);
if (k < 2) {
throw std::runtime_error("Number of folds must be greater than 1");
}
return k;
}
catch (const runtime_error& err) {
throw std::runtime_error(err.what());
}
catch (...) {
throw std::runtime_error("Number of folds must be an integer");
}});
auto seed_values = env.getSeeds();
program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
}
void list_dump(std::string& model)
{
auto data = platform::GridData(platform::Paths::grid_input(model));
std::cout << Colors::MAGENTA() << "Listing configuration input file (Grid)" << std::endl << std::endl;
int index = 0;
int max_hyper = 15;
int max_dataset = 7;
auto combinations = data.getGridFile();
for (auto const& item : combinations) {
if (item.first.size() > max_dataset) {
max_dataset = item.first.size();
}
if (item.second.dump().size() > max_hyper) {
max_hyper = item.second.dump().size();
}
}
std::cout << Colors::GREEN() << left << " # " << left << setw(max_dataset) << "Dataset" << " #Com. "
<< setw(max_hyper) << "Hyperparameters" << std::endl;
std::cout << "=== " << string(max_dataset, '=') << " ===== " << string(max_hyper, '=') << std::endl;
bool odd = true;
for (auto const& item : combinations) {
auto color = odd ? Colors::CYAN() : Colors::BLUE();
std::cout << color;
auto num_combinations = data.getNumCombinations(item.first);
std::cout << setw(3) << fixed << right << ++index << left << " " << setw(max_dataset) << item.first
<< " " << setw(5) << right << num_combinations << " " << setw(max_hyper) << item.second.dump() << std::endl;
odd = !odd;
}
std::cout << Colors::RESET() << std::endl;
}
std::string 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 list_results(json& results, std::string& model)
{
std::cout << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
std::cout << headerLine("Listing computed hyperparameters for model " + model);
std::cout << headerLine("Date & time: " + results["date"].get<std::string>() + " Duration: " + results["duration"].get<std::string>());
std::cout << headerLine("Score: " + results["score"].get<std::string>());
std::cout << headerLine(
"Random seeds: " + results["seeds"].dump()
+ " Discretized: " + (results["discretize"].get<bool>() ? "True" : "False")
+ " Stratified: " + (results["stratified"].get<bool>() ? "True" : "False")
+ " #Folds: " + std::to_string(results["n_folds"].get<int>())
+ " Nested: " + (results["nested"].get<int>() == 0 ? "False" : to_string(results["nested"].get<int>()))
);
std::cout << std::string(MAXL, '*') << std::endl;
int spaces = 7;
int hyperparameters_spaces = 15;
for (const auto& item : results["results"].items()) {
auto key = item.key();
auto value = item.value();
if (key.size() > spaces) {
spaces = key.size();
}
if (value["hyperparameters"].dump().size() > hyperparameters_spaces) {
hyperparameters_spaces = value["hyperparameters"].dump().size();
}
}
std::cout << Colors::GREEN() << " # " << left << setw(spaces) << "Dataset" << " " << setw(19) << "Date" << " "
<< "Duration " << setw(8) << "Score" << " " << "Hyperparameters" << std::endl;
std::cout << "=== " << string(spaces, '=') << " " << string(19, '=') << " " << string(8, '=') << " "
<< string(8, '=') << " " << string(hyperparameters_spaces, '=') << std::endl;
bool odd = true;
int index = 0;
for (const auto& item : results["results"].items()) {
auto color = odd ? Colors::CYAN() : Colors::BLUE();
auto value = item.value();
std::cout << color;
std::cout << std::setw(3) << std::right << index++ << " ";
std::cout << left << setw(spaces) << item.key() << " " << value["date"].get<string>()
<< " " << setw(8) << right << value["duration"].get<string>() << " " << setw(8) << setprecision(6)
<< fixed << right << value["score"].get<double>() << " " << value["hyperparameters"].dump() << std::endl;
odd = !odd;
}
std::cout << Colors::RESET() << std::endl;
}
/*
* Main
*/
int main(int argc, char** argv)
{
argparse::ArgumentParser program("b_grid", { project_version.begin(), project_version.end() });
manageArguments(program);
struct platform::ConfigGrid config;
bool dump, compute;
try {
program.parse_args(argc, argv);
config.model = program.get<std::string>("model");
config.score = program.get<std::string>("score");
config.discretize = program.get<bool>("discretize");
config.stratified = program.get<bool>("stratified");
config.n_folds = program.get<int>("folds");
config.quiet = program.get<bool>("quiet");
config.only = program.get<bool>("only");
config.seeds = program.get<std::vector<int>>("seeds");
config.nested = program.get<int>("nested");
config.continue_from = program.get<std::string>("continue");
if (config.continue_from == platform::GridSearch::NO_CONTINUE() && config.only) {
throw std::runtime_error("Cannot use --only without --continue");
}
dump = program.get<bool>("dump");
compute = program.get<bool>("compute");
if (dump && (config.continue_from != platform::GridSearch::NO_CONTINUE() || config.only)) {
throw std::runtime_error("Cannot use --dump with --continue or --only");
}
auto excluded = program.get<std::string>("exclude");
config.excluded = json::parse(excluded);
}
catch (const exception& err) {
cerr << err.what() << std::endl;
cerr << program;
exit(1);
}
/*
* Begin Processing
*/
auto env = platform::DotEnv();
config.platform = env.get("platform");
platform::Paths::createPath(platform::Paths::grid());
auto grid_search = platform::GridSearch(config);
platform::Timer timer;
timer.start();
if (dump) {
list_dump(config.model);
} else {
if (compute) {
struct platform::ConfigMPI mpi_config;
mpi_config.manager = 0; // which process is the manager
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &mpi_config.rank);
MPI_Comm_size(MPI_COMM_WORLD, &mpi_config.n_procs);
if (mpi_config.n_procs < 2) {
throw std::runtime_error("Cannot use --compute with less than 2 mpi processes, try mpirun -np 2 ...");
}
grid_search.go(mpi_config);
if (mpi_config.rank == mpi_config.manager) {
auto results = grid_search.loadResults();
list_results(results, config.model);
std::cout << "Process took " << timer.getDurationString() << std::endl;
}
MPI_Finalize();
} else {
// List results
auto results = grid_search.loadResults();
if (results.empty()) {
std::cout << "** No results found" << std::endl;
} else {
list_results(results, config.model);
}
}
}
std::cout << "Done!" << std::endl;
return 0;
}

56
src/Platform/b_list.cc
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#include <iostream>
#include <locale>
#include "Paths.h"
#include "Colors.h"
#include "Datasets.h"
const int BALANCE_LENGTH = 75;
struct separated : numpunct<char> {
char do_decimal_point() const { return ','; }
char do_thousands_sep() const { return '.'; }
std::string do_grouping() const { return "\03"; }
};
void outputBalance(const std::string& balance)
{
auto temp = std::string(balance);
while (temp.size() > BALANCE_LENGTH - 1) {
auto part = temp.substr(0, BALANCE_LENGTH);
std::cout << part << std::endl;
std::cout << setw(48) << " ";
temp = temp.substr(BALANCE_LENGTH);
}
std::cout << temp << std::endl;
}
int main(int argc, char** argv)
{
auto data = platform::Datasets(false, platform::Paths::datasets());
locale mylocale(std::cout.getloc(), new separated);
locale::global(mylocale);
std::cout.imbue(mylocale);
std::cout << Colors::GREEN() << "Dataset Sampl. Feat. Cls. Balance" << std::endl;
std::string balanceBars = std::string(BALANCE_LENGTH, '=');
std::cout << "============================== ====== ===== === " << balanceBars << std::endl;
bool odd = true;
for (const auto& dataset : data.getNames()) {
auto color = odd ? Colors::CYAN() : Colors::BLUE();
std::cout << color << setw(30) << left << dataset << " ";
data.loadDataset(dataset);
auto nSamples = data.getNSamples(dataset);
std::cout << setw(6) << right << nSamples << " ";
std::cout << setw(5) << right << data.getFeatures(dataset).size() << " ";
std::cout << setw(3) << right << data.getNClasses(dataset) << " ";
std::stringstream oss;
std::string sep = "";
for (auto number : data.getClassesCounts(dataset)) {
oss << sep << std::setprecision(2) << fixed << (float)number / nSamples * 100.0 << "% (" << number << ")";
sep = " / ";
}
outputBalance(oss.str());
odd = !odd;
}
std::cout << Colors::RESET() << std::endl;
return 0;
}

135
src/Platform/b_main.cc
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#include <iostream>
#include <argparse/argparse.hpp>
#include <nlohmann/json.hpp>
#include "Experiment.h"
#include "Datasets.h"
#include "DotEnv.h"
#include "Models.h"
#include "modelRegister.h"
#include "Paths.h"
#include "config.h"
using json = nlohmann::json;
void manageArguments(argparse::ArgumentParser& program)
{
auto env = platform::DotEnv();
program.add_argument("-d", "--dataset").default_value("").help("Dataset file name");
program.add_argument("--hyperparameters").default_value("{}").help("Hyperparameters passed to the model in Experiment");
program.add_argument("--hyper-file").default_value("").help("Hyperparameters file name." \
"Mutually exclusive with hyperparameters. This file should contain hyperparameters for each dataset in json format.");
program.add_argument("-m", "--model")
.help("Model to use " + platform::Models::instance()->tostring())
.action([](const 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 std::runtime_error("Model must be one of " + platform::Models::instance()->tostring());
}
);
program.add_argument("--title").default_value("").help("Experiment title");
program.add_argument("--discretize").help("Discretize input dataset").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
program.add_argument("--save").help("Save result (always save if no dataset is supplied)").default_value(false).implicit_value(true);
program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
try {
auto k = stoi(value);
if (k < 2) {
throw std::runtime_error("Number of folds must be greater than 1");
}
return k;
}
catch (const runtime_error& err) {
throw std::runtime_error(err.what());
}
catch (...) {
throw std::runtime_error("Number of folds must be an integer");
}});
auto seed_values = env.getSeeds();
program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
}
int main(int argc, char** argv)
{
argparse::ArgumentParser program("b_main", { project_version.begin(), project_version.end() });
manageArguments(program);
std::string file_name, model_name, title, hyperparameters_file;
json hyperparameters_json;
bool discretize_dataset, stratified, saveResults, quiet;
std::vector<int> seeds;
std::vector<std::string> filesToTest;
int n_folds;
try {
program.parse_args(argc, argv);
file_name = program.get<std::string>("dataset");
model_name = program.get<std::string>("model");
discretize_dataset = program.get<bool>("discretize");
stratified = program.get<bool>("stratified");
quiet = program.get<bool>("quiet");
n_folds = program.get<int>("folds");
seeds = program.get<std::vector<int>>("seeds");
auto hyperparameters = program.get<std::string>("hyperparameters");
hyperparameters_json = json::parse(hyperparameters);
hyperparameters_file = program.get<std::string>("hyper-file");
if (hyperparameters_file != "" && hyperparameters != "{}") {
throw runtime_error("hyperparameters and hyper_file are mutually exclusive");
}
title = program.get<std::string>("title");
if (title == "" && file_name == "") {
throw runtime_error("title is mandatory if dataset is not provided");
}
saveResults = program.get<bool>("save");
}
catch (const exception& err) {
cerr << err.what() << std::endl;
cerr << program;
exit(1);
}
auto datasets = platform::Datasets(discretize_dataset, platform::Paths::datasets());
if (file_name != "") {
if (!datasets.isDataset(file_name)) {
cerr << "Dataset " << file_name << " not found" << std::endl;
exit(1);
}
if (title == "") {
title = "Test " + file_name + " " + model_name + " " + to_string(n_folds) + " folds";
}
filesToTest.push_back(file_name);
} else {
filesToTest = datasets.getNames();
saveResults = true;
}
platform::HyperParameters test_hyperparams;
if (hyperparameters_file != "") {
test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_file);
} else {
test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_json);
}
/*
* Begin Processing
*/
auto env = platform::DotEnv();
auto experiment = platform::Experiment();
experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("14.0.3");
experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
experiment.setHyperparameters(test_hyperparams);
for (auto seed : seeds) {
experiment.addRandomSeed(seed);
}
platform::Timer timer;
timer.start();
experiment.go(filesToTest, quiet);
experiment.setDuration(timer.getDuration());
if (saveResults) {
experiment.save(platform::Paths::results());
}
if (!quiet)
experiment.report();
std::cout << "Done!" << std::endl;
return 0;
}

49
src/Platform/b_manage.cc
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#include <iostream>
#include <argparse/argparse.hpp>
#include "ManageResults.h"
#include "config.h"
void manageArguments(argparse::ArgumentParser& program, int argc, char** argv)
{
program.add_argument("-n", "--number").default_value(0).help("Number of results to show (0 = all)").scan<'i', int>();
program.add_argument("-m", "--model").default_value("any").help("Filter results of the selected model)");
program.add_argument("-s", "--score").default_value("any").help("Filter results of the score name supplied");
program.add_argument("--complete").help("Show only results with all datasets").default_value(false).implicit_value(true);
program.add_argument("--partial").help("Show only partial results").default_value(false).implicit_value(true);
program.add_argument("--compare").help("Compare with best results").default_value(false).implicit_value(true);
try {
program.parse_args(argc, argv);
auto number = program.get<int>("number");
if (number < 0) {
throw std::runtime_error("Number of results must be greater than or equal to 0");
}
auto model = program.get<std::string>("model");
auto score = program.get<std::string>("score");
auto complete = program.get<bool>("complete");
auto partial = program.get<bool>("partial");
auto compare = program.get<bool>("compare");
}
catch (const std::exception& err) {
std::cerr << err.what() << std::endl;
std::cerr << program;
exit(1);
}
}
int main(int argc, char** argv)
{
auto program = argparse::ArgumentParser("b_manage", { project_version.begin(), project_version.end() });
manageArguments(program, argc, argv);
int number = program.get<int>("number");
std::string model = program.get<std::string>("model");
std::string score = program.get<std::string>("score");
auto complete = program.get<bool>("complete");
auto partial = program.get<bool>("partial");
auto compare = program.get<bool>("compare");
if (complete)
partial = false;
auto manager = platform::ManageResults(number, model, score, complete, partial, compare);
manager.doMenu();
return 0;
}

29
src/Platform/modelRegister.h
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#ifndef MODEL_REGISTER_H
#define MODEL_REGISTER_H
static platform::Registrar registrarT("TAN",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
static platform::Registrar registrarTLD("TANLd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
static platform::Registrar registrarS("SPODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
static platform::Registrar registrarSLD("SPODELd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
static platform::Registrar registrarK("KDB",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
static platform::Registrar registrarKLD("KDBLd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
static platform::Registrar registrarA("AODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
static platform::Registrar registrarALD("AODELd",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
static platform::Registrar registrarBA("BoostAODE",
[](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostAODE();});
static platform::Registrar registrarSt("STree",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::STree();});
static platform::Registrar registrarOdte("Odte",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::ODTE();});
static platform::Registrar registrarSvc("SVC",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::SVC();});
static platform::Registrar registrarRaF("RandomForest",
[](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
#endif

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src/PyClassifiers/CMakeLists.txt
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include_directories(
${BayesNet_SOURCE_DIR}/lib/Files
${BayesNet_SOURCE_DIR}/lib/json/include
${BayesNet_SOURCE_DIR}/src/BayesNet
${Python3_INCLUDE_DIRS}
${TORCH_INCLUDE_DIRS}
)
add_library(PyWrap SHARED PyWrap.cc STree.cc ODTE.cc SVC.cc RandomForest.cc PyClassifier.cc)
#target_link_libraries(PyWrap ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy xgboost::xgboost ArffFiles)
target_link_libraries(PyWrap ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)

24
src/PyClassifiers/ODTE.cc
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#include "ODTE.h"
namespace pywrap {
ODTE::ODTE() : PyClassifier("odte", "Odte")
{
validHyperparameters = { "n_jobs", "n_estimators", "random_state" };
}
int ODTE::getNumberOfNodes() const
{
return callMethodInt("get_nodes");
}
int ODTE::getNumberOfEdges() const
{
return callMethodInt("get_leaves");
}
int ODTE::getNumberOfStates() const
{
return callMethodInt("get_depth");
}
std::string ODTE::graph()
{
return callMethodString("graph");
}
} /* namespace pywrap */

17
src/PyClassifiers/ODTE.h
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#ifndef ODTE_H
#define ODTE_H
#include "nlohmann/json.hpp"
#include "PyClassifier.h"
namespace pywrap {
class ODTE : public PyClassifier {
public:
ODTE();
~ODTE() = default;
int getNumberOfNodes() const override;
int getNumberOfEdges() const override;
int getNumberOfStates() const override;
std::string graph();
};
} /* namespace pywrap */
#endif /* ODTE_H */

96
src/PyClassifiers/PyClassifier.cc
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#include "PyClassifier.h"
namespace pywrap {
namespace bp = boost::python;
namespace np = boost::python::numpy;
PyClassifier::PyClassifier(const std::string& module, const std::string& className, bool sklearn) : module(module), className(className), sklearn(sklearn), fitted(false)
{
// This id allows to have more than one instance of the same module/class
id = reinterpret_cast<clfId_t>(this);
pyWrap = PyWrap::GetInstance();
pyWrap->importClass(id, module, className);
}
PyClassifier::~PyClassifier()
{
pyWrap->clean(id);
}
np::ndarray tensor2numpy(torch::Tensor& X)
{
int m = X.size(0);
int n = X.size(1);
auto Xn = np::from_data(X.data_ptr(), np::dtype::get_builtin<float>(), bp::make_tuple(m, n), bp::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), bp::object());
Xn = Xn.transpose();
return Xn;
}
std::pair<np::ndarray, np::ndarray> tensors2numpy(torch::Tensor& X, torch::Tensor& y)
{
int n = X.size(1);
auto yn = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), bp::make_tuple(n), bp::make_tuple(sizeof(y.dtype()) * 2), bp::object());
return { tensor2numpy(X), yn };
}
std::string PyClassifier::version()
{
if (sklearn) {
return pyWrap->sklearnVersion();
}
return pyWrap->version(id);
}
std::string PyClassifier::callMethodString(const std::string& method)
{
return pyWrap->callMethodString(id, method);
}
int PyClassifier::callMethodSumOfItems(const std::string& method) const
{
return pyWrap->callMethodSumOfItems(id, method);
}
int PyClassifier::callMethodInt(const std::string& method) const
{
return pyWrap->callMethodInt(id, method);
}
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y)
{
if (!fitted && hyperparameters.size() > 0) {
pyWrap->setHyperparameters(id, hyperparameters);
}
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = bp::incref(bp::object(Xn).ptr());
CPyObject yp = bp::incref(bp::object(yn).ptr());
pyWrap->fit(id, Xp, yp);
fitted = true;
return *this;
}
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
{
return fit(X, y);
}
torch::Tensor PyClassifier::predict(torch::Tensor& X)
{
int dimension = X.size(1);
auto Xn = tensor2numpy(X);
CPyObject Xp = bp::incref(bp::object(Xn).ptr());
PyObject* incoming = pyWrap->predict(id, Xp);
bp::handle<> handle(incoming);
bp::object object(handle);
np::ndarray prediction = np::from_object(object);
if (PyErr_Occurred()) {
PyErr_Print();
throw std::runtime_error("Error creating object for predict in " + module + " and class " + className);
}
int* data = reinterpret_cast<int*>(prediction.get_data());
std::vector<int> vPrediction(data, data + prediction.shape(0));
auto resultTensor = torch::tensor(vPrediction, torch::kInt32);
Py_XDECREF(incoming);
return resultTensor;
}
float PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
{
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = bp::incref(bp::object(Xn).ptr());
CPyObject yp = bp::incref(bp::object(yn).ptr());
float result = pyWrap->score(id, Xp, yp);
return result;
}
void PyClassifier::setHyperparameters(const nlohmann::json& hyperparameters)
{
this->hyperparameters = hyperparameters;
}
} /* namespace pywrap */

56
src/PyClassifiers/PyClassifier.h
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#ifndef PYCLASSIFIER_H
#define PYCLASSIFIER_H
#include "boost/python/detail/wrap_python.hpp"
#include <boost/python/numpy.hpp>
#include <nlohmann/json.hpp>
#include <string>
#include <map>
#include <vector>
#include <utility>
#include <torch/torch.h>
#include "PyWrap.h"
#include "Classifier.h"
#include "TypeId.h"
namespace pywrap {
class PyClassifier : public bayesnet::BaseClassifier {
public:
PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false);
virtual ~PyClassifier();
PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
// X is nxm tensor, y is nx1 tensor
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override { return *this; };
torch::Tensor predict(torch::Tensor& X) override;
std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); };
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override { return 0.0; };
float score(torch::Tensor& X, torch::Tensor& y) override;
std::string version();
std::string callMethodString(const std::string& method);
int callMethodSumOfItems(const std::string& method) const;
int callMethodInt(const std::string& method) const;
std::string getVersion() override { return this->version(); };
int getNumberOfNodes() const override { return 0; };
int getNumberOfEdges() const override { return 0; };
int getNumberOfStates() const override { return 0; };
std::vector<std::string> show() const override { return std::vector<std::string>(); }
std::vector<std::string> graph(const std::string& title = "") const override { return std::vector<std::string>(); }
bayesnet::status_t getStatus() const override { return bayesnet::NORMAL; };
std::vector<std::string> topological_order() override { return std::vector<std::string>(); }
void dump_cpt() const override {};
void setHyperparameters(const nlohmann::json& hyperparameters) override;
protected:
nlohmann::json hyperparameters;
void trainModel(const torch::Tensor& weights) override {};
private:
PyWrap* pyWrap;
std::string module;
std::string className;
bool sklearn;
clfId_t id;
bool fitted;
};
} /* namespace pywrap */
#endif /* PYCLASSIFIER_H */

15
src/PyClassifiers/PyClf.h
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#ifndef PYCLF_H
#define PYCLF_H
#include <string>
#include "DotEnv.h"
namespace PyClassifiers {
class PyClf {
public:
PyClf(const std::string& name);
virtual ~PyClf();
private:
std::string name;
};
} /* namespace PyClassifiers */
#endif /* PYCLF_H */

87
src/PyClassifiers/PyHelper.hpp
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#ifndef PYHELPER_HPP
#define PYHELPER_HPP
#pragma once
// Code taken and adapted from
// https ://www.codeproject.com/Articles/820116/Embedding-Python-program-in-a-C-Cplusplus-code
#include "boost/python/detail/wrap_python.hpp"
#include <boost/python/numpy.hpp>
#include <iostream>
namespace pywrap {
namespace p = boost::python;
namespace np = boost::python::numpy;
class CPyInstance {
public:
CPyInstance()
{
Py_Initialize();
np::initialize();
}
~CPyInstance()
{
Py_Finalize();
}
};
class CPyObject {
private:
PyObject* p;
public:
CPyObject() : p(NULL)
{
}
CPyObject(PyObject* _p) : p(_p)
{
}
~CPyObject()
{
Release();
}
PyObject* getObject()
{
return p;
}
PyObject* setObject(PyObject* _p)
{
return (p = _p);
}
PyObject* AddRef()
{
if (p) {
Py_INCREF(p);
}
return p;
}
void Release()
{
if (p) {
Py_XDECREF(p);
}
p = NULL;
}
PyObject* operator ->()
{
return p;
}
bool is()
{
return p ? true : false;
}
operator PyObject* ()
{
return p;
}
PyObject* operator = (PyObject* pp)
{
p = pp;
return p;
}
operator bool()
{
return p ? true : false;
}
};
} /* namespace pywrap */
#endif

255
src/PyClassifiers/PyWrap.cc
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#define PY_SSIZE_T_CLEAN
#include <stdexcept>
#include "PyWrap.h"
#include <string>
#include <map>
#include <sstream>
#include <boost/python/numpy.hpp>
#include <iostream>
namespace pywrap {
namespace np = boost::python::numpy;
PyWrap* PyWrap::wrapper = nullptr;
std::mutex PyWrap::mutex;
CPyInstance* PyWrap::pyInstance = nullptr;
auto moduleClassMap = std::map<std::pair<std::string, std::string>, std::tuple<PyObject*, PyObject*, PyObject*>>();
PyWrap* PyWrap::GetInstance()
{
std::lock_guard<std::mutex> lock(mutex);
if (wrapper == nullptr) {
wrapper = new PyWrap();
pyInstance = new CPyInstance();
PyRun_SimpleString("import warnings;warnings.filterwarnings('ignore')");
}
return wrapper;
}
void PyWrap::RemoveInstance()
{
if (wrapper != nullptr) {
if (pyInstance != nullptr) {
delete pyInstance;
}
pyInstance = nullptr;
if (wrapper != nullptr) {
delete wrapper;
}
wrapper = nullptr;
}
}
void PyWrap::importClass(const clfId_t id, const std::string& moduleName, const std::string& className)
{
std::lock_guard<std::mutex> lock(mutex);
auto result = moduleClassMap.find(id);
if (result != moduleClassMap.end()) {
return;
}
PyObject* module = PyImport_ImportModule(moduleName.c_str());
if (PyErr_Occurred()) {
errorAbort("Couldn't import module " + moduleName);
}
PyObject* classObject = PyObject_GetAttrString(module, className.c_str());
if (PyErr_Occurred()) {
errorAbort("Couldn't find class " + className);
}
PyObject* instance = PyObject_CallObject(classObject, NULL);
if (PyErr_Occurred()) {
errorAbort("Couldn't create instance of class " + className);
}
moduleClassMap.insert({ id, { module, classObject, instance } });
}
void PyWrap::clean(const clfId_t id)
{
// Remove Python interpreter if no more modules imported left
std::lock_guard<std::mutex> lock(mutex);
auto result = moduleClassMap.find(id);
if (result == moduleClassMap.end()) {
return;
}
Py_DECREF(std::get<0>(result->second));
Py_DECREF(std::get<1>(result->second));
Py_DECREF(std::get<2>(result->second));
moduleClassMap.erase(result);
if (PyErr_Occurred()) {
PyErr_Print();
errorAbort("Error cleaning module ");
}
// With boost you can't remove the interpreter
// https://www.boost.org/doc/libs/1_83_0/libs/python/doc/html/tutorial/tutorial/embedding.html#tutorial.embedding.getting_started
// if (moduleClassMap.empty()) {
// RemoveInstance();
// }
}
void PyWrap::errorAbort(const std::string& message)
{
std::cerr << message << std::endl;
PyErr_Print();
RemoveInstance();
exit(1);
}
PyObject* PyWrap::getClass(const clfId_t id)
{
auto item = moduleClassMap.find(id);
if (item == moduleClassMap.end()) {
errorAbort("Module not found");
}
return std::get<2>(item->second);
}
std::string PyWrap::callMethodString(const clfId_t id, const std::string& method)
{
PyObject* instance = getClass(id);
PyObject* result;
try {
if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
errorAbort("Couldn't call method " + method);
}
catch (const std::exception& e) {
errorAbort(e.what());
}
std::string value = PyUnicode_AsUTF8(result);
Py_XDECREF(result);
return value;
}
int PyWrap::callMethodInt(const clfId_t id, const std::string& method)
{
PyObject* instance = getClass(id);
PyObject* result;
try {
if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
errorAbort("Couldn't call method " + method);
}
catch (const std::exception& e) {
errorAbort(e.what());
}
int value = PyLong_AsLong(result);
Py_XDECREF(result);
return value;
}
std::string PyWrap::sklearnVersion()
{
PyObject* sklearnModule = PyImport_ImportModule("sklearn");
if (sklearnModule == nullptr) {
errorAbort("Couldn't import sklearn");
}
PyObject* versionAttr = PyObject_GetAttrString(sklearnModule, "__version__");
if (versionAttr == nullptr || !PyUnicode_Check(versionAttr)) {
Py_XDECREF(sklearnModule);
errorAbort("Couldn't get sklearn version");
}
std::string result = PyUnicode_AsUTF8(versionAttr);
Py_XDECREF(versionAttr);
Py_XDECREF(sklearnModule);
return result;
}
std::string PyWrap::version(const clfId_t id)
{
return callMethodString(id, "version");
}
int PyWrap::callMethodSumOfItems(const clfId_t id, const std::string& method)
{
// Call method on each estimator and sum the results (made for RandomForest)
PyObject* instance = getClass(id);
PyObject* estimators = PyObject_GetAttrString(instance, "estimators_");
if (estimators == nullptr) {
errorAbort("Failed to get attribute: " + method);
}
int sumOfItems = 0;
Py_ssize_t len = PyList_Size(estimators);
for (Py_ssize_t i = 0; i < len; i++) {
PyObject* estimator = PyList_GetItem(estimators, i);
PyObject* result;
if (method == "node_count") {
PyObject* owner = PyObject_GetAttrString(estimator, "tree_");
if (owner == nullptr) {
Py_XDECREF(estimators);
errorAbort("Failed to get attribute tree_ for: " + method);
}
result = PyObject_GetAttrString(owner, method.c_str());
if (result == nullptr) {
Py_XDECREF(estimators);
Py_XDECREF(owner);
errorAbort("Failed to get attribute node_count: " + method);
}
Py_DECREF(owner);
} else {
result = PyObject_CallMethod(estimator, method.c_str(), nullptr);
if (result == nullptr) {
Py_XDECREF(estimators);
errorAbort("Failed to call method: " + method);
}
}
sumOfItems += PyLong_AsLong(result);
Py_DECREF(result);
}
Py_DECREF(estimators);
return sumOfItems;
}
void PyWrap::setHyperparameters(const clfId_t id, const json& hyperparameters)
{
// Set hyperparameters as attributes of the class
PyObject* pValue;
PyObject* instance = getClass(id);
for (const auto& [key, value] : hyperparameters.items()) {
std::stringstream oss;
oss << value.type_name();
if (oss.str() == "string") {
pValue = Py_BuildValue("s", value.get<std::string>().c_str());
} else {
if (value.is_number_integer()) {
pValue = Py_BuildValue("i", value.get<int>());
} else {
pValue = Py_BuildValue("f", value.get<double>());
}
}
int res = PyObject_SetAttrString(instance, key.c_str(), pValue);
if (res == -1 && PyErr_Occurred()) {
Py_XDECREF(pValue);
errorAbort("Couldn't set attribute " + key + "=" + value.dump());
}
Py_XDECREF(pValue);
}
}
void PyWrap::fit(const clfId_t id, CPyObject& X, CPyObject& y)
{
PyObject* instance = getClass(id);
CPyObject result;
CPyObject method = PyUnicode_FromString("fit");
try {
if (!(result = PyObject_CallMethodObjArgs(instance, method.getObject(), X.getObject(), y.getObject(), NULL)))
errorAbort("Couldn't call method fit");
}
catch (const std::exception& e) {
errorAbort(e.what());
}
}
PyObject* PyWrap::predict(const clfId_t id, CPyObject& X)
{
PyObject* instance = getClass(id);
PyObject* result;
CPyObject method = PyUnicode_FromString("predict");
try {
if (!(result = PyObject_CallMethodObjArgs(instance, method.getObject(), X.getObject(), NULL)))
errorAbort("Couldn't call method predict");
}
catch (const std::exception& e) {
errorAbort(e.what());
}
Py_INCREF(result);
return result; // Caller must free this object
}
double PyWrap::score(const clfId_t id, CPyObject& X, CPyObject& y)
{
PyObject* instance = getClass(id);
CPyObject result;
CPyObject method = PyUnicode_FromString("score");
try {
if (!(result = PyObject_CallMethodObjArgs(instance, method.getObject(), X.getObject(), y.getObject(), NULL)))
errorAbort("Couldn't call method score");
}
catch (const std::exception& e) {
errorAbort(e.what());
}
double resultValue = PyFloat_AsDouble(result);
return resultValue;
}
}

49
src/PyClassifiers/PyWrap.h
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#ifndef PYWRAP_H
#define PYWRAP_H
#include "boost/python/detail/wrap_python.hpp"
#include <string>
#include <map>
#include <tuple>
#include <mutex>
#include <nlohmann/json.hpp>
#include "PyHelper.hpp"
#include "TypeId.h"
#pragma once
namespace pywrap {
/*
Singleton class to handle Python/numpy interpreter.
*/
using json = nlohmann::json;
class PyWrap {
public:
PyWrap() = default;
PyWrap(PyWrap& other) = delete;
static PyWrap* GetInstance();
void operator=(const PyWrap&) = delete;
~PyWrap() = default;
std::string callMethodString(const clfId_t id, const std::string& method);
int callMethodInt(const clfId_t id, const std::string& method);
std::string sklearnVersion();
std::string version(const clfId_t id);
int callMethodSumOfItems(const clfId_t id, const std::string& method);
void setHyperparameters(const clfId_t id, const json& hyperparameters);
void fit(const clfId_t id, CPyObject& X, CPyObject& y);
PyObject* predict(const clfId_t id, CPyObject& X);
double score(const clfId_t id, CPyObject& X, CPyObject& y);
void clean(const clfId_t id);
void importClass(const clfId_t id, const std::string& moduleName, const std::string& className);
PyObject* getClass(const clfId_t id);
private:
// Only call RemoveInstance from clean method
static void RemoveInstance();
void errorAbort(const std::string& message);
// No need to use static map here, since this class is a singleton
std::map<clfId_t, std::tuple<PyObject*, PyObject*, PyObject*>> moduleClassMap;
static CPyInstance* pyInstance;
static PyWrap* wrapper;
static std::mutex mutex;
};
} /* namespace pywrap */
#endif /* PYWRAP_H */

18
src/PyClassifiers/Pyclf.cc
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@ -1,18 +0,0 @@
#include "PyClf.h"
namespace PyClassifiers {
PyClf::PyClf(const std::std::string& name) : name(name)
{
env = platform::DotEnv();
}
PyClf::~PyClf()
{
}
} /* namespace PyClassifiers */

20
src/PyClassifiers/RandomForest.cc
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@ -1,20 +0,0 @@
#include "RandomForest.h"
namespace pywrap {
RandomForest::RandomForest() : PyClassifier("sklearn.ensemble", "RandomForestClassifier", true)
{
validHyperparameters = { "n_estimators", "n_jobs", "random_state" };
}
int RandomForest::getNumberOfEdges() const
{
return callMethodSumOfItems("get_n_leaves");
}
int RandomForest::getNumberOfStates() const
{
return callMethodSumOfItems("get_depth");
}
int RandomForest::getNumberOfNodes() const
{
return callMethodSumOfItems("node_count");
}
} /* namespace pywrap */

15
src/PyClassifiers/RandomForest.h
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@ -1,15 +0,0 @@
#ifndef RANDOMFOREST_H
#define RANDOMFOREST_H
#include "PyClassifier.h"
namespace pywrap {
class RandomForest : public PyClassifier {
public:
RandomForest();
~RandomForest() = default;
int getNumberOfEdges() const override;
int getNumberOfStates() const override;
int getNumberOfNodes() const override;
};
} /* namespace pywrap */
#endif /* RANDOMFOREST_H */

24
src/PyClassifiers/STree.cc
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@ -1,24 +0,0 @@
#include "STree.h"
namespace pywrap {
STree::STree() : PyClassifier("stree", "Stree")
{
validHyperparameters = { "C", "kernel", "max_iter", "max_depth", "random_state", "multiclass_strategy", "gamma", "max_features", "degree" };
};
int STree::getNumberOfNodes() const
{
return callMethodInt("get_nodes");
}
int STree::getNumberOfEdges() const
{
return callMethodInt("get_leaves");
}
int STree::getNumberOfStates() const
{
return callMethodInt("get_depth");
}
std::string STree::graph()
{
return callMethodString("graph");
}
} /* namespace pywrap */

17
src/PyClassifiers/STree.h
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@ -1,17 +0,0 @@
#ifndef STREE_H
#define STREE_H
#include "nlohmann/json.hpp"
#include "PyClassifier.h"
namespace pywrap {
class STree : public PyClassifier {
public:
STree();
~STree() = default;
int getNumberOfNodes() const override;
int getNumberOfEdges() const override;
int getNumberOfStates() const override;
std::string graph();
};
} /* namespace pywrap */
#endif /* STREE_H */

8
src/PyClassifiers/SVC.cc
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@ -1,8 +0,0 @@
#include "SVC.h"
namespace pywrap {
SVC::SVC() : PyClassifier("sklearn.svm", "SVC", true)
{
validHyperparameters = { "C", "gamma", "kernel", "random_state" };
}
} /* namespace pywrap */

13
src/PyClassifiers/SVC.h
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@ -1,13 +0,0 @@
#ifndef SVC_H
#define SVC_H
#include "PyClassifier.h"
namespace pywrap {
class SVC : public PyClassifier {
public:
SVC();
~SVC() = default;
};
} /* namespace pywrap */
#endif /* SVC_H */

6
src/PyClassifiers/TypeId.h
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@ -1,6 +0,0 @@
#ifndef TYPEDEF_H
#define TYPEDEF_H
namespace pywrap {
typedef uint64_t clfId_t;
}
#endif /* TYPEDEF_H */

18
src/PyClassifiers/XGBoost.cc
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@ -1,18 +0,0 @@
#include "XGBoost.h"
See https ://stackoverflow.com/questions/36071672/using-xgboost-in-c
namespace pywrap {
std::string XGBoost::version()
{
return callMethodString("1.0");
}
} /* namespace pywrap */

13
src/PyClassifiers/XGBoost.h
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@ -1,13 +0,0 @@
#ifndef XGBOOST_H
#define XGBOOST_H
#include "PyClassifier.h"
namespace pywrap {
class XGBoost : public PyClassifier {
public:
XGBoost() : PyClassifier("xgboost", "XGBClassifier") {};
~XGBoost() = default;
std::string version();
};
} /* namespace pywrap */
#endif /* XGBOOST_H */

835
stree_results.json
View File

@ -1,835 +0,0 @@
[
{
"date": "2021-04-11",
"time": "18:46:29",
"type": "crossval",
"classifier": "stree",
"dataset": "balance-scale",
"accuracy": "0.97056",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 10000.0, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.0135214",
"time_spent_std": "0.00111213",
"accuracy_std": "0.0150468",
"nodes": "7.0",
"leaves": "4.0",
"depth": "3.0"
},
{
"date": "2021-04-11",
"time": "18:46:29",
"type": "crossval",
"classifier": "stree",
"dataset": "balloons",
"accuracy": "0.86",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.000804768",
"time_spent_std": "7.74797e-05",
"accuracy_std": "0.285015",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:46:29",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer-wisc-diag",
"accuracy": "0.972764",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.2, \"max_iter\": 10000.0}",
"time_spent": "0.00380772",
"time_spent_std": "0.000638676",
"accuracy_std": "0.0173132",
"nodes": "3.24",
"leaves": "2.12",
"depth": "2.12"
},
{
"date": "2021-04-11",
"time": "18:46:30",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer-wisc-prog",
"accuracy": "0.811128",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.2, \"max_iter\": 10000.0}",
"time_spent": "0.00767535",
"time_spent_std": "0.00148114",
"accuracy_std": "0.0584601",
"nodes": "5.84",
"leaves": "3.42",
"depth": "3.24"
},
{
"date": "2021-04-11",
"time": "18:46:31",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer-wisc",
"accuracy": "0.966661",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00652217",
"time_spent_std": "0.000726579",
"accuracy_std": "0.0139421",
"nodes": "8.88",
"leaves": "4.94",
"depth": "4.08"
},
{
"date": "2021-04-11",
"time": "18:46:32",
"type": "crossval",
"classifier": "stree",
"dataset": "breast-cancer",
"accuracy": "0.734211",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.023475",
"time_spent_std": "0.00584447",
"accuracy_std": "0.0479774",
"nodes": "21.72",
"leaves": "11.36",
"depth": "5.86"
},
{
"date": "2021-04-11",
"time": "18:49:08",
"type": "crossval",
"classifier": "stree",
"dataset": "cardiotocography-10clases",
"accuracy": "0.791487",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "3.10582",
"time_spent_std": "0.339218",
"accuracy_std": "0.0192082",
"nodes": "160.76",
"leaves": "80.88",
"depth": "22.86"
},
{
"date": "2021-04-11",
"time": "18:50:01",
"type": "crossval",
"classifier": "stree",
"dataset": "cardiotocography-3clases",
"accuracy": "0.900613",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "1.05228",
"time_spent_std": "0.138768",
"accuracy_std": "0.0154004",
"nodes": "47.68",
"leaves": "24.34",
"depth": "8.84"
},
{
"date": "2021-04-11",
"time": "18:50:01",
"type": "crossval",
"classifier": "stree",
"dataset": "conn-bench-sonar-mines-rocks",
"accuracy": "0.755528",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.011577",
"time_spent_std": "0.00341148",
"accuracy_std": "0.0678424",
"nodes": "6.08",
"leaves": "3.54",
"depth": "2.86"
},
{
"date": "2021-04-11",
"time": "18:50:17",
"type": "crossval",
"classifier": "stree",
"dataset": "cylinder-bands",
"accuracy": "0.715049",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.301143",
"time_spent_std": "0.109773",
"accuracy_std": "0.0367646",
"nodes": "26.2",
"leaves": "13.6",
"depth": "6.82"
},
{
"date": "2021-04-11",
"time": "18:50:19",
"type": "crossval",
"classifier": "stree",
"dataset": "dermatology",
"accuracy": "0.971833",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 55, \"max_iter\": 10000.0}",
"time_spent": "0.0377538",
"time_spent_std": "0.010726",
"accuracy_std": "0.0206883",
"nodes": "11.0",
"leaves": "6.0",
"depth": "6.0"
},
{
"date": "2021-04-11",
"time": "18:50:19",
"type": "crossval",
"classifier": "stree",
"dataset": "echocardiogram",
"accuracy": "0.814758",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_features\": \"auto\", \"max_iter\": 10000.0}",
"time_spent": "0.00333449",
"time_spent_std": "0.000964686",
"accuracy_std": "0.0998078",
"nodes": "7.0",
"leaves": "4.0",
"depth": "3.54"
},
{
"date": "2021-04-11",
"time": "18:50:20",
"type": "crossval",
"classifier": "stree",
"dataset": "fertility",
"accuracy": "0.88",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_features\": \"auto\", \"max_iter\": 10000.0}",
"time_spent": "0.00090271",
"time_spent_std": "8.96446e-05",
"accuracy_std": "0.0547723",
"nodes": "1.0",
"leaves": "1.0",
"depth": "1.0"
},
{
"date": "2021-04-11",
"time": "18:50:21",
"type": "crossval",
"classifier": "stree",
"dataset": "haberman-survival",
"accuracy": "0.735637",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0171611",
"time_spent_std": "0.00334945",
"accuracy_std": "0.0434614",
"nodes": "23.4",
"leaves": "12.2",
"depth": "5.98"
},
{
"date": "2021-04-11",
"time": "18:50:21",
"type": "crossval",
"classifier": "stree",
"dataset": "heart-hungarian",
"accuracy": "0.827522",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.00493946",
"time_spent_std": "0.000738198",
"accuracy_std": "0.0505283",
"nodes": "10.16",
"leaves": "5.58",
"depth": "4.0"
},
{
"date": "2021-04-11",
"time": "18:50:21",
"type": "crossval",
"classifier": "stree",
"dataset": "hepatitis",
"accuracy": "0.824516",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.0021534",
"time_spent_std": "0.000133715",
"accuracy_std": "0.0738872",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:50:23",
"type": "crossval",
"classifier": "stree",
"dataset": "ilpd-indian-liver",
"accuracy": "0.723498",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0345243",
"time_spent_std": "0.015789",
"accuracy_std": "0.0384886",
"nodes": "16.04",
"leaves": "8.52",
"depth": "5.28"
},
{
"date": "2021-04-11",
"time": "18:50:24",
"type": "crossval",
"classifier": "stree",
"dataset": "ionosphere",
"accuracy": "0.953276",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.00881722",
"time_spent_std": "0.000843108",
"accuracy_std": "0.0238537",
"nodes": "3.16",
"leaves": "2.08",
"depth": "2.08"
},
{
"date": "2021-04-11",
"time": "18:50:24",
"type": "crossval",
"classifier": "stree",
"dataset": "iris",
"accuracy": "0.965333",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00357342",
"time_spent_std": "0.000400509",
"accuracy_std": "0.0319444",
"nodes": "5.0",
"leaves": "3.0",
"depth": "3.0"
},
{
"date": "2021-04-11",
"time": "18:50:36",
"type": "crossval",
"classifier": "stree",
"dataset": "led-display",
"accuracy": "0.703",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.222106",
"time_spent_std": "0.0116922",
"accuracy_std": "0.0291204",
"nodes": "47.16",
"leaves": "24.08",
"depth": "17.76"
},
{
"date": "2021-04-11",
"time": "18:51:18",
"type": "crossval",
"classifier": "stree",
"dataset": "libras",
"accuracy": "0.788611",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.08, \"max_iter\": 10000.0}",
"time_spent": "0.841714",
"time_spent_std": "0.0830966",
"accuracy_std": "0.0516913",
"nodes": "82.28",
"leaves": "41.64",
"depth": "28.84"
},
{
"date": "2021-04-11",
"time": "18:51:41",
"type": "crossval",
"classifier": "stree",
"dataset": "low-res-spect",
"accuracy": "0.883782",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.446301",
"time_spent_std": "0.0411822",
"accuracy_std": "0.0324593",
"nodes": "27.4",
"leaves": "14.2",
"depth": "10.74"
},
{
"date": "2021-04-11",
"time": "18:51:41",
"type": "crossval",
"classifier": "stree",
"dataset": "lymphography",
"accuracy": "0.835034",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.00539465",
"time_spent_std": "0.000754365",
"accuracy_std": "0.0590649",
"nodes": "9.04",
"leaves": "5.02",
"depth": "4.48"
},
{
"date": "2021-04-11",
"time": "18:51:43",
"type": "crossval",
"classifier": "stree",
"dataset": "mammographic",
"accuracy": "0.81915",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0227931",
"time_spent_std": "0.00328533",
"accuracy_std": "0.0222517",
"nodes": "7.4",
"leaves": "4.2",
"depth": "4.0"
},
{
"date": "2021-04-11",
"time": "18:51:43",
"type": "crossval",
"classifier": "stree",
"dataset": "molec-biol-promoter",
"accuracy": "0.767056",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.00130273",
"time_spent_std": "0.000105772",
"accuracy_std": "0.0910923",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:51:44",
"type": "crossval",
"classifier": "stree",
"dataset": "musk-1",
"accuracy": "0.916388",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.0116367",
"time_spent_std": "0.000331845",
"accuracy_std": "0.0275208",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:51:55",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_merluccius_nucleus_4d",
"accuracy": "0.835125",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 8.25, \"gamma\": 0.1, \"kernel\": \"poly\"}",
"time_spent": "0.208895",
"time_spent_std": "0.0270573",
"accuracy_std": "0.0220961",
"nodes": "10.52",
"leaves": "5.76",
"depth": "4.42"
},
{
"date": "2021-04-11",
"time": "18:52:04",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_merluccius_states_2f",
"accuracy": "0.915365",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.182198",
"time_spent_std": "0.0294267",
"accuracy_std": "0.020396",
"nodes": "18.04",
"leaves": "9.52",
"depth": "5.3"
},
{
"date": "2021-04-11",
"time": "18:52:41",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_trisopterus_nucleus_2f",
"accuracy": "0.800986",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.717113",
"time_spent_std": "0.209608",
"accuracy_std": "0.0218449",
"nodes": "29.88",
"leaves": "15.44",
"depth": "7.38"
},
{
"date": "2021-04-11",
"time": "18:52:44",
"type": "crossval",
"classifier": "stree",
"dataset": "oocytes_trisopterus_states_5b",
"accuracy": "0.922249",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.11, \"max_iter\": 10000.0}",
"time_spent": "0.0545047",
"time_spent_std": "0.00853014",
"accuracy_std": "0.0179203",
"nodes": "7.44",
"leaves": "4.22",
"depth": "3.6"
},
{
"date": "2021-04-11",
"time": "18:52:44",
"type": "crossval",
"classifier": "stree",
"dataset": "parkinsons",
"accuracy": "0.882051",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00795048",
"time_spent_std": "0.00176761",
"accuracy_std": "0.0478327",
"nodes": "8.48",
"leaves": "4.74",
"depth": "3.76"
},
{
"date": "2021-04-11",
"time": "18:52:48",
"type": "crossval",
"classifier": "stree",
"dataset": "pima",
"accuracy": "0.766651",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0750048",
"time_spent_std": "0.0213995",
"accuracy_std": "0.0297203",
"nodes": "17.4",
"leaves": "9.2",
"depth": "5.66"
},
{
"date": "2021-04-11",
"time": "18:52:48",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-MATERIAL",
"accuracy": "0.867749",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.00293318",
"time_spent_std": "0.000331469",
"accuracy_std": "0.0712226",
"nodes": "5.16",
"leaves": "3.08",
"depth": "3.02"
},
{
"date": "2021-04-11",
"time": "18:52:49",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-REL-L",
"accuracy": "0.632238",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0136311",
"time_spent_std": "0.00322964",
"accuracy_std": "0.101211",
"nodes": "16.32",
"leaves": "8.66",
"depth": "5.96"
},
{
"date": "2021-04-11",
"time": "18:52:50",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-SPAN",
"accuracy": "0.659766",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_iter\": 10000.0}",
"time_spent": "0.00524256",
"time_spent_std": "0.00158822",
"accuracy_std": "0.1165",
"nodes": "9.84",
"leaves": "5.42",
"depth": "4.58"
},
{
"date": "2021-04-11",
"time": "18:52:50",
"type": "crossval",
"classifier": "stree",
"dataset": "pittsburg-bridges-T-OR-D",
"accuracy": "0.861619",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00295627",
"time_spent_std": "0.000578594",
"accuracy_std": "0.0693747",
"nodes": "4.56",
"leaves": "2.78",
"depth": "2.68"
},
{
"date": "2021-04-11",
"time": "18:52:50",
"type": "crossval",
"classifier": "stree",
"dataset": "planning",
"accuracy": "0.73527",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"gamma\": 10.0, \"kernel\": \"rbf\", \"max_iter\": 10000.0}",
"time_spent": "0.0030475",
"time_spent_std": "0.000172266",
"accuracy_std": "0.0669776",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:52:51",
"type": "crossval",
"classifier": "stree",
"dataset": "post-operative",
"accuracy": "0.711111",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 55, \"degree\": 5, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.0018727",
"time_spent_std": "0.000481977",
"accuracy_std": "0.0753592",
"nodes": "2.64",
"leaves": "1.82",
"depth": "1.82"
},
{
"date": "2021-04-11",
"time": "18:52:52",
"type": "crossval",
"classifier": "stree",
"dataset": "seeds",
"accuracy": "0.952857",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 10000.0, \"max_iter\": 10000.0}",
"time_spent": "0.0203492",
"time_spent_std": "0.00518065",
"accuracy_std": "0.0279658",
"nodes": "9.88",
"leaves": "5.44",
"depth": "4.44"
},
{
"date": "2021-04-11",
"time": "18:52:52",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-australian-credit",
"accuracy": "0.678261",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.05, \"max_features\": \"auto\", \"max_iter\": 10000.0}",
"time_spent": "0.00205337",
"time_spent_std": "0.00083162",
"accuracy_std": "0.0390498",
"nodes": "1.32",
"leaves": "1.16",
"depth": "1.16"
},
{
"date": "2021-04-11",
"time": "18:53:07",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-german-credit",
"accuracy": "0.7625",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.290754",
"time_spent_std": "0.0653152",
"accuracy_std": "0.0271892",
"nodes": "21.24",
"leaves": "11.12",
"depth": "6.18"
},
{
"date": "2021-04-11",
"time": "18:53:09",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-heart",
"accuracy": "0.822963",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.0138923",
"time_spent_std": "0.00323664",
"accuracy_std": "0.044004",
"nodes": "14.56",
"leaves": "7.78",
"depth": "5.0"
},
{
"date": "2021-04-11",
"time": "18:56:43",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-image",
"accuracy": "0.955931",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 7, \"max_iter\": 10000.0}",
"time_spent": "4.27584",
"time_spent_std": "0.200362",
"accuracy_std": "0.00956073",
"nodes": "36.92",
"leaves": "18.96",
"depth": "10.8"
},
{
"date": "2021-04-11",
"time": "18:56:57",
"type": "crossval",
"classifier": "stree",
"dataset": "statlog-vehicle",
"accuracy": "0.793028",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.278833",
"time_spent_std": "0.0392173",
"accuracy_std": "0.030104",
"nodes": "23.88",
"leaves": "12.44",
"depth": "7.06"
},
{
"date": "2021-04-11",
"time": "18:57:07",
"type": "crossval",
"classifier": "stree",
"dataset": "synthetic-control",
"accuracy": "0.95",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.55, \"max_iter\": 10000.0}",
"time_spent": "0.205184",
"time_spent_std": "0.040793",
"accuracy_std": "0.0253859",
"nodes": "12.48",
"leaves": "6.74",
"depth": "6.5"
},
{
"date": "2021-04-11",
"time": "18:57:08",
"type": "crossval",
"classifier": "stree",
"dataset": "tic-tac-toe",
"accuracy": "0.984444",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.2, \"gamma\": 0.1, \"kernel\": \"poly\", \"max_iter\": 10000.0}",
"time_spent": "0.0123015",
"time_spent_std": "0.000423728",
"accuracy_std": "0.00838747",
"nodes": "3.0",
"leaves": "2.0",
"depth": "2.0"
},
{
"date": "2021-04-11",
"time": "18:57:09",
"type": "crossval",
"classifier": "stree",
"dataset": "vertebral-column-2clases",
"accuracy": "0.852903",
"norm": 1,
"stand": 0,
"parameters": "{}",
"time_spent": "0.00576833",
"time_spent_std": "0.000910332",
"accuracy_std": "0.0408851",
"nodes": "6.04",
"leaves": "3.52",
"depth": "3.34"
},
{
"date": "2021-04-11",
"time": "18:57:09",
"type": "crossval",
"classifier": "stree",
"dataset": "wine",
"accuracy": "0.979159",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.55, \"max_iter\": 10000.0}",
"time_spent": "0.0019741",
"time_spent_std": "0.000137745",
"accuracy_std": "0.022427",
"nodes": "5.0",
"leaves": "3.0",
"depth": "3.0"
},
{
"date": "2021-04-11",
"time": "18:57:10",
"type": "crossval",
"classifier": "stree",
"dataset": "zoo",
"accuracy": "0.957524",
"norm": 1,
"stand": 0,
"parameters": "{\"C\": 0.1, \"max_iter\": 10000.0}",
"time_spent": "0.00556221",
"time_spent_std": "0.000230106",
"accuracy_std": "0.0454615",
"nodes": "13.04",
"leaves": "7.02",
"depth": "7.02"
}
]

23
tests/CMakeLists.txt
View File

@ -1,18 +1,17 @@
if(ENABLE_TESTING) if(ENABLE_TESTING)
set(TEST_BAYESNET "unit_tests_bayesnet") set(TEST_BAYESNET "unit_tests_bayesnet")
set(TEST_PLATFORM "unit_tests_platform") include_directories(
include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet) ${BayesNet_SOURCE_DIR}/src/BayesNet
include_directories(${BayesNet_SOURCE_DIR}/src/Platform) ${BayesNet_SOURCE_DIR}/src/Platform
include_directories(${BayesNet_SOURCE_DIR}/lib/Files) ${BayesNet_SOURCE_DIR}/lib/Files
include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp) ${BayesNet_SOURCE_DIR}/lib/mdlp
include_directories(${BayesNet_SOURCE_DIR}/lib/json/include) ${BayesNet_SOURCE_DIR}/lib/folding
include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include) ${BayesNet_SOURCE_DIR}/lib/json/include
set(TEST_SOURCES_BAYESNET TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCES}) ${BayesNet_SOURCE_DIR}/lib/argparse/include
set(TEST_SOURCES_PLATFORM TestFolding.cc TestUtils.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc) ${CMAKE_BINARY_DIR}/configured_files/include
)
set(TEST_SOURCES_BAYESNET TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCES})
add_executable(${TEST_BAYESNET} ${TEST_SOURCES_BAYESNET}) add_executable(${TEST_BAYESNET} ${TEST_SOURCES_BAYESNET})
add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
target_link_libraries(${TEST_BAYESNET} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain) target_link_libraries(${TEST_BAYESNET} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
add_test(NAME ${TEST_BAYESNET} COMMAND ${TEST_BAYESNET}) add_test(NAME ${TEST_BAYESNET} COMMAND ${TEST_BAYESNET})
add_test(NAME ${TEST_PLATFORM} COMMAND ${TEST_PLATFORM})
endif(ENABLE_TESTING) endif(ENABLE_TESTING)

8
tests/TestBayesModels.cc
View File

@ -2,9 +2,9 @@
#include <catch2/catch_test_macros.hpp> #include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp> #include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp> #include <catch2/generators/catch_generators.hpp>
#include <std::vector> #include <vector>
#include <map> #include <map>
#include <std::string> #include <string>
#include "KDB.h" #include "KDB.h"
#include "TAN.h" #include "TAN.h"
#include "SPODE.h" #include "SPODE.h"
@ -126,7 +126,7 @@ TEST_CASE("Models features", "[BayesNet]")
auto raw = RawDatasets("iris", true); auto raw = RawDatasets("iris", true);
auto clf = bayesnet::TAN(); auto clf = bayesnet::TAN();
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv); clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 6); REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 7); REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "}); REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
REQUIRE(clf.graph("Test") == graph); REQUIRE(clf.graph("Test") == graph);
@ -136,6 +136,6 @@ TEST_CASE("Get num features & num edges", "[BayesNet]")
auto raw = RawDatasets("iris", true); auto raw = RawDatasets("iris", true);
auto clf = bayesnet::KDB(2); auto clf = bayesnet::KDB(2);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv); clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 6); REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 8); REQUIRE(clf.getNumberOfEdges() == 8);
} }

4
tests/TestBayesNetwork.cc
View File

@ -1,11 +1,11 @@
#include <catch2/catch_test_macros.hpp> #include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp> #include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp> #include <catch2/generators/catch_generators.hpp>
#include <std::string> #include <string>
#include "TestUtils.h" #include "TestUtils.h"
#include "Network.h" #include "Network.h"
void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::std::string& className) void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::string& className)
{ {
std::vector<pair<int, int>> network = { {0, 1}, {0, 2}, {1, 3} }; std::vector<pair<int, int>> network = { {0, 1}, {0, 2}, {1, 3} };
for (const auto& feature : features) { for (const auto& feature : features) {

95
tests/TestFolding.cc
View File

@ -1,95 +0,0 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include "TestUtils.h"
#include "Folding.h"
TEST_CASE("KFold Test", "[Platform][KFold]")
{
// Initialize a KFold object with k=5 and a seed of 19.
std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
auto raw = RawDatasets(file_name, true);
int nFolds = 5;
platform::KFold kfold(nFolds, raw.nSamples, 19);
int number = raw.nSamples * (kfold.getNumberOfFolds() - 1) / kfold.getNumberOfFolds();
SECTION("Number of Folds")
{
REQUIRE(kfold.getNumberOfFolds() == nFolds);
}
SECTION("Fold Test")
{
// Test each fold's size and contents.
for (int i = 0; i < nFolds; ++i) {
auto [train_indices, test_indices] = kfold.getFold(i);
bool result = train_indices.size() == number || train_indices.size() == number + 1;
REQUIRE(result);
REQUIRE(train_indices.size() + test_indices.size() == raw.nSamples);
}
}
}
map<int, int> counts(std::vector<int> y, std::vector<int> indices)
{
map<int, int> result;
for (auto i = 0; i < indices.size(); ++i) {
result[y[indices[i]]]++;
}
return result;
}
TEST_CASE("StratifiedKFold Test", "[Platform][StratifiedKFold]")
{
// Initialize a StratifiedKFold object with k=3, using the y std::vector, and a seed of 17.
std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
int nFolds = GENERATE(3, 5, 10);
auto raw = RawDatasets(file_name, true);
platform::StratifiedKFold stratified_kfoldt(nFolds, raw.yt, 17);
platform::StratifiedKFold stratified_kfoldv(nFolds, raw.yv, 17);
int number = raw.nSamples * (stratified_kfoldt.getNumberOfFolds() - 1) / stratified_kfoldt.getNumberOfFolds();
SECTION("Stratified Number of Folds")
{
REQUIRE(stratified_kfoldt.getNumberOfFolds() == nFolds);
}
SECTION("Stratified Fold Test")
{
// Test each fold's size and contents.
auto counts = map<int, std::vector<int>>();
// Initialize the counts per Fold
for (int i = 0; i < nFolds; ++i) {
counts[i] = std::vector<int>(raw.classNumStates, 0);
}
// Check fold and compute counts of each fold
for (int fold = 0; fold < nFolds; ++fold) {
auto [train_indicest, test_indicest] = stratified_kfoldt.getFold(fold);
auto [train_indicesv, test_indicesv] = stratified_kfoldv.getFold(fold);
REQUIRE(train_indicest == train_indicesv);
REQUIRE(test_indicest == test_indicesv);
// In the worst case scenario, the number of samples in the training set is number + raw.classNumStates
// because in that fold can come one remainder sample from each class.
REQUIRE(train_indicest.size() <= number + raw.classNumStates);
// If the number of samples in any class is less than the number of folds, then the fold is faulty.
// and the number of samples in the training set + test set will be less than nSamples
if (!stratified_kfoldt.isFaulty()) {
REQUIRE(train_indicest.size() + test_indicest.size() == raw.nSamples);
} else {
REQUIRE(train_indicest.size() + test_indicest.size() <= raw.nSamples);
}
auto train_t = torch::tensor(train_indicest);
auto ytrain = raw.yt.index({ train_t });
// Check that the class labels have been equally assign to each fold
for (const auto& idx : train_indicest) {
counts[fold][raw.yt[idx].item<int>()]++;
}
}
// Test the fold counting of every class
for (int fold = 0; fold < nFolds; ++fold) {
for (int j = 1; j < nFolds - 1; ++j) {
for (int k = 0; k < raw.classNumStates; ++k) {
REQUIRE(abs(counts.at(fold).at(k) - counts.at(j).at(k)) <= 1);
}
}
}
}
}

9
tests/TestUtils.cc
View File

@ -1,10 +1,11 @@
#include "TestUtils.h" #include "TestUtils.h"
#include "config.h"
class Paths { class Paths {
public: public:
static std::string datasets() static std::string datasets()
{ {
return "../../data/"; return { data_path.begin(), data_path.end() };
} }
}; };
@ -34,7 +35,7 @@ std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, m
return Xd; return Xd;
} }
bool file_exists(const std::std::string& name) bool file_exists(const std::string& name)
{ {
if (FILE* file = fopen(name.c_str(), "r")) { if (FILE* file = fopen(name.c_str(), "r")) {
fclose(file); fclose(file);
@ -44,7 +45,7 @@ bool file_exists(const std::std::string& name)
} }
} }
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::std::string& name, bool class_last, bool discretize_dataset) tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, bool discretize_dataset)
{ {
auto handler = ArffFiles(); auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff", class_last); handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff", class_last);
@ -78,7 +79,7 @@ tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<s
return { Xd, torch::tensor(y, torch::kInt32), features, className, states }; return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
} }
tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::std::string& name) tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name)
{ {
auto handler = ArffFiles(); auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff"); handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff");

4
tests/TestUtils.h
View File

@ -4,11 +4,11 @@
#include <string> #include <string>
#include <vector> #include <vector>
#include <map> #include <map>
#include <std::tuple> #include <tuple>
#include "ArffFiles.h" #include "ArffFiles.h"
#include "CPPFImdlp.h" #include "CPPFImdlp.h"
bool file_exists(const std::std::string& name); bool file_exists(const std::string& name);
std::pair<vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<string> features); std::pair<vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<string> features);
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y); std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
std::tuple<vector<vector<int>>, std::vector<int>, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name); std::tuple<vector<vector<int>>, std::vector<int>, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name);

0
data/diabetes.arff → tests/data/diabetes.arff
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0
data/ecoli.arff → tests/data/ecoli.arff
View File

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