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Merge pull request #5 from rmontanana/hiperparameters

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-Fix a big mistake in sortIndices method (removed unneeded loop)

-Add three hyperparameters to algorithm:
 * max_depth: maximum level of recursion when looking for cut point candidates.
 * min_length: minimum length of the interval of samples to be searched for candidates.
 * max_cut: Maximum number of cutpoints. This could be achieved in two ways: a natural number meaning the maximum number of outpoints in each feature of the dataset, or this number could be a number int the range (0, 1) meaning a proportion of the number of samples.
This commit is contained in:
Ricardo Montañana Gómez
2023-04-01 19:05:12 +02:00
committed by GitHub
parent e25ca378f0 c662a96da8
commit 0b35a15d62
23 changed files with 1121 additions and 389 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
.github/workflows/build.yml vendored
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@@ -3,9 +3,9 @@ on:
push:
branches:
- main
- test
- "*"
pull_request:
types: [opened, synchronize, reopened]
types: [ opened, synchronize, reopened ]
jobs:
build:
name: Build
@@ -13,25 +13,29 @@ jobs:
env:
BUILD_WRAPPER_OUT_DIR: build_wrapper_output_directory # Directory where build-wrapper output will be placed
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v3.2.0
with:
fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis
- name: Install sonar-scanner and build-wrapper
uses: SonarSource/sonarcloud-github-c-cpp@v1
- name: Install lcov & gcovr
run: |
sudo apt-get -y install lcov
sudo apt-get -y install gcovr
- name: Tests & build-wrapper
run: |
mkdir build
cmake -S . -B build -Wno-dev
build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Release
cd tests
mkdir build
cmake -S . -B build -Wno-dev
build-wrapper-linux-x86-64 --out-dir ${{ env.BUILD_WRAPPER_OUT_DIR }} cmake --build build/ --config Release
cd build
ctest -C Release --output-on-failure
make
ctest -C Release --output-on-failure --test-dir tests
cd ..
# gcovr -f CPPFImdlp.cpp -f Metrics.cpp --merge-mode-functions=separate --txt --sonarqube=coverage.xml
gcovr -f CPPFImdlp.cpp -f Metrics.cpp --txt --sonarqube=coverage.xml
- name: Run sonar-scanner
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
run: |
sonar-scanner --define sonar.cfamily.build-wrapper-output="${{ env.BUILD_WRAPPER_OUT_DIR }}"
sonar-scanner --define sonar.cfamily.build-wrapper-output="${{ env.BUILD_WRAPPER_OUT_DIR }}" \
--define sonar.coverageReportPaths=coverage.xml

3
.gitignore vendored
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@@ -35,4 +35,5 @@
.idea
cmake-*
**/CMakeFiles
.vscode/*
.vscode/*
**/gcovr-report

8
.vscode/launch.json vendored
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@@ -5,12 +5,14 @@
"version": "0.2.0",
"configurations": [
{
"name": "(lldb) Launch",
"type": "cppdbg",
"name": "lldb samplex",
"type": "lldb",
"request": "launch",
"targetArchitecture": "arm64",
"program": "${workspaceRoot}/sample/build/sample",
"args": [
"mfeat-factors"
"-f",
"glass"
],
"stopAtEntry": false,
"cwd": "${workspaceRoot}/sample/build/",

7
.vscode/settings.json vendored
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@@ -1,6 +1,9 @@
{
"sonarlint.connectedMode.project": {
"connectionId": "sonarcloud",
"connectionId": "rmontanana",
"projectKey": "rmontanana_mdlp"
}
},
"C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools",
"cmake.configureOnOpen": true,
"sonarlint.pathToCompileCommands": "${workspaceFolder}/build/compile_commands.json"
}

29
.vscode/tasks.json vendored
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@@ -1,29 +0,0 @@
{
"tasks": [
{
"type": "cppbuild",
"label": "C/C++: clang++ build active file",
"command": "/usr/bin/clang++",
"args": [
"-fcolor-diagnostics",
"-fansi-escape-codes",
"-g",
"${file}",
"-o",
"${fileDirname}/${fileBasenameNoExtension}"
],
"options": {
"cwd": "${fileDirname}"
},
"problemMatcher": [
"$gcc"
],
"group": {
"kind": "build",
"isDefault": true
},
"detail": "Task generated by Debugger."
}
],
"version": "2.0.0"
}

8
CMakeLists.txt
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@@ -1,7 +1,13 @@
cmake_minimum_required(VERSION 3.20)
project(mdlp)
if (POLICY CMP0135)
cmake_policy(SET CMP0135 NEW)
endif ()
set(CMAKE_CXX_STANDARD 11)
add_library(mdlp CPPFImdlp.cpp Metrics.cpp)
add_library(mdlp CPPFImdlp.cpp Metrics.cpp sample/sample.cpp)
add_subdirectory(sample)
add_subdirectory(tests)

126
CPPFImdlp.cpp
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@@ -2,22 +2,38 @@
#include <algorithm>
#include <set>
#include <cmath>
#include <limits>
#include "CPPFImdlp.h"
#include "Metrics.h"
namespace mdlp {
CPPFImdlp::CPPFImdlp(): indices(indices_t()), X(samples_t()), y(labels_t()),
metrics(Metrics(y, indices))
{
CPPFImdlp::CPPFImdlp(size_t min_length_, int max_depth_, float proposed) : min_length(min_length_),
max_depth(max_depth_),
proposed_cuts(proposed) {
}
CPPFImdlp::CPPFImdlp() = default;
CPPFImdlp::~CPPFImdlp() = default;
CPPFImdlp& CPPFImdlp::fit(samples_t& X_, labels_t& y_)
{
size_t CPPFImdlp::compute_max_num_cut_points() const {
// Set the actual maximum number of cut points as a number or as a percentage of the number of samples
if (proposed_cuts == 0) {
return numeric_limits<size_t>::max();
}
if (proposed_cuts < 0 || proposed_cuts > static_cast<float>(X.size())) {
throw invalid_argument("wrong proposed num_cuts value");
}
if (proposed_cuts < 1)
return static_cast<size_t>(round(static_cast<float>(X.size()) * proposed_cuts));
return static_cast<size_t>(proposed_cuts);
}
void CPPFImdlp::fit(samples_t &X_, labels_t &y_) {
X = X_;
y = y_;
num_cut_points = compute_max_num_cut_points();
depth = 0;
cutPoints.clear();
if (X.size() != y.size()) {
throw invalid_argument("X and y must have the same size");
@@ -25,18 +41,26 @@ namespace mdlp {
if (X.empty() || y.empty()) {
throw invalid_argument("X and y must have at least one element");
}
if (min_length < 3) {
throw invalid_argument("min_length must be greater than 2");
}
if (max_depth < 1) {
throw invalid_argument("max_depth must be greater than 0");
}
indices = sortIndices(X_, y_);
metrics.setData(y, indices);
computeCutPoints(0, X.size());
return *this;
computeCutPoints(0, X.size(), 1);
}
pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end)
{
size_t n, m, idxPrev = cut - 1 >= start ? cut - 1 : cut;
pair<precision_t, size_t> CPPFImdlp::valueCutPoint(size_t start, size_t cut, size_t end) {
size_t n;
size_t m;
size_t idxPrev = cut - 1 >= start ? cut - 1 : cut;
size_t idxNext = cut + 1 < end ? cut + 1 : cut;
bool backWall; // true if duplicates reach begining of the interval
precision_t previous, actual, next;
bool backWall; // true if duplicates reach beginning of the interval
precision_t previous;
precision_t actual;
precision_t next;
previous = X[indices[idxPrev]];
actual = X[indices[cut]];
next = X[indices[idxNext]];
@@ -57,15 +81,18 @@ namespace mdlp {
// Decide which values to use
cut = cut + (backWall ? m + 1 : -n);
actual = X[indices[cut]];
return { (actual + previous) / 2, cut };
return {(actual + previous) / 2, cut};
}
void CPPFImdlp::computeCutPoints(size_t start, size_t end)
{
void CPPFImdlp::computeCutPoints(size_t start, size_t end, int depth_) {
size_t cut;
pair<precision_t, size_t> result;
if (end - start < 3)
if (cutPoints.size() == num_cut_points)
return;
// Check if the interval length and the depth are Ok
if (end - start < min_length || depth_ > max_depth)
return;
depth = depth_ > depth ? depth_ : depth;
cut = getCandidate(start, end);
if (cut == numeric_limits<size_t>::max())
return;
@@ -73,18 +100,20 @@ namespace mdlp {
result = valueCutPoint(start, cut, end);
cut = result.second;
cutPoints.push_back(result.first);
computeCutPoints(start, cut);
computeCutPoints(cut, end);
computeCutPoints(start, cut, depth_ + 1);
computeCutPoints(cut, end, depth_ + 1);
}
}
size_t CPPFImdlp::getCandidate(size_t start, size_t end)
{
size_t CPPFImdlp::getCandidate(size_t start, size_t end) {
/* Definition 1: A binary discretization for A is determined by selecting the cut point TA for which
E(A, TA; S) is minimal amongst all the candidate cut points. */
size_t candidate = numeric_limits<size_t>::max(), elements = end - start;
size_t candidate = numeric_limits<size_t>::max();
size_t elements = end - start;
bool sameValues = true;
precision_t entropy_left, entropy_right, minEntropy;
precision_t entropy_left;
precision_t entropy_right;
precision_t minEntropy;
// Check if all the values of the variable in the interval are the same
for (size_t idx = start + 1; idx < end; idx++) {
if (X[indices[idx]] != X[indices[start]]) {
@@ -99,8 +128,8 @@ namespace mdlp {
// Cutpoints are always on boundaries (definition 2)
if (y[indices[idx]] == y[indices[idx - 1]])
continue;
entropy_left = precision_t(idx - start) / elements * metrics.entropy(start, idx);
entropy_right = precision_t(end - idx) / elements * metrics.entropy(idx, end);
entropy_left = precision_t(idx - start) / static_cast<float>(elements) * metrics.entropy(start, idx);
entropy_right = precision_t(end - idx) / static_cast<float>(elements) * metrics.entropy(idx, end);
if (entropy_left + entropy_right < minEntropy) {
minEntropy = entropy_left + entropy_right;
candidate = idx;
@@ -109,15 +138,16 @@ namespace mdlp {
return candidate;
}
bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end)
{
int k, k1, k2;
precision_t ig, delta;
precision_t ent, ent1, ent2;
bool CPPFImdlp::mdlp(size_t start, size_t cut, size_t end) {
int k;
int k1;
int k2;
precision_t ig;
precision_t delta;
precision_t ent;
precision_t ent1;
precision_t ent2;
auto N = precision_t(end - start);
if (N < 2) {
return false;
}
k = metrics.computeNumClasses(start, end);
k1 = metrics.computeNumClasses(start, cut);
k2 = metrics.computeNumClasses(cut, end);
@@ -125,37 +155,31 @@ namespace mdlp {
ent1 = metrics.entropy(start, cut);
ent2 = metrics.entropy(cut, end);
ig = metrics.informationGain(start, cut, end);
delta = log2(pow(3, precision_t(k)) - 2) -
(precision_t(k) * ent - precision_t(k1) * ent1 - precision_t(k2) * ent2);
delta = static_cast<float>(log2(pow(3, precision_t(k)) - 2) -
(precision_t(k) * ent - precision_t(k1) * ent1 - precision_t(k2) * ent2));
precision_t term = 1 / N * (log2(N - 1) + delta);
return ig > term;
}
// Argsort from https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes
indices_t CPPFImdlp::sortIndices(samples_t& X_, labels_t& y_)
{
indices_t CPPFImdlp::sortIndices(samples_t &X_, labels_t &y_) {
indices_t idx(X_.size());
iota(idx.begin(), idx.end(), 0);
for (size_t i = 0; i < X_.size(); i++)
stable_sort(idx.begin(), idx.end(), [&X_, &y_](size_t i1, size_t i2) {
stable_sort(idx.begin(), idx.end(), [&X_, &y_](size_t i1, size_t i2) {
if (X_[i1] == X_[i2])
return y_[i1] < y_[i2];
else
return X_[i1] < X_[i2];
});
});
return idx;
}
cutPoints_t CPPFImdlp::getCutPoints()
{
// Remove duplicates and sort
cutPoints_t output(cutPoints.size());
set<precision_t> s;
unsigned size = cutPoints.size();
for (unsigned i = 0; i < size; i++)
s.insert(cutPoints[i]);
output.assign(s.begin(), s.end());
sort(output.begin(), output.end());
return output;
cutPoints_t CPPFImdlp::getCutPoints() {
sort(cutPoints.begin(), cutPoints.end());
return cutPoints;
}
int CPPFImdlp::get_depth() const {
return depth;
}
}

39
CPPFImdlp.h
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@@ -1,29 +1,52 @@
#ifndef CPPFIMDLP_H
#define CPPFIMDLP_H
#include "typesFImdlp.h"
#include "Metrics.h"
#include <limits>
#include <utility>
#include <string>
namespace mdlp {
class CPPFImdlp {
protected:
indices_t indices;
samples_t X;
labels_t y;
Metrics metrics;
size_t min_length = 3;
int depth = 0;
int max_depth = numeric_limits<int>::max();
float proposed_cuts = 0;
indices_t indices = indices_t();
samples_t X = samples_t();
labels_t y = labels_t();
Metrics metrics = Metrics(y, indices);
cutPoints_t cutPoints;
size_t num_cut_points = numeric_limits<size_t>::max();
static indices_t sortIndices(samples_t&, labels_t&);
void computeCutPoints(size_t, size_t);
void computeCutPoints(size_t, size_t, int);
bool mdlp(size_t, size_t, size_t);
size_t getCandidate(size_t, size_t);
size_t compute_max_num_cut_points() const;
pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
public:
CPPFImdlp();
CPPFImdlp(size_t, int, float);
~CPPFImdlp();
CPPFImdlp& fit(samples_t&, labels_t&);
samples_t getCutPoints();
inline string version() { return "1.1.1"; };
void fit(samples_t&, labels_t&);
cutPoints_t getCutPoints();
int get_depth() const;
static inline string version() { return "1.1.1"; };
};
}
#endif

46
Metrics.cpp
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@@ -1,63 +1,71 @@
#include "Metrics.h"
#include <set>
#include <cmath>
using namespace std;
namespace mdlp {
Metrics::Metrics(labels_t& y_, indices_t& indices_): y(y_), indices(indices_), numClasses(computeNumClasses(0, indices.size())), entropyCache(cacheEnt_t()), igCache(cacheIg_t())
{
Metrics::Metrics(labels_t &y_, indices_t &indices_) : y(y_), indices(indices_),
numClasses(computeNumClasses(0, indices.size())) {
}
int Metrics::computeNumClasses(size_t start, size_t end)
{
int Metrics::computeNumClasses(size_t start, size_t end) {
set<int> nClasses;
for (auto i = start; i < end; ++i) {
nClasses.insert(y[indices[i]]);
}
return nClasses.size();
return static_cast<int>(nClasses.size());
}
void Metrics::setData(labels_t& y_, indices_t& indices_)
{
void Metrics::setData(const labels_t &y_, const indices_t &indices_) {
indices = indices_;
y = y_;
numClasses = computeNumClasses(0, indices.size());
entropyCache.clear();
igCache.clear();
}
precision_t Metrics::entropy(size_t start, size_t end)
{
precision_t p, ventropy = 0;
precision_t Metrics::entropy(size_t start, size_t end) {
precision_t p;
precision_t ventropy = 0;
int nElements = 0;
labels_t counts(numClasses + 1, 0);
if (end - start < 2)
return 0;
if (entropyCache.find({ start, end }) != entropyCache.end()) {
if (entropyCache.find({start, end}) != entropyCache.end()) {
return entropyCache[{start, end}];
}
for (auto i = &indices[start]; i != &indices[end]; ++i) {
counts[y[*i]]++;
nElements++;
}
for (auto count : counts) {
for (auto count: counts) {
if (count > 0) {
p = (precision_t)count / nElements;
p = static_cast<precision_t>(count) / static_cast<precision_t>(nElements);
ventropy -= p * log2(p);
}
}
entropyCache[{start, end}] = ventropy;
return ventropy;
}
precision_t Metrics::informationGain(size_t start, size_t cut, size_t end)
{
precision_t Metrics::informationGain(size_t start, size_t cut, size_t end) {
precision_t iGain;
precision_t entropyInterval, entropyLeft, entropyRight;
int nElementsLeft = cut - start, nElementsRight = end - cut;
int nElements = end - start;
precision_t entropyInterval;
precision_t entropyLeft;
precision_t entropyRight;
size_t nElementsLeft = cut - start;
size_t nElementsRight = end - cut;
size_t nElements = end - start;
if (igCache.find(make_tuple(start, cut, end)) != igCache.end()) {
return igCache[make_tuple(start, cut, end)];
}
entropyInterval = entropy(start, end);
entropyLeft = entropy(start, cut);
entropyRight = entropy(cut, end);
iGain = entropyInterval - ((precision_t)nElementsLeft * entropyLeft + (precision_t)nElementsRight * entropyRight) / nElements;
iGain = entropyInterval -
(static_cast<precision_t>(nElementsLeft) * entropyLeft +
static_cast<precision_t>(nElementsRight) * entropyRight) /
static_cast<precision_t>(nElements);
igCache[make_tuple(start, cut, end)] = iGain;
return iGain;
}

18
Metrics.h
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@@ -1,19 +1,25 @@
#ifndef CCMETRICS_H
#define CCMETRICS_H
#include "typesFImdlp.h"
namespace mdlp {
class Metrics {
protected:
labels_t& y;
indices_t& indices;
labels_t &y;
indices_t &indices;
int numClasses;
cacheEnt_t entropyCache;
cacheIg_t igCache;
cacheEnt_t entropyCache = cacheEnt_t();
cacheIg_t igCache = cacheIg_t();
public:
Metrics(labels_t&, indices_t&);
void setData(labels_t&, indices_t&);
Metrics(labels_t &, indices_t &);
void setData(const labels_t &, const indices_t &);
int computeNumClasses(size_t, size_t);
precision_t entropy(size_t, size_t);
precision_t informationGain(size_t, size_t, size_t);
};
}

3
README.md
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@@ -28,7 +28,8 @@ mkdir build
cd build
cmake ..
make
./sample iris
./sample -f iris -m 2
./sample -h
```
## Test

21
sample/.vscode/launch.json vendored Normal file
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@@ -0,0 +1,21 @@
{
"version": "0.2.0",
"configurations": [
{
"name": "lldb puro",
"type": "cppdbg",
// "targetArchitecture": "arm64",
"request": "launch",
"program": "${workspaceRoot}/build/sample",
"args": [
"-f",
"iris"
],
"stopAtEntry": false,
"cwd": "${workspaceRoot}/build/",
"environment": [],
"externalConsole": false,
"MIMode": "lldb"
},
]
}

2
sample/CMakeLists.txt
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@@ -1,5 +1,3 @@
cmake_minimum_required(VERSION 3.20)
project(main)
set(CMAKE_CXX_STANDARD 11)

176
sample/sample.cpp
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@@ -1,59 +1,187 @@
#include <iostream>
#include <vector>
#include <iomanip>
#include <chrono>
#include <algorithm>
#include <cstring>
#include <getopt.h>
#include "../CPPFImdlp.h"
#include "../tests/ArffFiles.h"
using namespace std;
using namespace mdlp;
const string PATH = "../../tests/datasets/";
int main(int argc, char** argv)
{
ArffFiles file;
string path = "../../tests/datasets/";
map<string, bool> datasets = {
{"mfeat-factors", true},
{"iris", true},
{"letter", true},
{"glass", true},
{"kdd_JapaneseVowels", false},
{"test", true}
/* print a description of all supported options */
void usage(const char *path) {
/* take only the last portion of the path */
const char *basename = strrchr(path, '/');
basename = basename ? basename + 1 : path;
cout << "usage: " << basename << "[OPTION]" << endl;
cout << " -h, --help\t\t Print this help and exit." << endl;
cout
<< " -f, --file[=FILENAME]\t {all, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors, test}."
<< endl;
cout << " -p, --path[=FILENAME]\t folder where the arff dataset is located, default " << PATH << endl;
cout << " -m, --max_depth=INT\t max_depth pased to discretizer. Default = MAX_INT" << endl;
cout
<< " -c, --max_cutpoints=FLOAT\t percentage of lines expressed in decimal or integer number or cut points. Default = 0 = any"
<< endl;
cout << " -n, --min_length=INT\t interval min_length pased to discretizer. Default = 3" << endl;
}
tuple<string, string, int, int, float> parse_arguments(int argc, char **argv) {
string file_name;
string path = PATH;
int max_depth = numeric_limits<int>::max();
int min_length = 3;
float max_cutpoints = 0;
const option long_options[] = {
{"help", no_argument, nullptr, 'h'},
{"file", required_argument, nullptr, 'f'},
{"path", required_argument, nullptr, 'p'},
{"max_depth", required_argument, nullptr, 'm'},
{"max_cutpoints", required_argument, nullptr, 'c'},
{"min_length", required_argument, nullptr, 'n'},
{nullptr, no_argument, nullptr, 0}
};
if (argc != 2 || datasets.find(argv[1]) == datasets.end()) {
cout << "Usage: " << argv[0] << " {mfeat-factors, glass, iris, letter, kdd_JapaneseVowels, test}" << endl;
return 1;
while (true) {
const auto c = getopt_long(argc, argv, "hf:p:m:c:n:", long_options, nullptr);
if (c == -1)
break;
switch (c) {
case 'h':
usage(argv[0]);
exit(0);
case 'f':
file_name = string(optarg);
break;
case 'm':
max_depth = stoi(optarg);
break;
case 'n':
min_length = stoi(optarg);
break;
case 'c':
max_cutpoints = stof(optarg);
break;
case 'p':
path = optarg;
if (path.back() != '/')
path += '/';
break;
case '?':
usage(argv[0]);
exit(1);
default:
abort();
}
}
if (file_name.empty()) {
usage(argv[0]);
exit(1);
}
return make_tuple(file_name, path, max_depth, min_length, max_cutpoints);
}
file.load(path + argv[1] + ".arff", datasets[argv[1]]);
void process_file(const string &path, const string &file_name, bool class_last, int max_depth, int min_length,
float max_cutpoints) {
ArffFiles file;
file.load(path + file_name + ".arff", class_last);
auto attributes = file.getAttributes();
int items = file.getSize();
auto items = file.getSize();
cout << "Number of lines: " << items << endl;
cout << "Attributes: " << endl;
for (auto attribute : attributes) {
for (auto attribute: attributes) {
cout << "Name: " << get<0>(attribute) << " Type: " << get<1>(attribute) << endl;
}
cout << "Class name: " << file.getClassName() << endl;
cout << "Class type: " << file.getClassType() << endl;
cout << "Data: " << endl;
vector<samples_t>& X = file.getX();
labels_t& y = file.getY();
for (int i = 0; i < 50; i++) {
for (auto feature : X) {
vector<samples_t> &X = file.getX();
labels_t &y = file.getY();
for (int i = 0; i < 5; i++) {
for (auto feature: X) {
cout << fixed << setprecision(1) << feature[i] << " ";
}
cout << y[i] << endl;
}
mdlp::CPPFImdlp test = mdlp::CPPFImdlp();
auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
auto total = 0;
for (auto i = 0; i < attributes.size(); i++) {
auto min_max = minmax_element(X[i].begin(), X[i].end());
cout << "Cut points for " << get<0>(attributes[i]) << endl;
cout << "Min: " << *min_max.first << " Max: " << *min_max.second << endl;
cout << "--------------------------" << setprecision(3) << endl;
test.fit(X[i], y);
for (auto item : test.getCutPoints()) {
for (auto item: test.getCutPoints()) {
cout << item << endl;
}
total += test.getCutPoints().size();
}
cout << "Total cut points ...: " << total << endl;
cout << "Total feature states: " << total + attributes.size() << endl;
}
void process_all_files(const map<string, bool> &datasets, const string &path, int max_depth, int min_length,
float max_cutpoints) {
cout << "Results: " << "Max_depth: " << max_depth << " Min_length: " << min_length << " Max_cutpoints: "
<< max_cutpoints << endl << endl;
printf("%-20s %4s %4s\n", "Dataset", "Feat", "Cuts Time(ms)");
printf("==================== ==== ==== ========\n");
for (const auto &dataset: datasets) {
ArffFiles file;
file.load(path + dataset.first + ".arff", dataset.second);
auto attributes = file.getAttributes();
vector<samples_t> &X = file.getX();
labels_t &y = file.getY();
size_t timing = 0;
int cut_points = 0;
for (auto i = 0; i < attributes.size(); i++) {
auto test = mdlp::CPPFImdlp(min_length, max_depth, max_cutpoints);
std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
test.fit(X[i], y);
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
timing += std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count();
cut_points += test.getCutPoints().size();
}
printf("%-20s %4lu %4d %8zu\n", dataset.first.c_str(), attributes.size(), cut_points, timing);
}
}
int main(int argc, char **argv) {
map<string, bool> datasets = {
{"glass", true},
{"iris", true},
{"kdd_JapaneseVowels", false},
{"letter", true},
{"liver-disorders", true},
{"mfeat-factors", true},
{"test", true}
};
string file_name;
string path;
int max_depth;
int min_length;
float max_cutpoints;
tie(file_name, path, max_depth, min_length, max_cutpoints) = parse_arguments(argc, argv);
if (datasets.find(file_name) == datasets.end() && file_name != "all") {
cout << "Invalid file name: " << file_name << endl;
usage(argv[0]);
exit(1);
}
if (file_name == "all")
process_all_files(datasets, path, max_depth, min_length, max_cutpoints);
else {
process_file(path, file_name, datasets[file_name], max_depth, min_length, max_cutpoints);
cout << "File name ....: " << file_name << endl;
cout << "Max depth ....: " << max_depth << endl;
cout << "Min length ...: " << min_length << endl;
cout << "Max cutpoints : " << max_cutpoints << endl;
}
return 0;
}
}

131
tests/ArffFiles.cpp
View File

@@ -2,87 +2,92 @@
#include <fstream>
#include <sstream>
#include <map>
#include <iostream>
using namespace std;
ArffFiles::ArffFiles()
{
}
vector<string> ArffFiles::getLines()
{
ArffFiles::ArffFiles() = default;
vector<string> ArffFiles::getLines() const {
return lines;
}
unsigned long int ArffFiles::getSize()
{
unsigned long int ArffFiles::getSize() const {
return lines.size();
}
vector<pair<string, string>> ArffFiles::getAttributes()
{
vector<pair<string, string>> ArffFiles::getAttributes() const {
return attributes;
}
string ArffFiles::getClassName()
{
string ArffFiles::getClassName() const {
return className;
}
string ArffFiles::getClassType()
{
string ArffFiles::getClassType() const {
return classType;
}
vector<vector<float>>& ArffFiles::getX()
{
vector<vector<float>> &ArffFiles::getX() {
return X;
}
vector<int>& ArffFiles::getY()
{
vector<int> &ArffFiles::getY() {
return y;
}
void ArffFiles::load(string fileName, bool classLast)
{
void ArffFiles::load(const string &fileName, bool classLast) {
ifstream file(fileName);
string keyword, attribute, type;
if (file.is_open()) {
string line;
while (getline(file, line)) {
if (line[0] == '%' || line.empty() || line == "\r" || line == " ") {
continue;
}
if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
stringstream ss(line);
ss >> keyword >> attribute >> type;
attributes.push_back({ attribute, type });
continue;
}
if (line[0] == '@') {
continue;
}
lines.push_back(line);
}
file.close();
if (attributes.empty())
throw invalid_argument("No attributes found");
if (classLast) {
className = get<0>(attributes.back());
classType = get<1>(attributes.back());
attributes.pop_back();
} else {
className = get<0>(attributes.front());
classType = get<1>(attributes.front());
attributes.erase(attributes.begin());
}
generateDataset(classLast);
} else
if (!file.is_open()) {
throw invalid_argument("Unable to open file");
}
string line;
string keyword;
string attribute;
string type;
string type_w;
while (getline(file, line)) {
if (line.empty() || line[0] == '%' || line == "\r" || line == " ") {
continue;
}
if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
stringstream ss(line);
ss >> keyword >> attribute;
type = "";
while (ss >> type_w)
type += type_w + " ";
attributes.emplace_back(attribute, trim(type));
continue;
}
if (line[0] == '@') {
continue;
}
lines.push_back(line);
}
file.close();
if (attributes.empty())
throw invalid_argument("No attributes found");
if (classLast) {
className = get<0>(attributes.back());
classType = get<1>(attributes.back());
attributes.pop_back();
} else {
className = get<0>(attributes.front());
classType = get<1>(attributes.front());
attributes.erase(attributes.begin());
}
generateDataset(classLast);
}
void ArffFiles::generateDataset(bool classLast)
{
void ArffFiles::generateDataset(bool classLast) {
X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
vector<string> yy = vector<string>(lines.size(), "");
int labelIndex = classLast ? attributes.size() : 0;
for (int i = 0; i < lines.size(); i++) {
auto yy = vector<string>(lines.size(), "");
int labelIndex = classLast ? static_cast<int>(attributes.size()) : 0;
for (size_t i = 0; i < lines.size(); i++) {
stringstream ss(lines[i]);
string value;
int pos = 0, xIndex = 0;
int pos = 0;
int xIndex = 0;
while (getline(ss, value, ',')) {
if (pos++ == labelIndex) {
yy[i] = value;
@@ -93,20 +98,20 @@ void ArffFiles::generateDataset(bool classLast)
}
y = factorize(yy);
}
string ArffFiles::trim(const string& source)
{
string ArffFiles::trim(const string &source) {
string s(source);
s.erase(0, s.find_first_not_of(" \n\r\t"));
s.erase(s.find_last_not_of(" \n\r\t") + 1);
return s;
}
vector<int> ArffFiles::factorize(const vector<string>& labels_t)
{
vector<int> ArffFiles::factorize(const vector<string> &labels_t) {
vector<int> yy;
yy.reserve(labels_t.size());
map<string, int> labelMap;
int i = 0;
for (string label : labels_t) {
for (const string &label: labels_t) {
if (labelMap.find(label) == labelMap.end()) {
labelMap[label] = i++;
}

39
tests/ArffFiles.h
View File

@@ -1,27 +1,44 @@
#ifndef ARFFFILES_H
#define ARFFFILES_H
#include <string>
#include <vector>
using namespace std;
class ArffFiles {
private:
vector<string> lines;
vector<pair<string, string>> attributes;
string className, classType;
string className;
string classType;
vector<vector<float>> X;
vector<int> y;
void generateDataset(bool);
public:
ArffFiles();
void load(string, bool = true);
vector<string> getLines();
unsigned long int getSize();
string getClassName();
string getClassType();
string trim(const string&);
vector<vector<float>>& getX();
vector<int>& getY();
vector<pair<string, string>> getAttributes();
vector<int> factorize(const vector<string>& labels_t);
void load(const string &, bool = true);
vector<string> getLines() const;
unsigned long int getSize() const;
string getClassName() const;
string getClassType() const;
static string trim(const string &);
vector<vector<float>> &getX();
vector<int> &getY();
vector<pair<string, string>> getAttributes() const;
static vector<int> factorize(const vector<string> &labels_t);
};
#endif

13
tests/CMakeLists.txt
View File

@@ -1,15 +1,12 @@
cmake_minimum_required(VERSION 3.14)
project(FImdlp)
# GoogleTest requires at least C++14
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD 11)
include(FetchContent)
include_directories(${GTEST_INCLUDE_DIRS})
FetchContent_Declare(
googletest
URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
googletest
URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
)
# For Windows: Prevent overriding the parent project's compiler/linker settings
set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
@@ -18,7 +15,7 @@ FetchContent_MakeAvailable(googletest)
enable_testing()
add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
add_executable(FImdlp_unittest ../CPPFImdlp.cpp ../ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp)
add_executable(FImdlp_unittest ../CPPFImdlp.cpp ArffFiles.cpp ../Metrics.cpp FImdlp_unittest.cpp)
target_link_libraries(Metrics_unittest GTest::gtest_main)
target_link_libraries(FImdlp_unittest GTest::gtest_main)
target_compile_options(Metrics_unittest PRIVATE --coverage)

345
tests/FImdlp_unittest.cpp
View File

@@ -1,22 +1,47 @@
#include "gtest/gtest.h"
#include "../Metrics.h"
#include "../CPPFImdlp.h"
#include "ArffFiles.h"
#include <fstream>
#include <iostream>
#include "ArffFiles.h"
#define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
try { \
stmt; \
} catch (const etype& ex) { \
EXPECT_EQ(whatstring, std::string(ex.what())); \
throw; \
} \
, etype)
namespace mdlp {
class TestFImdlp: public CPPFImdlp, public testing::Test {
class TestFImdlp : public CPPFImdlp, public testing::Test {
public:
precision_t precision = 0.000001;
TestFImdlp(): CPPFImdlp() {}
void SetUp()
{
X = { 4.7, 4.7, 4.7, 4.7, 4.8, 4.8, 4.8, 4.8, 4.9, 4.95, 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
y = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
precision_t precision = 0.000001f;
TestFImdlp() : CPPFImdlp() {}
string data_path;
void SetUp() override {
X = {4.7f, 4.7f, 4.7f, 4.7f, 4.8f, 4.8f, 4.8f, 4.8f, 4.9f, 4.95f, 5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f,
6.0f, 5.1f, 5.9f};
y = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
fit(X, y);
data_path = set_data_path();
}
void checkSortedVector()
{
static string set_data_path() {
string path = "../datasets/";
ifstream file(path + "iris.arff");
if (file.is_open()) {
file.close();
return path;
}
return "../../tests/datasets/";
}
void checkSortedVector() {
indices_t testSortedIndices = sortIndices(X, y);
precision_t prev = X[testSortedIndices[0]];
for (unsigned long i = 0; i < X.size(); ++i) {
@@ -25,27 +50,18 @@ namespace mdlp {
prev = X[testSortedIndices[i]];
}
}
void checkCutPoints(cutPoints_t& expected)
{
int expectedSize = expected.size();
EXPECT_EQ(cutPoints.size(), expectedSize);
for (unsigned long i = 0; i < cutPoints.size(); i++) {
EXPECT_NEAR(cutPoints[i], expected[i], precision);
void checkCutPoints(cutPoints_t &computed, cutPoints_t &expected) const {
EXPECT_EQ(computed.size(), expected.size());
for (unsigned long i = 0; i < computed.size(); i++) {
cout << "(" << computed[i] << ", " << expected[i] << ") ";
EXPECT_NEAR(computed[i], expected[i], precision);
}
}
template<typename T, typename A>
void checkVectors(std::vector<T, A> const& expected, std::vector<T, A> const& computed)
{
EXPECT_EQ(expected.size(), computed.size());
ASSERT_EQ(expected.size(), computed.size());
for (auto i = 0; i < expected.size(); i++) {
EXPECT_NEAR(expected[i], computed[i], precision);
}
}
bool test_result(samples_t& X_, size_t cut, float midPoint, size_t limit, string title)
{
bool test_result(const samples_t &X_, size_t cut, float midPoint, size_t limit, const string &title) {
pair<precision_t, size_t> result;
labels_t y_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
labels_t y_ = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
X = X_;
y = y_;
indices = sortIndices(X, y);
@@ -55,127 +71,228 @@ namespace mdlp {
EXPECT_EQ(result.second, limit);
return true;
}
void test_dataset(CPPFImdlp &test, const string &filename, vector<cutPoints_t> &expected,
vector<int> &depths) const {
ArffFiles file;
file.load(data_path + filename + ".arff", true);
vector<samples_t> &X = file.getX();
labels_t &y = file.getY();
auto attributes = file.getAttributes();
for (auto feature = 0; feature < attributes.size(); feature++) {
test.fit(X[feature], y);
EXPECT_EQ(test.get_depth(), depths[feature]);
auto computed = test.getCutPoints();
cout << "Feature " << feature << ": ";
checkCutPoints(computed, expected[feature]);
cout << endl;
}
}
};
TEST_F(TestFImdlp, FitErrorEmptyDataset)
{
TEST_F(TestFImdlp, FitErrorEmptyDataset) {
X = samples_t();
y = labels_t();
EXPECT_THROW(fit(X, y), std::invalid_argument);
EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have at least one element");
}
TEST_F(TestFImdlp, FitErrorDifferentSize)
{
X = { 1, 2, 3 };
y = { 1, 2 };
EXPECT_THROW(fit(X, y), std::invalid_argument);
TEST_F(TestFImdlp, FitErrorDifferentSize) {
X = {1, 2, 3};
y = {1, 2};
EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have the same size");
}
TEST_F(TestFImdlp, SortIndices)
{
X = { 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
indices = { 4, 3, 6, 8, 2, 1, 5, 0, 9, 7 };
checkSortedVector();
X = { 5.77, 5.88, 5.99 };
y = { 1, 2, 1 };
indices = { 0, 1, 2 };
checkSortedVector();
X = { 5.33, 5.22, 5.11 };
y = { 1, 2, 1 };
indices = { 2, 1, 0 };
checkSortedVector();
X = { 5.33, 5.22, 5.33 };
y = { 2, 2, 1 };
indices = { 1, 2, 0 };
TEST_F(TestFImdlp, FitErrorMinLengtMaxDepth) {
auto testLength = CPPFImdlp(2, 10, 0);
auto testDepth = CPPFImdlp(3, 0, 0);
X = {1, 2, 3};
y = {1, 2, 3};
EXPECT_THROW_WITH_MESSAGE(testLength.fit(X, y), invalid_argument, "min_length must be greater than 2");
EXPECT_THROW_WITH_MESSAGE(testDepth.fit(X, y), invalid_argument, "max_depth must be greater than 0");
}
TEST_F(TestFImdlp, TestShortDatasets)
{
TEST_F(TestFImdlp, FitErrorMaxCutPoints) {
auto testmin = CPPFImdlp(2, 10, -1);
auto testmax = CPPFImdlp(3, 0, 200);
X = {1, 2, 3};
y = {1, 2, 3};
EXPECT_THROW_WITH_MESSAGE(testmin.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
EXPECT_THROW_WITH_MESSAGE(testmax.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
}
TEST_F(TestFImdlp, SortIndices) {
X = {5.7f, 5.3f, 5.2f, 5.1f, 5.0f, 5.6f, 5.1f, 6.0f, 5.1f, 5.9f};
y = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
indices = {4, 3, 6, 8, 2, 1, 5, 0, 9, 7};
checkSortedVector();
X = {5.77f, 5.88f, 5.99f};
y = {1, 2, 1};
indices = {0, 1, 2};
checkSortedVector();
X = {5.33f, 5.22f, 5.11f};
y = {1, 2, 1};
indices = {2, 1, 0};
checkSortedVector();
X = {5.33f, 5.22f, 5.33f};
y = {2, 2, 1};
indices = {1, 2, 0};
}
TEST_F(TestFImdlp, TestShortDatasets) {
vector<precision_t> computed;
X = { 1 };
y = { 1 };
X = {1};
y = {1};
fit(X, y);
computed = getCutPoints();
EXPECT_EQ(computed.size(), 0);
X = { 1, 3 };
y = { 1, 2 };
X = {1, 3};
y = {1, 2};
fit(X, y);
computed = getCutPoints();
EXPECT_EQ(computed.size(), 0);
X = { 2, 4 };
y = { 1, 2 };
X = {2, 4};
y = {1, 2};
fit(X, y);
computed = getCutPoints();
EXPECT_EQ(computed.size(), 0);
X = { 1, 2, 3 };
y = { 1, 2, 2 };
X = {1, 2, 3};
y = {1, 2, 2};
fit(X, y);
computed = getCutPoints();
EXPECT_EQ(computed.size(), 1);
EXPECT_NEAR(computed[0], 1.5, precision);
}
TEST_F(TestFImdlp, TestArtificialDataset)
{
TEST_F(TestFImdlp, TestArtificialDataset) {
fit(X, y);
computeCutPoints(0, 20);
cutPoints_t expected = { 5.05 };
cutPoints_t expected = {5.05f};
vector<precision_t> computed = getCutPoints();
computed = getCutPoints();
int expectedSize = expected.size();
EXPECT_EQ(computed.size(), expected.size());
for (unsigned long i = 0; i < computed.size(); i++) {
EXPECT_NEAR(computed[i], expected[i], precision);
}
}
TEST_F(TestFImdlp, TestIris)
{
ArffFiles file;
string path = "../datasets/";
file.load(path + "iris.arff", true);
int items = file.getSize();
vector<samples_t>& X = file.getX();
TEST_F(TestFImdlp, TestIris) {
vector<cutPoints_t> expected = {
{ 5.4499998092651367, 5.75 },
{ 2.75, 2.85, 2.95, 3.05, 3.35 },
{ 2.4500000476837158, 4.75, 5.0500001907348633 },
{ 0.80000001192092896, 1.75 }
{5.45f, 5.75f},
{2.75f, 2.85f, 2.95f, 3.05f, 3.35f},
{2.45f, 4.75f, 5.05f},
{0.8f, 1.75f}
};
labels_t& y = file.getY();
auto attributes = file.getAttributes();
for (auto feature = 0; feature < attributes.size(); feature++) {
fit(X[feature], y);
vector<precision_t> computed = getCutPoints();
EXPECT_EQ(computed.size(), expected[feature].size());
for (auto i = 0; i < computed.size(); i++) {
EXPECT_NEAR(computed[i], expected[feature][i], precision);
}
}
vector<int> depths = {3, 5, 4, 3};
auto test = CPPFImdlp();
test_dataset(test, "iris", expected, depths);
}
TEST_F(TestFImdlp, ComputeCutPointsGCase)
{
TEST_F(TestFImdlp, ComputeCutPointsGCase) {
cutPoints_t expected;
expected = { 1.5 };
samples_t X_ = { 0, 1, 2, 2, 2 };
labels_t y_ = { 1, 1, 1, 2, 2 };
expected = {1.5};
samples_t X_ = {0, 1, 2, 2, 2};
labels_t y_ = {1, 1, 1, 2, 2};
fit(X_, y_);
checkCutPoints(expected);
auto computed = getCutPoints();
checkCutPoints(computed, expected);
}
TEST_F(TestFImdlp, ValueCutPoint)
{
TEST_F(TestFImdlp, ValueCutPoint) {
// Case titles as stated in the doc
samples_t X1a{ 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0 };
test_result(X1a, 6, 7.3 / 2, 6, "1a");
samples_t X2a = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
test_result(X2a, 6, 7.1 / 2, 4, "2a");
samples_t X2b = { 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
test_result(X2b, 6, 7.5 / 2, 7, "2b");
samples_t X3a = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
test_result(X3a, 4, 7.1 / 2, 4, "3a");
samples_t X3b = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
test_result(X3b, 4, 7.1 / 2, 4, "3b");
samples_t X4a = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.9, 4.0 };
test_result(X4a, 4, 6.9 / 2, 2, "4a");
samples_t X4b = { 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
test_result(X4b, 4, 7.5 / 2, 7, "4b");
samples_t X4c = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
test_result(X4c, 4, 6.9 / 2, 2, "4c");
samples_t X1a{3.1f, 3.2f, 3.3f, 3.4f, 3.5f, 3.6f, 3.7f, 3.8f, 3.9f, 4.0f};
test_result(X1a, 6, 7.3f / 2, 6, "1a");
samples_t X2a = {3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
test_result(X2a, 6, 7.1f / 2, 4, "2a");
samples_t X2b = {3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
test_result(X2b, 6, 7.5f / 2, 7, "2b");
samples_t X3a = {3.f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
test_result(X3a, 4, 7.1f / 2, 4, "3a");
samples_t X3b = {3.1f, 3.2f, 3.3f, 3.4f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f};
test_result(X3b, 4, 7.1f / 2, 4, "3b");
samples_t X4a = {3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.9f, 4.0f};
test_result(X4a, 4, 6.9f / 2, 2, "4a");
samples_t X4b = {3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.8f, 3.9f, 4.0f};
test_result(X4b, 4, 7.5f / 2, 7, "4b");
samples_t X4c = {3.1f, 3.2f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f, 3.7f};
test_result(X4c, 4, 6.9f / 2, 2, "4c");
}
TEST_F(TestFImdlp, MaxDepth) {
// Set max_depth to 1
auto test = CPPFImdlp(3, 1, 0);
vector<cutPoints_t> expected = {
{5.45f},
{3.35f},
{2.45f},
{0.8f}
};
vector<int> depths = {1, 1, 1, 1};
test_dataset(test, "iris", expected, depths);
}
TEST_F(TestFImdlp, MinLength) {
auto test = CPPFImdlp(75, 100, 0);
// Set min_length to 75
vector<cutPoints_t> expected = {
{5.45f, 5.75f},
{2.85f, 3.35f},
{2.45f, 4.75f},
{0.8f, 1.75f}
};
vector<int> depths = {3, 2, 2, 2};
test_dataset(test, "iris", expected, depths);
}
TEST_F(TestFImdlp, MinLengthMaxDepth) {
// Set min_length to 75
auto test = CPPFImdlp(75, 2, 0);
vector<cutPoints_t> expected = {
{5.45f, 5.75f},
{2.85f, 3.35f},
{2.45f, 4.75f},
{0.8f, 1.75f}
};
vector<int> depths = {2, 2, 2, 2};
test_dataset(test, "iris", expected, depths);
}
TEST_F(TestFImdlp, MaxCutPointsInteger) {
// Set min_length to 75
auto test = CPPFImdlp(75, 2, 1);
vector<cutPoints_t> expected = {
{5.45f},
{3.35f},
{2.45f},
{0.8f}
};
vector<int> depths = {1, 1, 1, 1};
test_dataset(test, "iris", expected, depths);
}
TEST_F(TestFImdlp, MaxCutPointsFloat) {
// Set min_length to 75
auto test = CPPFImdlp(75, 2, 0.2f);
vector<cutPoints_t> expected = {
{5.45f, 5.75f},
{2.85f, 3.35f},
{2.45f, 4.75f},
{0.8f, 1.75f}
};
vector<int> depths = {2, 2, 2, 2};
test_dataset(test, "iris", expected, depths);
}
TEST_F(TestFImdlp, ProposedCuts) {
vector<pair<float, size_t>> proposed_list = {{0.1f, 2},
{0.5f, 10},
{0.07f, 1},
{1.0f, 1},
{2.0f, 2}};
size_t expected;
size_t computed;
for (auto proposed_item: proposed_list) {
tie(proposed_cuts, expected) = proposed_item;
computed = compute_max_num_cut_points();
ASSERT_EQ(expected, computed);
}
}
}

43
tests/Metrics_unittest.cpp
View File

@@ -1,43 +1,40 @@
#include "gtest/gtest.h"
#include "../Metrics.h"
namespace mdlp {
class TestMetrics: public Metrics, public testing::Test {
class TestMetrics : public Metrics, public testing::Test {
public:
labels_t y;
samples_t X;
indices_t indices;
precision_t precision = 0.000001;
labels_t y_ = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
indices_t indices_ = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
precision_t precision = 0.000001f;
TestMetrics(): Metrics(y, indices) {}
void SetUp()
{
y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
indices = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
setData(y, indices);
TestMetrics() : Metrics(y_, indices_) {};
void SetUp() override {
setData(y_, indices_);
}
};
TEST_F(TestMetrics, NumClasses)
{
y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
TEST_F(TestMetrics, NumClasses) {
y = {1, 1, 1, 1, 1, 1, 1, 1, 2, 1};
EXPECT_EQ(1, computeNumClasses(4, 8));
EXPECT_EQ(2, computeNumClasses(0, 10));
EXPECT_EQ(2, computeNumClasses(8, 10));
}
TEST_F(TestMetrics, Entropy)
{
TEST_F(TestMetrics, Entropy) {
EXPECT_EQ(1, entropy(0, 10));
EXPECT_EQ(0, entropy(0, 5));
y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
y = {1, 1, 1, 1, 1, 1, 1, 1, 2, 1};
setData(y, indices);
ASSERT_NEAR(0.468996, entropy(0, 10), precision);
ASSERT_NEAR(0.468996f, entropy(0, 10), precision);
}
TEST_F(TestMetrics, InformationGain)
{
TEST_F(TestMetrics, InformationGain) {
ASSERT_NEAR(1, informationGain(0, 5, 10), precision);
y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
ASSERT_NEAR(1, informationGain(0, 5, 10), precision); // For cache
y = {1, 1, 1, 1, 1, 1, 1, 1, 2, 1};
setData(y, indices);
ASSERT_NEAR(0.108032, informationGain(0, 5, 10), precision);
ASSERT_NEAR(0.108032f, informationGain(0, 5, 10), precision);
}
}

4
tests/cover
View File

@@ -1,4 +0,0 @@
rm -fr lcoverage/*
lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
genhtml lcoverage/main_coverage.info --output-directory lcoverage
open lcoverage/index.html

2
tests/datasets/glass.arff
View File

@@ -114,7 +114,7 @@
@attribute 'Ca' real
@attribute 'Ba' real
@attribute 'Fe' real
@attribute 'Type' { 'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@attribute 'Type' {'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@data
1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'
1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0,0,'vehic wind float'

399
tests/datasets/liver-disorders.arff Executable file
View File

@@ -0,0 +1,399 @@
% 1. Title: BUPA liver disorders
%
% 2. Source information:
% -- Creators: BUPA Medical Research Ltd.
% -- Donor: Richard S. Forsyth
% 8 Grosvenor Avenue
% Mapperley Park
% Nottingham NG3 5DX
% 0602-621676
% -- Date: 5/15/1990
%
% 3. Past usage:
% -- None known other than what is shown in the PC/BEAGLE User's Guide
% (written by Richard S. Forsyth).
%
% 4. Relevant information:
% -- The first 5 variables are all blood tests which are thought
% to be sensitive to liver disorders that might arise from
% excessive alcohol consumption. Each line in the bupa.data file
% constitutes the record of a single male individual.
% -- It appears that drinks>5 is some sort of a selector on this database.
% See the PC/BEAGLE User's Guide for more information.
%
% 5. Number of instances: 345
%
% 6. Number of attributes: 7 overall
%
% 7. Attribute information:
% 1. mcv mean corpuscular volume
% 2. alkphos alkaline phosphotase
% 3. sgpt alamine aminotransferase
% 4. sgot aspartate aminotransferase
% 5. gammagt gamma-glutamyl transpeptidase
% 6. drinks number of half-pint equivalents of alcoholic beverages
% drunk per day
% 7. selector field used to split data into two sets
%
% 8. Missing values: none%
% Information about the dataset
% CLASSTYPE: nominal
% CLASSINDEX: last
%
@relation liver-disorders
@attribute mcv INTEGER
@attribute alkphos INTEGER
@attribute sgpt INTEGER
@attribute sgot INTEGER
@attribute gammagt INTEGER
@attribute drinks REAL
@attribute selector {1,2}
@data
85,92,45,27,31,0.0,1
85,64,59,32,23,0.0,2
86,54,33,16,54,0.0,2
91,78,34,24,36,0.0,2
87,70,12,28,10,0.0,2
98,55,13,17,17,0.0,2
88,62,20,17,9,0.5,1
88,67,21,11,11,0.5,1
92,54,22,20,7,0.5,1
90,60,25,19,5,0.5,1
89,52,13,24,15,0.5,1
82,62,17,17,15,0.5,1
90,64,61,32,13,0.5,1
86,77,25,19,18,0.5,1
96,67,29,20,11,0.5,1
91,78,20,31,18,0.5,1
89,67,23,16,10,0.5,1
89,79,17,17,16,0.5,1
91,107,20,20,56,0.5,1
94,116,11,33,11,0.5,1
92,59,35,13,19,0.5,1
93,23,35,20,20,0.5,1
90,60,23,27,5,0.5,1
96,68,18,19,19,0.5,1
84,80,47,33,97,0.5,1
92,70,24,13,26,0.5,1
90,47,28,15,18,0.5,1
88,66,20,21,10,0.5,1
91,102,17,13,19,0.5,1
87,41,31,19,16,0.5,1
86,79,28,16,17,0.5,1
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93,77,32,18,29,0.5,1
88,96,28,21,40,0.5,1
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91,72,155,68,82,0.5,2
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79,39,14,19,9,0.5,2
85,85,25,26,30,0.5,2
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84,92,68,37,44,0.5,2
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89,101,18,25,13,0.5,2
86,84,18,14,16,0.5,2
85,65,25,14,18,0.5,2
88,61,19,21,13,0.5,2
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95,50,29,25,50,0.5,2
91,75,24,22,11,0.5,2
83,40,29,25,38,0.5,2
89,74,19,23,16,0.5,2
85,64,24,22,11,0.5,2
92,57,64,36,90,0.5,2
94,48,11,23,43,0.5,2
87,52,21,19,30,0.5,2
85,65,23,29,15,0.5,2
84,82,21,21,19,0.5,2
88,49,20,22,19,0.5,2
96,67,26,26,36,0.5,2
90,63,24,24,24,0.5,2
90,45,33,34,27,0.5,2
90,72,14,15,18,0.5,2
91,55,4,8,13,0.5,2
91,52,15,22,11,0.5,2
87,71,32,19,27,1.0,1
89,77,26,20,19,1.0,1
89,67,5,17,14,1.0,2
85,51,26,24,23,1.0,2
103,75,19,30,13,1.0,2
90,63,16,21,14,1.0,2
90,63,29,23,57,2.0,1
90,67,35,19,35,2.0,1
87,66,27,22,9,2.0,1
90,73,34,21,22,2.0,1
86,54,20,21,16,2.0,1
90,80,19,14,42,2.0,1
87,90,43,28,156,2.0,2
96,72,28,19,30,2.0,2
91,55,9,25,16,2.0,2
95,78,27,25,30,2.0,2
92,101,34,30,64,2.0,2
89,51,41,22,48,2.0,2
91,99,42,33,16,2.0,2
94,58,21,18,26,2.0,2
92,60,30,27,297,2.0,2
94,58,21,18,26,2.0,2
88,47,33,26,29,2.0,2
92,65,17,25,9,2.0,2
92,79,22,20,11,3.0,1
84,83,20,25,7,3.0,1
88,68,27,21,26,3.0,1
86,48,20,20,6,3.0,1
99,69,45,32,30,3.0,1
88,66,23,12,15,3.0,1
89,62,42,30,20,3.0,1
90,51,23,17,27,3.0,1
81,61,32,37,53,3.0,2
89,89,23,18,104,3.0,2
89,65,26,18,36,3.0,2
92,75,26,26,24,3.0,2
85,59,25,20,25,3.0,2
92,61,18,13,81,3.0,2
89,63,22,27,10,4.0,1
90,84,18,23,13,4.0,1
88,95,25,19,14,4.0,1
89,35,27,29,17,4.0,1
91,80,37,23,27,4.0,1
91,109,33,15,18,4.0,1
91,65,17,5,7,4.0,1
88,107,29,20,50,4.0,2
87,76,22,55,9,4.0,2
87,86,28,23,21,4.0,2
87,42,26,23,17,4.0,2
88,80,24,25,17,4.0,2
90,96,34,49,169,4.0,2
86,67,11,15,8,4.0,2
92,40,19,20,21,4.0,2
85,60,17,21,14,4.0,2
89,90,15,17,25,4.0,2
91,57,15,16,16,4.0,2
96,55,48,39,42,4.0,2
79,101,17,27,23,4.0,2
90,134,14,20,14,4.0,2
89,76,14,21,24,4.0,2
88,93,29,27,31,4.0,2
90,67,10,16,16,4.0,2
92,73,24,21,48,4.0,2
91,55,28,28,82,4.0,2
83,45,19,21,13,4.0,2
90,74,19,14,22,4.0,2
92,66,21,16,33,5.0,1
93,63,26,18,18,5.0,1
86,78,47,39,107,5.0,2
97,44,113,45,150,5.0,2
87,59,15,19,12,5.0,2
86,44,21,11,15,5.0,2
87,64,16,20,24,5.0,2
92,57,21,23,22,5.0,2
90,70,25,23,112,5.0,2
99,59,17,19,11,5.0,2
92,80,10,26,20,6.0,1
95,60,26,22,28,6.0,1
91,63,25,26,15,6.0,1
92,62,37,21,36,6.0,1
95,50,13,14,15,6.0,1
90,76,37,19,50,6.0,1
96,70,70,26,36,6.0,1
95,62,64,42,76,6.0,1
92,62,20,23,20,6.0,1
91,63,25,26,15,6.0,1
82,56,67,38,92,6.0,2
92,82,27,24,37,6.0,2
90,63,12,26,21,6.0,2
88,37,9,15,16,6.0,2
100,60,29,23,76,6.0,2
98,43,35,23,69,6.0,2
91,74,87,50,67,6.0,2
92,87,57,25,44,6.0,2
93,99,36,34,48,6.0,2
90,72,17,19,19,6.0,2
97,93,21,20,68,6.0,2
93,50,18,25,17,6.0,2
90,57,20,26,33,6.0,2
92,76,31,28,41,6.0,2
88,55,19,17,14,6.0,2
89,63,24,29,29,6.0,2
92,79,70,32,84,7.0,1
92,93,58,35,120,7.0,1
93,84,58,47,62,7.0,2
97,71,29,22,52,8.0,1
84,99,33,19,26,8.0,1
96,44,42,23,73,8.0,1
90,62,22,21,21,8.0,1
92,94,18,17,6,8.0,1
90,67,77,39,114,8.0,1
97,71,29,22,52,8.0,1
91,69,25,25,66,8.0,2
93,59,17,20,14,8.0,2
92,95,85,48,200,8.0,2
90,50,26,22,53,8.0,2
91,62,59,47,60,8.0,2
92,93,22,28,123,9.0,1
92,77,86,41,31,10.0,1
86,66,22,24,26,10.0,2
98,57,31,34,73,10.0,2
95,80,50,64,55,10.0,2
92,108,53,33,94,12.0,2
97,92,22,28,49,12.0,2
93,77,39,37,108,16.0,1
94,83,81,34,201,20.0,1
87,75,25,21,14,0.0,1
88,56,23,18,12,0.0,1
84,97,41,20,32,0.0,2
94,91,27,20,15,0.5,1
97,62,17,13,5,0.5,1
92,85,25,20,12,0.5,1
82,48,27,15,12,0.5,1
88,74,31,25,15,0.5,1
95,77,30,14,21,0.5,1
88,94,26,18,8,0.5,1
91,70,19,19,22,0.5,1
83,54,27,15,12,0.5,1
91,105,40,26,56,0.5,1
86,79,37,28,14,0.5,1
91,96,35,22,135,0.5,1
89,82,23,14,35,0.5,1
90,73,24,23,11,0.5,1
90,87,19,25,19,0.5,1
89,82,33,32,18,0.5,1
85,79,17,8,9,0.5,1
85,119,30,26,17,0.5,1
78,69,24,18,31,0.5,1
88,107,34,21,27,0.5,1
89,115,17,27,7,0.5,1
92,67,23,15,12,0.5,1
89,101,27,34,14,0.5,1
91,84,11,12,10,0.5,1
94,101,41,20,53,0.5,2
88,46,29,22,18,0.5,2
88,122,35,29,42,0.5,2
84,88,28,25,35,0.5,2
90,79,18,15,24,0.5,2
87,69,22,26,11,0.5,2
65,63,19,20,14,0.5,2
90,64,12,17,14,0.5,2
85,58,18,24,16,0.5,2
88,81,41,27,36,0.5,2
86,78,52,29,62,0.5,2
82,74,38,28,48,0.5,2
86,58,36,27,59,0.5,2
94,56,30,18,27,0.5,2
87,57,30,30,22,0.5,2
98,74,148,75,159,0.5,2
94,75,20,25,38,0.5,2
83,68,17,20,71,0.5,2
93,56,25,21,33,0.5,2
101,65,18,21,22,0.5,2
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86,57,13,20,13,0.5,2
86,54,26,30,13,0.5,2
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91,67,32,26,13,1.0,1
91,80,21,19,14,1.0,1
92,60,23,15,19,1.0,1
91,60,32,14,8,1.0,1
93,65,28,22,10,1.0,1
90,63,45,24,85,1.0,2
87,92,21,22,37,1.0,2
83,78,31,19,115,1.0,2
95,62,24,23,14,1.0,2
93,59,41,30,48,1.0,2
84,82,43,32,38,2.0,1
87,71,33,20,22,2.0,1
86,44,24,15,18,2.0,1
86,66,28,24,21,2.0,1
88,58,31,17,17,2.0,1
90,61,28,29,31,2.0,1
88,69,70,24,64,2.0,1
93,87,18,17,26,2.0,1
98,58,33,21,28,2.0,1
91,44,18,18,23,2.0,2
87,75,37,19,70,2.0,2
94,91,30,26,25,2.0,2
88,85,14,15,10,2.0,2
89,109,26,25,27,2.0,2
87,59,37,27,34,2.0,2
93,58,20,23,18,2.0,2
88,57,9,15,16,2.0,2
94,65,38,27,17,3.0,1
91,71,12,22,11,3.0,1
90,55,20,20,16,3.0,1
91,64,21,17,26,3.0,2
88,47,35,26,33,3.0,2
82,72,31,20,84,3.0,2
85,58,83,49,51,3.0,2
91,54,25,22,35,4.0,1
98,50,27,25,53,4.0,2
86,62,29,21,26,4.0,2
89,48,32,22,14,4.0,2
82,68,20,22,9,4.0,2
83,70,17,19,23,4.0,2
96,70,21,26,21,4.0,2
94,117,77,56,52,4.0,2
93,45,11,14,21,4.0,2
93,49,27,21,29,4.0,2
84,73,46,32,39,4.0,2
91,63,17,17,46,4.0,2
90,57,31,18,37,4.0,2
87,45,19,13,16,4.0,2
91,68,14,20,19,4.0,2
86,55,29,35,108,4.0,2
91,86,52,47,52,4.0,2
88,46,15,33,55,4.0,2
85,52,22,23,34,4.0,2
89,72,33,27,55,4.0,2
95,59,23,18,19,4.0,2
94,43,154,82,121,4.0,2
96,56,38,26,23,5.0,2
90,52,10,17,12,5.0,2
94,45,20,16,12,5.0,2
99,42,14,21,49,5.0,2
93,102,47,23,37,5.0,2
94,71,25,26,31,5.0,2
92,73,33,34,115,5.0,2
87,54,41,29,23,6.0,1
92,67,15,14,14,6.0,1
98,101,31,26,32,6.0,1
92,53,51,33,92,6.0,1
97,94,43,43,82,6.0,1
93,43,11,16,54,6.0,1
93,68,24,18,19,6.0,1
95,36,38,19,15,6.0,1
99,86,58,42,203,6.0,1
98,66,103,57,114,6.0,1
92,80,10,26,20,6.0,1
96,74,27,25,43,6.0,2
95,93,21,27,47,6.0,2
86,109,16,22,28,6.0,2
91,46,30,24,39,7.0,2
102,82,34,78,203,7.0,2
85,50,12,18,14,7.0,2
91,57,33,23,12,8.0,1
91,52,76,32,24,8.0,1
93,70,46,30,33,8.0,1
87,55,36,19,25,8.0,1
98,123,28,24,31,8.0,1
82,55,18,23,44,8.0,2
95,73,20,25,225,8.0,2
97,80,17,20,53,8.0,2
100,83,25,24,28,8.0,2
88,91,56,35,126,9.0,2
91,138,45,21,48,10.0,1
92,41,37,22,37,10.0,1
86,123,20,25,23,10.0,2
91,93,35,34,37,10.0,2
87,87,15,23,11,10.0,2
87,56,52,43,55,10.0,2
99,75,26,24,41,12.0,1
96,69,53,43,203,12.0,2
98,77,55,35,89,15.0,1
91,68,27,26,14,16.0,1
98,99,57,45,65,20.0,1

24
tests/test
View File

@@ -1,12 +1,20 @@
if [ -d build ] ; then
rm -fr build
fi
if [ -d gcovr-report ] ; then
rm -fr gcovr-report
fi
cmake -S . -B build -Wno-dev
if test $? -ne 0; then
echo "Error in creating build commands."
exit 1
fi
cmake --build build
if test $? -ne 0; then
echo "Error in build command."
exit 1
fi
cd build
ctest --output-on-failure
cd ..
if [ ! -d gcovr-report ] ; then
mkdir gcovr-report
fi
rm -fr gcovr-report/* 2>/dev/null
#lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
#lcov --remove lcoverage/main_coverage.info 'v1/*' '/Applications/*' '*/tests/*' --output-file lcoverage/main_coverage.info -q
#lcov --list lcoverage/main_coverage.info
cd ..
gcovr --gcov-filter "CPPFImdlp.cpp" --gcov-filter "Metrics.cpp" --txt --sonarqube=tests/gcovr-report/coverage.xml