rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Add Makefile & tests

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2023-07-17 22:51:15 +02:00
parent f530e69dae
commit ca72a34131
10 changed files with 303 additions and 365 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
tests/CMakeLists.txt Normal file
View File

@@ -0,0 +1,12 @@
if(ENABLE_TESTING)
set(TEST_MAIN "unit_tests")
include_directories(src)
SET(GCC_COVERAGE_COMPILE_FLAGS "-fprofile-arcs -ftest-coverage --coverage")
SET(GCC_COVERAGE_LINK_FLAGS "--coverage")
set(TEST_SOURCES main.cc ../sample/ArffFiles.cc ../sample/CPPFImdlp.cpp ../sample/Metrics.cpp
../src/utils.cc ../src/Network.cc ../src/Node.cc ../src/Metrics.cc ../src/BaseClassifier.cc ../src/KDB.cc
../src/TAN.cc ../src/SPODE.cc ../src/Ensemble.cc ../src/AODE.cc ../src/Mst.cc)
add_executable(${TEST_MAIN} ${TEST_SOURCES})
target_link_libraries(${TEST_MAIN} PUBLIC "${TORCH_LIBRARIES}" Catch2::Catch2WithMain)
add_test(NAME ${TEST_MAIN} COMMAND ${TEST_MAIN})
endif(ENABLE_TESTING)

102
tests/main.cc Normal file
View File

@@ -0,0 +1,102 @@
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <vector>
#include <map>
#include <string>
#include <torch/torch.h>
#include "../sample/ArffFiles.h"
#include "../sample/CPPFImdlp.h"
#include "../src/KDB.h"
#include "../src/TAN.h"
#include "../src/SPODE.h"
#include "../src/AODE.h"
const string PATH = "data/";
using namespace std;
pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
{
vector<mdlp::labels_t>Xd;
map<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 };
}
TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
{
auto path = "../../data/";
map <pair<string, string>, float> scores = {
{{"diabetes", "AODE"}, 0.811198}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
{{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
{{"glass", "AODE"}, 0.78972}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
{{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333}
};
string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
auto handler = ArffFiles();
handler.load(path + static_cast<string>(file_name) + ".arff");
// Get Dataset X, y
vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
for (auto feature : handler.getAttributes()) {
features.push_back(feature.first);
}
// Discretize Dataset
vector<mdlp::labels_t> Xd;
map<string, int> maxes;
tie(Xd, maxes) = discretize(X, y, features);
maxes[className] = *max_element(y.begin(), y.end()) + 1;
map<string, vector<int>> states;
for (auto feature : features) {
states[feature] = vector<int>(maxes[feature]);
}
states[className] = vector<int>(maxes[className]);
SECTION("Test TAN classifier (" + file_name + ")")
{
auto clf = bayesnet::TAN();
clf.fit(Xd, y, features, className, states);
auto score = clf.score(Xd, y);
//scores[{file_name, "TAN"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "TAN"}]).epsilon(1e-6));
}
SECTION("Test KDB classifier (" + file_name + ")")
{
auto clf = bayesnet::KDB(2);
clf.fit(Xd, y, features, className, states);
auto score = clf.score(Xd, y);
//scores[{file_name, "KDB"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "KDB"
}]).epsilon(1e-6));
}
SECTION("Test SPODE classifier (" + file_name + ")")
{
auto clf = bayesnet::SPODE(1);
clf.fit(Xd, y, features, className, states);
auto score = clf.score(Xd, y);
// scores[{file_name, "SPODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "SPODE"}]).epsilon(1e-6));
}
SECTION("Test AODE classifier (" + file_name + ")")
{
auto clf = bayesnet::AODE();
clf.fit(Xd, y, features, className, states);
auto score = clf.score(Xd, y);
// scores[{file_name, "AODE"}] = score;
REQUIRE(score == Catch::Approx(scores[{file_name, "AODE"}]).epsilon(1e-6));
}
// for (auto scores : scores) {
// cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
// }
}