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Initial commit
This commit is contained in:
2
tests/.gitignore
vendored
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2
tests/.gitignore
vendored
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build
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build/*
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32
tests/CMakeLists.txt
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32
tests/CMakeLists.txt
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cmake_minimum_required(VERSION 3.14)
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project(FImdlp)
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# GoogleTest requires at least C++14
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set(CMAKE_CXX_STANDARD 14)
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include(FetchContent)
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include_directories(${GTEST_INCLUDE_DIRS})
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FetchContent_Declare(
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googletest
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URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip
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)
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# For Windows: Prevent overriding the parent project's compiler/linker settings
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set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
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FetchContent_MakeAvailable(googletest)
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enable_testing()
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add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
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add_executable(FImdlp_unittest ../CPPFImdlp.cpp ../Metrics.cpp FImdlp_unittest.cpp)
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target_link_libraries(Metrics_unittest GTest::gtest_main)
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target_link_libraries(FImdlp_unittest GTest::gtest_main)
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target_compile_options(Metrics_unittest PRIVATE --coverage)
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target_compile_options(FImdlp_unittest PRIVATE --coverage)
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target_link_options(Metrics_unittest PRIVATE --coverage)
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target_link_options(FImdlp_unittest PRIVATE --coverage)
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include(GoogleTest)
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gtest_discover_tests(Metrics_unittest)
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gtest_discover_tests(FImdlp_unittest)
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141
tests/FImdlp_unittest.cpp
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141
tests/FImdlp_unittest.cpp
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#include "gtest/gtest.h"
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#include "../Metrics.h"
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#include "../CPPFImdlp.h"
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#include <iostream>
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namespace mdlp {
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class TestFImdlp : public CPPFImdlp, public testing::Test {
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public:
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precision_t precision = 0.000001;
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TestFImdlp() : CPPFImdlp(false) {}
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void SetUp() {
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// 5.0, 5.1, 5.1, 5.1, 5.2, 5.3, 5.6, 5.7, 5.9, 6.0]
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//(5.0, 1) (5.1, 1) (5.1, 2) (5.1, 2) (5.2, 1) (5.3, 1) (5.6, 2) (5.7, 1) (5.9, 2) (6.0, 2)
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X = {5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9};
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y = {1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
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proposal = false;
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fit(X, y);
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}
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void setProposal(bool value) {
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proposal = value;
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}
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// void initIndices()
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// {
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// indices = indices_t();
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// }
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void checkSortedVector() {
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indices_t testSortedIndices = sortIndices(X);
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precision_t prev = X[testSortedIndices[0]];
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for (auto i = 0; i < X.size(); ++i) {
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EXPECT_EQ(testSortedIndices[i], indices[i]);
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EXPECT_LE(prev, X[testSortedIndices[i]]);
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prev = X[testSortedIndices[i]];
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}
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}
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void checkCutPoints(cutPoints_t &expected) {
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int expectedSize = expected.size();
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EXPECT_EQ(cutPoints.size(), expectedSize);
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for (auto i = 0; i < cutPoints.size(); i++) {
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EXPECT_NEAR(cutPoints[i], expected[i], precision);
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}
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}
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template<typename T, typename A>
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void checkVectors(std::vector<T, A> const &expected, std::vector<T, A> const &computed) {
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EXPECT_EQ(expected.size(), computed.size());
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ASSERT_EQ(expected.size(), computed.size());
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for (auto i = 0; i < expected.size(); i++) {
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EXPECT_NEAR(expected[i], computed[i],precision);
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}
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}
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};
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TEST_F(TestFImdlp, FitErrorEmptyDataset) {
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X = samples_t();
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y = labels_t();
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EXPECT_THROW(fit(X, y), std::invalid_argument);
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}
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TEST_F(TestFImdlp, FitErrorDifferentSize) {
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X = {1, 2, 3};
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y = {1, 2};
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EXPECT_THROW(fit(X, y), std::invalid_argument);
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}
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TEST_F(TestFImdlp, SortIndices) {
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X = {5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9};
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indices = {4, 3, 6, 8, 2, 1, 5, 0, 9, 7};
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checkSortedVector();
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X = {5.77, 5.88, 5.99};
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indices = {0, 1, 2};
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checkSortedVector();
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X = {5.33, 5.22, 5.11};
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indices = {2, 1, 0};
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checkSortedVector();
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}
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TEST_F(TestFImdlp, TestDataset) {
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proposal = false;
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fit(X, y);
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computeCutPointsOriginal(0, 10);
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cutPoints_t expected = {5.6499996185302734};
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vector<precision_t> computed = getCutPoints();
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computed = getCutPoints();
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int expectedSize = expected.size();
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EXPECT_EQ(computed.size(), expected.size());
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for (auto i = 0; i < expectedSize; i++) {
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EXPECT_NEAR(computed[i], expected[i], precision);
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}
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}
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TEST_F(TestFImdlp, ComputeCutPointsOriginal) {
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cutPoints_t expected = {5.65};
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proposal = false;
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computeCutPointsOriginal(0, 10);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, ComputeCutPointsOriginalGCase) {
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cutPoints_t expected;
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proposal = false;
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expected = {2};
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samples_t X_ = {0, 1, 2, 2};
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labels_t y_ = {1, 1, 1, 2};
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fit(X_, y_);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, ComputeCutPointsProposal) {
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proposal = true;
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cutPoints_t expected;
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expected = {};
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fit(X, y);
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computeCutPointsProposal();
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, ComputeCutPointsProposalGCase) {
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cutPoints_t expected;
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expected = {1.5};
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proposal = true;
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samples_t X_ = {0, 1, 2, 2};
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labels_t y_ = {1, 1, 1, 2};
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fit(X_, y_);
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checkCutPoints(expected);
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}
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TEST_F(TestFImdlp, GetCutPoints) {
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samples_t computed, expected = {5.65};
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proposal = false;
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computeCutPointsOriginal(0, 10);
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computed = getCutPoints();
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for (auto item: cutPoints)
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cout << setprecision(6) << item << endl;
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checkVectors(expected, computed);
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}
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}
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43
tests/Metrics_unittest.cpp
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43
tests/Metrics_unittest.cpp
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#include "gtest/gtest.h"
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#include "../Metrics.h"
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namespace mdlp {
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class TestMetrics: public Metrics, public testing::Test {
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public:
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labels_t y;
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samples_t X;
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indices_t indices;
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precision_t precision = 0.000001;
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TestMetrics(): Metrics(y, indices) {}
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void SetUp()
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{
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y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
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indices = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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setData(y, indices);
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}
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};
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TEST_F(TestMetrics, NumClasses)
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{
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y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
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EXPECT_EQ(1, computeNumClasses(4, 8));
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EXPECT_EQ(2, computeNumClasses(0, 10));
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EXPECT_EQ(2, computeNumClasses(8, 10));
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}
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TEST_F(TestMetrics, Entropy)
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{
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EXPECT_EQ(1, entropy(0, 10));
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EXPECT_EQ(0, entropy(0, 5));
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y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
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setData(y, indices);
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ASSERT_NEAR(0.468996, entropy(0, 10), precision);
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}
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TEST_F(TestMetrics, InformationGain)
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{
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ASSERT_NEAR(1, informationGain(0, 5, 10), precision);
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y = { 1, 1, 1, 1, 1, 1, 1, 1, 2, 1 };
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setData(y, indices);
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ASSERT_NEAR(0.108032, informationGain(0, 5, 10), precision);
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}
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}
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4
tests/cover
Executable file
4
tests/cover
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rm -fr lcoverage/*
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lcov --capture --directory ./ --output-file lcoverage/main_coverage.info
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genhtml lcoverage/main_coverage.info --output-directory lcoverage
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open lcoverage/index.html
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225
tests/datasets/iris.arff
Executable file
225
tests/datasets/iris.arff
Executable file
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% 1. Title: Iris Plants Database
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%
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% 2. Sources:
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% (a) Creator: R.A. Fisher
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% (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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% (c) Date: July, 1988
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%
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% 3. Past Usage:
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% - Publications: too many to mention!!! Here are a few.
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% 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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% Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
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% to Mathematical Statistics" (John Wiley, NY, 1950).
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% 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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% (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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% 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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% Structure and Classification Rule for Recognition in Partially Exposed
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% Environments". IEEE Transactions on Pattern Analysis and Machine
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% Intelligence, Vol. PAMI-2, No. 1, 67-71.
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% -- Results:
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% -- very low misclassification rates (0% for the setosa class)
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% 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
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% Transactions on Information Theory, May 1972, 431-433.
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% -- Results:
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% -- very low misclassification rates again
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% 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
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% conceptual clustering system finds 3 classes in the data.
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%
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% 4. Relevant Information:
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% --- This is perhaps the best known database to be found in the pattern
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% recognition literature. Fisher's paper is a classic in the field
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% and is referenced frequently to this day. (See Duda & Hart, for
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% example.) The data set contains 3 classes of 50 instances each,
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% where each class refers to a type of iris plant. One class is
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% linearly separable from the other 2; the latter are NOT linearly
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% separable from each other.
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% --- Predicted attribute: class of iris plant.
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% --- This is an exceedingly simple domain.
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%
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% 5. Number of Instances: 150 (50 in each of three classes)
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%
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% 6. Number of Attributes: 4 numeric, predictive attributes and the class
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%
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% 7. Attribute Information:
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% 1. sepal length in cm
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% 2. sepal width in cm
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% 3. petal length in cm
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% 4. petal width in cm
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% 5. class:
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% -- Iris Setosa
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% -- Iris Versicolour
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% -- Iris Virginica
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%
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% 8. Missing Attribute Values: None
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%
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% Summary Statistics:
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% Min Max Mean SD Class Correlation
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% sepal length: 4.3 7.9 5.84 0.83 0.7826
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% sepal width: 2.0 4.4 3.05 0.43 -0.4194
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% petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
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% petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
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%
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% 9. Class Distribution: 33.3% for each of 3 classes.
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@RELATION iris
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@ATTRIBUTE sepallength REAL
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@ATTRIBUTE sepalwidth REAL
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@ATTRIBUTE petallength REAL
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@ATTRIBUTE petalwidth REAL
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@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}
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@DATA
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
|
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4.6,3.1,1.5,0.2,Iris-setosa
|
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5.0,3.6,1.4,0.2,Iris-setosa
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||||
5.4,3.9,1.7,0.4,Iris-setosa
|
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4.6,3.4,1.4,0.3,Iris-setosa
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5.0,3.4,1.5,0.2,Iris-setosa
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||||
4.4,2.9,1.4,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
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5.4,3.7,1.5,0.2,Iris-setosa
|
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4.8,3.4,1.6,0.2,Iris-setosa
|
||||
4.8,3.0,1.4,0.1,Iris-setosa
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4.3,3.0,1.1,0.1,Iris-setosa
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5.8,4.0,1.2,0.2,Iris-setosa
|
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5.7,4.4,1.5,0.4,Iris-setosa
|
||||
5.4,3.9,1.3,0.4,Iris-setosa
|
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5.1,3.5,1.4,0.3,Iris-setosa
|
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5.7,3.8,1.7,0.3,Iris-setosa
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5.1,3.8,1.5,0.3,Iris-setosa
|
||||
5.4,3.4,1.7,0.2,Iris-setosa
|
||||
5.1,3.7,1.5,0.4,Iris-setosa
|
||||
4.6,3.6,1.0,0.2,Iris-setosa
|
||||
5.1,3.3,1.7,0.5,Iris-setosa
|
||||
4.8,3.4,1.9,0.2,Iris-setosa
|
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5.0,3.0,1.6,0.2,Iris-setosa
|
||||
5.0,3.4,1.6,0.4,Iris-setosa
|
||||
5.2,3.5,1.5,0.2,Iris-setosa
|
||||
5.2,3.4,1.4,0.2,Iris-setosa
|
||||
4.7,3.2,1.6,0.2,Iris-setosa
|
||||
4.8,3.1,1.6,0.2,Iris-setosa
|
||||
5.4,3.4,1.5,0.4,Iris-setosa
|
||||
5.2,4.1,1.5,0.1,Iris-setosa
|
||||
5.5,4.2,1.4,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
5.0,3.2,1.2,0.2,Iris-setosa
|
||||
5.5,3.5,1.3,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
4.4,3.0,1.3,0.2,Iris-setosa
|
||||
5.1,3.4,1.5,0.2,Iris-setosa
|
||||
5.0,3.5,1.3,0.3,Iris-setosa
|
||||
4.5,2.3,1.3,0.3,Iris-setosa
|
||||
4.4,3.2,1.3,0.2,Iris-setosa
|
||||
5.0,3.5,1.6,0.6,Iris-setosa
|
||||
5.1,3.8,1.9,0.4,Iris-setosa
|
||||
4.8,3.0,1.4,0.3,Iris-setosa
|
||||
5.1,3.8,1.6,0.2,Iris-setosa
|
||||
4.6,3.2,1.4,0.2,Iris-setosa
|
||||
5.3,3.7,1.5,0.2,Iris-setosa
|
||||
5.0,3.3,1.4,0.2,Iris-setosa
|
||||
7.0,3.2,4.7,1.4,Iris-versicolor
|
||||
6.4,3.2,4.5,1.5,Iris-versicolor
|
||||
6.9,3.1,4.9,1.5,Iris-versicolor
|
||||
5.5,2.3,4.0,1.3,Iris-versicolor
|
||||
6.5,2.8,4.6,1.5,Iris-versicolor
|
||||
5.7,2.8,4.5,1.3,Iris-versicolor
|
||||
6.3,3.3,4.7,1.6,Iris-versicolor
|
||||
4.9,2.4,3.3,1.0,Iris-versicolor
|
||||
6.6,2.9,4.6,1.3,Iris-versicolor
|
||||
5.2,2.7,3.9,1.4,Iris-versicolor
|
||||
5.0,2.0,3.5,1.0,Iris-versicolor
|
||||
5.9,3.0,4.2,1.5,Iris-versicolor
|
||||
6.0,2.2,4.0,1.0,Iris-versicolor
|
||||
6.1,2.9,4.7,1.4,Iris-versicolor
|
||||
5.6,2.9,3.6,1.3,Iris-versicolor
|
||||
6.7,3.1,4.4,1.4,Iris-versicolor
|
||||
5.6,3.0,4.5,1.5,Iris-versicolor
|
||||
5.8,2.7,4.1,1.0,Iris-versicolor
|
||||
6.2,2.2,4.5,1.5,Iris-versicolor
|
||||
5.6,2.5,3.9,1.1,Iris-versicolor
|
||||
5.9,3.2,4.8,1.8,Iris-versicolor
|
||||
6.1,2.8,4.0,1.3,Iris-versicolor
|
||||
6.3,2.5,4.9,1.5,Iris-versicolor
|
||||
6.1,2.8,4.7,1.2,Iris-versicolor
|
||||
6.4,2.9,4.3,1.3,Iris-versicolor
|
||||
6.6,3.0,4.4,1.4,Iris-versicolor
|
||||
6.8,2.8,4.8,1.4,Iris-versicolor
|
||||
6.7,3.0,5.0,1.7,Iris-versicolor
|
||||
6.0,2.9,4.5,1.5,Iris-versicolor
|
||||
5.7,2.6,3.5,1.0,Iris-versicolor
|
||||
5.5,2.4,3.8,1.1,Iris-versicolor
|
||||
5.5,2.4,3.7,1.0,Iris-versicolor
|
||||
5.8,2.7,3.9,1.2,Iris-versicolor
|
||||
6.0,2.7,5.1,1.6,Iris-versicolor
|
||||
5.4,3.0,4.5,1.5,Iris-versicolor
|
||||
6.0,3.4,4.5,1.6,Iris-versicolor
|
||||
6.7,3.1,4.7,1.5,Iris-versicolor
|
||||
6.3,2.3,4.4,1.3,Iris-versicolor
|
||||
5.6,3.0,4.1,1.3,Iris-versicolor
|
||||
5.5,2.5,4.0,1.3,Iris-versicolor
|
||||
5.5,2.6,4.4,1.2,Iris-versicolor
|
||||
6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
5.0,2.3,3.3,1.0,Iris-versicolor
|
||||
5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
5.7,2.8,4.1,1.3,Iris-versicolor
|
||||
6.3,3.3,6.0,2.5,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
7.1,3.0,5.9,2.1,Iris-virginica
|
||||
6.3,2.9,5.6,1.8,Iris-virginica
|
||||
6.5,3.0,5.8,2.2,Iris-virginica
|
||||
7.6,3.0,6.6,2.1,Iris-virginica
|
||||
4.9,2.5,4.5,1.7,Iris-virginica
|
||||
7.3,2.9,6.3,1.8,Iris-virginica
|
||||
6.7,2.5,5.8,1.8,Iris-virginica
|
||||
7.2,3.6,6.1,2.5,Iris-virginica
|
||||
6.5,3.2,5.1,2.0,Iris-virginica
|
||||
6.4,2.7,5.3,1.9,Iris-virginica
|
||||
6.8,3.0,5.5,2.1,Iris-virginica
|
||||
5.7,2.5,5.0,2.0,Iris-virginica
|
||||
5.8,2.8,5.1,2.4,Iris-virginica
|
||||
6.4,3.2,5.3,2.3,Iris-virginica
|
||||
6.5,3.0,5.5,1.8,Iris-virginica
|
||||
7.7,3.8,6.7,2.2,Iris-virginica
|
||||
7.7,2.6,6.9,2.3,Iris-virginica
|
||||
6.0,2.2,5.0,1.5,Iris-virginica
|
||||
6.9,3.2,5.7,2.3,Iris-virginica
|
||||
5.6,2.8,4.9,2.0,Iris-virginica
|
||||
7.7,2.8,6.7,2.0,Iris-virginica
|
||||
6.3,2.7,4.9,1.8,Iris-virginica
|
||||
6.7,3.3,5.7,2.1,Iris-virginica
|
||||
7.2,3.2,6.0,1.8,Iris-virginica
|
||||
6.2,2.8,4.8,1.8,Iris-virginica
|
||||
6.1,3.0,4.9,1.8,Iris-virginica
|
||||
6.4,2.8,5.6,2.1,Iris-virginica
|
||||
7.2,3.0,5.8,1.6,Iris-virginica
|
||||
7.4,2.8,6.1,1.9,Iris-virginica
|
||||
7.9,3.8,6.4,2.0,Iris-virginica
|
||||
6.4,2.8,5.6,2.2,Iris-virginica
|
||||
6.3,2.8,5.1,1.5,Iris-virginica
|
||||
6.1,2.6,5.6,1.4,Iris-virginica
|
||||
7.7,3.0,6.1,2.3,Iris-virginica
|
||||
6.3,3.4,5.6,2.4,Iris-virginica
|
||||
6.4,3.1,5.5,1.8,Iris-virginica
|
||||
6.0,3.0,4.8,1.8,Iris-virginica
|
||||
6.9,3.1,5.4,2.1,Iris-virginica
|
||||
6.7,3.1,5.6,2.4,Iris-virginica
|
||||
6.9,3.1,5.1,2.3,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
6.8,3.2,5.9,2.3,Iris-virginica
|
||||
6.7,3.3,5.7,2.5,Iris-virginica
|
||||
6.7,3.0,5.2,2.3,Iris-virginica
|
||||
6.3,2.5,5.0,1.9,Iris-virginica
|
||||
6.5,3.0,5.2,2.0,Iris-virginica
|
||||
6.2,3.4,5.4,2.3,Iris-virginica
|
||||
5.9,3.0,5.1,1.8,Iris-virginica
|
||||
%
|
||||
%
|
||||
%
|
||||
10177
tests/datasets/kdd_JapaneseVowels.arff
Executable file
10177
tests/datasets/kdd_JapaneseVowels.arff
Executable file
File diff suppressed because it is too large
Load Diff
20191
tests/datasets/letter.arff
Executable file
20191
tests/datasets/letter.arff
Executable file
File diff suppressed because it is too large
Load Diff
2306
tests/datasets/mfeat-factors.arff
Executable file
2306
tests/datasets/mfeat-factors.arff
Executable file
File diff suppressed because it is too large
Load Diff
12
tests/test
Executable file
12
tests/test
Executable file
@@ -0,0 +1,12 @@
|
||||
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
|
||||
Reference in New Issue
Block a user