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test: ⚡ Refactor tests to new version

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This commit is contained in:
Ricardo Montañana Gómez
2022-12-21 16:42:37 +01:00
parent 036b41a0eb
commit 5925dbd666
8 changed files with 100 additions and 41 deletions
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2
CPPFImdlp.h
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@@ -27,7 +27,7 @@ namespace mdlp {
~CPPFImdlp();
CPPFImdlp& fit(samples_t&, labels_t&);
samples_t getCutPoints();
inline string version() { return "0.9.7"; };
inline string version() { return "1.0.0"; };
};
}
#endif

12
README.md
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@@ -1,4 +1,5 @@
# mdlp
Discretization algorithm based on the paper by Fayyad & Irani [Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning](https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf)
The implementation tries to mitigate the problem of different label values with the same value of the variable:
@@ -19,4 +20,13 @@ cd build
cmake ..
make
./sample iris
```
```
## Test
To run the tests, execute the following commands:
```bash
cd tests
./test
```

2
sample/CMakeLists.txt
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@@ -3,4 +3,4 @@ project(main)
set(CMAKE_CXX_STANDARD 14)
add_executable(sample sample.cpp ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)
add_executable(sample sample.cpp ../tests/ArffFiles.cpp ../Metrics.cpp ../CPPFImdlp.cpp)

2
sample/sample.cpp
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@@ -1,8 +1,8 @@
#include "ArffFiles.h"
#include <iostream>
#include <vector>
#include <iomanip>
#include "../CPPFImdlp.h"
#include "../tests/ArffFiles.h"
using namespace std;
using namespace mdlp;

0
sample/ArffFiles.cpp → tests/ArffFiles.cpp
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0
sample/ArffFiles.h → tests/ArffFiles.h
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2
tests/CMakeLists.txt
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@@ -18,7 +18,7 @@ FetchContent_MakeAvailable(googletest)
enable_testing()
add_executable(Metrics_unittest ../Metrics.cpp Metrics_unittest.cpp)
add_executable(FImdlp_unittest ../CPPFImdlp.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)

121
tests/FImdlp_unittest.cpp
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@@ -1,6 +1,7 @@
#include "gtest/gtest.h"
#include "../Metrics.h"
#include "../CPPFImdlp.h"
#include "ArffFiles.h"
#include <iostream>
namespace mdlp {
@@ -10,10 +11,8 @@ namespace mdlp {
TestFImdlp(): CPPFImdlp() {}
void SetUp()
{
// 5.0, 5.1, 5.1, 5.1, 5.2, 5.3, 5.6, 5.7, 5.9, 6.0]
//(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)
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 };
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 };
algorithm = false;
fit(X, y);
}
@@ -55,6 +54,11 @@ namespace mdlp {
y = labels_t();
EXPECT_THROW(fit(X, y), std::invalid_argument);
}
TEST_F(TestFImdlp, FitErrorIncorrectAlgorithm)
{
algorithm = 2;
EXPECT_THROW(fit(X, y), std::invalid_argument);
}
TEST_F(TestFImdlp, FitErrorDifferentSize)
{
X = { 1, 2, 3 };
@@ -64,56 +68,111 @@ namespace mdlp {
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, TestDataset)
TEST_F(TestFImdlp, TestArtificialDatasetAlternative)
{
algorithm = 0;
algorithm = 1;
fit(X, y);
computeCutPoints(0, 10);
cutPoints_t expected = { 5.6499996185302734 };
computeCutPoints(0, 20);
cutPoints_t expected = { 5.0500001907348633 };
vector<precision_t> computed = getCutPoints();
computed = getCutPoints();
int expectedSize = expected.size();
EXPECT_EQ(computed.size(), expected.size());
for (auto i = 0; i < expectedSize; i++) {
for (auto i = 0; i < computed.size(); i++) {
EXPECT_NEAR(computed[i], expected[i], precision);
}
}
TEST_F(TestFImdlp, ComputeCutPoints)
TEST_F(TestFImdlp, TestArtificialDataset)
{
cutPoints_t expected = { 5.65 };
algorithm = false;
computeCutPoints(0, 10);
checkCutPoints(expected);
algorithm = 0;
fit(X, y);
computeCutPoints(0, 20);
cutPoints_t expected = { 5.0500001907348633 };
vector<precision_t> computed = getCutPoints();
computed = getCutPoints();
int expectedSize = expected.size();
EXPECT_EQ(computed.size(), expected.size());
for (auto 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();
vector<cutPoints_t> expected = {
{ 5.4499998092651367, 6.25 },
{ 2.8499999046325684, 3, 3.0499999523162842, 3.3499999046325684 },
{ 2.4500000476837158, 4.75, 5.0500001907348633 },
{ 0.80000001192092896, 1.4500000476837158, 1.75 }
};
labels_t& y = file.getY();
auto attributes = file.getAttributes();
algorithm = 0;
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);
}
}
}
TEST_F(TestFImdlp, TestIrisAlternative)
{
ArffFiles file;
string path = "../datasets/";
file.load(path + "iris.arff", true);
int items = file.getSize();
vector<samples_t>& X = file.getX();
vector<cutPoints_t> expected = {
{ 5.4499998092651367, 5.75 },
{ 2.8499999046325684, 3.3499999046325684 },
{ 2.4500000476837158, 4.75 },
{ 0.80000001192092896, 1.75 }
};
labels_t& y = file.getY();
auto attributes = file.getAttributes();
algorithm = 1;
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);
}
}
}
TEST_F(TestFImdlp, ComputeCutPointsGCase)
{
cutPoints_t expected;
algorithm = false;
expected = { 2 };
algorithm = 0;
expected = { 1.5 };
samples_t X_ = { 0, 1, 2, 2 };
labels_t y_ = { 1, 1, 1, 2 };
fit(X_, y_);
checkCutPoints(expected);
}
TEST_F(TestFImdlp, ComputeCutPointsalAlternative)
{
algorithm = true;
cutPoints_t expected;
expected = {};
fit(X, y);
computeCutPointsAlternative(0, 10);
checkCutPoints(expected);
}
TEST_F(TestFImdlp, ComputeCutPointsAlternativeGCase)
{
cutPoints_t expected;
@@ -124,14 +183,4 @@ namespace mdlp {
fit(X_, y_);
checkCutPoints(expected);
}
TEST_F(TestFImdlp, GetCutPoints)
{
samples_t computed, expected = { 5.65 };
algorithm = false;
computeCutPoints(0, 10);
computed = getCutPoints();
for (auto item : cutPoints)
cout << setprecision(6) << item << endl;
checkVectors(expected, computed);
}
}