test: Refactor tests to new version

This commit is contained in:
2022-12-21 16:42:37 +01:00
parent 036b41a0eb
commit 5925dbd666
8 changed files with 100 additions and 41 deletions

116
tests/ArffFiles.cpp Normal file
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@@ -0,0 +1,116 @@
#include "ArffFiles.h"
#include <fstream>
#include <sstream>
#include <map>
#include <iostream>
using namespace std;
ArffFiles::ArffFiles()
{
}
vector<string> ArffFiles::getLines()
{
return lines;
}
unsigned long int ArffFiles::getSize()
{
return lines.size();
}
vector<tuple<string, string>> ArffFiles::getAttributes()
{
return attributes;
}
string ArffFiles::getClassName()
{
return className;
}
string ArffFiles::getClassType()
{
return classType;
}
vector<vector<float>>& ArffFiles::getX()
{
return X;
}
vector<int>& ArffFiles::getY()
{
return y;
}
void ArffFiles::load(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(make_tuple(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
throw invalid_argument("Unable to open file");
}
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++) {
stringstream ss(lines[i]);
string value;
int pos = 0, xIndex = 0;
while (getline(ss, value, ',')) {
if (pos++ == labelIndex) {
yy[i] = value;
} else {
X[xIndex++][i] = stof(value);
}
}
}
y = factorize(yy);
}
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> yy;
yy.reserve(labels_t.size());
map<string, int> labelMap;
int i = 0;
for (string label : labels_t) {
if (labelMap.find(label) == labelMap.end()) {
labelMap[label] = i++;
}
yy.push_back(labelMap[label]);
}
return yy;
}

28
tests/ArffFiles.h Normal file
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@@ -0,0 +1,28 @@
#ifndef ARFFFILES_H
#define ARFFFILES_H
#include <string>
#include <vector>
#include <tuple>
using namespace std;
class ArffFiles {
private:
vector<string> lines;
vector<tuple<string, string>> attributes;
string className, 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<tuple<string, string>> getAttributes();
vector<int> factorize(const vector<string>& labels_t);
};
#endif

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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)

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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);
}
}