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Duplicate statistics tests in class

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This commit is contained in:
Ricardo Montañana Gómez 2023-09-28 00:45:15 +02:00
parent 00c6cf663b
commit ac89a451e3
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 258 additions and 3 deletions
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13
src/Platform/BestResults.cc
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@ -6,6 +6,7 @@
#include "BestResults.h"
#include "Result.h"
#include "Colors.h"
#include "Statistics.h"
#include <boost/math/distributions/chi_squared.hpp>
#include <boost/math/distributions/normal.hpp>
@ -475,15 +476,23 @@ namespace platform {
cout << efectiveColor << setw(12) << setprecision(9) << fixed << (double)ranksTotal[model] << " ";
}
cout << endl;
vector<string> vModels(models.begin(), models.end());
vector<string> datasets;
for (const auto& dataset : table.begin().value().items()) {
datasets.push_back(dataset.key());
}
double significance = 0.05;
if (friedman) {
double significance = 0.05;
vector<string> vModels(models.begin(), models.end());
friedmanTest(vModels, nDatasets, ranksTotal, significance);
// Stablish the control model as the one with the lowest averaged rank
int controlIdx = distance(ranks.begin(), min_element(ranks.begin(), ranks.end(), [](const auto& l, const auto& r) { return l.second < r.second; }));
auto wtl = computeWTL(controlIdx, vModels, table);
postHocHolm(controlIdx, vModels, nDatasets, ranksTotal, significance, wtl);
}
Statistics stats(vModels, datasets, table, significance);
stats.friedmanTest();
stats.postHocHolmTest();
}
void BestResults::reportAll()
{

2
src/Platform/CMakeLists.txt
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@ -8,7 +8,7 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc ReportConsole.cc ReportBase.cc)
add_executable(manage manage.cc Results.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc platformUtils.cc)
add_executable(list list.cc platformUtils Datasets.cc)
add_executable(best best.cc BestResults.cc Result.cc)
add_executable(best best.cc BestResults.cc Result.cc Statistics.cc)
target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
if (${CMAKE_HOST_SYSTEM_NAME} MATCHES "Linux")
target_link_libraries(manage "${TORCH_LIBRARIES}" libxlsxwriter.so ArffFiles mdlp stdc++fs)

209
src/Platform/Statistics.cc Normal file
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@ -0,0 +1,209 @@
#include "Statistics.h"
#include "Colors.h"
#include <boost/math/distributions/chi_squared.hpp>
#include <boost/math/distributions/normal.hpp>
namespace platform {
Statistics::Statistics(vector<string>& models, vector<string>& datasets, json data, double significance) : models(models), datasets(datasets), data(data), significance(significance)
{
nModels = models.size();
nDatasets = datasets.size();
};
void Statistics::fit()
{
if (nModels < 3 || nDatasets < 3) {
cerr << "nModels: " << nModels << endl;
cerr << "nDatasets: " << nDatasets << endl;
throw runtime_error("Can't make the Friedman test with less than 3 models and/or less than 3 datasets.");
}
computeRanks();
// Set the control model as the one with the lowest average rank
controlIdx = distance(ranks.begin(), min_element(ranks.begin(), ranks.end(), [](const auto& l, const auto& r) { return l.second < r.second; }));
computeWTL();
fitted = true;
}
map<string, float> assignRanks2(vector<pair<string, double>>& ranksOrder)
{
// sort the ranksOrder vector by value
sort(ranksOrder.begin(), ranksOrder.end(), [](const pair<string, double>& a, const pair<string, double>& b) {
return a.second > b.second;
});
//Assign ranks to values and if they are the same they share the same averaged rank
map<string, float> ranks;
for (int i = 0; i < ranksOrder.size(); i++) {
ranks[ranksOrder[i].first] = i + 1.0;
}
int i = 0;
while (i < static_cast<int>(ranksOrder.size())) {
int j = i + 1;
int sumRanks = ranks[ranksOrder[i].first];
while (j < static_cast<int>(ranksOrder.size()) && ranksOrder[i].second == ranksOrder[j].second) {
sumRanks += ranks[ranksOrder[j++].first];
}
if (j > i + 1) {
float averageRank = (float)sumRanks / (j - i);
for (int k = i; k < j; k++) {
ranks[ranksOrder[k].first] = averageRank;
}
}
i = j;
}
return ranks;
}
void Statistics::computeRanks()
{
map<string, float> ranksLine;
for (const auto& dataset : datasets) {
vector<pair<string, double>> ranksOrder;
for (const auto& model : models) {
double value = data[model].at(dataset).at(0).get<double>();
ranksOrder.push_back({ model, value });
}
// Assign the ranks
ranksLine = assignRanks2(ranksOrder);
if (ranks.size() == 0) {
ranks = ranksLine;
} else {
for (const auto& rank : ranksLine) {
ranks[rank.first] += rank.second;
}
}
}
// Average the ranks
for (const auto& rank : ranks) {
ranks[rank.first] /= nDatasets;
}
}
void Statistics::computeWTL()
{
// Compute the WTL matrix
for (int i = 0; i < nModels; ++i) {
wtl[i] = { 0, 0, 0 };
}
json origin = data.begin().value();
for (auto const& item : origin.items()) {
auto controlModel = models.at(controlIdx);
double controlValue = data[controlModel].at(item.key()).at(0).get<double>();
for (int i = 0; i < nModels; ++i) {
if (i == controlIdx) {
continue;
}
double value = data[models[i]].at(item.key()).at(0).get<double>();
if (value < controlValue) {
wtl[i].win++;
} else if (value == controlValue) {
wtl[i].tie++;
} else {
wtl[i].loss++;
}
}
}
}
void Statistics::postHocHolmTest()
{
if (!fitted) {
fit();
}
// Reference https://link.springer.com/article/10.1007/s44196-022-00083-8
// Post-hoc Holm test
// Calculate the p-value for the models paired with the control model
map<int, double> stats; // p-value of each model paired with the control model
boost::math::normal dist(0.0, 1.0);
double diff = sqrt(nModels * (nModels + 1) / (6.0 * nDatasets));
for (int i = 0; i < nModels; i++) {
if (i == controlIdx) {
stats[i] = 0.0;
continue;
}
double z = abs(ranks.at(models[controlIdx]) - ranks.at(models[i])) / diff;
double p_value = (long double)2 * (1 - cdf(dist, z));
stats[i] = p_value;
}
// Sort the models by p-value
vector<pair<int, double>> statsOrder;
for (const auto& stat : stats) {
statsOrder.push_back({ stat.first, stat.second });
}
sort(statsOrder.begin(), statsOrder.end(), [](const pair<int, double>& a, const pair<int, double>& b) {
return a.second < b.second;
});
// Holm adjustment
for (int i = 0; i < statsOrder.size(); ++i) {
auto item = statsOrder.at(i);
double before = i == 0 ? 0.0 : statsOrder.at(i - 1).second;
double p_value = min((double)1.0, item.second * (nModels - i));
p_value = max(before, p_value);
statsOrder[i] = { item.first, p_value };
}
cout << Colors::MAGENTA();
cout << " *************************************************************************************************************" << endl;
cout << " Post-hoc Holm test: H0: 'There is no significant differences between the control model and the other models.'" << endl;
cout << " Control model: " << models[controlIdx] << endl;
cout << " Model p-value rank win tie loss" << endl;
cout << " ============ ============ ========= === === ====" << endl;
// sort ranks from lowest to highest
vector<pair<string, float>> ranksOrder;
for (const auto& rank : ranks) {
ranksOrder.push_back({ rank.first, rank.second });
}
sort(ranksOrder.begin(), ranksOrder.end(), [](const pair<string, float>& a, const pair<string, float>& b) {
return a.second < b.second;
});
for (const auto& item : ranksOrder) {
if (item.first == models.at(controlIdx)) {
continue;
}
auto idx = distance(models.begin(), find(models.begin(), models.end(), item.first));
double pvalue = 0.0;
for (const auto& stat : statsOrder) {
if (stat.first == idx) {
pvalue = stat.second;
}
}
cout << " " << left << setw(12) << item.first << " " << setprecision(10) << fixed << pvalue << setprecision(7) << " " << item.second;
cout << " " << right << setw(3) << wtl.at(idx).win << " " << setw(3) << wtl.at(idx).tie << " " << setw(4) << wtl.at(idx).loss << endl;
}
cout << " *************************************************************************************************************" << endl;
cout << Colors::RESET();
}
bool Statistics::friedmanTest()
{
if (!fitted) {
fit();
}
// Friedman test
// Calculate the Friedman statistic
cout << Colors::BLUE() << endl;
cout << "***************************************************************************************************************" << endl;
cout << Colors::GREEN() << "Friedman test: H0: 'There is no significant differences between all the classifiers.'" << Colors::BLUE() << endl;
double degreesOfFreedom = nModels - 1.0;
double sumSquared = 0;
for (const auto& rank : ranks) {
sumSquared += pow(rank.second, 2);
}
// Compute the Friedman statistic as in https://link.springer.com/article/10.1007/s44196-022-00083-8
double friedmanQ = 12.0 * nDatasets / (nModels * (nModels + 1)) * (sumSquared - (nModels * pow(nModels + 1, 2)) / 4);
cout << "Friedman statistic: " << friedmanQ << endl;
// Calculate the critical value
boost::math::chi_squared chiSquared(degreesOfFreedom);
long double p_value = (long double)1.0 - cdf(chiSquared, friedmanQ);
double criticalValue = quantile(chiSquared, 1 - significance);
std::cout << "Critical Chi-Square Value for df=" << fixed << (int)degreesOfFreedom
<< " and alpha=" << setprecision(2) << fixed << significance << ": " << setprecision(7) << scientific << criticalValue << std::endl;
cout << "p-value: " << scientific << p_value << " is " << (p_value < significance ? "less" : "greater") << " than " << setprecision(2) << fixed << significance << endl;
bool result;
if (p_value < significance) {
cout << Colors::GREEN() << "The null hypothesis H0 is rejected." << endl;
result = true;
} else {
cout << Colors::YELLOW() << "The null hypothesis H0 is accepted. Computed p-values will not be significant." << endl;
result = false;
}
cout << Colors::BLUE() << "***************************************************************************************************************" << endl;
return result;
}
} // namespace platform

37
src/Platform/Statistics.h Normal file
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@ -0,0 +1,37 @@
#ifndef STATISTICS_H
#define STATISTICS_H
#include <iostream>
#include <vector>
#include <nlohmann/json.hpp>
using namespace std;
using json = nlohmann::json;
namespace platform {
struct WTL {
int win;
int tie;
int loss;
};
class Statistics {
public:
Statistics(vector<string>& models, vector<string>& datasets, json data, double significance = 0.05);
bool friedmanTest();
void postHocHolmTest();
private:
void fit();
void computeRanks();
void computeWTL();
vector<string> models;
vector<string> datasets;
json data;
double significance;
bool fitted = false;
int nModels = 0;
int nDatasets = 0;
int controlIdx = 0;
map<int, WTL> wtl;
map<string, float> ranks;
};
}
#endif // !STATISTICS_H