rmontanana/BayesNet
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Remove using namespace from Library

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This commit is contained in:
Ricardo Montañana Gómez
2023-11-08 18:45:35 +01:00
parent 92820555da
commit f9258e43b9
96 changed files with 1316 additions and 1340 deletions
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42
src/Platform/Experiment.cc
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@@ -6,7 +6,7 @@
#include "Paths.h"
namespace platform {
using json = nlohmann::json;
string get_date()
std::string get_date()
{
time_t rawtime;
tm* timeinfo;
@@ -16,7 +16,7 @@ namespace platform {
oss << std::put_time(timeinfo, "%Y-%m-%d");
return oss.str();
}
string get_time()
std::string get_time()
{
time_t rawtime;
tm* timeinfo;
@@ -27,9 +27,9 @@ namespace platform {
return oss.str();
}
Experiment::Experiment() : hyperparameters(json::parse("{}")) {}
string Experiment::get_file_name()
std::string Experiment::get_file_name()
{
string result = "results_" + score_name + "_" + model + "_" + platform + "_" + get_date() + "_" + get_time() + "_" + (stratified ? "1" : "0") + ".json";
std::string result = "results_" + score_name + "_" + model + "_" + platform + "_" + get_date() + "_" + get_time() + "_" + (stratified ? "1" : "0") + ".json";
return result;
}
@@ -81,7 +81,7 @@ namespace platform {
}
return result;
}
void Experiment::save(const string& path)
void Experiment::save(const std::string& path)
{
json data = build_json();
ofstream file(path + "/" + get_file_name());
@@ -99,20 +99,20 @@ namespace platform {
void Experiment::show()
{
json data = build_json();
cout << data.dump(4) << endl;
std::cout << data.dump(4) << std::endl;
}
void Experiment::go(vector<string> filesToProcess, bool quiet)
void Experiment::go(std::vector<std::string> filesToProcess, bool quiet)
{
cout << "*** Starting experiment: " << title << " ***" << endl;
std::cout << "*** Starting experiment: " << title << " ***" << std::endl;
for (auto fileName : filesToProcess) {
cout << "- " << setw(20) << left << fileName << " " << right << flush;
std::cout << "- " << setw(20) << left << fileName << " " << right << flush;
cross_validation(fileName, quiet);
cout << endl;
std::cout << std::endl;
}
}
string getColor(bayesnet::status_t status)
std::string getColor(bayesnet::status_t status)
{
switch (status) {
case bayesnet::NORMAL:
@@ -126,13 +126,13 @@ namespace platform {
}
}
void showProgress(int fold, const string& color, const string& phase)
void showProgress(int fold, const std::string& color, const std::string& phase)
{
string prefix = phase == "a" ? "" : "\b\b\b\b";
cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
std::string prefix = phase == "a" ? "" : "\b\b\b\b";
std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
}
void Experiment::cross_validation(const string& fileName, bool quiet)
void Experiment::cross_validation(const std::string& fileName, bool quiet)
{
auto datasets = platform::Datasets(discretized, Paths::datasets());
// Get dataset
@@ -142,14 +142,14 @@ namespace platform {
auto samples = datasets.getNSamples(fileName);
auto className = datasets.getClassName(fileName);
if (!quiet) {
cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
std::cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
}
// Prepare Result
auto result = Result();
auto [values, counts] = at::_unique(y);
result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
result.setHyperparameters(hyperparameters);
// Initialize results vectors
// Initialize results std::vectors
int nResults = nfolds * static_cast<int>(randomSeeds.size());
auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64);
@@ -162,7 +162,7 @@ namespace platform {
int item = 0;
for (auto seed : randomSeeds) {
if (!quiet)
cout << "(" << seed << ") doing Fold: " << flush;
std::cout << "(" << seed << ") doing Fold: " << flush;
Fold* fold;
if (stratified)
fold = new StratifiedKFold(nfolds, y, seed);
@@ -204,8 +204,8 @@ namespace platform {
accuracy_train[item] = accuracy_train_value;
accuracy_test[item] = accuracy_test_value;
if (!quiet)
cout << "\b\b\b, " << flush;
// Store results and times in vector
std::cout << "\b\b\b, " << flush;
// Store results and times in std::vector
result.addScoreTrain(accuracy_train_value);
result.addScoreTest(accuracy_test_value);
result.addTimeTrain(train_time[item].item<double>());
@@ -214,7 +214,7 @@ namespace platform {
clf.reset();
}
if (!quiet)
cout << "end. " << flush;
std::cout << "end. " << flush;
delete fold;
}
result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());