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Add generate-fold-files to b_main

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This commit is contained in:
Ricardo Montañana Gómez
2024-05-28 10:52:08 +02:00
parent b34af13eea
commit f5d5c35002
7 changed files with 58 additions and 10 deletions
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46
src/main/Experiment.cpp
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@@ -23,7 +23,7 @@ namespace platform {
{
std::cout << result.getJson().dump(4) << std::endl;
}
void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score)
void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score, bool generate_fold_files)
{
for (auto fileName : filesToProcess) {
if (fileName.size() > max_name)
@@ -47,7 +47,7 @@ namespace platform {
for (auto fileName : filesToProcess) {
if (!quiet)
std::cout << " " << setw(3) << right << num++ << " " << setw(max_name) << left << fileName << right << flush;
cross_validation(fileName, quiet, no_train_score);
cross_validation(fileName, quiet, no_train_score, generate_fold_files);
if (!quiet)
std::cout << std::endl;
}
@@ -74,7 +74,45 @@ namespace platform {
std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
}
void Experiment::cross_validation(const std::string& fileName, bool quiet, bool no_train_score)
void generate_files(const std::string& fileName, bool discretize, bool stratified, int seed, int nfold, torch::Tensor X_train, torch::Tensor y_train, torch::Tensor X_test, torch::Tensor y_test, std::vector<int>& train, std::vector<int>& test)
{
std::string file_name = Paths::experiment_file(fileName, discretize, stratified, seed, nfold);
auto file = std::ofstream(file_name);
json output;
output["seed"] = seed;
output["nfold"] = nfold;
output["X_train"] = json::array();
auto n = X_train.size(1);
for (int i = 0; i < X_train.size(0); i++) {
if (X_train.dtype() == torch::kFloat32) {
auto xvf_ptr = X_train.index({ i }).data_ptr<float>();
auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
output["X_train"].push_back(feature);
} else {
auto feature = std::vector<int>(X_train.index({ i }).data_ptr<int>(), X_train.index({ i }).data_ptr<int>() + n);
output["X_train"].push_back(feature);
}
}
output["y_train"] = std::vector<int>(y_train.data_ptr<int>(), y_train.data_ptr<int>() + n);
output["X_test"] = json::array();
n = X_test.size(1);
for (int i = 0; i < X_test.size(0); i++) {
if (X_train.dtype() == torch::kFloat32) {
auto xvf_ptr = X_test.index({ i }).data_ptr<float>();
auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
output["X_test"].push_back(feature);
} else {
auto feature = std::vector<int>(X_test.index({ i }).data_ptr<int>(), X_test.index({ i }).data_ptr<int>() + n);
output["X_test"].push_back(feature);
}
}
output["y_test"] = std::vector<int>(y_test.data_ptr<int>(), y_test.data_ptr<int>() + n);
output["train"] = train;
output["test"] = test;
file << output.dump(4);
file.close();
}
void Experiment::cross_validation(const std::string& fileName, bool quiet, bool no_train_score, bool generate_fold_files)
{
auto datasets = Datasets(discretized, Paths::datasets());
// Get dataset
@@ -137,6 +175,8 @@ namespace platform {
auto y_train = y.index({ train_t });
auto X_test = X.index({ "...", test_t });
auto y_test = y.index({ test_t });
if (generate_fold_files)
generate_files(fileName, discretized, stratified, seed, nfold, X_train, y_train, X_test, y_test, train, test);
if (!quiet)
showProgress(nfold + 1, getColor(clf->getStatus()), "a");
// Train model