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refactor gridsearch to have only one go method

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This commit is contained in:
Ricardo Montañana Gómez
2023-12-02 10:59:05 +01:00
parent 33cd32c639
commit 03e4437fea
5 changed files with 176 additions and 137 deletions
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267
src/Platform/GridSearch.cc
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@@ -38,10 +38,10 @@ namespace platform {
}
return json();
}
void showProgressComb(const int num, const int total, const std::string& color)
void showProgressComb(const int num, const int n_folds, const int total, const std::string& color)
{
int spaces = int(log(total) / log(10)) + 1;
int magic = 37 + 2 * spaces;
int magic = n_folds * 3 + 22 + 2 * spaces;
std::string prefix = num == 1 ? "" : string(magic, '\b') + string(magic + 1, ' ') + string(magic + 1, '\b');
std::cout << prefix << color << "(" << setw(spaces) << num << "/" << setw(spaces) << total << ") " << Colors::RESET() << flush;
}
@@ -63,18 +63,120 @@ namespace platform {
return Colors::RESET();
}
}
double GridSearch::processFileSingle(std::string fileName, Datasets& datasets, HyperParameters& hyperparameters)
void GridSearch::go()
{
timer.start();
auto grid_type = config.nested == 0 ? "Single" : "Nested";
auto datasets = Datasets(config.discretize, Paths::datasets());
auto datasets_names = processDatasets(datasets);
json results = initializeResults();
std::cout << "***************** Starting " << grid_type << " Gridsearch *****************" << std::endl;
std::cout << "input file=" << Paths::grid_input(config.model) << std::endl;
auto grid = GridData(Paths::grid_input(config.model));
Timer timer_dataset;
double bestScore = 0;
json bestHyperparameters;
for (const auto& dataset : datasets_names) {
if (!config.quiet)
std::cout << "- " << setw(20) << left << dataset << " " << right << flush;
auto combinations = grid.getGrid(dataset);
timer_dataset.start();
if (config.nested == 0)
// for dataset // for hyperparameters // for seed // for fold
tie(bestScore, bestHyperparameters) = processFileSingle(dataset, datasets, combinations);
else
// for dataset // for seed // for fold // for hyperparameters // for nested fold
tie(bestScore, bestHyperparameters) = processFileNested(dataset, datasets, combinations);
if (!config.quiet) {
std::cout << "end." << " Score: " << setw(9) << setprecision(7) << fixed
<< bestScore << " [" << bestHyperparameters.dump() << "]" << std::endl;
}
json result = {
{ "score", bestScore },
{ "hyperparameters", bestHyperparameters },
{ "date", get_date() + " " + get_time() },
{ "grid", grid.getInputGrid(dataset) },
{ "duration", timer_dataset.getDurationString() }
};
results[dataset] = result;
// Save partial results
save(results);
}
// Save final results
save(results);
std::cout << "***************** Ending " << grid_type << " Gridsearch *******************" << std::endl;
}
pair<double, json> GridSearch::processFileSingle(std::string fileName, Datasets& datasets, vector<json>& combinations)
{
int num = 0;
double bestScore = 0.0;
json bestHyperparameters;
auto totalComb = combinations.size();
for (const auto& hyperparam_line : combinations) {
if (!config.quiet)
showProgressComb(++num, config.n_folds, totalComb, Colors::CYAN());
auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
// Get dataset
auto [X, y] = datasets.getTensors(fileName);
auto states = datasets.getStates(fileName);
auto features = datasets.getFeatures(fileName);
auto className = datasets.getClassName(fileName);
double totalScore = 0.0;
int numItems = 0;
for (const auto& seed : config.seeds) {
if (!config.quiet)
std::cout << "(" << seed << ") doing Fold: " << flush;
Fold* fold;
if (config.stratified)
fold = new StratifiedKFold(config.n_folds, y, seed);
else
fold = new KFold(config.n_folds, y.size(0), seed);
for (int nfold = 0; nfold < config.n_folds; nfold++) {
auto clf = Models::instance()->create(config.model);
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, fileName);
clf->setHyperparameters(hyperparameters.get(fileName));
auto [train, test] = fold->getFold(nfold);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
auto X_train = X.index({ "...", train_t });
auto y_train = y.index({ train_t });
auto X_test = X.index({ "...", test_t });
auto y_test = y.index({ test_t });
// Train model
if (!config.quiet)
showProgressFold(nfold + 1, getColor(clf->getStatus()), "a");
clf->fit(X_train, y_train, features, className, states);
// Test model
if (!config.quiet)
showProgressFold(nfold + 1, getColor(clf->getStatus()), "b");
totalScore += clf->score(X_test, y_test);
numItems++;
if (!config.quiet)
std::cout << "\b\b\b, \b" << flush;
}
delete fold;
}
double score = numItems == 0 ? 0.0 : totalScore / numItems;
if (score > bestScore) {
bestScore = score;
bestHyperparameters = hyperparam_line;
}
}
return { bestScore, bestHyperparameters };
}
pair<double, json> GridSearch::processFileNested(std::string fileName, Datasets& datasets, vector<json>& combinations)
{
// Get dataset
auto [X, y] = datasets.getTensors(fileName);
auto states = datasets.getStates(fileName);
auto features = datasets.getFeatures(fileName);
auto className = datasets.getClassName(fileName);
double totalScore = 0.0;
double bestScore = 0.0;
json bestHyperparameters;
int numItems = 0;
// for dataset // for seed // for fold // for hyperparameters // for nested fold
for (const auto& seed : config.seeds) {
if (!config.quiet)
std::cout << "(" << seed << ") doing Fold: " << flush;
Fold* fold;
if (config.stratified)
fold = new StratifiedKFold(config.n_folds, y, seed);
@@ -82,10 +184,7 @@ namespace platform {
fold = new KFold(config.n_folds, y.size(0), seed);
double bestScore = 0.0;
for (int nfold = 0; nfold < config.n_folds; nfold++) {
auto clf = Models::instance()->create(config.model);
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, fileName);
clf->setHyperparameters(hyperparameters.get(fileName));
// First level fold
auto [train, test] = fold->getFold(nfold);
auto train_t = torch::tensor(train);
auto test_t = torch::tensor(test);
@@ -93,28 +192,50 @@ namespace platform {
auto y_train = y.index({ train_t });
auto X_test = X.index({ "...", test_t });
auto y_test = y.index({ test_t });
// Train model
if (!config.quiet)
showProgressFold(nfold + 1, getColor(clf->getStatus()), "a");
clf->fit(X_train, y_train, features, className, states);
// Test model
if (!config.quiet)
showProgressFold(nfold + 1, getColor(clf->getStatus()), "b");
totalScore += clf->score(X_test, y_test);
numItems++;
if (!config.quiet)
std::cout << "\b\b\b, \b" << flush;
for (const auto& hyperparam_line : combinations) {
Fold* nested_fold;
if (config.stratified)
nested_fold = new StratifiedKFold(config.nested, y_train, seed);
else
nested_fold = new KFold(config.nested, y_train.size(0), seed);
for (int n_nested_fold = 0; n_nested_fold < config.nested; n_nested_fold++) {
// Nested level fold
auto [train_nested, test_nested] = fold->getFold(n_nested_fold);
auto train_nested_t = torch::tensor(train_nested);
auto test_nested_t = torch::tensor(test_nested);
auto X_nexted_train = X_train.index({ "...", train_nested_t });
auto y_nested_train = y_train.index({ train_nested_t });
auto X_nested_test = X_train.index({ "...", test_nested_t });
auto y_nested_test = y_train.index({ test_nested_t });
// Build Classifier with selected hyperparameters
auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
auto clf = Models::instance()->create(config.model);
auto valid = clf->getValidHyperparameters();
hyperparameters.check(valid, fileName);
clf->setHyperparameters(hyperparameters.get(fileName));
// Train model
if (!config.quiet)
showProgressFold(nfold + 1, getColor(clf->getStatus()), "a");
clf->fit(X_nexted_train, y_nested_train, features, className, states);
// Test model
if (!config.quiet)
showProgressFold(nfold + 1, getColor(clf->getStatus()), "b");
bestScore += clf->score(X_nested_test, y_nested_test);
}
delete nested_fold;
}
}
delete fold;
}
return numItems == 0 ? 0.0 : totalScore / numItems;
return { bestScore, bestHyperparameters };
}
vector<std::string> GridSearch::processDatasets(Datasets& datasets)
{
// Load datasets
auto datasets_names = datasets.getNames();
if (config.continue_from != "No") {
if (config.continue_from != NO_CONTINUE()) {
// Continue previous execution:
// remove datasets already processed
if (std::find(datasets_names.begin(), datasets_names.end(), config.continue_from) == datasets_names.end()) {
@@ -139,7 +260,7 @@ namespace platform {
{
// Load previous results
json results;
if (config.continue_from != "No") {
if (config.continue_from != NO_CONTINUE()) {
if (!config.quiet)
std::cout << "* Loading previous results" << std::endl;
try {
@@ -157,100 +278,7 @@ namespace platform {
}
return results;
}
void GridSearch::goSingle()
{
auto datasets = Datasets(config.discretize, Paths::datasets());
auto datasets_names = processDatasets(datasets);
json results = initializeResults();
std::cout << "***************** Starting Single Gridsearch *****************" << std::endl;
std::cout << "input file=" << Paths::grid_input(config.model) << std::endl;
auto grid = GridData(Paths::grid_input(config.model));
// Generate hyperparameters grid & run gridsearch
// Check each combination of hyperparameters for each dataset and each seed
for (const auto& dataset : datasets_names) {
auto totalComb = grid.getNumCombinations(dataset);
if (!config.quiet)
std::cout << "- " << setw(20) << left << dataset << " " << right << flush;
int num = 0;
double bestScore = 0.0;
json bestHyperparameters;
auto combinations = grid.getGrid(dataset);
for (const auto& hyperparam_line : combinations) {
if (!config.quiet)
showProgressComb(++num, totalComb, Colors::CYAN());
auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
double score = processFileSingle(dataset, datasets, hyperparameters);
if (score > bestScore) {
bestScore = score;
bestHyperparameters = hyperparam_line;
}
}
if (!config.quiet) {
std::cout << "end." << " Score: " << setw(9) << setprecision(7) << fixed
<< bestScore << " [" << bestHyperparameters.dump() << "]" << std::endl;
}
json result = {
{ "score", bestScore },
{ "hyperparameters", bestHyperparameters },
{ "date", get_date() + " " + get_time() },
{ "grid", grid.getInputGrid(dataset) }
};
results[dataset] = result;
// Save partial results
save(results);
}
// Save final results
save(results);
std::cout << "***************** Ending Single Gridsearch *******************" << std::endl;
}
void GridSearch::goNested()
{
auto datasets = Datasets(config.discretize, Paths::datasets());
auto datasets_names = processDatasets(datasets);
json results = initializeResults();
std::cout << "***************** Starting Nested Gridsearch *****************" << std::endl;
std::cout << "input file=" << Paths::grid_input(config.model) << std::endl;
auto grid = GridData(Paths::grid_input(config.model));
// Generate hyperparameters grid & run gridsearch
// Check each combination of hyperparameters for each dataset and each seed
for (const auto& dataset : datasets_names) {
auto totalComb = grid.getNumCombinations(dataset);
if (!config.quiet)
std::cout << "- " << setw(20) << left << dataset << " " << right << flush;
int num = 0;
double bestScore = 0.0;
json bestHyperparameters;
auto combinations = grid.getGrid(dataset);
for (const auto& hyperparam_line : combinations) {
if (!config.quiet)
showProgressComb(++num, totalComb, Colors::CYAN());
auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
double score = processFileSingle(dataset, datasets, hyperparameters);
if (score > bestScore) {
bestScore = score;
bestHyperparameters = hyperparam_line;
}
}
if (!config.quiet) {
std::cout << "end." << " Score: " << setw(9) << setprecision(7) << fixed
<< bestScore << " [" << bestHyperparameters.dump() << "]" << std::endl;
}
json result = {
{ "score", bestScore },
{ "hyperparameters", bestHyperparameters },
{ "date", get_date() + " " + get_time() },
{ "grid", grid.getInputGrid(dataset) }
};
results[dataset] = result;
// Save partial results
save(results);
}
// Save final results
save(results);
std::cout << "***************** Ending Nested Gridsearch *******************" << std::endl;
}
void GridSearch::save(json& results) const
void GridSearch::save(json& results)
{
std::ofstream file(Paths::grid_output(config.model));
json output = {
@@ -262,7 +290,10 @@ namespace platform {
{ "seeds", config.seeds },
{ "date", get_date() + " " + get_time()},
{ "nested", config.nested},
{ "platform", config.platform },
{ "duration", timer.getDurationString(true)},
{ "results", results }
};
file << output.dump(4);
}