rmontanana/BayesNet
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Complete nested gridsearch

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This commit is contained in:
Ricardo Montañana Gómez 2023-12-03 12:37:25 +01:00
parent fb9b395748
commit 2e95e8999d
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
1 changed files with 43 additions and 11 deletions
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54
src/Platform/GridSearch.cc
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@ -172,8 +172,9 @@ namespace platform {
auto states = datasets.getStates(fileName);
auto features = datasets.getFeatures(fileName);
auto className = datasets.getClassName(fileName);
double bestScore = 0.0;
json bestHyperparameters;
int spcs_combinations = int(log(combinations.size()) / log(10)) + 1;
double goatScore = 0.0;
json goatHyperparameters;
// for dataset // for seed // for fold // for hyperparameters // for nested fold
for (const auto& seed : config.seeds) {
Fold* fold;
@ -182,9 +183,11 @@ namespace platform {
else
fold = new KFold(config.n_folds, y.size(0), seed);
double bestScore = 0.0;
json bestHyperparameters;
std::cout << "(" << seed << ") doing Fold: " << flush;
for (int nfold = 0; nfold < config.n_folds; nfold++) {
std::cout << nfold + 1 << flush;
if (!config.quiet)
std::cout << Colors::GREEN() << nfold + 1 << " " << flush;
// First level fold
auto [train, test] = fold->getFold(nfold);
auto train_t = torch::tensor(train);
@ -195,9 +198,12 @@ namespace platform {
auto y_test = y.index({ test_t });
auto num = 0;
json result_fold;
double score = 0.0;
double hypScore = 0.0;
double bestHypScore = 0.0;
json bestHypHyperparameters;
for (const auto& hyperparam_line : combinations) {
std::cout << "[" << ++num << "/" << combinations.size() << "] " << std::flush;
std::cout << "[" << setw(spcs_combinations) << ++num << "/" << setw(spcs_combinations)
<< combinations.size() << "] " << std::flush;
Fold* nested_fold;
if (config.stratified)
nested_fold = new StratifiedKFold(config.nested, y_train, seed);
@ -221,21 +227,47 @@ namespace platform {
// Train model
if (!config.quiet)
showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "a");
// clf->fit(X_nexted_train, y_nested_train, features, className, states);
clf->fit(X_nexted_train, y_nested_train, features, className, states);
// Test model
if (!config.quiet)
showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "b");
// score += clf->score(X_nested_test, y_nested_test);
score = 0.0;
hypScore += clf->score(X_nested_test, y_nested_test);
if (!config.quiet)
std::cout << "\b\b\b, \b" << flush;
}
std::cout << string(3 * config.nested + 2 * spcs_combinations + 4, '\b') << flush;
delete nested_fold;
score = score / config.nested;
hypScore /= config.nested;
if (hypScore > bestHypScore) {
bestHypScore = hypScore;
bestHypHyperparameters = hyperparam_line;
}
}
// Build Classifier with selected hyperparameters
auto clf = Models::instance()->create(config.model);
clf->setHyperparameters(bestHypHyperparameters);
// 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");
double score = clf->score(X_test, y_test);
if (!config.quiet)
std::cout << "\b\b\b\b\b, \b" << flush;
if (score > bestScore) {
bestScore = score;
bestHyperparameters = bestHypHyperparameters;
}
}
if (bestScore > goatScore) {
goatScore = bestScore;
goatHyperparameters = bestHyperparameters;
}
delete fold;
}
return { bestScore, bestHyperparameters };
return { goatScore, goatHyperparameters };
}
vector<std::string> GridSearch::processDatasets(Datasets& datasets)
{