rmontanana/BayesNet
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Begin output nested grid

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This commit is contained in:
Ricardo Montañana Gómez 2023-12-02 13:19:12 +01:00
parent 03e4437fea
commit fb9b395748
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
1 changed files with 14 additions and 7 deletions
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21
src/Platform/GridSearch.cc
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@ -174,7 +174,6 @@ namespace platform {
auto className = datasets.getClassName(fileName);
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) {
Fold* fold;
@ -183,7 +182,9 @@ namespace platform {
else
fold = new KFold(config.n_folds, y.size(0), seed);
double bestScore = 0.0;
std::cout << "(" << seed << ") doing Fold: " << flush;
for (int nfold = 0; nfold < config.n_folds; nfold++) {
std::cout << nfold + 1 << flush;
// First level fold
auto [train, test] = fold->getFold(nfold);
auto train_t = torch::tensor(train);
@ -192,16 +193,19 @@ namespace platform {
auto y_train = y.index({ train_t });
auto X_test = X.index({ "...", test_t });
auto y_test = y.index({ test_t });
auto num = 0;
json result_fold;
double score = 0.0;
for (const auto& hyperparam_line : combinations) {
std::cout << "[" << ++num << "/" << combinations.size() << "] " << std::flush;
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, test_nested] = 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 });
@ -216,14 +220,17 @@ namespace platform {
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);
showProgressFold(n_nested_fold + 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);
showProgressFold(n_nested_fold + 1, getColor(clf->getStatus()), "b");
// score += clf->score(X_nested_test, y_nested_test);
score = 0.0;
}
delete nested_fold;
score = score / config.nested;
}
}
delete fold;