rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Begin XBAODE tests

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2025-03-11 18:16:50 +01:00
parent a59689272d
commit 71b05cc1a7
10 changed files with 678 additions and 634 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
bayesnet/ensembles/XBAODE.cc
View File

@@ -36,7 +36,8 @@ namespace bayesnet {
std::vector<int> featuresSelected = featureSelection(weights_);
for (const int& feature : featuresSelected) {
std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
model->fit(dataset, features, className, states, weights_, smoothing);
// model->fit(dataset, features, className, states, weights_, smoothing);
dynamic_cast<XSpode*>(model.get())->fit(X_train, y_train, weights_, smoothing);
add_model(std::move(model), 1.0);
}
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
@@ -57,6 +58,7 @@ namespace bayesnet {
n_models = 0;
if (selectFeatures) {
featuresUsed = initializeModels(smoothing);
std::cout << "features used: " << featuresUsed.size() << std::endl;
auto ypred = predict(X_train_);
auto ypred_t = torch::tensor(ypred);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
@@ -103,7 +105,11 @@ namespace bayesnet {
featureSelection.erase(featureSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<XSpode>(feature);
dynamic_cast<XSpode*>(model.get())->fit(X_train_, y_train_, weights_, smoothing); // using exclusive XSpode fit method
dynamic_cast<XSpode*>(model.get())->fit(X_train, y_train, weights_, smoothing); // using exclusive XSpode fit method
// DEBUG
std::cout << "Model fitted." << std::endl;
std::cout << dynamic_cast<XSpode*>(model.get())->to_string() << std::endl;
// DEBUG
std::vector<int> ypred;
if (alpha_block) {
//
@@ -176,4 +182,4 @@ namespace bayesnet {
notes.push_back("Number of models: " + std::to_string(n_models));
return;
}
}
}