rmontanana/BayesNet
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Set smoothing as fit parameter

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 11:40:45 +02:00
parent 27a3e5a5e0
commit b34869cc61
30 changed files with 168 additions and 178 deletions
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12
bayesnet/ensembles/BoostA2DE.cc
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@@ -19,7 +19,7 @@ namespace bayesnet {
BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
{
}
std::vector<int> BoostA2DE::initializeModels()
std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
{
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
@@ -32,8 +32,7 @@ namespace bayesnet {
for (int j = i + 1; j < featuresSelected.size(); j++) {
auto parents = { featuresSelected[i], featuresSelected[j] };
std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
model->fit(dataset, features, className, states, weights_, smoothing);
models.push_back(std::move(model));
significanceModels.push_back(1.0); // They will be updated later in trainModel
n_models++;
@@ -42,7 +41,7 @@ namespace bayesnet {
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void BoostA2DE::trainModel(const torch::Tensor& weights)
void BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
//
// Logging setup
@@ -59,7 +58,7 @@ namespace bayesnet {
bool finished = false;
std::vector<int> featuresUsed;
if (selectFeatures) {
featuresUsed = initializeModels();
featuresUsed = initializeModels(smoothing);
auto ypred = predict(X_train);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
// Update significance of the models
@@ -97,8 +96,7 @@ namespace bayesnet {
pairSelection.erase(pairSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
model->fit(dataset, features, className, states, weights_, smoothing);
alpha_t = 0.0;
if (!block_update) {
auto ypred = model->predict(X_train);