Add items to .clang-format
This commit is contained in:
@@ -7,8 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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## [1.0.7] 2025-03-16
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### Internal
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- Add changes to .clang-format to ajust to vscode format style thanks to https://clang-format-configurator.site/
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## [1.0.7] 2025-03-16
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### Added
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@@ -10,175 +10,204 @@
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#include <random>
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#include <tuple>
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namespace bayesnet {
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XBAODE::XBAODE() : Boost(false) {
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validHyperparameters = {"alpha_block", "order", "convergence", "convergence_best", "bisection",
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"threshold", "maxTolerance", "predict_voting", "select_features"};
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}
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std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing) {
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torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
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std::vector<int> featuresSelected = featureSelection(weights_);
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for (const int &feature : featuresSelected) {
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std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
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model->fit(dataset, features, className, states, weights_, smoothing);
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add_model(std::move(model), 1.0);
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}
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notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
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std::to_string(features.size()) + " with " + select_features_algorithm);
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return featuresSelected;
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}
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void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) {
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X_train_ = TensorUtils::to_matrix(X_train);
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y_train_ = TensorUtils::to_vector<int>(y_train);
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if (convergence) {
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X_test_ = TensorUtils::to_matrix(X_test);
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y_test_ = TensorUtils::to_vector<int>(y_test);
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}
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fitted = true;
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double alpha_t;
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torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
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bool finished = false;
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std::vector<int> featuresUsed;
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n_models = 0;
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if (selectFeatures) {
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featuresUsed = initializeModels(smoothing);
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auto ypred = predict(X_train_);
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auto ypred_t = torch::tensor(ypred);
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std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
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// Update significance of the models
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for (const int &feature : featuresUsed) {
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significanceModels.pop_back();
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}
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for (const int &feature : featuresUsed) {
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significanceModels.push_back(alpha_t);
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}
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// VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
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// n_models);
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if (finished) {
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return;
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}
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}
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int numItemsPack = 0; // The counter of the models inserted in the current pack
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// Variables to control the accuracy finish condition
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double priorAccuracy = 0.0;
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double improvement = 1.0;
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double convergence_threshold = 1e-4;
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int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
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// Step 0: Set the finish condition
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// epsilon sub t > 0.5 => inverse the weights_ policy
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// validation error is not decreasing
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// run out of features
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bool ascending = order_algorithm == bayesnet::Orders.ASC;
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std::mt19937 g{173};
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while (!finished) {
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// Step 1: Build ranking with mutual information
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auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
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if (order_algorithm == bayesnet::Orders.RAND) {
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std::shuffle(featureSelection.begin(), featureSelection.end(), g);
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}
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// Remove used features
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featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
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[&](auto x) {
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return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
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featuresUsed.end();
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}),
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featureSelection.end());
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int k = bisection ? pow(2, tolerance) : 1;
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int counter = 0; // The model counter of the current pack
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// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k,
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// featureSelection.size());
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while (counter++ < k && featureSelection.size() > 0) {
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auto feature = featureSelection[0];
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featureSelection.erase(featureSelection.begin());
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std::unique_ptr<Classifier> model;
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model = std::make_unique<XSpode>(feature);
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namespace bayesnet
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{
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XBAODE::XBAODE() : Boost(false) {}
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std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing)
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{
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torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
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std::vector<int> featuresSelected = featureSelection(weights_);
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for (const int &feature : featuresSelected)
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{
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std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
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model->fit(dataset, features, className, states, weights_, smoothing);
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/*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
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* smoothing); // using exclusive XSpode fit method*/
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// DEBUG
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/*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
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* std::endl;*/
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// DEBUG
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std::vector<int> ypred;
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if (alpha_block) {
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//
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// Compute the prediction with the current ensemble + model
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//
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// Add the model to the ensemble
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add_model(std::move(model), 1.0);
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// Compute the prediction
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ypred = predict(X_train_);
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model = std::move(models.back());
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// Remove the model from the ensemble
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remove_last_model();
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} else {
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ypred = model->predict(X_train_);
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}
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// Step 3.1: Compute the classifier amout of say
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add_model(std::move(model), 1.0);
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}
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notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
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std::to_string(features.size()) + " with " + select_features_algorithm);
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return featuresSelected;
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}
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void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing)
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{
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X_train_ = TensorUtils::to_matrix(X_train);
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y_train_ = TensorUtils::to_vector<int>(y_train);
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if (convergence)
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{
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X_test_ = TensorUtils::to_matrix(X_test);
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y_test_ = TensorUtils::to_vector<int>(y_test);
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}
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fitted = true;
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double alpha_t;
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torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
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bool finished = false;
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std::vector<int> featuresUsed;
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n_models = 0;
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if (selectFeatures)
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{
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featuresUsed = initializeModels(smoothing);
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auto ypred = predict(X_train_);
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auto ypred_t = torch::tensor(ypred);
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std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
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// Step 3.4: Store classifier and its accuracy to weigh its future vote
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numItemsPack++;
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featuresUsed.push_back(feature);
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add_model(std::move(model), alpha_t);
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// VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
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// featuresUsed: %zu", finished, numItemsPack, n_models,
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// featuresUsed.size());
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} // End of the pack
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if (convergence && !finished) {
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auto y_val_predict = predict(X_test);
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double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
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if (priorAccuracy == 0) {
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priorAccuracy = accuracy;
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} else {
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improvement = accuracy - priorAccuracy;
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// Update significance of the models
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for (const int &feature : featuresUsed)
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{
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significanceModels.pop_back();
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}
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if (improvement < convergence_threshold) {
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// VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d
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// numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
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// numItemsPack, improvement, priorAccuracy, accuracy);
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tolerance++;
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} else {
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// VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d
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// numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
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// numItemsPack, improvement, priorAccuracy, accuracy);
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tolerance = 0; // Reset the counter if the model performs better
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numItemsPack = 0;
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for (const int &feature : featuresUsed)
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{
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significanceModels.push_back(alpha_t);
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}
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if (convergence_best) {
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// Keep the best accuracy until now as the prior accuracy
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priorAccuracy = std::max(accuracy, priorAccuracy);
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} else {
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// Keep the last accuray obtained as the prior accuracy
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priorAccuracy = accuracy;
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// VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
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// n_models);
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if (finished)
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{
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return;
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}
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}
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// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size:
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// %zu", tolerance, featuresUsed.size(), features.size());
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finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
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}
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if (tolerance > maxTolerance) {
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if (numItemsPack < n_models) {
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notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
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// VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated
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// of %d", numItemsPack, n_models);
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for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
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remove_last_model();
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int numItemsPack = 0; // The counter of the models inserted in the current pack
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// Variables to control the accuracy finish condition
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double priorAccuracy = 0.0;
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double improvement = 1.0;
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double convergence_threshold = 1e-4;
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int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
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// Step 0: Set the finish condition
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// epsilon sub t > 0.5 => inverse the weights_ policy
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// validation error is not decreasing
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// run out of features
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bool ascending = order_algorithm == bayesnet::Orders.ASC;
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std::mt19937 g{173};
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while (!finished)
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{
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// Step 1: Build ranking with mutual information
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auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
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if (order_algorithm == bayesnet::Orders.RAND)
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{
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std::shuffle(featureSelection.begin(), featureSelection.end(), g);
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}
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// VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
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// used.", n_models, featuresUsed.size());
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} else {
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notes.push_back("Convergence threshold reached & 0 models eliminated");
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// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated
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// n_models=%d numItemsPack=%d", n_models, numItemsPack);
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// Remove used features
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featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
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[&](auto x)
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{
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return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
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featuresUsed.end();
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}),
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featureSelection.end());
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int k = bisection ? pow(2, tolerance) : 1;
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int counter = 0; // The model counter of the current pack
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// VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k,
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// featureSelection.size());
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while (counter++ < k && featureSelection.size() > 0)
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{
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auto feature = featureSelection[0];
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featureSelection.erase(featureSelection.begin());
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std::unique_ptr<Classifier> model;
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model = std::make_unique<XSpode>(feature);
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model->fit(dataset, features, className, states, weights_, smoothing);
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/*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
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* smoothing); // using exclusive XSpode fit method*/
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// DEBUG
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/*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
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* std::endl;*/
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// DEBUG
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std::vector<int> ypred;
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if (alpha_block)
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{
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//
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// Compute the prediction with the current ensemble + model
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//
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// Add the model to the ensemble
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add_model(std::move(model), 1.0);
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// Compute the prediction
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ypred = predict(X_train_);
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model = std::move(models.back());
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// Remove the model from the ensemble
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remove_last_model();
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}
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else
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{
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ypred = model->predict(X_train_);
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}
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// Step 3.1: Compute the classifier amout of say
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auto ypred_t = torch::tensor(ypred);
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std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
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// Step 3.4: Store classifier and its accuracy to weigh its future vote
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numItemsPack++;
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featuresUsed.push_back(feature);
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add_model(std::move(model), alpha_t);
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// VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
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// featuresUsed: %zu", finished, numItemsPack, n_models,
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// featuresUsed.size());
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} // End of the pack
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if (convergence && !finished)
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{
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auto y_val_predict = predict(X_test);
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double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
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if (priorAccuracy == 0)
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{
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priorAccuracy = accuracy;
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}
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else
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{
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improvement = accuracy - priorAccuracy;
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}
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if (improvement < convergence_threshold)
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{
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// VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d
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// numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
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// numItemsPack, improvement, priorAccuracy, accuracy);
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tolerance++;
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}
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else
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{
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// VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d
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// numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
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// numItemsPack, improvement, priorAccuracy, accuracy);
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tolerance = 0; // Reset the counter if the model performs better
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numItemsPack = 0;
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}
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if (convergence_best)
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{
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// Keep the best accuracy until now as the prior accuracy
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priorAccuracy = std::max(accuracy, priorAccuracy);
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}
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else
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{
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// Keep the last accuray obtained as the prior accuracy
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priorAccuracy = accuracy;
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}
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}
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// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size:
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// %zu", tolerance, featuresUsed.size(), features.size());
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finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
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}
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if (tolerance > maxTolerance)
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{
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if (numItemsPack < n_models)
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{
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notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
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// VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated
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// of %d", numItemsPack, n_models);
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for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i)
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{
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remove_last_model();
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}
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// VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
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// used.", n_models, featuresUsed.size());
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}
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else
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{
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notes.push_back("Convergence threshold reached & 0 models eliminated");
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// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated
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// n_models=%d numItemsPack=%d", n_models, numItemsPack);
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}
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}
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if (featuresUsed.size() != features.size())
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{
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notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " +
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std::to_string(features.size()));
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status = bayesnet::WARNING;
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}
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notes.push_back("Number of models: " + std::to_string(n_models));
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return;
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}
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if (featuresUsed.size() != features.size()) {
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notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " +
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std::to_string(features.size()));
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status = bayesnet::WARNING;
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}
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notes.push_back("Number of models: " + std::to_string(n_models));
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return;
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}
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} // namespace bayesnet
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@@ -162,7 +162,7 @@ TEST_CASE("Bisection Best", "[BoostA2DE]")
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{"maxTolerance", 3},
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{"convergence", true},
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{"block_update", false},
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{"convergence_best", false},
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{"convergence_best", true},
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});
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clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 480);
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