From 306d3a4b55b7e29c967a7dd26c5e5e9082623d10 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?= Date: Sat, 22 Mar 2025 10:31:54 +0100 Subject: [PATCH] Reformat source --- bayesnet/ensembles/BoostAODE.cc | 31 +++++++------ bayesnet/ensembles/XBAODE.cc | 77 ++++++++++----------------------- 2 files changed, 39 insertions(+), 69 deletions(-) diff --git a/bayesnet/ensembles/BoostAODE.cc b/bayesnet/ensembles/BoostAODE.cc index c90a571..56eb6cc 100644 --- a/bayesnet/ensembles/BoostAODE.cc +++ b/bayesnet/ensembles/BoostAODE.cc @@ -4,25 +4,26 @@ // SPDX-License-Identifier: MIT // *************************************************************** -#include -#include -#include -#include #include "BoostAODE.h" #include "bayesnet/classifiers/SPODE.h" -#include +#include #include +#include +#include +#include +#include -namespace bayesnet { +namespace bayesnet +{ BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting) { } std::vector BoostAODE::initializeModels(const Smoothing_t smoothing) { - torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64); + torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64); std::vector featuresSelected = featureSelection(weights_); - for (const int& feature : featuresSelected) { + for (const int &feature : featuresSelected) { std::unique_ptr model = std::make_unique(feature); model->fit(dataset, features, className, states, weights_, smoothing); models.push_back(std::move(model)); @@ -32,7 +33,7 @@ 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 BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) + void BoostAODE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing) { // // Logging setup @@ -45,7 +46,7 @@ namespace bayesnet { // as explained in Ensemble methods (Zhi-Hua Zhou, 2012) fitted = true; double alpha_t = 0; - torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64); + torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64); bool finished = false; std::vector featuresUsed; n_models = 0; @@ -73,7 +74,7 @@ namespace bayesnet { // validation error is not decreasing // run out of features bool ascending = order_algorithm == Orders.ASC; - std::mt19937 g{ 173 }; + std::mt19937 g{173}; while (!finished) { // Step 1: Build ranking with mutual information auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted @@ -81,10 +82,8 @@ namespace bayesnet { std::shuffle(featureSelection.begin(), featureSelection.end(), g); } // Remove used features - featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) - { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}), - end(featureSelection) - ); + featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }), + end(featureSelection)); int k = bisection ? pow(2, tolerance) : 1; int counter = 0; // The model counter of the current pack // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size()); @@ -176,7 +175,7 @@ namespace bayesnet { } notes.push_back("Number of models: " + std::to_string(n_models)); } - std::vector BoostAODE::graph(const std::string& title) const + std::vector BoostAODE::graph(const std::string &title) const { return Ensemble::graph(title); } diff --git a/bayesnet/ensembles/XBAODE.cc b/bayesnet/ensembles/XBAODE.cc index 567e3cf..6af20d1 100644 --- a/bayesnet/ensembles/XBAODE.cc +++ b/bayesnet/ensembles/XBAODE.cc @@ -17,8 +17,7 @@ namespace bayesnet { torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64); std::vector featuresSelected = featureSelection(weights_); - for (const int &feature : featuresSelected) - { + for (const int &feature : featuresSelected) { std::unique_ptr model = std::make_unique(feature); model->fit(dataset, features, className, states, weights_, smoothing); add_model(std::move(model), 1.0); @@ -31,8 +30,7 @@ namespace bayesnet { X_train_ = TensorUtils::to_matrix(X_train); y_train_ = TensorUtils::to_vector(y_train); - if (convergence) - { + if (convergence) { X_test_ = TensorUtils::to_matrix(X_test); y_test_ = TensorUtils::to_vector(y_test); } @@ -42,25 +40,21 @@ namespace bayesnet bool finished = false; std::vector featuresUsed; n_models = 0; - if (selectFeatures) - { + if (selectFeatures) { featuresUsed = initializeModels(smoothing); auto ypred = predict(X_train_); auto ypred_t = torch::tensor(ypred); std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_); // Update significance of the models - for (const int &feature : featuresUsed) - { + for (const int &feature : featuresUsed) { significanceModels.pop_back(); } - for (const int &feature : featuresUsed) - { + for (const int &feature : featuresUsed) { significanceModels.push_back(alpha_t); } // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, // n_models); - if (finished) - { + if (finished) { return; } } @@ -76,18 +70,15 @@ namespace bayesnet // run out of features bool ascending = order_algorithm == bayesnet::Orders.ASC; std::mt19937 g{173}; - while (!finished) - { + while (!finished) { // Step 1: Build ranking with mutual information auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted - if (order_algorithm == bayesnet::Orders.RAND) - { + if (order_algorithm == bayesnet::Orders.RAND) { std::shuffle(featureSelection.begin(), featureSelection.end(), g); } // Remove used features featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(), - [&](auto x) - { + [&](auto x) { return std::find(featuresUsed.begin(), featuresUsed.end(), x) != featuresUsed.end(); }), @@ -96,8 +87,7 @@ namespace bayesnet int counter = 0; // The model counter of the current pack // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, // featureSelection.size()); - while (counter++ < k && featureSelection.size() > 0) - { + while (counter++ < k && featureSelection.size() > 0) { auto feature = featureSelection[0]; featureSelection.erase(featureSelection.begin()); std::unique_ptr model; @@ -110,8 +100,7 @@ namespace bayesnet * std::endl;*/ // DEBUG std::vector ypred; - if (alpha_block) - { + if (alpha_block) { // // Compute the prediction with the current ensemble + model // @@ -122,9 +111,7 @@ namespace bayesnet model = std::move(models.back()); // Remove the model from the ensemble remove_last_model(); - } - else - { + } else { ypred = model->predict(X_train_); } // Step 3.1: Compute the classifier amout of say @@ -138,40 +125,30 @@ namespace bayesnet // featuresUsed: %zu", finished, numItemsPack, n_models, // featuresUsed.size()); } // End of the pack - if (convergence && !finished) - { + if (convergence && !finished) { auto y_val_predict = predict(X_test); double accuracy = (y_val_predict == y_test).sum().item() / (double)y_test.size(0); - if (priorAccuracy == 0) - { + if (priorAccuracy == 0) { priorAccuracy = accuracy; - } - else - { + } else { improvement = accuracy - priorAccuracy; } - if (improvement < convergence_threshold) - { + if (improvement < convergence_threshold) { // VLOG_SCOPE_F(3, " (improvement=threshold) Reset. tolerance: %d // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, // numItemsPack, improvement, priorAccuracy, accuracy); tolerance = 0; // Reset the counter if the model performs better numItemsPack = 0; } - if (convergence_best) - { + if (convergence_best) { // Keep the best accuracy until now as the prior accuracy priorAccuracy = std::max(accuracy, priorAccuracy); - } - else - { + } else { // Keep the last accuray obtained as the prior accuracy priorAccuracy = accuracy; } @@ -180,29 +157,23 @@ namespace bayesnet // %zu", tolerance, featuresUsed.size(), features.size()); finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size(); } - if (tolerance > maxTolerance) - { - if (numItemsPack < n_models) - { + if (tolerance > maxTolerance) { + if (numItemsPack < n_models) { notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated"); // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated // of %d", numItemsPack, n_models); - for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) - { + for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) { remove_last_model(); } // VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features // used.", n_models, featuresUsed.size()); - } - else - { + } else { notes.push_back("Convergence threshold reached & 0 models eliminated"); // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated // n_models=%d numItemsPack=%d", n_models, numItemsPack); } } - if (featuresUsed.size() != features.size()) - { + if (featuresUsed.size() != features.size()) { notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size())); status = bayesnet::WARNING;