Add tests to 90% coverage
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168
bayesnet/ensembles/XBA2DE.cc
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168
bayesnet/ensembles/XBA2DE.cc
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// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <folding.hpp>
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#include <limits.h>
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#include "XBA2DE.h"
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#include "bayesnet/classifiers/XSPnDE.h"
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#include "bayesnet/utils/TensorUtils.h"
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namespace bayesnet {
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XBA2DE::XBA2DE(bool predict_voting) : Boost(predict_voting) {}
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std::vector<int> XBA2DE::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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if (featuresSelected.size() < 2) {
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notes.push_back("No features selected in initialization");
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status = ERROR;
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return std::vector<int>();
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}
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for (int i = 0; i < featuresSelected.size() - 1; i++) {
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for (int j = i + 1; j < featuresSelected.size(); j++) {
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std::unique_ptr<Classifier> model = std::make_unique<XSpnde>(featuresSelected[i], featuresSelected[j]);
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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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}
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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 XBA2DE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing) {
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//
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// Logging setup
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//
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// loguru::set_thread_name("XBA2DE");
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// loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
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// loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
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// Algorithm based on the adaboost algorithm for classification
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// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
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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 = 0;
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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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if (selectFeatures) {
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featuresUsed = initializeModels(smoothing);
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if (featuresUsed.size() == 0) {
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return;
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}
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auto ypred = predict(X_train);
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std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
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// Update significance of the models
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for (int i = 0; i < n_models; ++i) {
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significanceModels[i] = alpha_t;
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}
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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 == Orders.ASC;
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std::mt19937 g{173};
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std::vector<std::pair<int, int>> pairSelection;
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while (!finished) {
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// Step 1: Build ranking with mutual information
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pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
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if (order_algorithm == Orders.RAND) {
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std::shuffle(pairSelection.begin(), pairSelection.end(), g);
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}
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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, featureSelection.size());
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while (counter++ < k && pairSelection.size() > 0) {
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auto feature_pair = pairSelection[0];
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pairSelection.erase(pairSelection.begin());
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std::unique_ptr<Classifier> model;
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model = std::make_unique<XSpnde>(feature_pair.first, feature_pair.second);
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model->fit(dataset, features, className, states, weights_, smoothing);
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alpha_t = 0.0;
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if (!block_update) {
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auto ypred = model->predict(X_train);
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// Step 3.1: Compute the classifier amout of say
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std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
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}
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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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models.push_back(std::move(model));
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significanceModels.push_back(alpha_t);
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n_models++;
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// VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models,
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// featuresUsed.size());
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}
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if (block_update) {
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std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
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}
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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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}
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if (improvement < convergence_threshold) {
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// VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f
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// current: %f", tolerance, 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 numItemsPack: %d improvement: %f
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// prior: %f current: %f", tolerance, 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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// 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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}
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}
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// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(),
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// features.size());
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finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
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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 of %d", numItemsPack, n_models);
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for (int i = 0; i < numItemsPack; ++i) {
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significanceModels.pop_back();
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models.pop_back();
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n_models--;
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}
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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 n_models=%d numItemsPack=%d",
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// n_models, numItemsPack);
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}
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}
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if (pairSelection.size() > 0) {
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notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
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status = WARNING;
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}
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notes.push_back("Number of models: " + std::to_string(n_models));
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}
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std::vector<std::string> XBA2DE::graph(const std::string &title) const { return Ensemble::graph(title); }
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} // namespace bayesnet
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