From 5826702fc75d444131cfae31911074a2d084aded Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Wed, 20 Mar 2024 12:01:57 +0100
Subject: [PATCH] Remove weights backup

---
 bayesnet/ensembles/BoostAODE.cc | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/bayesnet/ensembles/BoostAODE.cc b/bayesnet/ensembles/BoostAODE.cc
index 19ab74f..c449b73 100644
--- a/bayesnet/ensembles/BoostAODE.cc
+++ b/bayesnet/ensembles/BoostAODE.cc
@@ -208,13 +208,9 @@ namespace bayesnet {
         // run out of features
         bool ascending = order_algorithm == Orders.ASC;
         std::mt19937 g{ 173 };
-        torch::Tensor weights_backup;
-        // LOG_SCOPE_FUNCTION(INFO);
-        // LOG_F(INFO, "Train model...");
         while (!finished) {
             // Step 1: Build ranking with mutual information
             auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
-            //LOG_S(INFO) << "1:featureSelection.size: " << featureSelection.size() << " featuresUsed.size: " << featuresUsed.size();
             VLOG_SCOPE_F(1, "featureSelection.size: %d featuresUsed.size: %d", featureSelection.size(), featuresUsed.size());
             if (order_algorithm == Orders.RAND) {
                 std::shuffle(featureSelection.begin(), featureSelection.end(), g);
@@ -226,10 +222,8 @@ namespace bayesnet {
             );
             int k = pow(2, tolerance);
             int counter = 0; // The model counter of the current pack
-            // LOG_S(INFO) << "k=" << k;
             VLOG_SCOPE_F(1, "k=%d", k);
             while (counter++ < k && featureSelection.size() > 0) {
-                // LOG_S(INFO) << "2:counter: " << counter << " numItemsPack: " << numItemsPack << " featureSelection.size: " << featureSelection.size();
                 VLOG_SCOPE_F(2, "counter: %d numItemsPack: %d featureSelection.size: %d", counter, numItemsPack, featureSelection.size());
                 auto feature = featureSelection[0];
                 featureSelection.erase(featureSelection.begin());
@@ -237,15 +231,10 @@ namespace bayesnet {
                 model = std::make_unique<SPODE>(feature);
                 model->fit(dataset, features, className, states, weights_);
                 torch::Tensor ypred;
-                //LOG_S(INFO) << "2:Begin model predict";
                 ypred = model->predict(X_train);
-                //LOG_S(INFO) << "2:End model predict";
                 // Step 3.1: Compute the classifier amout of say
-                weights_backup = weights_.clone();
                 std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
                 if (finished) {
-                    weights_ = weights_backup.clone();
-                    // LOG_S(INFO) << "2:** epsilon_t > 0.5 **";
                     VLOG_SCOPE_F(2, "** epsilon_t > 0.5 **");
                     break;
                 }
@@ -257,23 +246,18 @@ namespace bayesnet {
                 n_models++;
             }
             if (convergence && !finished) {
-                //LOG_S(INFO) << "3:Begin ensemble predict";
                 auto y_val_predict = predict(X_test);
-                //LOG_S(INFO) << "3:End ensemble predict";
                 double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
                 if (priorAccuracy == 0) {
                     priorAccuracy = accuracy;
-                    // LOG_S(INFO) << "3:First accuracyb_manage: " << std::to_string(priorAccuracy);
                     VLOG_SCOPE_F(3, "First accuracy: %f", priorAccuracy);
                 } else {
                     delta = accuracy - priorAccuracy;
                 }
                 if (delta < convergence_threshold) {
-                    // LOG_S(INFO) << "3:* tolerance: " << tolerance << " numItemsPack: " << numItemsPack << " delta: " << delta << " prior: " << priorAccuracy << " current: " << accuracy << std::endl;
                     VLOG_SCOPE_F(3, "(delta<threshold) tolerance: %d numItemsPack: %d delta: %f prior: %f current: %f", tolerance, numItemsPack, delta, priorAccuracy, accuracy);
                     tolerance++;
                 } else {
-                    // LOG_S(INFO) << "*Reset. tolerance: " << tolerance << " numItemsPack: " << numItemsPack << " delta: " << delta << " prior: " << priorAccuracy << " current: " << accuracy << std::endl;
                     VLOG_SCOPE_F(3, "*(delta>=threshold) Reset. tolerance: %d numItemsPack: %d delta: %f prior: %f current: %f", tolerance, numItemsPack, delta, priorAccuracy, accuracy);
                     tolerance = 0; // Reset the counter if the model performs better
                     numItemsPack = 0;
@@ -287,16 +271,13 @@ namespace bayesnet {
         if (tolerance > maxTolerance) {
             if (numItemsPack < n_models) {
                 notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
-                // LOG_S(INFO) << "4:Convergence threshold reached & " << numItemsPack << " models eliminated" << " of " << n_models << std::endl;
                 VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
-                weights_ = weights_backup;
                 for (int i = 0; i < numItemsPack; ++i) {
                     significanceModels.pop_back();
                     models.pop_back();
                     n_models--;
                 }
             } else {
-                // LOG_S(INFO) << "4:Convergence threshold reached & 0 models eliminated n_models=" << n_models << " numItemsPack=" << numItemsPack;
                 VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
                 notes.push_back("Convergence threshold reached & 0 models eliminated");
             }