rmontanana/BayesNet
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Update log output size type

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Ricardo Montañana Gómez 2024-03-21 19:24:51 +01:00
parent 45c048f635
commit 58d5a35a35
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
1 changed files with 4 additions and 4 deletions
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bayesnet/ensembles/BoostAODE.cc
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@ -215,7 +215,7 @@ namespace bayesnet {
while (!finished) { while (!finished) {
// Step 1: Build ranking with mutual information // Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
VLOG_SCOPE_F(1, "featureSelection.size: %d featuresUsed.size: %d", featureSelection.size(), featuresUsed.size()); VLOG_SCOPE_F(1, "featureSelection.size: %zu featuresUsed.size: %zu", featureSelection.size(), featuresUsed.size());
if (order_algorithm == Orders.RAND) { if (order_algorithm == Orders.RAND) {
std::shuffle(featureSelection.begin(), featureSelection.end(), g); std::shuffle(featureSelection.begin(), featureSelection.end(), g);
} }
@ -226,7 +226,7 @@ namespace bayesnet {
); );
int k = pow(2, tolerance); int k = pow(2, tolerance);
int counter = 0; // The model counter of the current pack int counter = 0; // The model counter of the current pack
VLOG_SCOPE_F(1, "k=%d featureSelection.size: %d", k, featureSelection.size()); VLOG_SCOPE_F(1, "k=%d featureSelection.size: %zu", k, featureSelection.size());
while (counter++ < k && featureSelection.size() > 0) { while (counter++ < k && featureSelection.size() > 0) {
VLOG_SCOPE_F(2, "counter: %d numItemsPack: %d", counter, numItemsPack); VLOG_SCOPE_F(2, "counter: %d numItemsPack: %d", counter, numItemsPack);
auto feature = featureSelection[0]; auto feature = featureSelection[0];
@ -248,7 +248,7 @@ namespace bayesnet {
models.push_back(std::move(model)); models.push_back(std::move(model));
significanceModels.push_back(alpha_t); significanceModels.push_back(alpha_t);
n_models++; n_models++;
VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %d", numItemsPack, n_models, featuresUsed.size()); VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
} }
if (convergence && !finished) { if (convergence && !finished) {
auto y_val_predict = predict(X_test); auto y_val_predict = predict(X_test);
@ -271,7 +271,7 @@ namespace bayesnet {
priorAccuracy = std::max(accuracy, priorAccuracy); priorAccuracy = std::max(accuracy, priorAccuracy);
// priorAccuracy = accuracy; // priorAccuracy = accuracy;
} }
VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %d features.size: %d", tolerance, featuresUsed.size(), features.size()); VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size(); finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
} }
if (tolerance > maxTolerance) { if (tolerance > maxTolerance) {