rmontanana/BayesNet
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Implement algorithm and add logging

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This commit is contained in:
Ricardo Montañana Gómez 2024-03-20 11:30:02 +01:00
parent 827b0dd893
commit 42e2be3263
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
3 changed files with 3574 additions and 18 deletions
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73
bayesnet/ensembles/BoostAODE.cc
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@ -8,6 +8,9 @@
#include "bayesnet/feature_selection/IWSS.h"
#include "BoostAODE.h"
#define LOGURU_WITH_STREAMS 1
#include "bayesnet/utils/loguru.cpp"
namespace bayesnet {
struct {
std::string CFS = "CFS";
@ -168,6 +171,12 @@ namespace bayesnet {
}
void BoostAODE::trainModel(const torch::Tensor& weights)
{
//
// Logging setup
//
loguru::set_thread_name("BoostAODE");
loguru::g_stderr_verbosity = loguru::Verbosity_OFF;;
loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
// Algorithm based on the adaboost algorithm for classification
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
fitted = true;
@ -187,7 +196,7 @@ namespace bayesnet {
return;
}
}
int numItemsPack = 0;
int numItemsPack = 0; // The counter of the models inserted in the current pack
// Variables to control the accuracy finish condition
double priorAccuracy = 0.0;
double delta = 1.0;
@ -196,72 +205,100 @@ namespace bayesnet {
// Step 0: Set the finish condition
// epsilon sub t > 0.5 => inverse the weights policy
// validation error is not decreasing
// 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);
}
// Remove used features
featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
{ return find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
{ return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
end(featureSelection)
);
int k = pow(2, tolerance);
if (tolerance == 0) {
}
int i = 0;
while (i < k && featureSelection.size() > 0) {
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());
std::unique_ptr<Classifier> model;
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) {
finished = true;
weights_ = weights_backup.clone();
// LOG_S(INFO) << "2:** epsilon_t > 0.5 **";
VLOG_SCOPE_F(2, "** epsilon_t > 0.5 **");
break;
}
// Step 3.4: Store classifier and its accuracy to weigh its future vote
numItemsPack++;
featuresUsed.insert(feature);
models.push_back(std::move(model));
significanceModels.push_back(alpha_t);
n_models++;
}
if (convergence) {
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;
}
// Keep the best accuracy until now as the prior accuracy
priorAccuracy = std::max(accuracy, priorAccuracy);
// priorAccuracy = std::max(accuracy, priorAccuracy);
priorAccuracy = accuracy;
}
finished = finished || tolerance == maxTolerance || featuresUsed.size() == features.size();
finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
}
if (tolerance == maxTolerance) {
notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
weights_ = weights_backup;
for (int i = 0; i < numItemsPack; ++i) {
significanceModels.pop_back();
models.pop_back();
n_models--;
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");
}
}
if (featuresUsed.size() != features.size()) {

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