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Upgrade version number to 1.0.5

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Fix dependency graph
Remove loguru library
This commit is contained in:
Ricardo Montañana Gómez
2024-04-20 18:00:40 +02:00
parent 1f06631f69
commit b956aa3873
10 changed files with 103 additions and 3956 deletions
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30
bayesnet/ensembles/BoostAODE.cc
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@@ -14,8 +14,6 @@
#include "bayesnet/feature_selection/IWSS.h"
#include "BoostAODE.h"
#include "bayesnet/utils/loguru.cpp"
namespace bayesnet {
BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
@@ -186,7 +184,6 @@ namespace bayesnet {
significanceModels = std::vector<double>(k, 1.0);
// 4. Move first n classifiers to models_bak
// backup the first n_models - k models (if n_models == k, don't backup any)
VLOG_SCOPE_F(1, "upd_weights_block n_models=%d k=%d", n_models, k);
for (int i = 0; i < n_models - k; ++i) {
model = std::move(models[0]);
models.erase(models.begin());
@@ -251,9 +248,6 @@ namespace bayesnet {
featureSelector->fit();
auto cfsFeatures = featureSelector->getFeatures();
auto scores = featureSelector->getScores();
for (int i = 0; i < cfsFeatures.size(); ++i) {
LOG_F(INFO, "Feature: %d Score: %f", cfsFeatures[i], scores[i]);
}
for (const int& feature : cfsFeatures) {
featuresUsed.push_back(feature);
std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
@@ -268,12 +262,6 @@ 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;
@@ -292,11 +280,6 @@ namespace bayesnet {
if (finished) {
return;
}
LOG_F(INFO, "Initial models: %d", n_models);
LOG_F(INFO, "Significances: ");
for (int i = 0; i < n_models; ++i) {
LOG_F(INFO, "i=%d significance=%f", i, significanceModels[i]);
}
}
int numItemsPack = 0; // The counter of the models inserted in the current pack
// Variables to control the accuracy finish condition
@@ -313,7 +296,6 @@ namespace bayesnet {
while (!finished) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
VLOG_SCOPE_F(1, "featureSelection.size: %zu featuresUsed.size: %zu", featureSelection.size(), featuresUsed.size());
if (order_algorithm == Orders.RAND) {
std::shuffle(featureSelection.begin(), featureSelection.end(), g);
}
@@ -324,7 +306,6 @@ namespace bayesnet {
);
int k = pow(2, tolerance);
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) {
auto feature = featureSelection[0];
featureSelection.erase(featureSelection.begin());
@@ -336,10 +317,6 @@ namespace bayesnet {
auto ypred = model->predict(X_train);
// Step 3.1: Compute the classifier amout of say
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
if (finished) {
VLOG_SCOPE_F(2, "** epsilon_t > 0.5 **");
break;
}
}
// Step 3.4: Store classifier and its accuracy to weigh its future vote
numItemsPack++;
@@ -347,7 +324,6 @@ namespace bayesnet {
models.push_back(std::move(model));
significanceModels.push_back(alpha_t);
n_models++;
VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
}
if (block_update) {
std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -357,15 +333,12 @@ namespace bayesnet {
double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
if (priorAccuracy == 0) {
priorAccuracy = accuracy;
VLOG_SCOPE_F(3, "First accuracy: %f", priorAccuracy);
} else {
improvement = accuracy - priorAccuracy;
}
if (improvement < convergence_threshold) {
VLOG_SCOPE_F(3, "(improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
tolerance++;
} else {
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;
}
@@ -373,20 +346,17 @@ namespace bayesnet {
priorAccuracy = std::max(accuracy, priorAccuracy);
// priorAccuracy = accuracy;
}
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();
}
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 = 0; i < numItemsPack; ++i) {
significanceModels.pop_back();
models.pop_back();
n_models--;
}
} else {
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");
}
}