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Add log and fix some mistakes in integration

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-25 20:35:13 +01:00
parent de7cf091be
commit c63baf419f
5 changed files with 3573 additions and 251 deletions
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81
src/experimental_clfs/XBAODE.cpp
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@@ -10,10 +10,14 @@
#include <tuple>
#include "XBAODE.h"
#include "TensorUtils.hpp"
#include <loguru.hpp>
#include <loguru.cpp>
namespace platform {
XBAODE::XBAODE() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)
{
validHyperparameters = { "alpha_block", "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
"predict_voting", "select_features" };
}
void XBAODE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
{
@@ -22,23 +26,23 @@ namespace platform {
y_train_ = TensorUtils::to_vector<int>(y_train);
X_test_ = TensorUtils::to_matrix(X_test);
y_test_ = TensorUtils::to_vector<int>(y_test);
maxTolerance = 5;
//
// Logging setup
//
// loguru::set_thread_name("XBAODE");
// loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
// loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
loguru::set_thread_name("XBAODE");
loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
loguru::add_file("XBAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
// Algorithm based on the adaboost algorithm for classification
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
double alpha_t = 0;
torch::Tensor weights_ = torch::full({ m }, 1.0, torch::kFloat64);
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
bool finished = false;
std::vector<int> featuresUsed;
int num_instances = m;
int num_attributes = n;
significanceModels.resize(num_attributes, 0.0);
significanceModels.resize(n, 0.0); // n possible spodes
aode_.fit(X_train_, y_train_, features, className, states, smoothing);
n_models = 0;
if (selectFeatures) {
featuresUsed = featureSelection(weights_);
aode_.set_active_parents(featuresUsed);
@@ -49,6 +53,8 @@ namespace platform {
for (const auto& parent : featuresUsed) {
significanceModels[parent] = alpha_t;
}
n_models = featuresUsed.size();
VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
if (finished) {
return;
}
@@ -78,46 +84,41 @@ namespace platform {
);
int k = bisection ? pow(2, tolerance) : 1;
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());
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());
aode_.add_active_parent(feature);
alpha_t = 0.0;
if (!block_update) {
std::vector<int> ypred;
if (alpha_block) {
//
// Compute the prediction with the current ensemble + model
//
// Add the model to the ensemble
n_models++;
significanceModels[feature] = 1.0;
aode_.add_active_parent(feature);
// Compute the prediction
ypred = aode_.predict(X_train_);
// Remove the model from the ensemble
significanceModels[feature] = 0.0;
aode_.remove_last_parent();
n_models--;
} else {
ypred = aode_.predict_spode(X_train_, feature);
}
// Step 3.1: Compute the classifier amout of say
auto ypred_t = torch::tensor(ypred);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
std::vector<int> ypred;
if (alpha_block) {
//
// Compute the prediction with the current ensemble + model
//
// Add the model to the ensemble
n_models++;
significanceModels[feature] = 1.0;
aode_.add_active_parent(feature);
// Compute the prediction
ypred = aode_.predict(X_train_);
// Remove the model from the ensemble
significanceModels[feature] = 0.0;
aode_.remove_last_parent();
n_models--;
} else {
ypred = aode_.predict_spode(X_train_, feature);
}
// Step 3.1: Compute the classifier amout of say
auto ypred_t = torch::tensor(ypred);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
// Step 3.4: Store classifier and its accuracy to weigh its future vote
numItemsPack++;
featuresUsed.push_back(feature);
aode_.add_active_parent(feature);
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_);
}
VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
} // End of the pack
if (convergence && !finished) {
auto y_val_predict = predict(X_test);
double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
@@ -127,10 +128,10 @@ namespace platform {
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);
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);
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;
}
@@ -142,13 +143,13 @@ namespace platform {
priorAccuracy = accuracy;
}
}
// VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", 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();
}
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);
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();
@@ -156,7 +157,7 @@ namespace platform {
}
} else {
notes.push_back("Convergence threshold reached & 0 models eliminated");
// VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
}
}
if (featuresUsed.size() != features.size()) {