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Ricardo Montañana Gómez
2025-03-22 10:31:54 +01:00
parent bf08b0de89
commit 306d3a4b55
2 changed files with 39 additions and 69 deletions
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31
bayesnet/ensembles/BoostAODE.cc
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@@ -4,25 +4,26 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <random>
#include <set>
#include <limits.h>
#include <tuple>
#include "BoostAODE.h"
#include "bayesnet/classifiers/SPODE.h"
#include <loguru.hpp>
#include <limits.h>
#include <loguru.cpp>
#include <loguru.hpp>
#include <random>
#include <set>
#include <tuple>
namespace bayesnet {
namespace bayesnet
{
BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
{
}
std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
{
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
for (const int& feature : featuresSelected) {
for (const int &feature : featuresSelected) {
std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
model->fit(dataset, features, className, states, weights_, smoothing);
models.push_back(std::move(model));
@@ -32,7 +33,7 @@ namespace bayesnet {
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
void BoostAODE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing)
{
//
// Logging setup
@@ -45,7 +46,7 @@ namespace bayesnet {
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
fitted = true;
double alpha_t = 0;
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
bool finished = false;
std::vector<int> featuresUsed;
n_models = 0;
@@ -73,7 +74,7 @@ namespace bayesnet {
// validation error is not decreasing
// run out of features
bool ascending = order_algorithm == Orders.ASC;
std::mt19937 g{ 173 };
std::mt19937 g{173};
while (!finished) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
@@ -81,10 +82,8 @@ namespace bayesnet {
std::shuffle(featureSelection.begin(), featureSelection.end(), g);
}
// Remove used features
featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
{ return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
end(featureSelection)
);
featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }),
end(featureSelection));
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());
@@ -176,7 +175,7 @@ namespace bayesnet {
}
notes.push_back("Number of models: " + std::to_string(n_models));
}
std::vector<std::string> BoostAODE::graph(const std::string& title) const
std::vector<std::string> BoostAODE::graph(const std::string &title) const
{
return Ensemble::graph(title);
}