294 lines
15 KiB
C++
294 lines
15 KiB
C++
#include <set>
|
|
#include <functional>
|
|
#include <limits.h>
|
|
#include <tuple>
|
|
#include <folding.hpp>
|
|
#include "bayesnet/feature_selection/CFS.h"
|
|
#include "bayesnet/feature_selection/FCBF.h"
|
|
#include "bayesnet/feature_selection/IWSS.h"
|
|
#include "BoostAODE.h"
|
|
|
|
#include "bayesnet/utils/loguru.cpp"
|
|
|
|
namespace bayesnet {
|
|
|
|
BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
|
|
{
|
|
validHyperparameters = {
|
|
"maxModels", "bisection", "order", "convergence", "threshold",
|
|
"select_features", "maxTolerance", "predict_voting"
|
|
};
|
|
|
|
}
|
|
void BoostAODE::buildModel(const torch::Tensor& weights)
|
|
{
|
|
// Models shall be built in trainModel
|
|
models.clear();
|
|
significanceModels.clear();
|
|
n_models = 0;
|
|
// Prepare the validation dataset
|
|
auto y_ = dataset.index({ -1, "..." });
|
|
if (convergence) {
|
|
// Prepare train & validation sets from train data
|
|
auto fold = folding::StratifiedKFold(5, y_, 271);
|
|
auto [train, test] = fold.getFold(0);
|
|
auto train_t = torch::tensor(train);
|
|
auto test_t = torch::tensor(test);
|
|
// Get train and validation sets
|
|
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
|
|
y_train = dataset.index({ -1, train_t });
|
|
X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
|
|
y_test = dataset.index({ -1, test_t });
|
|
dataset = X_train;
|
|
m = X_train.size(1);
|
|
auto n_classes = states.at(className).size();
|
|
// Build dataset with train data
|
|
buildDataset(y_train);
|
|
metrics = Metrics(dataset, features, className, n_classes);
|
|
} else {
|
|
// Use all data to train
|
|
X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
|
|
y_train = y_;
|
|
}
|
|
}
|
|
void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
|
|
{
|
|
auto hyperparameters = hyperparameters_;
|
|
if (hyperparameters.contains("order")) {
|
|
std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
|
|
order_algorithm = hyperparameters["order"];
|
|
if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
|
|
throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
|
|
}
|
|
hyperparameters.erase("order");
|
|
}
|
|
if (hyperparameters.contains("convergence")) {
|
|
convergence = hyperparameters["convergence"];
|
|
hyperparameters.erase("convergence");
|
|
}
|
|
if (hyperparameters.contains("bisection")) {
|
|
bisection = hyperparameters["bisection"];
|
|
hyperparameters.erase("bisection");
|
|
}
|
|
if (hyperparameters.contains("threshold")) {
|
|
threshold = hyperparameters["threshold"];
|
|
hyperparameters.erase("threshold");
|
|
}
|
|
if (hyperparameters.contains("maxTolerance")) {
|
|
maxTolerance = hyperparameters["maxTolerance"];
|
|
if (maxTolerance < 1 || maxTolerance > 4)
|
|
throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
|
|
hyperparameters.erase("maxTolerance");
|
|
}
|
|
if (hyperparameters.contains("predict_voting")) {
|
|
predict_voting = hyperparameters["predict_voting"];
|
|
hyperparameters.erase("predict_voting");
|
|
}
|
|
if (hyperparameters.contains("select_features")) {
|
|
auto selectedAlgorithm = hyperparameters["select_features"];
|
|
std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
|
|
selectFeatures = true;
|
|
select_features_algorithm = selectedAlgorithm;
|
|
if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
|
|
throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
|
|
}
|
|
hyperparameters.erase("select_features");
|
|
}
|
|
if (!hyperparameters.empty()) {
|
|
throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
|
|
}
|
|
}
|
|
std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
|
|
{
|
|
bool terminate = false;
|
|
double alpha_t = 0;
|
|
auto mask_wrong = ypred != ytrain;
|
|
auto mask_right = ypred == ytrain;
|
|
auto masked_weights = weights * mask_wrong.to(weights.dtype());
|
|
double epsilon_t = masked_weights.sum().item<double>();
|
|
if (epsilon_t > 0.5) {
|
|
// Inverse the weights policy (plot ln(wt))
|
|
// "In each round of AdaBoost, there is a sanity check to ensure that the current base
|
|
// learner is better than random guess" (Zhi-Hua Zhou, 2012)
|
|
terminate = true;
|
|
} else {
|
|
double wt = (1 - epsilon_t) / epsilon_t;
|
|
alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
|
|
// Step 3.2: Update weights for next classifier
|
|
// Step 3.2.1: Update weights of wrong samples
|
|
weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
|
|
// Step 3.2.2: Update weights of right samples
|
|
weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
|
|
// Step 3.3: Normalise the weights
|
|
double totalWeights = torch::sum(weights).item<double>();
|
|
weights = weights / totalWeights;
|
|
}
|
|
return { weights, alpha_t, terminate };
|
|
}
|
|
std::vector<int> BoostAODE::initializeModels()
|
|
{
|
|
std::vector<int> featuresUsed;
|
|
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
|
|
int maxFeatures = 0;
|
|
if (select_features_algorithm == SelectFeatures.CFS) {
|
|
featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
|
|
} else if (select_features_algorithm == SelectFeatures.IWSS) {
|
|
if (threshold < 0 || threshold >0.5) {
|
|
throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
|
|
}
|
|
featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
|
|
} else if (select_features_algorithm == SelectFeatures.FCBF) {
|
|
if (threshold < 1e-7 || threshold > 1) {
|
|
throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
|
|
}
|
|
featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
|
|
}
|
|
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);
|
|
model->fit(dataset, features, className, states, weights_);
|
|
models.push_back(std::move(model));
|
|
significanceModels.push_back(1.0);
|
|
n_models++;
|
|
}
|
|
notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
|
|
delete featureSelector;
|
|
return featuresUsed;
|
|
}
|
|
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;
|
|
double alpha_t = 0;
|
|
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
|
|
bool finished = false;
|
|
std::vector<int> featuresUsed;
|
|
if (selectFeatures) {
|
|
featuresUsed = initializeModels();
|
|
auto ypred = predict(X_train);
|
|
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
|
|
// Update significance of the models
|
|
for (int i = 0; i < n_models; ++i) {
|
|
significanceModels[i] = alpha_t;
|
|
}
|
|
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
|
|
double priorAccuracy = 0.0;
|
|
double improvement = 1.0;
|
|
double convergence_threshold = 1e-4;
|
|
int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
|
|
// 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 };
|
|
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);
|
|
}
|
|
// 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)
|
|
);
|
|
int k = pow(2, tolerance);
|
|
int counter = 0; // The model counter of the current pack
|
|
VLOG_SCOPE_F(1, "k=%d featureSelection.size: %zu", k, featureSelection.size());
|
|
while (counter++ < k && featureSelection.size() > 0) {
|
|
VLOG_SCOPE_F(2, "counter: %d numItemsPack: %d", counter, numItemsPack);
|
|
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;
|
|
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++;
|
|
featuresUsed.push_back(feature);
|
|
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 (convergence && !finished) {
|
|
auto y_val_predict = predict(X_test);
|
|
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;
|
|
}
|
|
// Keep the best accuracy until now as the prior accuracy
|
|
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");
|
|
}
|
|
}
|
|
if (featuresUsed.size() != features.size()) {
|
|
notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
|
|
status = WARNING;
|
|
}
|
|
notes.push_back("Number of models: " + std::to_string(n_models));
|
|
}
|
|
std::vector<std::string> BoostAODE::graph(const std::string& title) const
|
|
{
|
|
return Ensemble::graph(title);
|
|
}
|
|
} |