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Remove predict_single max_models

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This commit is contained in:
Ricardo Montañana Gómez
2024-03-19 11:35:43 +01:00
parent eb97a5a14b
commit 422129802a
5 changed files with 182 additions and 50 deletions
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43
bayesnet/ensembles/BoostAODE.cc
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@@ -23,7 +23,7 @@ namespace bayesnet {
{
validHyperparameters = {
"maxModels", "order", "convergence", "threshold",
"select_features", "tolerance", "predict_voting", "predict_single"
"select_features", "tolerance", "predict_voting"
};
}
@@ -63,10 +63,6 @@ namespace bayesnet {
void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("maxModels")) {
maxModels = hyperparameters["maxModels"];
hyperparameters.erase("maxModels");
}
if (hyperparameters.contains("order")) {
std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
order_algorithm = hyperparameters["order"];
@@ -79,10 +75,6 @@ namespace bayesnet {
convergence = hyperparameters["convergence"];
hyperparameters.erase("convergence");
}
if (hyperparameters.contains("predict_single")) {
predict_single = hyperparameters["predict_single"];
hyperparameters.erase("predict_single");
}
if (hyperparameters.contains("threshold")) {
threshold = hyperparameters["threshold"];
hyperparameters.erase("threshold");
@@ -168,24 +160,10 @@ namespace bayesnet {
delete featureSelector;
return featuresUsed;
}
torch::Tensor BoostAODE::ensemble_predict(torch::Tensor& X, SPODE* model)
{
if (initialize_prob_table) {
initialize_prob_table = false;
prob_table = model->predict_proba(X) * 1.0;
} else {
prob_table += model->predict_proba(X) * 1.0;
}
// prob_table doesn't store probabilities but the sum of them
// to have them we need to divide by the sum of the "weights" used to
// consider the results obtanined in the model's predict_proba.
return prob_table.argmax(1);
}
void BoostAODE::trainModel(const torch::Tensor& weights)
{
// Algorithm based on the adaboost algorithm for classification
// as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
initialize_prob_table = true;
fitted = true;
double alpha_t = 0;
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
@@ -203,19 +181,13 @@ namespace bayesnet {
return;
}
}
bool resetMaxModels = false;
if (maxModels == 0) {
maxModels = .1 * n > 10 ? .1 * n : n;
resetMaxModels = true; // Flag to unset maxModels
}
int numItemsPack = 0;
// Variables to control the accuracy finish condition
double priorAccuracy = 0.0;
double delta = 1.0;
double convergence_threshold = 1e-4;
int worse_model_count = 0; // number of times the accuracy is lower than the convergence_threshold
// Step 0: Set the finish condition
// if not repeatSparent a finish condition is run out of features
// n_models == maxModels
// epsilon sub t > 0.5 => inverse the weights policy
// validation error is not decreasing
bool ascending = order_algorithm == Orders.ASC;
@@ -239,11 +211,7 @@ namespace bayesnet {
model = std::make_unique<SPODE>(feature);
model->fit(dataset, features, className, states, weights_);
torch::Tensor ypred;
if (predict_single) {
ypred = model->predict(X_train);
} else {
ypred = ensemble_predict(X_train, dynamic_cast<SPODE*>(model.get()));
}
ypred = model->predict(X_train);
// Step 3.1: Compute the classifier amout of say
std::tie(weights_, alpha_t, exitCondition) = update_weights(y_train, ypred, weights_);
if (exitCondition) {
@@ -269,7 +237,7 @@ namespace bayesnet {
}
priorAccuracy = accuracy;
}
exitCondition = n_models >= maxModels && repeatSparent || worse_model_count > tolerance;
exitCondition = worse_model_count > tolerance;
}
if (worse_model_count > tolerance) {
notes.push_back("Convergence threshold reached & last model eliminated");
@@ -282,9 +250,6 @@ namespace bayesnet {
status = WARNING;
}
notes.push_back("Number of models: " + std::to_string(n_models));
if (resetMaxModels) {
maxModels = 0;
}
}
std::vector<std::string> BoostAODE::graph(const std::string& title) const
{

7
bayesnet/ensembles/BoostAODE.h
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@@ -16,20 +16,15 @@ namespace bayesnet {
void trainModel(const torch::Tensor& weights) override;
private:
std::unordered_set<int> initializeModels();
torch::Tensor ensemble_predict(torch::Tensor& X, SPODE* model);
torch::Tensor dataset_;
torch::Tensor X_train, y_train, X_test, y_test;
// Hyperparameters
bool repeatSparent = false; // if true, a feature can be selected more than once
int maxModels = 0;
bool bisection = false; // if true, use bisection stratety to add k models at once to the ensemble
int tolerance = 0;
bool predict_single = true; // wether the last model is used to predict in training or the whole ensemble
std::string order_algorithm; // order to process the KBest features asc, desc, rand
bool convergence = false; //if true, stop when the model does not improve
bool selectFeatures = false; // if true, use feature selection
std::string select_features_algorithm = "desc"; // Selected feature selection algorithm
bool initialize_prob_table; // if true, initialize the prob_table with the first model (used in train)
torch::Tensor prob_table; // Table of probabilities for ensemble predicting if predict_single is false
FeatureSelect* featureSelector = nullptr;
double threshold = -1;
};