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change boostaode ascending hyperparameter to order {asc,desc,rand}

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This commit is contained in:
Ricardo Montañana Gómez
2024-02-26 17:07:57 +01:00
parent 43dc79a345
commit fc3d63b7db
4 changed files with 59 additions and 19 deletions
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31
src/BoostAODE.cc
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@@ -10,7 +10,7 @@
namespace bayesnet {
BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
{
validHyperparameters = { "repeatSparent", "maxModels", "ascending", "convergence", "threshold", "select_features", "tolerance", "predict_voting" };
validHyperparameters = { "repeatSparent", "maxModels", "order", "convergence", "threshold", "select_features", "tolerance", "predict_voting" };
}
void BoostAODE::buildModel(const torch::Tensor& weights)
@@ -57,9 +57,13 @@ namespace bayesnet {
maxModels = hyperparameters["maxModels"];
hyperparameters.erase("maxModels");
}
if (hyperparameters.contains("ascending")) {
ascending = hyperparameters["ascending"];
hyperparameters.erase("ascending");
if (hyperparameters.contains("order")) {
std::vector<std::string> algos = { "asc", "desc", "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 [asc, desc, rand]");
}
hyperparameters.erase("order");
}
if (hyperparameters.contains("convergence")) {
convergence = hyperparameters["convergence"];
@@ -81,9 +85,9 @@ namespace bayesnet {
auto selectedAlgorithm = hyperparameters["select_features"];
std::vector<std::string> algos = { "IWSS", "FCBF", "CFS" };
selectFeatures = true;
algorithm = selectedAlgorithm;
select_features_algorithm = selectedAlgorithm;
if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
throw std::invalid_argument("Invalid selectFeatures value [IWSS, FCBF, CFS]");
throw std::invalid_argument("Invalid selectFeatures value, valid values [IWSS, FCBF, CFS]");
}
hyperparameters.erase("select_features");
}
@@ -96,14 +100,14 @@ namespace bayesnet {
std::unordered_set<int> featuresUsed;
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
int maxFeatures = 0;
if (algorithm == "CFS") {
if (select_features_algorithm == "CFS") {
featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
} else if (algorithm == "IWSS") {
} else if (select_features_algorithm == "IWSS") {
if (threshold < 0 || threshold >0.5) {
throw std::invalid_argument("Invalid threshold value for IWSS [0, 0.5]");
}
featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
} else if (algorithm == "FCBF") {
} else if (select_features_algorithm == "FCBF") {
if (threshold < 1e-7 || threshold > 1) {
throw std::invalid_argument("Invalid threshold value [1e-7, 1]");
}
@@ -120,7 +124,7 @@ namespace bayesnet {
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 " + algorithm);
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;
}
@@ -150,10 +154,14 @@ namespace bayesnet {
// n_models == maxModels
// epsilon sub t > 0.5 => inverse the weights policy
// validation error is not decreasing
bool ascending = order_algorithm == "asc";
std::mt19937 g{ 173 };
while (!exitCondition) {
// Step 1: Build ranking with mutual information
auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
std::unique_ptr<Classifier> model;
if (order_algorithm == "rand") {
std::shuffle(featureSelection.begin(), featureSelection.end(), g);
}
auto feature = featureSelection[0];
if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
bool used = true;
@@ -170,6 +178,7 @@ namespace bayesnet {
continue;
}
}
std::unique_ptr<Classifier> model;
model = std::make_unique<SPODE>(feature);
model->fit(dataset, features, className, states, weights_);
auto ypred = model->predict(X_train);

4
src/BoostAODE.h
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@@ -22,10 +22,10 @@ namespace bayesnet {
bool repeatSparent = false; // if true, a feature can be selected more than once
int maxModels = 0;
int tolerance = 0;
bool ascending = false; //Process KBest features ascending or descending order
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 algorithm = ""; // Selected feature selection algorithm
std::string select_features_algorithm = ""; // Selected feature selection algorithm
FeatureSelect* featureSelector = nullptr;
double threshold = -1;
};