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Create Boost class as Boost<x> classifiers parent

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Ricardo Montañana Gómez
2024-05-15 19:49:15 +02:00
parent 1f236a70db
commit 54496c68f1
9 changed files with 318 additions and 325 deletions
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bayesnet/ensembles/BoostA2DE.cc
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@@ -16,71 +16,44 @@
namespace bayesnet {
BoostA2DE::BoostA2DE(bool predict_voting) : Ensemble(predict_voting)
BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
{
validHyperparameters = {
"maxModels", "bisection", "order", "convergence", "convergence_best", "threshold",
"select_features", "maxTolerance", "predict_voting", "block_update"
};
}
void BoostA2DE::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 BoostA2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
void BoostA2DE::trainModel(const torch::Tensor& weights)
{
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("convergence_best")) {
convergence_best = hyperparameters["convergence_best"];
hyperparameters.erase("convergence_best");
}
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.contains("block_update")) {
block_update = hyperparameters["block_update"];
hyperparameters.erase("block_update");
}
Classifier::setHyperparameters(hyperparameters);
}
std::vector<std::string> BoostA2DE::graph(const std::string& title) const
{