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Add new hyperparameters to the Ld classifiers

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- *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU".
  - *ld_proposed_cuts*: number of cut points to return.
  - *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition.
  - *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.
This commit is contained in:
Ricardo Montañana Gómez
2025-06-29 13:00:34 +02:00
parent dafd5672bc
commit 9f3de4d924
10 changed files with 104 additions and 18 deletions
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48
bayesnet/classifiers/Proposal.cc
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@@ -7,13 +7,42 @@
#include "Proposal.h"
namespace bayesnet {
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
Proposal::~Proposal()
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_)
{
for (auto& [key, value] : discretizers) {
delete value;
}
void Proposal::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("ld_proposed_cuts")) {
ld_params.proposed_cuts = hyperparameters["ld_proposed_cuts"];
hyperparameters.erase("ld_proposed_cuts");
}
if (hyperparameters.contains("mdlp_max_depth")) {
ld_params.max_depth = hyperparameters["mdlp_max_depth"];
hyperparameters.erase("mdlp_max_depth");
}
if (hyperparameters.contains("mdlp_min_length")) {
ld_params.min_length = hyperparameters["mdlp_min_length"];
hyperparameters.erase("mdlp_min_length");
}
if (hyperparameters.contains("ld_algorithm")) {
auto algorithm = hyperparameters["ld_algorithm"];
hyperparameters.erase("ld_algorithm");
if (algorithm == "MDLP") {
discretizationType = discretization_t::MDLP;
} else if (algorithm == "BINQ") {
discretizationType = discretization_t::BINQ;
} else if (algorithm == "BINU") {
discretizationType = discretization_t::BINU;
} else {
throw std::invalid_argument("Invalid discretization algorithm: " + algorithm.get<std::string>());
}
}
if (!hyperparameters.empty()) {
throw std::invalid_argument("Invalid hyperparameters for Proposal: " + hyperparameters.dump());
}
}
void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
{
if (!torch::is_floating_point(X)) {
@@ -84,8 +113,15 @@ namespace bayesnet {
pDataset = torch::zeros({ n + 1, m }, torch::kInt32);
auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
// discretize input data by feature(row)
std::unique_ptr<mdlp::Discretizer> discretizer;
for (auto i = 0; i < pFeatures.size(); ++i) {
auto* discretizer = new mdlp::CPPFImdlp();
if (discretizationType == discretization_t::BINQ) {
discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::QUANTILE);
} else if (discretizationType == discretization_t::BINU) {
discretizer = std::make_unique<mdlp::BinDisc>(ld_params.proposed_cuts, mdlp::strategy_t::UNIFORM);
} else { // Default is MDLP
discretizer = std::make_unique<mdlp::CPPFImdlp>(ld_params.min_length, ld_params.max_depth, ld_params.proposed_cuts);
}
auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
discretizer->fit(Xt, yv);
@@ -93,7 +129,7 @@ namespace bayesnet {
auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);
iota(xStates.begin(), xStates.end(), 0);
states[pFeatures[i]] = xStates;
discretizers[pFeatures[i]] = discretizer;
discretizers[pFeatures[i]] = std::move(discretizer);
}
int n_classes = torch::max(y).item<int>() + 1;
auto yStates = std::vector<int>(n_classes);