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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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20
bayesnet/classifiers/KDBLd.cc
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@@ -7,7 +7,25 @@
#include "KDBLd.h"
namespace bayesnet {
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className)
{
validHyperparameters = validHyperparameters_ld;
validHyperparameters.push_back("k");
validHyperparameters.push_back("theta");
}
void KDBLd::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("k")) {
k = hyperparameters["k"];
hyperparameters.erase("k");
}
if (hyperparameters.contains("theta")) {
theta = hyperparameters["theta"];
hyperparameters.erase("theta");
}
Proposal::setHyperparameters(hyperparameters);
}
KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
{
checkInput(X_, y_);