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Add Notes to Proposal convergence

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This commit is contained in:
Ricardo Montañana Gómez
2025-07-08 18:50:09 +02:00
parent aa77745e55
commit e2a0c5f4a5
7 changed files with 13 additions and 9 deletions
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2
bayesnet/classifiers/KDBLd.cc
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@@ -8,7 +8,7 @@
#include <memory>
namespace bayesnet {
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className)
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className, KDB::notes)
{
validHyperparameters = validHyperparameters_ld;
validHyperparameters.push_back("k");

4
bayesnet/classifiers/Proposal.cc
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@@ -16,7 +16,7 @@
#include "TANLd.h"
namespace bayesnet {
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_)
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_, std::vector<std::string>& notes_) : pDataset(dataset_), pFeatures(features_), pClassName(className_), notes(notes_)
{
}
void Proposal::setHyperparameters(nlohmann::json& hyperparameters)
@@ -215,6 +215,8 @@ namespace bayesnet {
if (convergence_params.verbose) {
std::cout << "Converged after " << (iteration + 1) << " iterations" << std::endl;
}
notes.push_back("Converged after " + std::to_string(iteration + 1) + " of "
+ std::to_string(convergence_params.maxIterations) + " iterations");
break;
}

3
bayesnet/classifiers/Proposal.h
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@@ -18,7 +18,7 @@
namespace bayesnet {
class Proposal {
public:
Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_, std::vector<std::string>& notes);
void setHyperparameters(nlohmann::json& hyperparameters_);
protected:
void checkInput(const torch::Tensor& X, const torch::Tensor& y);
@@ -61,6 +61,7 @@ namespace bayesnet {
};
private:
std::vector<int> factorize(const std::vector<std::string>& labels_t);
std::vector<std::string>& notes; // Notes during fit from BaseClassifier
torch::Tensor& pDataset; // (n+1)xm tensor
std::vector<std::string>& pFeatures;
std::string& pClassName;

2
bayesnet/classifiers/SPODELd.cc
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@@ -7,7 +7,7 @@
#include "SPODELd.h"
namespace bayesnet {
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className)
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className, SPODE::notes)
{
validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
}

2
bayesnet/classifiers/TANLd.cc
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@@ -8,7 +8,7 @@
#include <memory>
namespace bayesnet {
TANLd::TANLd() : TAN(), Proposal(dataset, features, className)
TANLd::TANLd() : TAN(), Proposal(dataset, features, className, TAN::notes)
{
validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
}

2
bayesnet/ensembles/AODELd.cc
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@@ -7,7 +7,7 @@
#include "AODELd.h"
namespace bayesnet {
AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className, Ensemble::notes)
{
validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
}

7
tests/TestBayesModels.cc
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@@ -407,14 +407,15 @@ TEST_CASE("Check proposal checkInput", "[Models]")
{
class testProposal : public bayesnet::Proposal {
public:
testProposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_)
: Proposal(dataset_, features_, className_)
testProposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_, std::vector<std::string>& notes_)
: Proposal(dataset_, features_, className_, notes_)
{
}
void test_X_y(const torch::Tensor& X, const torch::Tensor& y) { checkInput(X, y); }
};
auto raw = RawDatasets("iris", true);
auto clf = testProposal(raw.dataset, raw.features, raw.className);
std::vector<std::string> notes;
auto clf = testProposal(raw.dataset, raw.features, raw.className, notes);
torch::Tensor X = torch::randint(0, 3, { 10, 4 });
torch::Tensor y = torch::rand({ 10 });
INFO("Check X is not float");