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Complete implementation with tests

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This commit is contained in:
Ricardo Montañana Gómez
2025-07-08 11:42:20 +02:00
parent 2c7352ac38
commit ed380b1494
13 changed files with 255 additions and 170 deletions
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53
bayesnet/classifiers/Proposal.cc
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@@ -11,6 +11,7 @@
#include "Classifier.h"
#include "KDB.h"
#include "TAN.h"
#include "SPODE.h"
#include "KDBLd.h"
#include "TANLd.h"
@@ -18,9 +19,8 @@ namespace bayesnet {
Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_)
{
}
void Proposal::setHyperparameters(const nlohmann::json& hyperparameters_)
void Proposal::setHyperparameters(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");
@@ -55,9 +55,6 @@ namespace bayesnet {
convergence_params.verbose = hyperparameters["verbose_convergence"];
hyperparameters.erase("verbose_convergence");
}
if (!hyperparameters.empty()) {
throw std::invalid_argument("Invalid hyperparameters for Proposal: " + hyperparameters.dump());
}
}
void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
@@ -209,7 +206,7 @@ namespace bayesnet {
// Phase 2: Build model with current discretization
classifier->fit(dataset, features, className, currentStates, weights, smoothing);
// Phase 3: Network-aware discretization refinement
currentStates = localDiscretizationProposal(currentStates, classifier->getModel());
@@ -228,51 +225,15 @@ namespace bayesnet {
return currentStates;
}
double Proposal::computeLogLikelihood(Network& model, const torch::Tensor& dataset)
{
double logLikelihood = 0.0;
int n_samples = dataset.size(0);
int n_features = dataset.size(1);
for (int i = 0; i < n_samples; ++i) {
double sampleLogLikelihood = 0.0;
// Get class value for this sample
int classValue = dataset[i][n_features - 1].item<int>();
// Compute log-likelihood for each feature given its parents and class
for (const auto& node : model.getNodes()) {
if (node.first == model.getClassName()) {
// For class node, add log P(class)
auto classCounts = node.second->getCPT();
double classProb = classCounts[classValue].item<double>() / dataset.size(0);
sampleLogLikelihood += std::log(std::max(classProb, 1e-10));
} else {
// For feature nodes, add log P(feature | parents, class)
int featureIdx = std::distance(model.getFeatures().begin(),
std::find(model.getFeatures().begin(),
model.getFeatures().end(),
node.first));
int featureValue = dataset[i][featureIdx].item<int>();
// Simplified probability computation - in practice would need full CPT lookup
double featureProb = 0.1; // Placeholder - would compute from CPT
sampleLogLikelihood += std::log(std::max(featureProb, 1e-10));
}
}
logLikelihood += sampleLogLikelihood;
}
return logLikelihood;
}
// Explicit template instantiation for common classifier types
template map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization<KDB>(
const torch::Tensor&, KDB*, torch::Tensor&, const std::vector<std::string>&,
const std::string&, const map<std::string, std::vector<int>>&, Smoothing_t);
template map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization<TAN>(
const torch::Tensor&, TAN*, torch::Tensor&, const std::vector<std::string>&,
const std::string&, const map<std::string, std::vector<int>>&, Smoothing_t);
template map<std::string, std::vector<int>> Proposal::iterativeLocalDiscretization<SPODE>(
const torch::Tensor&, SPODE*, torch::Tensor&, const std::vector<std::string>&,
const std::string&, const map<std::string, std::vector<int>>&, Smoothing_t);
}