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TANNew restructured with poor results

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This commit is contained in:
Ricardo Montañana Gómez 2023-08-04 20:11:22 +02:00
parent 64ac8fb4f2
commit a1c6ab18f3
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 34 additions and 24 deletions
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12
src/BayesNet/Network.cc
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@ -325,17 +325,17 @@ namespace bayesnet {
vector<thread> threads;
mutex mtx;
for (int i = 0; i < classNumStates; ++i) {
threads.emplace_back([this, &result, &evidence, i, &mtx]() {
// threads.emplace_back([this, &result, &evidence, i, &mtx]() {
auto completeEvidence = map<string, int>(evidence);
completeEvidence[getClassName()] = i;
double factor = computeFactor(completeEvidence);
lock_guard<mutex> lock(mtx);
// lock_guard<mutex> lock(mtx);
result[i] = factor;
});
}
for (auto& thread : threads) {
thread.join();
// });
}
// for (auto& thread : threads) {
// thread.join();
// }
// Normalize result
double sum = accumulate(result.begin(), result.end(), 0.0);
transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });

38
src/BayesNet/Proposal.cc
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@ -12,6 +12,7 @@ namespace bayesnet {
void Proposal::localDiscretizationProposal(map<string, vector<int>>& states, Network& model)
{
// order of local discretization is important. no good 0, 1, 2...
// although we rediscretize features after the local discretization of every feature
auto order = model.topological_sort();
auto& nodes = model.getNodes();
vector<int> indicesToReDiscretize;
@ -22,7 +23,7 @@ namespace bayesnet {
if (nodeParents.size() < 2) continue; // Only has class as parent
upgrade = true;
int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();
indicesToReDiscretize.push_back(index);
indicesToReDiscretize.push_back(index); // We need to re-discretize this feature
vector<string> parents;
transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });
// Remove class as parent as it will be added later
@ -30,7 +31,7 @@ namespace bayesnet {
// Get the indices of the parents
vector<int> indices;
transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });
// Now we fit the discretizer of the feature conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
// Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
vector<string> yJoinParents;
transform(yv.begin(), yv.end(), back_inserter(yJoinParents), [&](const auto& p) {return to_string(p); });
for (auto idx : indices) {
@ -43,19 +44,28 @@ namespace bayesnet {
auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
discretizers[feature]->fit(xvf, yxv);
//
//
//
auto tmp = discretizers[feature]->transform(xvf);
Xv[index] = tmp;
auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
iota(xStates.begin(), xStates.end(), 0);
//Update new states of the feature/node
states[feature] = xStates;
}
if (upgrade) {
// Discretize again X (only the affected indices) with the new fitted discretizers
for (auto index : indicesToReDiscretize) {
auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
iota(xStates.begin(), xStates.end(), 0);
//Update new states of the feature/node
states[pFeatures[index]] = xStates;
}
}
// if (upgrade) {
// // Discretize again X (only the affected indices) with the new fitted discretizers
// for (auto index : indicesToReDiscretize) {
// auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
// auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
// Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
// auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
// iota(xStates.begin(), xStates.end(), 0);
// //Update new states of the feature/node
// states[pFeatures[index]] = xStates;
// }
// }
}
void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
{

6
src/BayesNet/TANNew.cc
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@ -3,7 +3,6 @@
namespace bayesnet {
using namespace std;
TANNew::TANNew() : TAN(), Proposal(TAN::Xv, TAN::yv, TAN::features, TAN::className) {}
TANNew::~TANNew() {}
TANNew& TANNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
// This first part should go in a Classifier method called fit_local_discretization o fit_float...
@ -17,8 +16,9 @@ namespace bayesnet {
cout << "TANNew: Fitting model" << endl;
TAN::fit(TAN::Xv, TAN::yv, TAN::features, TAN::className, states);
cout << "TANNew: Model fitted" << endl;
//localDiscretizationProposal(states, model);
//addNodes();
localDiscretizationProposal(states, model);
addNodes();
model.fit(TAN::Xv, TAN::yv, features, className);
return *this;
}
Tensor TANNew::predict(Tensor& X)

2
src/BayesNet/TANNew.h
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@ -9,7 +9,7 @@ namespace bayesnet {
private:
public:
TANNew();
virtual ~TANNew();
virtual ~TANNew() = default;
TANNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "TAN") override;
Tensor predict(Tensor& X) override;