rmontanana/BayesNet
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Complete and fix KDB

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This commit is contained in:
Ricardo Montañana Gómez 2023-07-13 16:59:06 +02:00
parent 3fcf1e40c9
commit e52fdc718f
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
7 changed files with 75 additions and 37 deletions
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59
sample/main.cc
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@ -221,41 +221,44 @@ int main(int argc, char** argv)
cout << endl;
cout << "Class name: " << className << endl;
// Build Network
auto network = bayesnet::Network(1.0);
build_network(network, network_name, maxes);
network.fit(Xd, y, features, className);
cout << "Hello, Bayesian Networks!" << endl;
showNodesInfo(network, className);
//showCPDS(network);
cout << "Score: " << network.score(Xd, y) << endl;
cout << "PyTorch version: " << TORCH_VERSION << endl;
cout << "BayesNet version: " << network.version() << endl;
unsigned int nthreads = std::thread::hardware_concurrency();
cout << "Computer has " << nthreads << " cores." << endl;
cout << "****************** First ******************" << endl;
auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
cout << "conditionalEdgeWeight " << endl;
auto conditional = metrics.conditionalEdgeWeights();
cout << conditional << endl;
long m = features.size() + 1;
auto matrix = torch::from_blob(conditional.data(), { m, m });
cout << matrix << endl;
cout << "****************** Second ******************" << endl;
auto metrics2 = bayesnet::Metrics(Xd, y, features, className, network.getClassNumStates());
cout << "conditionalEdgeWeight " << endl;
auto conditional2 = metrics2.conditionalEdgeWeights();
cout << conditional2 << endl;
long m2 = features.size() + 1;
auto matrix2 = torch::from_blob(conditional2.data(), { m, m });
cout << matrix2 << endl;
// auto network = bayesnet::Network(1.0);
// build_network(network, network_name, maxes);
// network.fit(Xd, y, features, className);
// cout << "Hello, Bayesian Networks!" << endl;
// showNodesInfo(network, className);
// //showCPDS(network);
// cout << "Score: " << network.score(Xd, y) << endl;
// cout << "PyTorch version: " << TORCH_VERSION << endl;
// cout << "BayesNet version: " << network.version() << endl;
// unsigned int nthreads = std::thread::hardware_concurrency();
// cout << "Computer has " << nthreads << " cores." << endl;
// cout << "****************** First ******************" << endl;
// auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
// cout << "conditionalEdgeWeight " << endl;
// auto conditional = metrics.conditionalEdgeWeights();
// cout << conditional << endl;
// long m = features.size() + 1;
// auto matrix = torch::from_blob(conditional.data(), { m, m });
// cout << matrix << endl;
// cout << "****************** Second ******************" << endl;
// auto metrics2 = bayesnet::Metrics(Xd, y, features, className, network.getClassNumStates());
// cout << "conditionalEdgeWeight " << endl;
// auto conditional2 = metrics2.conditionalEdgeWeights();
// cout << conditional2 << endl;
// long m2 = features.size() + 1;
// auto matrix2 = torch::from_blob(conditional2.data(), { m, m });
// cout << matrix2 << endl;
cout << "****************** KDB ******************" << endl;
map<string, vector<int>> states;
for (auto feature : features) {
states[feature] = vector<int>(maxes[feature]);
}
states[className] = vector<int>(maxes[className]);
auto kdb = bayesnet::KDB(1);
auto kdb = bayesnet::KDB(2);
kdb.fit(Xd, y, features, className, states);
for (auto line : kdb.show()) {
cout << line << endl;
}
cout << "****************** KDB ******************" << endl;
return 0;
}

6
src/BaseClassifier.cc
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@ -8,7 +8,7 @@ namespace bayesnet {
BaseClassifier& BaseClassifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
{
dataset = torch::cat({ X, y.view({150, 1}) }, 1);
dataset = torch::cat({ X, y.view({y.size(0), 1}) }, 1);
this->features = features;
this->className = className;
this->states = states;
@ -86,4 +86,8 @@ namespace bayesnet {
Tensor y_pred = predict(X);
return (y_pred == y).sum().item<float>() / y.size(0);
}
void BaseClassifier::show()
{
model.show();
}
}

2
src/BaseClassifier.h
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@ -28,8 +28,8 @@ namespace bayesnet {
BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states);
Tensor predict(Tensor& X);
float score(Tensor& X, Tensor& y);
void show();
};
}
#endif

26
src/KDB.cc
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@ -12,7 +12,7 @@ namespace bayesnet {
sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
return indices;
}
KDB::KDB(int k, float theta = 0.03) : BaseClassifier(Network()), k(k), theta(theta) {}
KDB::KDB(int k, float theta) : BaseClassifier(Network()), k(k), theta(theta) {}
void KDB::train()
{
/*
@ -76,23 +76,39 @@ namespace bayesnet {
{
auto n_edges = min(k, static_cast<int>(S.size()));
auto cond_w = clone(weights);
cout << "Conditional edge weights cloned for idx " << idx << endl;
cout << cond_w << endl;
bool exit_cond = k == 0;
int num = 0;
while (!exit_cond) {
auto max_minfo = argmax(cond_w.index({ "...", idx })).item<int>();
auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>();
auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();
if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) {
try {
model.addEdge(features[idx], features[max_minfo]);
model.addEdge(features[max_minfo], features[idx]);
num++;
}
catch (const invalid_argument& e) {
// Loops are not allowed
}
}
cond_w.index_put_({ "...", max_minfo }, -1);
auto candidates = cond_w.gt(theta);
cond_w.index_put_({ idx, max_minfo }, -1);
cout << "Conditional edge weights cloned for idx " << idx << " After -1" << endl;
cout << cond_w << endl;
cout << "cond_w.index({ idx, '...'})" << endl;
cout << cond_w.index({ idx, "..." }) << endl;
auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);
auto candidates = candidates_mask.nonzero();
cout << "Candidates mask" << endl;
cout << candidates_mask << endl;
cout << "Candidates: " << endl;
cout << candidates << endl;
cout << "Candidates size: " << candidates.size(0) << endl;
exit_cond = num == n_edges || candidates.size(0) == 0;
}
}
vector<string> KDB::show()
{
return model.show();
}
}

5
src/KDB.h
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@ -10,9 +10,10 @@ namespace bayesnet {
float theta;
void add_m_edges(int idx, vector<int>& S, Tensor& weights);
protected:
void train();
void train() override;
public:
KDB(int k, float theta);
KDB(int k, float theta = 0.03);
vector<string> show();
};
}
#endif

13
src/Network.cc
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@ -245,5 +245,18 @@ namespace bayesnet {
}
return result;
}
vector<string> Network::show()
{
vector<string> result;
// Draw the network
for (auto node : nodes) {
string line = node.first + " -> ";
for (auto child : node.second->getChildren()) {
line += child->getName() + ", ";
}
result.push_back(line);
}
return result;
}
}

1
src/Network.h
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@ -44,6 +44,7 @@ namespace bayesnet {
torch::Tensor conditionalEdgeWeight();
vector<vector<double>> predict_proba(const vector<vector<int>>&);
double score(const vector<vector<int>>&, const vector<int>&);
vector<string> show();
inline string version() { return "0.1.0"; }
};
}