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Complete XA1DE integration

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-19 11:40:33 +01:00
parent 5556fbab03
commit c1531dba2a
3 changed files with 96 additions and 28 deletions
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67
src/experimental_clfs/XA1DE.cpp
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@@ -27,6 +27,7 @@ namespace platform {
Timer timer, timert;
timer.start();
timert.start();
// debug = true;
std::vector<std::vector<int>> instances = X;
instances.push_back(y);
int num_instances = instances[0].size();
@@ -36,6 +37,16 @@ namespace platform {
for (int i = 0; i < num_attributes; i++) {
statesv.push_back(*max_element(instances[i].begin(), instances[i].end()) + 1);
}
// std::cout << "* States: " << statesv << std::endl;
// std::cout << "* Weights: " << weights_ << std::endl;
// std::cout << "* Instances: " << num_instances << std::endl;
// std::cout << "* Attributes: " << num_attributes << std::endl;
// std::cout << "* y: " << y << std::endl;
// std::cout << "* x shape: " << X.size() << "x" << X[0].size() << std::endl;
// for (int i = 0; i < num_attributes - 1; i++) {
// std::cout << "* " << features[i] << ": " << instances[i] << std::endl;
// }
// std::cout << "Starting to build the model" << std::endl;
aode_.init(statesv);
aode_.duration_first += timer.getDuration(); timer.start();
std::vector<int> instance;
@@ -54,7 +65,7 @@ namespace platform {
// std::cout << "* Checking coherence... ";
// aode_.checkCoherenceApprox(1e-6);
// std::cout << "Ok!" << std::endl;
// aode_.show();
aode_.show();
// std::cout << "* Accumulated first time: " << aode_.duration_first << std::endl;
// std::cout << "* Accumulated second time: " << aode_.duration_second << std::endl;
// std::cout << "* Accumulated third time: " << aode_.duration_third << std::endl;
@@ -196,6 +207,26 @@ namespace platform {
return data;
}
//
// statistics
//
int XA1DE::getNumberOfNodes() const
{
return aode_.getNumberOfNodes();
}
int XA1DE::getNumberOfEdges() const
{
return aode_.getNumberOfEdges();
}
int XA1DE::getNumberOfStates() const
{
return aode_.getNumberOfStates();
}
int XA1DE::getClassNumStates() const
{
return aode_.statesClass();
}
//
// Fit
//
@@ -203,8 +234,7 @@ namespace platform {
XA1DE& XA1DE::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
{
auto X_ = to_matrix(X);
int a = 1;
std::vector<int> y_ = to_vector<int>(y);
auto y_ = to_vector<int>(y);
return fit(X_, y_, features, className, states, smoothing);
}
XA1DE& XA1DE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
@@ -215,8 +245,37 @@ namespace platform {
}
XA1DE& XA1DE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
{
double b = 1;
weights_ = to_vector<double>(weights);
return fit(dataset, features, className, states, smoothing);
}
//
// Predict
//
torch::Tensor XA1DE::predict(torch::Tensor& X)
{
auto X_ = to_matrix(X);
torch::Tensor y = torch::tensor(predict(X_));
return y;
}
torch::Tensor XA1DE::predict_proba(torch::Tensor& X)
{
auto X_ = to_matrix(X);
auto probabilities = predict_proba(X_);
auto n_samples = X.size(1);
int n_classes = probabilities[0].size();
auto y = torch::zeros({ n_samples, n_classes });
for (int i = 0; i < n_samples; i++) {
for (int j = 0; j < n_classes; j++) {
y[i][j] = probabilities[i][j];
}
}
return y;
}
float XA1DE::score(torch::Tensor& X, torch::Tensor& y)
{
auto X_ = to_matrix(X);
auto y_ = to_vector<int>(y);
return score(X_, y_);
}
}

24
src/experimental_clfs/XA1DE.h
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@@ -24,23 +24,23 @@ namespace platform {
virtual ~XA1DE() = default;
const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
XA1DE& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
int getNumberOfNodes() const override { return 0; };
int getNumberOfEdges() const override { return 0; };
int getNumberOfStates() const override { return 0; };
int getClassNumStates() const override { return 0; };
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
torch::Tensor predict(torch::Tensor& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override;
std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
float score(torch::Tensor& X, torch::Tensor& y) override;
int getNumberOfNodes() const override;
int getNumberOfEdges() const override;
int getNumberOfStates() const override;
int getClassNumStates() const override;
bayesnet::status_t getStatus() const override { return status; }
std::string getVersion() override { return version; };
float score(torch::Tensor& X, torch::Tensor& y) override { return 0; };
std::vector<std::string> show() const override { return {}; }
std::vector<std::string> topological_order() override { return {}; }
std::vector<std::string> getNotes() const override { return notes; }
@@ -57,7 +57,7 @@ namespace platform {
{
double sum = std::accumulate(weights_.begin(), weights_.end(), 0.0);
if (sum == 0) {
weights_ = std::vector<double>(weights_.size(), 1.0);
weights_ = std::vector<double>(num_instances, 1.0);
} else {
for (double& w : weights_) {
w = w * num_instances / sum;

33
src/experimental_clfs/Xaode.hpp
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@@ -31,7 +31,7 @@ namespace platform {
double duration_first = 0.0;
double duration_second = 0.0;
double duration_third = 0.0;
Xaode() : nFeatures_{ 0 }, statesClass_{ 0 }, totalSize_{ 0 }, matrixState_{ MatrixState::EMPTY } {}
Xaode() : nFeatures_{ 0 }, statesClass_{ 0 }, matrixState_{ MatrixState::EMPTY } {}
// -------------------------------------------------------
// init
// -------------------------------------------------------
@@ -84,9 +84,9 @@ namespace platform {
}
runningOffset += states_[i];
}
totalSize_ = index * statesClass_;
data_.resize(totalSize_);
dataOpp_.resize(totalSize_);
int totalSize = index * statesClass_;
data_.resize(totalSize);
dataOpp_.resize(totalSize);
classFeatureCounts_.resize(feature_offset * statesClass_);
classFeatureProbs_.resize(feature_offset * statesClass_);
@@ -98,12 +98,6 @@ namespace platform {
matrixState_ = MatrixState::COUNTS;
}
// Returns the dimension of data_ (just for info).
int size() const
{
return totalSize_;
}
// Returns current mode: INIT, COUNTS or PROBS
MatrixState state() const
{
@@ -116,7 +110,6 @@ namespace platform {
std::cout << "-------- Xaode.show() --------" << std::endl
<< "- nFeatures = " << nFeatures_ << std::endl
<< "- statesClass = " << statesClass_ << std::endl
<< "- totalSize_ = " << totalSize_ << std::endl
<< "- matrixState = " << (matrixState_ == MatrixState::COUNTS ? "COUNTS" : "PROBS") << std::endl;
std::cout << "- states: size: " << states_.size() << std::endl;
for (int s : states_) std::cout << s << " "; std::cout << std::endl;
@@ -543,6 +536,23 @@ namespace platform {
{
return statesClass_;
}
int nFeatures() const
{
return nFeatures_;
}
int getNumberOfStates() const
{
return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
}
int getNumberOfEdges() const
{
return nFeatures_ * (2 * nFeatures_ - 1);
}
int getNumberOfNodes() const
{
return (nFeatures_ + 1) * nFeatures_;
}
private:
// -----------
@@ -555,7 +565,6 @@ namespace platform {
// data_ means p(child=sj | c, superparent= si) after normalization.
// But in COUNTS mode, it accumulates raw counts.
std::vector<int> pairOffset_;
int totalSize_;
// data_ stores p(child=sj | c, superparent=si) for each pair (i<j).
std::vector<double> data_;
// dataOpp_ stores p(superparent=si | c, child=sj) for each pair (i<j).