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Refactor library and models to lighten data stored

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Refactro Ensemble to inherit from Classifier insted of BaseClassifier
This commit is contained in:
Ricardo Montañana Gómez 2023-08-07 12:49:37 +02:00
parent e74565ba01
commit 06db8f51ce
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
28 changed files with 134 additions and 189 deletions
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2
src/BayesNet/AODE.cc
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@ -2,7 +2,7 @@
namespace bayesnet {
AODE::AODE() : Ensemble() {}
void AODE::train()
void AODE::buildModel()
{
models.clear();
for (int i = 0; i < features.size(); ++i) {

2
src/BayesNet/AODE.h
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@ -5,7 +5,7 @@
namespace bayesnet {
class AODE : public Ensemble {
protected:
void train() override;
void buildModel() override;
public:
AODE();
virtual ~AODE() {};

16
src/BayesNet/AODELd.cc
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@ -2,27 +2,31 @@
namespace bayesnet {
using namespace std;
AODELd::AODELd() : Ensemble(), Proposal(Ensemble::Xv, Ensemble::yv, features, className) {}
AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
{
features = features_;
className = className_;
states = states_;
train();
for (const auto& model : models) {
model->fit(X_, y_, features_, className_, states_);
}
buildModel();
trainModel();
n_models = models.size();
fitted = true;
return *this;
}
void AODELd::train()
void AODELd::buildModel()
{
models.clear();
for (int i = 0; i < features.size(); ++i) {
models.push_back(std::make_unique<SPODELd>(i));
}
}
void AODELd::trainModel()
{
for (const auto& model : models) {
model->fit(dataset, features, className, states);
}
}
Tensor AODELd::predict(Tensor& X)
{
return Ensemble::predict(X);

4
src/BayesNet/AODELd.h
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@ -7,13 +7,15 @@
namespace bayesnet {
using namespace std;
class AODELd : public Ensemble, public Proposal {
private:
void trainModel();
void buildModel() override;
public:
AODELd();
virtual ~AODELd() = default;
AODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
vector<string> graph(const string& name = "AODE") override;
Tensor predict(Tensor& X) override;
void train() override;
static inline string version() { return "0.0.1"; };
};
}

1
src/BayesNet/BaseClassifier.h
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@ -10,6 +10,7 @@ namespace bayesnet {
virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
// X is nxm tensor, y is nx1 tensor
virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
virtual ~BaseClassifier() = default;
torch::Tensor virtual predict(torch::Tensor& X) = 0;
vector<int> virtual predict(vector<vector<int>>& X) = 0;

10
src/BayesNet/BayesMetrics.cc
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@ -2,7 +2,7 @@
#include "Mst.h"
namespace bayesnet {
//samples is nxm tensor used to fit the model
Metrics::Metrics(torch::Tensor& samples, vector<string>& features, string& className, int classNumStates)
Metrics::Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates)
: samples(samples)
, features(features)
, className(className)
@ -76,7 +76,7 @@ namespace bayesnet {
std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
return v;
}
double Metrics::entropy(torch::Tensor& feature)
double Metrics::entropy(const torch::Tensor& feature)
{
torch::Tensor counts = feature.bincount();
int totalWeight = counts.sum().item<int>();
@ -86,7 +86,7 @@ namespace bayesnet {
return entropy.nansum().item<double>();
}
// H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
double Metrics::conditionalEntropy(torch::Tensor& firstFeature, torch::Tensor& secondFeature)
double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature)
{
int numSamples = firstFeature.sizes()[0];
torch::Tensor featureCounts = secondFeature.bincount();
@ -115,7 +115,7 @@ namespace bayesnet {
return entropyValue;
}
// I(X;Y) = H(Y) - H(Y|X)
double Metrics::mutualInformation(torch::Tensor& firstFeature, torch::Tensor& secondFeature)
double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature)
{
return entropy(firstFeature) - conditionalEntropy(firstFeature, secondFeature);
}
@ -124,7 +124,7 @@ namespace bayesnet {
and the indices of the weights as nodes of this square matrix using
Kruskal algorithm
*/
vector<pair<int, int>> Metrics::maximumSpanningTree(vector<string> features, Tensor& weights, int root)
vector<pair<int, int>> Metrics::maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root)
{
auto mst = MST(features, weights, root);
return mst.maximumSpanningTree();

10
src/BayesNet/BayesMetrics.h
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@ -14,15 +14,15 @@ namespace bayesnet {
int classNumStates = 0;
public:
Metrics() = default;
Metrics(Tensor&, vector<string>&, string&, int);
Metrics(const Tensor&, const vector<string>&, const string&, const int);
Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
double entropy(Tensor&);
double conditionalEntropy(Tensor&, Tensor&);
double mutualInformation(Tensor&, Tensor&);
double entropy(const Tensor&);
double conditionalEntropy(const Tensor&, const Tensor&);
double mutualInformation(const Tensor&, const Tensor&);
vector<float> conditionalEdgeWeights(); // To use in Python
Tensor conditionalEdge();
vector<pair<string, string>> doCombinations(const vector<string>&);
vector<pair<int, int>> maximumSpanningTree(vector<string> features, Tensor& weights, int root);
vector<pair<int, int>> maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root);
};
}
#endif

60
src/BayesNet/Classifier.cc
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@ -7,59 +7,54 @@ namespace bayesnet {
Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
{
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
this->features = features;
this->className = className;
this->states = states;
checkFitParameters();
auto n_classes = states[className].size();
metrics = Metrics(samples, features, className, n_classes);
metrics = Metrics(dataset, features, className, n_classes);
model.initialize();
train();
if (Xv.empty()) {
// fit with tensors
model.fit(X, y, features, className);
} else {
// fit with vectors
model.fit(Xv, yv, features, className);
}
buildModel();
m = dataset.size(1);
n = dataset.size(0);
trainModel();
fitted = true;
return *this;
}
void Classifier::trainModel()
{
model.fit(dataset, features, className);
}
void Classifier::buildDataset(Tensor& ytmp)
{
ytmp = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
dataset = torch::cat({ dataset, ytmp }, 0);
}
// X is nxm where n is the number of features and m the number of samples
Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = X;
this->y = y;
Xv = vector<vector<int>>();
yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
dataset = X;
buildDataset(y);
return build(features, className, states);
}
void Classifier::generateTensorXFromVector()
{
X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
for (int i = 0; i < Xv.size(); ++i) {
X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
}
}
// X is nxm where n is the number of features and m the number of samples
Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
Xv = X;
generateTensorXFromVector();
this->y = torch::tensor(y, kInt32);
yv = y;
dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32);
for (int i = 0; i < X.size(); ++i) {
dataset.index_put_({ i, "..." }, torch::tensor(X[i], kInt32));
}
auto ytmp = torch::tensor(y, kInt32);
buildDataset(ytmp);
return build(features, className, states);
}
Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->dataset = dataset;
return build(features, className, states);
}
void Classifier::checkFitParameters()
{
auto sizes = X.sizes();
m = sizes[1];
n = sizes[0];
if (m != y.size(0)) {
throw invalid_argument("X and y must have the same number of samples");
}
if (n != features.size()) {
throw invalid_argument("X and features must have the same number of features");
}
@ -141,5 +136,4 @@ namespace bayesnet {
{
model.dump_cpt();
}
}

14
src/BayesNet/Classifier.h
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@ -10,28 +10,26 @@ using namespace torch;
namespace bayesnet {
class Classifier : public BaseClassifier {
private:
bool fitted;
void buildDataset(torch::Tensor& y);
Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
protected:
bool fitted;
Network model;
int m, n; // m: number of samples, n: number of features
Tensor X; // nxm tensor
vector<vector<int>> Xv; // nxm vector
Tensor y;
vector<int> yv;
Tensor samples; // (n+1)xm tensor
Tensor dataset; // (n+1)xm tensor
Metrics metrics;
vector<string> features;
string className;
map<string, vector<int>> states;
void checkFitParameters();
void generateTensorXFromVector();
virtual void train() = 0;
virtual void buildModel() = 0;
void trainModel();
public:
Classifier(Network model);
virtual ~Classifier() = default;
Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
void addNodes();
int getNumberOfNodes() override;
int getNumberOfEdges() override;

50
src/BayesNet/Ensemble.cc
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@ -3,54 +3,15 @@
namespace bayesnet {
using namespace torch;
Ensemble::Ensemble() : n_models(0), metrics(Metrics()), fitted(false) {}
Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
Ensemble::Ensemble() : Classifier(Network()) {}
void Ensemble::trainModel()
{
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
this->features = features;
this->className = className;
this->states = states;
auto n_classes = states[className].size();
metrics = Metrics(samples, features, className, n_classes);
// Build models
train();
// Train models
n_models = models.size();
for (auto i = 0; i < n_models; ++i) {
if (Xv.empty()) {
// fit with tensors
models[i]->fit(X, y, features, className, states);
} else {
// fit with vectors
models[i]->fit(Xv, yv, features, className, states);
}
// fit with vectors
models[i]->fit(dataset, features, className, states);
}
fitted = true;
return *this;
}
void Ensemble::generateTensorXFromVector()
{
X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
for (int i = 0; i < Xv.size(); ++i) {
X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
}
}
Ensemble& Ensemble::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = X;
this->y = y;
Xv = vector<vector<int>>();
yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
return build(features, className, states);
}
Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
Xv = X;
generateTensorXFromVector();
this->y = torch::tensor(y, kInt32);
yv = y;
return build(features, className, states);
}
vector<int> Ensemble::voting(Tensor& y_pred)
{
@ -132,7 +93,6 @@ namespace bayesnet {
}
}
return (double)correct / y_pred.size();
}
vector<string> Ensemble::show()
{

17
src/BayesNet/Ensemble.h
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@ -8,30 +8,17 @@ using namespace std;
using namespace torch;
namespace bayesnet {
class Ensemble : public BaseClassifier {
class Ensemble : public Classifier {
private:
Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
protected:
unsigned n_models;
bool fitted;
vector<unique_ptr<Classifier>> models;
Tensor X;
vector<vector<int>> Xv;
Tensor y;
vector<int> yv;
Tensor samples;
Metrics metrics;
vector<string> features;
string className;
map<string, vector<int>> states;
void virtual train() = 0;
void trainModel();
vector<int> voting(Tensor& y_pred);
void generateTensorXFromVector();
public:
Ensemble();
virtual ~Ensemble() = default;
Ensemble& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
Ensemble& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
Tensor predict(Tensor& X) override;
vector<int> predict(vector<vector<int>>& X) override;
float score(Tensor& X, Tensor& y) override;

5
src/BayesNet/KDB.cc
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@ -4,7 +4,7 @@ namespace bayesnet {
using namespace torch;
KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
void KDB::train()
void KDB::buildModel()
{
/*
1. For each feature Xi, compute mutual information, I(X;C),
@ -28,9 +28,10 @@ namespace bayesnet {
// 1. For each feature Xi, compute mutual information, I(X;C),
// where C is the class.
addNodes();
const Tensor& y = dataset.index({ -1, "..." });
vector <float> mi;
for (auto i = 0; i < features.size(); i++) {
Tensor firstFeature = X.index({ i, "..." });
Tensor firstFeature = dataset.index({ i, "..." });
mi.push_back(metrics.mutualInformation(firstFeature, y));
}
// 2. Compute class conditional mutual information I(Xi;XjIC), f or each

2
src/BayesNet/KDB.h
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@ -11,7 +11,7 @@ namespace bayesnet {
float theta;
void add_m_edges(int idx, vector<int>& S, Tensor& weights);
protected:
void train() override;
void buildModel() override;
public:
explicit KDB(int k, float theta = 0.03);
virtual ~KDB() {};

9
src/BayesNet/KDBLd.cc
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@ -2,7 +2,7 @@
namespace bayesnet {
using namespace std;
KDBLd::KDBLd(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
KDBLd& KDBLd::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...
@ -12,15 +12,10 @@ namespace bayesnet {
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
KDB::fit(KDB::Xv, KDB::yv, features, className, states);
KDB::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(KDB::Xv, KDB::yv, features, className);
return *this;
}
Tensor KDBLd::predict(Tensor& X)

2
src/BayesNet/Mst.cc
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@ -94,7 +94,7 @@ namespace bayesnet {
return result;
}
MST::MST(vector<string>& features, Tensor& weights, int root) : features(features), weights(weights), root(root) {}
MST::MST(const vector<string>& features, const Tensor& weights, const int root) : features(features), weights(weights), root(root) {}
vector<pair<int, int>> MST::maximumSpanningTree()
{
auto num_features = features.size();

2
src/BayesNet/Mst.h
View File

@ -13,7 +13,7 @@ namespace bayesnet {
int root = 0;
public:
MST() = default;
MST(vector<string>& features, Tensor& weights, int root);
MST(const vector<string>& features, const Tensor& weights, const int root);
vector<pair<int, int>> maximumSpanningTree();
};
class Graph {

26
src/BayesNet/Network.cc
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@ -20,7 +20,6 @@ namespace bayesnet {
classNumStates = 0;
fitted = false;
nodes.clear();
dataset.clear();
samples = torch::Tensor();
}
float Network::getmaxThreads()
@ -134,18 +133,22 @@ namespace bayesnet {
classNumStates = nodes[className]->getNumStates();
}
// X comes in nxm, where n is the number of features and m the number of samples
void Network::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& featureNames, const string& className)
void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className)
{
checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className);
this->className = className;
dataset.clear();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X , ytmp }, 0);
for (int i = 0; i < featureNames.size(); ++i) {
auto row_feature = X.index({ i, "..." });
dataset[featureNames[i]] = vector<int>(row_feature.data_ptr<int>(), row_feature.data_ptr<int>() + row_feature.size(0));;
}
dataset[className] = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
completeFit();
}
void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className)
{
checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className);
this->className = className;
this->samples = samples;
completeFit();
}
// input_data comes in nxm, where n is the number of features and m the number of samples
@ -153,14 +156,11 @@ namespace bayesnet {
{
checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className);
this->className = className;
dataset.clear();
// Build dataset & tensor of samples (nxm) (n+1 because of the class)
// Build tensor of samples (nxm) (n+1 because of the class)
samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
for (int i = 0; i < featureNames.size(); ++i) {
dataset[featureNames[i]] = input_data[i];
samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
}
dataset[className] = labels;
samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
completeFit();
}
@ -188,7 +188,7 @@ namespace bayesnet {
auto& pair = *std::next(nodes.begin(), nextNodeIndex);
++nextNodeIndex;
lock.unlock();
pair.second->computeCPT(dataset, laplaceSmoothing);
pair.second->computeCPT(samples, features, laplaceSmoothing);
lock.lock();
nodes[pair.first] = std::move(pair.second);
lock.unlock();
@ -328,12 +328,12 @@ namespace bayesnet {
mutex mtx;
for (int i = 0; i < classNumStates; ++i) {
threads.emplace_back([this, &result, &evidence, i, &mtx]() {
auto completeEvidence = map<string, int>(evidence);
completeEvidence[getClassName()] = i;
auto completeEvidence = map<string, int>(evidence);
completeEvidence[getClassName()] = i;
double factor = computeFactor(completeEvidence);
lock_guard<mutex> lock(mtx);
result[i] = factor;
});
});
}
for (auto& thread : threads) {
thread.join();

6
src/BayesNet/Network.h
View File

@ -8,11 +8,10 @@ namespace bayesnet {
class Network {
private:
map<string, unique_ptr<Node>> nodes;
map<string, vector<int>> dataset;
bool fitted;
float maxThreads = 0.95;
int classNumStates;
vector<string> features; // Including class
vector<string> features; // Including classname
string className;
int laplaceSmoothing = 1;
torch::Tensor samples; // nxm tensor used to fit the model
@ -44,7 +43,8 @@ namespace bayesnet {
int getClassNumStates();
string getClassName();
void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
void fit(torch::Tensor&, torch::Tensor&, const vector<string>&, const string&);
void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&);
void fit(const torch::Tensor&, const vector<string>&, const string&);
vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
//Computes the conditional edge weight of variable index u and v conditioned on class_node

20
src/BayesNet/Node.cc
View File

@ -84,7 +84,7 @@ namespace bayesnet {
}
return result;
}
void Node::computeCPT(map<string, vector<int>>& dataset, const int laplaceSmoothing)
void Node::computeCPT(const torch::Tensor& dataset, const vector<string>& features, const int laplaceSmoothing)
{
dimensions.clear();
// Get dimensions of the CPT
@ -94,10 +94,22 @@ namespace bayesnet {
// Create a tensor of zeros with the dimensions of the CPT
cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
// Fill table with counts
for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
auto pos = find(features.begin(), features.end(), name);
if (pos == features.end()) {
throw logic_error("Feature " + name + " not found in dataset");
}
int name_index = pos - features.begin();
for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
torch::List<c10::optional<torch::Tensor>> coordinates;
coordinates.push_back(torch::tensor(dataset[name][n_sample]));
transform(parents.begin(), parents.end(), back_inserter(coordinates), [&dataset, &n_sample](const auto& parent) { return torch::tensor(dataset[parent->getName()][n_sample]); });
coordinates.push_back(dataset.index({ name_index, n_sample }));
for (auto parent : parents) {
pos = find(features.begin(), features.end(), parent->getName());
if (pos == features.end()) {
throw logic_error("Feature parent " + parent->getName() + " not found in dataset");
}
int parent_index = pos - features.begin();
coordinates.push_back(dataset.index({ parent_index, n_sample }));
}
// Increment the count of the corresponding coordinate
cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
}

2
src/BayesNet/Node.h
View File

@ -26,7 +26,7 @@ namespace bayesnet {
vector<Node*>& getParents();
vector<Node*>& getChildren();
torch::Tensor& getCPT();
void computeCPT(map<string, vector<int>>&, const int);
void computeCPT(const torch::Tensor&, const vector<string>&, const int);
int getNumStates() const;
void setNumStates(int);
unsigned minFill();

22
src/BayesNet/Proposal.cc
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@ -2,7 +2,7 @@
#include "ArffFiles.h"
namespace bayesnet {
Proposal::Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_) : Xv(Xv_), yv(yv_), pFeatures(features_), pClassName(className_) {}
Proposal::Proposal(torch::Tensor& dataset_, vector<string>& features_, string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_), m(dataset_.size(1)), n(dataset_.size(0) - 1) {}
Proposal::~Proposal()
{
for (auto& [key, value] : discretizers) {
@ -16,7 +16,6 @@ namespace bayesnet {
auto order = model.topological_sort();
auto& nodes = model.getNodes();
vector<int> indicesToReDiscretize;
auto n_samples = Xf.size(1);
bool upgrade = false; // Flag to check if we need to upgrade the model
for (auto feature : order) {
auto nodeParents = nodes[feature]->getParents();
@ -30,13 +29,13 @@ namespace bayesnet {
parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());
// Get the indices of the parents
vector<int> indices;
indices.push_back(-1); // Add class index
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)
vector<string> yJoinParents;
transform(yv.begin(), yv.end(), back_inserter(yJoinParents), [&](const auto& p) {return to_string(p); });
vector<string> yJoinParents(indices.size());
for (auto idx : indices) {
for (int i = 0; i < n_samples; ++i) {
yJoinParents[i] += to_string(Xv[idx][i]);
for (int i = 0; i < n; ++i) {
yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
}
}
auto arff = ArffFiles();
@ -59,7 +58,7 @@ namespace bayesnet {
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);
pDataset.index_put_({ index, "..." }, torch::tensor(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
@ -69,16 +68,15 @@ namespace bayesnet {
}
void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
{
// Sharing Xv and yv with Classifier
Xv = vector<vector<int>>();
yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
pDataset = torch::zeros({ n + 1, m }, kInt32);
auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
// discretize input data by feature(row)
for (int i = 0; i < pFeatures.size(); ++i) {
for (auto i = 0; i < pFeatures.size(); ++i) {
auto* discretizer = new mdlp::CPPFImdlp();
auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
discretizer->fit(Xt, yv);
Xv.push_back(discretizer->transform(Xt));
pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));
auto xStates = vector<int>(discretizer->getCutPoints().size() + 1);
iota(xStates.begin(), xStates.end(), 0);
states[pFeatures[i]] = xStates;

9
src/BayesNet/Proposal.h
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@ -10,20 +10,21 @@
namespace bayesnet {
class Proposal {
public:
Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_);
Proposal(torch::Tensor& pDataset, vector<string>& features_, string& className_);
virtual ~Proposal();
protected:
torch::Tensor prepareX(torch::Tensor& X);
void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
torch::Tensor Xf; // X continuous nxm tensor
torch::Tensor y; // y discrete nx1 tensor
map<string, mdlp::CPPFImdlp*> discretizers;
int m, n;
private:
torch::Tensor& pDataset; // (n+1)xm tensor
vector<string>& pFeatures;
string& pClassName;
vector<vector<int>>& Xv; // X discrete nxm vector
vector<int>& yv;
};
}
#endif
#endif

2
src/BayesNet/SPODE.cc
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@ -4,7 +4,7 @@ namespace bayesnet {
SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
void SPODE::train()
void SPODE::buildModel()
{
// 0. Add all nodes to the model
addNodes();

2
src/BayesNet/SPODE.h
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@ -7,7 +7,7 @@ namespace bayesnet {
private:
int root;
protected:
void train() override;
void buildModel() override;
public:
explicit SPODE(int root);
virtual ~SPODE() {};

10
src/BayesNet/SPODELd.cc
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@ -2,7 +2,7 @@
namespace bayesnet {
using namespace std;
SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
SPODELd& SPODELd::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...
@ -12,15 +12,11 @@ namespace bayesnet {
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
SPODE::fit(SPODE::Xv, SPODE::yv, features, className, states);
SPODE::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(SPODE::Xv, SPODE::yv, features, className);
//model.fit(SPODE::Xv, SPODE::yv, features, className);
return *this;
}
Tensor SPODELd::predict(Tensor& X)

6
src/BayesNet/TAN.cc
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@ -5,16 +5,16 @@ namespace bayesnet {
TAN::TAN() : Classifier(Network()) {}
void TAN::train()
void TAN::buildModel()
{
// 0. Add all nodes to the model
addNodes();
// 1. Compute mutual information between each feature and the class and set the root node
// as the highest mutual information with the class
auto mi = vector <pair<int, float >>();
Tensor class_dataset = samples.index({ -1, "..." });
Tensor class_dataset = dataset.index({ -1, "..." });
for (int i = 0; i < static_cast<int>(features.size()); ++i) {
Tensor feature_dataset = samples.index({ i, "..." });
Tensor feature_dataset = dataset.index({ i, "..." });
auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
mi.push_back({ i, mi_value });
}

2
src/BayesNet/TAN.h
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@ -7,7 +7,7 @@ namespace bayesnet {
class TAN : public Classifier {
private:
protected:
void train() override;
void buildModel() override;
public:
TAN();
virtual ~TAN() {};

10
src/BayesNet/TANLd.cc
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@ -2,7 +2,7 @@
namespace bayesnet {
using namespace std;
TANLd::TANLd() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
TANLd& TANLd::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...
@ -12,15 +12,11 @@ namespace bayesnet {
y = y_;
// Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
fit_local_discretization(states, y);
generateTensorXFromVector();
// We have discretized the input data
// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
TAN::fit(TAN::Xv, TAN::yv, features, className, states);
TAN::fit(dataset, features, className, states);
localDiscretizationProposal(states, model);
generateTensorXFromVector();
Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
samples = torch::cat({ X, ytmp }, 0);
model.fit(TAN::Xv, TAN::yv, features, className);
//model.fit(dataset, features, className);
return *this;
}
Tensor TANLd::predict(Tensor& X)