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Implement predict and predict_proba

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Add samples and add parameters to main
This commit is contained in:
Ricardo Montañana Gómez
2023-07-01 14:45:44 +02:00
parent 79e7912ab3
commit 23f0b0f55c
13 changed files with 34538 additions and 43 deletions
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98
src/Network.cc
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@@ -11,7 +11,9 @@ namespace bayesnet {
void Network::addNode(string name, int numStates)
{
if (nodes.find(name) != nodes.end()) {
throw invalid_argument("Node " + name + " already exists");
// if node exists update its number of states
nodes[name]->setNumStates(numStates);
return;
}
nodes[name] = new Node(name, numStates);
if (root == nullptr) {
@@ -63,6 +65,7 @@ namespace bayesnet {
{
// remove problematic edge
nodes[parent]->removeChild(nodes[child]);
nodes[child]->removeParent(nodes[parent]);
throw invalid_argument("Adding this edge forms a cycle in the graph.");
}
@@ -72,20 +75,6 @@ namespace bayesnet {
{
return nodes;
}
void Network::buildNetwork()
{
// Add features as nodes to the network
for (int i = 0; i < features.size(); ++i) {
addNode(features[i], *max_element(dataset[features[i]].begin(), dataset[features[i]].end()) + 1);
}
// Add class as node to the network
addNode(className, *max_element(dataset[className].begin(), dataset[className].end()) + 1);
// Add edges from class to features => naive Bayes
for (auto feature : features) {
addEdge(className, feature);
}
addEdge("petalwidth", "petallength");
}
void Network::fit(const vector<vector<int>>& dataset, const vector<int>& labels, const vector<string>& featureNames, const string& className)
{
features = featureNames;
@@ -95,7 +84,6 @@ namespace bayesnet {
this->dataset[featureNames[i]] = dataset[i];
}
this->dataset[className] = labels;
buildNetwork();
estimateParameters();
}
@@ -128,4 +116,82 @@ namespace bayesnet {
node->setCPT(cpt);
}
}
pair<int, double> Network::predict_sample(const vector<int>& sample)
{
// Ensure the sample size is equal to the number of features
if (sample.size() != features.size()) {
throw std::invalid_argument("Sample size (" + to_string(sample.size()) +
") does not match the number of features (" + to_string(features.size()) + ")");
}
// Map the feature values to their corresponding nodes
map<string, int> featureValues;
for (int i = 0; i < features.size(); ++i) {
featureValues[features[i]] = sample[i];
}
// For each possible class, calculate the posterior probability
Node* classNode = nodes[className];
int numClassStates = classNode->getNumStates();
std::vector<double> classProbabilities(numClassStates, 0.0);
for (int classState = 0; classState < numClassStates; ++classState) {
// Start with the prior probability of the class
classProbabilities[classState] = classNode->getCPT()[classState].item<double>();
// Multiply by the likelihood of each feature given the class
for (auto& pair : nodes) {
if (pair.first != className) {
Node* node = pair.second;
int featureValue = featureValues[pair.first];
// We use the class as the parent state to index into the CPT
classProbabilities[classState] *= node->getCPT()[classState][featureValue].item<double>();
}
}
}
// Find the class with the maximum posterior probability
auto maxElem = std::max_element(classProbabilities.begin(), classProbabilities.end());
int predictedClass = std::distance(classProbabilities.begin(), maxElem);
double maxProbability = *maxElem;
return std::make_pair(predictedClass, maxProbability);
}
vector<int> Network::predict(const vector<vector<int>>& samples)
{
vector<int> predictions;
vector<int> sample;
for (int row = 0; row < samples[0].size(); ++row) {
sample.clear();
for (int col = 0; col < samples.size(); ++col) {
sample.push_back(samples[col][row]);
}
predictions.push_back(predict_sample(sample).first);
}
return predictions;
}
vector<pair<int, double>> Network::predict_proba(const vector<vector<int>>& samples)
{
vector<pair<int, double>> predictions;
vector<int> sample;
for (int row = 0; row < samples[0].size(); ++row) {
sample.clear();
for (int col = 0; col < samples.size(); ++col) {
sample.push_back(samples[col][row]);
}
predictions.push_back(predict_sample(sample));
}
return predictions;
}
double Network::score(const vector<vector<int>>& samples, const vector<int>& labels)
{
vector<int> y_pred = predict(samples);
int correct = 0;
for (int i = 0; i < y_pred.size(); ++i) {
if (y_pred[i] == labels[i]) {
correct++;
}
}
return (double)correct / y_pred.size();
}
}

5
src/Network.h
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@@ -15,6 +15,7 @@ namespace bayesnet {
string className;
int laplaceSmoothing;
bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
pair<int, double> predict_sample(const vector<int>&);
public:
Network();
Network(int);
@@ -24,9 +25,11 @@ namespace bayesnet {
map<string, Node*>& getNodes();
void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
void estimateParameters();
void buildNetwork();
void setRoot(string);
Node* getRoot();
vector<int> predict(const vector<vector<int>>&);
vector<pair<int, double>> predict_proba(const vector<vector<int>>&);
double score(const vector<vector<int>>&, const vector<int>&);
};
}
#endif

4
src/Node.cc
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@@ -41,6 +41,10 @@ namespace bayesnet {
{
return numStates;
}
void Node::setNumStates(int numStates)
{
this->numStates = numStates;
}
torch::Tensor& Node::getCPT()
{
return cpt;

1
src/Node.h
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@@ -27,6 +27,7 @@ namespace bayesnet {
torch::Tensor& getCPT();
void setCPT(const torch::Tensor&);
int getNumStates() const;
void setNumStates(int);
int getId() const { return id; }
};
}