rmontanana/BayesNet
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Set smoothing as fit parameter

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 11:40:45 +02:00
parent 27a3e5a5e0
commit b34869cc61
30 changed files with 168 additions and 178 deletions
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25
bayesnet/classifiers/Classifier.cc
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@@ -11,7 +11,7 @@
namespace bayesnet {
Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
{
this->features = features;
this->className = className;
@@ -22,9 +22,8 @@ namespace bayesnet {
auto n_classes = states.at(className).size();
metrics = Metrics(dataset, features, className, n_classes);
model.initialize();
model.setSmoothing(smoothing);
buildModel(weights);
trainModel(weights);
trainModel(weights, smoothing);
fitted = true;
return *this;
}
@@ -42,20 +41,20 @@ namespace bayesnet {
throw std::runtime_error(oss.str());
}
}
void Classifier::trainModel(const torch::Tensor& weights)
void Classifier::trainModel(const torch::Tensor& weights, Smoothing_t smoothing)
{
model.fit(dataset, weights, features, className, states);
model.fit(dataset, weights, features, className, states, smoothing);
}
// 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, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
Classifier& Classifier::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 Smoothing_t smoothing)
{
dataset = X;
buildDataset(y);
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
return build(features, className, states, weights);
return build(features, className, states, weights, smoothing);
}
// X is nxm where n is the number of features and m the number of samples
Classifier& Classifier::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)
Classifier& Classifier::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 Smoothing_t smoothing)
{
dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
for (int i = 0; i < X.size(); ++i) {
@@ -64,18 +63,18 @@ namespace bayesnet {
auto ytmp = torch::tensor(y, torch::kInt32);
buildDataset(ytmp);
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
return build(features, className, states, weights);
return build(features, className, states, weights, smoothing);
}
Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
{
this->dataset = dataset;
const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
return build(features, className, states, weights);
return build(features, className, states, weights, smoothing);
}
Classifier& Classifier::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)
Classifier& Classifier::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 Smoothing_t smoothing)
{
this->dataset = dataset;
return build(features, className, states, weights);
return build(features, className, states, weights, smoothing);
}
void Classifier::checkFitParameters()
{