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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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9
bayesnet/ensembles/AODELd.cc
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@@ -10,7 +10,7 @@ namespace bayesnet {
AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
{
}
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
{
checkInput(X_, y_);
features = features_;
@@ -21,7 +21,7 @@ namespace bayesnet {
states = fit_local_discretization(y);
// 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
Ensemble::fit(dataset, features, className, states);
Ensemble::fit(dataset, features, className, states, smoothing);
return *this;
}
@@ -34,11 +34,10 @@ namespace bayesnet {
n_models = models.size();
significanceModels = std::vector<double>(n_models, 1.0);
}
void AODELd::trainModel(const torch::Tensor& weights)
void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
for (const auto& model : models) {
model->setSmoothing(smoothing);
model->fit(Xf, y, features, className, states);
model->fit(Xf, y, features, className, states, smoothing);
}
}
std::vector<std::string> AODELd::graph(const std::string& name) const

4
bayesnet/ensembles/AODELd.h
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@@ -15,10 +15,10 @@ namespace bayesnet {
public:
AODELd(bool predict_voting = true);
virtual ~AODELd() = default;
AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_) override;
AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing) override;
std::vector<std::string> graph(const std::string& name = "AODELd") const override;
protected:
void trainModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
void buildModel(const torch::Tensor& weights) override;
};
}

12
bayesnet/ensembles/BoostA2DE.cc
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@@ -19,7 +19,7 @@ namespace bayesnet {
BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
{
}
std::vector<int> BoostA2DE::initializeModels()
std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
{
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
@@ -32,8 +32,7 @@ namespace bayesnet {
for (int j = i + 1; j < featuresSelected.size(); j++) {
auto parents = { featuresSelected[i], featuresSelected[j] };
std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
model->fit(dataset, features, className, states, weights_, smoothing);
models.push_back(std::move(model));
significanceModels.push_back(1.0); // They will be updated later in trainModel
n_models++;
@@ -42,7 +41,7 @@ namespace bayesnet {
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void BoostA2DE::trainModel(const torch::Tensor& weights)
void BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
//
// Logging setup
@@ -59,7 +58,7 @@ namespace bayesnet {
bool finished = false;
std::vector<int> featuresUsed;
if (selectFeatures) {
featuresUsed = initializeModels();
featuresUsed = initializeModels(smoothing);
auto ypred = predict(X_train);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
// Update significance of the models
@@ -97,8 +96,7 @@ namespace bayesnet {
pairSelection.erase(pairSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
model->fit(dataset, features, className, states, weights_, smoothing);
alpha_t = 0.0;
if (!block_update) {
auto ypred = model->predict(X_train);

4
bayesnet/ensembles/BoostA2DE.h
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@@ -17,9 +17,9 @@ namespace bayesnet {
virtual ~BoostA2DE() = default;
std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
protected:
void trainModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
private:
std::vector<int> initializeModels();
std::vector<int> initializeModels(const Smoothing_t smoothing);
};
}
#endif

12
bayesnet/ensembles/BoostAODE.cc
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@@ -16,14 +16,13 @@ namespace bayesnet {
BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
{
}
std::vector<int> BoostAODE::initializeModels()
std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
{
torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
std::vector<int> featuresSelected = featureSelection(weights_);
for (const int& feature : featuresSelected) {
std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
model->fit(dataset, features, className, states, weights_, smoothing);
models.push_back(std::move(model));
significanceModels.push_back(1.0); // They will be updated later in trainModel
n_models++;
@@ -31,7 +30,7 @@ namespace bayesnet {
notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
return featuresSelected;
}
void BoostAODE::trainModel(const torch::Tensor& weights)
void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
//
// Logging setup
@@ -48,7 +47,7 @@ namespace bayesnet {
bool finished = false;
std::vector<int> featuresUsed;
if (selectFeatures) {
featuresUsed = initializeModels();
featuresUsed = initializeModels(smoothing);
auto ypred = predict(X_train);
std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
// Update significance of the models
@@ -90,8 +89,7 @@ namespace bayesnet {
featureSelection.erase(featureSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<SPODE>(feature);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
model->fit(dataset, features, className, states, weights_, smoothing);
alpha_t = 0.0;
if (!block_update) {
auto ypred = model->predict(X_train);

4
bayesnet/ensembles/BoostAODE.h
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@@ -18,9 +18,9 @@ namespace bayesnet {
virtual ~BoostAODE() = default;
std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
protected:
void trainModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
private:
std::vector<int> initializeModels();
std::vector<int> initializeModels(const Smoothing_t smoothing);
};
}
#endif

5
bayesnet/ensembles/Ensemble.cc
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@@ -13,13 +13,12 @@ namespace bayesnet {
};
const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
void Ensemble::trainModel(const torch::Tensor& weights)
void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
n_models = models.size();
for (auto i = 0; i < n_models; ++i) {
// fit with std::vectors
models[i]->setSmoothing(smoothing);
models[i]->fit(dataset, features, className, states);
models[i]->fit(dataset, features, className, states, smoothing);
}
}
std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)

2
bayesnet/ensembles/Ensemble.h
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@@ -46,7 +46,7 @@ namespace bayesnet {
unsigned n_models;
std::vector<std::unique_ptr<Classifier>> models;
std::vector<double> significanceModels;
void trainModel(const torch::Tensor& weights) override;
void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
bool predict_voting;
};
}