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BayesNet 1.0.5
Bayesian Network Classifiers using libtorch from scratch
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Public Member Functions | Protected Member Functions | Protected Attributes | List of all members
bayesnet::Boost Class Reference
Inheritance diagram for bayesnet::Boost:
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Collaboration diagram for bayesnet::Boost:
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Public Member Functions

 Boost (bool predict_voting=false)
 
void setHyperparameters (const nlohmann::json &hyperparameters_) override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
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) override
 
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) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
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) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 

Protected Member Functions

std::vector< int > featureSelection (torch::Tensor &weights_)
 
void buildModel (const torch::Tensor &weights) override
 
std::tuple< torch::Tensor &, double, bool > update_weights (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)
 
std::tuple< torch::Tensor &, double, bool > update_weights_block (int k, torch::Tensor &ytrain, torch::Tensor &weights)
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 

Protected Attributes

torch::Tensor X_train
 
torch::Tensor y_train
 
torch::Tensor X_test
 
torch::Tensor y_test
 
bool bisection = true
 
int maxTolerance = 3
 
std::string order_algorithm
 
bool convergence = true
 
bool convergence_best = false
 
bool selectFeatures = false
 
std::string select_features_algorithm = Orders.DESC
 
FeatureSelect * featureSelector = nullptr
 
double threshold = -1
 
bool block_update = false
 
- Protected Attributes inherited from bayesnet::Ensemble
unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 

Detailed Description

Definition at line 27 of file Boost.h.

Constructor & Destructor Documentation

◆ Boost()

bayesnet::Boost::Boost ( bool predict_voting = false)
explicit

Definition at line 13 of file Boost.cc.

Member Function Documentation

◆ buildModel()

void bayesnet::Boost::buildModel ( const torch::Tensor & weights)
overrideprotectedvirtual

Implements bayesnet::Classifier.

Definition at line 71 of file Boost.cc.

◆ featureSelection()

std::vector< int > bayesnet::Boost::featureSelection ( torch::Tensor & weights_)
protected

Definition at line 102 of file Boost.cc.

◆ setHyperparameters()

void bayesnet::Boost::setHyperparameters ( const nlohmann::json & hyperparameters_)
overridevirtual

Implements bayesnet::BaseClassifier.

Definition at line 18 of file Boost.cc.

◆ update_weights()

std::tuple< torch::Tensor &, double, bool > bayesnet::Boost::update_weights ( torch::Tensor & ytrain,
torch::Tensor & ypred,
torch::Tensor & weights )
protected

Definition at line 123 of file Boost.cc.

◆ update_weights_block()

std::tuple< torch::Tensor &, double, bool > bayesnet::Boost::update_weights_block ( int k,
torch::Tensor & ytrain,
torch::Tensor & weights )
protected

Definition at line 150 of file Boost.cc.

Member Data Documentation

◆ bisection

bool bayesnet::Boost::bisection = true
protected

Definition at line 39 of file Boost.h.

◆ block_update

bool bayesnet::Boost::block_update = false
protected

Definition at line 48 of file Boost.h.

◆ convergence

bool bayesnet::Boost::convergence = true
protected

Definition at line 42 of file Boost.h.

◆ convergence_best

bool bayesnet::Boost::convergence_best = false
protected

Definition at line 43 of file Boost.h.

◆ featureSelector

FeatureSelect* bayesnet::Boost::featureSelector = nullptr
protected

Definition at line 46 of file Boost.h.

◆ maxTolerance

int bayesnet::Boost::maxTolerance = 3
protected

Definition at line 40 of file Boost.h.

◆ order_algorithm

std::string bayesnet::Boost::order_algorithm
protected

Definition at line 41 of file Boost.h.

◆ select_features_algorithm

std::string bayesnet::Boost::select_features_algorithm = Orders.DESC
protected

Definition at line 45 of file Boost.h.

◆ selectFeatures

bool bayesnet::Boost::selectFeatures = false
protected

Definition at line 44 of file Boost.h.

◆ threshold

double bayesnet::Boost::threshold = -1
protected

Definition at line 47 of file Boost.h.

◆ X_test

torch::Tensor bayesnet::Boost::X_test
protected

Definition at line 37 of file Boost.h.

◆ X_train

torch::Tensor bayesnet::Boost::X_train
protected

Definition at line 37 of file Boost.h.

◆ y_test

torch::Tensor bayesnet::Boost::y_test
protected

Definition at line 37 of file Boost.h.

◆ y_train

torch::Tensor bayesnet::Boost::y_train
protected

Definition at line 37 of file Boost.h.

The documentation for this class was generated from the following files:
  • /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/Boost.h
  • /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/Boost.cc
  • bayesnet
  • Boost
  • Generated by doxygen 1.11.0
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