LibraryFactory: LibraryFactory

In frbl/OnlineSuperLearner: Online SuperLearner package

Description

The goal of the LibraryFactory is to instantiate a set of models given to it. This factory will automatically expand any gridsearch-like grid passed to it.

Usage

LibraryFactory

Format

An object of class R6ClassGenerator of length 24.

Methods

initialize(ML.models.allowed = c('condensier::speedglmR6', 'condensier::glmR6', 'ML.H2O.gbm', 'ML.H2O.glm', 'ML.GLMnet', 'ML.H2O.randomForest', 'ML.randomForest', 'ML.SVM', 'ML.Local.Speedlm', 'ML.Local.lm', 'ML.NeuralNet', 'ML.XGBoost', verbose = FALSE)

This method is used to create object of this class. It expects a ML.models.allowed as a list which describes all of the models that are allowed for fabrication.

@param ML.models.allowed (default = c('condensier::speedglmR6', 'condensier::glmR6', 'ML.H2O.gbm', 'ML.H2O.glm', 'ML.GLMnet', 'ML.H2O.randomForest', 'ML.randomForest', 'ML.SVM', 'ML.Local.Speedlm', 'ML.Local.lm', 'ML.NeuralNet', 'ML.XGBoost', verbose = FALSE)) the list of ML models allowed to create objects for. The default is a list of all models in the OSL package.

@return a new instance of the library factory.

fabricate(SL.library)

Method that fabricates the models in the provided SL.library.

@param SL.library can be either a list of ML models, for which we will then choose the default hyper parameters, or a more specific specification of the models. This is in the form of a list and should be specified as follows: list(list(algorithm = 'the class of the algorithm', algorithm_params = list(hyper_parameter1=c(1,2,3)), params = list(nbins = c(39, 40), online = FALSE)))) in which algorithm_params are the hyperparameters for the learner, and params are the hyper parameters for the density estimator.

@return list a list of fabricated ML models encapsulated in DensityEstimator objects.

fabricate_grid(SL.library)

Function that will fabiricate a grid of SL estimators. See the specification of how to define an SL grid in the documentation for the fabricate function. Note that this function is usually not called from the outside, and merely used by the fabricate function

@param SL.library list the grid of SL estimators to use.

@return a list of fabricated SL models encapsulated in DensityEstimator objects.

fabricate_single_estimator_from_grid(entry)

Function to fabricate a set of estimator instances based on a single grid entry. These are all the instances of a specific type of ML algorithm. The result of thie function is a list of instances of ML.base objects.

@param entry list the entry of the grid for which an ML object needs to be created.

@return list of objects of the actual machine learning algorithm (ML.Base objects).

fabricate_single_density_estimator_from_grid(entry, algorithm_instances)

Function to initialize a density estimator objects based on the list of algorithms created in for example the fabricate_single_estimator_from_grid. Note the difference between an algrithm and a DensityEstimator. The density estimator object encapsulates an ML algorithm. These density estimators can also have parameters (e.g., the number of bins). These are specified in the entry argument.

@param entry list of details (name and number of bins, for example) used for initializing the algorithm.

@param entry list of hyperparameters used to generate the density algorithm.

@param algorithm_instances list of ML.Base objects which need to be injected in DensityEstimator objects.

fabricate_default(SL.library)

Function used to generate a list of densityestimator objects from a simple list of estimators. This will use the default settings and not apply gridsearch in any way.

@param SL.library a vector of strings with the names of the ML algorithms to use.

@return list of DensityEstimator objects

check_entry_validity(SL.library.entry)

Checks the validity of an entry when specified as part of a gridsearch initialization. It throws an error if the entry is not valid.

@param SL.library.entry the entry to check for validity.

check_entry_validity_of_character(SL.library.entry)

Checks the validity of an entry when specified as part of a list of ML estimators (no grid). Returns a list of errors if not valid.

@param SL.library.entry the entry to check for validity.

@return vector containing the errors detected (if any)

check_entry_validity_of_list(SL.library.entry)

Checks the validity of a list entry in a SL.library. This checks whether the arguments are correct and whether the provided values are in an excepted range. Returns a string of errors if not valid.

@param SL.library.entry the entry to check

@return vector containing the errors detected (if any)

inject_names_in_estimators(fabricatedLibrary)

Injects the names of each estimator in to a Density estimator object.

@param fabricatedLibrary the list of fabricated elements.

@return list of fabricated elements, now with their names injeced.

get_validity

Active method. Method to determine if the object is in a valid state. The method will throw whenever the state is not valid

get_allowed_ml_models

Active method. Returns a list of valid ML models.

@return list of names of ML models which are deemed valid.

frbl/OnlineSuperLearner documentation built on Feb. 9, 2020, 9:28 p.m.