OnlineSuperLearner.Predict: OnlineSuperLearner.Predict

In frbl/OnlineSuperLearner: Online SuperLearner package

Description

Class to perform any kind of prediction on the super learner objects. These methods were first included in the onlinesuperlearner class itself, but to improve clarity we moved them here. Most of the functions created in this file are exposed through the OnlineSuperLearner class. As such, it is not neccessary to create a sepearate instance of this class in order to get predictions.

Usage

OnlineSuperLearner.Predict

Format

An object of class R6ClassGenerator of length 24.

Methods

initialize(pre_processor = NULL, verbose = FALSE)

Initializes a new OnlineSuperLearner.Predict. This instance is then used by the OSL to perform predictions with.

@param pre_processor PreProcessor (default = NULL) an instance of the PreProcessor which was used to normalize the in and output values for the OSL.

@param verbose (default = FALSE) the verbosity of the prediction class.

predict(osl, data, relevantVariables, all_estimators = TRUE

Runs a prediction on the various estimators of the OSL. This means it runs the Discrete OSL, the cts OSL, and if specified, all separate learners.

@param osl OnlineSuperLearner a trained instance of the OnlineSuperLearner class.

@param data data.table the initial data to perform the prediction with. Note that this should be enough to provide values for all covariates.

@param relevantVariables list a list of RelevantVariable objects, the ones used in the training process.

@param all_estimators boolean (default = TRUE) whether or not to include the output of all candidate estimators in the output

@param discrete boolean (default = TRUE) = whether or not to include the output of the discrete super learner in the output

@param continuous boolean (default = TRUE) whether or not to include the output of the continuous super learner in the output

@param sample boolean (default = FALSE) is the goal to sample from the underlying densities, or should we predict a probability of an outcome?

@param plot (default = FALSE) if set to true, the algorithm will plot the outcomes to a file for further inspection. This is useful when inspecting the performance of the estimators.

@return list a list with two entries; normalized and denormalized. The normalized outcomes are the values scaled between 0-1 (using the PreProcessor), the denormalized outcomes are the values transformed back to their original range.

predict_osl(data, osl_weights, current_result, sl_library, relevantVariables

Performs a prediction using the continuous OSL. It does so using a set of osl_weights that represent the alphas for each of the algorithms.

@param data data.table the initial data to perform the prediction with. Note that this should be enough to provide values for all covariates.

@param osl_weights vector a vector containing all weights for the osl.

@param current_result an earlier prediction of outcomes. We use the current_result outcome table to reduce the number of predictions we have to make. It is essentially a form of dynamic programming, if we have already calculated it, we hit the cache and use the cached prediction.

@param sl_library list a list of all machine learning estimators

@param relevantVariables list a list of RelevantVariable objects, the ones used in the training process.

@return list a list with two entries; normalized and denormalized. The normalized outcomes are the values scaled between 0-1 (using the PreProcessor), the denormalized outcomes are the values transformed back to their original range.

predict_dosl(dosl, data, relevantVariables, current_result, sample = FALSE

Performs a prediction using the discrete OSL. It does so using the DOSL instance which can be retrieved from an OSL instance.

@param dosl the actual discrete superlearner as retrieved from / provided by a trained online super learner instance.

@param data data.table the initial data to perform the prediction with. Note that this should be enough to provide values for all covariates.

@param relevantVariables list a list of RelevantVariable objects, the ones used in the training process.

@param current_result an earlier prediction of outcomes. We use the current_result outcome table to reduce the number of predictions we have to make. It is essentially a form of dynamic programming, if we have already calculated it, we hit the cache and use the cached prediction.

@param sample boolean (default = FALSE) is the goal to sample from the underlying densities, or should we predict a probability of an outcome?

@return list a list with two entries; normalized and denormalized. The normalized outcomes are the values scaled between 0-1 (using the PreProcessor), the denormalized outcomes are the values transformed back to their original range.

predict_using_all_estimators(data, sl_library, sample = FALSE, plot = FALSE)

Perfomrs a prediction based on all estimators / candidate estimators trained by the online superlearner. @param data data.table the initial data to perform the prediction with. Note that this should be enough to provide values for all covariates.

@param sl_library list a list of all machine learning estimators

@param sample boolean (default = FALSE) is the goal to sample from the underlying densities, or should we predict a probability of an outcome?

@param plot (default = FALSE) if set to true, the algorithm will plot the outcomes to a file for further inspection. This is useful when inspecting the performance of the estimators.

@return list a list with two entries; normalized and denormalized. The normalized outcomes are the values scaled between 0-1 (using the PreProcessor), the denormalized outcomes are the values transformed back to their original range.

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