BaselearnerCustom: Create custom base-learner factory by using R functions.

In compboost: C++ Implementation of Component-Wise Boosting

Description

BaselearnerCustom creates a custom base-learner factory by setting custom R functions. This factory object can be registered within a base-learner list and then used for training.

Format

S4 object.

Usage

BaselearnerCustom$new(data_source, data_target, instantiateData, train,
  predict, extractParameter)

Arguments

data_source [Data Object]

Data object which contains the source data.

data_target [Data Object]

Data object which gets the transformed source data.

instantiateData [function]

R function to transform the source data. For details see the Details.

train [function]

R function to train the base-learner on the target data. For details see the Details.

predict [function]

R function to predict on the object returned by train. For details see the Details.

extractParameter [function]

R function to extract the parameter of the object returned by train. For details see the Details.

Details

The function must have the following structure:

instantiateData(X) { ... return (X.trafo) } With a matrix argument X and a matrix as return object.

train(y, X) { ... return (SEXP) } With a vector argument y and a matrix argument X. The target data is used in X while y contains the response. The function can return any R object which is stored within a SEXP.

predict(model, newdata) { ... return (prediction) } The returned object of the train function is passed to the model argument while newdata contains a new matrix used for predicting.

extractParameter() { ... return (parameters) } Again, model contains the object returned by train. The returned object must be a matrix containing the estimated parameter. If no parameter should be estimated one can return NA.

For an example see the Examples.

This class is a wrapper around the pure C++ implementation. To see the functionality of the C++ class visit https://schalkdaniel.github.io/compboost/cpp_man/html/classblearnerfactory_1_1_custom_blearner_factory.html.

Fields

This class doesn't contain public fields.

Methods

getData()

Get the data matrix of the target data which is used for modeling.

transformData(X)

Transform a data matrix as defined within the factory. The argument has to be a matrix with one column.

summarizeFactory()

Summarize the base-learner factory object.

Examples

# Sample data:
data.mat = cbind(1, 1:10)
y = 2 + 3 * 1:10

# Create new data object:
data.source = InMemoryData$new(data.mat, "my.data.name")
data.target = InMemoryData$new()

instantiateDataFun = function (X) {
  return(X)
}
# Ordinary least squares estimator:
trainFun = function (y, X) {
  return(solve(t(X) %*% X) %*% t(X) %*% y)
}
predictFun = function (model, newdata) {
  return(as.matrix(newdata %*% model))
}
extractParameter = function (model) {
  return(as.matrix(model))
}

# Create new custom linear base-learner factory:
custom.lin.factory = BaselearnerCustom$new(data.source, data.target,
  instantiateDataFun, trainFun, predictFun, extractParameter)

# Get the transformed data:
custom.lin.factory$getData()

# Summarize factory:
custom.lin.factory$summarizeFactory()

# Transform data manually:
custom.lin.factory$transformData(data.mat)

compboost documentation built on May 2, 2019, 6:40 a.m.