CORElearn: Interfaces for CORElearn package for data science pipelines.

In rbertolusso/intubate: Interface to Popular R Functions for Data Science Pipelines

Description

Interfaces to CORElearn functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_attrEval(data, ...)
ntbt_CoreModel(data, ...)
ntbt_discretize(data, ...)
ntbt_ordEval(data, ...)

Arguments

data	data frame, tibble, list, ...
...	Other arguments passed to the corresponding interfaced function.

Details

Interfaces call their corresponding interfaced function.

Value

Object returned by interfaced function.

Author(s)

Roberto Bertolusso

Examples

## Not run: 
library(intubate)
library(magrittr)
library(CORElearn)

## ntbt_attrEval: Attribute evaluation
## Original function to interface
attrEval(Species ~ ., iris, estimator = "ReliefFexpRank", ReliefIterations = 30)

## The interface puts data as first parameter
ntbt_attrEval(iris, Species ~ ., estimator = "ReliefFexpRank", ReliefIterations = 30)

## so it can be used easily in a pipeline.
iris %>%
  ntbt_attrEval(Species ~ ., estimator = "ReliefFexpRank", ReliefIterations = 30)

## ntbt_CoreModel: Build a classification or regression model
trainIdxs <- sample(x=nrow(iris), size=0.7*nrow(iris), replace=FALSE)
testIdxs <- c(1:nrow(iris))[-trainIdxs]

## Original function to interface
CoreModel(Species ~ ., iris[trainIdxs,], model = "rf",
          selectionEstimator = "MDL", minNodeWeightRF = 5,
          rfNoTrees = 100, maxThreads = 1)

## The interface puts data as first parameter
ntbt_CoreModel(iris[trainIdxs,], Species ~ ., model = "rf",
               selectionEstimator = "MDL", minNodeWeightRF = 5,
               rfNoTrees = 100, maxThreads = 1)

## so it can be used easily in a pipeline.
iris[trainIdxs,] %>%
  ntbt_CoreModel(Species ~ ., model = "rf",
                 selectionEstimator = "MDL", minNodeWeightRF = 5,
                 rfNoTrees = 100, maxThreads = 1)

## ntbt_discretize: Discretization of numeric attributes
## Original function to interface
discretize(Species ~ ., iris, method = "greedy", estimator = "ReliefFexpRank")

## The interface puts data as first parameter
ntbt_discretize(iris, Species ~ ., method = "greedy", estimator = "ReliefFexpRank")

## so it can be used easily in a pipeline.
iris %>%
  ntbt_discretize(Species ~ ., method = "greedy", estimator = "ReliefFexpRank")

## ntbt_ordEval: Evaluation of ordered attributes
dat <- ordDataGen(200)

## Original function to interface
ordEval(class ~ ., dat, ordEvalNoRandomNormalizers=100)

## The interface puts data as first parameter
ntbt_ordEval(dat, class ~ ., ordEvalNoRandomNormalizers=100)

## so it can be used easily in a pipeline.
dat %>%
  ntbt_ordEval(class ~ ., ordEvalNoRandomNormalizers=100)

## End(Not run)

rbertolusso/intubate documentation built on May 27, 2019, 3 a.m.