intubate (version 1.0.0)

CORElearn: Interfaces for CORElearn package 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.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

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)