Base learner to encode one single class of a categorical feature — BaselearnerCategoricalBinary • compboost

This class create a one-column one-hot encoded data matrix with ones at x == class_name and zero otherwise.

Format

S4 object.

Arguments

data_source: CategoricalDataRaw
The raw data object. Must be an object generated by CategoricalDataRaw.
class_name: (character(1))
The class for which a binary vector is created as data representation.
blearner_type: (character(1))
Type of the base learner (if not specified, blearner_type = "binary" is used). The unique id of the base learner is defined by appending blearner_type to the feature name: paste0(data_source$getIdentifier(), "_", class_name, "_", blearner_type).

Usage


BaselearnerCategoricalBinary$new(data_source, class_name)
BaselearnerCategoricalBinary$new(data_source, class_name, blearner_type)

Fields

This class doesn't contain public fields.

Methods

$summarizeFactory(): () -> ()
$transfromData(newdata): list(InMemoryData) -> matrix()
$getMeta(): () -> list()

Inherited methods from Baselearner

$getData(): () -> matrix()
$getDF(): () -> integer()
$getPenalty(): () -> numeric()
$getPenaltyMat(): () -> matrix()
$getFeatureName(): () -> character()
$getModelName(): () -> character()
$getBaselearnerId(): () -> character()

Examples

# Sample data:
x = sample(c("one","two"), 20, TRUE)
y = c(one = 0.8, two = -1.2)[x] + rnorm(20, 0, 0.2)
dat = data.frame(x, y)

# S4 API:
ds = CategoricalDataRaw$new(x, "cat")
bl = BaselearnerCategoricalBinary$new(ds, "one")

bl$getData()
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    0    0    1    0    1    0    0    0    1     0     0     0     1     1
#>      [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,]     1     0     1     1     0     1
bl$summarizeFactory()
#> Categorical base learner of feature cat and category one
bl$transformData(list(ds))
#> $design
#> 20 x 1 sparse Matrix of class "dgCMatrix"
#>        
#>  [1,] .
#>  [2,] .
#>  [3,] 1
#>  [4,] .
#>  [5,] 1
#>  [6,] .
#>  [7,] .
#>  [8,] .
#>  [9,] 1
#> [10,] .
#> [11,] .
#> [12,] .
#> [13,] 1
#> [14,] 1
#> [15,] 1
#> [16,] .
#> [17,] 1
#> [18,] 1
#> [19,] .
#> [20,] 1
#> 
bl$getBaselearnerId()
#> [1] "cat_one_binary"

# R6 API:
cboost = Compboost$new(dat, "y")
cboost$addBaselearner("x", "binary", BaselearnerCategoricalBinary)
cboost$train(500, 0)
#> Train 500 iterations in 0 Seconds.
#> Final risk based on the train set: 0.026
#> 
table(cboost$getSelectedBaselearner())
#> 
#> x_one_binary x_two_binary 
#>          251          249 
plotPEUni(cboost, "x", individual = FALSE)