One-hot encoded base learner for a categorical feature — BaselearnerCategoricalRidge • compboost

This base learner can be used to estimate effects of categorical features. The classes are included similar as in the linear model by using a one-hot encoded data matrix. Additionally, a Ridge penalty allows unbiased feature selection.

Format

S4 object.

Arguments

data_source: CategoricalDataRaw
Data container of the raw categorical feature.
blearner_type: (character(1))
Type of the base learner (if not specified, blearner_type = "ridge" is used). The unique id of the base learner is defined by appending blearner_type to the feature name: paste0(data_source$getIdentifier(), "_", blearner_type).
df: (numeric(1))
Degrees of freedom of the base learner(s).

Usage


BaselearnerCategoricalRidge$new(data_source, list(df))
BaselearnerCategoricalRidge$new(data_source, blearner_type, list(df))

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$new(x, "cat")
bl = BaselearnerCategoricalRidge$new(ds, list(df = 1))

bl$getData()
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    1    0    1    1    1    0    0    0    1     0     1     0     0     0
#> [2,]    0    1    0    0    0    1    1    1    0     1     0     1     1     1
#>      [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,]     0     1     0     1     0     0
#> [2,]     1     0     1     0     1     1
bl$summarizeFactory()
#> Categorical base learner of category cat

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

# R6 API:
cboost = Compboost$new(dat, "y")
cboost$addBaselearner("x", "binary", BaselearnerCategoricalRidge)
cboost$train(100, 0)
#> Train 100 iterations in 0 Seconds.
#> Final risk based on the train set: 0.015
#> 
table(cboost$getSelectedBaselearner())
#> 
#> x_binary 
#>      100 
plotPEUni(cboost, "x", individual = FALSE)