Flattens Functional Columns

Convert regular functional features (e.g. all individuals are observed at the same time-points) to new columns, one for each input value to the function.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreprocSimple.

Naming

The new names generally append a _1, ..., to the corresponding column name. However this can lead to name clashes with existing columns. This is solved as follows: If a column was called "x" and the feature is "mean", the corresponding new column will be called "x_mean". In case of duplicates, unique names are obtained using make.unique() and a warning is given.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFDAFlatten

Methods

Public methods

• PipeOpFDAFlatten$new()

• PipeOpFDAFlatten$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

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Method new()

Initializes a new instance of this Class.

Usage

PipeOpFDAFlatten$new(id = "fda.flatten", param_vals = list())

Arguments

id

(character(1))
Identifier of resulting object, default "fda.flatten".

param_vals

(named list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

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Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpFDAFlatten$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("fuel")
pop = po("fda.flatten")
task_flat = pop$train(list(task))