Skip to contents

Functional Principal Component Analysis

Source: R/PipeOpFPCA.R
mlr_pipeops_fda.fpca.Rd

This PipeOp applies a functional principal component analysis (FPCA) to functional columns and then extracts the principal components as features. This is done using a (truncated) weighted SVD.

To apply this PipeOp to irregular data, convert it to a regular grid first using PipeOpFDAInterpol.

For more details, see tf::tfb_fpc(), which is called internally.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as the following parameters:

  • pve :: numeric(1)
    The percentage of variance explained that should be retained. Default is 0.995.

  • n_components :: integer(1)
    The number of principal components to extract. This parameter is initialized to Inf.

Naming

The new names generally append a _pc_{number} to the corresponding column name. If a column was called "x" and the there are three principcal components, the corresponding new columns will be called "x_pc_1", "x_pc_2", "x_pc_3".

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpFPCA

Methods

Public methods

Inherited methods

Method new()

Initializes a new instance of this Class.

Usage

PipeOpFPCA$new(id = "fda.fpca", param_vals = list())

Arguments

id

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

param_vals

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

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpFPCA$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("fuel")
po_fpca = po("fda.fpca", n_components = 3L)
task_fpca = po_fpca$train(list(task))[[1L]]
task_fpca$data()
#>       heatan    h20   NIR_pc_1   NIR_pc_2     NIR_pc_3 UVVIS_pc_1 UVVIS_pc_2
#>        <num>  <num>      <num>      <num>        <num>      <num>      <num>
#>   1: 26.7810 2.3000  7.5675043  1.1371827 -0.377501250  11.000976 -2.7804661
#>   2: 27.4720 3.0000  6.2793462  1.9345782  0.160548831  -9.298016 -1.7955553
#>   3: 23.8400 2.0002  0.2348390 -0.1721228 -0.024101132  -3.078812 -0.6114645
#>   4: 18.1680 1.8500  0.2913332  0.2052379  0.040952119  -5.349049 -0.7030112
#>   5: 17.5170 2.3898  1.1406877  1.2563331  0.001074654  -8.497281 -1.2413718
#>  ---                                                                        
#> 125: 23.8340 2.1100  1.6980728  0.9841985  0.041623661  -6.427319 -0.2898928
#> 126: 11.8050 1.6200 -4.0420811  2.2340299 -0.029562942 -10.843930 -0.0918478
#> 127:  8.8315 1.4200 -5.5013835  2.8333813 -0.034304440 -12.227632 -0.1838102
#> 128: 11.3450 1.4800  2.1809306  1.4395300 -0.397523197   5.871679 -1.0021267
#> 129: 28.9940 2.5000  1.9734804 -0.1998806  0.034337704  -6.893096 -1.4463943
#>       UVVIS_pc_3
#>            <num>
#>   1:  0.13667224
#>   2: -0.46762503
#>   3: -0.27479445
#>   4:  0.08631440
#>   5: -0.19140105
#>  ---            
#> 125: -0.02865269
#> 126:  0.56113685
#> 127:  1.11134683
#> 128:  1.01323166
#> 129: -0.81054907