Smoothing Functional Columns

Smoothes functional data using tf::tf_smooth(). This preprocessing operator is similar to PipeOpFDAInterpol, however it does not interpolate to unobserved x-values, but rather smooths the observed values.

Parameters

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

• method :: character(1)
  One of:

  • "lowess": locally weighted scatterplot smoothing (default)

  • "rollmean": rolling mean

  • "rollmedian": rolling meadian

  • "savgol": Savitzky-Golay filtering

  All methods but "lowess" ignore non-equidistant arg values.

• args :: named list()
  List of named arguments that is passed to tf_smooth(). See the help page of tf_smooth() for default values.

• verbose :: logical(1)
  Whether to print messages during the transformation. Is initialized to FALSE.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFDASmooth

Methods

Public methods

• PipeOpFDASmooth$new()

• PipeOpFDASmooth$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

PipeOpFDASmooth$new(id = "fda.smooth", param_vals = list())

Arguments

id

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

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

PipeOpFDASmooth$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3pipelines)

task = tsk("fuel")
po_smooth = po("fda.smooth", method = "rollmean", args = list(k = 5))
task_smooth = po_smooth$train(list(task))[[1L]]
task_smooth
#> <TaskRegr:fuel> (129 x 4): Spectral Data of Fossil Fuels
#> * Target: heatan
#> * Properties: -
#> * Features (3):
#>   - tfr (2): NIR, UVVIS
#>   - dbl (1): h20
task_smooth$data(cols = c("NIR", "UVVIS"))
#>                                           NIR
#>                                     <tfd_reg>
#>   1:  [1]: (1,  0.3);(2,  0.3);(3,  0.3); ...
#>   2:  [2]: (1,  0.3);(2,  0.3);(3,  0.3); ...
#>   3:  [3]: (1,-0.01);(2,-0.01);(3,-0.01); ...
#>   4:  [4]: (1,-0.03);(2,-0.03);(3,-0.03); ...
#>   5:  [5]: (1, -0.1);(2, -0.1);(3, -0.1); ...
#>  ---                                         
#> 125:  [6]: (1,-0.04);(2,-0.04);(3,-0.04); ...
#> 126:  [7]: (1, -0.6);(2, -0.6);(3, -0.6); ...
#> 127:  [8]: (1, -0.8);(2, -0.8);(3, -0.8); ...
#> 128:  [9]: (1,-0.05);(2,-0.05);(3,-0.05); ...
#> 129: [10]: (1,  0.1);(2,  0.1);(3,  0.1); ...
#>                                      UVVIS
#>                                  <tfd_reg>
#>   1:  [1]: (1, 0.7);(2, 0.7);(3, 0.7); ...
#>   2:  [2]: (1,-0.9);(2,-0.9);(3,-0.9); ...
#>   3:  [3]: (1,-0.2);(2,-0.2);(3,-0.2); ...
#>   4:  [4]: (1,-0.5);(2,-0.5);(3,-0.5); ...
#>   5:  [5]: (1,-0.8);(2,-0.8);(3,-0.8); ...
#>  ---                                      
#> 125:  [6]: (1,-0.6);(2,-0.6);(3,-0.6); ...
#> 126:  [7]: (1,-0.9);(2,-0.9);(3,-0.9); ...
#> 127:  [8]: (1,-0.8);(2,-0.8);(3,-0.8); ...
#> 128:  [9]: (1, 0.6);(2, 0.6);(3, 0.6); ...
#> 129: [10]: (1,-0.8);(2,-0.8);(3,-0.8); ...