Author: Martin Binder
Date: 2025-06-16

# CPOs Built Into mlrCPO

#### Martin Binder

#### 2025-06-16

#### CPO Vignette Navigation

1. 1. First Steps (compact version)
2. mlrCPO Core (compact version)
3. CPOs Built Into mlrCPO (compact version)
4. Building Custom CPOs (compact version)

## Listing CPOs

Builtin CPOs can be listed with `listCPO()`.

    listCPO()[, c("name", "category", "subcategory")]

| name | category | subcategory
--- | --- | --- | ---
11 | cpoDropConstants | data | cleanup
12 | cpoDropMostlyConstants | data | cleanup
36 | cpoFixFactors | data | cleanup
10 | cpoCollapseFact | data | factor data preprocessing
4 | cpoAsNumeric | data | feature conversion
16 | cpoDummyEncode | data | feature conversion
14 | cpoImpactEncodeClassif | data | feature conversion
15 | cpoImpactEncodeRegr | data | feature conversion
13 | cpoProbEncode | data | feature conversion
55 | cpoQuantileBinNumerics | data | feature conversion
61 | cpoSelect | data | feature selection
62 | cpoSelectFreeProperties | data | feature selection
51 | cpoAddCols | data | features
50 | cpoMakeCols | data | features
1 | cpoApplyFun | data | general data preprocessing
53 | cpoModelMatrix | data | general
37 | cpoIca | data | numeric data preprocessing
54 | cpoPca | data | numeric data preprocessing
58 | cpoScale | data | numeric data preprocessing
59 | cpoScaleMaxAbs | data | numeric data preprocessing
60 | cpoScaleRange | data | numeric data preprocessing
64 | cpoSpatialSign | data | numeric data preprocessing
17 | cpoFilterFeatures | featurefilter | general
32 | cpoFilterAnova | featurefilter | specialised
19 | cpoFilterCarscore | featurefilter | specialised
28 | cpoFilterChiSquared | featurefilter | specialised
26 | cpoFilterGainRatio | featurefilter | specialised
25 | cpoFilterInformationGain | featurefilter | specialised
33 | cpoFilterKruskal | featurefilter | specialised
23 | cpoFilterLinearCorrelation | featurefilter | specialised
18 | cpoFilterMrmr | featurefilter | specialised
30 | cpoFilterOneR | featurefilter | specialised
35 | cpoFilterPermutationImportance | featurefilter | specialised
24 | cpoFilterRankCorrelation | featurefilter | specialised
29 | cpoFilterRelief | featurefilter | specialised
21 | cpoFilterRfCImportance | featurefilter | specialised
22 | cpoFilterRfImportance | featurefilter | specialised
20 | cpoFilterRfSRCImportance | featurefilter | specialised
27 | cpoFilterSymmetricalUncertainty | featurefilter | specialised
31 | cpoFilterUnivariate | featurefilter | specialised
34 | cpoFilterVariance | featurefilter | specialised
38 | cpoImpute | imputation | general
39 | cpoImputeAll | imputation | general
40 | cpoImputeConstant | imputation | specialised
48 | cpoImputeHist | imputation | specialised
49 | cpoImputeLearner | imputation | specialised
45 | cpoImputeMax | imputation | specialised
42 | cpoImputeMean | imputation | specialised
41 | cpoImputeMedian | imputation | specialised
44 | cpoImputeMin | imputation | specialised
43 | cpoImputeMode | imputation | specialised
47 | cpoImputeNormal | imputation | specialised
46 | cpoImputeUniform | imputation | specialised
8 | cpoCache | meta |
6 | cpoCase | meta |
9 | cpoCbind | meta |
5 | cpoMultiplex | meta |
7 | cpoTransformParams | meta |
68 | cpoWrap | meta | wrap
69 | cpoWrapRetrafoless | meta | wrap
65 | cpoOversample | subsampling | binary classif
63 | cpoSmote | subsampling | binary classif
66 | cpoUndersample | subsampling | binary classif
67 | cpoSample | subsampling | general
2 | cpoApplyFunRegrTarget | target | general target transformation
56 | cpoRegrResiduals | target | residual fitting
3 | cpoLogTrafoRegr | target | target transformation
52 | cpoMissingIndicators | tools | imputation
57 | cpoResponseFromSE | tools | predict.type

## NULLCPO

`NULLCPO` is the neutral element of `%>>%`. It is returned by some functions when no other CPO or Retrafo is present.

    NULLCPO
    #> NULLCPO
    is.nullcpo(NULLCPO)
    #> [1] TRUE
    NULLCPO %>>% cpoScale()
    #> scale(center = TRUE, scale = TRUE)
    NULLCPO %>>% NULLCPO
    #> NULLCPO
    print(as.list(NULLCPO))
    #> list()
    pipeCPO(list())
    #> NULLCPO

## Meta-CPO

### cpoWrap

A simple CPO with one parameter which gets applied to the data as CPO. This is different from a multiplexer in that its parameter is free and can take any value that behaves like a CPO. On the downside, this does not expose the argument’s parameters to the outside.

    cpa = cpoWrap()
    print(cpa, verbose = TRUE)
    #> Trafo chain of 1 cpos:
    #> wrap()
    #> Operating: feature
    #> ParamSet:
    #>             Type len Def Constr Req Tunable Trafo
    #> wrap.cpo untyped   -   -      -   -    TRUE     -
    head(iris %>>% setHyperPars(cpa, wrap.cpo = cpoScale()))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1   -0.8976739  1.01560199    -1.335752   -1.311052  setosa
    #> 2   -1.1392005 -0.13153881    -1.335752   -1.311052  setosa
    #> 3   -1.3807271  0.32731751    -1.392399   -1.311052  setosa
    #> 4   -1.5014904  0.09788935    -1.279104   -1.311052  setosa
    #> 5   -1.0184372  1.24503015    -1.335752   -1.311052  setosa
    #> 6   -0.5353840  1.93331463    -1.165809   -1.048667  setosa
    head(iris %>>% setHyperPars(cpa, wrap.cpo = cpoPca()))
    #>   Species       PC1        PC2         PC3          PC4
    #> 1  setosa -2.684126 -0.3193972  0.02791483  0.002262437
    #> 2  setosa -2.714142  0.1770012  0.21046427  0.099026550
    #> 3  setosa -2.888991  0.1449494 -0.01790026  0.019968390
    #> 4  setosa -2.745343  0.3182990 -0.03155937 -0.075575817
    #> 5  setosa -2.728717 -0.3267545 -0.09007924 -0.061258593
    #> 6  setosa -2.280860 -0.7413304 -0.16867766 -0.024200858
    # attaching the cpo applicator to a learner gives this learner a "cpo" hyperparameter
    # that can be set to any CPO.
    getParamSet(cpoWrap() %>>% makeLearner("classif.logreg"))
    #>             Type len  Def Constr Req Tunable Trafo
    #> wrap.cpo untyped   -    -      -   -    TRUE     -
    #> model    logical   - TRUE      -   -   FALSE     -

### cpoMultiplex

Combine many CPOs into one, with an extra `selected.cpo` parameter that chooses between them.

    cpm = cpoMultiplex(list(cpoScale, cpoPca))
    print(cpm, verbose = TRUE)
    #> Trafo chain of 1 cpos:
    #> multiplex(selected.cpo = scale, scale.center = TRUE, scale.scale = TRUE, pca.center = TRUE, pca.scale = FALSE)
    #> Operating: feature
    #> ParamSet:
    #>                  Type len   Def    Constr Req Tunable Trafo
    #> selected.cpo discrete   - scale scale,pca   -    TRUE     -
    #> scale.center  logical   -  TRUE         -   Y    TRUE     -
    #> scale.scale   logical   -  TRUE         -   Y    TRUE     -
    #> pca.center    logical   -  TRUE         -   Y    TRUE     -
    #> pca.scale     logical   - FALSE         -   Y    TRUE     -
    head(iris %>>% setHyperPars(cpm, selected.cpo = "scale"))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1   -0.8976739  1.01560199    -1.335752   -1.311052  setosa
    #> 2   -1.1392005 -0.13153881    -1.335752   -1.311052  setosa
    #> 3   -1.3807271  0.32731751    -1.392399   -1.311052  setosa
    #> 4   -1.5014904  0.09788935    -1.279104   -1.311052  setosa
    #> 5   -1.0184372  1.24503015    -1.335752   -1.311052  setosa
    #> 6   -0.5353840  1.93331463    -1.165809   -1.048667  setosa
    # every CPO's Hyperparameters are exported
    head(iris %>>% setHyperPars(cpm, selected.cpo = "scale", scale.center = FALSE))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1    0.8613268   1.1296201    0.3362663    0.140405  setosa
    #> 2    0.8275493   0.9682458    0.3362663    0.140405  setosa
    #> 3    0.7937718   1.0327956    0.3122473    0.140405  setosa
    #> 4    0.7768830   1.0005207    0.3602853    0.140405  setosa
    #> 5    0.8444380   1.1618950    0.3362663    0.140405  setosa
    #> 6    0.9119931   1.2587196    0.4083234    0.280810  setosa
    head(iris %>>% setHyperPars(cpm, selected.cpo = "pca"))
    #>   Species       PC1        PC2         PC3          PC4
    #> 1  setosa -2.684126 -0.3193972  0.02791483  0.002262437
    #> 2  setosa -2.714142  0.1770012  0.21046427  0.099026550
    #> 3  setosa -2.888991  0.1449494 -0.01790026  0.019968390
    #> 4  setosa -2.745343  0.3182990 -0.03155937 -0.075575817
    #> 5  setosa -2.728717 -0.3267545 -0.09007924 -0.061258593
    #> 6  setosa -2.280860 -0.7413304 -0.16867766 -0.024200858

### cpoCase

A CPO that builds data-dependent CPO networks. This is a generalized CPO-Multiplexer that takes a function which decides (from the data, and from user-specified hyperparameters) what CPO operation to perform. Besides optional arguments, the used CPO’s Hyperparameters are exported as well. This is a generalization of `cpoMultiplex`; however, `requires` of the involved parameters are not adjusted, since this is impossible in principle.

    s.and.p = cpoCase(pSS(logical.param: logical),
      export.cpos = list(cpoScale(),
      cpoPca()),
      cpo.build = function(data, target, logical.param, scale, pca) {
      if (logical.param || mean(data[[1]]) > 10) {
        scale %>>% pca
      } else {
        pca %>>% scale
      }
      })
    print(s.and.p, verbose = TRUE)
    #> Trafo chain of 1 cpos:
    #> case(scale.center = TRUE, scale.scale = TRUE, pca.center = TRUE, pca.scale = FALSE)
    #> Operating: feature
    #> ParamSet:
    #>                  Type len   Def Constr Req Tunable Trafo
    #> logical.param logical   -     -      -   -    TRUE     -
    #> scale.center  logical   -  TRUE      -   -    TRUE     -
    #> scale.scale   logical   -  TRUE      -   -    TRUE     -
    #> pca.center    logical   -  TRUE      -   -    TRUE     -
    #> pca.scale     logical   - FALSE      -   -    TRUE     -

The resulting CPO `s.and.p` performs scaling and PCA, with the order depending on the parameter `logical.param` and on whether the mean of the data’s first column exceeds 10. If either of those is true, the data will be first scaled, then PCA’d, otherwise the order is reversed. The all CPOs listed in `.export` are passed to the `cpo.build`.

### cpoCbind

`cbind` other CPOs as operation. The `cbinder` makes it possible to build DAGs of CPOs that perform different operations on data and paste the results next to each other.

    scale = cpoScale(id = "scale")
    scale.pca = scale %>>% cpoPca()
    cbinder = cpoCbind(scaled = scale, pcad = scale.pca, original = NULLCPO)

    # cpoCbind recognises that "scale.scale" happens before "pca.pca" but is also fed to the
    # result directly. The summary draws a (crude) ascii-art graph.
    print(cbinder, verbose = TRUE)
    #> Trafo chain of 1 cpos:
    #> cbind(scale.center = TRUE, scale.scale = TRUE, pca.center = TRUE, pca.scale = FALSE)
    #> Operating: feature
    #> ParamSet:
    #>                 Type len   Def Constr Req Tunable Trafo
    #> scale.center logical   -  TRUE      -   -    TRUE     -
    #> scale.scale  logical   -  TRUE      -   -    TRUE     -
    #> pca.center   logical   -  TRUE      -   -    TRUE     -
    #> pca.scale    logical   - FALSE      -   -    TRUE     -
    #> O>+   scale(center = TRUE, scale = TRUE)
    #> | |
    #> +<O   pca(center = TRUE, scale = FALSE)[not exp'd: tol = <NULL>, rank = <NULL>]
    #> |
    #> O   CBIND[scaled,pcad,original]
    #>
    head(iris %>>% cbinder)
    #>   scaled.Sepal.Length scaled.Sepal.Width scaled.Petal.Length scaled.Petal.Width
    #> 1          -0.8976739         1.01560199           -1.335752          -1.311052
    #> 2          -1.1392005        -0.13153881           -1.335752          -1.311052
    #> 3          -1.3807271         0.32731751           -1.392399          -1.311052
    #> 4          -1.5014904         0.09788935           -1.279104          -1.311052
    #> 5          -1.0184372         1.24503015           -1.335752          -1.311052
    #> 6          -0.5353840         1.93331463           -1.165809          -1.048667
    #>   scaled.Species pcad.Species  pcad.PC1   pcad.PC2    pcad.PC3     pcad.PC4
    #> 1         setosa       setosa -2.257141 -0.4784238  0.12727962  0.024087508
    #> 2         setosa       setosa -2.074013  0.6718827  0.23382552  0.102662845
    #> 3         setosa       setosa -2.356335  0.3407664 -0.04405390  0.028282305
    #> 4         setosa       setosa -2.291707  0.5953999 -0.09098530 -0.065735340
    #> 5         setosa       setosa -2.381863 -0.6446757 -0.01568565 -0.035802870
    #> 6         setosa       setosa -2.068701 -1.4842053 -0.02687825  0.006586116
    #>   original.Sepal.Length original.Sepal.Width original.Petal.Length
    #> 1                   5.1                  3.5                   1.4
    #> 2                   4.9                  3.0                   1.4
    #> 3                   4.7                  3.2                   1.3
    #> 4                   4.6                  3.1                   1.5
    #> 5                   5.0                  3.6                   1.4
    #> 6                   5.4                  3.9                   1.7
    #>   original.Petal.Width original.Species
    #> 1                  0.2           setosa
    #> 2                  0.2           setosa
    #> 3                  0.2           setosa
    #> 4                  0.2           setosa
    #> 5                  0.2           setosa
    #> 6                  0.4           setosa

    # the unnecessary copies of "Species" are unfortunate. Remove them with cpoSelect:
    selector = cpoSelect(type = "numeric")
    cbinder.select = cpoCbind(scaled = selector %>>% scale, pcad = selector %>>% scale.pca, original = NULLCPO)
    cbinder.select
    #> cbind(scale.center = TRUE, scale.scale = TRUE, pca.center = TRUE, pca.scale = FALSE)
    head(iris %>>% cbinder)
    #>   scaled.Sepal.Length scaled.Sepal.Width scaled.Petal.Length scaled.Petal.Width
    #> 1          -0.8976739         1.01560199           -1.335752          -1.311052
    #> 2          -1.1392005        -0.13153881           -1.335752          -1.311052
    #> 3          -1.3807271         0.32731751           -1.392399          -1.311052
    #> 4          -1.5014904         0.09788935           -1.279104          -1.311052
    #> 5          -1.0184372         1.24503015           -1.335752          -1.311052
    #> 6          -0.5353840         1.93331463           -1.165809          -1.048667
    #>   scaled.Species pcad.Species  pcad.PC1   pcad.PC2    pcad.PC3     pcad.PC4
    #> 1         setosa       setosa -2.257141 -0.4784238  0.12727962  0.024087508
    #> 2         setosa       setosa -2.074013  0.6718827  0.23382552  0.102662845
    #> 3         setosa       setosa -2.356335  0.3407664 -0.04405390  0.028282305
    #> 4         setosa       setosa -2.291707  0.5953999 -0.09098530 -0.065735340
    #> 5         setosa       setosa -2.381863 -0.6446757 -0.01568565 -0.035802870
    #> 6         setosa       setosa -2.068701 -1.4842053 -0.02687825  0.006586116
    #>   original.Sepal.Length original.Sepal.Width original.Petal.Length
    #> 1                   5.1                  3.5                   1.4
    #> 2                   4.9                  3.0                   1.4
    #> 3                   4.7                  3.2                   1.3
    #> 4                   4.6                  3.1                   1.5
    #> 5                   5.0                  3.6                   1.4
    #> 6                   5.4                  3.9                   1.7
    #>   original.Petal.Width original.Species
    #> 1                  0.2           setosa
    #> 2                  0.2           setosa
    #> 3                  0.2           setosa
    #> 4                  0.2           setosa
    #> 5                  0.2           setosa
    #> 6                  0.4           setosa

    # alternatively, we apply the cbinder only to numerical data
    head(iris %>>% cpoWrap(cbinder, affect.type = "numeric"))
    #>   Species scaled.Sepal.Length scaled.Sepal.Width scaled.Petal.Length
    #> 1  setosa          -0.8976739         1.01560199           -1.335752
    #> 2  setosa          -1.1392005        -0.13153881           -1.335752
    #> 3  setosa          -1.3807271         0.32731751           -1.392399
    #> 4  setosa          -1.5014904         0.09788935           -1.279104
    #> 5  setosa          -1.0184372         1.24503015           -1.335752
    #> 6  setosa          -0.5353840         1.93331463           -1.165809
    #>   scaled.Petal.Width  pcad.PC1   pcad.PC2    pcad.PC3     pcad.PC4
    #> 1          -1.311052 -2.257141 -0.4784238  0.12727962  0.024087508
    #> 2          -1.311052 -2.074013  0.6718827  0.23382552  0.102662845
    #> 3          -1.311052 -2.356335  0.3407664 -0.04405390  0.028282305
    #> 4          -1.311052 -2.291707  0.5953999 -0.09098530 -0.065735340
    #> 5          -1.311052 -2.381863 -0.6446757 -0.01568565 -0.035802870
    #> 6          -1.048667 -2.068701 -1.4842053 -0.02687825  0.006586116
    #>   original.Sepal.Length original.Sepal.Width original.Petal.Length
    #> 1                   5.1                  3.5                   1.4
    #> 2                   4.9                  3.0                   1.4
    #> 3                   4.7                  3.2                   1.3
    #> 4                   4.6                  3.1                   1.5
    #> 5                   5.0                  3.6                   1.4
    #> 6                   5.4                  3.9                   1.7
    #>   original.Petal.Width
    #> 1                  0.2
    #> 2                  0.2
    #> 3                  0.2
    #> 4                  0.2
    #> 5                  0.2
    #> 6                  0.4

### cpoTransformParams

`cpoTransformParams` wraps another `CPO` and sets some of its hyperparameters to the value of expressions depending on other hyperparameter values. This can be used to make a transformation of parameters similar to the `trafo` parameter of a `Param` in `ParamHelpers`, but it can also be used to set multiple parameters at the same time, depending on a single new parameter.

    cpo = cpoTransformParams(cpoPca(), alist(pca.scale = pca.center))
    retr = pid.task %>|% setHyperPars(cpo, pca.center = FALSE)
    getCPOTrainedState(retr)$control  # both 'center' and 'scale' are FALSE
    #> CPO Retrafo chain
    #> [RETRAFO pca(center = FALSE, scale = FALSE)]

    mplx = cpoMultiplex(list(cpoIca(export = "n.comp"), cpoPca(export = "rank")))
    !mplx
    #> Trafo chain of 1 cpos:
    #> multiplex(selected.cpo = ica, ica.n.comp = <NULL>, pca.rank = <NULL>)
    #> Operating: feature
    #> ParamSet:
    #>                  Type len    Def   Constr Req Tunable Trafo
    #> selected.cpo discrete   -    ica  ica,pca   -    TRUE     -
    #> ica.n.comp    integer   - <NULL> 1 to Inf   Y    TRUE     -
    #> pca.rank      integer   - <NULL> 1 to Inf   Y    TRUE     -
    mtx = cpoTransformParams(mplx, alist(ica.n.comp = comp, pca.rank = comp),
      pSS(comp: integer[1, ]), list(comp = 1))
    head(iris %>>% setHyperPars(mtx, selected.cpo = "ica", comp = 2))
    #>   Species         V1       V2
    #> 1  setosa  0.5040262 1.372772
    #> 2  setosa -0.5026081 1.277214
    #> 3  setosa -0.4470063 1.369134
    #> 4  setosa -0.7903465 1.261004
    #> 5  setosa  0.5165524 1.396033
    #> 6  setosa  1.3797295 1.270769
    head(iris %>>% setHyperPars(mtx, selected.cpo = "pca", comp = 3))
    #>   Species       PC1        PC2         PC3
    #> 1  setosa -2.684126 -0.3193972  0.02791483
    #> 2  setosa -2.714142  0.1770012  0.21046427
    #> 3  setosa -2.888991  0.1449494 -0.01790026
    #> 4  setosa -2.745343  0.3182990 -0.03155937
    #> 5  setosa -2.728717 -0.3267545 -0.09007924
    #> 6  setosa -2.280860 -0.7413304 -0.16867766

## Data Manipulation

### cpoScale

Implements the `base::scale` function.

    df = data.frame(a = 1:3, b = -(1:3) * 10)
    df %>>% cpoScale()
    #>    a  b
    #> 1 -1  1
    #> 2  0  0
    #> 3  1 -1
    df %>>% cpoScale(scale = FALSE)  # center = TRUE
    #>    a   b
    #> 1 -1  10
    #> 2  0   0
    #> 3  1 -10

### cpoPca

Implements `stats::prcomp`. No scaling or centering is performed.

    df %>>% cpoPca()
    #>         PC1           PC2
    #> 1 -10.04988  4.163336e-16
    #> 2   0.00000  0.000000e+00
    #> 3  10.04988 -4.163336e-16

### cpoDummyEncode

Dummy encoding of factorial variables. Optionally uses the first factor as reference variable.

    head(iris %>>% cpoDummyEncode())
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Speciessetosa
    #> 1          5.1         3.5          1.4         0.2             1
    #> 2          4.9         3.0          1.4         0.2             1
    #> 3          4.7         3.2          1.3         0.2             1
    #> 4          4.6         3.1          1.5         0.2             1
    #> 5          5.0         3.6          1.4         0.2             1
    #> 6          5.4         3.9          1.7         0.4             1
    #>   Speciesversicolor Speciesvirginica
    #> 1                 0                0
    #> 2                 0                0
    #> 3                 0                0
    #> 4                 0                0
    #> 5                 0                0
    #> 6                 0                0
    head(iris %>>% cpoDummyEncode(reference.cat = TRUE))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Speciesversicolor
    #> 1          5.1         3.5          1.4         0.2                 0
    #> 2          4.9         3.0          1.4         0.2                 0
    #> 3          4.7         3.2          1.3         0.2                 0
    #> 4          4.6         3.1          1.5         0.2                 0
    #> 5          5.0         3.6          1.4         0.2                 0
    #> 6          5.4         3.9          1.7         0.4                 0
    #>   Speciesvirginica
    #> 1                0
    #> 2                0
    #> 3                0
    #> 4                0
    #> 5                0
    #> 6                0

### cpoSelect

Select to use only certain columns of a dataset. Select by column index, name, or regex pattern.

    head(iris %>>% cpoSelect(pattern = "Width"))
    #>   Sepal.Width Petal.Width
    #> 1         3.5         0.2
    #> 2         3.0         0.2
    #> 3         3.2         0.2
    #> 4         3.1         0.2
    #> 5         3.6         0.2
    #> 6         3.9         0.4
    # selection is additive
    head(iris %>>% cpoSelect(pattern = "Width", type = "factor"))
    #>   Sepal.Width Petal.Width Species
    #> 1         3.5         0.2  setosa
    #> 2         3.0         0.2  setosa
    #> 3         3.2         0.2  setosa
    #> 4         3.1         0.2  setosa
    #> 5         3.6         0.2  setosa
    #> 6         3.9         0.4  setosa

### cpoDropConstants

Drops constant features or numerics, with variable tolerance

    head(iris) %>>% cpoDropConstants()  # drops 'species'
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width
    #> 1          5.1         3.5          1.4         0.2
    #> 2          4.9         3.0          1.4         0.2
    #> 3          4.7         3.2          1.3         0.2
    #> 4          4.6         3.1          1.5         0.2
    #> 5          5.0         3.6          1.4         0.2
    #> 6          5.4         3.9          1.7         0.4
    head(iris) %>>% cpoDropConstants(abs.tol = 0.2)  # also drops 'Petal.Width'
    #>   Sepal.Length Sepal.Width Petal.Length
    #> 1          5.1         3.5          1.4
    #> 2          4.9         3.0          1.4
    #> 3          4.7         3.2          1.3
    #> 4          4.6         3.1          1.5
    #> 5          5.0         3.6          1.4
    #> 6          5.4         3.9          1.7

### cpoFixFactors

Drops unused factors and makes sure prediction data has the same factor levels as training data.

    levels(iris$Species)
    #> [1] "setosa"     "versicolor" "virginica"

    irisfix = head(iris) %>>% cpoFixFactors()  # Species only has level 'setosa' in train
    levels(irisfix$Species)
    #> [1] "setosa"

    rf = retrafo(irisfix)
    iris[c(1, 100, 140), ]
    #>     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
    #> 1            5.1         3.5          1.4         0.2     setosa
    #> 100          5.7         2.8          4.1         1.3 versicolor
    #> 140          6.9         3.1          5.4         2.1  virginica
    iris[c(1, 100, 140), ] %>>% rf
    #>     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1            5.1         3.5          1.4         0.2  setosa
    #> 100          5.7         2.8          4.1         1.3    <NA>
    #> 140          6.9         3.1          5.4         2.1    <NA>

### cpoMissingIndicators

Creates columns indicating missing data. Most useful in combination with cpoCbind.

    impdata = df
    impdata[[1]][1] = NA
    impdata
    #>    a   b
    #> 1 NA -10
    #> 2  2 -20
    #> 3  3 -30

    impdata %>>% cpoMissingIndicators()
    #>       a
    #> 1  TRUE
    #> 2 FALSE
    #> 3 FALSE
    impdata %>>% cpoCbind(NULLCPO, dummy = cpoMissingIndicators())
    #>    a   b dummy.a
    #> 1 NA -10    TRUE
    #> 2  2 -20   FALSE
    #> 3  3 -30   FALSE

### cpoApplyFun

Apply an univariate function to data columns

    head(iris %>>% cpoApplyFun(function(x) sqrt(x) - 10, affect.type = "numeric"))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1    -7.741682   -8.129171    -8.816784   -9.552786  setosa
    #> 2    -7.786406   -8.267949    -8.816784   -9.552786  setosa
    #> 3    -7.832052   -8.211146    -8.859825   -9.552786  setosa
    #> 4    -7.855239   -8.239318    -8.775255   -9.552786  setosa
    #> 5    -7.763932   -8.102633    -8.816784   -9.552786  setosa
    #> 6    -7.676210   -8.025158    -8.696160   -9.367544  setosa

### cpoAsNumeric

Convert (non-numeric) features to numeric

    head(iris[sample(nrow(iris), 10), ] %>>% cpoAsNumeric())
    #>     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 14           4.3         3.0          1.1         0.1       1
    #> 50           5.0         3.3          1.4         0.2       1
    #> 118          7.7         3.8          6.7         2.2       3
    #> 43           4.4         3.2          1.3         0.2       1
    #> 150          5.9         3.0          5.1         1.8       3
    #> 148          6.5         3.0          5.2         2.0       3

### cpoCollapseFact

Combine low prevalence factors. Set `max.collapsed.class.prevalence` how big the combined factor level may be.

    iris2 = iris
    iris2$Species = factor(c("a", "b", "c", "b", "b", "c", "b", "c",
                            as.character(iris2$Species[-(1:8)])))
    head(iris2, 10)
    #>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1           5.1         3.5          1.4         0.2       a
    #> 2           4.9         3.0          1.4         0.2       b
    #> 3           4.7         3.2          1.3         0.2       c
    #> 4           4.6         3.1          1.5         0.2       b
    #> 5           5.0         3.6          1.4         0.2       b
    #> 6           5.4         3.9          1.7         0.4       c
    #> 7           4.6         3.4          1.4         0.3       b
    #> 8           5.0         3.4          1.5         0.2       c
    #> 9           4.4         2.9          1.4         0.2  setosa
    #> 10          4.9         3.1          1.5         0.1  setosa
    head(iris2 %>>% cpoCollapseFact(max.collapsed.class.prevalence = 0.2), 10)
    #>    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
    #> 1           5.1         3.5          1.4         0.2 collapsed
    #> 2           4.9         3.0          1.4         0.2 collapsed
    #> 3           4.7         3.2          1.3         0.2 collapsed
    #> 4           4.6         3.1          1.5         0.2 collapsed
    #> 5           5.0         3.6          1.4         0.2 collapsed
    #> 6           5.4         3.9          1.7         0.4 collapsed
    #> 7           4.6         3.4          1.4         0.3 collapsed
    #> 8           5.0         3.4          1.5         0.2 collapsed
    #> 9           4.4         2.9          1.4         0.2    setosa
    #> 10          4.9         3.1          1.5         0.1    setosa

### cpoModelMatrix

Specify which columns get used, and how they are transformed, using a `formula`.

    head(iris %>>% cpoModelMatrix(~0 + Species:Petal.Width))
    #>   Speciessetosa:Petal.Width Speciesversicolor:Petal.Width
    #> 1                       0.2                             0
    #> 2                       0.2                             0
    #> 3                       0.2                             0
    #> 4                       0.2                             0
    #> 5                       0.2                             0
    #> 6                       0.4                             0
    #>   Speciesvirginica:Petal.Width
    #> 1                            0
    #> 2                            0
    #> 3                            0
    #> 4                            0
    #> 5                            0
    #> 6                            0
    # use . + ... to retain originals
    head(iris %>>% cpoModelMatrix(~0 + . + Species:Petal.Width))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Speciessetosa
    #> 1          5.1         3.5          1.4         0.2             1
    #> 2          4.9         3.0          1.4         0.2             1
    #> 3          4.7         3.2          1.3         0.2             1
    #> 4          4.6         3.1          1.5         0.2             1
    #> 5          5.0         3.6          1.4         0.2             1
    #> 6          5.4         3.9          1.7         0.4             1
    #>   Speciesversicolor Speciesvirginica Petal.Width:Speciesversicolor
    #> 1                 0                0                             0
    #> 2                 0                0                             0
    #> 3                 0                0                             0
    #> 4                 0                0                             0
    #> 5                 0                0                             0
    #> 6                 0                0                             0
    #>   Petal.Width:Speciesvirginica
    #> 1                            0
    #> 2                            0
    #> 3                            0
    #> 4                            0
    #> 5                            0
    #> 6                            0

### cpoScaleRange

scale values to a given range

    head(iris %>>% cpoScaleRange(-1, 1))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1   -0.5555556  0.25000000   -0.8644068  -0.9166667  setosa
    #> 2   -0.6666667 -0.16666667   -0.8644068  -0.9166667  setosa
    #> 3   -0.7777778  0.00000000   -0.8983051  -0.9166667  setosa
    #> 4   -0.8333333 -0.08333333   -0.8305085  -0.9166667  setosa
    #> 5   -0.6111111  0.33333333   -0.8644068  -0.9166667  setosa
    #> 6   -0.3888889  0.58333333   -0.7627119  -0.7500000  setosa

### cpoScaleMaxAbs

Multiply features to set the maximum absolute value.

    head(iris %>>% cpoScaleMaxAbs(0.1))
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1   0.06455696  0.07954545   0.02028986       0.008  setosa
    #> 2   0.06202532  0.06818182   0.02028986       0.008  setosa
    #> 3   0.05949367  0.07272727   0.01884058       0.008  setosa
    #> 4   0.05822785  0.07045455   0.02173913       0.008  setosa
    #> 5   0.06329114  0.08181818   0.02028986       0.008  setosa
    #> 6   0.06835443  0.08863636   0.02463768       0.016  setosa

### cpoSpatialSign

Normalize values row-wise

    head(iris %>>% cpoSpatialSign())
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1    0.8037728   0.5516088    0.2206435  0.03152050  setosa
    #> 2    0.8281329   0.5070201    0.2366094  0.03380134  setosa
    #> 3    0.8053331   0.5483119    0.2227517  0.03426949  setosa
    #> 4    0.8000302   0.5391508    0.2608794  0.03478392  setosa
    #> 5    0.7909650   0.5694948    0.2214702  0.03163860  setosa
    #> 6    0.7841750   0.5663486    0.2468699  0.05808704  setosa

## Imputation

There are two general and many specialised imputation CPOs. The general imputation CPOs have parameters that let them use different imputation methods on different columns. They are a thin wrapper around `mlr`’s `impute()` and `reimpute()` functions. The specialised imputation CPOs each implement exactly one imputation method and are closer to the behaviour of typical CPOs.

#### General Imputation Wrappers

`cpoImpute` and `cpoImputeAll` both have parameters very much like `impute()`. The latter assumes that all columns of its input is somehow being imputed and can be preprended to a learner to give it the ability to work with missing data. It will, however, throw an error if data is missing after imputation.

    impdata %>>% cpoImpute(cols = list(a = imputeMedian()))
    #>     a   b
    #> 1 2.5 -10
    #> 2 2.0 -20
    #> 3 3.0 -30

    impdata %>>% cpoImpute(cols = list(b = imputeMedian()))  # NAs remain
    #>    a   b
    #> 1 NA -10
    #> 2  2 -20
    #> 3  3 -30
    impdata %>>% cpoImputeAll(cols = list(b = imputeMedian()))  # error, since NAs remain
    #> Error in assertPropertiesOk(present.properties, setdiff(allowed.properties, : Data returned by CPO trafo has property missings that impute declared in .properties.adding.
    #> properties in .properties.adding may not be present in trafo output.

    missing.task = makeRegrTask("missing.task", impdata, target = "b")
    # the following gives an error, since 'cpoImpute' does not make sure all missings are removed
    # and hence does not add the 'missings' property.
    train(cpoImpute(cols = list(a = imputeMedian())) %>>% makeLearner("regr.lm"), missing.task)
    #> Error in checkLearnerBeforeTrain(task, learner, weights): Task 'missing.task' has missing values in 'a', but learner 'regr.lm.impute' does not support that!

    # instead, the following works:
    train(cpoImputeAll(cols = list(a = imputeMedian())) %>>% makeLearner("regr.lm"), missing.task)
    #> Model for learner.id=regr.lm.impute; learner.class=CPOLearner
    #> Trained on: task.id = missing.task; obs = 3; features = 1
    #> Hyperparameters: impute.target.cols=character(0),impute.classes=list(),impute.cols=a=<ImputeMethod>,impute.dummy.classes=character(0),impute.dummy.cols=character(0),impute.dummy.type=factor,impute.force.dummies=FALSE,impute.impute.new.levels=TRUE,impute.recode.factor.levels=TRUE

#### Specialised Imputation Wrappers

There is one for each imputation method.

    impdata %>>% cpoImputeConstant(10)
    #>    a   b
    #> 1 10 -10
    #> 2  2 -20
    #> 3  3 -30
    getTaskData(missing.task %>>% cpoImputeMedian())
    #>     a   b
    #> 1 2.5 -10
    #> 2 2.0 -20
    #> 3 3.0 -30
    # The specialised impute CPOs are:
    listCPO()[listCPO()$category == "imputation" & listCPO()$subcategory == "specialised",
              c("name", "description")]
    #>                 name
    #> 40 cpoImputeConstant
    #> 48     cpoImputeHist
    #> 49  cpoImputeLearner
    #> 45      cpoImputeMax
    #> 42     cpoImputeMean
    #> 41   cpoImputeMedian
    #> ... (#rows: 10, #cols: 1)

## Feature Filtering

There is one general and many specialised feature filtering CPOs. The general filtering CPO, `cpoFilterFeatures`, is a thin wrapper around `filterFeatures` and takes the filtering method as its argument. The specialised CPOs each call a specific filtering method.

Most arguments of `filterFeatures` are reflected in the CPOs. The exceptions being: 1. for `filterFeatures`, the filter method arguments are given in a list `filter.args`, instead of in `...` 2. The argument `fval` was dropped for the specialised filter CPOs. 3. The argument `mandatory.feat` was dropped. Use `affect.*` parameters to prevent features from being filtered.

    head(getTaskData(iris.task %>>% cpoFilterFeatures(method = "variance", perc = 0.5)))
    #>   Sepal.Length Petal.Length Species
    #> 1          5.1          1.4  setosa
    #> 2          4.9          1.4  setosa
    #> 3          4.7          1.3  setosa
    #> 4          4.6          1.5  setosa
    #> 5          5.0          1.4  setosa
    #> 6          5.4          1.7  setosa
    head(getTaskData(iris.task %>>% cpoFilterVariance(perc = 0.5)))
    #>   Sepal.Length Petal.Length Species
    #> 1          5.1          1.4  setosa
    #> 2          4.9          1.4  setosa
    #> 3          4.7          1.3  setosa
    #> 4          4.6          1.5  setosa
    #> 5          5.0          1.4  setosa
    #> 6          5.4          1.7  setosa
    # The specialised filter CPOs are:
    listCPO()[listCPO()$category == "featurefilter" & listCPO()$subcategory == "specialised",
              c("name", "description")]
    #>                        name
    #> 32           cpoFilterAnova
    #> 19        cpoFilterCarscore
    #> 28      cpoFilterChiSquared
    #> 26       cpoFilterGainRatio
    #> 25 cpoFilterInformationGain
    #> 33         cpoFilterKruskal
    #> ... (#rows: 18, #cols: 1)