CPOs Built Into mlrCPO

Martin Binder

2021-01-11

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
37 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
56 cpoQuantileBinNumerics data feature conversion
62 cpoSelect data feature selection
63 cpoSelectFreeProperties data feature selection
52 cpoAddCols data features
51 cpoMakeCols data features
1 cpoApplyFun data general data preprocessing
54 cpoModelMatrix data general
38 cpoIca data numeric data preprocessing
55 cpoPca data numeric data preprocessing
59 cpoScale data numeric data preprocessing
60 cpoScaleMaxAbs data numeric data preprocessing
61 cpoScaleRange data numeric data preprocessing
65 cpoSpatialSign data numeric data preprocessing
17 cpoFilterFeatures featurefilter general
33 cpoFilterAnova featurefilter specialised
19 cpoFilterCarscore featurefilter specialised
29 cpoFilterChiSquared featurefilter specialised
27 cpoFilterGainRatio featurefilter specialised
26 cpoFilterInformationGain featurefilter specialised
34 cpoFilterKruskal featurefilter specialised
24 cpoFilterLinearCorrelation featurefilter specialised
18 cpoFilterMrmr featurefilter specialised
31 cpoFilterOneR featurefilter specialised
36 cpoFilterPermutationImportance featurefilter specialised
25 cpoFilterRankCorrelation featurefilter specialised
30 cpoFilterRelief featurefilter specialised
22 cpoFilterRfCImportance featurefilter specialised
23 cpoFilterRfImportance featurefilter specialised
20 cpoFilterRfSRCImportance featurefilter specialised
21 cpoFilterRfSRCMinDepth featurefilter specialised
28 cpoFilterSymmetricalUncertainty featurefilter specialised
32 cpoFilterUnivariate featurefilter specialised
35 cpoFilterVariance featurefilter specialised
39 cpoImpute imputation general
40 cpoImputeAll imputation general
41 cpoImputeConstant imputation specialised
49 cpoImputeHist imputation specialised
50 cpoImputeLearner imputation specialised
46 cpoImputeMax imputation specialised
43 cpoImputeMean imputation specialised
42 cpoImputeMedian imputation specialised
45 cpoImputeMin imputation specialised
44 cpoImputeMode imputation specialised
48 cpoImputeNormal imputation specialised
47 cpoImputeUniform imputation specialised
8 cpoCache meta
6 cpoCase meta
9 cpoCbind meta
5 cpoMultiplex meta
7 cpoTransformParams meta
69 cpoWrap meta wrap
70 cpoWrapRetrafoless meta wrap
66 cpoOversample subsampling binary classif
64 cpoSmote subsampling binary classif
67 cpoUndersample subsampling binary classif
68 cpoSample subsampling general
2 cpoApplyFunRegrTarget target general target transformation
57 cpoRegrResiduals target residual fitting
3 cpoLogTrafoRegr target target transformation
53 cpoMissingIndicators tools imputation
58 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=,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
#> 41 cpoImputeConstant
#> 49     cpoImputeHist
#> 50  cpoImputeLearner
#> 46      cpoImputeMax
#> 43     cpoImputeMean
#> 42   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
#> 33           cpoFilterAnova
#> 19        cpoFilterCarscore
#> 29      cpoFilterChiSquared
#> 27       cpoFilterGainRatio
#> 26 cpoFilterInformationGain
#> 34         cpoFilterKruskal
#> ... (#rows: 19, #cols: 1)